ITNG 2022 19th International Conference on Information Technology-New Generations

Modelling tools traditionally focus on one specific editing notation (such as text, diagrams, tables or forms), providing additional visualisation-only notations. For software-intensive systems with heterogeneous components and entailing different domain-specific aspects and different stakeholders, one editing notation is too little and voids many modelling benefits. Seamless blended modelling, which allows stakeholders to freely choose and switch between graphical and textual notations, can greatly contribute to increase productivity as well as decrease costs and time to market. In this paper we describe our work in bridging two powerful (meta) modelling platforms: Eclipse Modeling Framework, for the definition of tree-based and graphical DSMLs and models conforming to them, and JetBrains MPS, for the description of textual DSMLs and the projectional manipulation of textual models via multiple views. The possibility to visualise and edit the same information in these two platforms, otherwise disjoint, can greatly boost communication between stakeholders, who can freely select their preferred notation or switch from one to the other at any time.

In this paper, we propose a conceptual framework to facilitate the design and development of an automation system in which time-sensitive networking (TSN) is utilized for the backbone network and OPC UA is used for modeling of data exchange over TSN. As the configuration of OPC UA over TSN in a large automation setup can be a challenging task and requires specific expertise, we propose to add an abstract modeling layer that adopts the concepts of model-based development and component-based software engineering to facilitate the development of these systems. The proposed conceptual model can be automatically translated to the OPC UA modeling format. Such a modeling view will significantly reduce the complexity of OPC UA configurations, specially in large automation systems. Another benefit of the proposed framework is that the engineers, who do not have high levels of expertise in OPC UA, will be able to easily configure the OPC UA nodes in the automation system that utilize TSN for backbone communication.

Virtual worlds have the potential to mirror many aspects of real life. Immersive virtual worlds constructed through the use of Virtual Reality (VR) are useful in simulating the technology, equipment, and practices of many different fields. In the medical field, VR can be heavily relied upon to circumvent a wide variety of tools, human resources, and other objects that may be limited or difficult to procure at any given time. As a result, the goal of this research was to develop a low-stakes virtual environment (VE) in which medical students could practice developing skills necessary to their profession. As such, this environment needed to mirror, as closely as possible, an environment the medical students would see frequently during their practice. The result of this work is an application for use in patient handoffs, a situation where patient care is transferred from one medical professional to another. In order to achieve this, this work created a multiplayer VR environment with an immersive virtual world simulating standardized patient rooms and standard mediums of communication and interaction between users. While doing so, a framework was developed as the need for VR multiplayer, VR with voice communication, and a VR interaction system was seen to be needed for future VR multiplayer applications. This framework can be used to construct more applications for communication fueled environments, like the patient handoff.

One of the most common errors developers make is to provide incorrect string identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a significant percentage of defects observed in real-world codebases belong to this category. Existing work focuses on semantic static analysis, while this paper attempts to tackle challenges that can be solved using syntactic static analysis. While semantic state analysis is more powerful, it creates a greater computational burden on tool processing while simple static analysis may be computed faster, allowing for better integration in inline syntax-highlighting marker in a user interface or a quick pass through large codebases. This paper proposes a tool for quickly identifying defects at the time of injection due to dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in string identifiers. The proposed solution reduces the delta (time) between defect injection and discovery with the use of a dedicated just-in-time syntactic string identifier resolution tool. The solution focuses on modeling the nature of syntactic dependencies across the stack, and providing a tool that helps developers discover such dependencies. This tool was validated against a set of real-world codebases to analyze the significance of these defects.

The paper surveys the current state-of-the-art at promising US companies dedicated to drug discovery using computational methods. Computational fluid dynamics and molecular dynamics are only two methods that can be adapted to drug discovery using computational methods. Drug discovery is promising with the improvements in computer power and algorithms and is likely to play a more critical role.

Blended modeling is an emerging trend in Model-Driven Engineering for complex systems. It enables the modeling of diverse system-related aspects through multiple editing notations seamlessly, interchangeably, and collaboratively. Blended modeling is expected to significantly improve productivity and user-experience for multiple stakeholders. Case-specific solutions providing blended modeling, to a certain extent, for domain specific languages have been provided in the last few years. Nevertheless, a generic and language-agnostic full-fledged blended modeling framework has not been proposed yet.In this paper, we propose a comprehensive and generic blended modeling framework prototype that provides automated mechanism to generate graphical and textual notations from a given domain-specific modeling language. Moreover, it offers a flexible editor to get expert’s feedback on the mapping between graphical and textual notations. The proposed prototype is validated through a proof-of-concept on the Portable test and Stimulus Standard use-case. Our initial results indicate that the proposed framework is capable of being applied in different application scenarios and dealing with multiple domain-specific modeling standards.

