Reviewerly/exHarmony huggingface dataset:
The Flare of Xenophobia in America during Covid-19 Pandemic

Covid-19 pandemic contribute in creating xenophobic attitudes among American people. Recently, there are many reports about Asian-American people or Chinese people in America facing racisms and xenophobic attitudes; moreover there are also reports that Asian-American and Chinese people attacked by Americans. The aim of this study is to know how bad xenophobia in the middle of pandemic in America by analysing the data and what kinds of xenophobic attitudes do Asian-American and Chinese people frequently received during Covid-19 pandemic by society in America. This study uses descriptive research method to know the phenomena of xenophobic attitudes and its circumstance that happen at the present. We collect the data from reports on the various articles or news regarding to Asian-American and Chinese people experiencing racisms and xenophobic attitudes. The results are show that Asian-American and Chinese people received verbal and non-verbal attacks by American people, that is Hate-speech and physical Attacks. Xenophobic attitudes through verbal and non-verbal attacks, such as hate-speech and physical attacks have been surfaced by Asian-American people.
Blockchain periodic storage space recovery method based on blockchain consensus algorithm

Abstract Decentralized encryption digital cash has been proved to be a successful application mode, and the users of the application can also be the maintainers of this application. To solve the problem of fast growth of blockchain data on Ethereum platform, this paper proposes a method to recycle the periodic storage space of blockchain deployed on blockchain based on blockchain consensus algorithm. On the principle of decentralization, the problem of mutual trust between nodes is solved. And that stability value of the update storage node is recorded, and the high-stability node is selecte to store the newly generated data copy, so that the stability of data storage is improved. Finally, some bytecode fragments are replaced by a suitable algorithm. Tests show that the massive small file system supporting file deletion has no obvious decline in file reading and writing performance compared with the original one, and the storage space of smart contracts can be saved by nearly 46%.
Mediated Interactions via WhatsApp: a Social Space for Teacher Development in Brazil

Drawing on the seven “new learning” affordances in Cope and Kalantzis (E-learning ecologies: Principles for new learning and assessment, Routledge, pp. 1–45, 2017), an agenda for integrating digital technologies in the classroom, this chapter presents teacher education initiatives via WhatsApp, part of Taba Móvel, a Brazilian project which seeks to discuss the use of digital mobile technologies in teaching and learning. One of the benefits for integrating WhatsApp into any classroom is using a technology-enabled collaborative tool to increase learning opportunities in many different curricular areas.
Les spécificités des émoticônes chinoises

The use and creation of emoticons in China are linked not only to the latest events but also to traditional Chinese culture. Emoticons as a whole (emoticons, smileys and emojis) are part of the communication dynamic brought by the evolution of digital practices, allowing, very quickly, the expression of emotions and the subjectivity of the speaker. As a matter of economy, Chinese Internet users employ either typographic emoticons largely inspired by manga and 2D animations or very polysemic iconographic emoticons to express various emotions with a great humor and irony.
Mobile Virtual Reality (VR) for Science Projects: Ohm's Law Laboratory

The COVID-19 pandemic has undoubtedly brought devastation to the education system igniting innovation led by the Fourth Industrial Revolution. This paper proposes a mobile Virtual Reality (VR) application for teaching physics education to high school learners in a virtual laboratory. It was developed using Unity3D game engine, Visual Studio Community, Photoshop and 3D Studio Max. It was deployed on Android-based smartphones. The user comfort levels and VR content were tested. The results showed that about 50% of learners felt uncomfortable when using VR equipment. However, none reported any form of unease such as dizziness while being immersed in the virtual environment. Secondly, about 45% of learners mentioned that VR content was adequately presented. Furthermore, 65% of the users clearly understood the voice commands, and 35% needed to hear the commands repeatedly for them to be able to operate the application. The application offers an interactive and immersive 3D environment to learn the fundamentals of Ohm's Law. The application has the potential to address learning barriers experienced by learners who lack laboratory equipment but are expected to conduct scientific experiments. A learner with a smartphone and a head-mounted display will be able to make use of the proposed application in the comfort of their own home.
Residual neural network based joint transmit-receive antenna selection in MIMO system

