Pattern Analysis, Intelligent Security and the Internet of Things

As recent studies have discussed problem-based learning (PBL) as a popular model of fostering creativity, this paper aims to explore a research question: How can we bridge creativity and group work using elements of PBL to deepen understanding of PBL as a tool for creative learning? A theoretical framework aiming to respond to this question will be provided from a literature review. Accordingly, five elements include (1) group learning, (2) problem solving, (3) interdisciplinary learning, (4) project management, and (5) facilitation. These main elements show PBL is a suitable learning environment to develop individual creativity and to stimulate interplay of individual and group creativity. This study builds a theoretical model, urging a systematic view of PBL in creative facilitation and also indicates its practical significance and potential questions for future investigation.

Authentication with images or better known as graphical password is gaining its recognition as an alternative method to authenticate users, for it is claimed that images or pictures are easier to use and remember. The same method can be applied to password recovery, with the purpose to ease the process of users in regaining their account in case of forgotten passwords. A total of 30 participants were asked to use a prototype implementation of graphical password recovery and provide feedbacks. The data gained were analyzed in terms of attempts, timing, pattern, and user feedback. Overall, it was found that participants had no problem in using graphical password recovery despite they were new to it. Most of them preferred the choice-based method, even though they agreed that it provided less security. Graphical recovery has potential to be used more widely in current technology, although more works need to be done to balance the issues of usability and security.

In this paper, classification of brain wave using real-world data from Parkinson’s patients in producing an emotional model is presented. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aims to find the “best” classification for brain wave patterns in patients with Parkinson’s disease. This work performed is based on the four phases, which are first phase is raw data and after data processing using statistical features such as mean and standard deviation. The second phase is the sum of hertz, the third is the sum of hertz divided by the number of hertz, and last is the sum of hertz divided by total hertz. We are using five attributes that are patients, class, domain, location, and hertz. The data were classified using WEKA. The results showed that BayesNet gave a consistent result for all the phases from multilayer perceptron and K-Means. However, K-Mean gave the highest result in the first phase. Our results are based on a real-world data from Parkinson’s patients.

The past two decades have seen a remarkable shift in the knowledge- and information-sharing paradigm. In the crops domain, for example, the amount of information currently known about underutilized crops, for example, Bambara groundnut their genetics and agronomy are much richer than years before. That paradigm shift offers enormous potential for advancing knowledge representation systems to facilitate access to such data. However, inconsistencies in terminology, improper syntax, and semantics are main obstacles to sharing data and knowledge among disparate researchers. We present a formal framework for representing knowledge using OWL 2 RL ontologies and SWRL rules and to integrate and reason over data from multiple, heterogeneous underutilized crops data sources.

This paper aims to explore the changes of supervision model in higher education in relation to fostering creative PhD students in China. The changes are being made from the traditional Apprentice Master Model (AMM) to the modern Collaborative Cohort Model (CCM). According to the results of the empirical work done by questionnaire survey and interviews, this study shows in the background of the Big Science Era and according to theories on systematic view of creativity, the new CCM improves PhD students’ creativity to some extent; however, problems exist in the creativity development mechanisms. So this paper also explores the reasons of why the new mechanism of creativity development failed to play fully.

The era of big data has arrived, and a plethora of methods and tools are being used to manage and analyse the emerging huge volume, velocity, variety, veracity and volatility of information system data sources. In this paper, a particular aspect of a business domain is explored where the primary data being stored/accessed are not the data value itself (which is highly volatile), but the frequency of its change. Each data frequency has a chain of related data pertaining to it, whose links must be incorporated into this architecture. The volatility of data necessitates the use of in-memory architectures to reduce access/update times. Given these business requirements, different in-memory architectures are examined, using an experiment with sample data, in order to evaluate their worst case response times for a given test set of data analysis/manipulation operations. The results of this experiment are presented and discussed in terms of the most suitable architecture for this type of data, which is in-memory objects linked via hash table links.

This paper investigates linear and nonlinear navigations in Web-based learning (WBL) systems. The aim of the study was to identify whether the linear and the nonlinear navigations could be the factors that influence students’ engagement within WBL environment. An experimental study was conducted on seventy-two students from a university in Malaysia using a Web-based system for learning Basic Computer Networks and a self-report inventory. The results of this study suggested that the types of navigation support affected engagement from certain aspects.

