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Title: A flexible IPv6 mobility management architecture for SDN-based 5G mobile networks Abstract: With the advent of increasingly diverse services, applications, and use cases, the 5G mobility environment is expected to be highly heterogeneous. The distributed mobility management (DMM) is emerging as a promising approach for 5G, which provides decentralized handover and traffic management at the edge of the network. However, in order to meet the disparate mobility needs, the DMM process requires flexible design considerations. This paper presents a novel mobile node (MN) centered, software-defined networking (SDN)-based flexible mobility management architecture, named adaptive multimode mobility management (A3M), which can adaptively operate in multiple modes. The proposed A3M architecture incorporates a novel handover mode selection phase among the traditional handover phases during which a suitable mode of handover operation is evaluated. This makes the A3M handover process adaptable to the varying mobility requirements of the MN and enables it to provide differentiated handover management for MNs with different mobility profiles. The performance analysis of A3M shows that it offers optimal handover performance in terms of primary handover performance metrics such as session disruption delay, packet losses, and signaling costs, compared to the popular network-based DMM approaches.

Title: Design implications for adaptive augmented reality based interactive learning environment for improved concept comprehension in engineering paradigms Abstract: Augmented reality (AR) has tremendous potential as a teaching and learning tool in engineering education to enhance students? learning experience; it influences the students? spatial ability for real-time visualization. Furthermore, this helps to attain better concept comprehension pertaining to improved understanding of the topics. The present study provides evidence by developing an AR learning environment (ARLE) suitable for complicated theoretical topics of electronics engineering, which otherwise cannot be demonstrated using practical experiments. The idea of using different design variants; such as mobile and table-top for adaptive AR is also implemented. For this, 60 undergraduate students of electronics and electrical engineering were introduced to the ARLE system, in two different case studies. The first case validates the efficacy of using AR for learning the concept of stability in linear control systems through a questionnaire based survey where students reported the ARLE as an effective learning system for theoretical topics. Second case study provides comparative analysis for usability of two design variants of the ARLE in terms of manipulability and comprehensibility. Finally, the design implications for developed ARLE are discussed, which may be helpful for other researchers in creating learning environments for different courses of engineering education.

Title: Rapid jamming detection approach based on fuzzy in WSN Abstract: It is necessary for securing wireless sensor networks from jamming, since these networks are easily attacked by jammers. Jamming may be made by either external jammer node or internal node, which may become as an adversary in future. In this paper, two approaches are proposed in the cluster-based sensor network to detect the maliciousness level of nodes to secure sensor networks from jamming attacks. First approach detects maliciousness level of nodes using two modules, namely, certification module and monitoring module. Certification module defends the network from the jammers. Monitoring module discovers the sensor nodes that are jammed by a jammer. Second approach uses fuzzy logic for optimizing the jamming metrics to determine the occurrence of jamming accurately. The proposed system achieves 99.58% detection ratio to determine node's maliciousness level.

Title: Reliability prediction based on Birnbaum–Saunders model and its application to smart meter Abstract: For accelerated degradation testing, data analysis based on stochastic process has been drawn much attention. However, there is significant difference in reliability prediction based on different process. This paper proposes a unified distribution model combined with a stochastic process model in multiple accelerated stress degradation test. To solve the problem of heterogeneous population of pseudo failure data, the Birnbaum–Saunders model is considered as a unified distribution model for different Gaussian family process. To give an example, a detailed proof for substituting the Birnbaum–Saunders model for the first-passage time of Wiener process is provided. Then, the influence of the parameters of the Birnbaum–Saunders model was analyzed, which provided a basis for the Birnbaum–Saunders model to be selected as a unified model. To verify presented model, a case study of Smart Meter ADT is conducted. And the obtained results of this work is compared with former work of Smart Meter ADT modeling, which verifies the effectiveness of the proposed modeling method to heterogeneous population.

Title: Enhancing online security using selective DOM approach to counter phishing attacks Abstract: Today's computer era has paved the way for innovations like self-driving cars, quantum computing, and other ingenious advancements. As technology advances at a rapid pace, some issues yet remain unresolved. One of the issues is phishing, which dates back to the 1980s. Phishing is an art used by cybercriminals from the 1980s until to date targeting online users to harvest financial, confidential, and other sensitive information. The art and the methodologies used by cybercriminals have not evolved much from the AOL (American online) heydays. However, the counter mechanisms to defeat phishing have undergone considerable changes over the past two decades. Although sophisticated antiphishing systems are in place, statistics shows that phishing is a major threat. Our practical research proves that one of the state-of-the-art antiphishing systems can be bypassed using simple techniques. The research further demonstrates why today's antiphishing mechanisms fail and the need for a novel mechanism that will identify the authenticity of the website. In this manuscript, an antiphishing algorithm, PhishSec (PH-Sec), is introduced. PhishSec will not consider the URL of the website as the primary factor to determine the authenticity, rather take a reverse approach where the URL of the website is derived by analyzing the content of the visited website to establish its authenticity. To accomplish this, the HTML DOM (document object model) of a given web page on load is considered. This manuscript quotes the research results of the analysis of a state-of-the-art antiphishing system along with the introduced algorithm to counter-attack phishing. The introduced system detects phishing attacks with 99.21% of accuracy.

Title: Detection of multipath routing with passive delay measurements: Hypothesis testing approaches Abstract: Multipath routing, which is a common approach to achieve load balancing, will enable multiple active paths between two end-hosts. However, it will cause out-of-order delivery of packets, which will not only severely debilitate the performances of TCP (Transmission Control Protocol) but also will make many single-path-based applications work improperly. In this paper, we propose to utilize passive delay measurements between end-hosts, to detect multipath routing with two different hypothesis testing approaches, ie, t-Test and variance test. The motivation is that the obtained distributions of passive delays between end-to-end communication flows can be very similar to each other when they are routed by the identical end-to-end path, whereas distinct differences are observed among the ones of different end-to-end paths. Simulations based on NS2 validate the efficiency of both t-Test and variance test and demonstrate the robustness of t-Test against asynchronous measurements between different flows.

Title: Performance evaluation of some propensity score matching methods by using binary logistic regression model Abstract: The unit selection bias in treatment and control group affects negatively in evaluation of treatment effects. In some studies, the random units selected for the treatment and control group are out of control of the researcher and there may be differences between the units under consideration. This will cause the estimates obtained to be biased. Propensity score matching (PSM) has been used to reduce bias in estimation of treatment effect in observational data. Therefore, nearest neighbor (1:1), caliper, stratification, mahalanobis metric, full and combining propensity score and mahalanobis metric matching, which have been widely used as PSM methods, were compared in terms of the correct classification rates conducting a detailed Monte Carlo simulation study. In addition, standardized and percent reduction bias of covariates were evaluated for each of the PSM methods. It is suggested that stratification and full matching methods should be considered to study with high correct classification rate whereas caliper matching method should be prefered due to the low bias to make statistical inferences.

Title: Notes on power comparison between the sequential parallel comparison and other commonly-used designs Abstract: Because one excludes in the sequential parallel comparison design (SPCD) both placebo responders and patients assigned to the experimental treatment in period 1 from comparison in period 2, test procedures for the SPCD may lose efficiency. Assuming a random effects logistic regression model, we evaluate the loss of efficiency by comparing power of the SPCD with those for the parallel groups design with repeated measurements (PGDRM), simple crossover design (SCD), and parallel group design (PGD). Based on Monte Carlo simulations, we find that the increase in efficiency of the SPCD by use of the PGDRM and SCD can be significant, especially when the variation of responses between patients is large. The SPCD can be, however, of use if the relative treatment effect on placebo non-responders is of our primary interest.

Title: Congestion avoidance and fault detection in WSNs using data science techniques Abstract: Transmission rate is one of the contributing factors in the performance of wireless sensor networks. Congested network causes reduced network response time, queuing delay, and more packet loss. To address the issue of congestion, we have proposed transmission rate control methods. To avoid the congestion, we have adjusted the transmission rate at current node based on its traffic loading information. Multiclassification is done to control the congestion using an effective data science technique, namely support vector machine (SVM). In order to get less miss classification error, differential evolution (DE) and grey wolf optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE-SVM and GWO-SVM are more proficient than other classification techniques. Moreover, DE-SVM and GWO-SVM have outperformed the benchmark technique genetic algorithm-SVM by producing 3% and 1% less classification errors, respectively. For fault detection in wireless sensor networks, we have induced four types of faults in the sensor readings and detected the faults using the proposed enhanced random forest. We have made a comparative analysis with state of the art data science techniques based on two metrics, ie, detection accuracy and true positive rate. Enhanced random forest has detected the faults with 81% percent accuracy and outperformed the other classifiers in fault detection.

Title: Sustainable academic performance in higher education: a mixed method approach Abstract: The key purpose of this research is to discuss the role of trajectory movements from education and awareness of sustainability perspective in the higher educational sector. The study analyzes how the trajectory movement of students in various academic places of their interest influences sustainable academic performance. A person-administered survey approach was conducted initially followed by a focus group interview. The results of this research revealed that there are innumerable differences among university learners in terms of trajectory movement, and most of the students care only about a few common learning places like classrooms or laboratories. However, there are other trajectory movements that directly or indirectly influence sustainable academic performance. The research findings suggest that students and academicians are aware of not only the frequent trajectory movements, such as in the classroom, laboratory, and library but also the other trajectory movements inside and outside the campus which may severely affect sustainable academic performance. This study focuses on discovering the relationship between university students' trajectory movement tendencies and sustainable performance in higher educational institutions to fulfill sustainable development goals and how trajectory movements affect students' educational performance in various ways to ensure a better academic outcome for sustainable development.

Title: TraceChain: A blockchain-based scheme to protect data confidentiality and traceability Abstract: The risk of sharing data in cloud computing has gathered increasing attention. After the owner of some confidential data outsources the data to cloud storage services and shares it with others, the data owner lost the control to the data to a large extent. To achieve data sharing while keeping data confidentiality, attribute-based encryption (ABE) can be employed by cloud storage services. However, ABE can only guarantee that outsourced data on the cloud is decrypted by attribute-satisfying users but cannot restrict data from being accessed by dishonest users whose attributes also satisfy the access-control policy. It is impossible for the data owner to control the shared data after it has been decrypted by dishonest users, especially when a set of attribute-satisfying dishonest users may collude. To address this concern, we propose a traceable data sharing scheme called TraceChain. In TraceChain, data is encrypted over a new CP-ABE scheme called E-CP-ABE. Furthermore, the system parameters for generating the private key in E-CP-ABE are uploaded to the private blockchain and transactions are performed on the chain. The data owner can obtain the identity of users by monitoring system parameters simultaneously and control data sharing on the blockchain. To prove the security of our scheme, the security analysis is given in this paper. Meanwhile, experimental results also show that our system is viable and efficient.

