Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiuser Multivariate Multiorder Markov-Based Multimodal User Mobility Pattern Prediction

Excavating human’s temporal and spatial regularities hidden in trajectory data and predicting users’ mobility patterns are conducive to providing proactive smart services for people. Combining Markov transition and tensor theories to improve the prediction performance has proved to be effective. However, the existing state-of-the-art multivariate multiorder Markov model neglects the mutual influence among different users. In a practical trajectory system, people’s mobility patterns are influenced by their social relationships. Therefore, this article focuses on proposing a novel multiuser multivariate multiorder Markov model and a multimodal user mobility pattern prediction approach. First, we construct two concrete Markov trajectory transition models based on the single-user multivariate multiorder Markov model. Then, we propose a multiuser multivariate multiorder Markov model, including the influence model of multiple users and the multiuser Markov trajectory transition model. Afterward, two unified product-based power methods are developed to calculate the stationary joint eigentensor (SJE) for single-user and multiuser multivariate multiorder Markov models. Furthermore, an SJE-based multimodal prediction approach is proposed to realize precise mobility pattern prediction. Finally, we conduct a series of experiments based on real-world GPS trajectory data set to verify the performance of the proposed approaches. Experimental results demonstrate that the proposed multiuser multivariate multiorder Markov-based multimodal prediction approach can improve the trajectory prediction accuracy by highest up to 31.10% points compared with the Z-eigen-based approach.
A Low-cost SLAM Fusion Algorithm for Robot Localization

Autonomous navigation has been a popular research topic over the last two decades. The ability for a robot to solve the simultaneous localization and mapping (SLAM) problem is required to navigate unknown environments. One such example is a self-driving car: it needs to build maps of new environments and simultaneously localize itself. Many approaches for solving the SLAM problem have been proposed, but most of these require expensive LiDAR sensors. In this work, we investigate a strategy to solve the SLAM problem using an RGB-depth sensor Kinect v2, an open-source SLAM package called RTABMap, and an Extended Kalman Filter (EKF). Although SLAM algorithm we chose is computationally tractable and provides accurate results, the resulting pose data is available only at 1 Hz. To overcome the disadvantage of the low data frequency from RTAP-Map, we utilize an EKF filter to fuse in odometry estimates and obtain higher-rate (30 Hz) pose estimates. The system is implemented onboard a Jackal Unmanned Ground Vehicle (UGV), equipped with a Kinect v2 camera as the only external sensor. A motion capture system from Vicon is used to obtain a ground truth of the robot’s motions. Our results show good agreement between the pose estimates from our system and the ground truth. Our work demonstrates the ability of an inexpensive RGB-depth sensor such as the Kinect v2, combined with an open-source SLAM package and fused with high-rate odometry estimates through an EKF, to achieve good performance and accuracy results.
Data storage lock algorithm with cryptographic techniques

The cloud computing had its impact far and wide, and Enterprise solutions are getting migrated to different types of clouds. The services are delivered from the data centers which are located all over the world. As the data is roaming with less control in any data centers, data security issues in cloud are very challenging. Therefore we need multi-level authentication, data integrity, privacy and above all encryption to safeguard our data which is stored on to the cloud. The data and applications cannot be relocated to a virtual server without much degree of security concern as there can be much confidential data or mission-critical applications. In this paper, we propose Data Storage Lock Algorithm (DSLA) to store confidential data thereby provides secure data storage in cloud computing based on cryptographic standards.
Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform

Academic/Practical Relevance: Our intent is to identify the underlying factors and develop an order assignment policy that can help an on-demand meal delivery service platform to grow. Methodology: By analyzing transactional data obtained from an online meal delivery platform in Hangzhou (China) over a two-month period in 2015, we examine the impact of meal delivery performance on a customer's future orders. Through a simulation study, we illustrate the importance of incorporating our empirical results into the development of a smarter "order assignment policy". Results: We find empirical evidence that an "early delivery'' is positively correlated with customer retention: a 10-minute earlier delivery is associated with an increase of one order per month from each customer. However, we find that the negative effect on future orders associated with "late deliveries'' is much stronger than the positive effect associated with "early deliveries". Moreover, we show empirically that a driver's individual local area knowledge and prior delivery experience can reduce late deliveries significantly. Finally, through a simulation study, we illustrate how one can incorporate our empirical results in the development of an order assignment policy that can help a platform to grow its business through customer retention. Managerial Implications: Our empirical results and our simulation study suggest that to increase future customer orders, an on-demand service platform should address the issues arising from both the supply side (i.e., driver's local area knowledge and delivery experience) and the demand side (i.e., asymmetric impacts of early and late deliveries on future customer orders) into their operations.
An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks

Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.
Identity-Based Parallel Key-Insulated Proxy Signature Without Random Oracles

Exposure of private keys is the dangerous attack in the public key cryptosystems. We put forward an identity-based parallel key-insulated proxy signature (IBPKIPS) scheme to deal with the problem of key exposure in the identity-based proxy signature system. Our IBPKIPS scheme is secure without random oracles. In the IBPKIPS scheme, we use two different helper keys to renew the temporary private keys. Our IBPKIPS scheme is securer and more flexible than the identity-based proxy signature scheme.
Chinese Reviews Generation Based on HM-BiLSTM Model

HM-BiLSTM model is proposed to solve the problem of inaccurate expression of Chinese comment with specific topic, which is generated by existing deep learning models. Firstly, HM-BiLSTM model was created to generate comments with subject attention mechanism algorithm whose purpose was to assist to constrain the theme. Then, imitation writing sentence was retrieved to extract syntactic structure information from Chinese reviews corpus. The subject as well as structure information was both encoded. Lastly, Chinese comments were generated recurrently by HM-BiLSTM model under the constraints of the subject attention mechanism algorithm and syntactic structure information. The experiment results showed that the model generated comments with better quality and accurate theme.
Segregation of Plastic and Non-plastic Waste using Convolutional Neural Network

Due to industrialization and urbanization the rapid rise in the volumeand amount of hazardous waste and the disposal of it is becoming a burgeoning problem that the world is facing today. One of the best ways out for this problem is to collect, sort and reuse or recycle these waste. So this paper proposes an architecture which sorts waste materials into plastic and non-plastic using Convolutional Neural Networks (CNN). CNN is one among the efficient machine learning techniques, which is able to provide maximum learning efficiency. This technique requires less parameter for training compared to the standard neural network. A dataset of waste materials required for our setup is collected. They are trained and tested using CNN. The proposed architecture with CNN gives an accuracy of 0.978. The proposed design also consists of a prototype, which acts as a real-time classifier. This system reduces the human efforts in separating plastics from non-plastics.
A PatchMatch‐based Approach for Matte Propagation in Videos

Despite considerable advances in natural image matting over the last decades, video matting still remains a difficult problem. The main challenges faced by existing methods are the large amount of user input required, and temporal inconsistencies in mattes between pairs of adjacent frames. We present a temporally‐coherent matte‐propagation method for videos based on PatchMatch and edge‐aware filtering. Given an input video and trimaps for a few frames, including the first and last, our approach generates alpha mattes for all frames of the video sequence. We also present a user scribble‐based interface for video matting that takes advantage of the efficiency of our method to interactively refine the matte results. We demonstrate the effectiveness of our approach by using it to generate temporally‐coherent mattes for several natural video sequences. We perform quantitative comparisons against the state‐of‐the‐art sparse‐input video matting techniques and show that our method produces significantly better results according to three different metrics. We also perform qualitative comparisons against the state‐of‐the‐art dense‐input video matting techniques and show that our approach produces similar quality results while requiring only about 7% of the amount of user input required by such techniques. These results show that our method is both effective and user‐friendly, outperforming state‐of‐the‐art solutions.
Blockchain-Based Trustworthy Edge Caching Scheme for Mobile Cyber-Physical System

To improve mobile users’ quality-of-experience (QoE) in the mobile cyber-physical system (MCPS), caching layered-coding contents on edge nodes that are close to mobile users has been advocated as a promising solution, which can efficiently lower the content delivery delay and mitigate the overhead of backhaul network. However, due to the complexity of trust management and the limited caching capacities of edge nodes, designing an efficient edge caching scheme for mobile users becomes a challenge. Meanwhile, the content caching in MCPS also faces some security problems, where edge nodes may return incorrect results or viruses to mobile users, and mobile users would deliberately refuse to pay for caching services. To tackle these problems, we propose a novel blockchain-based trustworthy edge caching scheme for mobile users in MCPS. Specifically, we first exploit blockchain to supervise the caching transactions between the edge nodes and mobile users in a distributed manner, whereby the caching service information cannot be modified and denied by any entities. Furthermore, we devise a trust management mechanism for mobile users to search the trustworthy caching services from diversified edge nodes, where the trust degree of the edge node is real-time evaluated and updated by mobile users based on the quality of caching service. To take full advantage of caching resources, we design a max–min-based resource allocation algorithm, with which the trustworthy edge node could fairly allocate its caching resource based on mobile users’ optimal demands. The simulation results show that the presented scheme not only improves the utilities of edge nodes but also increases the QoE of mobile users.
Twelve Step Programs: An Update

Substance abuse, especially alcoholism, is a serious and long-standing problem that causes great personal, societal, and financial hardship. Although advances have been made in the medical treatment of these disorders, psychotherapeutic interventions are the current mainstay of treatment. In an effort to provide holistic treatment of addictive disorders, Alcoholics Anonymous was developed by two severe alcoholics in the 1930s. Alcoholics Anonymous (AA) has been adapted to the management of many different addictions and other chronic conditions such as HIV/AIDS, hepatitis, etc. since its inception; furthermore, twelve step programs and the related “disease concept of treatment,” including alcoholism and addiction, have heavily influenced inpatient and outpatient treatment.The twelve-step approach differs from other addiction treatment strategies. First, the program emphasizes a comprehensive treatment approach for the disease of addiction, addressing mental, physical, and spiritual components. Moreover, participation in each other's treatment is critical to sustained sobriety. With intensive work in the program, the twelve-step approach offers management of the addictive behavior as well as improvement in handling “life on life's terms. ”Twelve step treatments have significant positives such as being widely available for little to no cost and promising success via a holistic treatment strategy. However, the twelve step approach has been criticized for being overly religious, inflexible, and controlling. It has also been accused of fostering a substitute dependency. Despite the plentiful anecdotal reports of positive treatment outcomes, rigorous examination of treatment efficacy has been challenging and has produced varying results. However, more recent and detailed studies have generated optimistic findings.The benefits of enhanced knowledge regarding this treatment modality are readily apparent when serving chronically addicted clients in an increasingly managed-care environment. This article is a primer on the twelve-step model, with a history of AA, a brief examination of how a twelve-step program works, a review of research on this treatment modality, and a discussion of handling common issues reported by clients regarding this psychosocial approach.
A Review of IS Strategy Literature: Current Trends and Future Opportunities

ABSTRACT In this paper, we examine the IS strategy literature since 2008. Using Chen, Mocker, Preston, and Teubner’s (2010) review as a starting point, we identified 31 IS strategy studies published since 2008. Further, we evaluated previous frameworks for evaluating IS strategy research. Using the conceptions proposed by Chen, et al. (2010), IS Strategy as competitive advantage (I), IS functional strategy (II), and strategic alignment (III), we identify overarching themes and propose there is increased activity bridging the conceptions, providing interesting opportunities for future research. Directions for future research for individual conception as well as bridging conceptions are given.
Implementation of Fuzzy Mamdani Logic Method for Student Drop Out Status Analytics

One of The Contribution of Information Technology in the World of Education at University is the System of Executive. the Information that needed by the Executive Department is an Information Student Data that supposed to be drop out. all this time the Process to determine the Student Faculty of Science and Technology Status as Drop out at Sunan Gunung Djati (SGD) States Islamic University Bandung, had not to apply the Automatic Sysytem, based on this case, therefore we need a Solution to handle it, by Develop the Executive Information System. The Method that we need for Developing this System is by using the Fuzzy Mamdani Logic Method, there are 4 Steps to bring the Output of this Method 1. ) Creating a Fuzzy set, 2. ) Application of Function Implication 3. ) Composition Law, 4. ) Defuzzification. Based on this research, we will make a comparison between Manual Calculation and System Calculation, with the Accuracy 100%. The final result of this Method can be referenced for System Executive to Analyze the Student that should be given a Warning or Drop Out.
Preconditioned IDRStab algorithms for solving nonsymmetric linear systems

The IDRStab method, which combines the Induced Dimension Reduction (IDR) (s) method with higher-order stabilizing polynomials, is an effective method for solving large nonsymmetric linear systems. IDRStab can be implemented using different algorithms which are mathematically equivalent. In this paper, we illustrate preconditioned algorithms for three variants of IDRStab and describe their advantages. Numerical experiments show the differences in the convergence of the variants of IDRStab with preconditioning.
The Optimization of Redundancy Design for Solid State Transformer

As the essential device in future integrated energy system, the reliability of solid state transformer is highly related to the security and reliability of the distribution power grid. With the redundant sub-modules, the system reliability as well as the system cost of solid state transformer will both increase. The reliability of various redundancy designs for solid state transformer are analyzed and an objective function is studied for optimized solution. The redundancy design of relatively high reliability and low additional cost for various configurations are discussed, and the optimized solution which are appropriate in both reliability and economy is obtained.
Weak target echo detection based on low rank signal in multi-static passive radar

This paper considers weak target echo detection in multi-static passive radar system. A new detector adopting a test statistic based on the cross correlation of the principal left singular vectors of the reference and surveillance signal-plus-noise matrices is devised. Due to the exploitation of the low-rank structure of transmitted signal, it offers better performance than traditional detector. Simulation results verify the effectiveness of the proposed detection method.
Securing Hadoop Distributed File System using Intelligence based Sensitivity bits

Hadoop is a widely used analytical tool for analyzing large volume of data which are in terms of Exa & Zetta bytes. Hadoop Distributed File System(HDFS) is a well-known storage system which provides distributed storage using commodity hardware. Since Big Data consists of highly sensitive data like Electronic Health Records, Financial Data etc., harnessing big data access and security plays a vital role. The bottom most layer of security can be provided by encryption. Since the data stored is highly unstructured, wider in variety and the velocity at which data is stored is also unpredictable, traditionally used ciphers like AES, RSA, RC4 etc. will not work efficiently on Big Data. The solution must have to consider at-risk sensitive data, variety of data which needs intelligence to classify the data. When data blocks are stored in data nodes, with each block 4-bits called sensitivity bits has to be added. Two bits indicate the level of sensitivity and two more bits indicate the type of data. These 4-bits are used by the map and reduce functions to effectively encrypt the required data blocks alone and not all, in a time efficient manner by using the available commodity hardware.
Evaluating Practices and Developing Tools for Comparative Effectiveness Reviews of Diagnostic Test Accuracy

Background Existing methods for meta-analysis of diagnostic test accuracy focus primarily on a single index test rather than comparing two or more tests that have been applied to the same patients in paired designs. Objectives We develop novel methods for the joint meta-analysis of studies of diagnostic accuracy that compare two or more tests on the same participants. Development of methods We extend the bivariate meta-analysis method proposed by Reitsma et al. (J Clin Epidemiol. 2005; 58[10]:982–90) and modified by others to simultaneously meta-analyze M ≥ 2 index tests. We derive and present formulas for calculating the within-study correlations between the true-positive rates (TPR, sensitivity) and between the false-positive rates (FPR, one minus specificity) of each test under study using data reported in the studies themselves. The proposed methods respect the natural grouping of data by studies, account for the within-study correlation between the TPR and the FPR of the tests (induced because tests are applied to the same participants), allow for between-study correlations between TPRs and FPRs (such as those induced by threshold effects), and calculate asymptotically correct confidence intervals for summary estimates and for differences between summary estimates. We develop algorithms in the frequentist and Bayesian settings, using approximate and discrete likelihoods to model testing data. Application Published meta-analysis of 11 studies on the screening accuracy of detecting trisomy 21 (Down syndrome) in liveborn infants using two tests: shortened humerus (arm bone), and shortened femur (thigh bone). Secondary analyses included an additional 19 studies on shortened femur only. Findings In the application, separate and joint meta-analyses yielded very similar estimates. For example, in models using the discrete likelihood, the summary TPR for a shortened humerus was 35.3 percent (95% credible interval [CrI]: 26.9, 41.8%) with the novel method, and 37.9 percent (27.7 to 50.3%) when shortened humerus was analyzed on its own. The corresponding numbers for the summary FPR were 4.8 percent (2.8 to 7.5%) and 4.8 percent (3.0 to 7.4%). However, when calculating comparative accuracy, joint meta-analyses resulted in shorter confidence intervals compared with separate meta-analyses for each test. In analyses using the discrete likelihood, the difference in the summary TPRs is 0 percent (−8.9, 9.5%; TPR higher for shortened humerus) with the novel method versus 2.6 percent (−14.7, 19.8%) with separate meta-analyses. The standard deviation of the posterior distribution of the difference in TPR with joint meta-analyses is half of that with separate meta-analyses. Conclusions The joint meta-analysis of multiple tests is feasible. It may be preferable over separate analyses for estimating measures of comparative accuracy of diagnostic tests. Simulation and empirical analyses are needed to better define the role of the proposed methodology.