Reviewerly/exHarmony huggingface dataset:
Online Evaluation Method of College Students’ Mental Health Based on Campus Network Text Mining

Aiming at the problem that the mental health of contemporary college students is affected by many parties, and the evaluation accuracy and evaluation effect are poor, an online evaluation method of college students’ mental health based on campus network text mining is proposed. Mining and analyzing the association rules of the association features between texts, analyzing the monitoring quantitative index of the online evaluation of college students’ mental health; based on the regional transfer coefficient of existing mental health text information, the influencing factors of college students’ mental health are determined, and the online evaluation model of college students’ mental health is constructed. The membership degree of the factors is comprehensively calculated, and the corresponding fuzzy consistent matrix is used to compare the quantitative results of the index scales, and the interval number judgment matrix is constructed, which corresponds to the interval number weight of each secondary evaluation index one-to-one. The weight of the indicators is divided into the mental health assessment level, and the online assessment of college students’ mental health is completed. The experimental results show that the evaluation accuracy of the design method is more than 97.36%, the recall rate is more than 97%, and the highest packet loss rate is less than 0.3%, which proves that the online evaluation results of college students in this method are more accurate and that the accuracy and effect of the online evaluation of college students’ mental health are improved.
A Secure Real-Time IoT Data Stream Based on Improved Compound Coupled Map Lattices

A secure data stream is important for the real time communications of Internet of Things (IoT). A stream cipher with the characteristics of simple and high speed is suitable in the secure communications of IoT for its security. Some stream ciphers based on coupled map lattices (CML) were proposed. However, the original coupled map lattice shows evidence of correlation between the contiguous points. In this paper, we present an improved CML with a counter to overcome the weakness. The proposed scheme has the advantage of simplicity and suits the resource constrained IoT environment. We implement the proposed improved CML and analyze the proposed ciphers against some attacks. For the balance performance analysis, the numbers of 0 and 1 in the keystream are almost balanced, and the difference rates show that the proposed schemes have high key sensitivity. Finally, we present some experimental results of statistical random number tests for the output keystreams. Under the pass rates of the statistical test of NIST SP800-22, the proposed ciphers with improved CML, and compound CML are at least 95% and 97%, respectively. All the pass rates of the proposed stream ciphers are 100% for the statistical test of FIPS PUB 140-1.
WUAD (Wheelchair User Assisted Design): A VR-Based Strategy to Make Buildings More Accessible

Accessibility regulations towards building design and public places are more restrictive than in past years. Along with energy efficiency, accessibility is one of the pillars of an optimal building design. However, even buildings adapted for actual regulations are not always optimal, and there is still much to do to avoid adding more difficulties for people with disabilities. We proposed a wheelchair-user-assisted design methodology based on iterative bidirectional communication between a wheelchair simulator user and the building designer to reach the optimum specifications for an accessibility-friendly design. First-hand experience may be crucial to detect obstacles hardly noticeable to people with no impairments. To this end, VR technology was employed along with the mentioned wheelchair simulator to show the advantages of this kind of device when working on real-life experiences.
Can Remittance Inflow Fosters Economic Growth? What Matters? Evidence from Sub-Sahara African Countries

This study investigates the impact of remittance inflow on the economic growth of sub-Saharan African countries by considering the role of financial sector development, institutional quality, and economic freedom. The study includes 26 sub-Sahara African countries over the period 2010–2019. By employing the two-step system GMM, the finding shows remittance alone hurts economic growth. When remittance interacts with financial sector development, institutional quality, and economic growth, the coefficient of the interaction term is positive. The study concludes that a well-developed financial sector, better institutional quality, and economic freedom mitigates the negative impact of remittance inflow on economic growth.
Visual Gorgias: A Mechanism for the Visualization of an Argumentation Dialogue

The area of eXplainable Artificial Intelligence (XAI) has been given great attention during the last decade, due to the obscurity that exists on how a data-driven model makes a decision. One idea in order to make Artificial Intelligence (AI) systems more explainable is to use cognitive methods that humans use when reasoning. For instance, argumentation is a common method that humans use when they explain an opinion [13]. In this paper, we introduce a framework that produces the visualization of an explanation from an argumentation dialogue, and can be easily understood by any user. More specifically, we utilize the argumentation tree that Gorgias returns [7] for the acceptability of an argument, to create a graph that represents the explanation of the argumentation tree.
Performance Evaluation of Masked Face Recognition Using Deep Learning for Covid-19 Standard of Procedure (SOP) Compliance Monitoring

