Reviewerly/exHarmony huggingface dataset:
Multi-agent approach to foster regular physical activity in elderly users

The increase of life expectancy in last decades brought with it an increase in the number of technologies developed to assist this growing population segment. Some of the developed technologies focus on assisting older adults age in a more active way, remaining active and independent for longer. In accordance with the concept of active ageing we present a multi-agent approach, intended to foster healthy exercise habits among the elderly. This paper presents a multi-agent exercise system that positively affects elders' motivation to engage and maintain exercise habits. It details the theoretical foundations for our approach as well as the implementation details of each agent. A pilot study was conducted on 12 elderly users, testing three variants of the system, each involving a different combination of agents, and evaluating their impact on the participants. Results show that all three variants of the system were able to guide the participants through a diverse exercise routine, with the most complete system being the one most highly evaluated by the users.
Physical Layer Authentication Schemes Against Clone Node and Sybil Attacks in Wireless Industrial Internet

Security is becoming an increasingly important factor for any of today's wireless communications systems. For mission-critical wireless industrial networks, a new and increased level of significance is urgently required. Various cyber-attacks, such as a clone attack and sybil attack, seriously threaten industrial network security. Therefore, we propose a strategy utilizing identity and channel state information to detect clone attack and sybil attacks. Firstly, we collect identity to make a preliminary assessment between the two types of attacks. Secondly, the physical layer authentication approach is adopted to undertake attack detection, utilizing the channel state information differences between the legitimate and malicious nodes. We verify the effectiveness of the proposed approach through simulation and attack experiments.
Creative development of a modern preschooler

Raising a child as a creative person is very popular among parents nowadays. The article presents the experience of teachers in developing the creative potential of the personality of a child of preschool age. The authors have developed a methodology that allows, during art classes, to accurately diagnose the psychological problems of children and help cope with them. As a result of such work, there is a steady positive trend in the development of psycho-emotional stability of children regularly engaged in art activities.
Research on distributed data stream mining algorithms based on matrix weighted association rules

In order to overcome the low efficiency of traditional data mining algorithms without considering weighted association rules, this paper proposes a distributed data flow mining algorithm based on matrix weighted association rules. According to the way of separating metadata and data flow, garbage d ata processing in data flow is realized. By using sliding window and data summary structure to optimize PCA algorithm, the main component decision matrix is formed in the window, and the dimension of data in sliding window is reduced by using the decision matrix. The matrix weighted association rules are used to mine the distributed data. After dimensionality reduction, the transactions in the database are clustered according to the time distribution. The weighted analysis is carried out for each aggregation to obtain the weighted frequent item set with time and output the mining results. The experimental results show that the proposed algorithm has high efficiency and the highest accuracy of 98.9%.
Research on Human Action Recognition Algorithm Based on Sine Feature

Abstract Recent studies have shown that exploring features of the skeleton data is vital for human action recognition. Nevertheless, how to effectively extract discriminative features is still a challenging work. In this paper, we propose a novel method that extracts a sine feature for human action recognition from skeleton data. Kinect is used to extract human skeleton information (3D coordinates of each joint point) firstly, then two joint points are connected to define a skeleton vector according to the principle of human body structure, and the sine feature of each skeleton vector is calculated as a new pose description feature, SVM is used to classify the obtained features for human action classification. Experimental results on Cornell Activity Database are provided, and the results demonstrate the effectiveness of our approach.
Chemical Reaction Optimization for Mobile Robot Path Planning

The goal of mobile robot path planning (MRPP) is to generate a collision-free path while considering certain objective functions, such as path length and smoothness. Nowadays, the application areas of mobile robots have widened significantly. So, it is essential to develop algorithms that will allow them to maneuver effectively in complex environments with obstacles. In this paper, we have proposed a new methodology based on a metaheuristic approach named chemical reaction optimization (CRO) to solve this NP-hard problem. We have reconstructed the four fundamental operators and designed two new repair operators to obtain better performance. The results of our proposed algorithm are compared with ant colony optimization algorithm (ACO), particle swarm optimization (PSO) algorithm, and genetic algorithm (GA) to show its efficiency in terms of solution quality and computational time.
Aspects of Human-Centered Design Application in Control Information Systems

Principles, approaches and methodology of Human-Centered Design, set out in the ISO 9241-210 standard, give recommendations to developers according to the planning and implementation of information systems with interactive components. Analysis of the proposed rules and actions revealed the missing aspects, which, according to the authors, are essential for minimizing the influence of the human factor in information management systems.
A Case Study on Correctness Evaluation of Content Based Recommender System Based on Text, Semantic Text and Visual Similarity