Title: Health status diagnosis of distribution transformers based on big data mining Abstract: In the fault detection technology of distribution transformer, the traditional artificial intelligence algorithm has been unable to achieve its efficient analysis and processing, but also unable to achieve the elimination of misdiagnosis caused by improper interval segmentation in the process of fault diagnosis of distribution transformer. In a word, the problem that always exists in transformer fault diagnosis technology is the problem of discretization of fault data. In order to solve this problem, this paper creatively proposes a health condition diagnosis method of distribution transformer based on large data. This method innovatively proposes that the dissolved gas analysis value of distribution transformer is the conditional attribute, and the fault type is the decision attribute, and the fault decision table is established. The continuous attribute data in the decision table are discretized by using the optimization behavior of large data sets. Subsequently, the discretized decision table is simplified by using the big data theory, and the decision table of fault diagnosis rules is established, which greatly simplifies the difficulty of attribute simplification of decision table and makes diagnosis more convenient. Finally, an example shows that the proposed method can effectively discretize and reduce samples. Compared with traditional methods, it improves the accuracy of fault diagnosis.

Title: Examining the effect of interaction analysis on supporting students? motivation and learning strategies in online blog-based secondary education programming courses Abstract: Students? skilful use of self-regulatory learning strategies is becoming fundamental to the advent of blog-based learning. Moreover, the use of Interaction Analysis (IA) in studying the learning dynamics in Computer-Supported Collaborative Learning (CSCL) activities is on the increase, particularly aiming to support participants by means of IA self-monitoring tools. Within this framework, the main objective of this study is to investigate the effect IA indicators have on students? motivational orientations and learning strategies. Utilizing a pre-/post-test design to examine the main research questions, the authors of this paper collected data from 91 secondary education students, including the Motivation Strategies for Learning Questionnaire (MLSQ), administered before and after the completion of online programming activity. Overall research results indicate that the presence of IA indicators enhance students? motivation and learning strategies with respect to several dimensions. These findings support our initial suggestion, namely that tracking blog-based interactions facilitates student?s awareness, activating their metacognitive processes, and allowing thus students to self-regulate their activity.

Title: A stratified reservoir sampling algorithm in streams and large datasets Abstract: In data stream mining, a stream is a dataset of unknown size with continuously incoming elements, which is typically large enough so that a computer processing it does not have enough memory to hold it in its entirety and each element can be read only once and only in order. Classical sampling methods such as simple random sampling (SRS), stratified sampling and cluster sampling cannot be used on the stream data since the entire set is not available all at once and data cannot be reread. Vitter's (1985) Algorithm R is a reservoir sampling method which can be used to select an SRS from a data stream. In this article, we propose Algorithm SR which extends Algorithm R to a stratified reservoir sampling method with optimal allocation. We prove that the proposed method is asymptotically equivalent to classical stratified random sampling with optimal allocation. Implementation results show that the proposed method is efficient and can outperform Algorithm R.

Title: Design of a compact T-shaped slot antenna for wireless applications Abstract: In this paper, the design of a penta-band slot radiator for the WLAN (2.26-2.61 GHz), WiMAX (3.14-4.49 GHz), and C band (4-8 GHz) antenna design and fabrication process is proposed. The proposed system (antenna) has a dielectric substrate material and radiating patch (T-shaped) with an inverted-L slot (both left and right sides of the feed), circle slot, and comb-shaped ground structure on the back of the substrate. The proposed dimension of the antenna is 20 x 30 mm(2), and it is designed and fabricated on FR4 substrate. It can be designed to cover the frequency bands of 2.61, 3.12, 4.32, 6.54, and 7.62 GHz. It can be designed using advanced design system (ADS) and measured using vector network analyzer (VNA). The obtained simulation result of the proposed antenna is achieved with better return loss.

Title: Hypothesis testing in outcome-dependent sampling design under generalized linear models Abstract: In many large cohort studies, the major budge and cost typically arise from the assembling of primary covariates. Outcome-dependent sampling (ODS) designs are cost-effective sampling schemes which enrich the observed sample by selectively including certain subjects. We study the inference methods of hypothesis testing for a general ODS design under the generalized linear models. We develop a profile-likelihood-based family of tests and propose likelihood-ratio, Wald and score test statistics. Asymptotic properties of the proposed tests are established and the null limiting distributions are derived. The finite-sample behavior of the proposed methods is evaluated through simulation studies, and an application to a Wilms tumor data are illustrated.

Title: Iris-based continuous authentication in mobile ad hoc network Abstract: Mobile Ad hoc Networks (MANETs) are temporary in nature, with security concerns being more vulnerable than the other types of networks. Most of the works concentrate on either authentication or intrusion detection. This framework considers both intrusion detection and authentication continuously. It has been done for different layers of security for finding the intruder and eliminate from the network. Iris images have been used in this work for the purpose of authentication. Detection and response engines have been used for intrusion detection and high security in ad hoc network. The simulation results clearly show the iris-based continuous authentication detecting most of the intruders and eliminates from the network.

Title: Plenty is Plague: Fine-Grained Learning for Visual Question Answering Abstract: Visual Question Answering (VQA) has attracted extensive research focus recently. Along with the ever-increasing data scale and model complexity, the enormous training cost has become an emerging challenge for VQA. In this article, we show such a massive training cost is indeed plague. In contrast, a fine-grained design of the learning paradigm can be extremely beneficial in terms of both training ...

Title: Two meta-heuristics for solving the capacitated vehicle routing problem: the case of the Tunisian Post Office Abstract: Postal sector has a significant role in promoting and improving the services intended for companies and citizens via its various services and its capacity to provide a communication network which ensures rapidity in collecting, transferring and delivering correspondences, funds and goods across the world. Therefore, optimization of the routing system for collection and transport of letters and parcels constitutes an important component of an effective delivery management system. Generally, postal distribution problems are formulated as a Capacitated vehicle routing problem (CVRP) that consists of designing a set of routes, starting and terminating at a central depot and utilize a set of homogenous vehicles to deliver demands to a set of vertices. The objective is to minimize the total transportation cost. Due to its NP-Hardness, we develop in this paper a hybrid metaheuristic that embeds a Variable Neighborhood Search (VNS) in a Genetic Algorithm (GA) in order to accelerate the convergence of the GA to high quality solutions. This combination aims to take advantage of GA’s strength in the exploration and the VNS’s powerful exploitation of the solution space. We propose to include the VNS in the mutation operator of the GA so that the individual space is enlarged and more diversified. Hence, the hybrid algorithm is able to exploit and explore new regions of the search space. The proposed approach is compared to existing methods while applied on benchmark instances. Empirical results driven on five benchmark datasets with a total of 186 instances show that our proposed approach is very competitive in terms of the obtained solutions. Overall, our experiments illustrated that the Hybrid GA-VNS could be a very efficient method for solving the CVRP and its results are comparable with the results of the state-of-the-art. To operationalize our modeling and solution approach, we considered a real case study: the Tunisian Post Office. Results indicate that the proposed HGA-VNS approach improves considerably the solution regarding the existing methods adopted by the Tunisian Post Office.

Title: Methods of Metabolite Identification Using MS/MS Data Abstract: Researchers in bioinformatics and medical science fields have been developing various innovative information systems and software tools using emerging technologies. Metabolite profiling is one of the fields in which bioinformatics researchers are intensely developing various innovative methods and software tools. Metabolites are the intermediate and end products of metabolism, which is the set of life-sustaining chemical reactions in living organisms. Accurate and complete identification of metabolites can advance many medical science and bioinformatics fields by providing a direct way of observing metabolic activities. The analysis of tandem mass spectrometry (MS/MS) data has been a long-term computational challenge in bioinformatics. This study examines various existing and emerging methods and software tools for analyzing MS/MS data for metabolite identification and discusses challenges and future perspectives in this important bioinformatics research area.

Title: Detection of clone scammers in Android markets using IoT-based edge computing Abstract: Pirated application developers find an alternate way to publish pirated versions of the same Android mobile applications (apps) on different Android markets. Therefore, a centralized, automated scrutiny system among multiple app stores is inevitable to prevent publishing pirated or cloned version of these Android applications. In this paper, we proposed an Android clone detection system for Internet of things (IoT) (Droid-IoT) devices. First, the proposed system receives an original Android application package (APK) file along with possible candidate cloned APKs over the cloud network. The system uses an apkExtractor tool to extract Dalvik Executable (DEX) files for each subject program. The Jdex decompiler is used to extract Java source files from DEXs. Then, the bag-of-word model is used to extract tokenized features from source files. Further, the weighting filters are used to zoom the importance of each token. Moreover, Synthetic Minority Oversampling is applied to retrieve balanced features for better training of data. Finally, TensorFlow with Keras deep learning model is designed to predict clones in Android applications. The experimental results have shown that Droid-IoT can successfully detect cloned apps with an accuracy of up to 96%. The primary purpose of this system is to prevent the publishing of pirated apps among different app stores under different pirated names.

Title: Locality preserving partial least squares discriminant analysis for face recognition Abstract: We propose a locality preserving partial least squares discriminant analysis (LPPLSDA) which adds a locality preserving feature to the conventional partial least squares discriminant analysis(PLS-DA). The locality preserving feature captures the within group structural information via a similarity graph. The ability of LPPLS-DA to capture local structures allows it to be better suited for face recognition. We evaluate the performance of our proposed method on several benchmarked face databases which offer different levels of complexity in terms of sample size as well as image acquisition conditions. The experimental results indicate that, for each database used, the proposed method consistently outperformed the conventional PLS-DA method.

Title: Evolution of random access process: From Legacy networks to 5G and beyond Abstract: In this paper, we discuss the random access procedures in legacy networks, long-term evolution, LTE-Advanced, and 5G networks. Random access is the first and incumbent step for connection establishment between base station/eNodeB/gNodeB and user equipment. Keeping in view its importance, there has been a regular thrive to improve the procedures of random access in communication networks. With the advent of new concepts such as machine-type communication and Internet of Things, random access becomes extremely critical because huge number of devices try to connect to the network simultaneously. We present the evolution of random access process from legacy networks such as global system for mobile or wideband code division multiple access to 5G networks and beyond and their analysis. Simulations are performed in MATLAB to show various performance metrics such as access delay, total service time, and collision probability to demonstrate the pros and cons of one technique over another.

Title: Pricing insurance premia: a top down approach Abstract: Insurance plays an important economic and social role through its ability to transfer risk. In this paper, we focus on the largest insurance sector, the automobile sector. We model automobile insurance premia through a top down approach. Our approach is appealing since it defines the dynamics of the aggregate loss in a consistent way, and also provides a coherent definition of the joint distribution of the total losses and the car insurance premium. We show how to make this top down approach computationally tractable by using the class of affine point processes, which are intensity-based jump processes driven by affine jump diffusions. An affine point process is sufficiently flexible to account for both country global infrastructure and driving behaviour. Further it allows for efficient computation and calibration of a large class of insurance products.