Wearing a face mask can reduce the risk of Covid-19 transmission. As a reason, creating an effective masked face recognition model is critical for the development of an autonomous face mask wearing monitoring system. Manual way of face mask wearing monitoring is a tedious task especially in the crowd and large public areas. Furthermore, masked face recognition is complex due to variety of face mask wearing image appearances such as occlusions, calibrations, scene complexity and the types of face mask used. This paper provides the performance evaluation of the Deep Convolutional Neural Network (CNN) model and machine learning classifiers for masked face recognition. Specifically, DENSENET201, NASNETLARGE, INCEPTIONRESNETV2 and EFFICIENTNET (EFFNET) as a feature extractor. Then, the extracted features are classified by using Support Vector Machine (SVM), Linear Support Vector Machine (LSVM), Decision Tree (DT), K-nearest neighbour (KNN) and Convolutional Neural Network (CNN). The recognition model is evaluated on the face mask detection dataset. The experiment results have shown that DENSENET201-SVM and EFFNET-LSVM obtained the best classification accuracy of 0.9972. However, EFFNET-LSVM has the advantage of better computational time of feature extraction, classification as well as the size of features.
Research on dynamic path planning algorithm of spacecraft cluster based on cooperative particle swarm algorithm

In order to solve the problem of path planning for the spacecraft cluster to reach the dynamic target point under the premise of considering obstacle avoidance. In view of the fixed search radius, it will be difficult for the spacecraft to find a better value when it is close to the target point. This paper converts the orbital dynamics of each member spacecraft into an optimization problem considering constraints, and proposes an improved CPSO algorithm based on coordination. The path planning method of the traditional particle swarm optimization (CPSO): The dynamic radius search method that changes the search radius by changing the distance between them, and improves the CPSO algorithm based on this. The improved CPSO algorithm autonomously finds the optimal path of each member spacecraft at the current moment through the dynamic search radius, thereby obtaining the optimal solution for the dynamic path planning of the spacecraft cluster in three-dimensional space. The simulation results show that the use of the improved CPSO algorithm can not only obtain the optimal solution to the spacecraft cluster dynamic path planning problem, but also greatly reduce the fuel consumption in its path planning and improve the path stability of each member spacecraft.
Detection of Sickle Cell, Megaloblastic Anemia, Thalassemia and Malaria through Convolutional Neural Network

This paper presents an alternative method to diagnose Malaria and Anemia (Sickle Cell Anemia, Megaloblastic Anemia and Thalassemia) as well as to differentiate between them. First, different related high resolution images of blood samples are taken from multiple datasets. Second, Convolutional Neural Networks (CNN) technique is implemented and applied in order to process the images without the need of the standard protocol of Complete Blood Count (CBC) test. The implemented convolutional Neural Network has been designed using Python to train on a number of microscopic images. After completing the training phase, the built model has been tested on other images to classify them into normal blood cells, Malaria, Sickle cell anemia, Megaloblastic anemia or Thalassemia. Third, the diagnosis is made based on the outcomes. Finally, the accuracy of results is assessed. The total accuracy of the test is 93.4&#x0025;. The suggested approach yields promising outcomes that help diagnose blood samples faster, with low cost as well as without the need of an analysis laboratory.
Secure Information Transmission in Intelligent Transportation Systems Using Blockchain Technique

Cyber Physical System (CPS) is considered as an emerging mechanism where physical devices are managed and processed through automated computations. The Smart vehicular technology is considered as one of the emergent applications in CPS where the involvement of malevolent devices leads to reduce fully adoption risk by various organizations. Though researchers have proposed various cryptographic techniques in order to ensure a secure information transmission among vehicles, however, the existing technique may further leads to several storage, transparent, and cost efficient issues. Though researchers have proposed various blockchain schemes, however, there exist some severe issues that need to address. Further, number of miners elected to validate the block or transmitted information may further lead to increase the computation time and complexity in the network. The aim of this chapter is to propose a secure information transmission among vehicular devices by blockchain based technique. The proposed system generates the contracts with the gas used to encrypt files. The proposed phenomenon is validated over various security measures against conventional approach.
Analysis of Barriers to Implement Blockchain in Supply Chain Finance

