Reviewerly/exHarmony huggingface dataset:
Effectiveness and Limitation of Blockchain in Distributed Optimization: Applications to Energy Management Systems

A blockchain, which is well known as one of the distributed ledgers, has attracted in many research fields. In this paper, we discuss the effectiveness and limitation of a blockchain in distributed optimization. In distributed optimization, the original problem is decomposed, and the local problems are solved by multiple agents. In this paper, ADMM (Alternating Direction Method of Multipliers) is utilized as one of the powerful methods in distributed optimization. In ADMM, an aggregator is basically required for collecting the computation result in each agent. Using blockchains, the function of an aggregator can be contained in a distributed ledger, and an aggregator may not be required. As a result, tampering from attackers can be prevented. As an application, we consider energy management systems (EMSs). By numerical experiments, the effectiveness and limitation of blockchain-based distributed optimization are clarified.
A fatigue driving detection algorithm based on weighted D-S evidence theory

Fatigue driving detection is of great significance to public traffic safety. Traditional fatigue driving detection algorithm pursues the minimum classification error rate and ignores the loss caused by different misclassification. However, fatigue driving detection algorithm should pay more attention to the high cost misclassification samples to minimize the overall loss of the learning model. In order to solve this problem, the cost-sensitive learning and Dempster-Shafer evidence theory (D-S evidence theory) are combined to obtain a robust and good performance ensemble classifier.
Text Analysis of Community Governance Case based on Entity and Relation Extraction

In community governance, the development and application of information technology have led to a sharp increase in unstructured text reported through digital platforms. Traditional text information processing methods cannot effectively parse a large number of texts with different expressions. Based on the problems encountered in the community governance case text, this article proposes a targeted solution. The analysis of the community governance text is conducive to digging the deep-seated problems of community governance and assisting management with similar community structures. In this paper, we propose a novel entity and relation extraction method based on the BERT-BiLSTM-CRF model and OpenHowNet, establishing a set of entities related to community governance and a community governance entity relations diagram, which can effectively improve the efficiency and accuracy of information extraction.
A Metaheuristic Scheme Based on the Hunting Model of Yellow Saddle Goatfish

Animals have demonstrated clever hunting strategies when they collaborate with each other. These techniques involve swarm intelligence and have been applied to develop bio-inspired algorithms, a research field for solving optimization problems. In the last years, several methods arise from inspiration in nature to model collective animal behavior.
Recurrently-Trained Super-Resolution

We are motivated by the observation that for problems where inputs and outputs are in the same form such as in image enhancement, deep neural networks can be reinforced by retraining the network using a new target set to the output for the original target. As an example, we introduce a new learning strategy for super-resolution by recurrently training the same simple network. Unlike the existing self-trained SR, which involves a single stage of learning with multiple runs at test time, our method trains the same SR network multiple times with increasingly better targets requiring only a single inference at test time. At each stage of the proposed learning scheme, a new target for training is obtained by applying the most recently trained SR network to the original image and downscaling the resultant SR image to normalize the size. Even if downscaling is involved, we argue that the downscaled SR image acts as a better target compared to the old target. We could mathematically demonstrate that this process is similar to unsharp masking when it is linearly approximated and that this process makes the image sharper. However, unlike unsharp masking, the proposed recurrent learning tends to converge to a specific target. By retraining the existing network aiming at a more enhanced target, the proposed method can achieve a similar effect of applying SR multiple times without increasing implementation cost and inference time. To objectively verify the supremacy of our approach by experiments, we propose to use VIQET MOS, which does not require a reference image as a measure of image quality. As far as we know, our work of using an objective quality measure in image enhancement is the first one whose validity was verified by showing similar results to the actual user's subjective evaluation. The proposed recurrent learning scheme makes existing SR algorithms more useful by clearly improving the effect of SR. Codes are available at https://github.com/rtsr82/rtsr.git.
Development of Learning Media for Android-Based Budget Accounting

Abstract The purpose of this paper is to develop learning media for Android-based Budget Accounting; and testing the feasibility of learning media developed based on validation or assessment from media experts and students. Media feasibility validation was conducted by the learning technology lecturer and was tested on 50 students at the Accounting Study Program of one of the campuses in North Sumatera. In broad outline, the results of the validation of the media from the material and media aspects were classified as Very Feasible (VF) that is 89.06% and 89.88%, as well as the results of the validation of student trials from aspects of validity of concept, material preparation and implementation potential are Very Feasible (VF), that is 85.12%, 87.90% and 81.55%. The learning media products that are produced are files in the android package (apk) format.
Post-quantum Digital Signature Scheme for Personal Data Security in Communication Network Systems

