Test Case Prioritization (TCP) involves the rearrangement of test cases on a prioritized basis for various services. This research work focuses on TCP in web services, as it has been a growing challenge for researchers. Web services continuously evolve and hence require reforming and re-execution of test cases to ensure the accurate working of web services. This study aims to investigate gaps, issues, and existing solutions related to test case prioritization. This study examines research publications within popular selected databases. We perform a meticulous screening of research publications and selected 65 papers through which to answer the proposed research questions. The results show that criteria-based test case prioritization techniques are reported mainly in 41 primary studies. Test case prioritization models, frameworks, and related algorithms are also reported in primary studies. In addition, there are eight issues related to TCP techniques. Among these eight issues, optimization and high effectiveness are most discussed within primary studies. This systematic review has identified that a significant proportion of primary studies are not involved in the use of statistical methods in measuring or comparing the effectiveness of TCP techniques. However, a large number of primary studies use 'Average Percentage of Faults Detected' (APFD) or extended APFD metrics to compute the performance of techniques for web services.

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.

Reliable and secure data transmission in the application environment assisted by the wireless sensor network is one of the major challenges. Problem like blind forwarding and data inaccessibility affect the efficiency of overall infrastructure performance. This paper proposes routing protocol for forwarding and error recovery during packet loss. The same is achieved by energy and hops distance-based formulation of the routing mechanism. The reachability of the intermediate node to the source node is the major factor that helps in improving the lifetime of the network. On the other hand, intelligent hop selection increases the reliability over continuous data transmission. The number of hop count is factor of hop weight and available energy of the node. The comparison over the previous state of the art using QualNet-7.4 network simulator shows the effectiveness of proposed work in terms of overall energy conservation of network and reliable data delivery. The simulation results also show the elimination of blind forwarding and data inaccessibility.

Recent advancements in mobile device technology have enabled real-time positioning so that mobile patterns of people and favorable locations can be identified and related researches have become plentiful. One of the fields of research is the relationship between the object properties and the favored location to visit. The object properties of a person include personality, which is a major property jobs, income, gender, and age. In this study, we analyzed the relationship between the human personality and the preference of the location to visit. We used Spearman's Rank correlation coefficient, one of the many methods that can be used to determine the correlation between two variables. Instead of using actual data values, Spearman's Rank correlation coefficient deals with the ranks of the two data sets. In our research, the personality and the location data sets are used. Our personality data is ranked in five ranks and the location data is ranked in 8 ranks. Spearman's Rank correlation coefficient showed better results compared to Pearson linear correlation coefficient and Kendall rank correlation coefficient. Using Spearman's correlation coefficient, the degree of the relationship between the personality and the location preference is found to be 43%.

The goal of this paper is to analyze and build an algorithm to recognize hand gestures applying to smart home applications. The proposed algorithm uses image processing techniques combing with artificial neural network (ANN) approaches to help users interact with computers by common gestures. We use five types of gestures, namely those for Stop, Forward, Backward, Turn Left, and Turn Right. Users will control devices through a camera connected to computers. The algorithm will analyze gestures and take actions to perform appropriate action according to users requests via their gestures. The results show that the average accuracy of proposal algorithm is 92.6 percent for images and more than 91 percent for video, which both satisfy performance requirements for real-world application, specifically for smart home services. The processing time is approximately 0.098 second with 10 frames/sec datasets. However, accuracy rate still depends on the number of training images (video) and their resolution.

With the appearance of wireless spectrum crisis in traditional cellular network, device-to-device (D2D) communication has been regarded as a promising solution to ease heavy traffic burden by enabling precise content delivery among mobile users. However, due to the channel sharing, the interference between D2D and cellular users can affect the transmission rate and narrow the throughput in the network. In this paper, we firstly present a weighted interference minimization cluster formation model involving both social attribute and physical closeness. The weighted-interference, which is evaluated under the susceptible-infected(SI) model, is utilized to gather user in social and physical proximity. Then, we address the cluster formation problem via spectrum clustering with iterative operation. Finally, we propose the stable matching theory algorithm in order to maximize rate oriented to accomplish the one-to-one resource allocation. Numerical results show that our proposed scheme acquires quite well clustering effect and increases the accumulative transmission rate compared with the other two advanced schemes.

