Reviewerly/exHarmony huggingface dataset:
Review: Big Data Privacy and Security Risk and Solutions

Big Data is very dynamic issues in the current year, enables computing resources as a data to be provided as Information Technology services with high efficiency and effectiveness. The high amount of data in world is growing day by day. Data is growing very rapidly because of use of internet, smart phone and social network. Now size of the data is in Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyze this large amount of data. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the limits. However, the current scenario the growth rate of such large data creates number of challenges, such as the fast growth of data, access speed, diverse data, and security. This paper shows the fundamental concepts of Big Data. Privacy threats and security methods used in Big Data. With the development of various research application and recourses of Internet/Mobile Internet, social networks, Internet of Things, big data has become the very important topic of research across the world, at the same time, big data has security risks and privacy protection during different stages such as collecting, storing, analyzing and utilizing. This paper introduces security measures of big data, then proposes the technology to solve the security threats.
Comparative Study on “Sentence Rewriting for Semantic Parsing” and “Graph-Based Translation Via Graph Segmentation”

Abstract Machine translation is universally discussed by people worldwide nowadays along with the developments of computer science and artificial intelligence, however, sentence parsing and the translation errors between original language to target language are still difficult problems during the translation. This essay illustrates the main points of the two articles both of which concentrate on the methods to enhance the precision rate and accuracy of semantic parsing and translation, and makes a comparison between them. Even though these articles can increase the accuracy of translation, some of the backwards still exist and they are proposed to make the articles improved with their objectivity and precision.
Design and Analysis of Power Grid Marketing Data Atlas

The grid marketing and grid dispatching systems store four grid structures: stations, lines, transformers, and households. However, because the standards and functional focus of each system data are not unified, the data becomes isolated and not unified. As a result, there are errors in the relationship between the grid files themselves and the files in a single system, and there are also errors in the consistency and relationship of the cross-system grid files, which ultimately leads to inaccurate grid topology. Data graph technology is a graph technology developed based on big data graph database technology. It can also use multiple data knowledge extraction technology and voice recognition technology to intuitively and efficiently construct a grid topology model to realize automatic identification of grid topology. In order to realize the standardized management of the grid structure, it provides powerful conditions. Based on the investigation of a large number of graph databases, this article integrates factors such as distribution, query language, consistency, availability, fault tolerance, scalability, storage backend, etc., and fully combines some of the characteristics of the graph data itself. Starting from the data map technology, the automatic identification model of the power grid marketing data map is built, and the power grid marketing data map is applied to the real marketing data of a power supply company. It can also realize the difference analysis of the cross-system grid structure and find the data. And accurately locate the cause of the data problem, which improves the efficiency of data management for abnormal network topology.
Reproducible Analysis and Intelligent Scientific Criteria in Engineering Papers Classification Using Data Science

In this paper, we discuss, any work must be reproducible in order to influence research and contribute to our profession's knowledge. Nevertheless, studies show that 70% of university laboratory work cannot reproduce. Reproducible work with not always available data sets or procedures not clearly specified is uncommon in software engineering and complex specifications engineering. The lack of reproducible research prevents development, which means that researchers must replicate a scratch study. The RE researcher will read conference papers, find empirical articles and then review data that can (if available) be replicated in the empirical report. This paper deals with two aspects of the problem and discusses in RE articles, RE documents and theoretical documents. In learning and development of an automatic classification in RE and empirical document identification, recent conference papers and RE documents have been utilized. They use the ERRC approach for performing supervised lecture classifications using natural language therapy and machine learning. Our software is equated with a fundamental keyword approach. We study the paper collections from the engineering conference sponsored by IEEE on Requirements and the International Software Testing and Evaluation Symposium sponsored by IEEE in order to test our process. In all but a few cases, we examine that the procedure of ERRC worked superior than the reference procedure.
The Insecure Nature of Cyberspace

This chapter first briefs the development of the Internet and the substantial growth of users in the cyberspace. Accompanied with the convenience that the Internet has brought to the general public and shaped users’ daily behaviors, cyberspace has become an attractive virtual place for crime. While the type of cybercrime is diverse, the intuitive image of cyber criminals is usually hackers. “Real” hackers are equipped with computer programming ability, but they often start their cyberattacks with “social engineering.”
Deep Learning and RFID System Physical Anti-Collision

Radio Frequency Identification (RFID) technology has significant traits such as batch reading, non-line-of-sight communication, and teletransmission, which is a non-contact identification technology automatically.
A Causal Framework for Understanding Optimisation Algorithms

Over the last decades, plenty of exact and non-exact methods have been proposed to tackle NP-hard optimisation problems.
Applying group methods to work with students MSTU Baumana.