Title: Medical image DENOISING scheme using discrete wavelet transform and optimization with different noises Abstract: In Medical Image Processing (MIP), the images are influenced by various types of noises. Hence, reducing such noises becomes a crucial issue. The main objective is to denoise the noisy images effectively with reduced computational cost. This paper proposes a denoising technique using Discrete Wavelet Transform (DWT) and Social Spider Optimization (SSO) algorithm. Initially, DWT is applied over various noises over the input medical images. Then, the wavelet coefficients are optimized by applying the SSO algorithm. Finally, the inverse of DWT (IDWT) is applied over the optimized coefficients. The denoised image is obtained, and then, the PSNR evaluation is utilized for finding the superior performance. Experimental results using MATLAB show that the proposed algorithm has better performance when compared with the existing approaches.

Title: A novel software-defined networking approach for load balancing in data center networks Abstract: In today's modern era, the internet usage has been developing tremendously. In data center network (DCN), the traffic has been rising constantly in the past few years. Hence, greater traffic of network needs several services such as Domain Name Service (DNS) to manage. It is not possible for one server to manage entire requests coming from client because of huge amount of traffic. To resolve this issue, load balancing is used. The major purpose of load balancing is to forward incoming client requests and distribute the traffic across various servers using customized algorithm which is deployed in the load balancer. Traditional load balancers are very costly and inflexible hardware. A substitute of this hardware is to use software-defined network load balancers. The software-defined networking (SDN) load balancers offer the facility to programmers to design and construct their own strategy of load balancing, which makes the software-defined network load balancer flexible and programmable. This research proposes a novel algorithm that performs load balancing through calculating in advance the capacity of each and every switch across the path to which the packets are routed.

Title: Grey wolf assisted SIFT for improving copy move image forgery detection Abstract: Copy-move forgery is a general widespread type of digital image forgery, where a segment of an image is attached into a new portion of the similar image to hide or replicate the parts which are called forgered image. The forgered image appears original, as the objective region in spite of being forged, has attained the fundamental qualities of the similar image itself. The capability of the copy-move forgery detection (CMFD) technique is lacked due to some post-processing functions, like JPEG compression scaling, or rotation, etc. Therefore, this paper intends to develop a CMFD using Scale-invariant feature transform (SIFT), best-fin-first algorithm (BBF) and RANdom SAmple Consensus (RANSAC) directed by grey wolf optimization (GWO) algorithm. Initially, the keypoints are selected using SIFT principle, and BBF algorithm identifies the matched keypoints using keypoint threshold. Further, SIFT feature descriptor is determined, and the final extracted paired keypoints are given to RANSAC algorithm to remove all the mismatched keypoints. In this CMFD model, the parameters such as parameters keypoint threshold, maximum distance of inliers in RANSAC and distance threshold in SIFT features are optimized using GWO. The foremost purpose of this research work is maximizing the number of paired keypoints. Hence the proposed model is termed as GWO-based parameter optimization for CMFD (GWPO-CMD). The proposed model is compared over several other meta-heuristic-based keypoint threshold selections and proves its efficiency through diverse analysis.

Title: An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks Abstract: The evolution of wireless body area networks (WBAN) has changed the human life for its applications in the field of healthcare, fitness, entertainment and sports etc. However, two of the major challenges in the design of WBAN are energy efficiency and connectivity. The placement of relay nodes in a wireless body area network (WBAN) plays an important role in design of energy efficient and reliable WBAN. This problem is a joint problem of data routing and placement of relay nodes and formulated as a linear integer programming model. The main objective of the problem is to minimize the cost of relay nodes, energy consumption and distributing the loads uniformly on the relay nodes. Considering the hardness of the problem, we propose an adaptive cuckoo search based algorithm which uses an efficient fitness function and an adaptive step size proportional to the fitness function for placement of relay nodes. The set of relay nodes obtained by our proposed adaptive cuckoo search algorithm compared with cuckoo search as well as other state of the art algorithms via simulation results. The simulation results reveal that the proposed algorithm not only consumes less energy than its counterparts but also distributes the load evenly on the relay nodes. We consider two different postures of the body with 13 biosensors placed in fixed positions and 50–100 candidate sites for placement of relay nodes. Furthermore, we also consider 80 biosensors randomly deployed in a rectangular area with 50–300 candidate sites to study the scalability of our algorithm.

Title: Raising insects with an application to enhance students' self-confidence in interacting with insects Abstract: Insect phobia may last for a lifetime and needs to be overcome. This study aimed to design an application, Insect Garden, for students to learn about and experience how to raise four types of insect: Giant Asian Mantis, Japanese Rhinoceros Beetle, Formosan Stag Beetle, and Seven-Spotted Ladybug. It also investigated how the players' self-confidence in interacting with insects could be enhanced. Additionally, to understand the affective factors related to gameplay, this study explored the correlates between personality, insect phobia before gameplay, gameplay interest and self-confidence enhancement in interacting with insects. Data were collected from 211 eighth-grade students, from whom 175 useful data were subjected to confirmatory factor analysis and structural equation modeling. The results revealed that Extraversion is positively related to gameplay interest but not to Insect phobia, whereas Neuroticism is positively related to Insect phobia and gameplay interest. Insect phobia is negatively related to self-confidence enhancement in interacting with insects, but gameplay is positively related.

Title: Improved algorithms for ranking and unranking (k, m)-ary trees in B-order Abstract: Du and Liu (Eur J Comb 28:1312–1321, 2007) introduced (k, m)-ary trees as a generalization of k-ary trees. In a (k, m)-ary tree, every node on even level has degree k (i.e., has k children), and every node on odd level has degree m (which is called a crucial node) or is a leaf. In particular, a (k, m)-ary tree of order n has exactly n crucial nodes. Recently, Amani and Nowzari-Dalini (Bull Iranian Math Soc 45(4):1145–1158, 2019) presented a generation algorithm to produce all (k, m)-ary trees of order n in B-order using Zaks’ encoding, and showed that the generated ordering of this encoding results in a reverse-lexicographical ordering. They also proposed the corresponding ranking and unranking algorithms for (k, m)-ary trees according to such a generated ordering. These algorithms take $$\mathcal {O}(kmn^2)$$ time and space for building a precomputed table in which (k, m)-Catalan numbers (i.e., a kind of generalized Catalan numbers) are stored in advance. Then, each ranking and unranking algorithm can be performed subsequently in $$\mathcal {O}(n)$$ and $$\mathcal {O}(n\log n)$$ time, respectively. In this paper, we revisit the ranking and unranking problems. With the help of an encoding scheme called “right-distance” introduced by Wu et al. (Math Comput Model 53:1331–1335, 2011a; IEICE Trans Inf Syst E94–D:226–232, 2011b), we propose new ranking and unranking algorithms for (k, m)-ary trees of order n in B-order using Zaks’ encoding. We show that both algorithms can be improved in $$\mathcal {O}(kmn)$$ time and $$\mathcal {O}(n)$$ space without building the precomputed table.

Title: Autistic Innovative Assistant (AIA): an Android application for Arabic autism children Abstract: Most autism centers in Arabic countries rely on manual techniques in teaching children the basics of language, math, and social skills. Actually, these techniques may be limited in measuring the child's progress in a systematic way considering different fields. To overcome these limitations, we developed Autistic Innovative Assistant (AIA) which is an Android smartphone app that is dedicated to teach Arabic autistic children the necessary linguistic and mathematical basics in addition to improving their social skills through creating an interactive learning environment. The proposed app covers five main categories with an interactive quiz, included in each, as an assessment tool. The lessons included in each category are viewed as a series of colorful pictures with a written sentence and an audio message to describe the meaning of each, thereby attracting the child's attention and making learning a fun activity. Each child's supervisor can access his/her quizzes' records, measure the child's progress in different categories, and use the related recommendation reports to adjust the app usage pattern. AIA was tested by a group of autistic children for a one-month test period in a real life environment represented in "Jordan Specialized Center for Autism" and the results and feedback were very promising.

Title: Enhancing load bearing capacity of alkaline soil with agricultural and industrial waste by the stabilization process Abstract: In highway construction, the most important procedure is the sub-grade soil stabilization. The main motive of this research paper is to evaluate the value of industrial and agricultural waste as the soil admixture, and aimed to enhance the soil properties. Especially, for improving the alkaline soil’s load-bearing capacity, the current study explains the behavioral phase of alkaline soils blended along waste materials of industries like FA, TD, and agricultural waste material RHA, PM. The analysis is made to find the effect of both agricultural and industrial wastes on specific mix proportions on distinct soil properties like OMC, MDD, UCS, and CBR, and the comparison is also made. The results conclude all stabilizers such as FA, TD, RHA, and PM accomplish its optimal strength after the curing period of 7 days.

Title: The Price Of Anarchy In Closed-Loop Supply Chains Abstract: This paper measures the worst-case efficiency of price-only contracts in closed-loop supply chains (CLSCs) with the price of anarchy (PoA). We model a single-period Stackelberg game in which a manufacturer sells new products to a retailer and collects used products with exogenous retail price and collection price via three alternative reverse channels: (a) the manufacturer collects directly from customers, (b) the retailer collects for the manufacturer, and (c) a third party is awarded a collection subcontract from the manufacturer. We carry out a comprehensive investigation under push-pull configurations to observe how reverse channel structures with different gaming sequences of CLSC members influence the worst-case performance when the demand distribution is over the set of increasing generalized failure rate distributions. From our PoA analysis, we find that the pull system does not always outperform the push system, especially when the retailer is the leader, in contrast to the results for forward supply chains. While the PoA of the push system is dramatically sensitive to the quality condition of used products, the pull system has a constant efficiency loss that is independent of the quality condition. Instead, the PoA of the pull system solely changes with the gaming sequence of the manufacturer. We also find that manufacturer's direct collection is a better reverse channel choice compared to retailer's collection. Additional managerial insights are summarized for discussion.

Title: Optimal Start Time Of A Markdown Sale Under A Two-Echelon Inventory System Abstract: The importance of inventory management for perishable items has been steadily attracting attention. Because of the characteristics of items whose values drop precipitously or cannot be sold after a particular time, items should be disposed of by a markdown sale. Accordingly, the company makes the following decisions at the end of the selling season: (a) selection on which products to be discounted, (b) pricing of the product, and (c) timing of the sale. Extant literature on the inventory problem has mainly focused on investigating decisions on selecting products for discount and the amount of the discount. That is, the decision on the start time of the markdown sale was not extensively studied. This study focuses on the optimal combination of a start time of the markdown sale and an order quantity based on a newsvendor model. Under certain conditions in a decentralized system, the start time of a markdown sale, where the retailer obtains the highest profit, is the least profitable for the manufacturer. Therefore, we propose a revenue-sharing contract to avoid irrational ordering behavior by a retailer against a manufacturer. Centralization through the revenue-sharing contract improves the profits of the retailer and manufacturer compared to those earned in the decentralized system.

Title: On evaluating the collaborative research areas: A case study Abstract: The growth of social networks is ever-increasing. Many available scientific publications evidence the interest of researchers in this area. Within a time span of eight years from 2011 to 2018, approximately 2600, 230, 150, and 110 scientific articles were published from the USA, Iran, Saudi Arabia, and Turkey, respectively around this area of research. To comprehensively survey all the sub-fields and interests within this research area, the present paper proposes a novel density-based method for finding topic descriptors from academic articles. By employing a robust to noise fuzzy clustering algorithm, the terms are clustered, and by utilizing a modified Parzen window, k topic descriptors from each cluster are extracted. Besides, an optimization problem has been designed to detect the similarity between word pairs. By conducting the experiments, the research priorities for four countries within this time span have been found. Moreover, the closeness of the research in developing countries to the developed country have been measured. The experimental results show that for four years, the research topics in Turkey were close to the research topics in the USA on average, and the research topics in Saudi Arabia were close to the USA topics during the past two years. Additionally, the experimental comparison of the proposed method with two clustering baselines indicates the superiority of the proposed method in terms of precision, recall, and accuracy.

Title: Toughness, Forbidden Subgraphs, and Hamilton-Connected Graphs Abstract: A graph G is called Hamilton-connected if for every pair of distinct vertices {u, v} of G there exists a Hamilton path in G that connects u and v. A graph G is said to be t-tough if t center dot omega(G - X) <= |X| for all X subset of V (G) with omega(G - X) > 1. The toughness of G, denoted tau (G), is the maximum value of t such that G is t-tough (taking tau (K-n) = infinity for all n >= 1). It is known that a Hamilton-connected graph G has toughness tau (G) > 1, but that the reverse statement does not hold in general. In this paper, we investigate all possible forbidden subgraphs H such that every H-free graph G with tau (G) > 1 is Hamilton-connected. We find that the results are completely analogous to the Hamiltonian case: every graph H such that any 1-tough H-free graph is Hamiltonian also ensures that every H-free graph with toughness larger than one is Hamilton-connected. And similarly, there is no other forbidden subgraph having this property, except possibly for the graph K-1 ? P-4 itself. We leave this as an open case.

Title: An Enhanced Multiclass Support Vector Machine Model and its Application to Classifying File Systems Affected by a Digital Crime Abstract: The digital revolution we are witnessing nowadays goes hand in hand with a revolution in cybercrime. This irrefutable fact has been a major reason for making digital forensic (DF) a pressing and timely topic to investigate. Thanks to the file system which is a rich source of digital evidence that may prove or deny a digital crime. Yet, although there are many tools that can be used to extract potentially conclusive evidence from the file system, there is still a need to develop effective techniques for evaluating the extracted evidence and link it directly to a digital crime. Machine learning can be posed as a possible solution looming in the horizon. This article proposes an Enhanced Multiclass Support Vector Machine (EMSVM) model that aims to improve the classification performance. The EMSVM suggests a new technique in selecting the most effective set of parameters when building a SVM model. In addition, since the DF is considered a multiclass classification problem duo to the fact that a file system might be accecced by more than one application, the EMSVM enhances the class assignment mechanism by supporting multi-class classification. The article then investigates the applicability of the proposed model in analysing incriminating digital evidence by inspecting the historical activities of file systems to realize if a malicious program manipulated them. The results obtained from the proposed model were promising when compared to several machine-learning algorithms.

Title: Distribution theory following blinded and unblinded sample size re-estimation under parametric models Abstract: Asymptotic distribution theory for maximum likelihood estimators under fixed alternative hypotheses is reported in the literature even though the power of any realistic test converges to one under fixed alternatives. Under fixed alternatives, authors have established that nuisance parameter estimates are inconsistent when sample size re-estimation (SSR) follows blinded randomization. These results have helped to inhibit the use of SSR. In this paper, we argue for local alternatives to be used instead of fixed alternatives. We treat unavailable treatment assignments in blinded experiments as missing data and rely on single imputation from marginal distributions to fill in for missing data. With local alternatives, it is sufficient to proceed only with the first step of the EM algorithm mimicking imputation under the null hypothesis. Then, we show that blinded and unblinded estimates of the nuisance parameter are consistent, and re-estimated sample sizes converge to their locally asymptotically optimal values. This theoretical finding is confirmed through Monte-Carlo simulation studies. Practical utility is illustrated through a multiple logistic regression example. We conclude that, for hypothesis testing with a predetermined minimally clinically relevant local effect size, both blinded and unblinded SSR procedures lead to similar sample sizes and power.

Title: On the 12-Representability of Induced Subgraphs of a Grid Graph Abstract: The notion of a 12-representable graph was introduced by Jones, Kitaev, Pyatkin and Remmel in [Representing graphs via pattern avoiding words, Electron. J. Combin. 22 (2015) #P2.53]. This notion generalizes the notions of the much studied permutation graphs and co-interval graphs. It is known that any 12-representable graph is a comparability graph, and also that a tree is 12-representable if and only if it is a double caterpillar. Moreover, Jones et al. initiated the study of 12- representability of induced subgraphs of a grid graph, and asked whether it is possible to characterize such graphs. This question of Jones et al. is meant to be about induced subgraphs of a grid graph that consist of squares, which we call square grid graphs. However, an induced subgraph in a grid graph does not have to contain entire squares, and we call such graphs line grid graphs. In this paper we answer the question of Jones et al. by providing a complete characterization of 12-representable square grid graphs in terms of forbidden induced subgraphs. Moreover, we conjecture such a characterization for the line grid graphs and give a number of results towards solving this challenging conjecture. Our results are a major step in the direction of characterization of all 12-representable graphs since beyond our characterization, we also discuss relations between graph labelings and 12-representability, one of the key open questions in the area.

Title: The efficiency of constructed bivariate copulas for MEWMA and Hotelling's T-2 control charts Abstract: A multivariate exponentially weighted moving average (MEWMA) and Hotelling's T(2)control charts are types of multivariate control charts for monitoring the mean vector. In this paper, we propose an efficient construction of bivariate copulas on MEWMA and Hotelling's T-2 control charts. Observations are classified with Kendall's tau values as weak, moderate, and strong positive dependence by using a Monte Carlo simulation to measure the average run length as a performance metric. The numerical results obtained from the simulation show that the performances of the MEWMA and Hotelling's T-2 control charts were similar for small shifts (delta <= 0.01) but the MEWMA control chart showed higher performance for moderate to large shifts.

Title: Comparing multiple factor analysis and related metric scaling Abstract: Some statistical models, quite different in the symbolic mathematical sense, may provide similar results. After commenting two probability examples, we comment and compare multiple factor analysis (MFA) with related metric scaling (RMDS), two multivariate procedures dealing with mixed data. Each data set can be quantitative, binary, qualitative or nominal, and has been observed on the same individuals but coming from several sources. Then MFA and RMDS are two approaches for representing the individuals. We study the analogies and differences between both methodologies to guide users interested in performing multidimensional representations of mixed-type data. Though in general MFA and RMDS provide similar results, we prove that RMDS takes into account the association between the different sets of variables, providing, in some cases, better and more coherent representations. We also propose a parametric RMDS which includes MFA as a particular case. Article in memory of John C. Gower (1930-2019).

Title: Findings and implications of flipped science learning research: A review of journal publications Abstract: With reference to the Technology-based Learning model for flipped classrooms, a literature review on Social Sciences Citation Index (SSCI) papers published in the Web of Science (WOS) database was conducted, with the application of flipped learning in science education as the research topic. The study analyzed the existing research on author nationalities, application domains, research methods, participants, learning strategies, and research issues. Based on the analysis data, starting from 2015, there have been a large number of flipped classroom studies in science education. The studies were mainly from the United States; the application domains were mostly chemistry, physics, biology, and natural science and ecology. Over half of the studies adopted quantitative methods, and participants were mainly college students. In the before-class stage, the flipped classroom studies in science education mainly adopted instructional videos as the learning materials and used online learning systems as the medium for materials and online discussion. In the in-class stage, the major learning strategy was problem-based learning, while over half of the studies did not employ educational technology. In the after-class stage, most studies administered examinations to explore the learning outcomes. Accordingly, Potential research issues are proposed as a reference for future studies.

Title: Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation Abstract: Received Signal Strength Indicator (RSSI) is the measurement of the power in the radio signal and a parameter for distance-based measurements. Bluetooth Low Energy (BLE) is an advance implementation for Internet of Things (IoT). BLE beacons-based indoor positioning systems provide an easy and energy-efficient, low deployment cost solution for wide variety of applications including smart phones. This paper presents an in-depth experimental study of BLE RSSI in a dense indoor environment. Due to noise, fluctuations in RSSI occur, which produces distance estimation error, which ultimately affect position estimation accuracy. The main objective of this paper is to know the variations in RSSI and develop a radio propagation model in order to minimize the distance estimation and position estimation error. Based on our real-time experimental analysis using BLE modules, there is an average 1.32-m position estimation error in the presence of 10-dBm variation in RSSI. Moreover, we also observed that environmental specific radio propagation constants greatly affect distance and position estimation accuracy in BLE modules.

Title: Motivating Information Security Policy Compliance: Insights from Perceived Organizational Formalization Abstract: Psychological and behavioral characteristics are among the most important factors that instigate information security incidents. Although many previous studies have discussed the influencing factors of information security policy compliance behavior in an organization, few have considered the influence of organizational structures. In this study, the mechanism by which information security policy compliance behavioral intention is formed was studied by integrating the theory of planned behavior (TPB) and perceived organizational formalization. Data analysis was performed using the structural equation modeling (SEM) with data obtained from a survey of 261 company employees. The empirical results reveal that perceived organizational formalization significant affected cognitive processes theorized by TPB, behavioral habits, and deterrent certainty. This study suggests that formalized rules, procedures, and communications should be designed to improve employee information security policy compliance behavioral habits and intentions.

Title: Runtime performance evaluation and optimization of type-2 hypervisor for MIPS64 architecture Abstract: Over the last decade, virtualization technologies have seen unprecedented growth in the system-level domain and promptly have moved to the embedded system domain. While there are numerous applications of virtualization for off-the-shelf hardware such as security, sandboxing, testing tools, etc. However, there are few open-source virtualization solutions available for embedded systems. For real-time virtualization, good runtime performance is a deterministic factor for its practical use in the embedded domain. In this paper, the internal architecture of HTTM (an open-source type-2 hypervisor for MIPS64 architecture) was thoroughly analyzed. ISA virtualization unit and execution cycle control were two primary units that were profiled and explored for optimization. Dynamic Binary Translation (DBT) unit was modified to generate highly efficient code. While the reduction in switching between the translated code and management layer improved the overall execution cycle. The implementation of these strategies resulted in a 72–91% improvement in bandwidth benchmarks. Similarly, latency benchmarks show a 2–92% improvement from its vanilla version. Collectively producing an overall 1–5 times the improvement in execution time. The performance of optimized HTTM is also compared with Quick Emulator (QEMU). HTTM performs 44–80% better than QEMU in bandwidth benchmarks while QEMU performs better in latency operations.

