Reviewerly/exHarmony huggingface dataset:
Research on Information Visualization Design for Public Health Security Emergencies

Information visualization design is closely related to statistics, computer science, visual image design and other scientific fields and professions, and is used to provide the public with epidemic prevention and health information during the epidemic. Relevant data and information are provided by health, industry and information technology, transportation, customs, immigration management, civil aviation, railway, etc. The purpose is to analyze various information visualization cases for public health emergencies and how to convey information to the public. We analyze how to deal with the three user elements of user participation subject, user cognitive psychology and user interaction behavior in the excellent information visualization design cases. Combined with the above analysis, the development of the epidemic situation can be presented in a rich and intuitive way, so that the public can understand the transmission route of the epidemic and the number of cases, and the public will have less panic about it. At the same time, the correlation between data should be mined to help the audience to establish an effective logical thinking in the face of massive information. Moreover, attention should also be paid to the different ways of information visualization to bring different psychological feelings to the viewers. Through these three approaches, the effect of information visualization in public security emergencies in the era of big data can be improved, and the information of public health emergencies can be more accurately and dynamically controlled, which is conducive to improving the ability of relevant organizations to predict and respond to public health emergencies.
Empirical Analysis of Losses from Business-Email Compromise

We examine approximately nine months of data on losses from business email compromise (BEC) reported to the FBI’s Internet Crime Complaint Center in 2017. We describe the empirically observed loss distribution. We study differences in the amounts attempted stolen when the attacks were successful or not. We show that money stolen and transmitted internationally is less likely to be recovered. We also find, somewhat surprisingly, that illicit transfers to in-state banks are also more likely to succeed. Finally, we study state-level differences among BEC target selection and asset recovery.
One-Class Classification Approach Using Feature-Slide Prediction Subtask for Feature Data

One-class classification is machine learning problem where training data has only one class. Recently, various subtask-based one-class classification methods are proposed for image data. However, these methods are specialized for image. Applying them to feature data is difficult. In this paper, feature-slide prediction subtask is proposed for feature data. These additional feature vectors are annotated as how features are slid. Then, multi-class classifier is trained using self-labeled dataset. Since this classification model is built using data from only one-class, accuracy of feature-slide prediction for seen data is high relative to unseen data. Accordingly, OCC could be made using feature slide prediction subtask. Proposed methods are experimented using imbalanced-learn dataset.
Light Fidelity to Transfer Secure Data Using Advanced Encryption Standard Algorithm

Symmetric (secret) key encryption algorithms are formally used one key for encryption and decryption processes. One of the advantages of symmetric encryption is that it provides security at a much lower cost, both in term of key size and execution time. The algorithm adopted in this paper is the Advanced Encryption Standard (AES). This algorithm is considered official and secure for encryption due to the length of the encryption key. Therefore, the secre key for AES is generated by implementing Elliptic Curve Diffie Hellman (ECDH) based public key exchange protocol, while the communication process between sender and receiver is employed by using a light communication technology called light Fidelity (Li-Fi). Meanwhile, Li-Fi technology supposed to be very secure and fast much more than the Internet used in our daily life.
Development A Portable Solar Energy Measurement System

Abstract This project presents the design and development a portable measurement device for measure and monitor solar panel parameters by using Internet of Things (IoT) concept. Solar energy measurement plays a very important role in the measurement of parameter reading for the determination of output generated, but the challenge is only performed manually at the work site using a clamp meter or a multimeter. Furthermore, it was very difficult to get the value at that time, and the data recovery error occurred. There are three specific objectives have been used for the project. Firstly, the relevant circuits for this project are design and built the circuit by using software. The output of the measurement solar irradiance, ambient temperature, solar panel temperature, current and voltage value were displayed on LCD. Next, IoT concept is used for solar panel measurement and monitoring. The value of the measurement and monitoring is used ThingSpeak cloud and ThingView application on the smartphone. It can be collected the portable solar for the energy measurement system can monitor on site, anywhere and anytime using IoT platform.
An Approach to Automatic Evaluation of Instructional Videos

Instructional video quality evaluation is a heavy task due to the rapid growth of available instructional videos and limited experts. In order to solve this problem, this paper presents an approach to automatically evaluate instructional videos. Instructional video evaluation is different from general video evaluation in that the important characteristics of an instructional video is supporting the understanding of course content. Therefore, a metric of instructional video assessment is established. It includes five indicators, i.e. screen effect, subtitle, duration, format and quality. Image recognition and image processing technologies are used to score the indicators. Automatic evaluation model is constructed by machine learning. Our experimental results show that the automatic evaluation is basically consistent with the results of manual evaluation. Therefore, our approach significantly reduces the burden of instructional video evaluation and helps to improve video-based learning.
The Practice of Implementing Distance Learning During the Pandemic: a Regional Aspect

The article analyzes the experience of rapid implementation of distance learning in the Lipetsk region during the pandemic. The article considers the practice of distance learning in secondary school, higher school and additional professional education. The role of all stakeholders of the educational process in the distance learning mode in the period of isolation is updated. A comparative analysis of the regional experience, in the context of the all-Russian experience, demonstrated the general difficulties and problems in the implementation of distance learning during the pandemic.
Multifaceted Visual Analysis of Oceanographic Simulation Ensemble Data

The analysis of multirun oceanographic simulation data imposes various challenges ranging from visualizing multifield spatio-temporal data over properly identifying and depicting vortices to visually representing uncertainties. We present an integrated interactive visual analysis tool that enables us to overcome these challenges by employing multiple coordinated views of different facets of the data at different levels of aggregation.
Scaling up chatbots for corporate service delivery systems

A future-state architectural strategy designed to support chatbot integration with service delivery systems.
Coupled adversarial variational autoencoder

Generating image is a hot research topic in the field of deep learning, and it is challenging for generating high quality image pairs. The image pair refers to the corresponding image tuples with the same high-level features and different low-level features, generating high-quality image pairs has important applications in some specific fields. Currently, there are many methods to generate high quality images, but these methods cannot produce higher resolution image pairs. To address this problem, we proposed a novel model which consists of two adversarial variational autoencoders, each one aim at generating an image of pairs more accurately. We called this model CoAdVAE (coupled adversarial variational autoencoders), it can generate high quality image pairs due to introducing adversarial learning to the model. In the experiments, we applied the proposed model to three learning tasks, i.e., generating image pairs with different attributes, converting image attributes, and image dehazing. We show by experiments compared with related approaches on four datasets, Mnist, Celeba, AFHQ, and Fog_data that the proposed model can achieve the-state-of-the-art results.
Application of the CDIO standards for cyber-physical education in mechatronics and robotics in a research university on the example of development of digital electronic skills

One of the main problems of modern technical education is the lack of a specific standard for quality control of specialists’ knowledge. Cyber-physical education requires the introduction of new standards into educational programs. At National Research Nuclear University MEPhI, we have extensive experience in the implementation and use of CDIO standards in cyber-physical education to improve the qualification of trained specialists. This article contains the results of the introduction of CDIO standards into the training program for specialists in the field of mechatronics and robotics at National Research Nuclear University MEPhI on the example of the development of digital electronic skills.
Self-Adaption AAE-GAN for Aluminum Electrolytic Cell Anomaly Detection

Nowadays, the anomaly detection of aluminum electrolysis cell is a big problem in the aluminum electrolysis industry. The problem of unbalanced time series samples is common in industrial applications. The number of samples under normal conditions is much larger than that under abnormal conditions. In the electrolytic aluminum industry, this problem is even more serious, it is very difficult to find abnormal samples in industrial production because experts do not have a clear criterion to judge abnormalities. In traditional machine learning algorithms, such as support vector machine (SVM) and convolutional neural network (CNN), it is difficult to obtain high classification accuracy on the problem of class imbalance, and these methods tend to be more biased towards positive samples. In recent years, generative adversarial network (GAN) has become more and more popular in the field of anomaly detection. However, these methods need to find the best mapping from the actual space to the latent space in the anomaly detection stage, and the optimization process may bring new errors and take a long time. In this article, we use the ability of GAN to model complex high-dimensional image distribution, and propose a self-adaption AAE-GAN network based on adaptive changes of input samples. This time series anomaly detection method converts multi-dimensional time series data into a two-dimensional matrix, and only normal samples are needed in the training process, which effectively solves the above problems. The method we proposed is to use encoder and decoder to constitute a generator and a discriminator. During the training process, the generator and the discriminator are trained jointly and confrontationally, so that the mapping ability of the encoder can be fully reflected. In the anomaly detection stage, we determine whether the sample is abnormal according to the size of the reconstruction difference. Experimental results show that the detection accuracy and speed of this method are very high.
Deep image compression with multi-stage representation

While deep learning-based image compression methods have shown impressive coding performance, most existing methods are still in the mire of two limitations: (1) unpredictable compression efficiency gain when adopting convolutional neural networks with different depths, and (2) lack of an accurate model to estimate the entropy during the training process. To address these two problems, in this paper, a deep multi-stage representation based image compression (MSRIC) method is proposed. Owing to this architecture, the detail information of shallow stages and the compact information of deep stages can be utilized for image reconstruction. Furthermore, a data-dependent channel-wised factorized probability model (DCFPM) is adopted to increase the accuracy of entropy estimation. Experimental results indicate that the proposed method guarantees better perceptual performance at a wide range of bit-rates. Also, ablation studies are carried out to validate the above mentioned technologies.
Neutrosophic Logic-Based Document Summarization

Nowadays, rich quantity of information is offered on the Net which makes it hard for the clients to detect necessary information. Programmed techniques are desirable to effectively filter and search useful data from the Net. The purpose of purported text summarization is to get satisfied content handling with information variety. The main factor of document summarization is to extract benefit feature. In this paper, we extract word feature in three group called important words. Also, we extract sentence feature depending on the extracted words. With increasing knowledge on the Internet, it turns out to be an extremely time-consuming, exhausting, and boring mission to read the whole content and papers and get the relevant information on precise topics
Qsun: an open-source platform towards practical quantum machine learning applications

Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms have been proposed and tested on quantum virtual machines to surpass the limitations of quantum hardware. Our goal is to exploit further the variational quantum algorithms towards practical applications of quantum machine learning using state-of-the-art quantum computers. This paper first introduces our quantum virtual machine named Qsun, whose operation is underlined by quantum state wave-functions. The platform provides native tools supporting variational quantum algorithms. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of quantum machine learning: quantum linear regression and quantum neural network.
Building healthy Lagrangian theories with machine learning

The existence or not of pathologies in the context of Lagrangian theory is studied with the aid of Machine Learning algorithms. Using an example in the framework of classical mechanics, we make a proof of concept, that the construction of new physical theories using machine learning is possible. Specifically, we utilize a fully-connected, feed-forward neural network architecture, aiming to discriminate between ``healthy'' and ``non-healthy'' Lagrangians, without explicitly extracting the relevant equations of motion. The network, after training, is used as a fitness function in the concept of a genetic algorithm and new healthy Lagrangians are constructed. These new Lagrangians are different from the Lagrangians contained in the initial data set. Hence, searching for Lagrangians possessing a number of pre-defined properties is significantly simplified within our approach. The framework employed in this work can be used to explore more complex physical theories, such as generalizations of General Relativity in gravitational physics, or constructions in solid state physics, in which the standard procedure can be laborious.
Fuzzy logic controller based ship navigation system

Abstract This research work deals with more proficient and at ease to design the fuzzy logic controller (FLC) meant for course-changing. A new system in tuning a rule base in fuzzy controller system. Agreeing to the modification status of system error, the proposed real-time tuning algorithm is used in tuning the subsequent portion of fuzzy values, later the rule base is controlled. This proposed work also links the more performance of FLC at several working situations with conventional PID controller. In our proposed method we joined the PID with Fuzzy logic to build the strong and adaptive controller. The Simulation results are valuable to demonstrate this method.
The floating point: Tales of the unexpected

Digital computation is central to almost all scientific endeavors and has become integral to university physics education. Students collect experimental data using digital devices, process data using spreadsheets and graphical software, and develop scientific programming skills for modeling, simulation, and computational work. Issues associated with the floating-point representation of numbers are rarely explored. In this article, problems of floating point are divided into three categories: significant-figure limits, propagation of floating-point representation error, and rounding. For each category, examples are presented of unexpected ways, in which the digital representation of floating-point numbers can impact the veracity of scientific results. These examples cover aspects of classical dynamics, numerical integration, cellular automata, statistical analysis, and digital timing. Suggestions are made for curriculum enhancement and project-style investigations that reinforce the issues covered at a level suitable for physics undergraduate students.
Fault Detection of Train Height Valve Based on Nanodet-Resnet101

The train height valve can adjust the charging and deflating of the air spring according to the trainload. The normal operation of the height valve ensures the smooth operation of the train and prevents the track from being damaged due to wheel impact. Therefore, the maintenance of the height valve is very important. Traditional manual maintenance consumes a lot of manpower and material resources, at the same time, efficiency and accuracy are difficult to be guaranteed. In this paper, a fault detection algorithm based on Nanodet-Resnet101 is proposed. Through experiment, the network has a 97 percent accuracy rate in simultaneously detecting an image in 0.1 seconds. The experimental results show that the height valve fault detection network combined with NanoDet and Resnet101 has engineering significance
Self-Augmented Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) is quite challenging due to the large discrepancy introduced by cross-domain face images. The limited number of paired face images results in a severe overfitting problem in existing methods. To tackle this issue, we proposes a novel self-augmentation method named Mixed Adversarial Examples and Logits Replay (MAELR). Concretely, we first generate adversarial examples, and mix them with clean examples in an interpolating way for data augmentation. Simultaneously, we extend the definition of the adversarial examples according to cross-domain problems. Benefiting from this extension, we can reduce domain discrepancy to extract domain-invariant features. We further propose a diversity preserving loss via logits replay, which effectively uses the discriminative features obtained on the large-scale VIS dataset. In this way, we improve the feature diversity that can not be obtained from mixed adversarial examples methods. Extensive experiments demonstrate that our method alleviates the over-fitting problem, thus significantly improving the recognition performance of HFR.
A discrete chicken swarm optimization for traveling salesman problem

Abstract Traveling Salesman Problem (TSP) is a typical combinatorial optimization problem, and it is a NP-hard problem. The total number of routes increases exponentially with the number of cities, so it is great significance to design an effective algorithm to find the optimal solution accurately. Chicken Swarm Optimization (CSO) is a new intelligent optimization algorithm, which is mainly proposed for continuous problems. It has the advantages of fast convergence speed and high convergence accuracy. This paper proposed a Discrete Chicken Swarm Optimization (DCSO) for TSP. The CSO is discretized by introducing the methods of swap, order crossover and reverse order mutation, where the search space of the solution is enlarged, and the diversity of the solution is increased. The typical TSP models are simulated and compared with the Basic Ant Colony Optimization and Genetic Algorithm to verify the feasibility of the presented method.
Soft computing for abuse detection using <scp>cyber‐physical</scp> and social big data in cognitive smart cities

Abstract The internet of things (IoT) and the smart city crusade has seen an exponential increase in the number of data points we collect. Cities can use this data for an exceedingly heterogeneous range of purposes such as traffic, parking and public safety. Quite evidently, public safety is an essential pillar of these interconnected, instrumented and intelligent cities. The big data from cyber‐physical systems and social media platforms can be used for predictive policing to identify potential criminal activities, abuse, offenders, and victims of abuse. This study offers a systematic literature review on the use of soft computing techniques for abuse detection in the complex cyber–physical–social big data systems in cognitive smart cities. The objective is to define and identify the diverse concept of abuse and systematize techniques for automatic abuse detection for cyber abuse detection on social media and real‐time abuse detection using IoT. The cyber abuse studies on social media platforms have further been categorized as cyber‐hate and cyberbullying whereas the real‐time abuse includes studies using Internet of cyber‐physical systems. As in a cognitive smart city, citizens expect more from their urban environments with minimal intervention, this study helps to establish the need to capture situational context and awareness in real‐time and foster the need to develop a proactive as well as reactive safety mechanism to mitigate the risks of online abuse. The need of the hour is to entrench self‐learning, thinking and understanding capabilities into the physical and social world for reinforcing intelligent mechanisms which can detect assaults and disorderly conduct.
Smart Speaker Interaction Through ARM-COMS for Health Monitoring Platform

The authors have proposed an idea of augmented tele-presence system called ARM-COMS (ARm-supported eMbodied COmmunication Monitor System), which detects the orientation of a subject face by the face-detection tool based on an image processing technique, and mimics the head motion of a remote person to behave as if an avatar does during video communication. In addition to that, ARM-COMS makes appropriate reactions by audio signals during talk when a remote person speaks even without any significant motion in video communication. Based on the basic idea of ARM-COMS, this study focuses on AI speaker technology that answers questions in a natural language, and works on the development of an integrated system with health monitoring with ARM-COMS. This paper describes the prototype system developed in this study, shows some of the basic functions implemented in the prototype, and discusses how the combination of AI technology with ARM-COMS could work based on the experimental findings.