Handling the software complexity of modern vehicular systems has become very challenging due to their non-centralized nature and real-time requirements that they impose. Among many software development paradigms for these systems, model-based development excels for several reasons including its ability to verify timing predictability of software architectures of these systems using pre-runtime timing analysis techniques. In this work, we propose a comprehensive framework that captures the timing related information needed for the modeling languages to facilitate these timing analyses. We validate the applicability of the framework by comparing two modeling languages and their respective tool-chains, Rubus-ICE and APP4MC, that are used for software development in the vehicle industry. Based on our results, both modeling languages support the design and analysis of vehicle software, but with different. Both modeling languages support time-, event- and data-driven activation of software components and modeling of single- and multi-rate transactions. Amalthea targets applications on single nodes with multi-core architectures while RCM focuses on single-core single-node and distributed embedded systems with ongoing work for supporting single-node multi-core architectures. In comparison to Amalthea, RCM provides a generic message model which can easily be re-modeled according to protocol-specific properties.

Insulin infusion pumps are safety-critical systems that require the approval of regulatory agencies before commercialization to prevent hazard situations. Nowadays, many recalls are reported for insulin infusion pump systems, motivating the usage of a formal model-based approach to improve quality. However, the usage of such approaches increases costs and development time. Thus, this study aims to assist the quality assessment of such systems cost-effectively and time-efficient. We defined a coloured Petri nets model-based approach and conducted a case study on the ACCU-CHEK Spirit system to verify and validate a reference model, describing quality assessment scenarios. We also conducted an empirical evaluation of the approach with 12 modelers to verify productivity and reusability. Using the approach, 66.7% of the modelers stated no effort, while 8.3%, stated low effort, 16.7% medium effort, and 8.3% considerable effort. Given such results, we developed a web-based application to assist modelers in re-using the proposed approach. The usage of the approach can decrease development time and thus costs, increasing confidence in quality attributes such as safety and effectiveness.

Currently, there is a gap between university education in the area of software engineering and the corporate and industrial environment. In this way, it is necessary to explore new learning models to transmit complex knowledge more quickly and interconnect these different environments. For that, the STEPES-BD was developed on the first half of 2020, in the midst of the COVID-19 Pandemic. In this project, the students have put into practice some concepts from three different undergrad and graduate courses of Computer and Electronic Engineering in the Area of Informatics of the Aeronautics Institute of Technology (Instituto Tecnológico de Aeronáutica – ITA) in Brazil. To approach these different worlds, it was used the Interdisciplinary Problem-Based Learning (IPBL), the Scrum Framework (SF), and also several emerging Information Technologies of public domain. This article aims to present the main results obtained by students in just one semester of 17 weeks, in a collaborative and cooperative manner, allowing them to implement a computer system prototype to help solve a real-world problem of managing health information for the COVID-19 Pandemic. In this article, it is described how the IPBL, the SF, its ceremonies, roles, and artifacts were adapted. The assessment of students experience in the STEPES-BD Project was carried out using the Tuckman Model, extracting quantitative and qualitative results, comparing the results achieved with related works, and acquiring students’ perceptions through project-end questionnaires.

The evolution of distributed and cloud-based systems has led the computing community to converge on Microservice Architecture (MSA) as a preferred solution to distributed software design. Established design methodologies applied to MSA (e.g., Data-, Model-, and Domain-Driven Design) assist in decision-making about business capacity and functionality encapsulated by the microservice. An expected result of microservice design is a well-defined Application Programming Interface (API) that facilitates the access of microservice and system capabilities. However, even with the extensive documentation and defined frameworks guiding practitioners in their execution of MSA, challenges exist in defining and exposing clean APIs. Further, the industry’s current focus on maximizing business capacities exposed by distributed systems emphasizes the importance of improving API design and implementation. To this end, API-First Design is emerging as a viable approach to MSA and API design. API-First principles suggest that all capabilities of an organization and its systems are exposed via an API and that the foundation of system design is the definition of clear and well-defined APIs. A significant challenge associated with API-First Design lies in the infancy of the topic and the necessity for peer-reviewed research defining guidelines for adoption and a baseline for future research. This paper seeks to move the state of the API-First Design methodology forward by exploring publications of the academic community and grey literature available on the topic. The paper concludes with a discussion about future research opportunities that may advance the understanding and adoption of API-First Design.

