Information Technology - New Generations

Password cracking tools have given hackers the ability to solve hashes in minutes. These same tools can also be used for penetration testing to determine weak passwords within our own infrastructure. In using products, such as, Cain and Abel or Ophcrack, organizations can gain insight and awareness that could be the stronghold in keeping accounts and personal health information (PHI) safe. Cain and Abel, and Ophcrack, which are the two password cracking tools tested, can be both useful and very dangerous at the same time. While many can learn from these products, so can their adversaries. In using these products to test our own password strengths we can foresee vulnerabilities that we may have been overlooked. As new software is created, passwords will become easier to crack. Technology knows no boundaries in many aspects, which is why securing our networks, strengthening our physical and logical security, and mitigating every risk possible, becomes of utmost importance in this technology-ridden world.

While tools and technology are important, people are the most important element of a cybersecurity strategy. A properly implemented cybersecurity strategy engages every member of an organization in achieving mission success and in perpetually improving its cybersecurity posture.

Wireless Local Area Networks (WLAN) are gaining popularity due to the ease of use and ubiquity. Notwithstanding, their inherent characteristics make them more vulnerable to security breaches compared to wired networks. IEEE 802.11ac specification is currently the widely used WLAN standard deployed by most organizations. We study the impact of security on 802.11AC WLANs using different security modes (No Security, Personal and Enterprise Security) using a test WLAN. The performance analysis is based on throughput, delay, jitter, loss ratio and connection time. Our experiments indicate a performance improvement when no security is implemented relative to other security modes. For throughput performance, improvements ranged between 1.6 and 8.2% depending on the transport (TCP/UDP) and network (IPv4/IPv6) layer protocol. Improvements between 2.8 and 7.9% was observed when no security is implemented for delay. Jitter, Loss Ratio and connection time experienced between 1.3 and 18.6% improvement in performance. Though the performance degradation because of implementing security measures on 802.11ac WLANs appear relatively insignificant per the study, we believe the situation could be different when a heterogeneously complex setup is used. However, other factors (e.g. channel congestion, interference etc.) may equally be responsible for the performance degradation in WLANs that may not be necessarily security related.

Neural Networks have become the state-of-the-art technique in the field of Natural Language Processing (NLP). Many models attempt to learn and extend facts on graph-based knowledge bases (KBs). These datasets and models are valuable resources for many NLP tasks but are occasionally limited by data incompleteness. Previous work limited the number of relationships the model would learn from. In this paper we attempt to train a Neural Tensor Network (NTN) using 97 relationships from the Never Ending Language Learner (NELL) knowledge base. We compare its performance with previous NTNs trained with 11 relationships from Wordnet and 13 relationships from Freebase. Our model has achieved significant accuracy given the limited number of tuples per relationship in NELL’s KB.

Image annotation is a procedure to interpret the semantic concepts associated with image objects and represent them as their textual descriptions. Automatic and manual techniques have been extensively discussed in recent years to annotate the image objects, but are not without limitations. Automatic image annotation techniques mainly consider a single classifier and a descriptor type to annotate the image objects. Furthermore, thesaurus based extensions and human-centered revisions of the annotations are usually not possible. The fine-tuning of classifiers is generally not supported. In contrast to this, manual image annotation improves the accuracy, but tedious to annotate huge collections of image objects. Alternatively, semi-automatic image annotation techniques are human-centered, enhances the efficiency, and also speed-up the annotation process by machine intervention. In this research, a semi-automatic image annotation framework is proposed to address limitations in automatic and manual image annotation techniques. Our image annotation framework considers multiple descriptors and artificial neural networks to annotate the image objects. Along with that, a voting mechanism is provided to recommend the suitable annotations extendible by thesaurus and human revisions. Revised and extended annotations employed further to fine-tune the classifiers. Image annotation framework is instantiated and tested on a real dataset by implementing an image annotation tool.

As information technologies are embedding in increasing number of spheres of life of states and societies, dependence on the use of them transforms in new vulnerabilities and security concerns for entire states. The article is devoted to the analysis of the modern state of information security in the Republic of Kazakhstan. This article reviews the law, standards, technologies and incidents in the field of information security. Some statistics of information security events, threats and incidents are considered. The measures to provide information security in Kazakhstan including the basic norms of information security of information systems, resources and networks are described. The importance of timely elimination of vulnerabilities, exclusion and prevention of information security threats and incidents is highlighted. In conclusion the authors emphasizes that effective ways to solve the existing problems of low security clearance is full implementation of the specific recommendations are made to improve the information security of the country.

This report serves as a brief review of United Kingdom’s (UK) National Health Service’s (NHS) information system infrastructure and the various security threats that could lead to potential breaches of personal health information hosted on the network. Specifically, the document will address details of the NHS Spine infrastructure and how its components, users, and security mechanisms have a great impact on the NHS’ overall ability to provide quality service to the UK’s healthcare customers. The report will also provide an overview of the NHS system, its sub-components, and how they are all connected via the Spine infrastructure to serve UK citizens seeking healthcare assistance. Lastly, the report will touch on recommendations for role-based access control and identity management.

Cybersecurity competitions are exciting for the game participants; however, the excitement and educational value do not necessarily transfer to audiences because audiences may not be experts in the field. To improve the audiences’ comprehension and engagement levels at these events, we have proposed a virtual commentator architecture for cybersecurity competitions. Based on the architecture, we have developed a virtual animated agent that serves as a commentator in cybersecurity competition. This virtual commentator can interact with audiences with facial expressions and the corresponding hand gestures. The commentator can provide several types of feedback including causal, congratulatory, deleterious, assistive, background, and motivational responses. In addition, when producing speech, the lips, tongue, and jaw provide visual cues that complement auditory cues. The virtual commentator is flexible enough to be employed in the Collegiate Cyber Defense Competitions environment. Our preliminary results demonstrate the architecture can generate phonemes with timestamps and behavioral tags. These timestamps and tags provide solid building blocks for implementing desired responsive behaviors.

Malicious software (malware) has been extensively employed for illegal purposes and thousands of new samples are discovered every day. The ability to classify samples with similar characteristics into families makes possible to create mitigation strategies that work for a whole class of programs. In this paper, we present a malware family classification approach using VGG16 deep neural network’s bottleneck features. Malware samples are represented as byteplot grayscale images and the convolutional layers of a VGG16 deep neural network pre-trained on the ImageNet dataset is used for bottleneck features extraction. These features are used to train a SVM classifier for the malware family classification task. The experimental results on a dataset comprising 10,136 samples from 20 different families showed that our approach can effectively be used to classify malware families with an accuracy of 92.97%, outperforming similar approaches proposed in the literature which require feature engineering and considerable domain expertise.

For analysis information technology and computer system vulnerabilities, this paper benefits from “systematic review analysis: 2000–2015” with two-time searches: One established using suitable keywords, the second performed inside references used by selected papers.A detailed approach for analysis vulnerabilities of an organization includes physical and infrastructure of an organization, software, networks, policies, and information system vulnerabilities.Our findings highlight the following to be the most important vulnerabilities of networks: buffer overruns, operating environment, resource exhaustion, race conditions, standardization of canonical form, and violation of trust, injection attacks, cross-site scripting, non-secure cryptography storage and failure to restrict URL access.

The concept of self proxy signature (SPS) scheme was proposed by Kim and Chang in 2007. In a self proxy signatures, the signer wants to protect his original keys by generating temporary key pairs for a time period and then revoke them. The temporary keys can be generated by delegating the signing right to himself. Thus, in SPS the user can prevent the exposure of his private key from repeated use. If we are considering the existence of quantum computers, then scheme proposed by Kim and Chang’s is no more secure since its security is based on the hardness of discrete logarithm assumption. In this paper we propose the first lattice based self proxy signature scheme. Since hard problems of lattices are secure against quantum attacks, therefore, our proposed scheme is secure against quantum computer also. We designed our scheme on NTRU lattices since NTRU lattices are most efficient lattices than general lattices.

SecAOnto (Security Assessment Ontology) aims at formalizing the knowledge on “Security Assessment”. A conceptual formalization of this area is needed, given that there is an overlap of the “Information Security” and “Systems Assessment” areas, concepts are ambiguous, terminology is confounding, and important concepts are not defined. Nineteen papers on ontology, out of 80 papers of interest, have been selected to be discussed. Most of them are proposals of ontologies on information security; here we propose an ontology to deal specifically with security assessment aspects and particularities. SecAOnto is OWL-based, is publicly available and is devised to be used as a common and extensible model for security assessment. Its foundation comes from glossaries, vocabularies, taxonomies, ontologies, and market’s guidelines. The initial version of the ontology, its core model, as well as an application are presented. Our proposal is meant to be useful for security researchers who wish to formalize knowledge in their systems, methods and techniques.

Mobile forensics has been gaining in demand and significance with fast-growing number of users for mobile devices such as smartphones. Mobile forensics tools provide important capabilities for digital forensic investigators to extract, examine, and analyze evidence data uncovered from mobile devices. Due to the limitations of various tools, this paper argues for an integrated approach to mobile forensic tool capabilities through combined use of different tools. This study provides empirical data that demonstrates the benefit of integrating the strengths of two different mobile forensic tools, Cellebrite UFED and Oxygen Forensics, in evidence extraction from two sample Samsung Galaxy smartphones.