The main attention of the authors of the article is paid to the study of group methods in work with students of the M.V. N.E. Bauman. The expediency of introducing various forms of group methods is determined, as a result of which, relationships with peers are improved and the ability to independently solve life problems is revealed, as well as the process of adaptation to studying at a university is facilitated, attitudes and social attitudes towards a healthy lifestyle change.
Blockchain Technology: Developers Cultivate Novel Applications for Societal Benefits

Blockchain, a buzz word in the second decade of the current century, is a decentralized technology enabling facilitation of distributed ledger with immutable transactions, updated time-to-time at every node. The beauty is such an absence of centralized authority, transactions, or digital assets that can be shared across a P2P network. This technology was conceived way back in the 90s by a group of researchers. Though the primary aim was for time-stamping documents, after two decades, in 2009, the first blockchain, i.e., bitcoin came into the market and soon became the talk of the time. Other popular cryptocurrencies in blockchain such as Ethereum, Litecoin, Ripple, and Stellar also emerged in the form of intangible currencies working on the very principle of cryptography. These days, Ethereum has become more popular since it is featured prominently for “smart contracts” for decentralized and self-executing agreements. There are so many advantages of blockchain over the traditional system. Blockchain is enriched by its dominating characteristics such as decentralized, distributed, secure and faster, transparent, immutable and insusceptible to tampering. Every node in blockchain can propose new transactions, to validate this transaction consensus mechanism play a crucial role in a structured way, i.e., proof of work and the proof of stake mechanism. Thus in a nutshell blockchain is a cutting-edge technology that proves it is time-saving, cost-saving, and rigid security. It is also advantageous over inefficient, expensive, and vulnerable transactions. It creates resistance against fraud, cybersecurity attacks without involving centralized authority. Moreover, it is secure with a cryptographic shield that contains a hash function, public-key cryptography, and digital signatures, and so on. Though phenomenal growth of blockchain in various applications like cybersecurity, supply chain, online data storage, networking, IoT, insurance, government, multimedia, and real estate is witnessed, few domains remain untouched by developers and tech savvies. The sole aim of this chapter is to focus on such untouched domains, besides showcasing the evolution of this novel platform. This chapter aims to cover the basic essence of blockchain with basic architecture and working principles. The societal applications will be dealt with in-depth. The chapter will present a lucid flow of the blockchain technology details with the potential for further exploration.
Development, Application and Future Development Trend of Computer Software Technology from the Perspective of Double Height

With the rapid development of computer software design technology and development tools, the scale and complexity of application software are getting larger and larger, which brings higher requirements for the safety and reliability of computer application software. This article mainly studies the development and application of computer software technology and its future development trend under the double-high vision. The host computer and target computer in this article are connected through Ethernet. Due to the randomness of the value, in the process of generating the test case set, the boundary value is often not guaranteed, so it must be forced to generate. According to the user’s pre-setting, the boundary value will be generated first when the number of test cases occupied by this value is greater than 2, and the test cases will be randomly obtained and added to it. The data shows that through the optimization of the interface configuration, the overall performance of the system in the experiment is improved to 42.77%. The results show that the software self-recovery strategy adopted in this paper can perceive the current state of the software in time and effectively ensure the normal operation of the software.
The design of the C mobile earth station simulator based on C language

Abstract The communication equipment software of the Global Maritime Distress and Safety System is based on DOS platform, and the equipment operation simulator used for training and assessment is generally developed by VC, VB and FLASH technology. There are a lot of difference between the simulator and the real machine on the interface and the operating response. The operation of the simulator is done with mouse, while the operation of the real machine must be done with the keyboard only on board ships. The Inmarsat-C mobile earth station simulator has being developed by C language. The simulator terminal operating interface and response is identical with the real machine operation because the developing language of the simulator is the same as the real machine. Because the adoption of the doubly-linked list data structure, program structure is clear and code is simplified.
Zero-Shot Feature Selection via Transferring Supervised Knowledge