In large-scale MIMO system, as the number of antennas increases, the huge computational complexity makes traditional antenna selection algorithms impossible to effectively apply. This paper propose a joint transmitreceive antenna selection model based on ResNet. We utilize the optimal antenna selection algorithm to create labels for all channel matrices, which based on maximizing channel capacity criterion. Then using large-scale channel data to train a powerful residual neural network classifier. Consequently the trained model can classify the corresponding label for each channel matrix in the test set and select the optimal antenna subset. Experimental results show that the method can effectively decrease the number of antenna selection and its communication performance outperforms compared methods
The system of accounting for the results of intellectual activity of scientists as the development of the information system "Proceedings of Scientists"

The article describes a WEB-system developed by the authors that implements services related to the formation and provision of multifaceted information about the results of scientific activities (publications, copyright certificates and reports at scientific events) of employees of an organization or a group of organizations. The system is focused both on the end user interested in obtaining specific data, and on the administrative staff, who generates reporting materials for the parent organization. The information base of the system contains metadata on the following classes of objects: persons (authors), organizations and their subdivisions; publications at analytical, monographic and summary levels; copyright certificates; scientific events (conferences, symposia, seminars); reports. The system includes two modules - an administrative one (intended for entering and editing data) and a user one, which is a special search engine that searches for information, visualizes it, provides navigation among related resources and exports data. A distinctive feature of the system is the introduced concept of "equivalent" objects. Objects are considered equivalent if they are represented in the system by different metadata, but referring to the same physical entity. Such objects are "persons" corresponding to one author with different spellings of the surname in the bibliographic descriptions of publications; organizations with different variants of names; articles published unchanged in various languages. In accordance with modern requirements for reporting on publications, the system reflects the sources of research funding, as well as the affiliations indicated in the articles for each author.
Privacy-Preserving Mechanisms for Heterogeneous Data Types

Due to the pervasiveness of Interconnected devices, large amounts of heterogeneous data types are being continuously collected. Regardless of the benefits that come from sharing data, exposing sensitive and private information arises serious privacy concerns. To prevent unwanted disclosures and, hence, to protect users' privacy, several privacy-preserving mechanisms have been proposed. However, the data heterogeneity and the inherent correlations among the different data types have been disregarded when developing such mechanisms. Our goal is to develop privacy-preserving mechanisms that are suitable for data heterogeneity and data correlation. These aspects will also be considered to develop mechanisms to achieve private learning.
Adversarial Trends in Mobile Communication Systems: From Attack Patterns to Potential Defenses Strategies

Understanding attack patterns and attacker behavior has always been a prominent security research topic to provide insights into adversarial trends and defense strategies. In this paper, we demonstrate the process of analyzing adversarial trends in mobile communication systems using a conceptual threat modeling framework combined with graph analysis methodologies. We model 60 attacks using the Bhadra framework [] and conduct graph-theory-based analysis to deduce insights. We observed the attack patterns, the diversity of attack paths given an attacker’s ability or target impact, and the importance of each technique from a network graph viewpoint and discussed potential defense strategies that mobile operators can deploy accordingly. Our main contribution is demonstrating the potential of Bhadra for analyzing the security posture of an operator’s network and simplifying the complexity of the mobile networks to communicate the security analysis results.
Emotion Recognition using Deep Learning Techniques and Word Embeddings

Emotion recognition is a topic of vital importance in the world of Big Data. The accurate classification, analysis and interpretation of emotional content is highly desired in a wide spectrum of applications. In this work we examine the performance of Deep Learning models for an emotion recognition task. These models can also be applied to any classification task as well as text-related tasks in general. We examine the performance of deep learning methods in textual emotion recognition under different feature selection parameters such as one-hot-enconding, custom word2vec and more. An experimental study was designed and various deep learning methods were evaluated in publicly available datasets. The results are quite interesting and indicate the advantages of each method and each examined training approach.
Implementation of the Incremental Model in Building a Web-Based Information System for Wealth Reporting of State Civil Apparatus

