Reviewerly/exHarmony huggingface dataset:
Evaluating A Library Automation System Application in Senayan Library Management System (Slims): The Human Organization Technology (HOT) FIT Model in Indonesia

SLiMS is a web-based Free Open-Source Software (FOSS) that can be used to build systems in the implementation of library automation systems. While automation system in offer numerous advantages in library, they are often criticized for lack of interaction between librarians and the software and accessibility issues. This research investigated the application of library automation system on Senayan Library Management System (SLiMS) used Human organization Technology (HOT) FIT. HOT FIT is one of the models commonly used to evaluate the implementation of existing systems in an institution. In this model there are 3 aspects discussed, namely, Human, organization, and Technology. The three aspects in this research are Human (user), organization (library), Technology (SLiMS). In addition, this research explores indicators to improve SLiMS service in library. Using the HOT FIT, this research find that librarians need to improve their empathy when assisting library users. Using HOT FIT model is particularly offering positive change to librarians which able to explain comprehensive evaluation to provide information system core component approach (library stakeholder). This investigation recommends further education to library visitors and students who use library service on library information system specifically on information search and collection.
Multi-aircraft cooperative detection strategy based on formation

Abstract A filter algorithm combining Interactive Multiple Model (IMM) algorithm with adaptive transition probability and Unscented Kalman Filter (UKF) is proposed for the issue of state estimation of highly maneuvering targets with unknown motions. The centralized fusion structure is adopted to process measurements of multi-aircraft to improve the accuracy of target state estimation. The cooperative detection strategy based on formation is studied from two aspects of the aircrafts number and configuration radius, and conclusions are drawn through simulation: In general, the more the number of aircrafts, the better the detection effect; when the configuration radius is 250km and 100km, the better cooperative detection effect can be obtained; considering the cost issue, 4 aircrafts can be selected for cooperative detection in a rectangular formation with a half diagonal of 100km.
The Security of a Blockchain-Based File System in a Software Defined Network Framework

Developing distributed form of file security systems using Blockchain technology. Based on the idea of cloud storage as it is a leading storage technology for huge data storage. Blockchain is one of the trending technology for decentralized data storage systems that ensures privacy, confidentiality, data security, authentication, and integrity. As SDN network provides support to have various nodes in the network for the secure transaction of data from source to destination. Blockchain helps in keeping track of block data by constructing the gateway to make it immutable. BCFS refers to Blockchain-Based File System Security in SDN. In the designed system, a Web-Based Interface is developed an authorized entity can upload file data the user’s file is projected to encryption process and the block data is shared among the various nodes in the network. Along with Unique Document ID, encrypted random key, and hash data. This hash data value holds the file path and preserves in the blockchain into their corresponding block data folders. Detection of node failure across the network an automatic short path is chosen by the network and detection of an attack based on entropy value.
SATISFACTION LEVEL OF DEGREE STUDENTS ON E-LEARNING DURING COVID-19 SITUATION (A COMPARATIVE STUDY OF RURAL AND URBAN AREA IN BHADRADRI KOTHAGUDEM DISTRICT)

In December 2019, Wuhan, China, experienced an unidentified viral pandemic of pneumonia. The World Health Organization (WHO) declared the finding of a new corona virus, SARS-Cov-2, on January 9, 2020. In the Covid-19 pandemic situation education sector has faced enormous provocations. More than 200 countries across the world have been distressed with the covid-19. Conforming to UNESCO, above 32 crore Indian students have been concerned by the covid-19 led lockdown. Following the shake of Covid-19, HonorablePrime Minister of India Sri.Narendra Modi declared a lockdown beginning on March 25, 2020 and lasting until May 31, 2020. This lockdown has severely disrupted India's education sector, forcing students and faculty to remain at home and continue their education online.&#x0D; The Commissioner of Collegiate Education in Telangana State formally began Online Classes during the Lockdown period. The present paper is an attempt to a comparative study and analyzes the Satisfaction level of Degree students on online classes during covid 19 situation of Rural and Urban area in Bhadradri Kothagudem district, Telangana State. It also aims to investigate the students' opinions and issues encountered during online classes. The Online survey was carried out with the help of Google Form send to students’ through e-mail and WhatsApp and collected responses from about 140 Degree students of Government and Private colleges in Bhadradri district, Telangana State. It demonstrates that the majority of students attending to online classes using smart phones with Android operating systems. For e-learning, the majority of students have used the Zoom online platform. Students have experienced a variety of issues, including Live streaming and Audio disruptions, insufficient data, power outages, lack of clarity in lectures, missing face-to-face connection, and a general lack of enthusiasm. During this Covid-19 circumstance, students from rural areas have had a difficult time accessing online lectures and study materials.
A Computer Vision-Based Attention Generator using DQN