Recommender systems provide suggestions to appropriate recipients based on suggestions and inputs given by the clients or users. The system guides the user in a tailored way to objects of interest in a larger space of possible options. In this paper, with the help of a case study the analysis of the results are obtained for different algorithms used for recommender systems—text based, semantic text based, visual similarity based product similarity. Correctness measure is performed using mean reciprocal rank metric. The paper concludes that no single algorithm is suitable for all recommender systems. Based on the type of user preferences, ensemble and hybrid algorithms are the future for recommendation.
Research and Application of Police UAVs Target Tracking Based on Improved Camshift Algorithm

We proposed an improved Camshift algorithm for target tracking of police UAVs. To improve the traditional Camshift algorithm is vulnerable similar backgrounds, we propose an improved algorithm which combining LBP texture features with H and S extracted in HSV; By combining Kalman with Camshift, a prediction mechanism is introduced to improve the problem of easily influenced by object sheltering.
An Analysis of EWOM Text that Contribute to EWOM Helpfulness

Electronic markets are increasingly becoming first choice for shopping by customers. The electronic word of mouth (EWOM) plays a significant role in their online buying behavior and loyalty formations. It supports in reducing uncertainty and risk confronted by customers in online shopping. The EWOM helpfulness has emerged as a popular area of research both for business and academic communities in recent times. The role of comprehensibility and credibility of EWOM text and their impact on EWOM helpfulness has not been investigated much in existing research studies. In this paper, we build a EWOM helpfulness predictive model through machine learning (ML) methods and examine the influence of comprehensibility and credibility features implanted in the textual content of EWOM on the EWOM helpfulness. We have used text analytics and ML techniques to extract comprehensibility and credibility features from the EWOM text. In addition, linguistic and sentimental features of EWOM text have also been used for EWOM helpfulness prediction. A number of popular ML methods have been applied to predict the helpfulness of EWOM dataset extracted from Amazon.com . This study finds that comprehensibility and sentimental information of review text play an important role to outline EWOM helpfulness. Moreover, the number of right spell, wrong spell, adjectives, nouns, and adverbs embedded in EWOM text are also effective predictor of EWOM helpfulness.
The Research on General Case-Based Reasoning Method Based on TF-IDF

With the continuous expansion of the application field of Case-Based Reasoning (CBR) technology, it is increasingly difficult for programmers to acquire and express professional knowledge. Therefore, the demand for general case retrieval model based on Case-Based Reasoning is rising. This paper first gives a structured expression of professional knowledge, and combines the Case-Based Reasoning method with the scientific measurement of keyword weight (Term Frequency-Inverse Document Frequency, TF-IDF) to design the case organization, case retrieval and case retaining in CBR technology. It provides an effective method for general case retrieval model.
Where to Eat? An Automatic Summarization System for Opinions in Restaurant Reviews

Technology has been an essential part of our lives. When going out to a new restaurant or cafe, people usually use websites or applications to lookup nearby places and then select one based on average rating. However, the average rating is sometimes not enough to predict the quality of the restaurant as people have different perspectives and priorities when evaluating a restaurant. In this paper, a summarization system for opinions in restaurant reviews is proposed. The system is a helpful tool for users on-the-go to help them make better judgments about the quality of a restaurant while saving their time. This is done by automatically and quickly providing the users with a summary of the opinions in the restaurant's reviews. The proposed summarization system has been implemented in a mobile location-based application for Arabic restaurant reviews and it achieved a high usability score.
Dynamic Scene Deblurring Based on Semantic Information Supplement

Abstract To solve the problem of semantic information dilution in network propagation, a semantic information supplement mechanism (SIS) is proposed to improve the performance of dynamic scene deblurring algorithm. Based on GANs structure, our generator is to recycle the semantic information and features spanning across multiple receptive scales to restore a sharp image, when a blur image is given. What’s more, in order to better integrate semantic information with the latent-feature and solve the problem of training difficulty in very-deep network, we put forward a long and short skip-connection method. Extensive experiments show that our Semantic Information Supplement network (SIS-net) achieves both qualitative and quantitative improvements against state-of-the-art methods.
Confidence limits for conformance proportions in normal mixture models