Title: Group feature screening via the F statistic Abstract: Feature screening is crucial in the analysis of ultrahigh dimensional data, where the number of variables (features) is in an exponential order of the number of observations. In various ultrahigh dimensional data, variables are naturally grouped, giving us a good rationale to develop a screening method using joint effect of multiple variables. In this article, we propose a group screening procedure via the F-test statistic. The proposed method is a direct extension of the original sure independence screening procedure, when the group information is known, for example, from prior knowledge. Under certain regularity conditions, we prove that the proposed group screening procedure possesses the sure screening property that selects all effective groups with a probability approaching one at an exponential rate. We use simulations to demonstrate the advantages of the proposed method and show its application in a genome-wide association study. We conclude that the grouping method is very useful in the analysis of ultrahigh dimensional data, as the optimal F-test can detect true signals with desired properties.

Title: A new binary grasshopper optimization algorithm for feature selection problem Abstract: The grasshopper optimization algorithm is one of the recently population-based optimization techniques inspired by the behaviours of grasshoppers in nature. It is an efficient optimization algorithm and since demonstrates excellent performance in solving continuous problems, but cannot resolve directly binary optimization problems. Many optimization problems have been modelled as binary problems since their decision variables varied in binary space such as feature selection in data classification. The main goal of feature selection is to find a small size subset of feature from a sizeable original set of features that optimize the classification accuracy. In this paper, a new binary variant of the grasshopper optimization algorithm is proposed and used for the feature subset selection problem. This proposed new binary grasshopper optimization algorithm is tested and compared to five well-known swarm-based algorithms used in feature selection problem. All these algorithms are implemented and experimented assessed on twenty data sets with various sizes. The results demonstrated that the proposed approach could outperform the other tested methods.

Title: CoPModL: Construction Process Modeling Language and Satisfiability Checking Abstract: Process modeling has been widely investigated in the literature and several general purpose approaches have been introduced, addressing a variety of domains. However, generality goes to the detriment of the possibility to model details and peculiarities of a particular application domain. As acknowledged by the literature, known approaches predominantly focus on one aspect between control flow and data, thus neglecting the interplay between the two. Moreover, process instances are not considered or considered in isolation, neglecting, among other aspects, synchronization points among them. As a consequence, the model is an approximation of the real process, limiting its reliability and usefulness in particular domains. This observation emerged clearly in the context of a research project in the construction domain, where preliminary attempts to model inter-company processes show the lack of an appropriate language.

Title: Efficient workflow scheduling in cloud computing for security maintenance of sensitive data Abstract: The seamless conveying of information's considering internet as a virtual space, connecting all users from the world is cloud computing. In simpler terms it is a means of storing and accessing information over internet irrespective of place and time. The cloud proceeds with the process of storing, retrieving and allowing access, on demand as a paid service. As the benefits of cloud is attractive, the number of users adopting to the cloud also increases, so the workflow management becomes tedious and more challenging in the cloud. The decision to provide with an enhanced work flow management requires proper scheduling recollecting value of the information. So the paper proposes with an efficient method of managing the work flow considering the value of the information by categorizing between the much value and non-value information's and framing the algorithm that functions as scheduler using the parallel implementation in natural process of genetic algorithm (GA) with secured frame work for the information's of high value. The proposed work shows an overwhelmed performance than the conventional techniques in the time taken for the execution and its total cost. The performance of the scheduler is validated in the WORKFLOWSIM on the grounds of time of execution and total cost.

Title: A hybrid decision dependent maintenance model of failure rate and virtual age classes using modified Weibull intensity Abstract: In this article, we introduced a new hybrid model for repairable systems using the modified Weibull distribution. The model expresses the efficiency of the maintenance by a reduction in the virtual age of the system and a change in the parameters of the failure intensity. Depending on the maintenance decisions, the parameters of the failure intensity may shift and the virtual age of the system is reduced to zero or lower value. Both of Kijima type 1 and Kijima type 2 models were considered in modeling the virtual age of the system. The maximum likelihood estimation is considered to determine the estimation of the model parameters. The obtained results were applied on sets of simulated data, taking into account the left truncated and the right censored cases. In order to illustrate the usefulness of the proposed model, two real data sets were applied, it has been shown that the model can provide a better fit than its sub-models. In some cases, we have seen that the change in the parameters can also change the shape of the hazard function. The confidence intervals for the estimated parameters were obtained using the parametric bootstrap and the bias-corrected accelerated bootstrap methods.

Title: Robust estimation in partially linear regression models with monotonicity constraints Abstract: Partially linear models are important tools in statistical modeling, combining the flexibility of non-parametric models and the simple interpretation of linear models. Monotonicity constraints appear naturally in certain problems when the response is known to increase with one of the covariates. Estimation methods for partially linear models with monotonicity constraints have been proposed in recent years. These methods have a good performance when all the observations follow the assumed model. However, if a small proportion of atypical observations is present in the sample, these estimators become unreliable. A robust estimation method for these models is proposed and applied to two real data sets. A Monte Carlo simulation study is performed, in which the proposed estimators are compared to existing ones in different situations, both with clean and contaminated samples.

Title: A lightweight and compromise-resilient authentication scheme for IoTs Abstract: Internet of Things (IoTs) connects billions of devices through the Internet having billion of data points. The security and privacy of those data points are very important and considered as a major concern. Due to the resource constraints nature of IoTs, the security solutions for IoTs must be secure and lightweight in terms of processing and storage. However, many existing security solutions specifically in the field of authentication are not suitable for IoTs due to the computation involved. Alternatively, the lightweight existing solutions are vulnerable to various attack(s). In this paper, a lightweight and compromise-resilient authentication (LCA) scheme for IoT is proposed. The proposed LCA scheme is lightweight because it uses lightweight hash and XOR operations. The security analysis manifests that the proposed scheme is compromise resilient against numerous security attacks. The proposed LCA scheme is compared with existing authentication schemes based on execution time, security, and computation cost. The outcomes show that the proposed LCA scheme is more secure and robust as compared to the existing authentication schemes proposed for IoTs.

Title: Credible and economic multimedia service optimization based on game theoretic in hybrid cloud networks Abstract: The cloud network has the advantages in efficiently offloading the large-scale Internet traffic, which is considered as a promising architecture to provide the satisfactory multimedia services for mobile users. However, most current studies lack the joint consideration of economic and security of services in hybrid cloud networks. In this paper, a novel multimedia service optimization mechanism is proposed hereby to meet the user's requirements mentioned above while guaranteeing the reliability of service. Firstly, a credible scheme is designed to help the mobile users distinguish the reliable cloud providers. Meanwhile, a blockchain-based content credibility approach is further designed to guarantee the reliability and integrity of video contents. Moreover, a noncooperative Stackelberg game model is presented to maximize the profit of each party. Furthermore, the equilibrium of this game is achieved by the methods of backward induction and gradient descent. Finally, extensive simulations demonstrate that our solution has efficient performance in terms of secure service ratio, utility, service pricing, etc.

Title: High-quality tweet generation for online behavior security management based on semantics measurement Abstract: Behavior security management refers to monitoring and guiding the user's opinions in online social networks to reduce their harmful influence to social public security. Pushing designed tweets with specific contains to them is one of the promising ways to solve this problem. In this paper, we developed a new method for high-quality supervision tweet generation, which not only considers the semantics of the tweets but also includes the supervision requirement of different aspects. Firstly, we collect millions of tweets of six typical events. Following, we construct a sentiment lexicon suitable for online behavior analysis, and we also construct a lexicon for tweet preprocessing and emotional score calculation. Secondly, to include the semantics during sentence similarity calculation, we employ the tweets collected to train the word2vec model and employ the sum of the word vectors in specific sentences to form the sentence vector. Finally, we employ the TextRank to generate the supervision tweet. Experimental results based on data collected showed that the proposed methods outperform other related traditional methods, which can be used for effective social security management.

Title: A survey of energy-aware cluster head selection techniques in wireless sensor network Abstract: Recently, wireless sensor networks (WSNs) are becoming very famous as they are inexpensive and easy to maintain and manage. The network contains a group of sensor nodes, which are capable of sensing, computing, and transmitting. Energy efficiency is one of the most important challenging problems in WSN. Sensor nodes have inadequate energy and installed in remote areas. Hence, it is difficult to restore the batteries in WSN. Therefore, to maximize the network lifetime, appropriate clustering techniques and cluster head (CH) selection methods should be implemented. The main idea behind the clustering technique is that it clusters the sensor nodes and reduces the composed data simultaneously and then, it broadcasts the data. In this process, CH selection is an essential part. Therefore, this survey paper provides an overview of the clustering techniques for reducing energy consumption by reviewing several CH selection techniques in WSN that provide high energy efficiency. Several techniques have been employed for CH selection based on partitional clustering, optimization, low-energy adaptive clustering hierarchy, hierarchical, distributed, and other classification methods. Finally, an analysis is done based on the implementation tools, metrics employed, accuracy, and achievements of the considered CH selection techniques.

Title: Multiple input and multiple output and energy-aware peering routing protocol for energy consumption in sensor networks Abstract: Wireless sensor network consumes large number of energy-constrained nodes that are used to monitor the external devices while transferring the information in the sensor networks. At the time of the information transmission process, node contains high energy, and battery of node may be recharged continuously, which leads to reduction of the entire information transmission system performance. This paper introduces the multiple input and multiple output (MIMO) method with energy-efficient protocol for reducing the energy consumption in the network. Initially, the network coverage is determined by applying the shadow fading sensing model, and the clusters are formed with the help of the particle dual clustering process. After the cluster is formed, the information has been transmitted with the help of the energy-aware peering routing protocol (EPR), which reduces the network traffic and also improves the energy efficiency with efficient manner. Then, the efficiency of the system is analyzed with the help of experimental results in terms of coverage fraction, accuracy of the cluster, and energy consumption.

Title: Statistical inference for semiparametric varying -coefficient spatial autoregressive models under restricted conditions Abstract: This article considers statistical inference for restricted semiparametric varying-coefficient spatial autoregressive(SVCSAR) models. We propose a restricted estimation method for parametric and nonparametric components, and a Lagrange-multiplier-type test for testing hypotheses on the parametric component restrictions of SVCSAR models. Under mild conditions, we obtain the asymptotic normality for the resulting estimator of the parametric vector and the optimal convergence rate for that of nonparametric functions. Simulation studies are carried out to investigate the finite sample performance of the proposed method. The method is exemplified with Boston housing price data.

Title: Joint optimization of software time-to-market and testing duration using multi-attribute utility theory Abstract: An optimal software release strategy is a well-investigated issue in software reliability literature. Comprehensive testing is expected before releasing the software into the market to enhance the reliability and security of the software device. In recent years, few analysts have recommended the scheme for software projects that support releasing the software early in the market and continue the testing process for an added period in the field environment even after the software is distributed. These studies are based on one common assumption that the efficiency of the software engineers in detecting the faults occurs at a consistent rate throughout the testing phase. However, bug-identification rate may experience discontinuity at the software release time. In software engineering, the time-point at which fault detection rate changes is termed as change-point. Consequently, an alternative software release policy is proposed in the present paper, which offers a generalized framework for fault detection phenomenon using the unified approach. An extensive analysis of software time-to-market and testing duration based on cost-efficiency and reliability measures is discussed by considering the change in tester’s fault detection rate. A multi-criteria decision making technique known as multi-attribute utility theory is applied to optimize the software release policy under field-testing (FT) and no field-testing (NFT) frameworks. The relevance of the optimization problem is illustrated using a numerical example, comprising both the exponential and S-shaped bug-detection process.