As the number of cyber-attacks continue to increase, the need for data protection increases as well. Deep Packet Inspection is a highly effective way to reveal suspicious content in the headers or the payloads in any packet processing layer, except when the payload is encrypted. DPI is an essential inspector of packet payloads as it is applied to many different layers of the OSI model. The DPI tasks include intrusion detection, exfiltration detection and parental filtering. This can be a great advantage as layer-independent attacks are becoming more prevalent. It allows for inspection of all layers for attacks. However, there are challenges that come with Deep Packet Inspection. Some include the decrease of throughput of the system, attacks through the Secured Socket Layer and intrusion fingerprint matching. These challenges do not constitute as grounds to eliminate DPI as a method, but instead obstacles to be aware of in case difficulties with implementation prevails.

Ransomware attacks have been growing worldwide since they appeared around 2012. The idea of ransomware attacks is, encrypting and locking the files on a computer until the ransom is paid. These attacks usually enter the system by using Trojans, which has malicious programs that run a payload that encrypts and locks the files. The basic goal of this type of attack is getting money, so hackers usually unlock the files when they receive the money, but really there is no guarantee of that. Ransomware attacks have various versions such as Reveton, CryptoWall, WannaCry, and Petya. The Petya attack is the attack that this paper discusses, especially the most recent version of it, which is referred as NotPetya. This paper defines the NotPetya attack, explains how it works, and where and how it spreads. Also, this paper discusses four solutions available to recover after a system infected by the NotPetya attack and propose the best solution depending on intense research about the recovering solutions of this attack.

The Internet of Things (IOT) has become an attractive and inviting technology that enables gathering information about all interconnected devices on real-time. These interconnected physical devices have a unique identifiers and the ability to communicate each other using its sensor technology and transfer data over a network. The collected information also provide significant opportunity for different businesses to have insight about these data by applying effective data analytics on them. Internet of Things have also revealed a huge security vulnerability that range from its authentication to its trust management, and a threat to its embedded devices. This research paper explores and discusses the challenges of Internet of things (IOT) that includes: its vulnerability, security and Privacy of IOT, current analytics of IOT, Imminent ownership threat, trust management, IOT Models, its road map, and make recommendation on how to resolve its security challenges.

In this paper, we consider a scenario where a sender transmits ciphertexts to multiple receivers using a public-key encryption scheme, and at a later point of time, wants to retrieve the plaintexts, without having to request the receivers’ help in decrypting the ciphertexts, and without having to locally store a separate recovery key for every receiver the sender interacts with. This problem, known as public key encryption with sender recovery has intuitive solutions based on hybrid encryption-based key encapsulation mechanism and data encapsulation mechanism (KEM/DEM) schemes. We propose a KEM/DEM-based solution that is CCA2-secure, allows for multiple receivers, only requires the receivers to be equipped with public/secret keypairs (the sender needs only a single symmetric recovery key), and uses an analysis technique called plaintext randomization that results in greatly simplified, clean, and intuitive proofs compared to prior work in this area. We instantiate our protocol for public key encryption with sender recovery with the Cramer-Shoup hybrid encryption scheme.

Information and Communication Technology is giving rise to new technologies and solutions that were not possible a few years ago. Electronic voting is one of the technologies that has emerged. One of the subsets of e-voting is mobile voting. Mobile voting is the use of mobile phones to cast a vote outside the restricted electoral boundaries. Mobile phones are pervasive; they offer connection anywhere, at any time. However, utilising a fast-growing medium such as the mobile phone to cast a vote, poses various security threats and challenges such as viruses, Trojans and worms. Many approaches for mobile phone security were based on running a lightweight intrusion detection software on the mobile phone. Nevertheless, such security solutions failed to provide effective protection as they are constrained by the limited memory, storage and computational resources of mobile phones. This paper compared and evaluated two intrusion detection and prevention systems named Suricata and Snort to equate, among the two security systems the one suitable to secure mobile voting application called XaP, while casting a vote. Simulations were used to evaluate the two security systems and results indicated that Suricata is more effective, reliable, accurate and secure than Snort when comes to protecting XaP.

Service providers depend on the ability to host, analyze, and exchange the personal data of users. Legal and contractual frameworks aim to protect the rights of users regarding this data. However, a confluence of factors render these rights difficult to guarantee. This paper evaluates the potential of blockchain technology as a mechanism for achieving transparency and accountability in the realm of personal data collection.

With a fast-growing demand for properly trained cybersecurity professionals to defend our cyber space and information systems, effective cybersecurity education programs and courses with consistent and reliable quality control and evaluation are necessary to prepare qualified workforce for the cybersecurity industry. The national Centers of Academic Excellence in Cyber Defense Education (CAE-CDE) designation program jointly sponsored by the US National Security Agency (NSA) and Department of Homeland Security (DHS) is a rigorous certification and national standard for maintaining quality of cybersecurity education. This paper explains the CAE-CDE program criteria and requirements and discusses the important role of the designation in cybersecurity education and workforce development. This paper illustrates the educational value and impact of the CAE-CDE program with case studies of three different institutions: (1) University of Missouri—St. Louis, which has obtained the CAE-CDE and Security Policy Development and Compliance Focus Area designations; (2) American Public University System, which has just completed the application for CAE-CDE; and (3) Robert Morris University, which is in the process of applying for the CAE-CDE designation.

This paper describes some software optimizations for the classical Data Encryption Standard (DES) cipher DES applicable for modern processor architectures that have SIMD instructions. Performance is gained by processing several messages in parallel, compared to processing single messages serially. An added value that the proposed optimizations offer is that the resulting implementations are also side channel protected, unlike other implementations that are found in open source libraries. For comparison, when measured on the latest Intel server processor (Architecture Codename Skylake), our side channel safe implementation is 3.2× faster than that of OpenSSL.

Now a day’s individuals and organizations are adopting cloud at a faster rate, due to which cloud traffic is increasing at a pace which is difficult to manage (Mamta Madan, Int J Cloud Comput Serv Archit 4:9–20, 2014). The virtualization plays a vital role to implement cloud computing but virtualization technologies add an additional level of complexity for the consumers and cloud providers. Cloud overlay network technology introduces the same visibility challenges as most exist for encapsulation methods. In this paper we present Virtual eXtensible LANs (VXLAN) based packet capturing and filtering mechanism for cloud overlay networks. This mechanism can provide cloud users and providers, detail visibility and information of VXLAN based network traffic traversing in cloud environment. Furthermore, we present design and development of real time VXLAN based virtual cloud overlay network environment. The proposed mechanism was tested in the Linux operating system based virtual environment.

We consider a Wireless Sensor Networks (WSN) in a planar structure with uniform distribution of the sensors and with a two-level hierarchical topology. At lower level, the clustering architecture is adopted in which the sensed information is transferred from nodes to a cluster head (CH). At CH level, CHs transfer information, hop-by-hop, towards to the sink located at the center of the sensed area. We propose a Markovian model to evaluate the end-to-end transfer delay. The numerical results reveal that the traffic carried by CHs near the sink is higher than the traffic carried by CHs located near the perimeter of the sensed area, as it could be expected. Furthermore, for a given radial distance between the source and the sink, the transfer delay depends on the angular orientation between both. This asymmetric behavior is revealed by the model.

The cloud computing paradigm represents a shift in the way companies deal with customizable and resourceful platforms to deploy software. It has been receiving increasing attention, partly due to its claimed financial and functional benefits. Cloud computing providers provide organizations with access to computing services without the need for those organizations to own the providing infrastructure. However, migration of legacy information systems to the cloud is not simple. This field is very dynamic and related technologies are rapidly evolving. For instance, Small and Medium Enterprises (SMEs) may not necessarily be well prepared to deal with issues such as multi-tenancy, elasticity, interoperability, and cloud services. With such issues in view, we searched for different types of tools referenced in the literature to support migration to the cloud and discussed related challenges and advantages of their use by SMEs.

This study describes an automated medicine dispenser that has enhanced functions provided by with the advent of the Internet of Things (IoT) paradigm. The prototype device outlined here is capable of delivering two different medicines at approximately the same time. This work describes the development of a medicine dispenser that incorporates some of the features of IoT devices for the home. It uses an Archimedes screw to deliver the tablets and incorporates basic levels of visual communication with the client, such as an LCD display and dispensing push buttons. The prototype developed illustrates the possible applications for the home that can be provided by the IoT paradigm.

In wireless sensor networks, each wireless sensor node records events occurred in its observation area with their observation time. Each wireless sensor node possesses its own local clock whose drift and offset are generally different from the others. In conventional clock synchronization methods, wireless sensor nodes exchanges control messages with their local clock values and estimate their transmission delay. However, it is difficult to adjust their local clocks since transmission delay of control messages are difficult to estimate. By using observation records of the commonly observed events by neighbor wireless sensor nodes, this paper proposes a novel method to estimate the relative drift and offset between local clocks of the neighbor wireless sensor nodes. Here, each sensor node only detects the occurrences of events and cannot achieve the locations where the events occur. Hence, commonly observed events between neighbor wireless sensor nodes are required to be detected. Our proposed method applies a heuristic that multiple observation records in neighbor wireless sensor nodes whose intervals are the same are estimated to be commonly observed events.