The reliable collection, processing and storage of the sensitive personal data constitute an important research topic in the European context, especially after the General Data Protection Regulation started to produce its legal effects. The data encryption has been the traditional way of ensuring the sensitive data is legitimately accessed. Nevertheless, traditional encryption systems have become vulnerable to quantum computer-based attacks. This fostered the research efforts that look for encryption schemes that are immune to quantum computers-based attacks. This paper describes one of the few digital signature schemes, which is essentially immune to quantum computers-based attacks. The optimized version of the scheme is offered in the article. This scheme is much more efficient than the original one and has the stronger security guarantees. Furthermore, it may run efficiently on traditional computer systems. The suitability of the proposed digital signature scheme is assessed using real-world data, which demonstrates that it can be used to secure large amounts of personal sensitive data.
Benchmarking pre‐trained Encoders for real‐time Semantic Road Scene Segmentation

Abstract Semantic segmentation, i.e. assigning each pixel in an image a class to which it belongs, can be a part of the implementation for the perception model of autonomous vehicles. Over the last years multiple powerful neural network architectures for solving this task have been developed. In this work, a simple lightweight but modular, fully convolutional encoder‐decoder network has been implemented and multiple, publicly available pre‐trained classification networks are evaluated on their encoder performance on the Cityscapes [8] benchmark dataset with respect to their accuracy and their inference speed.
Approximate Private Quantum Channels on Fermionic Gaussian Systems

The private quantum channel (PQC) maps any quantum state to the maximally mixed state for the discrete as well as the bosonic Gaussian quantum systems, and it has fundamental meaning on the quantum cryptographic tasks and the quantum channel capacity problems.In this paper, we primally introduce a notion of approximate private quantum channel (ε-PQC) on fermionic Gaussian systems (i.e., ε-FPQC), and construct its explicit form of the fermionic (Gaussian) private quantum channel.First of all, we suggest a general structure for ε-FPQC on the fermionic Gaussian systems with respect to the Schatten p-norm class, and then we give an explicit proof of the statement in the trace norm case.In addition, we study that the cardinality of a set of fermionic unitary operators agrees on the ε-FPQC condition in the trace norm case.This result may give birth to intuition on the construction of emerging fermionic Gaussian quantum communication or computing systems.
Construction and Evaluation of QOL Specialized Dictionary SqolDic Using Vocabulary Meaning and QOL Scale

Agents that build interactive relationships with people can provide appropriate support and generate behaviors by accurately grasping the state of the person. This study focuses on the quality of life (QOL), which can be assessed multidimensionally, and aims to estimate QOL scores in the process of human interaction. Although vision-based estimation has been the main method for QOL estimation, we proposed a new text-based estimation method. We created a QOL-specific dictionary called SqolDic, which is based on large-scale Japanese textual data. To evaluate the effectiveness of SqolDic, we implemented a system that outputs the time-series variation of a user’s conversation content and the QOL scores based on it. In an experiment for estimating the content of user conversations based on a QOL scale by inputting data from actual human conversations, we achieved a maximum estimation accuracy of 91.2%. Additionally, in an experiment to estimate QOL score variability, we successfully estimated the mental health state and one of the QOL scales with a smaller distribution of error than that in previous studies. The experimental results demonstrated the effectiveness of our system in estimating conversation content and QOL scores as well as the effectiveness of our newly proposed QOL dictionary.
Automated Completion of Partial Configurations as a Diagnosis Task Using FastDiag to Improve Performance

The completion of partial configurations might represent an expensive computational task. Existing solutions, such as those which use modern constraint satisfaction solvers, perform a complete search, making them unsuitable on large-scale configurations. In this work, we propose an approach to define the completion of a partial configuration like a diagnosis task to solve it by applying the FastDiag algorithm, an efficient solution for preferred minimal diagnosis (updates) in the analyzed partial configuration. We evaluate our proposed method in the completion of partial configurations of random medium and large-size features models and the completion of partial configurations of a feature model of an adapted version of the Ubuntu Xenial OS. Our experimental analysis shows remarkable improvements in our solution regarding the use of classical CSP-based approaches for the same tasks.
Health Monitoring with Artificial Intelligence

Health is very important, but many people do not care about health. In developing countries, public awareness of the importance of health is minimal. This is because some people cannot easily consult a doctor about their health problems due to limited time, money, and much more. Time and money can be an obstacle for someone when they want to run routine health checks, especially for someone who has a busy schedule. We have developed an artificial intelligence-based application that can be used by people to accurately check their health from the symptoms provided by the user. People also usually did not have enough time to have a medical check-up in the hospital. What if there is a place where we can find about health problem just by entering symptoms or just scanning an ECG or you can check whether the prescribed medicine is supposed to be used the way you are told to. Such a problem can be solved by using medical chatbot by giving proper guidance regarding healthy living. This application is also designed to be easy to use anywhere and anytime with the hope that public awareness about health will get better.
QNet: A Quick Deep Neural Network for Real-Time Semantic Segmentation

Real-time semantic segmentation plays an important role in intelligent industrial scenarios like autonomous driving, and the core challenge is to consider both performance and efficiency. In this paper, we present a quick network called QNet to address this problem. The overall design of the proposed network is an asymmetric encoder-decoder architecture. The encoder is based on a new Bottleneck architecture that combines channel split and channel shuffle, which reduces network parameters while improving the level of extracting features. An improved spatial pyramid multi-feature fusion module is adopted to the decoder, which can enlarge the receptive field and make up for the loss of spatial information, with only a small computational complexity. Experiments on Camvid and Cityscapes datasets show that without using any pre-trained model, the proposed QNet achieves a state-of-the-art trade-off between performance and efficiency, better than other methods. Specifically, in a single GTX 1080Ti GPU, it can run at over 38 FPS on the Camvid test dataset with 51.8% mean IoU, and 37 FPS with 75.28% category IoU on the Cityscapes test dataset.
Innovative tools for developing soft skills in the context of education transformation

The purpose of the article is to substantiate the use of modern educational technologies, means and tools of influence to achieve the desired result in any field of education, providing specialists with competitiveness and personnel leadership in the digital era. To analyze the current situation in the education system and develop proposals for their improvement, this article uses the results of many years of research obtained within the framework of the author’s scientific and pedagogical school on the development of the communicative potential of a future specialist and its implementation in future practical professional activities. Particular attention is paid to the study of the effectiveness of interactive technologies aimed on the development of students’ cross-cutting competencies and communication skills, as well as modern teaching trends, such as networking and edutainment, the value of which is allowing specialists to better navigate in a rapidly changing world, in various new directions, modern studying courses and technologies and understand the flows of new information. The authors of the article studied in what directions, with the use of what educational technologies it is necessary to develop skills that will be in demand in the next decades.
Calculating the Personalized Treatment Strategy by Genetic Algorithm Using Optimal Complexity Models

The task of calculating a personalized patient treatment strategy for optimization of his output condition is not new. It is possible to formalize the problem in such a way that a machine can find the optimal solution in a matter of seconds by using optimization methods. However, this problem has not been solved by the combination of such mechanisms as genetic algorithm (GA), analytic hierarchy process (AHP) and group method of data handling (GMDH) yet. Their collective use makes it possible to find a variety of individual treatment method combinations, providing a solution for the applied problem of finding a treatment for patients with congenital heart defects. The paper describes the steps of the single algorithm formation for calculating optimal treatment strategies and presents its testing results.
Performance Analysis of Separately Excited DC Motor with Wavelet Neural Network based Controller

The direct current motors are used in various applications such as defence, industries, robotics, which are the major power consumers. The optimal control of input parameters can decrease the power consumption and enhance the performance, reliability and life span of these machines. Although the separately excited DC motors provides constant speed output which is stimulated to the variations and unpredictability in inputs such as presences of nonlinearities such as saturation, friction and noise propagation along the series of unit processes degrade its performance. Thus optimal controlling of the parameters through the motor control unit is required to achieve smooth operation. This article is applied to the Wavelet Neural Network (WNN) based controller which uses the wavelet transforms as the activation function. This helps in continuous training and optimization of the controller parameters with minimum time duration. The different controllers such as proportional-integral, NN based proportional-integral-derivative, WNN based proportional-integral-derivative with filter, WNN based proportional-integral-proportional-derivative (WNN-PIPD) are compared in terms of rise time, maximum overshoot, steady state error etc. The inherent approximation capability and learning abilities of the proposed WNN-PIPD controller improve the degree of tolerance and adaptability concerning other controllers.
Evaluation Grade Prediction Method with Limited Information from Experts