Today's computer systems are widely integrated with CPU and GPU to achieve considerable performance, but energy consumption of such system directly affects operational cost, maintainability and environmental problem, which has been aroused wide concern by researchers, computer architects, and developers. To cope with energy problem, we propose a task-scheduling framework to reduce energy under performance constraint by rationally allocating the tasks across the CPU and GPU. The framework first collects the estimated energy consumption of programs and performance information. Next, we use above information to formalize the scheduling problem as the 0-1 knapsack problem. Then, we elaborate our experiment on typical platform to verify proposed scheduling framework. The experimental results show that our proposed algorithm saves 14.97% energy compared with that of the time-oriented policy and yields 37.23% performance improvement than that of energy-oriented scheme on average.

This research aims to contribute to the discipline of knowledge management (KM) in Higher Education (HE), and is conducted using the case of the study programme (SP) for Integrated Academic Studies (IAS) of Technics and Informatics (TI). A deduction-induction methodology is applied, using a Plan-Do-Check-Act (PDCA) approach to design a suitable KM concept for making improvements to the SP IAS TI on the standardisation platform. Based on the application of the International Classification of Standards (ICS), the research analyses the correlations and competences acquired during the courses within the accredited TI study programme of two-course studies. On the basis of the results of a comparative analysis of the developmental learning process and educational goals, it is shown that knowledge management in correlation with standardisation leads to the improvement of study programmes in the upcoming accreditation cycle on the standardisation platform. Frequent innovations within standardised thematic fields (daily, weekly, monthly, etc.) indicate the need for appropriate changes to the SP. The contribution to KM is through an original analysis of annual samples of more than 90,000 standardised sources in correlation with the TI study programme. Expert System (ES) modelling and the original results enable an analogous contribution to the future KM of other SPs.

Developed from Digital Multimedia Broadcasting (DMB), DMB+ changes the application focus fundamentally. The plus symbol "+" indicates that DMB+ is no longer for digital radio/videos, instead it works as a general data transmission technique. The merits of wireless transmission, broad coverage, high quality, information security and low building/maintenance cost make DMB+ suitable for a wide range of applications with numerous terminals and users, such as public information release, alarm and administration etc. The concept of DMB+ base station enables DMB+ to be used in building independent networks and for remote control and management, as well as for Industry 4.0. DMB+ will access into a new field that till now has been dominated by communication techniques, will open a huge commercial opportunity.

Social commerce, integration of social media and e-commerce, provides potential opportunities for consumers to talk about their ideas and exchange product-related information on online shopping platforms. Given the substantial prospects related to business opportunities and consumers' perceptions, this study explores the factors driving fulfillment of the psychological contract in social commerce platforms. This research proposes, examines, and proves a theoretical model for the post-purchase behavior of the consumers, through an empirical investigation of online questionnaire-based data, gathered from 367 consumers in a cross-sectional setting. Results show that the fulfillment of psychological contract, consumer loyalty, and affective commitment fully mediate the impact of consumer trust on platform and repurchase intention. The level of commitment toward the consumer by the platform is strongly related to the degree of consumer trust, which is reciprocated through re-purchase intentions. This study offers essential theoretical implications with regards to the social exchange theory, attribution theory, and an moderating effect of the platform empathy on consumers' trust-psychological contract fulfillment relationship. Likewise, this research has significant implications for practitioners and managers.

Operating system (OS) kernel function call graphs have been widely used in OS analysis and defense. However, most existing methods and tools for generating function call graphs are designed for application programs, and cannot be used for generating OS kernel function call graphs. This paper proposes a virtualization-based call graph generation method called Acquire in Trap (AIT). When target kernel functions are called, AIT dynamically initiates a system trap with the help of a virtualization technique. It then analyzes and records the calling relationships for trap handling by traversing the kernel stacks and the code space. Our experimental results show that the proposed method is feasible for both Linux and Windows OSs, including 32 and 64-bit versions, with high recall and precision rates. AIT is independent of the source code, compiler and OS kernel architecture, and is a universal method for generating OS kernel function call graphs.