A wireless sensor network (WSN) is a network consisting of small nodes with constrained capabilities to sense, collect, and transmit sensed data in many application areas such as the healthcare system, the automotive industry, sports, and open space surveillance. WSNs communicate through wireless mediums and are accessible to anyone, which makes sensor nodes vulnerable to various forms of attack. Considering the energy-constrained nature of sensor nodes, denial of service (DoS) attacks on these nodes are popular. This paper examines DoS attacks and proposes countermeasures based on use of the clustering technique. The method is compared with other related protocols, and the results show that our method is able to effectively detect and defend against DoS attacks in WSNs.

This paper aims at introducing a methodology focused on student-centered learning and aided by an educational collaborative and graphical tool. Through it, we enable students to interact with abstract topics as well as interact with each other. Our motivation was the lack of capability to represent knowledge and abstractions faced by students that work alone. In this regard, we present as result a tool to be used in the whole educational processes, together with a teaching-learning methodology that is described from multiple points of view.

Companies that have been successful in implementing software project management, have focused efforts on people-oriented topics, for example, communication and teamwork. In order to effectively disseminate the attributes that the organization expects from a newly formed professional and what the university prepares, it is necessary to adopt ways of teaching that will encourage the involvement of these young people. It is in this context that active teaching methodologies, such as Game Based Learning, have emerged to include processes of experimentation and social interactivity. This work aims to identify and prioritize the practices inherent to Communication Management in Software Projects, that allow to perfect a game for teaching and learning. The steps of this research were: (1) identification of communication management practices and processes in the literature, (2) prioritization of practices and processes through the use of the AHP method, (3) conducting cycles of application of the object of study: an online board game and finally (4). The results allow to conclude that there was an improvement in the number of correct answers after the students played the game, especially in practices Communicate changes efficiently, Accurately collect requirements and Communicate frequently with interested parties. So, it is possible to prove statistically that the game increased students’ knowledge about these practices.

The teaching of software development is not trivial, as it faces a great challenge that is to combine theory and practice of many concepts. Agile methodologies combined with active methodologies, such as Problem-based Learning (PBL), generate opportunities in the development of software projects in academic scenarios. However, some teams do not distribute activities evenly among members, hindering the learning and the project development. In this paper, we report a case study on the use of gamification as a motivating agent in the teaching-learning process of the agile Scrum methodology of software development combined with PBL. The gamification process was supported by a game called LevelLearn, that was created by the authors. At the end of the semester, a questionnaire about the influence of the game during the course was answered by the students. The results were satisfactory and showed that gamification motivated students and influenced the involvement with the developed project.

The use of serious games has emerged as a differentiated strategy to promote the teaching of essential concepts and techniques in several areas of knowledge. To contribute to the student’s formation process of the Software Project Management, this research presents the development and validation of an electronic board serious game, named SCRUMI, to the teaching of concepts inherent to the SCRUM framework. The evaluation of the proposed game was carried out according to some criteria such as usability, quality of the question and presentation of the activities, applicability and motivation. The main results showed that the game is presented as a good alternative to be explored in the classroom.

Unemployment in South Africa is calculated to be approximately between 25 and 35% depending on whether or not one includes the number of people who are unemployed but not actively looking for a position. Even among people with some tertiary education, the unemployment rate is just under 20% and university graduates have an unemployment rate of approximately 7%. These high values have encouraged the South African Revenue Service to offer tax incentives to companies which maintain internships or learnerships. This paper looks specifically at how the Scrum Methodology can be introduced in internships in the IT industry to help prepare interns for Agile projects. Students who attend traditional programming courses are typically prepared for a Waterfall environment where the student is handed a specification and expected to write a program. Usually, students have never encountered an Agile environment during their training. This paper describes an internship which introduces Scrum during the internship by using Scrum in the internship itself.

Agile development aims at switching the focus from processes to interactions between stakeholders, from heavy to minimalistic documentation, from contract negotiation and detailed plans to customer collaboration and prompt reaction to changes. With these premises, requirements traceability may appear to be an overly exigent activity, with little or no return-of-investment. However, since testing remains crucial even when going agile, the developers need to identify at a glance what to test and how to test it. That is why, even though requirements traceability has historically faced a firm resistance from the agile community, it can provide several benefits when promoting precise alignment of requirements with testing. This paper reports on our experience in promoting traceability of requirements and testing in the data communications for mission-critical systems in an industrial Scrum project. We define a semi-automated requirements tracing mechanism which coordinates four traceability techniques. We evaluate the solution by applying it to an industrial project aiming at enhancing the existing Virtual Router Redundancy Protocol by adding Simple Network Management Protocol support.

During the 1st Semester of 2017, at the BrazilianAeronautics Institute of Technology (Instituto Tecnologico de Aeronautica, ITA), a successful Interdisciplinary Problem-Based Learning (IPBL) experience took place. At that time, almost 30 undergraduate and graduate students from three different courses within just 17 academic weeks had the opportunity of conceptualizing, modeling, and developing a Computer System based on Big Data, Internet of Things, and other emerging technologies for governmental organizations and private sectors. The purpose of this system was to aggregate data and integrate actors, such as Patients, Hospitals, Physicians, and Suppliers for decision making processes related to crises management involving events of health systems, such as epidemics, that needs to manage data and information. Differently from other existing products from Universities, Research Centers, Governmental Agencies, Public and/or Private companies, this product was developed and tested in just 17 academic weeks, applying the Scrum agile method and its best practices available in the market. This experience was stored in a Google site and implemented as a Proof of Concept (PoC). It represents just one example of how to address the old problems of teaching, learning, and developing complex intelligent academic computer projects to solve health system problems, by collaboratively using the Scrum agile method with Python or Java, Spark, NoSQL databases, Kafka, and other technologies. The major contribution of this paper is the use of agile testing to verify and validate an academic health system case study.

Disasters and crises, whether climatic, economic, or social are undesirably frequent in everyday lives. In such situations, lives are lost mainly because of inadequate management, lack of qualified and accurate information, besides other factors that prevent full situational awareness, including software failures. The goal of this paper is to report the agile conceptualization, design, build, and demonstration of a computerized system, containing correct-by-construction software, to safely manage critical information, during alerts or crises situations. On this research, the following challenges and requirements were tackled: formal specifications, aerospatial-level reliability, agile development, embedded systems, controlled testability, and product assessment. An Interdisciplinary Problem-Based Learning (IPBL), involving a Scrum of Scrums Agile Framework was adapted for managing the cohesive, productive, and collaborative development team of around 100 undergrad and graduate students remotely working. In addition, the following hardware technologies, for supporting the software development were used: environment sensors, Radio Frequency Identification (RFID), and Unmanned Aerial Vehicles (UAVs). Other software technologies were also used, as well cloud-based web-responsive platforms and mobile applications to geographically manage resources at real-time. Finally, the ANSYS® SCADE (Safety-Critical Application Development Environment) was employed to support the embedded and correct-by-construction module of this system, according to Model-Driven Architecture (MDA) and Model-Driven Development (MDD).

The proposal of a Corporate Governance Model called Service-Focused Operation Methodology (MOCA) was carried out, applied in Public and Private Partnerships (PPP) to improve services quality offered by the Brazilian states. This PPP model enabled several Service Center (in portuguese Central de Atendimento—CA) implementation projects supported by several multidisciplinary knowledge areas that involve projects and governments. However, this article explored an aspect of how a MOCA’s use with new technologies embedded in projects provide continuous improvements in results. In this case, for example, a demand study was applied to Planning and Control of Operations (PCO) in a use of Research and Development (R&D) to enable Artificial Intelligence algorithms for Planning Optimization in service production lines, aiming at improve citizen service aspects. In this CA PCO environment, a project outcomes set have been consolidated to demonstrate an impact that MOCA’s use with new computational technologies can bring to society. The effective integration results for this R&D; MOCA applied in PCO; obtained from stabilized proof of concepts; providing data collection and more accurate performance information in each CA, collected directly by an ERP used. From these data, the design of service production lines was performed using the following methodologies: (1) Descriptive Statistics, (2) Temporal Series and (3) Temporal Underground Neural Networks (ANNT). A Temporal Neural Networks (ANNT) was obtained, using recursive corrections in demand balancing by attendant performance. Using these technologies, a more accurate performance forecast to estimates attendants work was achieved in order to obtain a more realistic operational planning.