A significant obstacle to achieving autonomous driving (AD) and advanced driver-assistance systems (ADAS) functionality in passenger vehicles is high-fidelity perception at a sufficiently low cost of computation and sensors. An area of research that aims to address this challenge takes inspiration from human foveal vision by using attention-based sensing. This work presents an end-to-end computer vision-based Deep Q-Network (DQN) technique that intelligently selects a priority region of an image to place greater attention to achieve better perception performance. This method is evaluated on the Berkeley Deep Drive (BDD) dataset. Results demonstrate that a substantial improvement in perception performance can be attained – compared to a baseline method – at a minimal cost in terms of time and processing.
Analysis of Radar Detection Accuracy Deviation Based on Goodness of Fit Test

In this paper, through the analysis of the accuracy of the target information detected by the warning radar, the description of the related goodness of fit test model and the calculation of the detection reference trajectory based on the least square algorithm are given. After the track comparison, the detection track deviation of the warning radar is calculated, and then the track deviation is tested by the goodness of fit test. This method solves the problem that the hypothesis test is done under the assumption that the population is normal, but the hypothesis itself is not necessarily true, and it needs to collect samples (&#x03A7;<inf>1</inf>,&#x2026;,&#x03A7;<inf>n</inf>) to test it. This method reduces the number of samples collected and the complexity of data fitting. An application example shows that the detection accuracy of radar can be improved by using goodness of fit test to analyze the deviation of radar detection accuracy after detecting target trajectory.
Transforming Power of Scientific Knowledge

Knowledge, and especially scientific knowledge, is crucial in terms of transforming societies, and making social structures manageable and sustainable. This can be seen in the fact that the universities which are at the top of the global university rankings are mostly located in countries that are the center of attraction. The aim of this study is to examine the positive effects of academic productivity, which is of great importance in optimizing social conditions, on human development on a global scale with a statistically provable technique. In order to understand this relationship more clearly, our study has compared countries according to their academic productivity using the Multidimensional Scaling Analysis method and established a relationship between this and human development. As a result of the analysis carried out within the scope of the study, which used data from 178 countries from the time period of 1996-2019, it was concluded that the relationship between academic productivity and human development was only at a medium level. This suggests that, in addition to the need for scientific knowledge to be used for the improvement of social conditions, scientific knowledge is limited to certain organizations, which leads to the elitism of scientific knowledge.
Risk assessment of aircraft landing phase based on neural network cloud and Monte Carlo

In view of the complex factors affecting the landing risk of military aircraft after the mission, the assessment method is not systematic, and the process is fuzzy, random and uncertain. In this paper, a risk assessment model based on neural network cloud is constructed and implemented by Monte Carlo simulation. Firstly, four parameters, namely ground speed, elevation angle, vertical acceleration and ground distance deviation, are selected to describe the risk situation of landing phase. Roulette algorithm is used to generate 100 groups of cloud data randomly, and the probability membership degree is determined by cloud generator as the training data of GA-BP neural network. The simulation evaluation system is formed after learning and training of neural network, and the landing number of a certain army aircraft is calculated The rationality and scientificity of the evaluation system is verified by an example, which proves that it has certain practical significance and reference value.
Optimization strategy of college English online teaching supported by information technology

Information technology is an important decisive force to realize the informationization and modernization of education. In the process of teaching students, teachers need to make full use of some modern teaching methods, and transform the original abstract and obscure English knowledge into more intuitive and easy-to-understand interesting knowledge, so as to provide a good environment for students to learn English, so as to improve students' interest in learning English. With the support of information technology, the online teaching of college English is optimized, and a new online teaching model of college English is constructed from three dimensions: creating teaching resources, innovating teaching activities and creating teaching evaluation, which can provide reference for the effective implementation of online teaching, thus better promoting the informationization, intelligence and three-dimensional development of online and offline teaching of college English.
Research on Knowledge Consensus Mechanism of Network Learning Space Based on Block Chain

