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Title: A new efficient TKHC-based image sharing scheme over unsecured channel Abstract: The major problems of Visual Secret Sharing (VSS) are the pixel expansion and lossy recovery. The former creates large-sized shared images and makes their handling, storage, and speed transmission via networks challenging, whereas the latter leads to poor contrast of the recovered images. In addition, sharing a huge volume of images and transmitting the shared images through one less channel is a critical problem of VSS where any unauthenticated user can attack, discover the generated shares, and recover the secret image. In this paper, an efficient TKHC algorithm is proposed to augment the privacy and safety of the shared images. Moreover, the new TKHC-based VSS scheme is utilized to sharing a huge RGB and grayscale images which are subjected to be encrypted and decrypted by means of TKHC and providing strong security to transmit all the generated shares via one public channel. In comparison to the existing schemes, the proposed scheme shows significant improvement in encryption quality with lightweight computation cost. Furthermore, it withstands the known-plaintext and brute-force attacks and overall creates a balance between security, cost, and performance.

Title: A WSQ-based flipped learning approach to improving students' dance performance through reflection and effort promotion Abstract: Flipped learning has received significant emphasis in recent years. Through this approach, students' self-learning ability can be cultivated and the time for in-class practice and teachers' and students' interaction are increased. In the past, there has been much research confirming its benefits in learning performance. Meanwhile, some studies have also pointed out that with proper educational strategies, students can be guided to have better learning engagement and higher-order thinking, which could improve their learning performance. In this study, a WSQ (Watch-Summary-Question)-based flipped learning approach was implemented in a dance course. Moreover, an experiment was conducted to investigate the impacts of the proposed approach. The participants were 173 college students from 2 classes, which were assigned to learn with the WSQ-based flipped learning approach and the conventional flipped learning approach, respectively. From the 12-week experiment, it was found that the WSQ-based flipped learning approach effectively promoted the students' dance performance and guided them to make more reflections. Furthermore, the proposed approach also engaged the students in making more efforts to think and to perfect their dance techniques.

Title: Goodness of fit tests for Rayleigh distribution based on quantiles Abstract: In the recent studies, much attention has been paid to the usefulness and importance of quantile functions as an alternative approach in statistical modeling, analysis of data and information theory. In the present paper, some new divergence measures based on quantile are proposed and then utilizing these divergence measures, some goodness of fit tests for Rayleigh distribution are constructed. Monte Carlo simulations are performed for various alternatives and sample sizes in order to compare the proposed tests with other goodness of fit tests for Rayleigh distribution in the literature. Simulation results show that in comparison with the existing tests, our proposed tests have good performances. Finally, illustrative examples for use of the proposed tests are presented and analyzed.

Title: Social learning analytics for determining learning styles in a smart classroom Abstract: Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart classroom. Specifically, this paper proposes to determine the learning styles of the students in a smart classroom using SLA. In this proposal is analyzed external data from the web and social networks to build knowledge models about the students, in order to improve the learning processes that occur in the smart classrooms. In general, these SLA tasks will be organized in autonomous cycles, in order to integrate them with each other. The autonomic cycle will automate the execution of those tasks and the generation of knowledge models, in such a way to permanently monitor the learning process, observing it, analyzing it and determining the student learning styles. For the development of the SLA tasks, we will use concepts from the Semantic Mining, Text Mining, Data Mining, among other domains. Finally, we experiment in a test scenario, with results very interesting.

Title: Improved l-diversity: Scalable anonymization approach for Privacy Preserving Big Data Publishing Abstract: In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k-anonymity, l-diversity, and t-closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k-anonymity and l-diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l-Diversity (ImSLD) approach which is the extension of Improved Scalable k-Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k-anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class.

Title: Hash-balanced binary tree-based public auditing in vehicular edge computing and networks Abstract: In recent years, cloud storage technology is developed maturely, so data owners could deposit data to relieve the storage pressure. In vehicular communication networks, vehicles tend to use low-latency networks to transfer the valid road information and vehicle information generated by smart sensors in real time. Vehicular edge computing (VEC) networks satisfy the low-latency requirements of the vehicular communication networks. However, vehicles have no space to store a copy of the data locally like using a local storage service. Vehicles, as users of cloud storage, pay attention to the integrity of the data especially. Unfortunately, many existing auditing schemes for integrity are not fully applicable to the VEC paradigm, because of significant differences in security assumptions. In this paper, a dynamic public audit protocol is proposed to the VEC paradigm. First, a new threat model is defined to formalize noncollusive and collusive attacks according to VEC servers and cloud server providers. Second, we propose a novel auditing protocol based on hash-balanced binary tree (HBBT) and describe the auditing process and dynamic operations. Third, security analysis and experimental analysis are completed to demonstrate the safety and effectiveness of the proposed scheme.

Title: Analysis of user satisfaction of shared bicycles based on SEM Abstract: As the ideal transportation mode for the last mile trips of the residents in the city, shared bicycles are becoming more and more popular, and the market of the shared bicycles is experiencing rapid development. The explosive growth of shared bicycles brings many conveniences but also issues. There are many shared bicycles illegally parked around the areas where people are concentrated, which not only caused huge traffic impact, but also affected the normal life of urban residents seriously. Although there have been many studies on public bicycles before, the research on shared bicycles is scattered, fragmented, and there are few quantitative studies. To explore the current operation status of shared bicycle, the cause of the problem, management methods, and provide practical suggestions and opinions on the future operation and development of shared bicycles in the city, after investigating the shared bicycle travel data in Yangpu District, Shanghai, the data obtained is summarized and analyzed with the statistical methods. AMOS and SPSS22.0 are used to analyze the data in depth in this paper. Also, with the establishment of hypothesis, a path diagram and structural equation model is proposed, and path coefficient is calculated. The model is identified, evaluated, and corrected. The extent of the influence of the factors on user satisfaction of the shared bicycle is determined. All the latent variables proposed in this paper passed the significance test. Moreover, there are significant positive correlations between various factors analyzed in this paper and user satisfaction of shared bicycles.

Title: Sample based side sensitive group runs control chart to detect shifts in the process mean Abstract: In this article, we propose side sensitive group runs based univariate control chart using sample based 'transition probability matrix' (tpm) namely 'Sample based Side Sensitive Group Runs' (S-SSGR) control chart. The zero state ATS performance of the S-SSGR chart and the CRL based 'Side Sensitive Group Runs' (SSGR) chart is exactly same but better than the 'Side Sensitive Synthetic' (SSS) chart and the 'Group Runs' (GR) Chart. Also the steady state ATS performance of the S-SSGR chart is better as compared to the GR and the SSGR charts.

Title: Identify the user presence by GLRT and NP detection criteria in cognitive radio spectrum sensing Abstract: Spectrum sensing in cognitive radio networks is vital and is used for identifying the user presence or absence in the available spectrum. Energy detection and matched filter detection are the few methods to identify the user presence in the spectrum. There are various authors that proposed their research on spectrum sensing using matched filter detection with fixed threshold and predefined dynamic threshold. In this paper, authors proposed the novel matched filter detection method with dynamic threshold by using generalized likelihood ratio test (GLRT) and Neyman Pearson (NP) observer detection criteria. Due to which the probability of detection (P-D) is increased, probability of false alarm (P-fa) and probability of missed detection (P-md) has been reduced when compare with the existing methods. The results are simulated using MATLab Software and also plotted the receiver operating characteristic (ROC) curve for estimation of the receiver sensitivity.

Title: Ambient intelligence-based smart classroom model Abstract: This paper introduces the smart classroom learning model based on the concept of ambient intelligence. By analyzing a smart classroom, the ambient intelligence system detects a student and determines their level of fatigue based on the data about their previous daily academic activities. This information is then used to assign the student the appropriate learning strategy. The paper describes relevant factors for developing the model. The model was tested on a sample of 80 students. By analyzing the information available through ambient intelligence, it was possible to utilize the smart classroom to provide students with the adequate learning strategy in accordance with the criteria compatible with the expected leaning outcomes. The research results have shown positive effects of the application of the ambient intelligence-based smart classroom model on learning results.

Title: Prioritizing the components of e-learning systems by using fuzzy DEMATEL and ANP Abstract: Institutions and universities have started using e-learning systems to reach the potential students from all over the world by decreasing costs of investments. The speed of technological developments increases the importance of e-learning systems and their technology-based components. E-learning systems also decrease the costs of both institutions and students with effective learning way. But, the main problem of e-learning systems is that investment to the right and demanded components is important to actualize cost benefits. The main aim of this study is to analyze the relations of the components of e-learning systems and prioritize them in detail for stakeholders. To solve this problem in this study, causal relations among the components are analyzed by using fuzzy DEMATEL. After determining the causal relations, importance and priorities of the components are calculated according to these relations with the help of fuzzy analytic network process. The application includes 19 components of e-learning systems under three main cluster as e-learning, education and technology. The results of this study supports that the most important components of e-learning systems are technology-based components and these are also the most affected and affecting components of the e-learning systems.

Title: Modification of the adaptive Nadaraya-Watson kernel method for nonparametric regression (simulation study) Abstract: In this research, a new improvement of the Nadaraya-Watson kernel non parametric regression estimator is proposed and the bandwidth of this new improvement is obtained depending on the three different statistical indicators: robust mean, median and harmonic mean of kernel function instead of using geometric and arithmetic mean, or R. Simulation study is presented, including comparisons with four others Nadaraya-Watson kernel estimators (classical methods). The proposed estimator in the case of harmonic mean is more accurate than all classical methods for all simulations based on MSE criteria.

Title: Caching in heterogeneous satellite networks with fountain codes Abstract: In this paper, we investigate the performance of caching schemes based on fountain codes in a heterogeneous satellite network. We consider multiple cache-aided hubs, which are connected to a geostationary satellite through backhaul links. With the aim of reducing the average number of transmissions over the satellite backhaul link, we propose the use of a caching scheme based on fountain codes. We derive a simple analytical expression of the average backhaul transmission rate and provide a tight upper bound on it. Furthermore, we show how the performance of the fountain code-based caching scheme is similar to that of a caching scheme based on maximum distance separable codes.

Title: Effects of between-batch variability on the type I error rate in biosimilar development Abstract: Biological products are known to have some between-batch variation. However, the traditional method to assess biosimilarity does not consider such between-batch variation. Beta-binomial models and linear random effect models are considered in order to incorporate between-batch variation for the binary endpoints and the continuous endpoints, respectively. In this article, emphasis is on the beta-binomial models for the binary endpoint case. For the linear random effect models of the continuous endpoint case, we cite relevant references along with conducting some simulation studies. Overall, we show that the type I error rates are inflated when biosimilarity is evaluated by the traditional method, which ignores between-batch variation.

Title: A dynamic swift association scheme for wireless body area networks Abstract: Among numerous promising applications of wireless sensor networks is wireless body area networks. Wireless body area networks are used for real-time vital signs monitoring of patients in a remote setting. In addition to their dedicated sensing tasks, the implanted sensor nodes need to effectively utilize their resources to enhance the lifetime of the network. For wireless body area networks, IEEE 802.15.4 low data rate low-power wireless Personal Area Networks (PANs) standard is adopted. The said standard uses fixed superframe structure operation, which restricts the movement of the nodes, and has degraded performance in the mobility of nodes. Two superframe parameters, ie, beacon order and superframe order, decide the beacon interval and duty cycle of the standard. Both beacon order and superframe order have a strong impact on the performance of network parameters. In this research work, a dynamic swift association scheme is proposed to enhance the performance of a mobile node with a scenario where node moves from its parent PAN and joins the neighbor PAN. Dynamic swift association scheme is compared with IEEE 802.15.4 in terms of association time, association success rate, average throughput, and energy consumption. Simulation results using network simulator version 2 confirm superior performance of dynamic swift association scheme against IEEE 802.15.4 standard.

Title: Applying cuckoo search based algorithm and hybrid based neural classifier for breast cancer detection using ultrasound images Abstract: Ultrasound examination is one of the most convenient and appropriate processes used for the diagnosis of tumors that make use of ultrasound images. Ultrasound imaging is a noninvasive modality utilized commonly for the detection of breast cancer, which is a common and dangerous cancer found in women. This paper proposes an approach for the detection of breast cancer using ultrasound images using MKF-cuckoo search (MKF-CS) algorithm and hybrid based neural (H-BN) classifier. In pre-processing, the input images to be diagnosed are pre-processed by ROI extraction using a novel algorithm, four way search. The pre-processed image is allowed to perform segmentation using MKF-CS algorithm. The key features, such as mean, variance, standard deviation, and so on, are extracted in feature extraction and are fed to the proposed H-BN classifier. Based on the training data, H-BN classifier classifies the data into benign or malignant tumor classes, for the detection of breast cancer. To evaluate the performance of the proposed MKFCS-HBN approach, three metrics, such as accuracy, sensitivity, and specificity, are utilized. The experimental results show that MKFCS-HBN could attain the maximum performance with an accuracy of 0.8889, the sensitivity of 1, and specificity of 0.85 and thus, prove its effectiveness.

Title: Day-ahead optimal scheduling of microgrid with adaptive grasshopper optimization algorithm Abstract: Recently, the microgrid (MG) structure day-ahead scheduling is an important aspect and achieved an optimal operation by maximizing the utility function. In this paper, a day-ahead scheduling of MG and their optimal operation are analyzed with the help of the proposed adaptive algorithm. For the optimal analysis of MG, adaptive grasshopper algorithm (AGOA) with cuckoo search (CS) is proposed. The CS algorithm is utilized to update the learning functions of the GOA, and the optimal performances are evaluated. Here, the photovoltaic (PV), wind turbine (WT), battery, and diesel generator (DG) are considered to analyze the optimal scheduling issues, and the main aim is to minimize their generating and operational cost functions. In addition, to maximize the profit of operations in MG, the load demand must be satisfied according to their constraints and objectives. The multiobjective function is defined as the cost functions of MG such as the fuel cost, generation cost, state of charge (SOC), direct cost, reserve cost, and penalty cost, respectively. The proposed method is implemented in MATLAB/Simulink platform and tested with the IEEE 57-bus system and IEEE 118-bus system. In order to verify the effectiveness of the proposed method, this is compared with the existing methods such as whale optimization algorithm (WOA) and cuttlefish algorithm (CFA), respectively. Before the comparative study, the real-time data of PV and WT are analyzed for the 24 hours. The SOC of the proposed method is analyzed and is about 80%.

Title: Performance analysis of LTE physical layer using hardware cosimulation techniques and implementation on FPGA for communication systems Abstract: The recent mobile networks provide large data access using long-term evolution networks. Long-term evolution networks are a third-generation partnership project standard that provides different speed limits for uplink and downlink. The different network operators have different bandwidth requirements to provide different services. Long-term evolution improves the efficiency of the network. The analysis of long-term evolution is performed using different signal processing algorithms that require a realistic, flexible, and standard compliant simulation environment. This study emphasizes to measure the performance of long-term evolution downlink and uplink physical layer based on Release 11, 12, and 13. The analysis of orthogonal frequency division multiple access for downlink and single-carrier frequency division multiplexing access for uplink using different modulation schemes like quadrature amplitude modulation (4, 16, 64) and different antenna configurations in long-term evolution physical layer is analyzed using hardware cosimulation platform.

Title: Generating survival times with time-varying covariates using the Lambert W Function Abstract: Simulation studies provide an important statistical tool in evaluating survival methods, requiring an appropriate data-generating process to simulate data for an underlying statistical model. Many studies with time-to-event outcomes use the Cox proportional hazard model. While methods for simulating such data with time-invariant predictors have been described, methods for simulating data with time-varying covariates are sorely needed. Here, we describe an approach for generating data for the Cox proportional hazard model with time-varying covariates when event times follow an Exponential or Weibull distribution. For each distribution, we derive a closed-form expression to generate survival times and link the time-varying covariates with the hazard function. We consider a continuous time-varying covariate measured at regular intervals over time, as well as time-invariant covariates, in generating time-to-event data under a number of scenarios. Our results suggest this method can lead to simulation studies with reliable and robust estimation of the association parameter in Cox-Weibull and Cox-Exponential models.

Title: Shewhart-type monitoring schemes with supplementary w-of-w runs-rules to monitor the mean of autocorrelated samples Abstract: The simplicity of the Shewhart charts makes it popular in practice; however, it is insensitive to detecting small shifts. In an effort to preserve its simplicity but increase its detection ability, in this paper, we propose two Shewhart-type charts supplemented with w-of-w runs-rules to monitor the mean of autocorrelated samples using a first-order autoregressive model. It is shown that the higher the level of autocorrelation, the poor the proposed schemes perform. Hence, we implement the skipping sampling strategy which involves sampling of nonconsecutive observations to form the rational subgroups to compute the corresponding sample means. The Markov chain approach is used to derive zero- and steady-state closed-form expressions of the average run-length (ARL). To supplement the specific shift performance metric, i.e. ARL, we compute the overall performance metric so that these schemes can also be evaluated from a global point of view. A real-life example is provided to illustrate the implementation of the monitoring schemes proposed here.

Title: Enhancing classroom interaction: the integration of image-sharing projection software in social science and humanities classrooms Abstract: Fostering student-student and student-faculty interactions involves not only pedagogical design but also classroom technology. Image-sharing projection software, which allows multiple students to simultaneously share images from their electronic devices to the classroom's screens, offers a new form of communication in medium to large-size social science and humanities classes. While there is a body of literature that analyzes the effectiveness and best practices of clickers and image-sharing software in STEM classes, few studies have evaluated the potential of image-sharing projection software and its impact on student engagement in social science and humanities undergraduate courses. Based on a case study of a general education introductory social science course, this paper demonstrates how giving undergraduate students the ability to share visual depictions of their ideas increased engagement among those students who report not feeling comfortable interacting in medium to large-size class discussions. Overall, this case study illustrates the potential of image-sharing projection software to simultaneously address several known challenges to collaborative learning and to increase the types of students who participate in "the doing" of active-learning pedagogy, especially the idea-sharing component.

Title: Online collaborative tool usage for review meetings in software engineering courses Abstract: The instructors generally utilize conventional methods in teaching software engineering courses, where the students are provided theoretical knowledge based on text books or lecture notes. Usage of collaborative tools may be a solution to the problems of not practicing the depth of the components of the subject. This study proposes the usage of a collaborative tool, namely, Google Docs in a software engineering course based on predefined scenarios. The review meeting subject was selected for this purpose and students' reactions were assessed with a survey after the completion of the experiments. The survey data were analysed using least square regression method. The results have shown that efficiency, certainty, satisfaction, advantage, complexity, learnability, and intention are indicators of the adoption of the online collaborative tool.

Title: Two approximations of renewal function for any arbitrary lifetime distribution Abstract: The renewal functions (RFs) of most distribution functions do not have closed-form expressions while such expressions are desired for the optimization problems involved RF. Many efforts have been made to develop approximations of RF. However, it seems that no RF approximation is accurate enough in the entire time range. In this paper, we propose two RF approximations. The first approximation is obtained through smoothly connecting two limiting relations and fairly accurate in the entire time range. The second approximation has the same function form as the first part of the first approximation but the model parameter is determined in a different way so as to achieve higher accuracy for small to moderate time range. The expressions of the proposed approximations are simple and applicable for any arbitrary lifetime distribution. Their accuracy is analyzed and, the appropriateness and usefulness are illustrated by a numerical example.

Title: The relation between openness and creativity is moderated by attention to peers' ideas in electronic brainstorming Abstract: An emerging body of research has focused on students' creativity in group contexts, with the assumption that students could be inspired by peers' ideas. Although students' openness and attention to peers' ideas are claimed to play important roles in their creativity in group settings, there is little empirical research that tests this assumption. This study examined the moderating effect of attention to peers' ideas in the relation between openness and creativity in electronic brainstorming. Participants were 91 undergraduate students who took about 10 min to complete a creative idea generation task during electronic brainstorming. Regression analyses found that students who were characterized by high openness were more creative, but only when they showed more attention to peers' ideas. This suggests that electronic brainstorming can be useful for enhancing the creativity of some students.

Title: Exploring the effects of automated tracking of student responses to teacher feedback in draft revision: evidence from an undergraduate EFL writing course Abstract: This study aimed to investigate the impact of using an automated tracking system on the writing performance of English as Foreign Language (EFL) students in a 13-week academic writing course. Sixty-eight first year university students participated in the study. They received the same instruction on academic writing and were allocated to one of two conditions: experimental (N = 36) or control (N = 32). Participants in the experimental condition could use the automated tracking system to generate analysis of teacher feedback on their draft essays and of their subsequent revisions in response to the feedback received, while those in the control condition could not. The results of this study show that the system could not only support students to reflect on the quality of their revisions but also likely result in improvements in their revised texts. The findings of this study would contribute to the body of literature on effects of using technology to facilitate student reflection on multiple-draft writing.

Title: Research outlook and state-of-the-art methods in context awareness data modeling and retrieval Abstract: As the data or information gets increased in various applications, it is very much essential to make the retrieval and modeling easier and simple. Number of modeling aspects already exists for this crisis. Yet, context awareness modeling plays a significant role in this. However, there requires some advancement in modeling system with the incorporation of advanced technologies. Hence, this survey intends to formulate a review on the context-aware modeling in two aspects: context data retrieval and context data modeling. Here, the literature analyses on diverse techniques associated with context awareness modeling. It reviews 60 research papers and states the significant analysis. Initially, the analysis depicts various applications that are contributed in different papers. Subsequently, the analysis also focuses on various features such as web application, time series model, intelligence models and performance measure. Moreover, this survey gives the detailed study regarding the chronological review and performance achievements in each contribution. Finally, it extends the various research issues, mainly the adoption of Evolutionary algorithms, which can be useful for the researchers to accomplish further research on context-aware system.

Title: A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle Abstract: The factors that influence the inherent reliability of machine tool spindle are a mixture of various uncertainties and it leads that the reliability modeling and analysis of machine tool spindle can’t be dealt with by one mathematical theory. Meanwhile, the reliability data of the machine tool spindle for reliability modeling and analysis is often insufficient, and data of different types such as accumulated historical data, expert opinions, simulation data, etc. are used to make up for the lack of data. Thus, the unified quantification of mixed uncertainties and the data characterization of different types are the major premises for reliability modeling and analysis of machine tool spindle. By considering this, this paper makes use of the advantage of imprecise probability theory in quantizing the multiple types of data and the advantage of the Bayes theory in data fusion, and proposes a new inherent reliability modeling and analysis method based on imprecise Dirichlet model. In the proposed method, imprecise probability theory is used to quantify mixed uncertainties, imprecise Dirichlet model is built to characterize the different types of reliability data. After analyzing the inherent reliability variation regularity, an inherent reliability model is built, and the proposed method is verified by the inherent reliability calculation of a certain heavy-duty CNC machine tool’s milling spindle. This study can provide new method, theory and reference for reliability modeling and analysis when there are various uncertainties mixed and multiple data existed.

Title: An efficient technique for image compression and quality retrieval using matrix completion Abstract: In this paper, an efficient technique for image compression and quality retrieval using matrix completion is presented. The proposed technique is based on low-rank matrix completion using singular value truncation and thresholding. Here, an image is decomposed using singular value decomposition (SVD) to obtain a low rank of image data, which is approximated in compressed form. Later on, singular value thresholding algorithm is exploited to retrieve visual quality of the compressed image. The presented method is easily applicable for various visual characteristics of the image for different compression efficiency. A detailed analysis has been presented to show the efficiency of proposed method in term of compression as well as quality retrieval. It is evident from experimental results that a maximum of 80% compression is achieved with acceptable visual quality as per human vision system (HVS).

Title: Fuzzy-based decisive approach for call admission control in the LTE networks Abstract: Long term evolution (LTE) is a user-friendly network in providing a user requested service, and the growth of LTE is reported exponentially due to the attractive applications. The existence of the huge number of users and a massive presence of user demands in the network raise a question on the Quality-of-Service (QoS). In order to assure the required QoS with the available resources in the network, the paper proposes a call admission control scheme using a fuzzy-based decisive approach. The proposed method works based on the available resources in the network and allocates extra resource blocks when there is a lag in the demanded QoS. The flexible and user-friendly service is assured to three groups of users, for which the users are categorized based on the requested service as soon the service is requested. The simulation environment is developed to perform the fuzzy-based call admission control in LTE such that the results prove that the proposed method outperformed the existing methods in terms of delay, throughput, cell power, and call drops. The delay, throughput, cell power, and call drops using the proposed method are 0.1103 s, 1,294,932 bps, 44.2071 dBm, and 328, respectively. Also, the proposed method has the minimum call blocking probability of 0.0527 and 0.2901 for handoff users and new users. It has the call dropping probability of 0.0528 and 0.3357 for handoff users and new users, respectively.

Title: Differential evolution CapsNet model for QoS routing enhancement in wireless networks Abstract: One of the nondeterministic polynomial (NP) hard problem in the area of wireless networks is the quality of service (QoS) routing problem. This has always been a key area wherein research is required to enhance the QoS metrics considered with respect to the considered network model. In this paper, mobile ad hoc network (MANET) model is identified for which an effective QoS routing protocol which meets the set QoS constraints is designed employing the novel variant of differential evolution (DE) model. The ultimate aim of the proposed work is to identify an optimal feasible path for the MANET model with the set objective function criterion being met. A variant of DE capsule net model is built by hybridizing it with the nature-inspired firefly (FF) algorithmic approach. The effectiveness of the proposed technique is achieved by minimizing the set objective function on satisfying the QoS constraints in the MANETs. The developed technique chose better QoS path with respect to the best fitness value than that of the route replay the shortest path algorithm of ad hoc on-demand distance vector (AODV) protocol. The modeled variant DE-FF model achieves better QoS metrics with all the set constrained being met. Simulated results depict the superiority and better performance of the developed DE-FF model than that of the other methods considered for comparison from the existing literature works.

Title: Phase-I robust parameter estimation of simple linear profiles in multistage processes Abstract: This paper addresses the problem of robust parameter estimation of simple linear profiles in multistage processes in the presence of outliers in Phase I. In this regard, two robust approaches, namely the Huber's M-estimator and the MM estimator, are proposed to estimate the parameters of the process in Phase I in the presence of outliers in historical data. In addition, the U statistic is applied to the robust parameter estimates to remove the effect of the cascade property in multistage processes and as a result, to obtain adjusted robust estimates of the parameters of simple linear profiles. The performance of the proposed methods is evaluated under weak and strong autocorrelations involved in some numerical experimentation. The results show that the proposed robust methods perform efficiently to eliminate the effects of the outliers and the cascade property and demonstrate a better performance compared to the one of the classical approach under both weak and strong autocorrelations. Moreover, based on the results, the proposed MM estimator provides much better estimates of the parameters compared to the M estimator.

Title: A multi-analysis on privacy preservation of association rules using hybridized approach Abstract: Nowadays, extensively obtainable personal data has made Privacy-Preserving Data Mining (PPDM) issues a significant one. PPDM handles securing the privacy of sensitive knowledge or personal data without leaking the utility of the data. Several techniques have been introduced with the concern of privacy, yet there exist certain limitations in PPDM in achieving the feasible standards. Hence, this paper intends to develop a sanitization and restoration model by concerning objective functions like, Hiding Failure rate, Information Preservation rate, False Rules generation rate, Degree of Modification, Compression Ratio, tampering and Low Pass Filter for better preservation of privacy data. In sanitization and restoration, a key is generated optimally using Hybrid model named Genetic Algorithm with Crow Search Algorithm (GA-CSA). Moreover, the sensitive data is restored efficiently by the authorized user at the receiving end. Finally, the proposed GA-CSA approach is compared over conventional schemes such as Firefly (FF), Self-Adaptive FF Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution approach and the enhanced outcomes are obtained.

Title: TempoX: A disciplined approach for data management in multi-temporal and multi-schema-version XML databases Abstract: Although multi-temporal XML databases supporting schema versioning are used in several domains, like e-commerce, e-health, and e-government, existing database management systems and XML tools do not provide any support for managing (inserting, updating, and deleting) temporal XML data or temporal XML schema versioning. Besides, whereas much research work has focused in the last decade on schema versioning in temporal XML databases, any attention has been devoted to manipulating data in such databases. To fill this theoretical and practical gap, we propose in this paper a generic approach, named TempoX (Temporal XML), for data manipulation in multi-temporal and multi-schema-version XML databases. Indeed, we (i) define a new multi-temporal XML data model supporting temporal schema versioning, named TempoXDM (Temporal XML Data Model), (ii) introduce the principles on which our approach is based, and (iii) provide the specifications of the basic data manipulation operations: "insert", "replace", "evolve", and "delete". Moreover, to show the feasibility of TempoX, we use it to propose a temporal XML update language, named TempoXUF (Temporal XQuery Update Facility), as an extension of the W3C XQuery Update Facility language to temporal and versioning aspects. Furthermore, to validate our language proposal, we develop a system prototype, named TempoXUF-Manager, that supports TempoXUF. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: A note on the asymptotic properties of the estimators in a semiparametric regression model Abstract: In this paper, we investigate the parametric component and nonparametric component estimators in a semiparametric regression model based on -mixing random errors. The r-th mean consistency and uniform consistency are established under some suitable conditions. Finally, a simulation to study the numerical performance of the consistency for the nearest neighbor weight function estimators is provided. The results obtained in the paper complement the existing ones of independent random errors to the case of -mixing random errors.

Title: Reservation based resource allocation in 5G new radio standard Abstract: In fifth generation new radio, due to the increase in the number of mobile devices, there exist a demand for a large amount of spectrum. Resource scheduling service plays an important role to fulfill the demand for a large spectrum. To cater this spectrum crisis, a flexible and reliable reservation-based spectrum sharing technique is proposed. The computational results shows that compared with existing techniques, the reservation-based resource allocation (RBRA) technique can significantly increase the resource effective utilization, where not only the user with best channel characteristics is allocated the free spectrum but also a user with acceptable channel characteristics is assigned with free spectrum thereby maintaining the fairness.

Title: Development of a practical system for computerized evaluation of descriptive answers of middle school level students Abstract: Assessment plays an important role in education. Recently proposed machine learning-based systems for answer grading demand a large training data which is not available in many application areas. Creation of sufficient training data is costly and time-consuming. As a result, automatic long answer grading is still a challenge. In this paper, we propose a practical system for long or descriptive answer grading that can assess in a small class scenario. The system uses an expert-written reference answer and computes the similarity of a student answer with it. For the similarity computation, it uses several word level and sentence level similarity measures including TFIDF, Latent Semantic Indexing, Latent Dirichlet Analysis, TextRank summarizer, and neural sentence embedding-based InferSent. The student answer might contain certain facts that do not occur in the model answer. The system identifies such sentences, examine their relevance and correctness, and assigns extra marks accordingly. In the final phase, the system uses a clustering-based confidence analysis. The system is tested on an assessment of school-level social science answer books. The experimental results demonstrate that the system evaluates the answer books with high accuracy, the best root mean square error value is 0.59 on a 0-5 scoring scale.

Title: ROC analysis using covariate balancing propensity scores with an application to biochemical predictors for thyroid cancer Abstract: Biomarker evaluation is important for diagnosing clinical diseases. Covariate adjusted receiver operating characteristic (ROC) regression has been used to identify significant biomarker candidates. Here, we show that the statistical significance of a biomarker can be affected by its prevalence. We propose a novel method that incorporates covariate prevalence information in the ROC regression. This approach is based on covariate balancing propensity scores proposed by Imai and van Dyk. Our method produces higher AUC values, demonstrating improved discrimination ability compared to direct ROC regression or unadjusted ROC analysis; this method can be used to improve biomarker development and can be implemented by an artificial intelligence (AI) system. Extensive simulation studies and data from a thyroid cancer study illustrate the advantages of our approach.

Title: Model for data codification in hierarchical classifications with application to the biodiversity domain Abstract: The conception and treatment of complex classifications or typologies (as hierarchical tables), mainly of nature components, has for long constituted a major concern for researchers. Hence, several codification methods were developed in order to address and facilitate the management of such tables. Most of these methods uses alphanumeric codes, that remain specific to each case (type of entity), in the sense that their transposition to other cases is rarely feasible. This article proposes a new standardized codification method, applicable to various hierarchical schemas. Implemented in the development of a complex system of biodiversity data management, this method consists in creating for each table a fixed number of levels, coded using the same type of strings, based on 'numeric characters'. This method makes it possible to manage different tables with the same module and facilitates the automatic incrementation and updating of the code (=position). The new model has the advantage of offering possibilities to generate, update or delete a code, without disrupting the codes of the other elements, in addition to the gain in response time of the requests. It gives hope that in some cases (as geo-databases) to merge different tables whose merger was not previously obvious. CO 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Predictive density criterion for SETAR models Abstract: This paper deals with the problem of joint determination of the delay parameter and autoregressive orders of the Self-Exciting Threshold Autoregressive (SETAR) models. More specifically, we propose a variant of the Predictive Density Criterion (PDC) for the purpose of SETAR model selection. The performance of this variant is evaluated by means of Monte Carlo experiments. Our results indicate that PDC serves as an effective tool for jointly selecting the correct order and delay.

Title: Gene data classification using Map Reduce based linear SVM Abstract: Nowadays, the microarray classification for various diseases is a challenging one. The real disadvantage of sequence of gene information is the "scourge of dimensionality issue"; this frustrates the meaningful data of dataset and, what is more, prompts computational unsteadiness. Accordingly, choosing pertinent qualities is a basic in microarray information investigation. The majority of the existing plans utilize a two-stage form: selection of features/extraction pursued by order. In this paper, a factual test, ie, forward selection depending upon map reduce, is suggested to choose the significant highlights. Subsequently, the selection of relevant features, ie, linear-based Support Vector Machine (SVM) using map reduce based classifier, is likewise suggested to order the microarray information. These calculations are effectively executed on Hadoop system, and relative investigation is finished utilizing different datasets.

Title: Trusted information project platform based on blockchain for sharing strategy Abstract: South Korea invests a budget of trillions in national R&D projects every year, and has achieved excellent performance doing so each year. However, since the projects are planned, evaluated, and managed by different departments and institutions, duplicate planning and submission leads to insufficient sharing of research results. Currently, the National Technology Information Service (NTIS) inspects project duplication based on keywords, which leads to duplicate planning among departments and closed management of research results. Since the NTIS builds in centralized systems, the inspection systems supports one-way management for duplication checking and information sharing. Therefore, we propose a new platform, called the Trusted Information Project Platform (TIP-Platform), for easily checking for project duplication, sharing research results, and updating research results. TIP-Platform adopts a new concept for user authority setting, the distributed ledger structure, transaction structure, and service. For the adaption, the TIP-Platform uses blockchain technology that performs recording and management via blocks by distributing the right to record and managing transactions. This platform makes it easy for anyone to view and use project-related information such as research results and duplication review. In this paper, we describe how the TIP-Platform can achieve excellent research results through information sharing of a project. This platform needs to be based on trust, because it shares information and continually updates information.

Title: Extended age maintenance models and its optimization for series and parallel systems Abstract: In this paper, extended preventive replacement models for series and parallel system with n independent non-identical components are proposed. It is assumed that the system suffers from two types of failure. One is repairable (type-I) failure, at that time the system can be rectified by minimal repair. Another is non-repairable (type-II) failure, then the whole system is replaced. In the proposed models, the system is replaced at the planned time, at random working time, or at the time when type-II failure occurs, with options whichever occurs first or whichever occurs last. The average cost rate (ACR) function and the failure rate function (FRF) of the series and parallel system under the different cases are obtained respectively. Moreover, the optimal preventive replacement time of models based on minimization of the ACR function is obtained theoretically. Numerical examples are presented to evaluate the cost of the system and verify the performance of our results.

Title: Differential Privacy-Based Location Protection in Spatial Crowdsourcing Abstract: Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks’ location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.

Title: Universal modification of vector weighted method of dependent trials Abstract: Vector weighted Monte Carlo algorithms for the estimation of linear functionals of set of solutions of systems of the 2nd kind integral equations are studied. A universal modification of the vector weighted method of dependent trials with branching of the Markov chain trajectory relative to the elements of matrix weight is constructed. It is shown that the computational cost of the constructed algorithms is finite in the case when the solutions of systems of the 2nd kind are bounded. The application of constructed method of dependent trials is presented for some problems of the radiation transfer theory with allowance for polarization and multigroup transport theory.

Title: MCMC4Extremes: an R package for Bayesian inference for extremes and its extensions Abstract: The R package, MCMC4Extremes, provides functions which estimate posterior points for extreme-value distributions such as the Generalized Pareto Distribution (GPD), the Generalize Extreme Value (GEV), and the new extension dual Gamma Generalized Extreme Value (GGEV). Inference is performed under the Bayesian paradigm. The Markov chain Monte Carlo (MCMC) method is used to obtain the posterior points. Moreover, the package allows the identification of predictive distributions for the estimated parameters. It performs estimation for return levels plots, and fit measures such as AIC, BIC, and DIC.

Title: Discrete multivariate associated kernel estimators using two multiplicative bias correction methods Abstract: Two multiplicative bias correction (MBC) approaches for nonparametric multivariate associated kernel estimators for joint probability mass functions in the context of discrete supported data are proposed. Both techniques reach an optimal rate of convergence of the mean integrated squared error. We show some properties of the MBC multivariate associated kernel estimators like bias, variance and mean integrated square error. A simulation study and an application on a real count data set illustrate the performance of the MBC estimators based on the Dirac Discrete Uniform associated kernel in terms of the integrated squared error and integrated squared bias.

Title: Bayesian methods for time series of count data Abstract: In this paper, we consider Bayesian methods for analyzing time series of count data under a Poisson regression model with a latent auto-regressive process embedded as an additive error term. We propose two different methods; the first method samples the latent variables one by one while the second method samples them jointly. The two methods are compared by simulation studies and an example employing real data. In terms of relative bias and root-mean-squared-errors, the two methods perform almost the same. However, the mixing performance of the first method is better than the second method for most of the simulation scenarios.

Title: Pricing credit default swaps with Parisian and Parasian default mechanics Abstract: This paper proposes Parisian and Parasian default mechanics for modeling the credit risks of the CDS (credit default swap) contracts. Unlike most of the structural models used in the literature, our new model assumes that the default will occur only if the price of the reference asset stays below a certain level for a pre-described period of time. To work out the corresponding CDS price, a general pricing formula containing the unknown no-default probability is derived first. It is then shown that the determination of such a probability is equivalent to the valuation of a Parisian or Parasian down-and-out binary options, depending on how the time is recorded. After the option price is solved with a theta finite difference scheme, the CDS price is obtained through the derived general pricing formula. Finally, some numerical experiments are carried out to study the effects of the new default mechanics on the CDS prices.

Title: State-of-art review of information diffusion models and their impact on social network vulnerabilities Abstract: With the development of information society and network technology, people increasingly depend on information found on the Internet. At the same time, the models of information diffusion on the Internet are changing as well. However, these models experience the problem due to the fast development of network technologies. There is no thorough research in regards to the latest models and their applications and advantages. As a result, it is essential to have a comprehensive study of information diffusion models. The primary goal of this research is to provide a comparative study on the existing models such as the Ising model, Sznajd model, SIR model, SICR model, Game theory and social networking services models. We discuss several of their applications with the existing limitations and further categorizations. Vulnerabilities and privacy challenges of information diffusion models are extensively explored. Furthermore, categorization including strengths and weaknesses are discussed. Finally, limitations and recommendations are suggested with diverse solutions for the improvement of the information diffusion models and envisioned future research directions. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: A systematic review of IoT communication strategies for an efficient smart environment Abstract: The massive increase in actuators, industrial devices, health care devices, and sensors, has led to the implementation of the Internet of Things (IoT), fast and flexible information technology communication between the devices. As such, responding to the needs in speedily way, and matching the smart services with modified requirements, IoT communications have facilitated the interconnections of things between applications, users, and smart devices in order to gain extra advantage of the numerous services of the Internet. In this paper, the authors first provided a comprehensive analysis on the IoT communication strategies and applications for smart devices based on a systematic literature review. Then, the communication strategies and applications are categorized into four main topics including device-to-device, device-to-cloud, device-to-gateway, and device-to-application scenarios. Furthermore, a technical taxonomy is presented to classify the existing papers according to search-based methodology in the scientific databases. The technical taxonomy presents five categories for IoT communication applications including monitoring-based communications, routing-based communications, health-based communications, intrusion-based communications, and resource-based communications. The evaluation factors and infrastructure attributes are discussed based on some technical questions. Finally, some new challenges and forthcoming issues of future IoT communications are presented.

Title: Holoentropy based Correlative Naive Bayes classifier and MapReduce model for classifying the big data Abstract: Big data is the recent imminent technology, which can provide large benefits to the business administration. Owing to such huge volume, it becomes very complicated to ensure effective analysis by the existing techniques. The complications can be related to analyze, capture, sharing, storage, and visualization of the data. To tackle these challenges, a novel classification technique using Holoentropy based Correlative Naive Bayes classifier and MapReduce Model (HCNB-MRM) is proposed. The proposed HCNB, which is designed by combining the Holoentropy function with the correlative based Naive Bayes classifier deals with both high-dimensional data sets as well as extensive datasets to improve the benchmark, and classify the data based on dependent assumption. Therefore, the proposed HCNB-MRM is used to make the process simpler and to choose the best features from big dataset. The proposed HCNB with the MapReduce Model maximizes the performance of big data classification using probability index table, and posterior probability of the testing data samples. The performance of the proposed HCNB-MRM is evaluated using three metrics, such as accuracy, sensitivity, and specificity. From the experimental results, it is analyzed that the proposed HCNB-MRM obtains a high classification accuracy of 93.5965% and 94.3369% for the localization dataset, and skin dataset when compared with the existing techniques.

Title: A progressive mean control chart for COM-Poisson distribution Abstract: The Conway-Maxwell Poisson (COM-Poisson) distribution is a generalization of the Poisson distribution and encompasses the geometric, the Poisson and the Bernoulli distribution as special cases. This distribution can be used to model over- or under- dispersed data in manufacturing processes. For monitoring such data, a flexible memory-type control chart based on the progressive mean (PM) statistic (regarded as CMP-PM chart) is developed in the present paper. Through a simulation study, we investigate the run-length distribution of the proposed chart. The performance comparison study shows that the CMP-PM chart outperforms the Sellers, the GEWMA and the mu-CUSUM charts at almost all levels of shifts for both over- and under-dispersed data. Finally, the application of the proposed chart is given through an illustrative example.

Title: Research on the construction of quantum induction universe network Abstract: The universe is filled with electromagnetic fields, quantum fields, and the other different field. They penetrate and interact with each other. Just as the interaction between magnetic materials are due to the magnetic field around the magnet contacting another magnet, between the quanta can also interact with each other through magnetic fields. Through the study of quantum mechanics and related theories of brain science, the article puts forward the following viewpoints: between quantum, which has entanglement state, cannot only interact through magnetic fields but also generate quantum induction fields to generate quantum induction waves. The entangled quantum generated spin waves are connected to each other based on quantum inductive waves to form a quantum inductive network. Then, the information is transmitted through resonance to realize the communication between all things. Thus, all things in the universe can be interconnected based on quantum entanglement effects. By analyzing the construction mode of the internet network, the paper proposes the architecture of the quantum induction communication system and the basic topology of a quantum induction internet network. Several thought experiment schemes were designed, and the information transmission between the ordinary objects and between the people and ordinary objects is studied as an example. The analysis points out the main problems faced by the construction of the quantum induction internet. It points out that the significance of constructing of the network of All things interconnect based on quantum entanglement effect is that all living bodies and nonliving bodies in the heaven and earth can be connected and communicate with each other.

Title: SGXPool: Improving the performance of enclave creation in the cloud Abstract: Deploying user data or programs in cloud risks divulging their privacy because the cloud-side supervisors, such as system administrators, can leverage the higher privilege to snoop the user data. Based on the trusted execution environment (TEE) technology, Intel Software Guard eXtension (SGX) is a practical remedy to user privacy, which employs hardware-assisted enclave to wrap the sensitive data, preventing them from the disclosure. However, the application performance is hurt due to the CPU-expensive and frequent operations on the enclave creation and destruction. In this paper, we propose SGXPool, an application-level framework of resource management to relieve the above issue. SGXPool first uses a preallocated resource pool to assign/revoke the enclave on demand and avoids its cost of dynamical creation and destruction. Then, SGXPool utilizes another resource pool to constrain the threading overhead such as thread initialization, scheduling, and destruction. In addition, SGXPool exposes the simple and clear interface, allowing users or programmers to apply SGXPool without any intrusive modifications on the original SGX application. We implement SGXPool in the typical web servers which use the multithreading to handle the concurrent user requests. The evaluation results show that SGXPool can improve the performance of the original multithreading system up to 19 times. Meanwhile, the original security of SGX is maintained.

Title: Two-Tier Matching Game in Small Cell Networks for Mobile Edge Computing Abstract: Mobile edge computing (MEC) enables computing services at the network edge closer to mobile users (MUs) to reduce network transmission latency and energy consumption. Deploying edge computing servers in small base stations (SBSs), operators make profit by offering MUs with computing services, while MUs purchase services to solve their own computation tasks quickly and energy-efficiently. In this context, it is of particular importance to optimize computing resource allocation and computing service pricing in each SBS, subject to its limited computing and communication resources. To address this issue, we formulate an optimization problem of computing resource management and trading in small-cell networks and tackle this problem using a two-tier matching. Specifically, the first tier targets at the association algorithm between MUs and SBSs to achieve maximum social welfare, and the second tier focuses on the collaboration algorithm among SBSs to make efficient usage of limited computing resources. We further show that the two proposed algorithms contribute to stable matchings and achieve weak Pareto optimality. In particular, we verify that the first algorithm arrives at a competitive equilibrium. Simulation results demonstrate that our proposed algorithms can achieve a better network social welfare than baseline algorithms while retaining a close-optimal performance.

Title: DTN performance analysis of multi-asset Mars-Earth communications Abstract: The delay-/disruption-tolerant networking (DTN) architecture is considered the key enabling technology for future space communications, as confirmed by the current standardization within CCSDS and the experiments carried out onboard the International Space Station. Despite the scientific community efforts to analyze DTN architecture performance, most of the studies have focused on individual protocols, or have considered simple test cases, thus missing a whole system view. To bridge these research gaps, this paper presents a comprehensive analysis of DTN performance in Mars-Earth communications, considering a realistic and complex end-to-end scenario, where multiple assets and multiple data flows are involved, as envisioned for future space missions. To this end, a virtualized testbed based on ION software was used for an extensive emulation campaign, focusing particularly on Bundle and Licklider Protocol interaction with the CGR routing algorithm.

Title: Multiple vehicle tracking and classification system with a convolutional neural network Abstract: This paper proposes a traffic monitoring system that detects, tracks, and classifies multiple vehicles on the road in real time using various digital image processing techniques and the process of machine learning based on a convolutional neural network (CNN). With this system, a video camera is installed on the road, and calibration is used to obtain the projection equation of the actual road on the image plane. Several image processing techniques, such as background modeling, background extraction, edge detection, and object tracking, are used to develop and implement a prototype system. The proposed system also uses a transfer learning process that is more efficient than starting CNN from scratch. This maximizes training efficiency and increases prediction accuracy in vehicle classification. Preliminary experimental results demonstrate that multiple vehicle tracking and classification are possible while calculating vehicle speed. The ultimate goal of this study is to develop a single digital video camera system with embedded machine learning process that can monitor and distinguish multiple vehicles simultaneously in multiple lanes.

Title: T-TOHIP: Trust-based topology-hiding multipath routing in mobile ad hoc network Abstract: In recent years, security in mobile ad hoc network (MANET) is the active research topic in both industry and academia because of the rising availability of tiny, adapted mobile devices, and the multipath routing protocols have been received much attention in MANET. Accordingly, this paper introduces the trust-based topology-hiding multipath routing algorithm for the MANET. The proposed model finds the security factor of each node in the MANET, and the neighbor nodes are selected based on the defined security factor. The proposed routing method has four models for defining the security factor, namely the trusted model, energy model, delay model, and the mobility model. The proposed multipath routing determines the secured route between the sender and receiver based on the selected neighbor nodes. Finally, data communication is performed through the selected multipath. The performance of the proposed multipath routing is analyzed with the existing methods, such as topology-hiding multipath routing protocol, Fractional lion optimization to topology-hiding multi-path routing, and Adaptive Fractional lion optimization to topology-hiding multi-path routing for the performance metrics, such as throughput, delay, energy, and packet drop rate (PDR). Simulation results show that the proposed multipath routing has the better values of 0.330566, 0.376754, 0.319369, and 0.380559 after 50 s of simulation for throughput, delay, energy, and PDR than the existing models when there is an attack on the node.

Title: Smart-city medium access for smart mobility applications in Internet of Things Abstract: In this paper, we target the wireless medium access problem under rapid mobility conditions in smart cities. We take advantage of cloud computing and put forward an agile vehicular cloud and mobile Internet of Things communication framework. The framework is employing the standardized Long Term Evolution (LTE) while improving the quality of service (QoS) in modern mobile applications. It minimizes the communication delay and error rates in smart real-time transportation applications while maintaining the highest network throughput via enhanced medium access protocol. The objective of this study is to improve the connection between the vehicles and the roadside units, which are equipped with real-time devices. The proposed approach integrates Markovian process with IEEE 802.16 to analyze various QoS metrics, namely throughput, error rate, and the average packet delay.

Title: Context aware ontology-based hybrid intelligent framework for vehicle driver categorization Abstract: In public vehicles, one of the major concerns is driver's level of expertise for its direct proportionality to safety of passengers. Hence, before a driver is subjected to certain type of vehicle, he should be thoroughly evaluated and categorized with respect to certain parameters instead of only one-time metric of having driving license. These aspects may be driver's expertise, vigilance, aptitude, experience years, cognition, driving style, formal education, terrain, region, minor violations, major accidents, and age group. The purpose of this categorization is to ascertain suitability of a driver for certain vehicle type(s) to ensure passengers' safety. Currently, no driver categorization technique fully comprehends the implicit as well as explicit characteristics of drivers dynamically. In this paper, machine learning-based dynamic and adaptive technique named D-CHAITs (driver categorization through hybrid of artificial intelligence techniques) is proposed for driver categorization with an objective focus on driver's attributes modeled in DriverOntology. A supervised mode of learning has been employed on a labeled dataset, having diverse profiles of drivers with attributes pertinent to drivers' perspectives of demographics, behaviors, expertise, and inclinations. A comparative analysis of D-CHAIT with three other machine learning techniques (fuzzy logic, case-based reasoning, and artificial neural networks) is also presented. The efficacy of all techniques was empirically measured while categorizing the drivers based on their profiles through metrics of accuracy, precision, recall, F-measure performance, and associated costs. These empirical quantifications assert D-CHAIT as a better technique than contemporary ones. The novelty of proposed technique is signified through preprocessing of feature attributes, quality of data, training of machine learning model on more relevant data, and adaptivity.

Title: Hybrid Salp Swarm Algorithm for integrating renewable distributed energy resources in distribution systems considering annual load growth Abstract: Load growth in electrical distribution networks became naturally occurring due to industrial development and human population demand growth. Consequently, system losses are continually raised while the voltage profile is reduced. This paper presents a novel hybrid method to determine the best locations and sizes of single and multiple of different renewable distributed energy resources (DER). The presented hybrid method is based on Salp Swarm Algorithm (SSA) and combined power loss sensitivity (CPLS). Integration of photovoltaics (PV) and wind turbines (WT) in distribution network is used to enhance the system voltage, minimize system losses and increase the system capacity. The effect of annual load growth in system load and system operating constraints are taken in consideration. IEEE 33-bus and 69-bus radial distribution systems (RDS) are used to validate the presented algorithm for integrating the DERS in distribution system. In addition, the presented algorithm is compared with different recent optimization algorithms in order to prove its effectiveness and superiority. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: GUI-based software modularization through module clustering in edge computing based IoT environments Abstract: With edge computing-based Internet-of-Things environments widely recognized, major software technologies are rapidly integrated. Among the various areas of such technologies, software evolution and maintenance are essential yet challenging tasks both for research and in practice. In particular, for large-scale software, the maintenance costs are often much more than the development cost. In order to help to resolve this issue, software module clustering approaches have been studied to build adequate modules with high cohesion and low coupling; such modular structures can help the comprehension and maintenance of complex systems. The existing studies apply either structural, semantic, or historic-based factors as clustering criteria; however, to the best of our knowledge, no previous study considers Graphical User Interface (GUI) as a factor for software module clustering. Because GUI is linked to the functionalities of a software system, it can be a useful source to find cohesive modules and generate well-defined modular structures. Thus, in this study, we propose a GUI-based approach for software module clustering to improve the quality of software modularization. Quantitative and qualitative experiments are performed to evaluate the performance of the proposed approach and comparing it with three existing approaches; using two open source software systems. The evaluation results show that our proposed approach generates feasible software modules and provides closer estimates to actual modularization than those of the existing approaches.

Title: Formulation of logarithmic type estimators to estimate population mean in successive sampling in presence of random non response and measurement errors Abstract: This paper considers the problem of estimation of population mean of the study character in two-occasion successive sampling in presence of non-response and measurement error. A logarithmic type imputation technique has been developed to reduce the nuisance effect of non-response in sample surveys. Utilizing information available on a highly positively or highly negatively correlated auxiliary variable, a logarithmic type estimator is proposed and its properties, including bias and mean square error, are discussed. An optimal replacement policy has been derived, and effect of measurement errors on the mean square error of the estimator has been studied. Empirical studies have been carried out using both real and simulated data, and suitable recommendations have been put forward to the survey statisticians for applications in real life problems.

Title: Signal discrimination without denoising Abstract: This article reveals that two previously established tests for autocovariance equality between stationary autoregressive moving average (ARMA) processes can also be used to discriminate between harmonic signals embedded in noise without the need for any reconstruction or modeling. A third test is also introduced and used for the same purpose. An application involving functional magnetic resonance imaging (fMRI) as well as an extension involving a generalized linear chirp (GLC) process are presented at the end.

Title: Necessary sample sizes for specified closeness and confidence of matched data under the skew normal setting Abstract: Previous researchers have shown how to compute a priori confidence interval (as opposed to sample-based confidence intervals) for means, assuming normal distributions (Trafimow 2017; Trafimow and MacDonald 2017); or for locations, assuming skew normal distributions (Trafimow et al. 2018). The present work extends a priori thinking to an important case not addressed previously, where the researcher is interested in the difference between means or the difference in locations across two matched samples. The proposed procedure can be used under the assumption that both samples come from normal distributions or skew normal distributions. Computer simulations support the equations presented, along with an example with real data.

Title: Linear regression with bivariate response variable containing missing data. Strategies to increase prediction precision Abstract: In this study we focus on prediction precision for linear regression with a bivariate response variable, where the response variable of primary interest contains missing data in the training data set. We derive and provide the maximum likelihood solution and a Bayesian method based on a conjugate prior distribution. In particular we evaluate strategies in how to "borrow prediction strength" from the full set of data to prediction associated with the missing data. Regularization of the maximum likelihood estimator is theoretically shown to be beneficial, and we derive methods for how to implement such regularization under frequentist and Bayesian inference, including available software as a R-package.

Title: Optimal handover scheme for device-to-device communication in highly mobile LTE HetNets Abstract: Device-to-device (D2D) communication was primarily proposed as a new concept to improve the cellular network performance by allowing direct communications among user equipment (UE). Because of the mobile nature of the UE, the D2D link tends to fail more often. In order to guarantee seamless proximity service (ProSe) continuity for the mobile devices, a competent mobility management technique is necessary. In this work, an optimal D2D handover (HO) scheme in LTE heterogeneous network is proposed for guaranteeing seamless ProSe continuity for the UE in D2D communication. The proposed optimal D2D HO scheme tries to improve the duration of D2D communication by not only considering the signal quality between the D2D UE pairs but also the distance between them. The simulation results show that the proposed scheme improves the D2D mode duration, even in high mobility, thereby reducing frequent HO of the D2D UE, which is in D2D communication.

Title: An efficient, font independent word and character segmentation algorithm for printed Arabic text Abstract: Characters segmentation is a necessity and the most critical stage in Arabic OCR system. It has attracted the interest of a wide range of researchers. However, the nature of the Arabic cursive script poses extra challenges that need further investigation. Therefore, having a reliable and efficient Arabic OCR system that is independent of font variations is highly required. In this paper, an indirect, font-in dependent word and character segmentation algorithm for printed Arabic text investigated. The proposed algorithm takes a binary line image as an input and produces a set of binary images consisting of one character or ligature as an output. The segmentation performed at two levels: a word segmentation performed in the first level, by employing a vertical projection at the input line image along with using Interquartile Range (IQR) method to differentiate between word gaps and within word gaps. A projection profile method used as a second level of segmentation along with a set of statistical and topological features, which are font-independent, to identify the correct segmentation points from all potential points. The APTI dataset used to test the proposed algorithm with a variety of font type, size, and style. The algorithm experimented on 1800 lines (approximately 24,816 words) with an average accuracy of 97.7% for words segmentation and 97.51% for characters segmentation. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Score level fusion in multi-biometric identification based on zones of interest Abstract: In this paper, we present a new multibiometric fusion method for the identification of persons using two modalities, the iris and the fingerprint. Each modality is separately processed to generate a vector of scores. The fusion method is applied at the score level. A preliminary study based on the k-means clustering method, for each modality, led us to split the score range into three zones of interest relevant to the proposed identification method. The fusion is then applied to the extracted regions using two approaches. The first one achieves the classification by the decision tree combined to the weighted sum (BCC), while the second approach is based on the fuzzy logic (BFL). Several tests were conducted to evaluate the performance of the proposed methods on standard biometric databases using four metrics, namely, False Accept Rate, False Reject Rate, Enrollee False Accept Rate and Recognition Rate. The obtained results are very interesting since they illustrate clearly that the proposed fusion approaches outperform those based on a single modality. In addition, we showed that the BCC fusion approach achieves slightly better performance compared to the BFL. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Bayesian estimation of the survival characteristics for Hjorth distribution under progressive type-II censoring Abstract: In this paper, the Bayes estimation procedure for the parameters and survival characteristics (survival and hazard functions) of the Hjorth distribution has been proposed with progressively type-II censored data. The Bayes estimators are derived with gamma prior and evaluated under squared error loss function. It is known that the censored observations create the complexity in Bayes estimation procedures. Therefore, two approximation techniques, namely Tierney-Kadane approximation method and Markov Chain Monte Carlo method have been used to compute the approximate Bayes estimators. The proposed estimates are compared with the usual maximum likelihood estimators through Monte Carlo simulations. Lastly, a medical data set has been considered to show the applicability of the proposed model as well study in real life scenario.

Title: Load balancing in cloud computing using water wave algorithm Abstract: Cloud computing as of late is a rising innovation for giving various administrations through internet to fulfil the requisites of clients dependent on needs with minimal expense. The client can benefit all kind of administration without building, observing, and keeping up the assets. Researchers developed distinctive asset planning calculation to satisfy the client requisites as per their condition. This article proposes a Water Wave Algorithm (WWA) for resource scheduling in cloud environment. The results for throughput, Response time, Turnaround time, Migration time, Resource utilization, Fault tolerance, and scalability over FCFS, MCT, MET, and OLB algorithms and the results prove that WWA provides better results for throughput, response time, resource utilization, and scalability.

Title: A machine learning based framework for IoT device identification and abnormal traffic detection Abstract: Network security is a key challenge for the deployment of Internet of Things (IoT). New attacks have been developed to exploit the vulnerabilities of IoT devices. Moreover, IoT immense scale will amplify traditional network attacks. Machine learning has been extensively applied for traffic classification and intrusion detection. In this paper, we propose a framework, specifically for IoT devices identification and malicious traffic detection. Pushing the intelligence to the network edge, this framework extracts features per network flow to identify the source, the type of the generated traffic, and to detect network attacks. Different machine learning algorithms are compared with random forest, which gives the best results: Up to 94.5% accuracy for device-type identification, up to 93.5% accuracy for traffic-type classification, and up to 97% accuracy for abnormal traffic detection.