With the innovation of financial technology and the development of supply chain finance, the supply chain finance system has become increasingly complex and risks from various aspects have started to emerge. And blockchain, as an emerging technology, can optimize the existing system. Therefore, implementing the blockchain technology into traditional supply chain finance has become important issue However, various barriers from grey transaction behaviours may affect the implementation and further affect the improvement of supply chain finance. Although currently studies have provided more suggestions, however, they lack to explore the implementation barriers based on the grey transaction behaviours under supply chain finance. The main purpose of this research is to explore grey transaction behaviours in supply chain finance and how these behaviours hinder the implementation of blockchain technology in supply chain finance. To obtain relevant indicators, this study used the Delphi method to interview senior supply chain finance professionals. This study is an initially exploration, we provide a different research direction.
An Feature Fusion Object Detector for Autonomous Driving in Mining Area

An accurate and reliable object detection is an important building block for the most autonomous driving system. Specially, in some scenarios like autonomous driving in the mining area, the detection of small and hard samples are a great challenges for perception system. In this paper, we propose a multi-scales fusion and attention-based model to improve the performance of the object detection for different scales and camouflaged obstacles in the mining area, like trucks, rocks, person and so on. Based on our proposed model, we have developed a new object detector, called FANet, which achieves much better efficiency than prior arts by using some optimization strategies. When we test the proposed model at our datasets, it can achieve 77.76 AP50 in 19 ms on a Titan X, compared to 68.16 AP50 in 22 ms by YOLOv3. The work has successfully apply in the autonomous driving system in mining area.
An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Abstract Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.
Evaluation of an Avatar Robot with a Physically Immersive Telepresence

Ageing societies and an increase in non-communicable diseases across the world are causing a rise in the number of people restricted to stay at home. There are various technologies to connect people remotely. Telepresence systems as an example aim to enrich the means of communication and allows more intimate contact. A missing aspect in telepresence research is the effect of physical engagement on presence and immersion. In this paper, we develop a telepresence system using a cycling wheelchair. We did our experiment with different participants to complete a prescribed track with varying gear ratios. Our findings show that increasing the physical engagement through interactive parameters such as the gear-ratio, feeling of resistance, or simply through engagement over time improve the user&#x2019;s experience of presence and immersion.
Speech as a Driver of Intergroup Violence: A Literature Review

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A workflow for supporting the evolution requirements of RDF-based semantic warehouses

Semantic data integration aims to exploit heterogeneous pieces of similar or complementary information for enabling integrated browsing and querying services. A quite common approach is the transformation from the original sources with respect to a common graph-based data model and the construction of a global semantic warehouse. The main problem is the periodic refreshment of the warehouse, as the contents from the data sources change. This is a challenging requirement, not only because the transformations that were used for constructing the warehouse can be invalidated, but also because additional information may have been added in the semantic warehouse, which needs to be preserved after every reconstruction. In this paper, we focus on this particular problem using a semantic warehouse that integrates data about stocks and fisheries from various information systems, we detail the requirements related to the evolution of semantic warehouses and propose a workflow for tackling them.
Factors of raising international ratings of Ukrainian higher education institutions in the conditions of digital transformation of society

The article is devoted to the statistical analysis of the tools for assessing the international competitiveness of higher education institutions (HEIs) on the basis of the Webometric ranking of universities in terms of digital transformation.The ratings of ZVO as a modern information tool for education management are studied. A brief review and analysis of research and publications of domestic and foreign scientists on the role of ratings as a mechanism for assessing free economic education as educational and research centers.Using the professional statistical data processing program STATGRAPHICS Centurion XV.I and the principal components method, the specifics of Webometric indicators and their impact on the global ranking of the world's leading universities were analyzed. Based on statistical assessments, the reasons for insufficiently high rating positions of national free economic zones in the Webometrics system have been identified.Proposals have been developed to increase the representation of Ukrainian free economic zones in the Internet space, to raise their international rating according to Webometric criteria and to qualitatively develop the domestic scientific and educational school in the context of world trends.The results of the study can be used by the governing bodies and management of Ukrainian free economic zones in the development and adjustment of development strategies, as well as specialists who develop, implement and evaluate the state policy of Ukraine in the field of higher education.
SURRL: Structural Unsupervised Representations for Robot Learning