In this paper we propose to apply domain ontologies in agile software development to reduce the ambiguity caused by using natural language as ubiquitous language to report user stories. To justify and demonstrate our approach, we present a case study that combines Scrum and Behaviour-Driven Development (BDD) in the development of an educational support system, which was built to support the activities of the Medicine Programme of Federal University of São Carlos (UFSCar) in Brazil. Starting from a reference ontology for the Higher Education domain, we gradually specialized this ontology for this programme. Since we selected the Evaluation Management module of this system for our case study, we applied the Evaluation Process Ontology to that programme, and defined user stories to identify the feature set to be implemented. For evaluation and validation purposes, we assessed the quality of all ontologies used in this work according to structural and functional dimensions.

Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) are widely applied in a variety market segments to handle with real complex problems. The ability to deal with tasks in real time is essential in an environment that uses large volume do information available. In each new project, a decision-making system using ANN with time reduction and data processing is a key issue to test various learning algorithms; containing a variety of parameters when using this technology. From this starting point, the MLPs used data collected from a specific phenomenon and, based on statistical estimators, applied a data extraction algorithm for stratified sampling, aiming to reduce the time of ANN processing. In this context, this work proposes a Stratified Sampling algorithm (SSA), which was developed to minimize processing MLPs time without losing coverage and assertiveness, when comparing with training conducted on a population database. The case study consisted of a ANN performance influence with a population database and with its sample data obtained by the SSA model. This procedure with the RNAs aimed to evaluate the following properties: (1) meet the pre-established criteria of reliability of the model; (2) have a computer-automated procedure; (3) sort and select records more correlated, and (4) maintain sampling results within a track of assertiveness of total results obtained. From the realization of this case study, it was possible to identify the following gains made by the (1) reduction of ANN processing time by providing: (2) optimization of processing time; (3) automatic network selection; and (4) automatic parameters selection for training algorithms.

The National Institute of Educational Research and Studies (INEP) provides ENADE data for Higher Education Institutions (IES) from Brazil. This data is a rich source of support in improving the quality of education offered by these IES, but requires the application of data mining techniques to achieve the standards of the learning process and thus achieve improved academic performance of students in different courses. This paper aims to present the steps of mining the data provided by INEP, which will enable the identification of standards for the IES analyzed, as well as serve as a guide for other IES that wish to follow a similar process.

Condition of a patient in an intensive care unit is assessed by monitoring multiple correlated variables with individual observations. Individual monitoring of variables leads to misdiagnosis. Therefore, variability of the correlated variables needs to be monitored simultaneously by deploying a multivariate control chart. Once the shift from the accepted range is detected, it is vital to identify the variables that are responsible for the variance shift detected by the chart. This will aid the medical practitioners to take the appropriate medical intervention to adjust the condition of the patient. In this paper, Multivariate Exponentially Weighted Moving Variance chart has been used as the variance shift identifier. Once the shift is detected, authors for the first time have used ANNIGMA to identify the variables responsible for variance shifts in the condition of the patient and rank the responsible variables in terms of the percentage of their contribution to the variance shift. The performance of the proposed ANNIGMA has been measured by computing average classification accuracy. A case study based on real data collected from ICU unit shows that ANNIGMA not only improve the diagnosis but also speed up the variable identification for the purpose of appropriate medical diagnosis.

In this article, we investigate which features are required to enhance a credit scoring model for a Brazilian retail enterprise. In order to find attributes that can improve the performance of classifier algorithms for credit granting, a national and an international survey were carried out. A logistic regression classifier was used and the main result has improved the performance of data mining classifiers. The main contribution of this article was the verification that additional financial and behavioral data increase defaulting prediction performance on credit granting.

Sentiment Analysis (SA) employs Natural Language Processing (NLP) techniques in order to infer emotional states and subjective information contained in texts. Generally, previously trained machine learning models are used to identify the polarity of an opinion concerning a given target (e.g. film, book, etc.). Therefore, engineering features in order to create the training set for the learning model is a central task in SA problems. Additionally, finding properly labeled datasets for NLP models containing non-English text is a big challenge. Thus, we aim to contribute to SA in texts written in Brazilian Portuguese (PtBR) by validating the use of ConvNet, a convolutional neural network (CNN) that works with character-level inputs, in analyzing the polarity of product reviews in PtBR. The results obtained from our experiments confirm the model’s efficiency.

Satellite Image Time Series (SITS) is a set of images taken from the same satellite scene at different times. The mining of SITS is challenging task because it requires spatiotemporal data analysis. An example of the need for SITS mining is the analysis of solar flares and their evolving. Thematic Spatiotemporal Association Rules (TSARs) are associations that show spatiotemporal relationships among the values of the thematics attributes. By employing TSARs, we propose an approach to track the evolving of visual features of SITS images and their meaning. Our approach, called Miner of Thematic Spatiotemporal Associations for Images (MiTSAI), considers the data extracting and transformation, the thematic spatiotemporal association rule mining (TSARs), and the post-processing of the mined TSARs, that relate the visual features and their meaning. Our experiment shows that the proposed approach improves the domain expert team understanding of Solar SITS. Moreover, MiTSAI presented an acceptable time performance being able of extracting and processing TSARs using a long period of historical data faster than the period needed for the arrival of new data in the database.

The risk of default has grown as a concern for financial institutions. In a scenario of uncertainties, the correct decision is essential in the granting of credit. A predictive model of default risk and the linking of conflict management strategies can be critical in reducing financial losses and in decision-making doubts. This article presents an information system, called DeRis (Default Risk Information System), designed to support activities in the management of default risk in the context of a bank focused on the granting of credit. It covers a default prediction model based on conflict indicators, management, and financial indicators, a reasoner and visualization elements. Collecting historical data and sorting indicators is also possible. Through an experimental study, quantitative and qualitative data were collected. The feasibility of using DeRis was verified through an experimental study.

Note-taking software often far surpasses its paper-and-pencil counterpart when measured in metrics such as availability and reliability. However, there is ample opportunity relating to the analysis and organization of notes in structures often called folders, notebooks, or projects within various software. ShovelWare is a project designed for an ongoing field research project analyzing the Bronze and Iron Ages of Mongolia. Accessible through a web interface and cross-platform mobile application, it is a replacement for manual data collection on paper and excessive, error-ridden input into digital spreadsheets. We propose a machine learning algorithm that classifies notes using a variety of metrics, sorting them into graph structures to provide initial insights into the similarity of field notes. As a result, ShovelWare will allow archaeologists to more quickly and cleanly view and share their data. The algorithm, as well as the note-taking structure, are planned with hopes of scalability and applicability into more disciplines.

This paper presents a method for time series forecasting based on pattern recognition. As the system receives samples of time series, each of them representing one variable from the set of variables that describe the behavior of an application model, these samples are evaluated using a PCA algorithm, where each sample is represented by a feature vector. Different feature vectors (each of them representing a different sample of a particular case) are compared for pattern recognition. Once this sequence of steps is well performed, it’s possible to estimate time series for different states between those represented by the previously analyzed samples. As an example for application of this method, a case study is presented for some variables under specific flight conditions. The chosen application for this case study, helicopter flight dynamics is a relevant study, for it can be used, for example, to provide precise data to a flight simulator, which implies in an important issue for pilot training, and subsequently, this type of application may help reducing the probability of pilot's faults in real flight missions. To demonstrate the applicability of the method, this paper shows results obtained when the system generated forecasts for flight dynamics variables in a specific scenario of initial conditions and while the helicopter performed a maneuver of response to collective command. Finally, some considerations are made about the work shown in this paper as the results, discussions and conclusions are presented.

Mobile devices have become so ubiquitous and their computational capabilities have increased so much that they have been deemed as first-class resource providers in modern computational paradigms. Particularly, novel Mobile Cloud Computing paradigms such as Dew Computing promote offloading heavy computations to nearby mobile devices. Not only this requires to produce resource allocators to take advantage of device resources, but also mechanisms to quantify current and future energy availability in target devices. We propose a model to produce hour-wise estimations of battery availability by inspecting past device owner’s activity and relevant device state variables. The model includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. Comparisons against a relevant related work in terms of the Mean Squared Error metric shows that our method provides more accurate battery availability predictions in the order of several hours ahead.

One of the important tasks in purchasing cosmetics is the selection process. Swatching is the best way in shade-matching the look and feel of cosmetics. However, swatching the lipstick color on skins is far from being a good representation of the lips color. This paper aims to develop a virtual lipstick try-on application based on augmented reality and color prediction model. The goal of the color prediction model is to predict the RGB of the worn lips color given an undertone color of the lips and a lipstick shade. We have studied the performance of several learning models including simple and multiple linear regression, reduced-error pruning decision tree, M5P model tree, support vector regression, stacking technique, and random forests. We find that ensemble methods work best. However, since ensemble methods win only a small margin, our application is implemented with a simpler algorithm that is faster to train and to test, the M5P. The detection and tracking of lips are implemented using the OpenFace toolkit’s facial landmark detection sub-module. Measuring the prediction accuracy with MAE and RMSE, we have demonstrated that our approach that predicts worn lips colors performs better than without the prediction. Lipstick shades that resemble human skins have been shown to give more accurate results than dark shades or light pink shades.