This paper describes the conduction of a tutorial to characterize the experience with dealing with nonconformities of open government data. The goal was to analyze data quality to identify limitations and errors relative to the use of open government data from the viewpoint of researchers and users in the context of Brazilian Student Financing (FIES). We planned the tutorial in seven steps to analyze the FIES data quality dimensions and present the results of difficulties and challenges faced by the participants.

ETL (Extract, Transform, and Load) is an essential process required to perform data extraction from data sources, transforming the extracted data into an integrated format and loading the integrated data into a data warehouse. Moreover, the ETL process can be performed in a real-time way. This paper presents a survey of real-time ETL process applied to data warehousing environments. The related studies were collected by means of a systematic review conducted by a methodological protocol. Moreover, we grouped the studies by categories, which had not been made before in the literature. The results showed that we have a wide range of opening opportunities of original research, as there are a lot of initial ideas and in turn they have not yet been tested, validated, and compared to related work.

This article presents a new method for modeling graph databases using the entity-relationship model. We analyze four modeling techniques available in the literature, identify the strengths and weaknesses of each one, and propose a method that minimizes the query path while maintaining the clustering base. We performed practical tests on graph bases resulting from these models. The innovation of the new proposal is the participation of the database designer who, through their knowledge of the application’s business rules, interferes in the most appropriate type of mapping in the final model.

Here we study the problem of matched record clustering in unsupervised entity resolution. We build upon a state-of-the-art probabilistic framework named the Data Washing Machine (DWM). We introduce a graph-based hierarchical 2-step record clustering method (GDWM) that first identifies large, connected components or, as we call them, soft clusters in the matched record pairs using a graph-based transitive closure. That is followed by breaking down the discovered soft clusters into more precise entity profiles in a hierarchical manner using an adapted graph-based modularity optimization method. Our approach provides several advantages over the original implementation of the DWM, mainly a significant speed-up, increased precision, and overall increased F1 scores. We demonstrate the efficacy of our approach using experiments on multiple synthetic datasets. Our results also provide some evidence of the utility of graph theory-based algorithms despite their sparsity in the literature on unsupervised entity resolution.

A great variety of real-world problems can be satisfactorily solved by automatic agents that use adaptive learning techniques conceived to deal with data stream scenarios. The success of such agents depends on their ability to detect changes and on using such information to conveniently adapt their decision-making modules. Several detecting change methods have been proposed, with emphasis on the M-DBScan algorithm, which is the basis of this proposal. However, none of these methods is able to capture the meaning of the identified changes. Thus, the main contribution of this work is to propose an extended version of M-DBScan, called Semantic-MDBScan, with such ability. The proposed approach was validated through artificial datasets representing distinct scenarios. The experiments show that Semantic-MDBScan in fact achieves the intended goal.

With the massive use of e-learning and Learning Management Systems (LMS) in the education domain, the development of methods and techniques for evaluating the usability of systems is required. This is a critical and important task, as different user profiles interact with educational systems. For example, teachers and students of different ages and limitations (cognitive or physical) demand user-friendly systems. We present a systematic literature review, describing and comparing works that address the usability and accessibility assessment of e-learning and m-learning systems. The study provides a current view of the available methodological resources, in addition to pointing out gaps in the literature. This review is aimed at researchers seeking to improve the interface of educational systems.

A method of alternate user authentication that relies on sensory data from a smartwatch has been explored in this paper. This attempt to beef up the authentication security was made by taking the user-defined hand gesture into account while wearing a smartwatch. Eventually, the preset hand gesture would work similar way to the password-based authentication scheme. In our experiment, we recorded the 3D coordinate values measured by the accelerometer and gyroscope over a set of gestures. We experimented with 50 gesture samples comprising of five different gesture patterns and ten repeated samples for each pattern. We developed an Android WearOS smartwatch app for sensor data collection, implemented our method of sensor data processing, and performed a series of experiments to demonstrate the potential of this method to achieve high accuracy.

The Advanced Encryption Standard (AES) was standardized in 2001 by NIST and has become the de facto block cipher used today. AES is a block cipher with a block size of 128 bits and is based on the proposal by Rijmen and Daemen, named “R i j n d a e l”. The R i j n d a e l proposal includes a definition for a block cipher with 256 bits block size (and a 256-bits key), which we call here R i j n d a e l256. This variant has not been standardized. This paper describes software optimization methods for fast computations of R i j n d a e l256 on modern x86-64 platforms equipped with AES-NI and with vector AES-NI instructions. We explore several implementation methods and report a speed record for R i j n d a e l256 at 0.27 cycles per byte.