Abstract With the development of artificial intelligence, using machine learning methods to build evaluation models has attracted more and more attention. However, training anevaluation model often needs a lot of labeling samples annotated by experts. It is very difficult to get enough labeling data with a limited group of experts. This paper proposes a method to learn the evaluation model with limited information from experts. This method has two stages. In the first stage, we build a large training set with ordinary people by comparing every two samples. After that, we train a Siamese Network with the paired comparison data set to get a score for each sample. In the second stage, we map the scores to evaluation grades with the help of experts. In the experiments, we use the UCI wine quality data set to evaluate our method. Experimental results demonstrate that we get a basically equivalent accuracy (0.5% decrease) with only1.45% samples labeled byexperts.
Innovation and Coordination a SWOT Analysis of Artificial Intelligence Industry in Guangdong Province

This paper discusses the current development of artificial intelligence industry in Guangdong province, and uses SWOT analysis to explore the advantages, disadvantages, opportunities and threats of the artificial intelligence industry in the Guangdong province. Based on the analysis, this paper gives some suggestions for the policy makers: firstly, it is necessary to speed up breakthroughs in core technologies and key components; secondly, Guangdong should gather high-end intelligence resources of AI industry; finally, it is recommended to increase policy support and build an ecosystem with deep integration of AI and real economy.
Sentiment Classification of Financial Microblogs through Automatic Text Summarization

With the rapid development of social media, sentiment classification of financial microblogs has been a research hotpot. Online financial microblogs contain a wealth of information on market and usually express emotions or opinions. However, microblogs contain so much noise that it is difficult to make sentiment classification correctly. In this paper, we propose a two-stage model to improve the performance of sentiment classification through automatic text summarization techniques. The automatic text summarization model built on encoder-decoder model as well as attention mechanism can compress the original text and obtain the informative words, which can filter the noise in microblogs and capture significant semantics. Then we build the sentiment classifier based on hierarchical attention network. To make full use of sentiment semantics, we fuse every original blog and its generated summary to train our classifier. We establish two datasets based on Sina Weibo, one is for training the summarization model, another is for sentiment classification. Experimental results indicate that our model outperforms the other baselines on real datasets.
Problems of information and psychological analysis confrontations in the logic of metamodern psychology

The analysis of the phenomenon of «information-psychological confrontation» as a direction of scientific research and practical activities of subjects in the realities of the metamodern of the XXI century is presented. The transformation of the world community, the development of technologies, AI, databases, biotechnologies (BEGEN), their complex application for destructive purposes have exposed new threats. The aim of the work was the theoretical substantiation of the phenomenon of «information-psychological confrontation » in the logic of the methodology of the psychology of the metamodern, and the refinement of the conceptual elements of the probabilistic model of the selection of technologies. The research methodology and methodology includes a system-activity approach and principles: virtuality, interactivity, digitality and oscillation. Associated activities within the confrontation have become a single genetic basis for its deployment. The general scientific method of cognition, included a theoretical and methodological analysis of sources, generalization, analysis, comparison. Systematization provided the flow of empirical and theoretical data using the methods of discourse and comparative analysis. In order to interpret paradigmatic texts, the techniques of hermeneutics, methods of metamodern psychology and political psychology were extracted. As a result of the analysis of the Anglo-Saxon model of confrontation, the essence is revealed, the methodology is refined, the author's edition of the definition is introduced. Analysis of promising technologies, problems of hybridization of confrontation, stages of their development, have determined the range of issues of psychological science to counter threats and challenges. The promising technologies of confrontation and the conceptual elements of the probabilistic model of their selection have been substantiated. It is based on the principle of oscillation with its mechanism for eliminating rigid structures and introducing conceptual diversity through flexible tools. In conditions of aggravated geopolitical rivalry, an adequate analysis and reaction of power structures is in demand. The development of theoretical foundations and bringing the confrontation system to a new level becomes relevant and is a novelty. In conclusion, the direction of further scientific research of various aspects of information and psychological confrontation was determined.
Role‐based attention in deep reinforcement learning for games

Abstract Reinforcement learning method that learns while interacting with the environment, relies heavily on the concept of state as the input to the policy and value function. In the task, the view of agent contains a lot of information, and it is difficult for the agent to learn to ignore the irrelevant information and focus on the key information. Inspired by recent work in attention models for computer vision, we present a role‐based attention model for reinforcement learning. The proposed model uses convolutional neural networks to generate soft attention maps, adding crucial role information in the task, forcing the agent to focus on important features and distinguish task‐related information. To validate the performance in complex problems, the proposed approach is evaluated in a challenging scenario, Football Academy in Google Research Football Environment , a newly released reinforcement learning environment with physics‐based three‐dimensional simulator. The experimental results demonstrate that agents using role‐based attention mechanism can perform better in football games.