Traffic lights detection and recognition research has grown every year. Time is coming when autonomous vehicle can navigate in urban roads and streets and intelligent systems aboard those cars would have to recognize traffic lights in real time. This article proposes a traffic light recognition (TLR) device prototype using a smartphone as camera and processing unit that can be used as a driver assistance. A TLR device has to be able to visualize the traffic scene from inside of a vehicle, generate stable images, and be protected from adverse conditions. To validate this layout prototype, a dataset was built and used to test an algorithm that uses an adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs) to detect traffic lights. The application of AdaBSF and subsequent classification with SVM to the dataset achieved 100% precision rate and recall of 65%. Road testing shows that the TLR device prototype meets the requirements to be used as a driver assistance device.

This paper presents an approach to prepare data to feed visual metaphors in a multiple view interactive environment. We implemented a tool that supports programmers and users to prepare datasets from different domains to feed visual metaphors. To analyze the effectiveness of the approach, we conducted a case study with the data of the Brazilian National Health System (known as SUS—Sistema Unico de Saude in Portuguese). The results obtained are an initial evidence of the feasibility of the approach that support the preparation of data to a format suitable to the characteristics of visual metaphors. The case study illustrates scenarios in which both programmers and users are able to prepare datasets from different domains to feed visual metaphors that comprise a multiple view interactive visualization infrastructure.

With reproducible research becoming a de facto standard in computational sciences, many approaches have been explored to enable researchers in other disciplines to adopt this standard. In this paper, we explore the importance of reproducible research in the field of document analysis and recognition and in the Computer Science field as a whole. First, we report on the difficulties that one can face in trying to reproduce research in the current publication standards. These difficulties for a large percentage of research may include missing raw or original data, a lack of tidied up version of the data, no source code available, or lacking the software to run the experiment. Furthermore, even when we have all these tools available, we found it was not a trivial task to replicate the research due to lack of documentation and deprecated dependencies. In this paper, we offer a solution to these reproducible research issues by utilizing container technologies such as Docker. As an example, we revisit the installation and execution of OCRSpell which we reported on and implemented in 1994. While the code for OCRSpell is freely available on github, we continuously get emails from individuals who have difficulties compiling and using it in modern hardware platforms. We walk through the development of an OCRSpell Docker container for creating an image, uploading such an image, and enabling others to easily run this program by simply downloading the image and running the container.

With accelerated advances in internet technologies users make listen to a staggering amount of multimedia data available worldwide. Musical genres are descriptions that are used to characterize music in music stores, radio stations and now on the Internet. Music choices vary from person to person, even within the same geographical culture. Presently Apple’s iTunes and Napster classify the genre of each song with the help of the listener, thus manually. We propose to develop an automatic genre classification technique for jazz, metal, pop and classical using neural networks using supervised training which will have high accuracy, efficiency and reliability, and can be used in media production house, radio stations etc. for a bulk categorization of music content.

Recently, the NLP community has focused on finding methods for learning good vectorial word representations. These vectorial representations must be good enough to capture semantic relationships between words using simple vector arithmetic operations. Currently, two methods stand out: GloVe and word2vec. We argue that the proper usage of knowledge bases such as WordNet, Freebase and Paraphrase can improve even further the results of such methods. Although the attempt to incorporate information from knowledge bases in vectorial word representations is not new, results are not compared to that of GloVe nor word2vec. In this paper, we propose a method to incorporate the knowledge of Paraphrase knowledge base into GloVe. Results show that such incorporation improves GloVe’s original results for at least three different benchmarks.

Empathy plays an important role in social interactions, such an effective teaching-learning process in a teacher-student relationship, and company-client or employee-customer relationship to retain potential clients and provide them with greater satisfaction. Increasingly, the Computer-Mediated Communication (CMC) support people in their interactions, especially when the interlocutors are geographically distant from one another. In CMC, there are different approaches to promote empathy in social or human-computer interactions. However, a little explored approach to gaining empathy in CMC is the use of the theory of Neurolinguistics that presents the possibility of developing a Preferred Representation System (PRS) for cognition in humans. This paper presents an initial experimental evaluation of the NeuroMessenger, a collaborative messenger library that uses the theory of Neurolinguistics to promote empathy by PRS identification and suggestion of textual matching based on the given PRS, using psychometry and text mining. The results showed that there was a difference between the means of grades in the empathy evaluation, in favor of NeuroMessenger. Although it is initial study, the results are encouraging, and more research on textual matching to gain empathy is needed.

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible; source codes and corpora are available on GitHub (https://github.com/hankcs/sub-character-cws).

Insurance Companies use Business Intelligence (BI) and Business Analytics (BA) to quantify their business and to predict their growth with the help of BI solutions. The primary objective of this paper is to build a software solution which provides a platform for insurance companies and ecommerce to find a set of tools and solutions that can be implemented for their business data analytics. The BI Dashboard application can be used by insurance companies to implement the concept of Cross-Selling and Up-selling of insurance products to their customers. The Ecommerce web based application is used to implement the concept of group-based collaborative marketing of products which internally uses data mining and clustering algorithms.

In this paper, a speech pattern analysis framework for tone language speaker discrimination systems is proposed. We hold the hypothesis that speech feature variability is an efficient means for discriminating speakers. To achieve this, we exploit prosody-related acoustic features (pitch, intensity and glottal pulse) of corpus recordings obtained from male and female speakers of varying age categories: children (0–15), youths (16–30), adults (31–50), seniors (above 50)—and captured under suboptimal conditions. The speaker dataset was segmented into three sets: train, validation and test set—in the ratio of 70%, 15% and 15%, respectively. A 41 × 14 self-organizing map (SOM) architecture was then used to model the speech features, thereby determining the relationship between the speech features, segments and patterns. Results of a speech pattern analysis indicated wide F0 variability amongst children speakers compared with other speakers. This gap however closes as the speaker ages. Further, the intensity variability among speakers was similar across all speaker classes/categories, while glottal pulse exhibited significant variation among the different speaker classes. Results of SOM feature visualization confirmed high inter-variability—between speakers, and low intra-variability—within speakers.

The binary logistic regression is a machine learning tool for classification and discrimination that is widely used in business analytics and medical research. Transforming continuous predictors to improve model performance of logistic regression is a common practice, but no systematic method for finding optimal transformations exists in the statistical or data mining literature. In this paper, the problem of selecting transformations of continuous predictors to improve the performance of logistic regression models is considered. The proposed method is based upon the point-biserial correlation coefficient between the binary response and a continuous predictor. Several examples are presented to illustrate the proposed method.

Multi-core processing units have been the answer to ever increasing demand of computational power of modern software. One of the main issues with the adoption of new hardware is portability of legacy software. In this specific case, in order for legacy sequential software to maximize the exploitation of the computational benefits brought by multi-core processors, it has to undergo a parallelization effort. Although there is a common agreement and well-specified support for parallelizing sequential algorithms, there is still a lack in supporting software engineers in identifying and assessing parallelization potentials in a legacy sequential application. In this work we provide a generic parallelization approach which supports the engineering in maximizing performance gain through parallelization while minimizing the cost of the parallelization effort. We evaluate the approach on an industrial use-case at ABB Robotics.

This work introduces a novel test case prioritization method that combines clustering methods, dimensionality reduction techniques (DRTs), and combinatorial-based two-way prioritization for GUI and web applications. The use of clustering with interleaved cluster prioritization increases the diversity of the earliest selected test cases. The study applies four DRTs, four clustering algorithms, and three inter-cluster ranking methods to three GUI and one web applications in order to determine the best combination of methods. We compare the proposed clustering and dimensionality reduction approaches to random and two-way inter-window prioritization techniques. The outcome of the study indicates that the Principal Component Analysis (PCA) dimensionality reduction technique and Mean Shift clustering method outperform other techniques. There is no statistical difference between the three inter-cluster ranking criteria. In comparison to two-way inter-window prioritization, the Mean Shift clustering algorithm with PCA or Independent Component Analysis (FICA) generally produces faster rates of fault detection in the studies.

In this paper, an approach based on Petri nets for the design process of video games is presented. A WorkFlow net is used to represent the activities the player will perform in a video game. The main areas of the virtual world that the player will encounter during the game are modeled through a kind of Petri net named State Graph. A timed version of the WorkFlow net (that models the activities of the game) and of the State Graph (that models the topological map of the game) is presented in order to produce an estimated time that corresponds to the effective duration a player will need to complete a specific level of a game. The simulation software CPN Tools is used to simulate both models and show, through a kind of quantitative analysis, the influence that one model has over the other. The video game Silent Hill II is used to illustrate the proposed approach.

Although significant research has been dedicated to developing biometric solutions for motorized vehicles, there are currently no survey works charting the progress in this field. This paper discusses a selection of biometrics research focusing on improving vehicle safety and protecting vehicles against theft. Specifically, we discuss research that focuses on detecting a driver’s impaired ability to control the vehicle due to drowsiness, intoxication, or a medical emergency; developing techniques for identifying and preventing intrusions into the vehicle; and discovering driver distractions from within and without the vehicle. We also comment on the potential effectiveness, user-friendliness, privacy, security, and other aspects of the proposed approaches and identify directions for future research.We supplement this paper with a comprehensive list of other works in the field, which is accessible from Gofman and Villa (Extended database of biometrics research for automotive applications, 2017. http://www.fullerton.edu/cybersecurity/research/Extended-Database-of-Biometrics-Research-for-Automotive-Applications.php).