Conformance proportions are important numerical indices for quality assessments. When the population is characterized by a normal mixture model, estimating conformance proportions can be a practical issue. To account for the inherent structure of normal mixture models, universal and individual conformance proportions are first defined for the purpose of evaluating the overall population and specific subpopulations of interest, respectively. On the basis of generalized fiducial quantities, a systematic method is then proposed in this paper to obtain confidence limits for the two classes of conformance proportions. The simulation results demonstrate that the proposed method can maintain the empirical coverage rate sufficiently close to the nominal level. In addition, two examples are given to illustrate the proposed method.
Attention-based BiLSTM Model for Answer Extraction in Question Answering System

Answer extraction is the core technology of question answering (QA) system. Most of the previous works were based on syntactic features and knowledge base, which highly required the language knowledge. Accordingly, we proposed an attention-based bidirectional long short-term memory (BiLSTM) neural network model for answer extraction to automatically extract and analyze semantic features. The model remembers information in both forward and backward sequences to extract more complete semantic features. The attention mechanism assigns appropriate attention weights according to the semantic similarity of question-answer pairs, which enables the network to focus on the significant information and raises the probability of selecting the correct answer. We also study the prevailing semantic similarity calculation methods and choose the most suitable cosine similarity as the calculation criterion. Experimental results demonstrate that the proposed model outperforms the compared models.
Cryptocurrency as the currency of the future: a case study among Algebra University College students

The cryptocurrencies were designed to be a medium of exchange. The blockchain technology on which cryptocurrencies are based on offers many possibilities for computer science and all future businesses. For the past decade experts as well as laypeople have been experiencing cryptocurrencies in extremes. They either have a very positive attitude or a very negative attitude towards them. Experts who have very positive attitudes towards them believe that cryptocurrencies create new ways of conducting business and new ways of trust relationships are managed. Experts who have very negative attitudes towards them often emphasize the fact that they are often linked to negative connotations such as being a tool for criminal activities or skipping social responsibilities such as tax avoidance and corruption. They also emphasize the fact that it is a new, unexplored technology and an unstable market. The purpose of research conducted among students of Algebra University College was to explore their opinions and ways of using blockchain technologies in the present and their attitudes toward using cryptocurrencies in the future.
Last Mile Healthcare Through Micro-entrepreneurs

Rapid unplanned urbanization and unhealthy lifestyle habits are contributing to a significant increase in non-communicable diseases (NCD). As per WHO [1], almost three quarters of NCD deaths occur in low and middle-income countries. This problem is compounded due to lack of awareness and accessibility to affordable healthcare. In such countries, with physical infrastructure lagging and access to primary healthcare itself a challenge, a shift from treating illness to promoting and maintaining wellness through health enablers like outreach healthcare providers is emerging as a viable alternative. While the community health worker (CHW) model has shown to influence better health outcomes in rural areas, generating adequate financial incentives for CHWs to sustain the program is a challenge. In this paper, we explore the architecture of a digital system that can address the demand for quality primary healthcare by creating a new channel of technology-enabled healthcare micro-entrepreneurs who will act as the last mile connect between the hospitals and the citizens. We present the pilot results conducted with 45 health navigators at Channapatna, India.
Why Predicative Sets?

The predicativist program for the foundations of mathematics, initiated by Poincaré and first developed by Weyl, seeks to establish certainty in mathematics without revolutionizing it. The program was later extensively pursued by Feferman, who developed proofs systems for predicative mathematics, and showed that a very large part of classical analysis can be developed within them. Both Weyl and Feferman worked within type-theoretic frameworks. In contrast, set theory is almost universally accepted now as the foundational theory in which the whole of mathematics can and should be developed. We explain how to reconstruct Weyl’s ideas and system within the set-theoretical framework, and indicate the advantages that this approach to predicativity and to set theory has from both the foundational as well as the computational points of views.
Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis

Grgić, Demijan Karaula, Mislav Babac, Marina Bagić Podobnik, VedranIn recent years, multi-agent systems have been augmented with alternative social network data like Twitter for preforming target inference. To this extent, public figures tracked by agent system usually have key social value in social networks. This social value can additionally drift in public approval based on social network communication. To test the connection in public approval and social communication, we perform analysis of presidential Twitter account activity during the two-year period 2017–2018. Sentiment analysis was used on the processed tweets in order to test such a data set's predictability in gaining an insight into a 7, 14 and 21 days (1, 2 and 3 weeks) significant presidential job approval rating change. To this extent, five different supervised machine learning algorithms are used: Random Forest, Xgboost, AdaBoost, AdaBag and ExtraTrees. Results indicate that voter approval rating has slight future predictability based on Twitter activity and emotional sentiment analysis possibly indicating consistency with the human nature of positive news and outcomes resonating with people for a much shorter period than negative ones.
Distribution of heavy metals in anthropogenic soils: on the example of the industrial zone of Moscow