Title: Path-flow matching: Two-sided matching and multiobjective evolutionary algorithm for traffic scheduling in cloud date center network Abstract: Improving the operational efficiency of data center has always been an important direction for the development of ICT. In this paper, we apply two-sided matching decision-making process in game theory to traffic scheduling problem in data center network. From the perspective of matching between flow and path, the traffic scheduling is properly arranged. We first propose and model the path-flow matching problem, considering the preference ordering, then formulate the problem as a multiobjective optimization problem with the target to ensure the stability and satisfaction from the matching scheme, and design a preference-based path-flow ordering method Extended PIAS, and finally propose a lightweight scheduling algorithm LinkGame based on multiobjective evolutionary algorithm. Compared with the previous scheduling methods (ECMP, Hedera, and Fincher), experiment results demonstrate that LinkGame can simultaneously consider the stability and satisfaction of the matching results, with improved bandwidth utilization and flow completion time.

Title: Proposition of new alternative tests adapted to the traditional T-2 test Abstract: New alternative tests of the T-2 Hotelling's test for testing hypotheses on the mean vector of a normal p-variate population were proposed. These tests were based on comedian robust estimator of the covariance matrix using an asymptotic T-2 distribution and a parametric bootstrap distribution to the null distribution of the statistical tests. The performance of these new tests was evaluated under normal and non-normal distributions through Monte Carlo simulations. The contaminated normal populations were also considered to evaluate the effects of outliers in performance of the tests. The type I error rats and power were computed in all Monte Carlo simulations by using the R software. The parametric bootstrap test based on the T-2 test statistic had equivalent performance of the T-2 original test. This test was recommended because it is easy to implement and computationally fast.

Title: Adaptive cooperative sensing in cognitive radio networks with ensemble model for primary user detection Abstract: Opportunistic spectrum sharing ability enables higher spectrum utilization in cognitive radio networks. Detecting the presence of primary user in the network is the most important functionality in cognitive radio network as the cognitive users cannot use the spectrum with interference to primary users. Most solutions proposed for primary user detection suffer from hidden terminal problem resulting from multipath fading and shadow effects. The work focus on Rayleigh and Nakagami fading channel with comparable nonfading AWGN channel in cognitive radio. An ensemble model to detect the presence of primary user with high confidence is proposed in this work. The approach is based on training machine learning models with energy vectors in presence and absence of primary users. The trained model is then used to predict the primary user based on the energy vector.

Title: Monte Carlo power comparison of seven most commonly used heteroscedasticity tests Abstract: Assumption of the classical linear regression model states that the disturbances should have a constant (equal) variance. When this requirement is not met, the loss in efficiency in using ordinary least squares may be substantial and the biases in estimated standard errors may lead to invalid inferences. This problem is known as heteroscedasticity. There are many tests for heteroscedasticity, we want to know which test is more powerful. We use Monte Carlo simulation to compare the power of seven most commonly used tests for detecting heteroscedasticity, namely, Breusch-Pagan test, Glejser test, Goldfeld-Quandt test, Harvey-Godfrey test, Harrison-McCabe test, Park test, and White test for six common types of heteroscedasticity. Simulation results show that the Harrison-McCabe test has generally the most power in all of the six common types of heteroscedasticity and the White test has generally the least power in all of the six common types of heteroscedasticity.

Title: Digital transformation in entrepreneurial firms through information exchange with operating environment Abstract: Changing digital technologies and innovation threaten established business models. Increasing uncertainty in the operating environment often drives the need to transform the core business model of firms through a process of digital business transformation (DBT). In this study, we conduct a longitudinal study of two digital startups in the crowdfunding domain and identify the core attributes driving such transformation in digital ventures. We build a framework that examines how DBT takes place in entrepreneurial firms through information exchange with the environment. This research will help entrepreneurs and managers of such firms design and develop reactive business models for market success.

Title: Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics Abstract: In this article, we consider an emergent problem in the sensor fusion area in which unreliable sensors need to be identified in the absence of the ground truth. We devise a novel solution to the problem using the theory of replicator dynamics that require mild conditions compared to the available state-of-the-art approaches. The solution has a low computational complexity that is linear in terms o...

Title: The effect of SOS Table learning environment on mobile learning tools acceptance, motivation and mobile learning attitude in English language learning Abstract: The aim of this study is to demonstrate effectiveness of mobile game called SOS Table in the context of the subject "Tenses in English" within the framework of mobile learning tools acceptance, learning language motivation and mobile learning attitude of the students. The target group of the research consists of preparatory class students of a school of foreign languages in a state university in Turkey. The research was carried out with mixed method research. The quantitative paradigm-based section of this study was designed with a single-group pre-test and post-test model. In this model, three different scales were applied to the participants. The participants used the SOS Table mobile game developed by the researchers for 8 weeks. After the applications, semi-structured interviews were conducted with seven participants. The quantitative data of the study were analyzed with paired sampled t-Test and the qualitative data were subjected to thematic content analysis. The results of the study indicated that the mobile application named SOS Table increased both the mobile learning tools acceptance of the participants and motivation in English, and mediated the positive attitude development for mobile learning in English. The qualitative data obtained also supported these findings.

Title: Estimation of semiparametric mixed analysis of covariance model Abstract: A semiparametric mixed analysis of covariance model is postulated. This model is estimated by imbedding restricted maximum likelihood estimation and smoothing splines regression into the backfitting algorithm along with the bootstrap method. To mitigate overparameterization, the heterogeneous effect of covariates across groups of experimental units is assumed to affect the response through a nonparametric function. Simulation studies exhibited the capability of the postulated model (and estimation procedures) in increasing predictive ability and stabilizing variance components estimates even for small sample size and with minimal covariate effect, and regardless of the extent of misspecification error. The method also exhibits relative advantage even for unbalanced cases.

Title: Fast computation of global solutions to the single-period unit commitment problem Abstract: The single-period unit commitment problem has significant applications in electricity markets. An efficient global algorithm not only provides the optimal schedule that achieves the lowest cost, but also plays an important role for deriving the market-clearing price. As of today, the problem is mainly solved by using a general-purpose mixed-integer quadratic programming solver such as CPLEX or Gurobi. This paper proposes an extremely efficient global optimization algorithm for solving the problem. We propose a conjugate function based convex relaxation and design a special dual algorithm to compute a tight lower bound of the problem in $${\mathcal {O}}(n\log n)$$ complexity. Then, a branch-and-bound algorithm is designed for finding a global solution to the problem. Computational experiments show that the proposed algorithm solves test instances with 500 integer variables in less than 0.01 s, whereas current state-of-the-art solvers fail to solve the same test instances in one hour. This superior performance of the proposed algorithm clearly indicates its potential in day-ahead and real-time electricity markets.

Title: The influence of big data analysis of intelligent manufacturing under machine learning on start-ups enterprise Abstract: In order to solve the problem that enterprises waste resources and make wrong decision caused by lack of comprehensive grasp of production, so as to realise intelligent manufacturing under the background of big data and improve the level of intelligent manufacturing of the newly created enterprises, machine learning method is used to make reasonable predictions on the customer's favourite products and combinations. The research results show that the diagnosis and introspection are predicted through the manufacturing data collection assessment to realise intelligent manufacturing system, optimisation of intelligent manufacturing decision space, provide visual guidance for intelligent manufacturing.

Title: Key characteristics in designing massive open online courses (MOOCs) for user acceptance: an application of the extended technology acceptance model Abstract: In spite of the proliferation of Massive Open Online Courses (MOOCs) in higher education, factors influencing user acceptance of MOOCs are not well understood. This study is intended to investigate key characteristics of user acceptance from interface design (i.e. usability), content quality (i.e. perceived quality), and emotional arousal (i.e. perceived enjoyment) of MOOCs within the framework of Technology Acceptance Model (TAM). Six hundred and sixty-eight college students were invited to complete a self-reported questionnaire measuring TAM constructs and three hypothesized variables drawn from MOOC characteristics. The results from path analysis showed that all path coefficients were statistically significant. Perceived ease of use, perceived usefulness and perceived enjoyment significantly affected students' behavioral intention to use MOOCs, while both perceived usefulness and behavioral intention yielded a significant influence on perceived effective use of MOOCs. Usability and perceived quality had a strong indirect impact on behavioral intention and perceived effective use through the mediators of perceived ease of use, perceived usefulness and perceived enjoyment. This study demonstrated that the extended TAM with MOOC characteristics provides an effective means to understand students' acceptance of MOOCs.

Title: A refined order release method for achieving robustness of non-repetitive dynamic manufacturing system performance Abstract: The operational quality and reliability of a manufacturing system is greatly influenced by uncertain or variable environments, therefore robustness is one of the most important indicators for measuring the operational quality of the non-repetitive dynamic manufacturing system. Controlling the order release to limit work in process at a stable level and protect throughput from variation is crucial to achieving robustness of manufacturing system performance. To deal with the influences of bottleneck severity and variable resource on system performance, a refined order release method is presented, which releases order periodically based on the corrected aggregate load and continuously based on the bottleneck buffer load. The operational quality of this method with the classical order release method under non-repetitive dynamic manufacturing system is compared by modeling and simulation. The results show that the refined order release method is more robust for general flow shop with higher protective capacity and resource variability.

Title: On the time to first spotting in wildland fires Abstract: Protecting human life and property from wildfires is a primary concern to wildland fire management agencies. Under certain environmental conditions and wildfire situations, burning embers can be lofted across a natural or man-made barrier devoid of fuel, such as a river or road, resulting in new ignitions downwind of the main advancing fire front. This phenomenon, referred to as a "spot fire" or "spotting," can put a considerable strain on firefighting operations and pose a grave risk to wildland-urban interface communities. In this article, we formulate the process of spot fire development, and derive the distribution of the time to the first spot fire occurring beyond a barrier to fire spread. A simulator is developed in the framework of generating burning embers from an active wildfire that may result in a spot fire. With the generated data, we demonstrate how to estimate the rate of developing spot fires and identify significant covariates based on data in two practical formats.

Title: Secure and energy aware multi-hop routing protocol in WSN using Taylor-based hybrid optimization algorithm Abstract: The advancements of Wireless sensor network (WSN) in large number of applications made it common. However, the energy is a major challenge in the WSN environment as the battery-operated sensor nodes in the network consumes huge amount of energy during transmission. This work addresses the energy issue and provides an energy efficient multi-hop routing in WSN named Taylor based Cat Salp Swarm Algorithm (Taylor C-SSA) by modifying C-SSA with Taylor series. This method undergoes two stages for attaining multi-hop routing, which includes selection of cluster head (CH), and transmission of data. Initially, the energy-efficient cluster heads are selected using the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for effective data transmission, the sensor nodes sends data over the CH, which transmits the data to the base station through the selected optimal hop. The optimal hop selection is done using the proposed Taylor C-SSA. Moreover, the security aware multi-hop routing is performed by introducing trust model that involves indirect trust, integrity factor, direct trust, and data forwarding rate. The proposed Taylor C-SSA algorithm shows best performance in terms of energy, number of alive nodes, delay, and throughput values of 0.129, 42, 0.291, and 0.1, respectively.