This article presents a research concerning password management and single sign-on for accessing Internet applications. Many Internet applications require users to subscribe to their services and authenticate themselves through the use of login credentials. The number of such applications is increasing exponentially, which caused ineffective login credential management among users. This study was conducted with two objectives (i) to identify how users manage their usernames and passwords and (ii) to examine whether users see the benefits of single sign-on. To achieve these objectives, a focus group interview was conducted on students from a local university. The results of the study suggested that the students did not practise proper password management. Further, it suggested that single sign-on may not be the immediate solution to improve the students’ password management.

Software is an intangible computer’s component, and their requirements are the greatest challenge to handle but yet the most important. In order to ensure the requirements are in good quality, defect management is one of the promising efforts to adopt. This paper aims to provide a literature review regarding defect management for software requirements and the relevance of the defect management to improve requirements and product quality. The paper is structured based on a systematic literature review method which is constructed from significant questions. The findings on the literatures show that many efforts have been done to improve defect management effort in several stages of software development life cycle. However, the efforts are scarce in the requirement engineering phase. This paper provides a foundation study to pursue research in improving requirements and eventually product quality through defect management.

This paper presents steps taken in finding the effectiveness of software team members using decision tree technique. Data sets from software engineering (SE) students were collected to establish pattern relationship among four predictor variables—prior academic achievements, personality types, team personality diversity, and software methodology—as input to determine team effectiveness outcome. There are three main stages involved in this study, which are data collection, data mining using decision tree, and evaluation stage. The results indicate that the decision tree technique is able to predict 69.17 % accuracy. This revealed that the four predictor variables are significant and thus should consider in building a team performance prediction model. Future research will be carried to obtain more data and use a hybrid algorithm to improve the model accuracy. The model could facilitate the educators in developing strategic planning methods in order to improve current curriculum in SE education.

This paper presents a review of the literature on data completeness with the aim to learn the different forms of completeness measure proposed to date. By learning the features of the completeness measures in the literature, an understanding of the similarities (and differences) of those measures will be provided, and the gaps in the current completeness measure proposals will be identified. Definitions of data completeness and comparison of several types of completeness measures proposed to date will be presented. In particular, for each proposal, the definition of the reference data set which is used in completeness measurement and the method used to measure completeness are examined. This paper concludes by pointing out the gaps in the current literature that will be addressed in the future work.

Cloud computing (CC) is a computing model in which technology resources are delivered over the Internet. Nowadays, it has becoming one of the most popular tools used in educational institutions. The salient features of CC can be exploited for both teaching and administration purposes. This paper aims to look into the acceptance of CC in Malaysian polytechnics (MP) and identify areas that need improvement in terms of awareness. To achieve this aim, related papers in cloud computing were reviewed so as to evaluate the extensiveness of the implementation of CC in MPs. A survey was conducted among polytechnic lecturers. The results of the survey were analyzed, and it revealed that there is positive acceptance of CC in MPs in terms of readiness and perception. However, there is still a lacking on security awareness. Therefore, improvement in terms of creating security awareness among the polytechnic lecturers and strengthen the knowledge on cloud among lecturers are needed.

The Big-Five personality traits may influence people’s usage of social networking sites (SNSs), and that of children may not be the same as adolescents or adults. This study investigated the relationships between elementary school students’ personality traits and their usage of SNSs. Two hundred and forty 6th graders in Taiwan were involved in this work. The results indicated there were no gender differences in students’ SNS usage. Extraversion had a significantly positive relationship with SNSs’ usage for sharing, branding, monitoring, and learning. Emotional stability had a positive correlation with using SNSs for relaxing. Learning was the most frequent activity carried out on SNSs among the elementary school students examined in this work, and few of them used such sites for expressing, branding, sharing, or organizing.

With the high increase in the transmission of digital data over secured and unsecured communication channels, security and privacy of such data are critical in this present day of cyberspace and it is a concern to both the transmitter and the receiver. This paper proposes a cryptographic encryption technique of mpeg digital video images based on RGB layer pixel values. The cryptographic encryption technique made use of the Red, Green, and Blue channel in the encryption and securing of the digital images. The programming and implementation were done using MATLAB.

This paper reports on an initiative that determines the most appropriate technique for supporting students’ programming ability. The proposed technique combines pair programming (PP) and SECI process that is a knowledge management (KM) model. Combining PP and SECI resulted in the formation of four approaches, which are named as NSNR, NSYR, YSNR, and YSYR. In those four approaches, the subjects who are students of IT-related programs in a higher learning institution complete a set of programming questions. The approaches were then compared based on the subjects’ scores in their program codes. Descriptive statistics was used to analyze the gathered data. Generally, the results show that switching the roles (driver and navigator) in PP enhances good quality of coding. Through this study, an initial formation of the KM model and programming technique is contributed in enhancing program quality. Further, future work to be considered can be a rigorous theoretical formation for constructing other important determinants to enhance program quality because the findings of this research are minimal to SECI model and pair programming technique only.