Feature selection, an effective technique for dimensionality reduction, plays an important role in many machine learning systems. Supervised knowledge can significantly improve the performance. However, faced with the rapid growth of newly emerging concepts, existing supervised methods might easily suffer from the scarcity and validity of labeled data for training. In this paper, the authors study the problem of zero-shot feature selection (i.e., building a feature selection model that generalizes well to “unseen” concepts with limited training data of “seen” concepts). Specifically, they adopt class-semantic descriptions (i.e., attributes) as supervision for feature selection, so as to utilize the supervised knowledge transferred from the seen concepts. For more reliable discriminative features, they further propose the center-characteristic loss which encourages the selected features to capture the central characteristics of seen concepts. Extensive experiments conducted on various real-world datasets demonstrate the effectiveness of the method.
Improved Path Planning with Memory Efficient A* Algorithm and Optimization of Narrow Passages

Path planning is an important type of problem that occurs in various transportation related areas and has led to a several algorithms to solve it. In the current paper, we present a fast solution for path planning by optimizing the A* algorithm in grid-based maps with obstacles arranged to create rooms or as walls. We also optimize the behavior in narrow passages. Besides A* we use the Memory Efficient A* algorithm (MEA*) and improve it by changing the driving and searching behavior in narrow passages to find a faster way to the target location. The four considered variants of algorithms (original A* and MEA* algorithms and both with detection and special handling in narrow passages) are evaluated in computational experiments based on the number of steps (distance) required to reach the goal. These comparisons were performed on different types and sizes of maps.
Action camera: First person perspective or hybrid in motion?

In this article, we discuss the usage of action cameras in research. First, we elaborate on the idea of the camera providing a first-person perspective, possibly giving access to the research participant’s subjectivity, and discuss this critically. Our discussion of these issues is based on data that was produced in two different research settings where action cameras were distributed to groups of students; one setting was an outdoor museum and the other a classroom. Second, we examine how using the action camera in research creates different hybrids involving the camera, the person carrying it, and both present and absent others. These hybridisation processes become evident in different ways. We argue that the camera is treated as a hybrid in four different forms. Arguing with these hybrids enables us to more adequately highlight aspects of the research process than understanding the action camera as providing a first-person-perspective.
Further Observation to Investigate Tendency of Obtaining Potential Appropriate Respondents to Questions at Q&amp;A Sites with Expansion of Categories

In order to eliminate mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for statements have experimentally been obtained. Factor scores have been then estimated by using the feature values of statements. So far, the possibility of detecting respondents who can appropriately answer a newly posted question has been established for limited several categories e.g. Auction, PC, etc. It has been shown that the distance and the appearance times could lead to selecting users who are able to appropriately answer a question. In order to inspect these tendencies, the objectivity of this paper is to investigate if any further tendency of introducing proper respondents to a questioner could be observed by analyzing the Q&A statements other than those already used and expanding their categories. As a result of analysis, the tendencies that particular several users are inclined to appear in each category has been verified. Quantitative evaluation has also shown the outstanding tendency of answer statements.
Solving infinite-horizon optimalcontrol problems of the time-delayedsystems by a feed forward neural network model

A numerical method using neural network for solving infinite-horizon time-delayed optimal control problems is studied. The problem is first transformed, using a Páde approximation, to one without a time-delayed argument. By a suitable change of variable, the obtained non-delay infinite-horizon optimal control problem is converted to a finite-horizon nonlinear optimal control problem. We try to approximate the solution of Hamiltonian conditions based on the Pontryagin minimum principle (PMP). For this purpose, we introduce an error function that contains all PMP conditions. We then minimize the error function where weights and biases associated with all neurons are unknown. Substituting the optimal values of the weights and biases in the trial solutions, we obtain the optimal solution of the original problem. Several examples are given to show the efficiency of the method.
Lingual-Agnostic Meta-Learning for Low-Resource Part-of-Speech Tagging

Current deep learning based cross-lingual Part-of-Speech (POS) tagging methods are limited by their ability to achieve fast learning and generalization when the data in the target language is scarce. In this paper, we integrate a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better POS tagging. Based on the Model-Agnostic Meta-Learning framework (MAML), we propose a Lingual-Agnostic Meta-Learning (LAML) for cross-lingual low-resource POS tagging. The proposed LAML models cross-lingual POS tagging as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource languages. Also, different from the original MALM, LAML distinguishes parameters into shared parameters for represent learning and parameters to be adapted to different languages. We demonstrate that the proposed model outperforms the multilingual, joint-learning based approaches and enables us to train a competitive POS tagging system with only a fraction of samples.
Exploiting Adaptation Behavior of an Autonomous Vehicle to Achieve Fail-Safe Reconfiguration

Autonomous Vehicles (AV) operate in a safety-critical dynamic environment. Frequently occurring environmental uncertainties and random component failures might result into hazardous events, sometimes even into an accident, if left undetected. Moreover, in the event of random errors, highly integrated automotive systems might suffer from the butterfly effect, where a small failure in a component can lead to an unexpected unsafe behavior within the entire system. While an AV operates in a dynamic environment, traditional safety assurance mechanisms like Fault Tree Analysis (FTA), Failure Mode Effect and Criticality Analysis (FMECA), etc. are plays a vital role, but not sufficient enough to ensure safety (at runtime) as they are based on static worst-case assumptions. One possible way to overcome this is through runtime monitoring, where the AV and its behavior can be monitored during operation. In case of an unplanned behavior or safety-critical deviation appear, the monitor can manage the system to assume a safe state via reconfiguration process. We present our work where we exploit the adaptation behavior of AV, to ensure its safe operation at runtime. At this end, we investigate the adaptation behavior of an Adaptive Cruise Control (ACC) of an AV, followed by implementing different possible adaptation techniques.
Real-Time Facial Emotions Analysis in Videos

Deciphering human facial expressions is an integral part of achieving a seamless human to machine communication. This may assist in various cognitive tasks and convey important information such as emotions, intentions and opinions in a medium where machines can understand beyond the traditional binary form of communication. In this paper, we present a significant progress in this direction. We adapted a machine learning approach based on a neural network to help recognize human facial expressions in real time. With a core revolving around a multi-layered convolutional network responsible to train a model, we were able to successfully detect frontal faces from a video stream and encode, in real time, the seven emotions of joy, disgust, neutral, anger, fear, sadness and surprise.
DOSA: Dravidian Code-Mixed Offensive Span Identification Dataset

This paper presents the Dravidian Offensive Span Identification Dataset (DOSA) for under-resourced Tamil-English and Kannada-English code-mixed text. The dataset addresses the lack of code-mixed datasets with annotated offensive spans by extending annotations of existing code-mixed offensive language identification datasets. It provides span annotations for Tamil-English and Kannada-English code-mixed comments posted by users on YouTube social media. Overall the dataset consists of 4786 Tamil-English comments with 6202 annotated spans and 1097 Kannada-English comments with 1641 annotated spans, each annotated by two different annotators. We further present some of our baseline experimental results on the developed dataset, thereby eliciting research in under-resourced languages, leading to an essential step towards semi-automated content moderation in Dravidian languages. The dataset is available in https://github.com/teamdl-mlsg/DOSA
E-Focused Crawler and Hierarchical Agglomerative Clustering Approach for Automated Categorization of Feature-Level Healthcare Sentiments on Social Media

The chapter focus on the data science field and it concentrates on the initial stage as a novel focused crawler used for collecting the reviews then the next stage of a database similar to bigdata (sentiments) preprocessing along with it cleanses to deal with noise as well as the missing data in many reviews. The frequent pattern growth of associating rule mining, part of speech (POS), information on interaction, and sentiwordnet2 is used to detect and extract feature words frequently, and therefore, features and sentimental words are necessary to extract the orientation detection. Finally, for the grouping of contraception procedures, we use data science hierarchical agglomerative clustering algorithm.
Virtual Assistant: A Multi-paradigm Dialog Workflow System for Visitor Registration During a Pandemic Situation

This paper summarizes the findings related to the use of a conversational user interface to support a contactless business visitor registration in a pandemic situation, where requirements to infection control measures might reshape the visitor journey. We explore services, experience models and technology necessary to prepare and support touchless services and prototype a relevant vertical of such a system. Our findings are supportive to the claim that conversational interfaces are perceived safe, when considering infection control. However, the usability of such interfaces needs to be further refined for a holistic touchless operation, as well as a close consideration to the design of the dialogue flow.
Countermeasures of Computer Security Storage and Cloud Computing Technology

Abstract With the development and application of the Internet, it has greatly changed the way people live, produce and learn, and has been widely used in all fields of society. In the process of computer operation, massive data information is generated, which provides important technical support for computer storage for the effective processing of these data information. The Internet is a double-edged sword, which provides convenience, and is threatened by computer virus, illegal access, hardware damage and so on at the same time in the process of computer information storage, which leads to the collapse of computer system to bring huge economic losses for enterprises and individuals. This paper first expounds the security problems of computer security storage and the characteristics of cloud computing technology, and combines the characteristics of cloud computing technology to explain the application of cloud computing technology in computer security storage.