This paper aims to promote high-quality and large-scale knowledge sharing in network learning space. Firstly, it systematically combs the way and process of knowledge consensus, and proposes a process model of knowledge consensus in network learning space. Secondly, using the characteristics of anti-tampering, traceability and decentralization of blockchain technology, we study and design the knowledge rights and interests recording mechanism. The mechanism includes two parts: knowledge consensus rights record and knowledge consensus process supervision, in order to solve the problem of user rights protection, remove obstacles for the development of network learning space, and finally achieve the purpose of promoting knowledge sharing.
Fast Transfer Navigation for Autonomous Robots

Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.
Use subsequent neural network to analyze ICEMI

Among the security issues of embedded systems, electromagnetic (EM) information leakage problem is difficult to detect and prevent. When the embedded system is operating, different commands will emit different EM signals. In this paper, we propose a NN architecture to analyze the EM leakage and effectively improve the accuracy. Comparing with a previous research, the accuracy is greatly improved from 38% up to 66%.
PIRMedic

Passive Infra-Red (PIR) sensors are an integral part of modern living. They have diverse applications ranging from automatic lighting and heating control in smart buildings, towel dispensers in washrooms, security alarms (for intrusion detection) to even human detection robots (for search and rescue). Unfortunately, PIR sensors are prone to failures during deployment due to reasons such as environmental damage, incorrect installation and component degradation among others that can lead to incorrect or faulty data. Currently, such failures are typically detected using either : (a) heavily engineered data-driven, statistical approaches that can have high false positive rates due to unseen data patterns or (b) expensive methods that use additional hardware such as video cameras or a golden reference sensor. The second approach inhibits scalability.
Mobile Dimensions Detection For Forensic Investigation

This paper proposed a canny edge detection-based approach for measuring the dimension of an object of mobile type captured in an image from the given distance. The purpose of the scheme is to implement aspects of forensic investigation helpful for our society. It makes use of mobile dimension detection algorithms to narrow it down to a range of mobile phones in a grievous event. The scheme not only identifying the mistrusting individual but also assists the forensics investigation team. In addition to that, the technique support to track the suspected individual as well as also concerns for tapping the communication records of concern person. It has been tested on wide varieties of mobile images of different model from same or different companies. The superiority of the proposed scheme is that excellent results are obtained while comparing the real dimension of an object with the object in an image.
STRESS INFLUENCE ON PROFESSIONAL ACTIVITY OF ACADEMIC STAFF AND STRESS MANAGEMENT IN INSTITUTIONS OF HIGHER EDUCATION

Subject under consideration in the article is the problem of stress in professional activity of academic staff. It has revealed modern scientific ideas about stress. The reasons, manifestations, consequences of professional stress of a university lecturer have been analyzed on the basis of applied sociological research conducted among the academic staff of National University of Life and Environmental Sciences of Ukraine. The article presents the research results of professional stress impact on professional activity of academic staff. It has been shown that the professional activity of a lecturer is accompanied by numerous stress factors that determine professional stress and have negative influence on his/her professional activity effectiveness. It has been noted that the violation of adaptive mechanisms, low stress resistance with prolonged chronic influence of stress factors, leads to a number of psychosomatic diseases. The strategies of overcoming behavior in stressful situations have been studied among the scientific and pedagogical staff of NULES of Ukraine. The features of overcoming professional stress among teachers with different stress levels have been revealed. Gender characteristics of higher school teachers have been established: a high susceptibility of women to stress, due to their expressiveness, the desire for success in professional career and in personal life, as well as a high frequency of stress development among young teachers, since they have to gain credibility with students. It has been identified that professional stress among teachers with working experience is mainly due to numerous educational reforms and toughening requirements. The importance of stress resistance has been noted, which is associated with innate qualities, but can be strengthened by competent planning and administration responsibilities, the creation of a comfortable working environment by the heads of the faculties and departments, the work of psychologists and the lecturers themselves.
Research on the University Library Anonymous Customer Complaint System Based on Blockchain Technology

In 2016, Zhang Xiaotong and his collaborators published Research on Internal Control Information System Based on Anonymity of Dual Complexity of Computation, which introduced two algorithms RSA and ELGAMAL into the system. In 2017, Yan Hanbing et al. commented and improved Zhang Xiaotong's scheme. In Zhang's and Yan's methods, 17 equations were used for in the six stages. Based on this concept, this paper proposes a new scheme which is different from the former one. It contains 6 stages, but the number of equations can be reduced to 10. This solution does not need to pre-compute to prevent CPU idle. It adopts Elgamal single algorithm and simplifies the unused parts of Elgamal algorithm, and also modifies the RSA algorithm to achieve better computational efficiency.
Reference-Based Compression of FASTQ Data Using Longest Match Model

