Reviewerly/exHarmony huggingface dataset:
An Introduction of NoSQL Databases Based on Their Categories and Application Industries

The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise’s database must be accessed as fast as possible, but the Relational Database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a NoSQL database, which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of NoSQL databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate NoSQL database for different industries.
Evolutionary Convolutional Neural Networks Using ABC

Convolutional neural networks (CNNs) have been used over the past years to solve many different artificial intelligence (AI) problems, providing significant advances in some domains and leading to state-of-the-art results. Nonetheless, the design of CNNs architecture remains to be a meticulous and cumbersome process that requires the participation of specialists in the field. In this work, we have explored the neuro-evolution application to the automatic design of CNN topologies, developing a novel solution based on Artificial Bee Colony (ABC). The MNIST dataset is used to evaluate the proposed method, which is proved being highly competitive with the state-of-the-art.
Distinguishability classes, resource sharing, and bound entanglement distribution

Suppose a set of $m$-partite, $m\ensuremath{\ge}3$, pure orthogonal fully separable states is given. We consider the task of distinguishing these states perfectly by local operations and classical communication (LOCC) in different $k$ partitions, $1<k<m$. Based on this task, it is possible to classify the sets of product states into different classes. For tripartite systems, a classification of the sets with explicit examples is presented. Few important cases related to the aforesaid task are also studied when the number of parties, $m\ensuremath{\ge}4$. These cases never appear for a tripartite system. However, to distinguish any LOCC indistinguishable set, entanglement can be used as resource. An important objective of the present study is to learn about the efficient ways of resource sharing among the parties. We also find an interesting application of multipartite product states, which are LOCC indistinguishable in a particular $k$ partition. Starting from such product states, we constitute a protocol to distribute bound entanglement between two spatially separated parties by sending a separable qubit.
Online Non-Negative Multi-Modality Feature Template Learning for RGB-Assisted Infrared Tracking

Infrared sensors have been deployed in many video surveillance systems because of the insensibility of their imaging procedure to some extreme conditions (e.g. low illumination condition, dim environment). To reduce human labor in video monitoring and perform intelligent infrared video understanding, an important issue we need to consider is how to locate the object of interest in consecutive video frames accurately. Therefore, developing a robust object tracking algorithm for infrared videos is necessary. However, the infrared information may not be reliable (e.g. thermal crossover), and appearance modeling with only the infrared modality may not be able to achieve good results. To address these issues, with the wide deployment of RGB-infrared camera systems, this paper proposes an infrared tracking framework in which information from RGB-modality will be exploited to assist the infrared object tracking. Specifically, within the tracking framework, in order to deal with the contaminated features caused by large appearance variations, an online non-negative feature template learning model is designed. The non-negative constraint enables the model to capture the local part-based characteristic of the target appearance. To ensure more important modality contribute more in appearance representation, an adaptive modality importance weight learning scheme is also incorporated in the proposed feature learning model. To guarantee the model optimality, an iterative optimization algorithm is derived. The experimental results on various RGB-infrared videos show the effectiveness of the proposed method.
Recomputation of dense layers for the performance improvement of DCNN

Gradient descent optimization of learning has become a paradigm for training deep convolutional neural networks (DCNN). However, utilizing other learning strategies in the training process of the DCNN has rarely been explored by the deep learning (DL) community. This serves as the motivation to introduce a non-iterative learning strategy to retrain neurons at the top dense or fully connected (FC) layers of DCNN, resulting in, higher performance. The proposed method exploits the Moore-Penrose Inverse to pull back the current residual error to each FC layer, generating well-generalized features. Further, the weights of each FC layers are recomputed according to the Moore-Penrose Inverse. We evaluate the proposed approach on six most widely accepted object recognition benchmark datasets: Scene-15, CIFAR-10, CIFAR-100, SUN-397, Places365, and ImageNet. The experimental results show that the proposed method obtains improvements over 30 state-of-the-art methods. Interestingly, it also indicates that any DCNN with the proposed method can provide better performance than the same network with its original Backpropagation (BP)-based training.
Trust more, serverless

The increasingly popular and novel Function-as-a-Service (FaaS) clouds allow users the deployment of single functions. Compared to Infrastructure-as-a-Service or Platform-as-a-Service, this enables providers even more aggressive and rigorous resource sharing and liberates customers from tedious maintenance tasks. However, as a crucial factor of cloud adoption, FaaS clouds need to provide security and privacy guarantees in order to allow sensitive data processing.
Hand-raising gesture detection in real classrooms using improved R-FCN

This paper proposes a novel method for hand-raising detection in real classroom environments. Different from traditional motion detection, the hand-raising detection is quite challenging due to complex backgrounds, various gestures and low resolutions. To solve these challenges, we build up a large-scale dataset from videos of real classrooms, and propose a novel neural network architecture based on region-based, fully convolutional networks (R-FCN). Specifically, we first design an adaptive templates selection algorithm for various gestures of hand-raising detection. Secondly, for better detection of small-size hands, we design a feature pyramid to simultaneously capture the detail and highly semantic features. Compared with state-of-the-art object detection algorithms, our method achieves impressive results on our hand-raising dataset. After extensive testing, the accuracy of the hand-raising detection achieves 90% in mean Average Precision (mAP), which can satisfy real applications.
telingo = ASP + Time