Title: Pathway to presence: an investigation of ambient awareness in online learning environments Abstract: Due to the pervasiveness of online technology, especially social media and social networking sites (SNS) like Twitter and Facebook were are surrounded by a constant stream of information about other individuals. On the other hand, even though distance and online learning has become mainstream, there are still concerns leveled at many of these educational offerings, one of them being the relative social isolation of online and distance students. In this study, we investigate if and how ambient awareness, a concept originated in research in interpersonal effects of SNS, can play an important role in how salient social impressions of peers emerge and how this leads to perceptions of social presence among students. To this end, we use an impression formation paradigm in a four-week online distance course (N = 51). We found that students were able to form impressions of their peers through ambient awareness. These impressions differed in prevalence from the ones gathered through direct social interaction. In a larger second sample (N = 169), we find ambient awareness to be a mediator between the sociability of the learning environment and perceptions of social presence. Implications, limitations and suggestions for further research are discussed.

Title: A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks Abstract: As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level.

Title: Nonparametric multiplicative bias correction for von Mises kernel circular density estimator Abstract: In this paper, we apply the multiplicative bias correction (MBC) techniques for von Mises (vM) kernel density estimator in the context of circular data. Some properties of the MBC-vM kernel circular density estimators (bias, variance and mean integrated squared error) are shown. The choice of bandwidth is investigated by adapting the popular cross-validation techniques. The performances of the MBC estimators based on vM kernel are illustrated by a simulation study and real application for circular data. In general, in terms of integrated squared bias (ISB) and integrated squared error (ISE), the proposed estimators outperform the standard vM kernel estimator.

Title: Design of experiments and machine learning to improve robustness of predictive maintenance with application to a real case study Abstract: When deploying predictive analytics in a Big Data context, some concerns may arise regarding the validity of the results obtained. The reason for this is linked to flaws which are intrinsic to the nature of the Big Data Analytics methods themselves. In this article a new approach is proposed with the aim of mitigating new problems which arise. This novel method consists of a two-step workflow in which a Design of Experiments (DOE) study is conducted prior to the usual Big Data Analytics and machine learning modeling phase. The advantages of the new approach are presented and an industrial application of the method in predictive maintenance is described in detail.

Title: Computing the effect of measurement errors on efficient variant of the product and ratio estimators of mean using auxiliary information Abstract: This article presents an efficient variant of the usual product and ratio methods of estimation of population mean of a study variable Y in the context of simple random sampling when the observations of both study variable and auxiliary variable are supposed to be commingled with measurement error. The bias and mean squared error of proposed class of estimators have been derived and studied under measurement error. Monte Carlo simulation and numerical studies have been carried out to study the properties of the estimators and compared with mean square error and percentage relative efficiency of the estimator when variables are free from measurement errors.

Title: Nonparametric estimation of a quantile density function under L-p risk via block thresholding method Abstract: Here we propose a new quantile density function estimator via block thresholding methods and investigate its asymptotic convergence rates under L-p risk with over Besov balls. We show that the considered estimator achieves optimal or near optimal rates of convergence according to the values of the parameter nu of the Besov classes . We show that this estimator attain optimal and nearly optimal rates of convergence over a wide range of Besov function classes, and in particular enjoys those faster rates without the extraneous logarithmic penalties that given in Chesneau et al. A simulation study shows new proposed estimator performs better at the tails than existing competitors.

Title: An efficient predictive analytics system for high dimensional big data Abstract: The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data. Predictive analytics has grown in prominence alongside the emergence of big data. In this paper, an efficient predictive analytics system for high dimensional big data is proposed by enhancing scalable random forest (SRF) algorithm on the Apache Spark platform. SRF is enhanced by optimizing the hyperparameters and prediction performance is improved by reducing the dimensions. The effectiveness of the proposed system is examined on five real-world datasets. Experimental results demonstrated that the proposed system achieves the highly competitive performance compared with RF algorithm implemented by Spark MLlib. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Testing symmetry for additive distortion measurement errors data Abstract: This paper studies how to estimate and test the symmetry of a continuous variable under the additive distortion measurement errors setting. The unobservable variable is distorted in a additive fashion by an observed confounding variable. Firstly, a direct plug-in estimation procedure is proposed to calibrate the unobserved variable. Next, we propose four test statistics for testing whether the unobserved variable is symmetric or not. The asymptotic properties of the proposed estimators and test statistics are investigated. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators and test statistics. These methods are applied to analyze a real dataset for an illustration.

Title: A learning analytics approach to investigating pre-service teachers' change of concept of engagement in the flipped classroom Abstract: To determine the impact of cognitive style on change of concept of engagement in the flipped classroom, a sequential analysis from the perspective of Bloom's Taxonomy was conducted to establish if significant differences existed between the learning achievements and engagement of students with different cognitive styles. The participants were pre-service teachers who had registered for a school-based curriculum development methodology course in China, with a total of 53 students from two classes, who performed a total of 1,599 behaviors in the discussion sections. The results show that the concepts of "evaluate" (31.52%) and "analyze" (27.77%) were the two most frequent behaviors in the Bloom's Taxonomy. The "remember" and "evaluate" were the significant starting behaviors for all of the students; "remember" and "evaluate" were the significant starting behaviors for the intuitive-style students, while "understand" was a significant starting behavior for the analytical-style students. This confirms that the students with different cognitive styles implemented multiple strategies for learning. It is also found that the flipped learning promoted the students' achievement. The conclusions suggest that it is important to match the cognitive styles of students as well as the instructional methods in order to improve students' learning.

Title: On Berry-Esseen bound of wavelet estimators in nonparametric regression model under asymptotically negatively associated assumptions Abstract: This article is concerned with the estimating problem of nonparametric regression model. Under certain regularity conditions, we derive the Berry-Esseen bound for wavelet estimators of the unknown regression function with asymptotically negatively associated assumptions. Also, we present a numerical simulation study to verify the validity of the results established here.

Title: Frequent itemset-based feature selection and Rider Moth Search Algorithm for document clustering Abstract: Document clustering has recently been paid great attention in retrieval, navigation, and summarization of huge volumes of documents. With a better document clustering approach, computers can organize a document corpus automatically to a meaningful cluster for enabling efficient navigation, and browsing of the corpus. Document navigation and browsing is a valuable complement to the deficiencies of information retrieval technologies. This paper introduces Modsup-based frequent itemset and Rider Optimization-based Moth Search Algorithm (Rn-MSA) for clustering the documents. At first, the input documents are given to the pre-processing step, and then, the extraction is carried out based on TF-IDF and Wordnet features. Once the extraction is done, the feature selection is carried out based on frequent itemset for the establishment of feature knowledge. At last, the document clustering is done using the proposed Rn-MSA, which is designed by combining Rider Optimization Algorithm (ROA), and the Moth Search Algorithm (MSA). The performance of the document clustering based on proposed Modsup + Rn-MSA is evaluated in terms of precision, recall, F-Measure, and accuracy. The developed document clustering method achieves the maximal precision of 95.90%, maximal recall of 96.41%, maximal F-Measure of 96.41%, and the maximal accuracy of 95.12% that indicates its superiority.

Title: Maximum likelihood estimation of the parameters of student's t Birnbaum-Saunders distribution: a comparative study Abstract: In the last decade, Diaz-Garcia and Leiva-Sanchez (2005, 2007) proposed a generalized Birnbaum-Saunders distribution based on elliptically contoured distributions. A special case of this generalization is Student's t Birnbaum-Saunders distribution. This flexible lifetime distribution generalizes both the Cauchy Birnbaum-Saunders distribution and the two-parameter Birnbaum-Saunders distribution. In this comparison paper, we discuss maximum likelihood estimation methods for the parameters of this distribution. We numerically illustrate and examine the performances of all discussed methods using extensive Monte Carlo simulations and illustrative examples. Furthermore, we analyze real-life data to assess the practical usage of the considered generalized family of distributions, and to illustrate the discussed estimation methods.

Title: Improved methods for parameter estimation of gray model GM(1,1) based on new background value optimization and model application Abstract: In the gray prediction, the GM(1,1) model has been applied widely, but the conventional GM(1,1) model prediction shows big errors sometimes. Many researchers improve prediction accuracy by changing the basic structure of model. The paper tries to make improvements in the models parameter estimation on the basis of new background value optimization without changing the models structure, and gives the following four methods: (1) estimating the parameter of gray model GM(1,1) with the optimized value of exponential curve as the background value; (2) estimating the parameter of gray model GM(1,1) with the optimized value of power function curve as the background value; (3) estimating the parameter of gray model GM(1,1) with the optimized value of polynomial curve as the background value; (4) estimating the parameter of gray model GM(1,1) with the optimized value of interpolation function as the background value. The last part of the paper builds the GM(1,1) model of China's total clean energy consumption with the four improved methods. The simulation and prediction results show that these methods all improve the prediction accuracy of model significantly.

Title: On zero-inflated hyper-Poisson distribution and its applications Abstract: Here we consider a zero-inflated version of the hyper-Poisson distribution and study some of its important properties through deriving its probability generating function and expressions for factorial moments, mean, variance, recursion formulae for probabilities, raw moments, and factorial moments. The estimation of the parameters of the zero-inflated hyper-Poisson distribution is attempted. The distribution has been fitted to certain real life data sets and thereby shown that the proposed model gives better fit to the data sets compared to existing models such as zero-inflated Poisson distibution (ZIPD), zero-inflated negative binomial distribution (ZINBD), zero-inflated Conway-Maxwell Poisson distribution (ZICMPD), and zero-inflated generalized Poisson distribution (ZIGPD). Further, Rao's efficient score test procedure is applied for examining the significance of the parameters and a simulation study is carried out for assessing the performance of the maximum likelihood estimators of the parameters of the distribution.

Title: Variational inference for varying-coefficient model Abstract: In this paper, we propose a variational Bayesian method for estimation of varying-coefficient model. Within the local likelihood framework, we develop variational updates for the approximated posterior and obtain variational lower bound. Mean-field assumption naturally simplifies the estimation procedure, and overcomes the computational burden of traditional Bayesian methods in nonparametric setting. We also propose a Metropolis-Hastings algorithm to select the bandwidth. We conduct simulation study to demonstrate proposed procedure, and apply the proposed estimation method in the analysis of stock return data.

Title: Cloud traffic prediction based on fuzzy ARIMA model with low dependence on historical data Abstract: Traffic prediction with high accuracy has become a vital and challenging issue for resource management in cloud computing. It should be noted that one of the prominent factors in resource management is accurate traffic prediction based on a few data points and within a short time period. The autoregressive integrated moving average (ARIMA) model is a suitable model to predict traffic in short time periods. However, it requires a massive amount of historical data to achieve accurate results. On the other hand, the fuzzy regression model is adequate for prediction using less historical data. Aforementioned by these considerations, in this paper, a combination of ARIMA and fuzzy regression called fuzzy autoregressive integrated moving average (FARIMA) is used to forecast traffic in cloud computing. Besides, we adopt the FARIMA model by using the sliding window, called SOFA, concept to determine models with higher prediction accuracy. Accuracy comparison of these models based on the root means square error and coefficient of determination demonstrates that SOFA is about 5.4 and 0.009, respectively, which is the superior model for traffic prediction.

Title: GLS detrending in nonlinear unit root test Abstract: This article proposes to apply the GLS detrending method for the unit root test procedure developed by Kruse [2011. A new unit root test against ESTAR based on a class of modified statistics. Statistical Papers 52 (1):71-85]. The Monte Carlo simulations made indicate that the proposed test is more powerful than the Kruse (2011) test. Using the proposed test, it was examined whether the consumer price index permanent or transitory for 25 countries. According to the results obtained, by using the Kruse test, we find that unit root hypothesis was rejected only in 5 countries while using the GLS Kruse test, the unit root hypothesis was rejected in 15 countries.

Title: The limit property of a risk model based on entrance processes Abstract: In this article, we investigate the risk model based on an entrance process with stochastic investment, where an insurance company invest risky stock market with a geometric Brownian motion, and risk-free market. Under the assumptions that the entrance process is a renewal process and the claims sizes are pairwise strong quasi-asymptotically independent, which belong to the different heavy-tailed distribution classes,the finite-time and infinite-time asymptotic estimates of the risk model with stochastic investment are obtained.

Title: Aiming for smart wind energy: A comparison analysis between wind speed forecasting techniques Abstract: Smart cities are one of the promising application areas where the energy management (supply/dispatch) issues have the potential to positively affect the society. In the common era of information and technology, the consumers expect the required amount of energy to be present at anytime. This energy is obtained through renewable and nonrenewable resources. Smart wind energy is the idea of efficient energy generation utilizing the wind while satisfying green expectations. However, the random characteristics caused by various external factors have significant effects on wind speeds, thus introducing difficulties in power systems operations and energy generation. Forecasting techniques can be effectively used to predict future wind speeds and power generation to optimize the energy output. The objective of this study is to provide a framework for a local smart-city wind energy harvesting model. Our study evaluates wind speed forecasting models based on synced weather characteristics (air temperature, humidity, and pressure), against models based on wind speed data itself. The produced models are implemented via k-nearest neighbors and linear regression. Two different data sets are addressed for performance comparison purposes. The first one is 5 years of 10-minute time span measurements at Middle East Technical University campus. The second data set is 3 years of the same time span at a site in California, USA. The accuracy of predictions is found to be higher for the data sets with lower variance (METU data set). However, the models fit the higher variance (NREL data set) data better than the lower one.

Title: Hybrid-based novel approach for resource scheduling using MCFCM and PSO in cloud computing environment Abstract: Cloud computing is a growing environment. Many of the users are interested to outsource their data in cloud; however, load balancing in cloud is still at risk. Resource allocation plays a major role in load balancing. In this scheduling problem, independent task in cloud computing can allocate resource by the summary of modified canopy fuzzy c-means algorithm (MCFCMA). To allocate task to their corresponding resource, particle swarm-based optimization algorithm (PSO) is used. In proposed scheme, first independent task selected based on load feed-back, cluster the requested task using MCFCMA and schedule task to each virtual machine. VM selects parallel execution in virtual machine manager. Calculate feature value using PSO algorithm. Allocate resource to the task. Since our proposed system selects resource based on parallel execution, it reduces load balancing in Cloud Quantum Computation (CQC). The proposed system overcomes issues in load balancing and load scheduling; this can be proved by its precision and privacy calculation.

Title: Modified Lilliefors goodness-of-fit test for normality Abstract: The first aim of the paper is to put into practice the -corrected Lilliefors goodness-of-fit test for normality (LF). This modification consists in varying a formula of calculating the empirical distribution function (EDF). Values of constants in the formula depend on values of sample skewness and excess kurtosis, which is recommended in order to increase the power of the LF test. Critical values are obtained with the Monte Carlo method for sample sizes and at a significance level The power of several normality tests for a wide collection of alternative distributions is calculated. Alternative distributions are divided into 12 groups according to their skewness and excess kurtosis. The second aim is to propose a similarity measure between the normal distribution and an alternative distribution. The third aim is to propose two new alternative distributions created in order to obtain the desired values of skewness and excess kurtosis. The fourth aim is to calculate the power of tests for new alternative distributions. The paper shows that values of constants in the formula of calculating the EDF influences the power of the LF test. The performance of the new proposal through the analysis of real data sets is illustrated.

Title: Multi-response based personalized treatment selection with data from crossover designs for multiple treatments Abstract: In this work we propose a novel method for treatment selection based on individual covariate information when the treatment response is multivariate and data are available from a crossover design. Our method covers any number of treatments and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to estimate an ordering of treatments based on ranked lists of treatment performance measures such as smooth conditional means and conditional probability of a response for one treatment dominating others. An empirical study demonstrates the performance of the proposed method in finite samples.

Title: GP-ELM-RNN: Garson-pruned extreme learning machine based replicator neural network for anomaly detection Abstract: Replicator Neural Network (RNN) is a popular algorithm for anomaly detection, but finding optimal number of hidden layers and then finding optimal number of neurons in each hidden layer is quite a challenging and time-consuming task. Extreme Learning Machines (ELM) are neural networks with single-hidden layer but the learning algorithm is different and faster than back-propagation. ELM-based RNNs solve our problem of determining the number of hidden layers and the learning algorithm is also faster than gradient-descent based RNN. The problem of identifying the optimal number of neurons in the hidden layer can be solved by Garson algorithm. In this work, the author propose an optimal Replicator Neural Network which is optimized using ELM learning and Garson algorithm for anomaly detection. The experimental results show that the proposed method is fast as well as highly accurate.

Title: Exponentially weighted moving average control chart using auxiliary variable with measurement error Abstract: Control chart is one of the important industrial tools used for monitoring the stability of manufacturing processes. Measurement error plays an important role in quality control process and adversely affects the shift detection ability of control charts. In this article, we examined the effect of measurement error on auxiliary variable based exponentially weighted moving average (EWMA-Z) control chart by using three different techniques (i) covariate method (ii) multiple measurements (iii) linearly increasing variance. The performance of EWMA-Z control chart is measured through ARL and SDRL by using Monte-Carlo simulation method. An example is provided with real data set for the implementation of EWMA-Z control chart in the presence of measurement error.

Title: Trust-based forest monitoring system using Internet of Things Abstract: We introduce trust-based event-driven (TBED) infrastructure for forest monitoring and reporting. TBED endows the real-time local monitoring along with spatial monitoring. None of the work till now considers both local and spatial monitoring. To the best of our knowledge, TBED is the first attempt in this domain, where event-related information is reported to the consumers along with the effective area. TBED is based on the event-driven architecture in which device management and monitoring authorities, middleware event handler (MEH), and consumers are involved. Additionally, we present a trust mechanism to evaluate the sensor node trust for reliable information. At last, we present a case study of fire monitoring to depict the real-life scenario.

Title: Robust estimation in restricted linear regression Abstract: Linear regression is a well-known method to predict further values for dependent variable given observed independent variables. Sometimes, there is prior information about regression coefficients. Therefore, restricted regression takes into account prior information and combines it with the sample information. Unfortunately, restricted regression has same assumptions as a linear regression. In this paper, we propose robust estimation of restricted linear models. In the application part, it is demonstrated that the proposed methods are convenient method to give impressive results in the presence of outliers with stochastic or nonstochastic restriction.

Title: Inference about the bivariate new extended Weibull distribution based on complete and censored data Abstract: In this article, we have discussed the problem of point estimation of the three unknown parameters of a bivariate new extended Weibull distribution under complete and randomly right-censored samples. The expectation-maximization algorithm is used to estimate the unknown parameters. Simulation experiments are performed to see the effectiveness of the estimators for complete and censored data. One dataset has been considered to illustrate the practical utility of the article.

Title: Power analysis of several normality tests: A Monte Carlo simulation study Abstract: In statistical inference, oftentimes the data are assumed to be normally distributed. Consequently, testing the validity of the normality assumption is an integral part of such statistical analyses. Here, we investigate twelve currently available tests for normality using Monte-Carlo simulation. Alternative distributions are used to calculate the empirical power of the tests studied here. The distributions considered arise from three different categories: symmetric short-tailed, symmetric long-tailed and asymmetric. In addition, power is calculated for several contaminated alternatives. As a direct consequence of this study, we recommend a two-tier approach: (i) observe the shape of the empirical data distribution using graphical methods, then (ii) select an appropriate test based on the likely distributional shape and the corresponding sample size. In general, with respect to power considerations, it is observed that for asymmetric distributions, the Shapiro-Wilk and Ryan-Joiner tests perform fairly well for all sample sizes studied here. Additionally, the Jarque-Bera, Modified Jarque-Bera, and Ryan-Joiner tests perform fairly well for contaminated normal distributions. The popular methods available in current software packages, such as the Shapiro-Wilk test, the Ryan-Joiner Normality test, and the Anderson-Darling goodness of test, work at least moderately well for most of the cases we considered.