The implementation of the submission of the State Civil Apparatus Wealth Report (LHKASN) is an obligation for all State Civil Apparatus (ASN) based on Ministry of State Apparatus Empowerment and Bureaucratic Reforms policy. Currently, the implementation of LHKASN in one of the state financial institutions still uses paper media as an LHKASN form. so that it can cause a human error in calculating assets. Besides, the LHKASN verification process is still carried out by typing the name of the ASN as an obligation to report one by one on the receipt which requires a lot of time so that the process is not efficient. Another problem is that no application can help monitor the discipline of ASN in delivering LHKASN, as well as data management related to the implementation of LHKASN which still uses the file system which is vulnerable to redundancies and data inconsistencies. Therefore, we need a system that can solve the existing problems. The method to be used in this research is the Incremental Model as a system development method with the Unified Modeling Language as a tool in designing the system. Meanwhile, for system testing, the method to be used is black-box testing by combining Boundary Value Analysis and Decision Table Testing. The result of this research is a web-based LHKASN application that can assist the reporting, verification, disciplinary monitoring, and data management processes in the implementation of LHKASN.
Research on Simulation System of Rubber Particle Mat Based on Web Technology

Abstract The current design of rubber granular floor mats is limited to a single fixed plane sample drawing, and it consumes a lot of time, manpower and material resources in obtaining customer design requirements. In response to this situation, the use of Web development technology to realize the free combination of particles in proportion, generate simulation application scenarios, provide interface operations, and finally complete the order process. By constructing a particle probability distribution model, establishing a particle position coordinate matrix, and developing a particle mat simulation system, based on the sample quality of the sampled data, it is compared with the real artificial mechanical product. Finally, by mixing and matching 9 different color values according to the industrial production ratio, randomly combining 10 groups and comparing with the real products of the same color ratio, quantifying the color difference, contrast, and particle position offset to obtain the production simulation floor mat sample and the actual product. The finished product is quite close. The system generates simulated particle mat maps and real sample maps with a high degree of simulation, and provides an interface that allows users to directly match the particle combination program that suits their needs, saving labor cycles and improving work efficiency.
Benchmarking NewSQL Cloud-Native or Cloud-Hosted Databases

Evaluating the performance of high-impact components when building a solution is the process that provides an informed and fitting choice. Choosing a database system and the proper infrastructural configuration underneath it is however a process that can prove to be extremely complex. In the context of the highly volatile NewSQL database systems and Cloud hosting, choosing the fitting combination is not only difficult but can be daunting. This paper provides an overview of the implications of creating reliable benchmarking for NewSQL Cloud hosted databases. It includes the steps involved in accurate performance evaluations, existing tools, proposes a systematic approach for narrowing the number of combinations to test for and lists the standard benchmarks as well as alternatives. With this work we aim to introduce clarity and structure for the database benchmarking process, based on existing research and the current gaps therein.
A Model of Chinese Named Entity Recognition in the Field of Oil and Gas Exploration Using Domain Dictionary

In view of the scarcity of labeled data in the task of Chinese named entity recognition in the field of oil and gas exploration, the large number of entity types and the difficulty of pre-defining, and the problem of entity combination and nesting in many domain entities, this paper proposes a model of Chinese named entity recognition in the field of oil and gas exploration using domain dictionary (OGDNER) which uses the domain dictionary to match domain documents, and designs a boundary correction algorithm to reduce noises generated in the dictionary matching process. In addition, the “Break-Tie” labeling mode is proposed as well as the input of the neural network is optimized to improve the effect of model. Finally, the effectiveness of the model is verified by comparing it with other models on the data set of oil and gas exploration.
A Lane Level Bi-Directional Hybrid Path Planning Method Based on High Definition Map

A global reference path generated by a path search algorithm based on a road-level driving map cannot be directly used to complete the efficient autonomous path-following motion of autonomous vehicles due to the large computational load and insufficient path accuracy. To solve this problem, this paper proposes a lane-level bidirectional hybrid path planning method based on a high-definition map (HD map), which effectively completes the high-precision reference path planning task. First, the global driving environment information is extracted from the HD map, and the lane-level driving map is constructed. Real value mapping from the road network map to the driving cost is realized based on the road network information, road markings, and driving behavior data. Then, a hybrid path search method is carried out for the search space in a bidirectional search mode, where the stopping conditions of the search method are determined by the relaxation region in the two search processes. As the search process continues, the dimension of the relaxation region is updated to dynamically adjust the search scope to maintain the desired search efficiency and search effect. After the completion of the bidirectional search, the search results are evaluated and optimized to obtain the reference path with the optimal traffic cost. Finally, in an HD map based on a real scene, the path search performance of the proposed algorithm is compared with that of the simple bidirectional Dijkstra algorithm and the bidirectional BFS search algorithm. The results show that the proposed path search algorithm not only has a good optimization effect, but also has a high path search efficiency.
Feature mining method of information system operation and maintenance big data based on window function