Social Networks (SNs) become a major source of reporting new events that happen in real life even before the news channels and other media sources report them nowadays. The objective of this paper is to conduct the systematic literature review (SLR) to identify the most frequently used SN for reporting and analyzing the real-time events worldwide. Furthermore, we recognize the features and techniques used for mining the real-time events from SNs. To determine the literature related to event mining (EM) and SNs, the SLR process has been used. The SLR searching phase resulted 692 total studies from different online databases that went through three phases of screening, and finally 145 papers out of 692 were chosen to include in this SLR as per inclusion criteria and RQs. Based on the data analysis of the selected 145 studies, this paper has concluded that the Twitter micro-blogging SN is the most used SN to repot the events in textual format. The most common features used are n-gram and TF-IDF. Results also showed that support vector machine (SVM) and naive Bayes (NB) are the most frequently used techniques for SNEM. This SLR presents the list of SNs, features, and techniques that are reporting the SN events that can be helpful for other researchers for selection of SNs and techniques for their research.

In the fast-changing world of the early twenty-first century, education is also changing. The use of ICT in education lends itself to more student-centered learning settings. Given this changing landscape of teacher education, the purpose of this chapter is to explore new educational approaches to enhance teachers’ ICT capabilities in the twenty-first-century learning environment. The literature indicates a brief explanation of twenty-first-century education about the roles of (i) student, (ii) teacher, (iii) curriculum, (iv) classroom, and (v) information and communication of technology (ICT). The new approach in education nowadays is introduced, which is personalized learning environment (PLE). PLE enables learners to organize their learning, provides the freedom to choose content, and allows communication and collaboration with others easily. In conclusion, the chapter concludes with recommendations for continued improvements in twenty-first-century education in order to ensure the opportunities of higher education remain open to as many students as possible.

Peace provides the freedom to express our views, to relate with others people and create cooperation, and social networks (SNs) provide that platform. SNs can play a very important role to improve peacebuilding (Pb) applications as current peace-related studies witness that violence- and Pb-related reports are communicated through different SNs applications. People and victims of the conflicts make use of SNs and its applications to cast their concerns. However, the major setback of these SNs is to manage the huge amount of SNs data and to extract the topic specific (Pb related) information. There is lack of research done on SNCA by Pb perspective. Therefore, the objective of this research is to perform CA, means to identify which (

SN

) what (

data

) how (

to extract

)? Furthermore, what features and techniques should be used for content analysis (CA) of Pb-related data? This research proposes framework for automatic SNs data extraction (DE) and CA to achieve our objective. The proposed framework shows that Twitter is most popular SN for Pb CA purpose and proposed framework presents the searching criteria and custom filters to extract the topic specific data. Moreover, the research proposes to use lexical analysis (LA) method to extract the SNs features, first-order context representation (CR) technique to represent the context of the extracted features, DBSCAN clustering algorithm for data management by making different clusters, ranking algorithm, Log-likelihood ratio, and SVM techniques for CA and classification. The proposed framework aims to help in conducting SNCA to support Pb application in order to take important information from the sea of SNs data to predict violence-related information or incidents that will help peacekeepers for communicating and maintaining peace-related news (may it be natural disaster or manmade terrorism activities) around the world.

Handwriting is individualistic where it presents various types of features represent the writer’s characteristics. Not all the features are relevant for Writer Identification (WI) process and some are irrelevant. Removing these irrelevant features called as feature selection process. Feature selection select only the importance features and can improve the classification accuracy. This chapter investigated feature selection process using tree-base structure method in WI domain. Tree-base structure method able to generate a compact subset of non-redundant features and hence improves interpretability and generalization. Random forest (RF) of tree-base structure method is used for feature selection method in WI. An experiment is carried out using image dataset from IAM Hand-writing Database. The results show that RF tree successively selects the most significant features and gives good classification performance as well.

Interest in collaboration is a natural outgrowth of the trend in education towards active learning. Many researchers have found advantages of collaborative learning; it improves academic performance, promotes soft skills development (communication, collaboration, problem solving and critical thinking skills) and increases satisfaction in the learning experience. However, several studies have reported the opposite. Therefore, this paper aims to determine the factors to be considered in creating an effective online collaborative learning environment. In order to achieve the aims, this study was conducted qualitatively in the form of a document review. The results indicate three main factors that affect the effectiveness of online collaborative learning environments such as learning environment, learning design and learning interaction. An online learning interaction model is also proposed according to the results. This study will continue to determine the elements that can clarify all the factors which have been identified in this study.