Revolutionary advances have occurred in robot learning research, where a resurgence in reinforcement learning (RL) algorithms has fueled breakthroughs in acquiring complicated robotic skills without human intervention. Unfortunately, one limitation that has hampered RL methods is that RL may be quite inefficient, that is, it may cost unrealistic learning time and prohibitively large numbers of trajectories to provide implausible models for achieving multitask learning. In a broader perspective, the realization of robotics control by RL relies heavily on the availability of compact and expressive representations of the state spaces. In this article, we propose to learn vivid general structural representations by utilizing structural prior knowledge of robots. Particularly, a novel framework called structural unsupervised representations for robot learning (SURRL) is presented to enable multitask learning. The task-agnostic sample trajectories are leveraged to learn the structural representations that are constructed by graph autoencoder (GAE) in an unsupervised fashion. When learning a new task, the learned structural representations can be directly adopted for subsequent policy learning without training from scratch. Extensive experiments on continuous robotic environments, including dexterous manipulation tasks, characterize our method’s effectiveness to learn optimal policies for handling multiple tasks learning, demonstrating significant improvements over other competitive methods.
Collaborative and Incremental Learning for Modulation Classification With Heterogeneous Local Dataset in Cognitive IoT

Among 6G high-density connection applications, the cognitive Internet of Things (IoT) is considered to be an effective means to cope with the problem of spectrum resource shortage and reduce energy consumption. The modulation classification with the functions of primary user detection and signal recognition has become the core technology in the cognitive IoT. However, the remarkable surge in the generated data makes it intractable to store and analyze such a large amount of data in a centralized manner. Meanwhile, due to the heterogeneity of IoT devices, the collected data in distributed devices may be non-independent-identical distribution, resulting in heterogeneous local datasets problem. Therefore, we introduce the federated learning (FL) and propose federated incremental learning (FIL) method to address the problem of heterogeneous local datasets in modulation classification. Specifically, we regard the private classes of a particular local device as incremental classes and incremental learning is adopted to learn the classification knowledge of private classes. In order to avoid excessive dispersion between the global model parameters and local model parameters, we utilize knowledge distillation in the local model training stage for imposing similar constrain of parameters on different local models. Simulation results have proven the effectiveness of our proposed method.
A Relational Model for One-Shot Classification of Images and Pen Strokes

We show that a deep learning model with built-in relational inductive bias can bring benefits to sample-efficient learning, without relying on extensive data augmentation. Our study shows that excellent results can be achieved with a model in which the relational inductive bias is applied to images, while building an efficient one-shot classifier on top of raw strokes is more challenging. The proposed one-shot classification model performs relational matching of a pair of inputs in the form of local and pairwise attention. Our approach solves with almost perfect accuracy the one-shot image classification Omniglot challenge when combined with a Hungarian matching algorithm and attains competitive results on the same task on characters represented as rotation-augmented strokes.
Deep view synthesis with compact and adaptive Multiplane Images

Multiplane Images (MPIs) have shown to be excellent scene representations to synthesize new scene views. Indeed, MPIs are able to model challenging occlusions and reflections, and allow to render novel images in real time and with angular consistency. However, their memory footprint constitutes their major limitation. In this work, we propose a learning-based method that computes compact and adaptive MPIs. Our network promotes sparsity in the MPIs to only keep the necessary scene information. Besides, we adapt the depth sampling to the given scene to optimize the available memory and increase the synthesis quality with a restricted number of planes. Moreover, in contrast to recent work, our approach does not need individual training per scene and is able to generalize well to unseen scenarios. An extensive evaluation shows the superiority of our approach with respect to the state of the art on diverse view synthesis datasets.