Reviewerly/exHarmony huggingface dataset:
A Study on the Efficiency Analysis of Education and Training Operating Institutions in a New Technology Field Using Data Envelopment Analysis

이 연구의 목적은 신기술 분야 교육훈련기관을 대상으로 자료포락분석(Data Envelopment Analysis)를 활용하여 운영 효율성을 분석하는 것으로 자료포락분석 모형 중 CCR 모형, CCR 초효율성(Super-CCR) 모형, BCC 모형, 여유분 초효율성(Super-SBM) 모형을 통해 규모 효율성을 분석하였다. 교육훈련기관의 상대적 효율성 분석을 위해 훈련 과정 수, 훈련 시간, 인당 평균 훈련비, 총훈련비, 훈련 인원, 수료 인원, 취업 인원, 전반적 만족도를 변수로 설정하였다. 기술효율성 분석 결과, 전체 56개 기관 중 30.4%인 17개 기관이 다른 비교 기관들에 비해 운영 효율성이 있는 것으로 나타났고, 이 중 4개 기관은 효율성 분석에서 10회 이상 참조 기관으로 활용되었다. 또한, 규모 효율성 분석 결과, 비효율적 원인이 기술적 요인뿐만 아니라 규모와 연관 있는 기관은 32개 기관으로 나타나 효율성 개선을 위해 규모적 측면을 고려할 필요가 있다. 교육훈련기관의 효율성 제고를 위해서는 훈련공급방식을 개선하고, 서열화된 성과관리 방식보다는 효율성 중심의 훈련품질 관리를 실시해야 하며, 상대적으로 효율적인 교육훈련기관을 통한 우수사례를 확산하는 방안이 필요하다.The purpose of this study was to analyzes the operational efficiency of education and training institutions that operate education and training in the new technology field by using Data Envelopment Analysis(DEA). For this purpose, the researcher analyzed education and training institutions’ operational efficiency through the CCR model, the CCR Super-CCR model, the BCC model, and the Super-SBM model. For the analysis of relative efficiency, the number of training courses, training hours, average training cost per person, total training cost, number of trainees, number of graduates, number of employees, and overall satisfaction were set as variables. As a result of the analysis, 17 institutions, 30.4% of the total 56 institutions, were confirmed to be more efficient than other institutions. It was confirmed that 4 institutions were used as reference institutions more than 10 times when comparing efficiency. Also as a result of the scale efficiency analysis, 32 institutions were found to be related to size as well as technical factors for inefficiency, and it is necessary to consider the scale aspect to improve efficiency. Based on the results of this study, were suggested to need to improve the training supply method, the need for efficiency-oriented training quality management away from the rank-and-file performance management method, and need to sharing of the best practices.
Analysis of Educational Needs and Priorities for Happiness Indicators of Vocational High School Teachers

Analysis of Educational Needs and Priorities for Happiness Indicators of Vocational High School Teacher - vocational high school teachers;teacher happiness Indicators;educational needs;Educational Priorities;The Locus for Focus model
Security of entanglement-based QKD with realistic parametric down-conversion sources

Abstract The paper analyzes security aspects of practical entanglement-based quantum key distribution (QKD), namely, BBM92 or entanglement-based BB84 protocol. Similar to prepare-and-measure QKD protocols, practical implementations of the entanglement-based QKD have to rely upon non-ideal photon sources. A typical solution for entanglement generation is the spontaneous parametric down-conversion. However, this process creates not only single photon pairs, but also quantum states with more than two photons, which potentially may lead to security deterioration. We show that this effect does not impair the security of entanglement-based QKD systems. We also review the available security proofs and show that properties of the entanglement source have nothing to do with security degradation.
Programming by Example Made Easy

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee , which leverages an “entity-action” model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee ’s effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers.
ANALYSIS OF THE APPLICATION OF FORENSIC TOOLS IN CRIME SCENE INVESTIGATION IN NIGERIA; A CASE STUDY OF BENIN METROPOLIS.