The rapid growth of instructional videos repositories and their widespread use as a tool to support education have raised the need of studies to assess the quality of those educational resources and their impact on the quality of learning process that depends on them. Khan Academy (KA) repository is one of the prominent educational videos' repositories. It is famous and widely used by different types of learners, students and teachers. To better understand its characteristics and the impact of such repositories on education, we gathered a huge amount of KA data using its API and different web scraping techniques, then we analyzed them. This paper reports the first quantitative and descriptive analysis of Khan Academy repository (KA repository) of open video lessons. First, we described the structure of repository. Then, we demonstrated some analyses highlighting content-based growth and evolution. Those descriptive analyses spotted the main important findings in KA repository. Finally, we focused on users' interactions with video lessons. Those interactions consisted of questions and answers posted on videos. We developed interaction profiles for those videos based on the number of users' interactions. We conducted regression analysis and statistical tests to mine the relation between those profiles and some quality related proposed metrics. The results of analysis showed that all interaction profiles are highly affected by video length and reuse rate in different subjects. We believe that our study demonstrated in this paper provides valuable information in understanding the logic and the learning mechanism inside learning repositories, which can have major impacts on the education field in general, and particularly on the informal learning process and the instructional design process. This study can be considered as one of the first quantitative studies to shed the light on Khan Academy as an open educational resources (OER) repository. The results presented in this paper are crucial in understanding KA videos repository, its characteristics and its impact on education.

Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

This paper presents a fire detection algorithm with a minimal false detection rate, intended for a thermal imaging surveillance environment, whose properties vary depending on temporal conditions of day or night and environmental changes. This algorithm was designed to minimize the false detection alarm rate while ensuring a high detection rate, as required in fire detection applications. It was necessary to reduce false fire detections due to non-flame elements occurring when existing fixed threshold-based fire detection methods were applied. To this end, adaptive flame thresholds that varied depending on the characteristics of input images, as well as the center of gravity of the heat-source and hot-source regions, were analyzed in an attempt to minimize such non-flame elements in the phase of selecting flame candidate blocks. Also, to remove any false detection elements caused by camera shaking, one of the most frequently raised issues at outdoor sites, preliminary decision thresholds were adaptively set to the motion pixel ratio of input images to maximize the accuracy of the preliminary decision. Finally, in addition to the preliminary decision results, the texture correlation and intensity of the flame candidate blocks were averaged for a specific period of time and tested for their conformity with the fire decision conditions before making the final decision. To verify the fire detection performance of the proposed algorithm, a total of ten test videos were subjected to computer simulation. As a result, the fire detection accuracy of the proposed algorithm was determined to be 94.24%, with minimum false detection, demonstrating its improved performance and practicality compared to previous fixed threshold-based algorithms.

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

Security administrators of companies and organizations need to come up with proper countermeasures against cyber-attacks considering infrastructures and security policies in their possession. In order to develop and verify such countermeasures, the administrators should be able to reenact both cyber-attacks and defenses. Simulations can be useful for the reenactment by overcoming its limitations including high risk and cost. If the administrators are able to design various scenarios of cyber-attacks and to develop simulation models from their viewpoints, they can simulate desired situations and observe the results more easily. It is challenging to simulate cyber-security issues, because there is lack of theoretical basis for modeling a wide range of the security field as well as pre-defined basic components used to model cyber-attacks. In this paper, we propose a modeling method for cyber-security simulations by developing a basic component and a composite model, called Abstracted Cyber-Security Unit Model (ACSUM) and Abstracted Cyber-security SIMulation model (ACSIM), respectively. The proposed models are based on DEVS(Discrete Event systems Specification) formalism, a modeling theory for discrete event simulations. We develop attack scenarios by sequencing attack behaviors using ACSUMs and then model ACSIMs by combining and abstracting the ACSUMs from a security perspective. The concepts of ACSUM and ACSIM enable the security administrators to simulate numerous cyber-security issues from their viewpoints. As a case study, we model a worm scenario using ACSUM and simulate three types of simulation models based on ACSIM from a different security perspective.

In recent years, digital content has become easily accessible because of internet technology. Representative examples of such digital content include various types, such as music, TV, (program, sport, drama etc.) and films. However, there are cases where internet technology is used in illegal ways without the authorization of the copyright holder for digital content. Such actions have a direct impact on copyright owners' profits and further affect the development of the digital culture industry adversely. Therefore, in this study, we analyze features to detect piracy sites that cause copyright infringement. Further, we develop a piracy site detection crawler based on these features and present the analysis of its performance.

Compared to the past infrastructure networks, the current smart grid network can improve productivity and management efficiency. However, as the Industrial Internet of Things (IIoT) and Internet-based standard communication protocol is used, external network contacts are created, which is accompanied by security vulnerabilities from various perspectives. Accordingly, it is necessary to develop an appropriate cybersecurity guideline that enables effective reactions to cybersecurity threats caused by the abuse of such defects. Unfortunately, it is not easy for each organization to develop an adequate cybersecurity guideline. Thus, the cybersecurity checklist proposed by a government organization is used. The checklist does not fully reflect the characteristics of each infrastructure network. In this study, we proposed a cybersecurity framework that reflects the characteristics of a microgrid network in the IIoT environment, and performed an analysis to validate the proposed framework.

Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.

In this paper we first propose a new cryptosystem based on our data hiding scheme (2,9,8) introduced in 2019 with high security, where encrypting and hiding are done at once, the ciphertext does not depend on the input image size as existing hybrid techniques of cryptography and steganography. We then exploit our automata approach presented in 2019 to design two algorithms for exact and approximate pattern matching on secret data encrypted by our cryptosystem. Theoretical analyses remark that these algorithms both have O(n) time complexity in the worst case, where for the approximate algorithm, we assume that it uses ⌈(1-ε)m)⌉ processors, where ε, m and n are the error of our string similarity measure and lengths of the pattern and secret data, respectively. In searchable encryption, our cryptosystem is used by users and our pattern matching algorithms are performed by cloud providers.

This paper focuses on the simplified receiver design for multi-user circular block oversampled filter bank (CB-OSFB) uplink systems. Through application of discrete Fourier transform (DFT), the special banded structure and circular properties of the modulation matrix in the frequency domain of each user are derived. By exploiting the newly derived properties, a simplified zero-forcing (ZF) receiver is proposed for multi-user CB-OSFB uplink systems in the multipath channels. In the proposed receiver, the matrix inversion operation of the large dimension multi-user equivalent channel matrix is transformed into DFTs and smaller size matrix inversion operations. Simulation is given to show that the proposed ZF receiver can dramatically reduce the computational complexity while with almost the same symbol error rate as that of the traditional ZF receiver.

In this article, a new Aloha-based tag identification protocol is presented to improve the reading efficiency of the EPC C1 Gen2-based UHF RFID system. Collision detection (CD) plays a vital role in tag identification process which determines the efficiency of anti-collision protocols since most Aloha-based protocols optimize the incoming frame length based on the collisions in current frame. Existing CD methods are ineffective in identifying collision, resulting in a degradation of identification performance. Our proposed algorithm adopts an enhanced CD (ECD) scheme based on the EPC C1 Gen2 standard to optimize identification performance. The ECD method can realize timely and effective CD by detecting the pulse width of the randomly sent by tags. According to the ECD, the reader detects the slot distribution and predicts tag cardinality in every collision slot. The tags involved in each collision slot are identified by independently assigned sub-frames. A large number of numerical results show that the proposed solution is superior to other existing anti-collision protocols in various performance evaluation metrics.