Title: A new intelligent intrusion detector based on ensemble of decision trees Abstract: Artificial intelligence and machine learning are in widespread use nowadays in order to develop automatic and precise models for different tasks especially in the Internet. In this paper, by the use of machine learning techniques, an intrusion detection system is proposed. An intrusion detection system is involved extensive mass of data; such data is naturally characterized with repetitions and noise which leads to the reduction in the stability and the accuracy of the intrusion detection system. Hence, the issue of reducing features dimensions for achieving a smaller subset of features which can precisely express the results and status of network observations has attracted a lot of researchers’ attention. In the proposed method, by using gradually feature removal method, 16 critical features were selected for representing various network visits. By combining ant colony algorithm and ensemble of decision trees, we proposed an efficient and stable classifier for judging a network visit to be normal or not. Despite the selection of 16 features, high accuracy, i.e. 99.92%, and the average value of Matthews correlation coefficient 0.91 are obtained.

Title: Comments on "an extended Gompertz-Makeham distribution with application to lifetime data" Abstract: Abd El-Bar 2018, 'An extended Gompertz-Makeham distribution with application to lifetime data', Communication in Statistics - Simulation and Computation, 47, 2454-2475) introduced a four-parameter extension of the Gompertz-Makeham distribution, and derived some statistical and reliability measures like moments, moment generating function, conditional moments, among many others. In this short communication, we show that some of the closed-form expressions derived by the author cannot be used since they depend on power series expansions which are not convergent. We provide, therefore, simple and correct expressions to compute such properties.

Title: DL-IDS: a deep learning-based intrusion detection framework for securing IoT Abstract: The Internet of Things (IoT) is comprised of numerous devices connected through wired or wireless networks, including sensors and actuators. Recently, the number of IoT applications has increased dramatically, including smart homes, vehicular ad hoc network (VANETs), health care, smart cities, and wearables. As reported in IHS Markit (see ), the number of connected devices is projected to jump from approximately 27 billion in 2017 to 125 billion in 2030, an average annual increment of 12%. Security is a critical issue in today's IoT field because of the nature of the architecture, the types of devices, different methods of communication (mainly wireless), and the volume of data being transmitted over the network. Security becomes even more important as the number of devices connected to the IoT increases. To overcome the challenges of securing IoT devices, we propose a new deep learning-based intrusion detection system (DL-IDS) to detect security threats in IoT environments. There are many IDSs in the literature, but they lack optimal features learning and data set management, which are significant issues that affect the accuracy of attack detection. Our proposed module combines the spider monkey optimization (SMO) algorithm and the stacked-deep polynomial network (SDPN) to achieve optimal detection recognition; SMO selects the optimal features in the data sets and SDPN classifies the data as normal or anomalies. The types of anomalies detected by DL-IDS include denial of service (DoS), user-to-root (U2R) attack, probe attack, and remote-to-local (R2L) attack. Extensive analysis indicates that the proposed DL-IDS achieves better performance in terms of accuracy, precision, recall, and F-score.

Title: Risk and complexity in scenario optimization Abstract: Scenario optimization is a broad methodology to perform optimization based on empirical knowledge. One collects previous cases, called "scenarios", for the set-up in which optimization is being performed, and makes a decision that is optimal for the cases that have been collected. For convex optimization, a solid theory has been developed that provides guarantees of performance, and constraint satisfaction, of the scenario solution. In this paper, we open a new direction of investigation: the risk that a performance is not achieved, or that constraints are violated, is studied jointly with the complexity (as precisely defined in the paper) of the solution. It is shown that the joint probability distribution of risk and complexity is concentrated in such a way that the complexity carries fundamental information to tightly judge the risk. This result is obtained without requiring extra knowledge on the underlying optimization problem than that carried by the scenarios; in particular, no extra knowledge on the distribution by which scenarios are generated is assumed, so that the result is broadly applicable. This deep-seated result unveils a fundamental and general structure of data-driven optimization and suggests practical approaches for risk assessment.

Title: Comparison of distribution selection methods Abstract: Many methods have been suggested to choose between distributions. There has been relatively less study to examine whether these methods accurately recover the distributions being studied. Hence, this research compares several popular distribution selection methods through a Monte Carlo simulation study and identifies which are robust for several types of discrete probability distributions. In addition, we study whether it matters that the distribution selection method does not accurately pick the correct probability distribution by calculating the expected distance, which is the amount of information lost for each distribution selection method compared to the generating probability distribution.

Title: Data-driven Begins with DATA; Potential of Data Assets Abstract: The objective of this study is to analyze the potential of company data assets for data-driven, fact-based decision-making in product portfolio management (PPM). Data assets are categorized from the PPM standpoint, including (product/customer/ horizontal ellipsis ) master data, transactional data, and interaction data (e.g., IoT data). The study combines literature review and qualitative analysis of eight international companies. The findings underline the crucial role of corporate-widely combined and governed data model. Company business IT is adjusted against the corporate-level data model. The order of importance is data first, and the technology second. The data-driven mind-set and culture creation are also important. The implications include understanding the role and potential of combined data assets that form the basis for data-driven PPM. Facts based on company data assets are essential for decision-making instead of "gut feeling" and emotions. The utilization of the unused potential of data assets is promoted in the transformation toward data-driven PPM.

Title: THE SEMITOTAL DOMINATION PROBLEM IN BLOCK GRAPHS Abstract: A set D of vertices in a graph G is a dominating set of G if every vertex outside D is adjacent in G to some vertex in D. A set D of vertices in G is a semitotal dominating set of G if D is a dominating set of G and every vertex in D is within distance 2 from another vertex of D. Given a graph G and a positive integer k, the semitotal domination problem is to decide whether G has a semitotal dominating set of cardinality at most k. The semitotal domination problem is known to be NP-complete for chordal graphs and bipartite graphs as shown in [M.A. Henning and A. Pandey, Algorithmic aspects of semitotal domination in graphs, Theoret. Comput. Sci. 766 (2019) 46-57]. In this paper, we present a linear time algorithm to compute a minimum semitotal dominating set in block graphs. On the other hand, we show that the semitotal domination problem remains NP-complete for undirected path graphs.

Title: Asymptotic Enumeration of Non-Uniform Linear Hypergraphs Abstract: A linear hypergraph, also known as a partial Steiner system, is a collection of subsets of a set such that no two of the subsets have more than one element in common. Most studies of linear hypergraphs consider only the uniform case, in which all the subsets have the same size. In this paper we provide, for the first time, asymptotically precise estimates of the number of linear hypergraphs in the non-uniform case, as a function of the number of subsets of each size.

Title: Iterative user and expert feedback in the design of an educational virtual reality biology game Abstract: This study focuses on an educational game titled Cellverse, a two-player cross-platform VR project intended to teach high school biology students about cell structure and function. In Cellverse, players work in pairs to explore a human lung cell and diagnose and treat a dangerous genetic disorder. Cellverse is being designed by the Collaborative Learning Environments in Virtual Reality (CLEVR) team, an interdisciplinary team consisting of game designers, educational researchers, and graduate and undergraduate students. Using a design-based research approach, we have enlisted the help of both subject matter experts and user testers to iteratively design and improve Cellverse. The objective of this paper is to share how user and expert feedback can inform and enhance the development of learning games. We describe how we gather and synthesize information to review and revise our game from in-game observations, semi-structured interviews, and video data. We discuss the input of subject matter experts, present feedback from our user testers, and describe how input from both parties influenced the design of Cellverse. Our results suggest that including feedback from both experts and users has provided information that can clarify gameplay, instruction, subject portrayal, narrative, and in-game goals.

Title: Precision matrix estimation under data contamination with an application to minimum variance portfolio selection Abstract: In this article, we consider the problem of estimating the precision matrix when the sample data contains cellwise contamination. For the widely employed methodologies (e.g. Graphical Lasso), using the sample covariance matrix as an input matrix potentially deteriorates the precision matrix estimation performance in the presence of outliers. We propose several robust alternatives for the covariance matrix, which are constructed by combining robust correlation estimators with robust variation measures. Through extensive numerical studies we demonstrate the robust performance of our proposed approaches compared to the standard methods based on the sample covariance matrix. Further, we apply our proposals to a real data application, aimed at studying the optimal portfolio allocations in Shanghai Stock Exchange Composite index. The results show that the proposed alternatives provide desirable out-of-sample performance.

Title: Factors affecting usability of 3D model learning in a virtual reality environment Abstract: The aim of the present study was to explore the learning usability factors of 3D modeling in virtual reality environment (VRE). Users' 3D modeling usability factors in VR were investigated through principal component analysis (PCA). Fifty industrial design students participated in the 3D modeling learning in VR experience experiment, and their average age was 23.9 years (SD = 1.6). System Usability Scale (SUS) scores and user experience of the 3D modeling interface of the participants in the VR environment were also assessed. The results show that there are three major usability factors in 3D modeling learning in VR work: interactive quality, dynamic compatibility, and flow effects. In addition, the users' learning experiences of the 3D modeling in VR are described. The results can be provided to 3D modeling software developers as a key reference in VR learning interface design.

Title: A model for defining project lifecycle phases: Implementation of CMMI level 2 specific practice Abstract: Carefully considering the Capability Maturity Model Integration (CMMI) Level 2 specific practice to “define project life cycle phases” (SP 1.3) is a key requirement, particularly when it comes to small and medium-sized software development organizations. This is a necessary step to help these organizations get nearer to achieving CMMI Level 2 certification. In this paper, we, therefore, report on our latest empirical study that recently explored both the perceptions and experiences of practitioners about SP 1.3 implementation. During our research, we visited three firms and carried out three in-depth interviews. We developed a SP 1.3 model using the different experiences and opinions of practitioners regarding SP 1.3 implementation. The four essential stages of this model are plan, design, review, and update/rework. Practitioners will receive advice in this model about implementing SP 1.3 effectively.

Title: ON THE rho-EDGE STABILITY NUMBER OF GRAPHS Abstract: For an arbitrary invariant rho(G) of a graph G the rho-edge stability number es(rho)(G) is the minimum number of edges of G whose removal results in a graph H subset of G with rho(H) not equal rho(G) or with E(H) = empty set. In the first part of this paper we give some general lower and upper bounds for the rho-edge stability number. In the second part we study the chi'-edge stability number of graphs, where chi' = chi'(G) is the chromatic index of G. We prove some general results for the so-called chromatic edge stability index es(chi')(G) and determine es(chi')(G) exactly for specific classes of graphs.

Title: A modified randomized device for estimation of population mean of quantitative sensitive variable with measure of privacy protection Abstract: This work presents a randomization device for the estimation of population mean related to quantitative sensitive characteristics using blank card strategy. Measure of privacy protection for the proposed randomized response models have been described. The proposed technique provides the unbiased estimation procedures of population mean along with enhanced privacy protection of the respondents. Empirical studies are performed to support the theoretical results and efficiency of the models are also verified by suggesting an unified measure which show the dominance of the proposed models over existing models. Results are analyzed and suitable recommendations are put forward to the survey practitioners.