In this paper, a window function based feature mining method for the operation and maintenance of big data in information system is proposed. The time clustering feature vector is combined with window function to reduce the dimension of operation and maintenance data of high-dimensional information system. The operation and maintenance data feature subset is segmented according to the similar feature level, and the redundant features of operation and maintenance data are removed to complete the information system operation and maintenance big data feature mining. The simulation results show that the proposed method has better clustering effect, fewer iterations and shorter mining time.
Acceptance: A Forgotten Dimension of Usability in Ambient Intelligent Environments

Ambient Intelligence plays a vital role in developing assistive technologies like smart homes, aiding disabled and elderly living, and emergency assistance and learning systems. As an evolving applied science criterion, Ambient Intelligence is taken to the next level by considering behavioural and humane aspects into day-to-day applications such as traffic, education and smart homes etc. Although recent progress has indicated significant breakthroughs in computational and cognitive directions, the Human-Computer-Interaction dimension is not effectively applied. Interaction principles such as accessibility, usability and learnability from the user acceptance aspect are not significantly explored. The embodiment of these principles can lead to effective usability by a potential user. Through this paper, the author would like to identify the significance of usability towards user acceptance for qualifiable ambience in intelligent environments. This paper presents the perspective-based reflection of the potential and younger user group validated against the correlation of user's educational backdrop and experience towards technological acceptance. Also, a novel philosophical/policy level phenomenon to determine the principles through which usability in such environments can be made acceptable is discussed.
ReflectU: A Mirror-Based Intelligent Interactive System for Intuitive Remote Control

Large interactive displays are widely used in industrial scenarios to enhance ubiquitous and seamless human–machine interactions. However, few studies have paid attention to design implicit interaction that users can directly manipulate physical circumstance without touch or specific gesture. This article proposes ReflectU, a novel reflection-based approach that leverages mirror reflection for a natural and implicit interactive method for remote control, i.e., user will be able to directly interact with physical circumstance just via the reflection of their bare hands. We compare its performance with that of other two generally known devices: Wii Remoter and Microsoft Kinect. Moreover, performance metrics of ReflectU are evaluated in real-life scenarios and provide evidence in convincing performance in both the tasks requiring instant targeting and trajectory control. Furthermore, ReflectU is reported by users to be the most intuitive and satisfactory approach among all three candidates in user studies. Future industrial applications of the reflection-base mirror approach are discussed.
Cloud Storage Service Architecture Providing the Eventually Consistent Totally Ordered Commit History of Distributed Key-Value Stores for Data Consistency Verification

Cloud storage services are one of the most popular cloud computing service types these days. Various cloud storage services such as Amazon S3, DropBox, Google Drive, and Microsoft OneDrive currently support billions of users. Nevertheless, data consistency of the underlying distributed key-value store of cloud storage services remains a serious concern, making potential customers of cloud services hesitate to migrate their data to the cloud. Researchers have explored how to allow clients to verify the behavior of untrusted cloud storage services with respect to consistency models. However, previous proposals are limited because they rely on a strongly consistent history server to provide a totally ordered history for clients. This work presents Relief, a novel cloud storage service exposing an eventually consistent totally ordered commit history of the underlying distributed key-value store to enable client-side data consistency verification for various consistency models. By empirically evaluating our system, we demonstrate that Relief is an efficient solution to overcome the limitation of previous approaches.
Chapter 5. A multilingual net of lexical resources

In this chapter, we explore how to develop and encode the relationship between wordnets for different languages using some Nordic and Baltic wordnets to illustrate the variety of approaches. We also briefly touch on how these wordnets have been enhanced or augmented with various types of lexical information, such as framenet frames as well as syntagmatic and sentiment information.