The article presents the results of the assessment of the distribution of heavy metals content in anthropogenic transformed soils of the industrial zone of Moscow. Excesses of norms are shown, geochemical associations are obtained. The work proved to exceed the maximum permissible concentrations of cadmium, lead, copper, nickel, zinc, arsenic for eleven test sites per 13.95 hectares of industrial zone in 57% of the obtained empirical data on the content of pollutants in the soil. In the investigated area, the contents of lead, copper, nickel and zinc are not uniformly distributed and are within wide range. No excess of the norm was recorded for mercury. The isolation of two geochemical associations of manmade genesis is justified. One association includes cadmium lead, copper, and zinc with the system-forming element copper. The other consists of nickel and mercury.
Automatic eNodeB state management in LTE networks using Semi-Supervised Learning with Adversarial Autoencoder

It is crucial to identify the cause immediately when a failure occurs at the base station called eNodeB in LTE networks. However, a huge amount of log data generated from the eNodeB prevents the human operator to quickly identify the cause of failure. In order to improve the network operation efficiency, machine learning technique is used to analyze Key Performance Indicator (KPI) data generated from eNodeB and classify the operational status of the eNodeB. However an issue classification with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate raw performance metric data. To address this issue, we propose a method that employs Adversarial Autoencoder (AAE), which is a semi-supervised learning method. We evaluate the proposed method using eNodeB log data collected from a service provider LTE network. We confirm that our approach achieves on average 94% accuracy and yields high accuracy even for the class with a small amount of labeled data.
Cryptanalysis and improvement of a blind multi-document signcryption scheme

Blind signcryption is used to maintain the anonymity and privacy of the sender from other participants in an unsecured public network. It has vast applications for privacy related mechanisms such as electronic voting and electronic auction systems. Various blind signcryption schemes were introduced in the last two decades, depending upon the computational cost and the level of security. Some of these schemes were proven to be insecure and need further improvement. We analyzed a recently proposed blind signcryption scheme for multiple digital documents based on a hyperelliptic curve. Our cryptanalysis shows that the proposed blind signcryption scheme is not secure against the existing attacks. An adversary, with the knowledge of public parameters, can modify the signcrypted text of his choice. The successful cryptanalysis shows that the proposed scheme is unable to provide the security attributes of authentication and message integrity. To overcome the security flaws, we propose a modified version of this scheme.
Cloud-based Cellular Network Planning System: Proof-of-Concept Implementation for GSM in AWS

Frequency planning for radio network has always been a time consuming, low reward task. Although, it is a must for a quality network. This paper presents a new pattern to plan radio frequency in global system for mobile communications, using cloud-services to automatically configure, optimize and heal a network, with the advantage of this services making both the implementation and its execution faster, without the need to acquire specific hardware. Our implementation analyses the current state of the network and uses interference as a metric to plan and heal networks. We compare our pattern to a realistic scenario and an already implemented pattern.
Computer assisted instruction (CAI)-based learning: instructional need for pupils’ conceptual understanding of water cycle?

Abstract This study is aimed to identify the Computer Assisted Instruction (CAI) based on learning as an instructional need for students’ conceptual understanding about water cycle. Qualitative descriptive method was used in this study. The data collection used mini interviews and test. Participants were three students average 11-13 years old from different schools. The multimedia-based CAI application used by the researcher to understand pupils’ conceptual understanding of the water cycle is implemented on laptops or desktop computers used Windows operating system. The result showed that pupils’ conceptual understanding of the water cycle are still lack and have many misconceptions can be seen from the results of students’ test when drew water cycle. The sustainable development will be achieved if the instructional needs through improvement of CAI are fulfilled.
Detection Algorithm of Untwisted or Broken Strands of Transmission Line Based on FRAC

The detection of untwisted or broken strands of the transmission line plays an important role in ensuring the longterm and steady operation of the transmission system. This paper proposes an enhanced Faster R-CNN with Attention Channel (FRAC) and a sliding crop algorithm and use them in an well-designed algorithm flow suitable for the detection of the strands defects. Firstly, the image is merged with an attention map produced by attention network and become a 4-channel picture. Then the sliding crop algorithm cuts it into subimages before sending it into our proposed FRAC. The experiment results demonstrate that this algorithm flow has a significant improvement over the original Faster R-CNN network.