Title: Diagnosis of Parkinson's disease from electroencephalography signals using linear and self-similarity features Abstract: An early stage detection of Parkinson's disease (PD) is crucial for its appropriate treatment. The quality of life degrades with the advancement of the disease. In this paper, we propose a natural (time) domain technique for the diagnosis of PD. The proposed technique eliminates the need for transformation of the signal to other domains by extracting the feature of electroencephalography signals in the time domain. We hypothesize that two inter-channel similarity features, correlation coefficients and linear predictive coefficients, are able to detect the PD signals automatically using support vector machines classifier with third degree polynomial kernel. A progressive feature addition analysis is employed using selected features obtained based on the feature ranking and principal component analysis techniques. The proposed approach is able to achieve a maximum accuracy of 99.1 +/- 0.1%. The presented computer-aided diagnosis system can act as an assistive tool to confirm the finding of PD by the clinicians.

Title: Energy efficient distributed lightweight authentication and encryption technique for IoT security Abstract: Internet of Things (IoT) is an intelligent technology and service that mutually communicates information between human and devices or between Internet-based devices. In IoT security, the authentication should be distributed and lightweight. Token generation should be based on the trust worthiness of the devices, and it needs to ensure fast authentication and authorization. In this paper, we propose an Energy Efficient Distributed Lightweight Authentication and Encryption (EEDLAE) technique for IoT security. In our proposed technique, the receiver generates the token for each sender. The token expiration time is determined based on the trust value of each sender, and sleep period of the receiver radio is determined based on its remaining energy. For encryption, Counter with Cipher Block Chaining-Message Authentication Code (CCM) is applied. Experimental results show that EEDLAE technique can hold out against affected packets, higher resilience against node capture, increased throughput, and residual energy, compared with the existing technique.

Title: Multicategory large margin classification with unequal costs Abstract: In this paper, we propose a multicategory large margin classification with unequal costs. In addition to extending the standard multicategory SVM to the case of unequal costs, we also develop an unequal costs classification for psi-loss and propose an efficient algorithm for computation. Besides commonly used L-2 penalty, the adaptive LASSO is also examined to remove irrelevant variables. Theoretically, we derive the Bayes rules under the generalized cost and reduce the infinite sum-to-zero constraint to a finite constraint. Numerically, we demonstrate the good performance of our methodology on two simulated examples and one real-life dataset.

Title: Machine vision gait-based biometric cryptosystem using a fuzzy commitment scheme Abstract: In this paper, a fuzzy commitment scheme is applied with a machine vision gait-based biometric system to enhance system security. The proposed biometric cryptosystem has two phases: enrolment and verification. Each of them comprises three main stages: feature extraction, reliable components extraction, and fuzzy commitment scheme. Gait features are extracted from gait images using local ternary pattern (LTP), and then, the average of one complete gait cycle using the gait energy image (GEl) concept is calculated. The average images are joined using a 2D joint histogram, which is reduced using principal component analysis (PCA) to produce the final feature vector. To enhance the robustness of the system, only highly robust and reliable bits from the feature vector are extracted. Finally, the fuzzy commitment scheme is used to secure feature templates. Bose–Chaudhuri–Hocquenghem codes (BCH) are used for key encoding in the enrolment phase and for decoding in the verification phase. The proposed system is tested using the CMU MoBo and CASIA A databases. The experimental results show that the best error rate for the CMU MoBo database is obtained when using a fast walk for enrolment and verification, where we obtain 0% for the false acceptance rate (FAR) and 0% for the false rejection rate (FRR) for a key length equal to 50 bits. The best error rate for CASIA A dataset is obtained when using the 45-degree direction to the image plane view for enrolment and verification, where we obtain 0% for the false acceptance rate (FAR) and 0% for the false rejection rate (FRR) for a key length equal to 45 bits.

Title: Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation Abstract: This work proposes a binary nonlinear bi-objective optimization model for the problem of planning the sustainable cultivation of crops. The solution to the problem is a planting schedule for crops to be cultivated in predefined plots, in order to minimize the possibility of pest proliferation and maximize the profit of this process. Biological constraints were also considered. Exact methods, based on the nonlinear model and on a linearization of that model were proposed to generate Pareto optimal solutions for the problem of sustainable cultivation, along with a metaheuristic approach for the problem based on a genetic algorithm and on constructive heuristics. The methods were tested using semi-randomly generated instances to simulate real situations. According to the experimental results, the exact methodologies performed favorably for small and medium size instances. The heuristic method was able to potentially determine Pareto optimal solutions of good quality, in a reduced computational time, even for high dimension instances. Therefore, the mathematical models and the methods proposed may support a powerful methodology for this complex decision-making problem.

Title: Nonlinear chance-constrained problems with applications to hydro scheduling Abstract: We present a Branch-and-Cut algorithm for a class of nonlinear chance-constrained mathematical optimization problems with a finite number of scenarios. Unsatisfied scenarios can enter a recovery mode. This class corresponds to problems that can be reformulated as deterministic convex mixed-integer nonlinear programming problems with indicator variables and continuous scenario variables, but the size of the reformulation is large and quickly becomes impractical as the number of scenarios grows. The Branch-and-Cut algorithm is based on an implicit Benders decomposition scheme, where we generate cutting planes as outer approximation cuts from the projection of the feasible region on suitable subspaces. The size of the master problem in our scheme is much smaller than the deterministic reformulation of the chance-constrained problem. We apply the Branch-and-Cut algorithm to the mid-term hydro scheduling problem, for which we propose a chance-constrained formulation. A computational study using data from ten hydroplants in Greece shows that the proposed methodology solves instances faster than applying a general-purpose solver for convex mixed-integer nonlinear programming problems to the deterministic reformulation, and scales much better with the number of scenarios.

Title: Formalising and animating multiple instances in BPMN collaborations Abstract: The increasing adoption of modelling methods contributes to a better understanding of the flow of processes, from the internal behaviour of a single organisation to a wider perspective where several organisations exchange messages. In this regard, BPMN collaborations provide a suitable modelling abstraction. Even if this is a widely accepted notation, only a limited effort has been expended in formalising its semantics, especially for what it concerns the interplay among control features, data handling and exchange of messages in scenarios requiring multiple instances of interacting participants. In this paper, we face the problem of providing a formal semantics for BPMN collaborations including elements dealing with multiple instances, i.e., multi-instance pools and sequential/parallel multi-instance tasks. For an accurate account of these features, it is necessary to consider the data perspective of collaboration models, thus supporting data objects, data collections and data stores, and different execution modalities of tasks concerning atomicity and concurrency. Beyond defining a novel formalisation, we also provide a BPMN collaboration animator tool, named MIDA, faithfully implementing the formal semantics. MIDA can also support designers in debugging multi-instance collaboration models.

Title: Orchestrating big data analytics capability for sustainability: A study of air pollution management in China Abstract: Under rapid urbanization, cities are facing many societal challenges that impede sustainability. Big data analytics (BDA) gives cities unprecedented potential to address these issues. As BDA is still a new concept, there is limited knowledge on how to apply BDA in a sustainability context. Thus, this study investigates a case using BDA for sustainability, adopting the resource orchestration perspective. A process model is generated, which provides novel insights into three aspects: data resource orchestration, BDA capability development, and big data value creation. This study benefits both researchers and practitioners by contributing to theoretical developments as well as by providing practical insights.

Title: Predicting student understanding by modeling interactive exploration of evidence during an online science investigation Abstract: This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students' interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the interactive map to make scientific observations for an evidence-based hypothesis. The examination of classification error using an artificial neural network, compared against the majority class for prediction, suggests that student performance on several metrics of critical thinking can be classified based on different patterns in interactions with visual representations. Two alternative methods are compared in this study for training and evaluating data-mined models of student performance. In accordance with the general consensus in the literature, the error estimates for models' predictions were less variable using a student-level cross-validation. Implications of these findings for open-ended inquiry-based learning environments are discussed.

Title: Little rewards, big changes: Using exercise analytics to motivate sustainable changes in physical activity Abstract: Even using simple techniques like taking the stairs, many individuals struggle to maintain the motivation to be physically active. Health gamification systems can aid this goal by providing points earned through exercise that are redeemable for tangible extrinsic rewards. Using self-determination theory, we conduct research on one such system and investigate rewards’ effectiveness to promote exercise considering reward value, redemption frequency patterns, and fitness levels. We find that rewards do significantly increase activity levels, and this effect is larger for advanced users who redeem multiple times for higher value rewards. We close by offering future research avenues and advice to optimize reward portfolios.

Title: Energy loss prediction in nonoriented materials using machine learning techniques: A novel approach Abstract: Traditional extrapolation performed by machine designers for energy loss estimation results in the decrease of the overall efficiency of electrical machines. Therefore, state-of-the-art techniques need to be developed in order to accurately predict the energy loss in electrical machines for their improved performance. To this end, machine learning techniques have been employed to predict accurate energy loss at different frequencies and induction levels under rotational conditions. Such types of flux exist near the teeth of the stator in synchronous machines. In transformers, rotational flux arises at the bends and corners of the stators. It was observed that the random forest machine learning algorithm has the least mean square error and as such is the most suited algorithm, which can be used for the accurate prediction of energy loss in nonoriented materials.

Title: Spanning Trees with Disjoint Dominating and 2-Dominating Sets Abstract: In this paper, we provide a structural characterization of graphs having a spanning tree with disjoint dominating and 2-dominating sets.

Title: Subject independent emotion recognition from EEG using VMD and deep learning Abstract: Emotion recognition from Electroencephalography (EEG) is proved to be a good choice as it cannot be mimicked like speech signals or facial expressions. EEG signals of emotions are not unique and it varies from person to person as each one has different emotional responses to the same stimuli. Thus EEG signals are subject dependent and proved to be effective for subject dependent emotion recognition. However, subject independent emotion recognition plays an important role in situations like emotion recognition from paralyzed or burnt face, where EEG of emotions of the subjects before the incidents are not available to build the emotion recognition model. Hence there is a need to identify common EEG patterns corresponds to each emotion independent of the subjects. In this paper, a subject independent emotion recognition technique is proposed from EEG signals using Variational Mode Decomposition (VMD) as a feature extraction technique and Deep Neural Network as the classifier. The performance evaluation of the proposed method with the benchmark DEAP dataset shows that the combination of VMD and Deep Neural Network performs better compared to the state of the art techniques in subject-independent emotion recognition from EEG.

Title: Gradient analysis of Markov-type control schemes and its applications Abstract: This paper presents formulas for gradients of the Average Run Length (ARL) and other Run Length characteristics by control scheme parameters, in the context of Markov Chain approach to analysis of Markov-type control schemes. We illustrate use of these formulas in several problems related to control charting, including (a) design of control schemes, (b) inference on Run Length characteristics based on Phase-1 data and (c) performance analysis for highly complex input data distributions.