Abstract Crime scene investigation is a significant and vital stage in criminal investigation. The process is a very long and tedious exercise that involves meticulous documentation of the crime scene leading to collection and recovery of evidence in order to deduce the events of a crime. Doing this, requires the application of some scientific or forensic tools which enables the prosecution to produce credible evidence with which to get justice for the victim. Objectives; This study was therefore conducted to investigate the application of forensic tools in crime scene investigation in Nigerian using Benin City as a case study. Method; In the study, investigators of the various investigative units in Edo State Criminal Investigations Department in Benin City within the three local government areas that make up Benin metropolis was the study population. Data was collected using a semi-structured questionnaire and key informant interview from senior serving and retired police officers. 110 respondents were initially drawn as the sample for the study, however, only 98 results were received and were used for the final sample. Using an interview schedule and statistical analysis technique, the data were critically analyzed. Results; Results from the study showed non usage of any form of forensic evidence in the various investigations carried out by the Police. Challenges, such as poor training, inadequate funding and interference from diverse quarters contribute to limit the effective use of forensic investigation in the conduct of criminal investigations. Conclusion; The Police being the chief execution arm of government and by implication other law enforcement agencies, the legal community and the Nigerian public are yet to fully grasp the nucleus or important roles of forensics science in crime investigation. On this premise, it was recommended that specialized forensic units be established for criminal investigations which should be adequately funded.
Spurious Outlier in Cell Counter for Total White Blood Cell Count Parameter in Laboratory External Quality Assurance Program

Abstract Background &amp; Aims: In the External Quality Assessment Program (EQAP), medical laboratories' test results are compared to their peers by statistical analysis. The present study aims to highlight issues related to the root cause for the outliers in EQAP, the factors which lie outside the laboratory operation. Material and Method: Indian Society of Haematology and Blood Transfusion and All India Institute of Medical Science (ISHTM AIIMS) EQAP samples were analyzed in HORIBA Yumizen H 550 and HORIBA Pentra 60 cell counter. Randox international quality assessment scheme hematology (RIQAS) was analyzed on Yumizen H 550 cell counter. Result: All CBC parameters performance except Total WBC were found satisfactory as Z score is below three in ISHTM AIIMS EQAS on Yumizen H 550 in both laboratories. In contrast, Yumizen H 550 cell counter showed unsatisfactory performance as the Z score was more than three in both laboratories. In RIQAS Yumizen H 550 cell, the counter-performance of all CBC parameters is satisfactory for all samples as SDI is less than three. Conclusion: Horiba Yumizen H 550 cell counter gives spurious outlier in the total WBC parameter for ISHTM AIIMS EQAS. Switching to other EQAPs can provide the solution to a constant outlier. Keywords: Cell counter, External quality assurance, Yumizen H 550, RQAS, Total WBC count
Performance comparison of representative chatbots based on deep learning

Chatbots have always been a hot topic in the field of natural language processing, which aims to train models to understand input content and give cognitively logical answers. With the continuous development of artificial intelligence technology, the performance of chat robots has continuously made breakthroughs, and has been widely used in many fields such as smart home, e-commerce, and automatic consultation. In this article, we introduce the basic techniques and methods of deep learning-based chatbots, including recurrent neural network (RNN), long short-term memory (LSTM), Seq2Seq model, attention mechanism, etc. We also review a chatbot designed using RNN and quality testing result. We finally summarize the existing research issues of the chatbot and discuss its possible future works.
Resume recommendation based on text similarity

With the vigorous development of the mobile Internet era, traditional offline recruitment methods are gradually fading out of people's sight. Internet recruitment has become the first choice of many job seekers and companies due to its advantages of low cost and high efficiency. However, suitable positions for job seekers and resume information for recruiters are often drowned in a sea of information. How to find an effective resume recommendation algorithm is still an open issue. To this end, this paper proposes a personalized resume recommendation method based on text classification. Specifically, by encoding and extracting the feature information of different resumes, and scoring the matching degree between job seekers and job information based on the XGBoost algorithm, personalized job information recommendations are made for job seekers. A large number of experiments have verified the effectiveness of the method in this paper, which can provide new insights for the efficient matching of resumes and job information.
Analysis of sentiment analysis model based on deep learning

A traditional yet important topic in the study of natural language processing is sentiment analysis. Deep learning models have gradually taken over as one of the primary techniques for resolving sentiment analysis issues over the last ten years. Common deep learning models targeting sentiment analysis tasks include both recurrent and convolutional neural networks, as well as the BERT model. The current research examines the classificational accuracy of numerous deep learning models with diverse structural types in order to compare their performance in sentiment analysis. Results from the experiments suggest that the pre-training BERT model achieves the highest accuracy, while the convolutional neural network appears to sustain better results on sentiment analysis than standard recurrent neural networks.
Music as an existential phenomenon and the main trends of its understanding in the history of culture