Governance in Information and Communication Technology (ICT) is a relevant area of research and practice. The Audit Court of the Union (ACU) periodically evaluates all Brazilian Federal Public Organizations in order to disseminate the importance of implementing good governance system and raising awareness of positive contribution of adopting Good Practices. As a result of these evaluations, it is clear that evolution of ICT Governance in Federal Public Administration (FPA) in Brazil is growing slowly. The number of organizations that effectively adopt good governance practices is low when compared to all other FPAs. This article presents results of a survey conducted at Public Organizations that have improved capability (iGovTI) stages according to the ACU. The iGovTI is the index of governance in which the organization is inserted, it can be (initial, basic, intermediate and enhanced). Knowing this, this survey collected used practices and identifies the profiles of organizations and their interviewees. As main results, we recognize that the Good Practices used in Enhanced Organizations are the same known in academia as well as in industry (for instance, COBIT, ITIL, CMMI, MPS.Br, PMBOK). Another relevant result is the evidence that even Enhanced Organizations do not adopt practices in topics considered relevant, including project management, information security and risk management.

The Precipitation-Runoff Modeling System (PRMS) is used to study and simulate hydrological environment systems. It is common for an environmental scientist to execute hundreds of PRMS model runs to learn different scenarios in a study field. If the study case is complex, this procedure can be very time-consuming. Also, it is very hard to create different scenarios without an efficient method. In this paper, we propose a PRMS scenario web application. It can execute multiple model runs in parallel and automatically rent extra servers based on needs. The control strategy introduced in the paper guarantees that the expense is within the planned budget and can remind a system manager if the quantified user feedback score crosses the predefined threshold. The application has user-friendly interfaces and any user can create and execute different PRMS model scenarios by simply clicking buttons. The application can support other environmental models besides PRMS by filling the blueprint file.

Defining system business models and rules using conceptual schemas is not an easy task. The Object Management Group’s standard SBVR—Semantics of Business Vocabulary and Rules, define semantics for expressing business focused vocabularies and rules. It is based on a meta-model with roots on formal first-order logic. In order to create valid business vocabulary and rules, based on this standard, business analysts need tools that can facilitate modeling efforts using the standard. In this work, we contribute with a proposed tool that would allow business analyst designers to define the keywords that represent each of the Logical Formulation Concepts in the SBVR Meta-model. Therefore, this approach would enable the creation of Controlled Natural Language Libraries based on the SBVR meta-model that can be dynamically included in SBVR Modeling projects. These Language Libraries can then be used to model business rules and vocabularies in the tool. As a result, one can support multiple controlled natural languages and represent the SBVR metamodel using the tool. Examples of different controlled natural languages using the proposed SBVR editor tool are provided.

In this paper, an approach based on Possibilistic WorkFlow nets is proposed to model mutiplayer game scenarios. In a multiplayer game scenario, the actions of a player may influence, directly or indirectly, the actions of other players. This type of interaction may change the normal flow of activities in the game causing eventually then an uncertain behavior. In this work, the routing structure of WorkFlow nets to model the activities of a multiplayer video game is used. The uncertain reasoning of a possibilistic Petri net is then used to represent the possible interactions between the players. The resulting model (Multiplayer Game activity Model) can then simulate and analyze (qualitatively and quantitatively) the game’s behavior in terms of its gameplay. The video game Tom Clancy’s Ghost Recon: Wildlands is used to illustrate the proposed approach.

The significance of security requirements in building safety and security critical systems is widely acknowledged. However, given the multitude of security requirements engineering methodologies that exists today, selecting the best suitable methodology remains challenging. In a previous work, we proposed a generic evaluation methodology to elicit and evaluate the anticipated characteristics of a security requirements engineering methodology with regards to the stakeholders’ working context. In this article, we provide the empirical evaluation of three security requirements engineering methodologies KAOS, STS and SEPP with respect to the evaluation criteria elicited for network SRE context. The study show that none of them provide good support to derive network security requirements.

This paper reviews the main concepts related to software testing, its difficulties and the impossibility of a complete software test. Then, it proposes an approach to predict which module is defective, aiming to assure the usually limited software test resources will be wisely distributed to maximize the coverage of the modules most prone to defects. The used approach employs the recently proposed Kaizen Programming (KP) to automatically discover high-quality nonlinear combinations of the original features of a database to be used by the classification technique, replacing a human in the feature engineering process. Using a NASA open dataset with Software metrics of over 9500 modules, the experimental analysis shows that the new features can significantly boost the detection of detective modules, allowing testers to find 216% more defects than with a random module selection; this is also an improvement of 1% when compared to the original features.

This research discusses the detailed experiences of recruitment and training of Noyce interns to become qualified STEM teachers. In this paper, both successful experiences and challenges in the first 4 years of a NSF Noyce project are discussed with the three-tier model. In addition, this model has proven to be effective by using two types of evidences. First, the survey data collected from over 25 STEM teacher candidates is described. Secondly, the actual interview data and student feedback are reported.

Finding associations between itemsets within two categories (e.g., drugs and adverse effects, genes and diseases) are very important in many domains. However, these association mining tasks often involve computation-intensive algorithms and a large amount of data. This paper investigates how to leverage MapReduce to effectively mine the associations between itemsets within two categories using a large set of unstructured data. While existing MapReduce-based association mining algorithms focus on frequent itemset mining (i.e., finding itemsets whose frequencies are higher than a threshold), we proposed a MapReduce algorithm that could be used to compute all the interestingness measures defined on the basis of a 2 × 2 contingency table. The algorithm was applied to mine the associations between drugs and diseases using 33,959 full-text biomedical articles on the Amazon Elastic MapReduce (EMR) platform. Experiment results indicate that the proposed algorithm exhibits linear scalability.

Computational science is simply the application of computing capabilities to the solution of problems in the real-world. It is considered as one of the five college majors on the rise. Computational science is officially listed as a new study area in the Computer Science Curricula 2013 (CS2013), by the Joint Task Force on Computing Curricula Association for Computing Machinery (ACM) and IEEE Computer Society. The authors introduced Binomial Simulation Method (BSM) as a new algorithm in the study of Computer Science. BSM is a simple and effective method to model real-world phenomena that involve up and down movements, and has been successfully used in many financial calculations. This work extends the previous study significantly with comparison with Black-Sholes and Monte Carlo simulation; it is intended to help the study of computational science in order to realize the potential power to solve more and more challenging real-world problems. We believe that it is very important to incorporate BSM into computational science education.

Cloud computing has become increasingly popular as a means of providing computational resources to ubiquitous computing tasks. Our research specifically defines computing resource needs while developing an architecture for processing and analyzing microbiome data sets. We propose a specialized cloud architecture with processing capabilities defined by various toolchains and bioinformatics scripts. This “Bioinformatics-as-a-Service” cloud architecture, named BioCloud, is in the optimization stage for processing bioinformatic requests, and allowing multi-tenant access of resources through a simple to use web-based graphical user interface. We’ll be compiling a list of Bioinformatics tools, some of which will be discussed in this paper, that will be optional components in our Biocloud platform. These tools will become apart of the plug-and-play system envisioned by the BioCloud team.

This paper presents the implementation of an algorithm based on elementary computer vision techniques that allow an UAV (Unmanned Aerial Vehicle) to identify obstacles (including another UAV) and to avoid them, using only a trivial 5MP camera and applying six mathematical treatments on image. The proposed algorithm of this paper was applied in a drone in real flight. The algorithm proved to be quite efficient both in identifying obstacles ahead and in transposing the same obstacles. Another interesting result is that the algorithm can identify small mobile obstacles and avoid collisions, for example other drones.

Analyzing and understanding spatial data that vary over time is a complex task. Usually, the data is arranged in a tabular or text forms. We develop the ST-Vis (SpatioTemporal Visualization) tool to provide a visual representation of spatiotemporal data, which must help users to understand a temporal variation in a region when combining the parallel coordinates graph with a geographic map, a temporal texture, and a table. The temporal texture maps the linear form of the variation of the Normalized Difference Vegetation Index (NDVI), resulting in a representation of colors texture, and each texture cell refers to a period. ST-Vis provides a simultaneous spatiotemporal representation of data, and the visualizations interact with each other through animations. We evaluated ST-Vis by interviewing some domain experts, computer science, and other field students, who experienced ST-Vis tool. The results show that ST-Vis allows the understanding of spatiotemporal data through the generated visualizations. This tool simultaneously displays visualizations, which have interactions with each other through animations.