We describe telingo, an extension of the ASP system clingo with temporal operators over finite linear time and provide insights into its implementation. telingo takes temporal logic programs as input whose rules contain only future and present operators in their heads and past and present operators in their bodies. Moreover, telingo extends the grammar of clingo's input language with a variety of temporal operators that can even be used to represent nested temporal formulas. By using clingo's interface for manipulating the abstract syntax tree of non-ground programs, temporal logic programs are transformed into regular ones before grounding. The resulting regular logic program is then solved incrementally by using clingo's multi-shot interface. Notably, this involves the consecutive unfolding of future temporal operators that is accomplished via external atoms. Finally, we provide an empirical evaluation contrasting standard incremental ASP programs with their temporal counterparts in telingo's input language.
Fast Semisupervised Learning With Bipartite Graph for Large-Scale Data

As the captured information in our real word is very scare and labeling sample is time cost and expensive, semisupervised learning (SSL) has an important application in computer vision and machine learning. Among SSL approaches, a graph-based SSL (GSSL) model has recently attracted much attention for high accuracy. However, for most traditional GSSL methods, the large-scale data bring higher computational complexity, which acquires a better computing platform. In order to dispose of these issues, we propose a novel approach, bipartite GSSL normalized (BGSSL-normalized) method, in this paper. This method consists of three parts. First, the bipartite graph between the original data and the anchor points is constructed, which is parameter-insensitive, scale-invariant, naturally sparse, and simple operation. Then, the label of the original data and anchors can be inferred through the graph. Besides, we extend our algorithm to handle out-of-sample for large-scale data by the inferred label of anchors, which not only retains good classification result but also saves a large amount of time. The computational complexity of BGSSL-normalized can be reduced to O(ndm+nm2) , which is a significant improvement compared with traditional GSSL methods that need O(n2d+n3) , where n , d , and m are the number of samples, features, and anchors, respectively. The experimental results on several publicly available data sets demonstrate that our approaches can achieve better classification accuracy with less time costs.
BIFFOA: A Novel Binary Improved Fruit Fly Algorithm for Feature Selection

Feature selection is an important method to reduce the number of attributes of high-dimensional data and an essential preprocess work in classification.It eliminates irrelevant, redundant, and noisy features improves the performance of the model and reduces the computational burden.Fruit fly optimization algorithm is a new algorithm proposed in recent years, which imitates the foraging behavior of fruit fly.To the best of our knowledge, it has not been systematically applied to feature selection.This paper uses the fruit fly optimization algorithm as a search strategy and designs a wrapper-based feature selection method, named binary improved fruit fly optimization algorithm (BIFFOA).Besides, four different strategies based on evolutionary population dynamics (EPD) and new mutation operators are employed to enhance the BIFFOA.The extensive experiments on 25 datasets (see Table 1) show that the performance of the BIFFOA is better than several state-of-the-art algorithms.
Group Reidentification with Multigrained Matching and Integration

The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem.Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership. In this paper, we propose a novel conceptof group granularity by characterizing a group image by multi-grained objects: individual persons and sub-groups of two andthree people within a group. To achieve robust group Re-ID,we first introduce multi-grained representations which can beextracted via the development of two separate schemes, i.e. onewith hand-crafted descriptors and another with deep neuralnetworks. The proposed representation seeks to characterize bothappearance and spatial relations of multi-grained objects, and isfurther equipped with importance weights which capture varia-tions in intra-group dynamics. Optimal group-wise matching isfacilitated by a multi-order matching process which in turn,dynamically updates the importance weights in iterative fashion.We evaluated on three multi-camera group datasets containingcomplex scenarios and large dynamics, with experimental resultsdemonstrating the effectiveness of our approach. The published dataset can be found in \url{http://min.sjtu.edu.cn/lwydemo/GroupReID.html}
To react, or not to react: Patterns of reaction to API deprecation

Application Programming Interfaces (API) provide reusable functionality to aid developers in the development process. The features provided by these APIs might change over time as the API evolves. To allow API consumers to peacefully transition from older obsolete features to new features, API producers make use of the deprecation mechanism that allows them to indicate to the consumer that a feature should no longer be used. The Java language designers noticed that no one was taking these deprecation warnings seriously and continued using outdated features. Due to this, they decided to change the implementation of this feature in Java 9. We question as to what extent this issue exists and whether the Java language designers have a case. We start by identifying the various ways in which an API consumer can react to deprecation. Following this we benchmark the frequency of the reaction patterns by creating a dataset consisting of data mined from 50 API consumers totalling 297,254 GitHub based projects and 1,322,612,567 type-checked method invocations. We see that predominantly consumers do not react to deprecation and we try to explain this behavior by surveying API consumers and by analyzing if the API’s deprecation policy has an impact on the consumers’ decision to react.
Hybrid image processing approach for autonomous crack area detection and tracking using local digital image correlation results applied to single-fiber interfacial debonding