Cybersecurity ethics has emerged as a new and increasingly significant area for research and education. New and complex ethical dilemmas and conflicts in values and judgments arise as new cybersecurity technologies and policies are constantly explored and implemented to defend our cyberspace for a safe and secure living and work environment. As various cyber threats, attacks, and risks pose increasing challenges to the diverse and interconnected world we live in, there is an increasing demand for quality cybersecurity education to prepare and produce qualified and ethically competent professionals to address the cybersecurity challenges. The national Centers of Academic Excellence in Cyber Defense Education (CAE-CDE) designation by the U.S. National Security Agency and Department of Homeland Security (NSA/DHS) is a high-quality program that promotes excellence in cybersecurity education for developing qualified cyber talent. Strong curriculum and courses supported by regular mentoring are essential to successful preparation of cybersecurity professionals. This research paper proposes a new credit course in cybersecurity ethics supported by an adopted comprehensive model of mentoring for an undergraduate cybersecurity education program at a CAE-CDE designated university in the United States. The curriculum proposal will present the rationale, course description, mappings of learning outcomes and topics to the CAE-CDE knowledge unit, suggested methods of assessment, and mentoring activities. The goal of this research is to contribute a new course design with ethical mentoring to enrich and enhance national and international cybersecurity curriculum and education.

Despite the security policies that organizations follow to defend against cyber crimes, phishing attacks are still among the most popular ways the criminals use to steal user’s credentials. Spear phishing, fake websites, fraudulent emails, smishing, and vishing all fall under the umbrella of phishing attacks. Recent procedures followed by many organizations tend to develop anti-phishing tools that identify fraudulent emails and websites, which are embedded either implicitly within the web browsers and email applications or explicitly as an online service. In this research, we have evaluated the effectiveness of six online checking tools in detecting potentially malicious websites. Six URL scanning engines from the best well-known engines in the VirusTotal website were tested on a list of legitimate and malicious URLs, which were collected from well-known anti-phishing frameworks, including PhishTank. In order to find the most efficient anti-phishing tool, the detection accuracy, precision, recall, and F1-Score were calculated for each engine. The results showed that among website checking tools, Sophos achieved a detection accuracy of 99.23% and a precision value of 100%.

Ensuring trust between Internet of Things (IoT) devices is crucial to ensure the quality and the functionality of the system. However, with the dynamism and distributed nature of IoT systems, finding a solution that not only provides trust among IoT systems but is also suitable to their nature of operation is considered a challenge. In recent years, Blockchain technology has attracted significant scientific interest in research areas such as IoT. A Blockchain is a distributed ledger capable of maintaining an immutable log of transactions happening in a network. Blockchain is seen as the missing link towards building a truly decentralized and secure environment for the IoT. This paper gives a taxonomy and a side by side comparison of the state of the art methods securing IoT systems with Blockchain technology. The taxonomy aims to evaluate the methods with respect to security functions, suitability to IoT, viability, main features, and limitations.

Driven by the need to offer digital solutions to the population, the healthcare sector requires computational solutions with features of security, immutability and traceability for data transactions on the Electronic Health Record (EHR). An EHR is defined as a repository of healthcare information stored and transmitted in a secure and accessible way by authorized users. To address this important area of research, this work investigated state of the art practice and studies that addressed the development and validation of computational solutions with Blockchain technology applied to the following areas of an EHR lifecycle: (i) modeling and standardization (ii) data storage techniques, (iii) standards for data interoperability, and (iv) data retrieval and visualization solutions. Based on the results found, this study presents an analysis of the main advances and opportunities identified in the use of Blockchain technology in the development of healthcare applications.

Patient’s privacy, electronic health records’ confidentiality, integrity and all related e-health security issues are the most critical elements of a successful digital health, as they support building trust between patients and healthcare stakeholders. Blockchain technology appears to cover a wide range of these elements. However, the use of this emergent technology in healthcare domain, still has many security and privacy challenges that need to be overcome. Recently, many research works are focusing on such issues leading to a growing literature. In the perspective to review this literature in a systematic way to deeply investigate the use of blockchain technology in healthcare for security enhancement and privacy protection, this paper proposes a protocol that could be used to conduct successfully this systematic literature review (SLR). The proposed protocol follows the PRISMA-P 2015 Guidelines. At a closer look, we indicate the use of snowballing search and automated search (on eight electronic data sources) to carry out the intended SLR, identify five pertinent research questions, and specify the related inclusion/exclusion criteria. All methods for selection process, data collection and data analysis that will be used in the intended SLR are described in this protocol.

The objective of this paper is to describe the front-end and the backend of an open-source web-application that can be integrated in any website and for which the storage of single sign-on (SSO) authentication is provided in an Ethereum network. The backend of this application is shared with a browser-based platform or Android platform. Ethereum network facilitated the implementation of peer-to-peer multi-node blockchain in distributed ledger technology. We use smart contract code for user creation and authentication. A contract is a collection of code (its functions) and data (its state) that resides at a specific address on the Ethereum blockchain. The smart contract made our proposed web app self-verifying, self-executing, and tamper resistant. The proposed software system can be used as two factor authentications in combination with passwords for servers, for payments authorizations, in banking and automotive industry.

Colorectal cancer is a major cause of death. As a result, cancer detection using supervised learning methods from endoscopic images is an active research area. Regarding early-stage colorectal cancer, preparing a significant number of labeled endoscopic images is impractical. We devise a technique for detecting early-stage colorectal cancer in this study. This technique consists of a 2D complex discrete wavelet packet transform and principal component analysis. As this technique does not require supervised learning, detection is feasible even in the absence of labeled data. In the endoscopic image, this technique correctly classifies early-stage colorectal cancer and normal regions with 92% accuracy. This approach outperforms the local binary pattern method.

Three-dimensional (3D) models have been used as essential tools in medical training. In this study, we visualize 3D models of human organs with graphics software for the purpose of training medical students. This study investigates whether 3D organ visualizations will be more recognizable to medical students than two-dimensional (2D) organ images. In our experiments, the models were shown to health science students to determine how useful they were in training and we compared the use of 3D models with 2D images. We conclude that the 3D organ models we used are more likely to be recognized by the students.

During the 1st Semester of 2020, 25 students from the Aeronautics Institute of Technology (Instituto Tecnológico de Aeronáutica – ITA) in São José dos Campos, SP, Brazil developed the academic project “Specific Technological Solutions for Special Patients with Big Data”, in Portuguese Projeto STEPES-BD: Soluções Tecnológicas Específicas para Pacientes Especiais e Sistemas em Bancos de Dados. They have accepted the challenge of using a technological approach to help manage and combat the Sars-CoV-2 Virus Pandemic (COVID-19). At that time, the lack of shared data between public and private agencies and the need for faster information flow were considered the main information for combating the spread of the disease to guide the development of an Information Management System for the COVID-19 Pandemic, involving the essential data from Electronic Health Records (EHRs). The combination of some emerging technologies like Big Data and Blockchain, together with the Scrum Framework (SF) and the Interdisciplinary Problem-Based Learning (IPBL) enabled those students from three different academic courses to develop a computer system prototype based on the pressing needs caused by this disease. This article describes the development of the main deliverables made by those graduate students in just 17 academic weeks right from the beginning of the COVID-19 Pandemic crisis in the 1st Semester of 2020.

The objective of this paper is to develop a Machine learning model that can classify cancer patients. Gene mutation-based treatment has a very good success ratio, but only a few cancer institutes follow it. This research uses natural language processing techniques to remove unwanted text and convert the categorical data into numerical data using response coded and one-hot encoded. Then we apply various classification algorithms to classify the training data. The proposed system has the advantage of reducing the time to analyze and classify clinical data of patients, which translates into less wait time for patients in order to get results from pathologists. The results of our experiment will demonstrate that the Stacking Classifier algorithm with One-Hot encoding and Term Frequency – Inverse Document Frequency (TF-IDF) techniques perform better than other Machine Learning methods with around 67% accuracy on the test data.

Identifying genetic mutations that contribute to cancer tumors is the key to diagnosing cancer and finding specific gene mutation-based treatment. It is a very challenging problem and a time-consuming job. Currently, clinical pathologists classify cancer manually, and they need to analyze and organize every single genetic mutation in cancer tumors from clinical text. The text data analysis can be automated using Deep Learning and Machine Learning classification techniques to ease the manual work needed to extract information from clinical text. This paper aims to analyze the performance of Machine Learning and Deep Learning methods to classify cancer from gene mutation-based clinical text data. This paper uses Natural Language Processing techniques, namely, CountVectorizer and TfidfVectorizer, and Keras API’s One-Hot encoding and to-categorical utility, to vectorize the categorical and text data and transform them into numerical vectors. Machine Learning classification algorithms and Deep Learning methods are then applied to the extracted features, and the most accurate combination of feature extraction and a classifier is discovered. Keras API’s Embedding Layer (Word Embeddings) and Bidirectional Long-Term Short-Term Memory (Bidirectional LSTM) techniques using original and augmented text data from NLPAug library are applied as Deep Learning methods. The Keras Word Embeddings using augmented text data ha performed the highest with an accuracy of 80.67%, the weighted average precision of 0.81, recall of 0.81, F1 score of 0.81, and the log loss of 0.6391. As for the Machine Learning classification algorithms, Random Forest and Stacking classifiers are explored within this paper, and the highest accuracy of 67.02% is achieved from the Random Forest classifier with the weighted average precision of 0.70, recall of 0.67, F1 score of 0.65, and the log loss of 1.0523.