It is said that the overlooking rate of early gastric cancer in endoscopic examination reaches 20–25% in Japan, and it is desirable to develop a detection method for early gastric cancer from endoscopic images to reduce the overlooking rate. We propose a new method for detecting early gastric cancer from endoscopic images using the wavelet transform modulus maxima (WTMM). First, our method converts the original image into the CIE L*a*b* color space. Next, we apply the dyadic wavelet transform (DYWT) to the a* component image and compute the WTMM of the high frequency component. It is shown that the WTMM of the abnormal parts tends to become smaller than the WTMM of the normal parts. We describe the method detecting the abnormal parts based on these features in detail, we show experimental results demonstrating that the proposed method are able to detect the regions suspected of being early gastric cancer from endoscopic images.

We propose a new detection method of early esophageal cancer from endoscopic image by using the dyadic wavelet transform (DYWT) and the four-layered neural network (NN). We prepare 6500 appropriate training images to make a NN classifier for early esophageal cancer. Each training image is converted into HSV and CIEL*a*b* color spaces, and each fusion image is made from the S (saturation), a* (complementary color), and b* (complementary color) components. The fusion image is enhanced contrast so as to emphasize the difference of the pixel values between the normal and abnormal regions, and we use only high pixel values of this image for learning in the neural network. We can obtain the important image features by applying the inverse DYWT to processed image. We describe our proposed method in detail and present experimental results demonstrating that the detection result of the proposed method is superior to that of the deep learning technique utilizing the endoscopic image marked an early esophageal cancer by a doctor.

Optical flow is defined as pixel motion between two images. Hence, in order to estimate optical flow, an energy model is proposed. This model considers: a data term and a regularization term. Data term is an optical flow error estimation and regularization term imposes spatial smoothness. Most of traditional variational models use a linearized version of data term, which fails when the displacement of the object is larger than their own size. Last years the precision of optical flow method has been increased due to the use of additional information, which comes from correspondences computed between two images obtained by: SIFT, Deep-matching or exhaustive search. This paper presents an experimental study to evaluate strategies for locating exhaustive correspondences improving flow estimation. We considered different location for matching: random location, uniform location, maximum of the gradient and maximum error of the optical flow estimation. Best performance (minimum EPE and AAE error) was obtained by the Uniform Location which outperforms reported results in the literature.

This paper aims to present some applications in Geospatial Technology area, from the use of UAV, Communication, IT and High Performance Computing tools. Important research topics in this area are the detection of changes in multiple images of a soil region to security, deforestation identification and changes in plantations, river courses, shorelines and glaciers. Students, teachers and researchers from four BRIC countries: Brazil, Russia, India and China are conducting joint research in low-cost systems involving aircraft powered by solar energy (Russian team), communication systems over long distances (Chinese team), change detection algorithms in the soil (Brazilian team) and cloud based distributed systems for identification of the type of change detected (Indian team). Some intermediate results already achieved individually by each team are also presented and discussed in this study.

Makerspace is an innovative concept of working in a community based, semi-organized groups working to tinker, design, fabricate or develop, and market the ideas. The involvement of the community is vital to the success of every application of the makerspace concept. Some areas, like Science, Technology, Engineering, and Management (STEM) are among the extremely popular areas in the makerspace, while other popular areas include programming, and curriculum development. In such areas, where the community involvement is expected to be high, it is possible that the makerspace can’t accommodate all the individuals along with their equipment, at one place. Since the cost to develop the makerspace is high, we will be interested to see that how the small makerspaces can interact with the central makerspace to share the ideas and resources. In this paper, we investigate the popular areas of community involvement, opportunities and challenges in forming the distributed makerspaces, and also provide the analyses of their productivity in terms of problems solving, fabrication of small components, and software development. A model is proposed that provides a framework of conducting activities in distributed makerspaces and integrating the activities (project components) to form a product.

With the blooming of data created for example by IoT devices, the possibility to handle all information coming from healthcare applications is becoming increasingly challenging. Cognitive computing systems can be used to analyse large information volume by providing insights and recommendations to represent, access, integrate, and investigate data in order to improve outcomes across many domains, including healthcare. This paper presents an ontology-based system for the eHealth domain. It provides semantic interoperability among heterogeneous IoT devices and facilitates data integration and sharing. The novelty of the proposed approach lies in exploiting semantic web technologies to explicitly describe the meaning of sensor data and define a common communication strategy for information representation and exchange.

Aiming at avoiding high costs in the production and analysis of new drug candidates, databases containing molecular information have been generated, and thus, computational models can be constructed from these data. The quantitative study of structure-activity relationships (QSAR) involves building predictive models that relate chemical descriptors for a compound set and biological activity with respect to one or more targets in the human body. Datasets manipulated by researchers in QSAR analyses are generally characterized by a small number of instances, which can affect the accuracy of the resulting models. In this context, transfer learning techniques that take information from other QSAR models to the same biological target would be desirable, reducing efforts and costs for evaluating new chemical compounds. This article presents a novel transfer learning method that can be applied to build QSAR regression models by Support Vectors Regression (SVR). The SVR-Adapted method for Transfer Learning (ATL) was compared with standard SVR method regarding values of mean squared error. From experimental studies, the performance of both methods was evaluated for different proportions of the original training set. The obtained results show that transfer learning is capable to exploit knowledge from models built from other datasets, which is effective primarily for small target training datasets.

People with articulation and phonological disorders need training to plan and to execute sounds of speech. Compared to other children, children with Down Syndrome have significantly delayed speech development because they present developmental disabilities, mainly apraxia of speech. In practice, speech therapists plan and perform trainings of articulatory and non-articulatory sounds such as blow production and popping lips in order to assist speech production. Mobile applications can be integrated into the clinical treatment to transcend the boundaries of clinics and schedules and therefore reach more people at any time. The use of artificial intelligence and machine learning techniques can improve this kind of application. The aim of this pilot study is to assess speech recognition methods prioritizing the training of sounds for speech production, particularly the non-articulatory sounds. These methods apply Mel-Frequency Cepstrum Coefficients and Laplace transform to extract features, as well as traditional statistical tests and Support Vector Machine (SVM) to recognize sounds. This study also reports experimental results regarding the effectiveness of the methods on a set of 197 sounds. Overall, SVM provides higher accuracy.

Diabetes has become a serious health concern. The development of highly evolved blood glucose measurement devices have led to tremendous improvements in glucose monitoring and diabetic management. Tracking and maintaining traceability between glucose measurements, insulin doses and carbohydrate intake can provide useful information to physicians, health professionals, and patients. This paper presents an information system, called GLUMIS (GLUcose Management Information System), aimed to support diabetes management activities. It encompasses a rule-based method for predicting future glucose values, a reasoner and visualization elements. Through integration with glucose measurement devices it is possible to collect historical treatment data and with REALI system insulin doses and dietary habits can be processed. Through an experimental study, quantitative and qualitative data was collected. An analysis was applied and shown that GLUMIS is feasible and capable of resulting interesting rules that can help diabetics.

Interferential Current (IFC) therapy is routinely used on patients in order to reduce pain, to speed up the healing of wounds in muscles, and to strengthen muscles and bodily structure. The van der Pauw (vdP) technique is a process used to measure a material’s sheet resistance when the material has a geometric shape that has a uniform thickness, but whose other two dimensions are arbitrary. By combining these techniques, the skin’s sheet resistance can be measured before, after, and during IFC therapy, and this will show a disturbance in skin sheet resistance caused by accidental burning. This technique can also be extended to monitoring TENS (transcutaneous electrical nerve stimulation) and other types of e-stim monitoring.

In museum, the light plays important role in viewing the value of the collections, but light might cause gradual objects damage. The light damages are permanent and cumulative. No object can be recovered from light damage. Resting objects from the effects of light does not mean that they could handle more light; the object will not “heal”. Typically, it is visible light that fades (or bleaches) colors. This light would come from the sun shining directly into museum. UV light will not only fade colors but it will cause “yellowing, chalking, weakening, and/or disintegration” of objects. UV light not only comes from the sun but also comes from some sources of artificial lighting, such as fluorescent. IR light heats the surface of objects, which then leads to the same conditions as Incorrect Temperature IR light comes from the sun as well as Incandescent lighting. In this paper, smart system is built to control the lights in the museum. The system is mainly consisting of thermal sensors that detect the presence of humans, DC LED spotlights, Arduino boards and Zigbee modules for a wireless communication to send data to a server. Once the visitor stops by the display, the thermal sensors will be able to detect that visitor and prepare to calculate several measurements. The system is already installed to be working in the Main Museum in the college of Archaeology and Anthropology as a prototype in Yarmouk University. The measurements show how the system is reliable and effective.

This paper presents a method for the development of artificial neural networks (ANN) that consists in the use of a search space algorithm to adjust the components of an ANN’s initial structure, based on the performance obtained by different network configurations. Also, it is possible to represent an ANN’s structure as a genetic sequence, which enables directly loading a corresponding genetic sequence to instantly generate and run a previously trained ANN. This paper also shows some results obtained by different ANNs developed by this method, which demonstrate its features by analyzing its accuracy and trueness. As an example for application of this method, a case study is presented for a specific flight simulation, using data obtained from a helicopter’s flight dynamics simulator for ANN training. Helicopter flight dynamics is a relevant study, for it can be used, for example, to provide precise data to a flight simulator, which implies in an important issue for pilot training, and subsequently, this type of application may help reducing the probability of pilot’s faults in a real flight mission. Finally, some considerations are made about the work shown in this paper as the results, discussions and conclusions are presented.

Generating constrained triangulation of point sites distributed in the plane is an important problem in computational geometry. We present theoretical and experimental investigation results for generating triangulations for polygons and point sites that address node degree constraints. We characterize point sites that have almost all vertices of odd degree. We present experimental results on the node degree distribution of Delaunay triangulation of point sites generated randomly. Additionally, we present a heuristic algorithm for triangulating a given normal annular region with increased number of even degree vertices.

The open-shop scheduling problem is concerned with the allocation of tasks to resources, especially when resources are scarce. The problem has many practical applications in the production, manufacturing, testing, and telecommunication domains. In this paper, we study the non-preemptive open-shop scheduling problem with more than two machines using two metaheuristic algorithms: cuckoo search and ant colony optimization. The proposed algorithms are implemented using Python, and tested on the Taillard benchmarks. Favorable results comparisons are reported.

In the continuous power-down problem one considers a device, which has states OFF, ON, and an infinite number of intermediate states. The state of the device can be switched at any time. In the OFF state the device consumes zero energy and in the ON state it works at its full power consumption. The intermediate states consume only some fraction of energy proportional to usage time but switching back to the ON state has various switch up cost depending on the state. Requests for service, i.e. for when the device has to be in the ON state, are not known in advance; power-down problems are thus studied in the framework of online competitive analysis. Power-down can be used to model the control of traditional power generation in an electrical grid predominantly supplied by renewable energy. We analyze a number of systems, namely “linear”, “optimal-following”, “progressive”, “logarithmic” as well as “exponential”, and give competitive ratios for these systems. We show that highly competitive systems must have schedules which are accelerated from the offline solution.

Timing models include crucial information that is required by the timing analysis engines to verify timing behavior of vehicular embedded systems. The extraction of this information from these systems is challenging due to the software complexity, distribution of functionality and multiple criticality levels. To meet this challenge, this paper presents a comprehensive end-to-end timing model for multi-criticality vehicular distributed embedded systems. The model is comprehensive, in the sense that it captures detailed timing information and supports various types of real-time network protocols used in the vehicular domain. Moreover, the paper provides a method to extract these models from the software architectures of these systems. The proposed model is aligned with the component models and standards in the vehicular domain that support the pipe-and-filter communication among their basic building elements.

The Internet of Things (IoT) is today separated by different vertically oriented platforms for integration of all the different devices. Developers who aim to access other platforms and access that data are forced to manually adapt their interfaces to the specific platform API and data models.This paper highlights the work of the BIG IoT project that aims at launching an IoT marketplace and ecosystem as part of the European Platform Initiative (IoT EPI).We will present the setup of and the results of integration of the use cases that have been implemented in Northern Germany and Barcelona.Please refer to www.big-iot.eu for further details.

Although website usability is one of the prominent factors that determine the success of web-based businesses, extant research on website usability does not focus on the diversity of the users who actually use the website. Drawing on Hofstede’s cultural values and Nisbett’s cognitive style framework, this study proposes that individuals perceive website usability differently. The results from our online survey suggest that users who are more exposed to Western culture are more responsive to personalized and customizable features in B2C fashion websites, since their level of individualism is strongly associated with all aspects of the MUG usability requirements.

This article is devoted to ensuring the reliability of information systems. An approach based on risk assessment and neutralization is used to increase the reliability of information systems. This approach allows for early risk assessment of the software development process and determines the most effective mitigation strategies.

This article is devoted to ensuring the reliability of information systems. An approach based on risk assessment and neutralization is used to increase the reliability of information systems. This approach allows for early risk assessment of the software development process and determines the most effective mitigation strategies.

Fragility is a syndrome characterized by reduced physical and cognitive reserves making the elderly more vulnerable to adverse events, hospitalizations, falls, loss of independence, and death. Inertia sensors have been applied to quantify motion assessment in Timed Up and Go (TUG) test, accelerometers are used during balance assessment, and algorithms differentiate fragile, pre-fragile, and robust elderly people.Objective: Developing a multifunctional sensor to evaluate fragility, based on marker phenotype and deficit accumulation index.Methods: Primary, exploratory, interventional, analytical, and transversal study with a technological approach. The study will be developed, in partnership with researchers from the Federal University of Itajubá-MG using high-tech, multifunctional, and cost-effective sensor equipment in combination with a 3-axis gyroscope, a 3-axis accelerometer, electromyography and frequency meter, analysis of movement quality, energy expenditure, gait velocity, change in balance, heart rate variability during movement, and quality of quadriceps muscle contraction.The data will be analyzed by software developed after the prototyping of the equipment. The fragility analysis procedure will not cause any damage or impairment to the health of the elderly participants, since the items used during the procedure will be the sensor, the measurement of the instruments, the Barthel Index, the Mental State Examination, and the Self-rated fragility assessment.The validation of the sensor will not cause damage or impairment to the health of the participants.Locations: Samuel Libânio Clinical Hospital, in the clinics of Health Clinic, Dementia, and Assistance Nucleus Nursing Education, and in the Basic Health Units of the municipality of Pouso Alegre-MG.Casuistry: Convenience sample.Eligibility criteria: 300 elderly people, 60 years of age or older, both sexes, signing the Free and Informed Consent Form (TCLE), and approval by the Research Ethics Committee of University of Vale do Sapucaí (UNIVÁS).Criteria for non-inclusion: Elderly people with immobility or severe cognitive impairment that impedes understanding of the orientation towards the TUG.Exclusion criteria: The waiving of continuing the study after the signing of the TCLE.

This article presents an investigation about the performance of classification algorithms used for predicting payments’ default. Classifiers used for modelling the data set include: Logistic Regression; Naive-Bayes; Decision Trees; Support Vector Machine; k-Nearest Neighbors; Random Forests; and Artificial Neural Networks. These classifiers were applied to both balanced and original data using the Weka data mining tool. Results from experiments revealed that Logistics Regression and Naive Bayes classifiers had the best performance for the chosen data set.

Clinical health informatics is a new innovation in healthcare systems to transform paper-based systems to electronic systems. Health information is enhancing care coordination, quality and efficiency, but there are concerns related to protecting security and confidentiality of data. The main aspect of using a different electronic package in hospitals depends on important factors such as confidentiality, integrity and availability of health data. This paper is an integrative review of the evidence to compare the Confidentiality, Integrity and Availability (CIA) model in different Electronic Health Records [1] and identify the contributing factors in selecting different vendors in hospitals. The Johns Hopkins Nursing Evidence-Based Practice model was used to appraise the quality of studies related to health informatics. Forty-five titles were reviewed and, after reviewing 27 abstracts and contents, seven papers were included in this study. According to the reviewed evidence, a health information framework includes “Confidentiality, Integrity and Availability Triad, MEDITECH, Cerner and EPIC were the most popular hospital software packages because of being user-friendly, accessibility, lower cost and high security.

Recent widely-known hacking exploits have increased the focus on computer and network security. System users need systems to provide confidentiality, integrity, availability and authenticity for their data. Access control, firewalls, and antivirus software are three ways to provide system security. They address different aspects of computer security with complementary advantages and disadvantages.

The paper is dedicated to building big data processing methods and image classification using machine learning algorithms. Machine learning methods and their application to computer vision tasks, in particular to image classification, are investigated. Supervised learning applied to image classification is considered. Computational experiments and comparative analysis of various machine learning methods applied to image classification problem are carried out.

The students’ portal is a student management information system developed in-house for higher education institution in Kazakhstan. It represents a major part among university information systems. This paper reviews the common features of the general portal structure. The architecture of the new students’ portal framework is presented (Boranbayev, Nonlinear Anal. 71:1633–1637, 2009). It consists of a web application developed for the university, dedicated for students and staff members of the department of Student Affairs. It was developed in the last 6 years with such technologies like IBM WebSphere, Java EJB, JavaScript, HTML, and Oracle Database. The university’s system designers and application development team constantly work on enhancing and improving it. The software architecture of the developed portal is shared among various web applications at the university (Boranbayev and Boranbayev, Development and optimization of information systems for health insurance billing. Seventh International Conference on Information Technology: New Generations (ITNG 2010), Las Vegas, Nevada, USA, 2010, pp. 1282–1284). This architecture and experience may be used by various development teams developing local applications for universities, either in-house or with the help of suppliers and vendors.In addition, the paper discusses how the students’ portal components were developed. The research contributes towards the higher education field worldwide by providing a solution that could be followed for building university portals with various components.

In the interest of minimizing bandwidth usage, a modified Huffman code structure is proposed, with an accompanying algorithm, to achieve excellent lossless compression ratios while maintaining a quick compression and decompression process. This is important as the usage of internet bandwidth increases greatly with each passing year, and other existing compression models are either too slow, or not efficient enough. We then implement this data structure and algorithm using English text compression as the data and discuss its application to other data types. We conclude that if this algorithm were to be adopted by browsers and web servers, bandwidth usage could be reduced significantly, resulting in cut costs and a faster internet.