The article outlines two directions of comprehension of the existential phenomenon of musical art (cataphatic and apophatic) throughout the history of culture within the framework of the Christian-Eurocentric tradition. The basis of methodological research includes Christian-idealistic and Christian-anthropological positions. These provisions of objective idealism contain a part of the Creator's Plan in the realization of his Image. The presentation of the main trends is presented in a contrasting ratio, which is characteristic of musical compositional thinking. The discourse of the problem is expressed in a cataphatic-preaching form. In the process of considering this phenomenon, the method of synthesis of ideas about music was applied as a reflection of the human being of such domestic thinkers as A. S. Klyuev, A. F. Losev, V. V. Medushevsky, M.O. Orlov, O.V. Leontieva and foreign philosophers R. Ingarden, A. Schopenhauer, F. England, D. Turner. The author makes an attempt to update the musical discourse from the perspective of Eurocentric Christian traditions by including the language of theology in it. Research in a given direction reveals the deep foundations of musical creativity, which arouse interest in the social significance of the master creator, leveled by post-industrial society. The scientific novelty of the research is contained in the choice of sources that reveal the European understanding of the existential essence of musical art as a historically formed part of the musicological discourse. According to the results of the study, it can be concluded that, unlike other types of art, music remains irreconcilably resistant to various forms of evaluation and paraphrasing, explanation and description. The essence of human existence is fully reflected in the musical art.
Improving your company's cyber security training

Even the best security technology can't eliminate the threats to your organisation. Human error will always present a risk. It's important, then, that every organisation takes a careful look at its cyber security training to determine whether it's up to the job.
Artificial Intelligence Technology in Computer Vision and Network Field

In the era of rapid development of information, science and technology are constantly progressing, and artificial intelligence (AI) technology is also developing continuously. With the rapid growth of social economy, it has promoted the development of modern industries to a certain extent. The development of computer technology has also triggered a new revolution in AI technology. AI technology can use computers to recognize big data, image information and human movement. People’s lives. The purpose of this book is to explore the application of AI techniques in computer vision and networking. This article explains the concepts of AI, computer vision. Analyze the development and prediction of AI, conduct appropriate analysis on the development of AI in the field of vision and computer networks, and conduct a number of basic technical research and model design around multiple technical issues in the computer and network fields, enhancing the field of computational vision The relevant AI research content has important scientific value and significance. Finally, a questionnaire is used to collect and analyze the application of AI in the two fields. Through experimental authentication, the image recognition accuracy of this paper is 99%, and it is more friendly to some middle-aged and elderly users.
Multi-object Tracking Based on YOLOX and DeepSORT Algorithm

The implementation of 5G/6G network provides high-speed data transmission with a peak transmission rate of up to 10 Gbit/s, which solves the problems of blurred video and low transmission rate in monitoring systems. Faster and higher-definition surveillance images provide good conditions for tracking multiple targets in surveillance video. In this context, this paper uses a two-stage processing algorithm to complete the multi-target tracking task based on the surveillance video in the 5G/6G network, realizing the continuous tracking of multiple targets and solving the problem of target loss and occlusion well. The first stage uses YOLOX to detect the target and passes the detection data to the DeepSORT algorithm of the second stage as the input of Kalman Filtering, and then use the deep convolutional network to extract the features of the detected frames and compare them with the previously saved features. The algorithm can better continuously track multiple targets in different scenarios and achieve the real-time effect of the processing of monitoring video, which has certain significance for solving the problems of large-scale dense pedestrian detection and tracking and pedestrian multi-object tracking for pedestrians in the future 5G/6G video surveillance network.
Application Research of Electronic Invoice System Based on Blockchain Technology—Taking Shenzhen City as an Example

This paper starts with the operation principle of blockchain electronic invoice system, the operation effect and development bottleneck in Shenzhen. Based on this, it explains how blockchain technology is integrated into electronic invoices system, analyzes the operation effect of Shenzhen blockchain electronic invoices system, and finally analyzes the bottlenecks in the application of blockchain electronic invoices system of Shenzhen Taxation Bureau based on index construction. Provide experience and reference for improving the application of national electronic invoices system.
Self-adaptive evolutionary of non-tuned neural network—coding and implementation

The purpose of this chapter is to present a self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm that can be used to improve the SLFFNN training process. In the SaE-ELM, the two randomly allocated matrices (i.e., the bias of hidden neurons and input weights) are defined using an optimization process while the output weight is calculated in a similar manner as the original ELM. The process considered in the SaE-ELM algorithm results in the self-adaptation of the strategies of trial vector production and control parameter selection in the differential evolution algorithm by using previous experience to produce optimistic solutions in the strategy pool. The SaE-ELM code developed throughout this chapter can be easily applied to a variety of nonlinear problems with only a knowledge of the required user-defined parameters—a detailed understanding of the mathematical formulations of the model is not required for successful implementation. Moreover, a calculator is introduced to apply the trained SaE-ELM-based models for complex nonlinear real-world environmental problems. Finally, several new user-defined input parameters associated with this model are introduced and discussed. These parameters relate to the differential evolution algorithm used to optimize the hidden neurons and include the population size and generation number. The impact of both these variables on the model output is investigated.
Double circulant complementary dual codes over $ \mathbb{F}_4 $

&lt;abstract&gt;&lt;p&gt;Linear codes with complementary-duals (LCD codes) are linear codes that trivially intersect with their dual (Massey, 1992). In this paper, we study double circulant codes (DC codes), which are a special class of quasi-cyclic codes, over $ \mathbb{F}_4 $ that are LCD. The main techniques used are as follows: Chinese reminder theory (CRT) decomposition in the line of (Ling et al. 2001), explicit enumeration, and asymptotics. In particular, we show that the class of codes considered here is asymptotically good.&lt;/p&gt;&lt;/abstract&gt;
A Conceptual Framework of a Blockchain Application in a Manufacturing Supply Chain

This paper presents a conceptual framework to apply blockchainBlockchain technology to two implementation areas in a supply chainSupply chain. It specifically intends to provide product tracking information to all stakeholders in the product development, assembly and subsequent delivery phases, manage information of supplied components to perform the assemblies where it is included, and track injected components. Information flow and exchange are supported by blockchainBlockchain. This article contributes to this emerging technology by providing an overview of blockchainBlockchain and its application to an industrial supply chainSupply chain by presenting the real problems encountered in the chain, examines the implications of information centralization, traceability, and transparency, and identifies some potential challenges in the work performed by collaborating companies in quality, receipt, and shipment of goods terms, among others.
Improved Ant Colony Optimization Based Binary Search for Point Pattern Matching for Efficient Image Matching

Point Pattern Matching (PPM) is an approach to establish correspondence between two related patterns by pairing up of points. PPM is widely used in the field of computer vision and pattern recognition. The existing approaches for PPM has either high computational complexity or the search space is large. To overcome this drawback, an Improved Ant Colony Optimization based Binary Search for Point Pattern Matching (IACOBSPPM) has been proposed. The algorithm chooses a query image point value from the query image point pattern and finds the reduced search space in the stored image point pattern based on the length of the point value. The query image point value is searched only in the reduced search space. When a match is found, the next query image point value of the same length is searched only from the matching position of the previous query image point value, further reducing the search space. The computational complexity of the proposal is less compared to the existing approaches for point pattern matching. Experimental results prove the efficiency of IACOBSPPM algorithm.
A Blockchain-Based Service-Oriented Framework to Enable Cooperation of Swarm Robots

Swarm robotics has been proposed to achieve tasks by the cooperation and coordination among distributed simple robots. To overcome the practical issues of deploying a swarm robotic system, we present a blockchain-based service-oriented framework to ensure the secured cooperation among heterogeneous swarm robots. Our work adopts smart contracts working on the blockchain to design service functions for swarm robots. The major service functions for the robot nodes include requesting the services, selecting suitable services based on the quality and creating composite services to achieve complex tasks. Considering the market-oriented cooperation condition of the real world, we design a service trading mechanism to take into account utilities of service requesters and providers. We conduct a series of experiments to evaluate the framework, and the results show the promising of the presented approach.
Balancing Measurement Efficiency, Test Diversity and Security for Item Selection in On-the-Fly Assembled Multistage Adaptive Testing via a Multi-objective Evolutionary Algorithm

On-the-fly assembled multistage adaptive testing (OMST) is a recently emerging computer-administered test form and has been employed in a variety of large-scale examinations. Item selection is a key challenge in OMST, which adaptively assembles real-time question modules by selecting a set of questions tailored for examinees from item bank. Existing question module assembly recognizes the measurement efficiency, test diversity and security as crucial evaluation criteria for item selection. Nevertheless, most of the studies on OMST concentrate on improving the measurement efficiency in item selection, while few of them devote to making a trade-off between the three criteria. In this paper, we propose to use a dynamic indicator-based multi-objective evolutionary algorithm with reference point adaptation, termed AR-DMOEA, for striking a balance between the three criteria in the item selection of OMST. A diversity-strengthened population initialization strategy is suggested to generate diverse individuals in the initial iteration of AR-DMOEA. A set of knowledge-guided offspring generation operators are designed to reproduce high-quality offspring individuals during the optimization of AR-DMOEA. Empirical results on five OMST datasets demonstrate that AR-DMOEA effectively balances the measurement efficiency, test diversity and security in the item selection of OMST. The effectiveness of the suggested initialization strategy and offspring generation operators are also validated by comparison between AR-DMOEA with the two strategies and that without the two strategies.