This paper proposes a reference-based lossless compression algorithm for FASTQ files using hash table. Firstly, the original FASTQ file is split into B blocks in order to take advantage of multi-thread parallelism. Secondly, each block is divided into three parts: metadata, quality score, and base sequence. After that, we use run-length coding and incremental coding to preprocess the quality score and metadata streams respectively. For the base sequence, we use a carefully designed longest matching model to quickly map it onto the reference sequence so that the longest match of each base sequence in the reference sequence can be obtained, and the match result can be saved for further compression. Finally, the three pre-processed data streams are compressed using algorithms such as zpaq and lpaq9m. Our method is tested against data sets of different read lengths generated on different sequencing platforms, it achieves compression ratio in the range of 0.102-0.229, which is significantly better than the same results from other advanced lossless compression algorithms.
A Combination of Enhanced WordNet and BERT for Semantic Textual Similarity

The task of measuring sentence similarity deals with computing the likeness between a pair of sentences by adopting Natural Language Processing techniques (Euclidean distance, Jaccard distance, Manhattan distance, etc.) as well as embedding techniques (word2vec, GloVe, Flair, etc.). For the purpose of determining sentence similarity, this paper proposes a novel, ensemble learning approach which uses the WordNet corpus and the Bidirectional Encoder Representations from Transformers (BERT) in order to consider the context of words in sentences while computing the similarity scores. The accuracy of the proposed model is computed by calculating the Pearson and Spearman scores for the sentence pairs from the Sentences Involving Compositional Knowledge (SICK) dataset. On analyzing the results, the proposed approach is observed to outperform existing state-of-the-art semantic textual similarity models since it returns the highest correlation scores. Further, this paper also introduces a possible machine learning approach for the same and evaluates its scope and drawbacks.
A study on English online learning system framework based on clustering analysis

In the process of English language learning, college students often find it hard to obtain specialized knowledge after class, or effectively exchange what they've learned with others. To grasp this language, they need constant study and exchange to acquire relatively comprehensive information on it. In response to this situation, various learning systems have popped up as an assistance to the study of English. This paper also focuses on system construction, and probes into the framework of online English learning system through cluster analysis. It is expected that this paper can provide some practical content for system development researchers.
Comparative Study of Different Machine Learning Models for Measurement of QoE Based on MOS in Wireless Networks

Mean opinion score (MOS) is a measurement of the Quality of Experience (QoE) used in the quality of audio and video content. It is a subjective measure of quality of voice. QoE provides a good video looks and audio sound. In our paper, we consider the MOS as the parameter for measurement of QoE in wireless networks. MOS measures human-judged overall quality of an event or experience. MOS score does not give the Quality of Service (QoS) directly, rather than it gives QoE. It is understood that good experiences come from good services. So, when we get the good experiences, the QoS must be also good. Bringing QoS and QoE together is truly the best approach for network performance and the satisfaction of end user. In our paper, we consider five different machine learning models (logistic regression, decision tree classifier, K neighbors classifier, Gaussian Naive Bayes, random forest classifiers) and train them with the dataset and we get the accuracy score accordingly. We make a comparative study of above-mentioned models to obtain highest predicted value with maximum accuracy score on the basis of MOS value. Here, random forest classifiers provide the highest predicted value than other models with maximum accuracy score.
ConVIScope: Visual Analytics for Exploring Patient Conversations

The proliferation of text messaging for mobile health is generating a large amount of patient-doctor conversations that can be extremely valuable to health care professionals. We present ConVIScope, a visual text analytic system that tightly integrates interactive visualization with natural language processing in analyzing patient-doctor conversations. ConVIScope was developed in collaboration with healthcare professionals following a user-centered iterative design. Case studies with six domain experts suggest the potential utility of ConVIScope and reveal lessons for further developments.
FB-KEA: A Feature-Based Keyword Extraction Algorithm for Improving Hit Performance

In the age of Internet, people usually use search engines by giving keywords to find the information they need. For content providers, the reference performance of a given content is majorly determined by the set of keywords. In literature, there have been many studies proposed algorithms for finding keywords. However, most of them cannot reflect important properties, such as the expert’s knowledge and trends. This work proposes a novel keyword extraction algorithm, called FB-KEA, which extracts the keywords based on the features including semantic, expert experience as well as trend of hot search. Experimental results show that the proposed FB-KEA has significant improvements in terms of hit ratio, as compared with traditional methods.