This chapter presents a comparison and analysis of six feature extraction methods which were often cited in the literature, namely wavelet packet decomposition (WPD), Hjorth parameter, mean, coherence, cross-correlation and mutual information for the purpose of person authentication using EEG signals. The experimental dataset consists of a selection of 5 lateral and 5 midline EEG channels extracted from the raw data published in UCI repository. The experiments were designed to assess the capability of the feature extraction methods in authenticating different users. Besides, the correlation-based feature selection (CFS) method was also proposed to identify the significant feature subset and enhance the authentication performance of the features vector. The performance measurement was based on the accuracy and area under ROC curve (AUC) values using the fuzzy-rough nearest neighbour (FRNN) classifier proposed previously in our earlier work. The results show that all the six feature extraction methods are promising. However, WPD will induce large vector set when the selected EEG channels increases. Thus, the feature selection process is important to reduce the features set before combining the significant features with the other small feature vectors set.

Drug abuse is a threat to national development. Generally, drugs can be identified based on the structure of its molecular components. This procedure is becoming more unreliable with the introduction of new amphetamine-type stimulants (ATS) molecular structures which are increasingly complex and sophisticated. An in-depth study is crucial to accurately identify the unique characteristics of molecular structure in ATS drug. Therefore, this chapter is meant for exploring the usage of shape descriptors (SD) to represent the drug molecular structure. Two-dimensional (2D) united moment invariant (UMI) and three-dimensional (3D) Zernike are selected and their performances are analyzed using drug chemical structures obtained from United Nations Office of Drugs and Crime (UNODC) and various sources. The evaluation identifies the most interesting method to be further explored and adapted in the future work to fully compatible with ATS drug identification domain.

Assessing risk associated with computational grid is an essential need for both the resource providers and the users who runs applications in grid environments. In this chapter, we modeled the prediction process of risk assessment (RA) in grid computing utilizing meta-learning approaches in order to improve the performance of the individual predictive models. In this chapter, four algorithms were selected as base classifiers, namely isotonic regression, instance base knowledge (IBK), randomizable filtered classified tree, and extra tree. Two meta-schemes, known as voting and multi schemes, were adopted to perform an ensemble risk prediction model in order to have better performance. The combination of prediction models was compared based on root mean-squared error (RMSE) to find out the best suitable algorithm. The performance of the prediction models is measured using percentage split. Experiments and assessments of these methods are performed using nine datasets for grid computing risk factors. Empirical results illustrate that the prediction performance is enhanced by predictive model using ensemble methods.

Risk Assessment is a common practice in the information system security domain, besides that it is a useful tool to assess risk exposure and drive management decisions. Cloud computing has been an emerging computing model in the IT field. It provides computing resources as general utilities that can be leased and released by users in an on-demand fashion. It is about growing interest in many companies around the globe, but adopting cloud computing comes with greater risks, which need to be assessed. The main target of risk assessment is to define appropriate controls for reducing or eliminating those risks. The goal of this paper was to use an ensemble technique to increase the predictive performance. The main idea of using ensembles is that the combination of predictors can lead to an improvement of a risk assessment model in terms of better generalization and/or in terms of increased efficiency. We conducted a survey and formulated different associated risk factors to simulate the data from the experiments. We applied different feature selection algorithms such as best-first and random search algorithms and ranking methods to reduce the attributes to 4, 5, and 10 attributes, which enabled us to achieve better accuracy. Six function approximation algorithms, namely Isotonic Regression, Randomizable Filter Classifier, Kstar, Extra tree, IBK, and the multilayered perceptron, were selected after experimenting with more than thirty different algorithms. Further, the meta-schemes algorithm named voting is adopted to improve the generalization performance of best individual classifier and to build highly accurate risk assessment model.

Illicit Web content filtering is a content-based analysis technique, applied to censor inappropriate contents on the Internet. Web content filtering can recognize undesirable contents through the application of AI techniques, linguistic analysis, or machine learning to classify Web pages into a set of predefined categories. However, the capacity to distinguish between useful and harmful Web content remains a major research challenge, which usually leads to the problem of under-blocking and over-blocking. Further, the extraction of best term representation for classifier presents a major limitation due to curse of dimensionality, where a feature can have the same term frequency (TF) in two or more categories but has different semantic meanings such as illicit pornography and sex education context also known as ambiguous issues. Besides, the high dimensionality of features on a Web page, even for moderate size, it has made the term representation value for classifier more complex, which affects the performance of classification. Thus, this research proposes a modified term weighting scheme (TWS) for narrative and discrete Web in order to increase the classification performance. Characteristics of pornography Web site were extracted and significant characteristics were identified and mapped against term weighting factors. Initial result revealed that other criteria such as rare feature have potential to be regarded as significant criteria in TWS technique to distinguish high-similarity Web content.