Local digital image correlation is a popular method for accurate full field displacement measurements. However, the technique struggles at autonomously tracking emerging and propagating cracks. We proposed a hybrid approach which utilizes image processing techniques in combination with local digital image correlation to autonomously monitor cracks in a mechanically loaded specimen. Our approach can extract and track crack surfaces and provide a volume-based visualization of the crack growth. This approach was applied to single-fiber composite experimental results with interfacial debonding from the literature. Results quantitatively show that strong interfacial fiber/matrix bonding leads to slower interfacial crack growth, delays interfacial crack growth in the matrix, requires higher loadings for crack growth and shows a specific crack path distinct from the one obtained for weak interfaces. The approach was also validated against a manual approach where a domain scientist extracts a crack using a polygon extraction tool. The method can be used on any local digital image correlation results involving damage observations.
Privacy-preserving decentralized ABE for secure sharing of personal health records in cloud storage

Personal health record (PHR) system has become an important platform for health information exchange, in which patients can effectively manage and share personal health information in cloud storage. In general, all health information will be encrypted before they are uploaded to the cloud server. However, the cloud server is unreliable and the sensitive information of users may be disclosed in this process. The general encryption algorithm cannot protect both confidentiality and privacy of the uploaded information. Attribute-based encryption (ABE) provides a solution to it. In this paper, we propose a privacy-preserving decentralized ABE scheme for secure sharing of PHR based on Lewko and Waters′s scheme, in which an anonymous secret key issuing protocol is used to hide GIDs. This protocol can make the authorities generate the correct decryption key for users without knowing their GIDs. In addition, the one-way anonymous key agreement is used to hide the attributes in the access policy. The presented scheme keeps the security of the Lewko and Waters′s scheme and removes the random oracle. Finally, we show that the security of the proposed scheme is reduced to static assumptions based on dual system encryption instead of other strong assumptions.
Mistakes in UML Diagrams: Analysis of Student Projects in a Software Engineering Course

The Unified Modelling Language (UML) is being widely accepted as a modelling notation for visualizing software systems during design and development. UML has thus become part of many software engineering course curricula at universities worldwide, providing a recognized tool for practical training of students in understanding and visualizing software design. It is however common that students have difficulties in absorbing UML in its complexity, and often repeat the same mistakes that have been observed by course tutors in previous years. Having a catalogue of such mistakes could hence increase the effectiveness of both teaching and learning of UML diagrams. In this paper, we introduce such a catalogue, consisting of 146 types of mistakes in eight types of diagrams. As the main contribution of this study, we use this catalogue to guide the analysis of student projects within a software engineering course. In total, over 2,700 diagrams submitted over 12 weeks of a semester by 123 students were analysed to identify the frequency of mistakes (from the catalogue), correlations of the mistakes between different diagram types, correlation of the quality of student projects to exam results, student behaviour in terms of introducing and fixing the mistakes over time, and other interesting insights. The analysis is described together with its setup and execution, and all datasets and detailed guidebook to the catalogue of all mistakes is made available for download.
Self-characteristics based Energy-Efficient Resource Scheduling for Cloud

Energy optimization in cloud computing is more attractive and much consideration in resource management, where, energy consumption is also one of the important metrics that should be included to resource management technique apart from performance and utilization of resource metric. The majority of existing methodologies for scheduling of resources are focusing on cost and execution time. In fact, the resource failures also distract the execution of cloud resource provisioning, which results in more operative cost and time. In this paper, the self-optimized energy efficient resource management strategy has been consider, which gives the optimal solution to maximize the resource utilization and identify the faulty resources to avoid misleading of scheduling. This strategy, allocate the resources to the workloads submitted by the cloud user by the fulfillment of the QoS with less SLA violation rate and maximizes the resource utilization at low cost. The novelty of this paper is to applied Antlion Optimization algorithm to find the optimal resource. The experimental results are evidence of utmost performance of proposed work.
THE EFFECT OF USERS’ BEHAVIORAL INTENTION ON GAMIFICATION AUGMENTED REALITY IN STEM (GAR-STEM) EDUCATION

As people attach increasing importance to the learning environment and design of teaching materials in STEM, how to design teaching materials that enable students to seamlessly integrate the learning of scientific investigation, engineering design, mathematical reasoning, and technical skills has become increasingly important. The purpose of this research was to explore the behavioral intention of GAR-STEM teaching application users. The results showed that the best predictor of practicability, entertainment, and media interactivity for intention towards GAR-STEM app design was usage attitude (R2= 0.547), followed by intent to use (R2 = 0.528), practicability (R2 = 0.408), and entertainment (R2 = 0.186). The proposed model explained 52.8% of the variance in behavioral intention. The overall findings suggested that usage attitude and intent to use may augment its function as a key factor for the procedural intention toward GAR-STEM app design. Keywords: augmented reality, gamification apps, importance-performance matrix analysis, STEM education.
MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15 percent compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86 and 90 percent, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7 percent time overhead to execute for a data center with 1,500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload.