Reviewerly/exHarmony huggingface dataset:
Bat algorithm and cuckoo search algorithm

Both the bat algorithm and the cuckoo search algorithm are nature-inspired optimization algorithms, and they have been shown to be flexible and effective in solving various optimization problems. This chapter introduces both algorithms in detail, with an emphasis on the main ideas and implementations. Examples are also given to show how these algorithms work.
Relating Factors of Tertiary Institution Students’ Cybersecurity Behavior

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution students undergo lot of cybersecurity issues due to their constant Internet exposure, however there is a lack in literature with regards to tertiary institution students' cybersecurity behaviors. This research aimed at linking the factors responsible for tertiary institutions students' cybersecurity behavior, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behavior (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); Familiarity with Cyber-Threats (FCT), thus exploring the relationship between the factors and the students' Cybersecurity Behaviors (CSB). A cross-sectional online survey was used to gather data from 450 undergraduate and postgraduate students from tertiary institutions within Klang Valley, Malaysia. Correlation Analysis was used to find the relationships existing among the cybersecurity behavioral factors via SPSS version 25. Results indicate that all factors were significantly related to the cybersecurity behaviors of the students apart from Perceived Severity. Practically, the study instigates the need for more cybersecurity training and practices in the tertiary institutions.
Employ Multimodal Machine Learning for Content Quality Analysis

User-generated content varies drastically, and todays mainstream media sites have a lot of information presented in graphic form. So the task of identifying high-quality content becomes increasingly important, and it can improve overall reading time and ctr(click-through rate estimates). Traditional quality assessment method based only on single modes, for example in image quality assessment area it can divide into IQA method and NR-IQA method, and in text quality assessment area we use different indicators such as readability and smoothness, but there are some limitations in analyzing quality from single mode, many information will lose by single modal analysis. In this paper we propose a multimodal quality recognition approach. We use Transformers-XL which is more efficient and accurate for text feature extract, and use inceptionv3 for the original image feature extract, in order to solve the problem of multi-graph fusion we use NeXtVLAD for graph fusion. Then we use siamese network to score the content quality, and the rank loss is used as the optimization objective. We crawled many data from different website such as WeiboTwitter to constitute the validation data set. Compare with other method such as single modal method and other multimodal deep learning method, our approach got a state of art result.
Modified Gbest-Guided ABC Algorithm Approach Applied to Various Nonlinear Problems

A mathematical process which is used to search the maxima or minima of an objective function in the valid search space is known as the optimization. A wide range of nature-inspired optimization tactics such as spider monkey optimization, ant colony optimization and particle swarm optimization are used to find the most desirable solution and one of them is artificial bee colony (ABC) algorithm which is a population-based metaheuristic optimization approach. In the proposed work, a modification in gbest-guided ABC (GABC) algorithm is introduced by integrating some properties of Gaussian ABC algorithm. The aim of this paper is to overcome certain impediments of original ABC algorithm such as low speed of convergence, weak exploitation capability and solutions easily trapped by local optima. Experimental results by proposed algorithm are tested on several benchmark functions that show the modified GABC can improve upon ABC and GABC algorithms in most of the experiments.
Sentinel-2 Image Dehazing using Correlation between Visible and Infrared Bands

Several atmospheric phenomena often contaminate multispectral remote sensing images. Haze is one of the atmospheric phenomena that obscure the transparency of the scene. It affects the quality of an acquired satellite image. In this paper, we propose the dehazing method for Sentinel-2 satellite imagery. The correlation of the visible bands and infrared bands (narrow near-infrared (NIR) and short-wave infrared (SWIR) bands) are considered for constructing the linear combination formulas. The linear coefficients are determined by using the multiple linear regression of the haze-free image samples. The results show that our proposed method can be used to remove the haze, both thin and thick haze, especially the haze in the smoking area.
A motion-in-depth model based on inter-ocular velocity to estimate direction in depth

Perception of motion in depth is one of the most important visual functions for living in the three-dimensional world. Two binocular cues have been investigated for motion in depth: inter-ocular velocity difference (IOVD) and changing disparity (CD). IOVD provides direction information directly by comparing velocity signals from the two retinas. In this study, we propose for the first time a motion-in-depth model of IOVD that predicts motion-in-depth direction. The model is based on a psychophysical assumption that there are four channels tuned to different directions in depth (Journal of Physiology 235 (1973) 17–29). We modeled these channels by combining outputs of low-level motion detectors that are sensitive to left and right retinal stimulation. Using these channels, we constructed a model of motion in depth that successfully predicted a variety of psychophysical results including direction discrimination, perceived direction, spatial frequency tuning, effect of speed on rotation in depth, effect of lateral motion direction, and effect of binocular and temporal correlations.
Revenue sharing in edge-cloud systems: A Game-theoretic perspective

We explore the design of revenue sharing mechanisms for an Edge-Cloud computing system from a game-theoretic perspective. Different from traditional cloud computing, the providers in an Edge-Cloud system are independent and self-interested. The system adopts a task distribution mechanism to maximize the total revenue received from clients and employs a revenue sharing mechanism to split the received revenue among service providers. Under system-level mechanisms, service providers game with the system in order to maximize their own utilities by strategically allocating their resources. We introduce a game-theoretic framework to model the competition among the service providers as a non-cooperative game. We show the existence of Nash equilibrium in the game theoretically and experimentally. We find that at Nash equilibria, the revenue sharing design based directly on actual contributions of service providers results in significantly worse system-level performance than revenue sharing mechanisms based on marginal contributions. We find that the reason for this seemingly counter-intuitive result is that revenue sharing mechanisms based on marginal contributions discourage providers with less powerful resources from contributing resources to the system at equilibrium state. Our framework offers an economics approach to the understanding of Edge-Cloud systems and provides fundamental insights into their revenue sharing design.
Dual-Branch Network With a Subtle Motion Detector for Microaction Recognition in Videos

By involving only subtle motions of body parts, video-based microaction recognition is a very important but challenging problem. Most existing action recognition methods are developed for general actions, and the current state-of-the-art methods usually largely rely on high-layer features learned from convolutional neural networks (CNNs). High-layer CNN features usually contain more semantic information but less detailed information. However, detailed information can be important for microactions due to the motion subtleness of such actions. In this paper, we propose to more effectively learn midlayer CNN features for enhancing microaction recognition. More specifically, we develop a new dual-branch network for microaction recognition: one branch uses the high-layer CNN features for classification, and the second branch further explores the midlayer CNN features for classification. In the second branch, we introduce a novel subtle motion detector consisting of three modules: 1) a discriminative spatial-temporal feature learning module, which further learns the subtle motion features corresponding to the discriminative spatial-temporal regions, 2) a parallel multiplier attention module, which further refines the features learned in channels and spatial-temporal domains, and 3) an activation fusion module, which fuses the max and average activations from midlayer CNN features for classification. In the experiments, we build a new microaction video dataset, where the micromotions of interest are mixed with other larger general motions such as walking. Comprehensive experimental results verify that the proposed method yields new state-of-the-art performance in two microaction video datasets, while its performance on two generalaction video datasets is also very promising.
Description of Interface Between Semiconductor Electrostatic Qubit and Josephson Junction in Tight Binding Model

The interface between superconducting Josephson junction and semiconductor position-based qubit implemented in coupled semiconductor q-dots is described such that it can be the base for electrostatic interface between superconducting and semiconductor quantum computer. Modification of Andreev Bound State in Josephson junction by the presence of semiconductor qubit in its proximity and electrostatic interaction with superconducting qubit is spotted by the minimalist tight-binding model. The obtained analytical results allow in creating interface between semiconductor quantum computer and superconducting quantum computer. They open the perspective of construction of QISKIT like software that will describe both types of quantum computers as well as their interface. Key words: electrostatic entanglement, interface between semiconductor and superconducting quantum computer, tight-binding BdGe equations, analytical solutions, topological states of matter, hybrid quantum computer
Real-Time Traffic Sign Detection using Capsule Network

In the past few years, Deep learning has emerged as an enormous technology which has applications in Image classification and Natural language processing, Recommendation System, Automatic Machine Translation, Handwriting Recognition, etc. Traffic Sign Detection (TSD) systems are used to regulate the drivers during driving and to increase the road safety by reducing accidents. Automatic detection of traffic signs is also important for automated intelligent driving vehicle or driver assistance systems. In previous Traffic Sign Detection Systems, locations of the Traffic Signs are estimated using an object pose elimination problem and the problem is modelled using Convolution Neural Network (CNN). In this work, we are proposing a model using Capsule Network (CapsNet) which overcomes the limitations of CNN and provides training accuracy of 95 percent and training accuracy of 98 percent.
A MinHash approach for fast scanpath classification

The visual scanpath describes the shift of visual attention over time. Characteristic patterns in the attention shifts allow inferences about cognitive processes, performed tasks, intention, or expertise. To analyse such patterns, the scanpath is often represented as a sequence of symbols that can be used to calculate a similarity score to other scanpaths. However, as the length of the scanpath or the number of possible symbols increases, established methods for scanpath similarity become inefficient, both in terms of runtime and memory consumption. We present a MinHash approach for efficient scanpath similarity calculation. Our approach shows competitive results in clustering and classification of scanpaths compared to established methods such as Needleman-Wunsch, but at a fraction of the required runtime. Furthermore, with time complexity of and constant memory consumption, our approach is ideally suited for real-time operation or analyzing large amounts of data.
Design of Limb Rehabilitation Training System Based on Virtual Reality Technology

Traditional rehabilitation exercises take a long time and require high training intensity. Moreover, repeating the same boring training action will reduce the enthusiasm of the patient to participate in rehabilitation training. A task-oriented virtual reality rehabilitation training system for rehabilitation people with certain athletic ability is designed and developed in this paper. It can improve the efficiency and fun of rehabilitation training. The system uses MEMS sensors to capture human motion capture in real time, and the human body motion is identified by constructing a human body wireless domain network based on Zigbee technology. With the Oculus Rift S wearable head-mounted device, the patient's rehabilitation process in a virtual environment. After testing, the rehabilitation training task based on VR technology effectively promoted the initiative of patients to participate in rehabilitation training, and it meets the needs of rehabilitation training.
Modified Smooth Variable Structure Filter for Radar Target Tracking

The Smooth Variable Structure Filter (SVSF) is an effective method for radar target tracking and capable of providing robust target state estimation under model uncertainty. The traditional SVSF utilizing sign function or saturation function as the switching function in the correction gain suffers from the cut-off switching operations and the imprecise correction gain when the uncertainty rises unexpectedly. Therefore, the undesired chattering phenomenon occurs and the posteriori estimation error of the target velocity and acceleration greatly increases. In this paper, a modified form of SVSF using the hyperbolic tangent function as the "soft" switching principle is proposed to suppress the chattering phenomenon. The proposed method, referred to as the Tanh-SVSF, improves the tracking accuracy without increasing any computational burden. Simulation results demonstrate the advantage of the proposed Tanh-SVSF over traditional methods in terms of the accuracy of target motion state estimation.
A Comprehensive Study of Sentiment Analysis in Big Data Applications

Sentiment analysis [SA] is a computational analysis of sentiments or opinions, emotions, views, subjectivity expressed in text or associated with big data such as reviews, blogs, discussions, news, comments, feedback etc., about things such as electronic products, movies, public or private services, organizations, individuals, issues, events, topics, and their attributes. It represents a large problem space and this field has become a very active area. This survey paper deals with a comprehensive analysis of the research work carried out for the duration of 2010–2019 in the relevance of conventional methods, enhanced methods and associated techniques employed for the sentiment analysis research problems. Evidently, this article includes an analysis of the majority of the research work done by earlier researchers with respect to the related domain. However, we anticipate that the references mentioned will wrap up the most important theoretical aspects, and this review would be eventually helpful to subsequent researchers for upcoming research trends.
Enriching Topic Coherence on Reviews for Cross-Domain Recommendation

Abstract With the advent of e-commerce sites and social media, users express their preferences and tastes freely through user-generated content such as reviews and comments. In order to promote cross-selling, e-commerce sites such as eBay and Amazon regularly use such inputs from multiple domains and suggest items with which users may be interested. In this paper, we propose a topic coherence-based cross-domain recommender model. The core concept is to use topic modeling to extract topics from user-generated content such as reviews and combine them with reliable semantic coherence techniques to link different domains, using Wikipedia as a reference corpus. We experiment with different topic coherence methods such as pointwise mutual information (PMI) and explicit semantic analysis (ESA). Experimental results presented demonstrate that our approach, using PMI as topic coherence, yields 22.6% and using ESA yields 54.4% higher precision as compared with cross-domain recommender system based on semantic clustering.
Models and Modeling

The terms model and modeling are everyday terms. In science, however, different meanings are ascribed to them, especially when they are viewed from the two perspectives of mathematics and physics. In this chapter, we will examine whether and how these terms are defined in the two disciplines and what significance the use of models and modeling has in education.
A Novel Fuzzy Evaluation Method on Teaching Quality of College English based on Preference Selection Index

Abstract The evaluation of teaching quality is an important part of the quality management of teaching in colleges and universities. Many evaluation systems exist shortcomings, which are not suitable for the evaluation of English class. Because English subject has its own characteristics, in which the evaluation subject taken on students is more suitable than on teachers. This paper will propose a college English evaluation index system on the basis of students as the evaluation subject. Since the evaluation indicators are mainly qualitative indicators, which makes the evaluation value fuzzy, this paper proposes a new evaluation method of college English classroom teaching evaluation based on linguistic terms, triangular fuzzy numbers and preference selection index method. Finally, an algorithm case is analyzed to illustrate the effectiveness and feasibility of the proposed method.
Big Data Retrieval using HDFS with LZO Compression

Any type of organization depends on accurate data analytics to make better decisions. Users of these organizations request access from different resources like processes or executors. When processing this request of users, the data retrieval speed is low and also data is inaccurate for some conditions. To solve this issue, a system may be proposed having Hadoop Distributed File system (HDFS) with Lempel-Ziv-Oberhumer(LZO). The first step in the proposed technique is to retrieve and mine the data from respective database. The next step is to cluster the extracted data and optimize it using HDFS and LZO compression method. In the last step, if the compressed data is found similar to user requested data, the final data has to be visualized to the user. The proposed retrieving process in big data gives better performance and reduced execution time.
Characteristics and Learning Behavior of Active Learners on SWU-MOOC

In 2017, the SWU-MOOC project began from the collaboration between Srinakharinwirot University and Thai cyber University project to developing 10 courses on innovation and technology. There are over 3,000 enrolled students, with more than 40% of students completing the course. This research aimed at analyzing characteristics and learning behavior of active learners on SWU-MOOC. The sample involved 773 active learners who filled the questionnaires after completing the course. The data was analyzed using percentage. The findings were as follow: (1) most of the active learners' characteristics in gender ranged from 64.94% for female students, Occupation ranged from 63.26% for University students, and age ranged from 60.28% for 18-24 years. (2) Learning behavior in terms of frequency in attending SWU-MOOC took 2-3 times per week (42.82%) and in terms of length of study per time, 30-60 minutes (25.61%) of spent time had the highest percentage.
Imbalanced Kernel Extreme Learning Machines for Fault Detection of Aircraft Engine

Abstract To deal with class imbalance learning (CIL) problems, a novel algorithm is proposed based on kernel extreme learning machine (KELM), named KELM-CIL. To solve it, two algorithms are developed from the dual and primal spaces, respectively, thus yielding D-KELM-CIL and P-KELM-CIL. However, both D-KELM-CIL and P-KELM-CIL are not sparse algorithms. Hence, a sparse strategy based on Cholesky factorization is utilized to realize their sparseness, producing CD-KELM-CIL and CP-KELM-CIL. For large-size problems, a probabilistic trick is applied to accelerate them further, hence obtaining PCD-KELM-CIL and PCP-KELM-CIL. To test the effectiveness and efficacy of the proposed algorithms, experiments on benchmark datasets are carried out. When the proposed algorithms are applied to fault detection of aircraft engine, they show good generalization performance and real-time performance, especially for CP-KELM-CIL and PCP-KELM-CIL, which indicates that they can be developed as candidate techniques for fault detection of aircraft engine.
Cognitive Decision Support System for Avionics Analytics

The combination of information from different sources is increasingly critical for Air Traffic Management (ATM) systems. It is not just about providing decision-making functional support to pilots, controllers, and unmanned aircraft but also dealing with non-functional ATM aspects such as cybersecurity. The use of knowledge representation through an Avionics Analytics Ontology (AAO) is an attractive solution to develop Decision Support Systems (DSSs) for ATM. This paper presents details of an AAO-based DSS prototype demonstrating the feasibility and performance of such a cognitive DSS. The AAO-DSS prototype is built of physics-based sensing, informatics-based processing, and human-derived communications for reporting, decisions, and actions. The prototype considers a case study which highlights benefits by means of simulating realistic airspace collision avoidance situations in downscaled scenarios, aircraft takeoffs and landing with drones flying nearby, and collision-avoidance airspace situations. This paper also shows preliminary performance analysis results (including information uncertainty estimates and cross-concept impacts in the AAO).
Information hiding in motion data of virtual characters

Abstract As an import branch of information security, image/video-based information hiding system has been investigated extensively in the past few decades. Meanwhile, 3D applications are increasingly popular in various areas such as animation, virtual, and augmented reality. This work explores the feasibility of data hiding in 3D applications and proposes a novel method to encrypt a message in the motion data of 3D virtual character. For the human visual system, it is challenging to notice the subtle details in dynamic scenarios at a fast frame rate. Therefore, the message encryption in 3D character movements offers an intuitive solution to information hiding. Our work can be divided into two stages. The first stage optimally selects a group of joints which introduces minimal modification on the global presentation of the character motion to maintain the maximum visual consistency between the embedding motion data and the original motion data. The second stage is to encrypt the secret data based on the Sudoku magic matrix and hide the data in the optimal selection of joints. The experimental results show that our system has a high hiding capacity and excellent visual quality.