In this paper we present a Stock Backtesting Engine which would test historical data using pairs trading strategy. We implemented pairs trading strategy and ran it on historical data. We collect the S&P 500 data from the Internet and store it in a database. We then allow users to enter a set of stocks to find cointegrated pairs among them. We also provide an option to find the cointegrated pairs in all of S&P 500 stocks. Once the cointegrated stocks are selected we run the backtesting algorithm on these pairs and find from a given set of stocks, all the pairs of stocks that exhibit cointegration properties. Once such pairs are identified, this program would use pairs trading methods to calculate trades for each stock. Finally we provide the analysis of trades executed by the algorithm with average and daily data, and plot a chart of daily profit and loss with pairs trading strategy to showcase the effectiveness of the trading strategy.

Quora is an online platform that empowers people to learn from each other. On Quora, users can post questions and connect with others who contribute unique insights and quality answers. But as with any other social media or online platform, there is the potential for misuse. A key challenge in maintaining the integrity of such an online platform is to classify and flag negative content. On Quora, the challenge is to identify insincere questions. Insincere questions could be those founded upon false premises, are disparaging, inflammatory, intended to arouse discrimination in any form, or intend to make a statement rather than look for helpful answers. We propose to develop a text classification model that correctly labels questions as sincere or insincere on the Quora platform. For this purpose, we used the Quora Insincere Questions Classification dataset, which is available on Kaggle. We first trained classical machine learning models such as Logistic regression and SVMs to establish a baseline on the performance. However, to leverage the large dataset, we used neural network-based models. We trained several models including standard neural networks, and LSTM based models. The best model that we obtained is a two-layer Bidirectional LSTM network that takes word embeddings as inputs. The classification accuracy and F1-score for this model were 96% and 0.64, respectively.

The objective of this paper is to the use of the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and performs well on the large, sparse, and imbalanced music/movie dataset. In this project, we compare various PMF-based models and apply them to the recommendation system. We design and develop a Mix Probabilistic Matrix Factorization (MixPMF) model for music recommendation. This new model will take advantage of user network mapping and artist tag information and forms the effective rating matrix and thus will be efficient in recommending music/movies to new users. Simulation results show the advantage of our model.

Text summarization creates a brief and succinct summary of the original text. The summarized text highlights the main text’s most interesting points without omitting crucial details. There is a plethora of applications on the market that include news summaries, such as Inshort and Blinklist which not only save time but also effort. The method of manually summarizing a text can be time-consuming. Fortunately, using algorithms, the mechanism can be automated. We apply three text summarization algorithms on the Amazon Product Review dataset from Kaggle: extractive text summarization using NLTK, extractive text summarization using TextRank, and abstractive text summarization using Seq-to-Seq. We present the advantages and disadvantages for these three methods.

The search and identification of people lost in an emergency is a very important activity, it is carried out to assist in human lives in danger, and the unavailability of support technologies. Unmanned aerial vehicles (UAV) act directly in this activity for greater capacity in the coverage of the area in a shorter time of operation. A brief state-of-the-art survey is presented, and specific needs are raised for accuracy and practical application. In this joint work, low-processing image recognition alternatives for use in UAV will be explored to face challenges such as location in large areas, small targets, and orientation. Assessments are performed on real images using Inception, SSD, and Yolo. For tracking, MIL, KCF, and Boosting techniques are applied to empirically observe the results of test missions. Preliminary results show that the proposed method is viable for action in the search and rescue of people.

This paper presents an application for Virtual Reality (VR) interfaces for virtual hands which will allow us to compare interaction between virtual hands and VR controllers in Virtual Environments (VEs). Development for human-computer interaction in VEs needs improvement to accommodate the growth and need for applications inside VR. Virtual hands are growing more prevalent with many devices detecting the location and mimicry of the user’s own hands inside the VE. Virtual hands can also be implemented via VR Gloves to more precisely pinpoint the movements of hands. This work also implements interaction mediums that can be used by virtual hands or VR controllers to directly manipulate and control virtual objects and virtual interfaces. Unity was used to generate the VE and to render the input mediums and interactable objects. SteamVR was used to connect the input mediums to Unity. The HTC Vive Pro Eye was used to connect the user to the VE. The two input mediums that were compared are the HTC Vive Controllers and the HI5 gloves. All of these components come together to form an immersive and consistent means to compare input mediums in different kinds of interactions.

Light Detection and Ranging (LiDAR) sensors have been employed in many different ways over time and continue to be utilized today. These sensors produce point clouds which are large and complex data sets that are a collection of position points across a 3D space. As LiDAR point cloud data can be highly complex, it can often be difficult to conduct analysis and visualization of the data sets. A web tool was developed to analyze and visualize this type of data, ensuing in an interactive and readable representation of the data. The data obtained for this tool is from LiDAR sensors located on street lights directly adjacent to the University of Nevada, Reno to analyze traffic information. In order to ensure the effectiveness of the tool, a user study was conducted to test the functionality and assess possible improvements.

Social media is a popular pastime in our current society. There are numerous and diverse social media applications available to use. The study presented in this paper aimed to determine which application is easiest to use and most preferred by users. The apps considered were four of the most popular existing social media applications: Facebook, Twitter, Instagram, and TikTok. The participants in the study were timed while publishing a picture, a text, and a video using each application, and were asked to comment and provide their level of linking on each post they made. Post-questionnaire answers reveal that the majority of participants found Facebook the easiest and more preferable application to use. Experiment results also show that publishing videos on Facebook is quicker than on the other three media apps. On the other hand, publishing pictures and liking/commenting take about the same time on all four apps considered in our study.

Estimating vehicle operating costs (VOCs) allows individuals and organizations to make informed decisions about vehicle usage. As a wide variety of cars and roadway conditions exist, a relatively large amount of input must be provided to any VOC model. Developed as part of a civil engineering research project funded by the U.S. Department of Transportation, five VOC models were run initially in Microsoft Excel. While this early solution was practical and operational, to improve usability, including efficiency of data input, flexibility of running the models, and presentation of results, an alternative solution, a web-based application, was also designed and implemented. The VOC models that can be run on both Excel and the web-based application are: fuel economy, oil consumption, tire wear, mile-age-related vehicle depreciation, and repair and maintenance. This paper briefly introduces the VOC models, describes the two software interfaces created for running them, and presents the results of a user study conducted to evaluate and compare the two interfaces. The study involved 17 participants and focused on usability characteristics and the quality of the user experience. The independent variable was “user interface,” with two test conditions: Excel interface and web-based interface. The participants answered an entry questionnaire, performed tasks using both interfaces, and completed an exit questionnaire. Several dependent variables were measured and analyzed, including task completion time, number of incorrect data entries, and number of clarification questions asked to the user study facilitator. The results obtained showed that the web-based solution consistently outperformed the Excel-based solution, although the latter received some positive feedback as well.

When an environmental research project grows, technical concerns over system scalability, data exposure, and third-party application support are overlooked. This paper presents a system, the Microservice-based Envirosensing Support Applications (MESA), that provides a scalable environment and data infrastructure solutions for the NSF-funded Solar Energy-Water-Environment Nexus project. MESA can be broken into 4 major parts: a suite of microservices exposed over an API, an overarching service discovery, a series of tables replicated from an existing monolith, and the applications that MESA lends its support. In order to evaluate the capability of MESA, the features of this system were compared against three other existing microservice-based research systems. MESA features were more robust than two of the other systems, but was found lacking when compared to the last, as it does not lender support to advanced techniques like HPC or Machine Learning.

The main challenges in Internet of Things (IoT) refer to the varied capabilities of things, the huge amount of data they produce, the heterogeneity of this data, and the diverse offered services. For each application domain and for each vendor, there is usually a specific and proprietary IoT platform, with no de facto standards currently being found or expected in the near future. Therefore, ensuring the semantic interoperability of things between different types of IoT platforms and applications is one of the major problems in this area. This paper aims to identify current conceptual and practical findings for the semantic interoperability problem in IoT through a Systematic Literature Review (SLR). By searching digital libraries and using the snowballing technique, 314 manuscripts were selected for data extraction from this SLR, which allowed us to come up with the current main issues and solutions related to this matter. The results obtained with this SLR and reported in this paper can help researchers and practitioners to find better solutions for this problem.

Machine Learning based designs provide an extensive means to recognize patterns and do various kinds of predictions. In this paper we apply machine learning to the Internet of Things architecture to optimize access to smart parking. We assume that the system will detect which driver parked at which spot, and also will recognize driver’s habit of returning to the car. This way we predict that the spot of the driver walking towards the parking lot or garage will soon be available for other drivers. Drivers looking for a parking spot will receive such information in advance, in a form of “Spot N will be available in X minutes”. The design operates over the features offered by smartphone devices: to determine the parking spot, determine the walking driver’s position and also serving as a base for a mobile application, so the system is convenient to use and doesn’t require additional infrastructure.

We propose CSIFill, a novel system to predict the Channel State Information (CSI) in indoor wireless networks. CSIFill can estimate the CSI on different frequency subcarriers by using the CSI measurements from neighboring frequencies. CSIFill is different from traditional estimation techniques which attempt to recreate the wireless channel and instead relies on already collected CSI data, to predict the CSI on different wireless frequencies. This is especially useful in indoor wireless networks where an Access Point (AP) needs to periodically measure the CSI on other frequency channels to find better data rates. CSIFill can be used to automatically determine when to switch to another channel to obtain better service without any additional probing or overhead. Our initial results with CSIFill have been very encouraging. CSIFill was evaluated using real world experimental CSI data and was found to accurately estimate CSI data for up to 7.5 MHz channel bandwidth using a shallow neural network.

In this paper we aim to design a brand new corporate structure which merges Physical and Digital Technology in World-Class Business, whilst utilizing new technologies based on digitalization drivers. We propose a method in which we have introduced a state of the art Organizational structure that surveys our world-class Business Model case studies and all of the mandatory technologies which shape the fundamental framework of such innovative business ecosystems that end up as the most important input for the decision making process in management levels. In the upcoming research we will be comparing this model with other models in different aspects like comparability in utilizing brand new technologies to building up the dominant share of global market.

This research aims to understand how soundtrack elements can stimulate an emotional response in viewers whilst watching film and how sound professionals utilise sound for this purpose. The article reviews research on affective audio in film and informs ten semi-structured interviews with film sound professionals. These interviews cover a variety of topics, all with a focus on sound design. The interview transcripts were thematically analysed, and six themes were drawn from them and are discussed in detail and explored through case-study examples. A workflow for sound design is synthesised using each theme as a step in the post-production process, intended as a way of visualising how this research can aid the film-sound profession and inform future research in the field. It is concluded that sound is currently used to bring about affective responses in specific instances, when other emotional devices are not already in use. The use of sound as a narrative aid is found to be more prevalent, and indeed currently seen as more important within the industry than for specific affective use. Further research is suggested to enhance understanding of affective audio and to develop a framework for its widespread effective implementation in film.

Algorithms for covering and simplifying a 1.5D terrain have been extensively investigated. Minimally covering a 1.5D terrain is an intractable problem. We present a critical review of existing approximation algorithms for simplifying a 1.5 D terrain by a fewer number of vertices. We introduce the problem of simplifying a 1.5D chain with a fewer number of vertices subject to visibility requirement. We then present the development of an efficient algorithm for chain approximation with minimal effect on external visibility.

One of the dynamic properties of Petri nets is liveness, which ranges from L0 to L4 depending on the severity of the condition. The structure required for the existence of strictly L3-live transitions is referred to as the strictly L3-live structure. The strictly L3-live structure consists of three elements: a repeating closed circuit (L3-circuit), a transition that cancels the liveness of the L3-circuit (CircuitBreaker), and a place that receives a token supplied by the L3-circuit (k-place). To detect these elements, we use the partially-conservative, partially-repetitive, and bounded structurally properties of general Petri nets, as well as solving the matrix inequalities, which are necessary and sufficient conditions for the three structural properties, using the SAT solver to detect the three elements that constitute the strictly L3-live structure.

The home state is a property of Petri nets. The home state is a state that can be returned to from all markings, and is the stable state in the system. In this study, we define a quasi-home state as a state in which the conditions of the home state are relaxed. The quasi-home state is the home state in submarkings, which are abstracted markings. The submarking method can compress the state space while giving meaning to the marking. In this study, we defined four submarking methods. The user specifies the place to be abstracted and the submarking method, and submarking is obtained. The home state is obtained through the submarking’s dynamic analysis. This study aims to improve the net analysis’ efficiency using the submarking method to determine the quasi-home state.