Many Identity-Based two-party Key Agreement protocols have been proposed in recent years. Some of them are built on pairing maps, whereas some others could eliminate the pairings in order to decrease the complexity of computation. In this paper, we proposed a secure pairing-free Identity-Based two-party Key Agreement protocol which besides supporting security requirements uses less computational cost in comparison with existing related works.

Due to the high computation cost of bilinear pairings, pairing-free cryptosystems have received widespread attention recently. Various pairing-free two-party key agreement protocols in the context of public key cryptography (PKC) have been studied. To avoid complex certificate management in traditional PKC and key escrow problem in identity-based ones, several certificateless cryptosystems have been proposed in this research area. In this paper, we proposed a secure and efficient certificateless pairing-free two-party key agreement protocol. In comparison with related works, our protocol requires less computational cost.

Ganoderma boninense (G. boninense) is one of the critical palm oil diseases that have caused major loss in palm oil production, especially in Malaysia. Current detection methods are based on molecular and non-molecular approaches. Unfortunately, both are expensive and time consuming. Meanwhile, wireless sensor networks (WSNs) have been successfully used in precision agriculture and have a potential to be deployed in palm oil plantation. The success of using WSN to detect anomalous events in other domain reaffirms that WSN could be used to detect the presence of G. boninense, since WSN has some resource constraints such as energy and memory. This paper focuses on feature selection to ensure only significant and relevant data that will be collected and transmitted by the sensor nodes. Sixteen soil features have been collected from the palm oil plantation. This research used rough set technique to do feature selection. Few algorithms were compared in terms of their classification accuracy, and we found that genetic algorithm gave the best combination of feature subset to signify the presence of Ganoderma in soil.

Graphical user authentication (GUA) is an alternative replacement for traditional password that used text-based form. Even though GUA has high usability and security, it is also facing security attacks that legitimate from the traditional password such as brute force, shoulder surfing, dictionary attack, social engineering, and guessing attacks. The proposed category-based graphical user authentication (CGUA) scheme is developed for web application and based on image category. This category image is inspired from the Hanafuda Japanese card game. The scheme also involved several security features such as decoys, randomly assigned, hashing, limited login attempts, and random characters to strengthen the CGUA scheme. Overall, the proposed CGUA scheme was able to mitigate known attacks based on the security features analysis.

Due to the resource constrained property of mobile ad hoc networks (MANETs), making their application more lightweight is one of the challenging issues. In this area, there exists a large variety of cryptographic protocols especially in the context of public key cryptosystems. The difficulty of managing complex public key infrastructures in traditional cryptosystems and the key escrow problem in identity-based ones persuaded many researchers to propose appropriate certificateless cryptosystems for such an environment. In this area, a protocol, named ID-RSA, has been proposed to authenticate the public key in RSA based schemes in the basis of certificateless cryptosystems. Although the security of this protocol is proved, the use of bilinear pairings made it computationally expensive. In this paper, we improved the performance of ID-RSA by the use of elliptic curve based algebraic groups instead of multiplicative ones over finite fields as the output of bilinear pairings. Our results show that our secure protocol is significantly more lightweight than ID-RSA.

Cloud computing has an immense need of file monitoring techniques to be applied so that system-specific configuration files are not modified by the virtual machine users or cloud insiders/outsiders for carrying out unwanted operations such as privilege escalation or system misconfiguration. The article critically describes the previous work done in the area of file monitoring and why there is a need for lightweight and efficient mechanism for its deployment in cloud. A centralized cum distributed approach for file monitoring of VMs on a host in cloud and the initial outcomes of its execution are presented in this article. The scheme does not use any database for storing file integrity, which eventually results in increased computational and storage efficiency. The technique is presented for the use by admins for ensuring integrity of specific files at VM hosts but can be upgraded to provide support to user-specified files as well.

In this paper, a bookmarklet-triggered literature metadata extraction system using cloud plugins is designed to find metadata of the publisher Web pages. First, we propose selector-syntax extractors using CSS-like syntax. Furthermore, we deploy them in the cloud. Finally, a bookmarklet-triggered way is proposed to execute cloud script to extract metadata of current Web pages. Compared with current methods, this system works across browser platforms with flexibility and extensibility and without installing additional plugins.