Reviewerly/exHarmony huggingface dataset:
테러이용수단으로 방사능 물질 안전관리 : 미국의 방사능물질 관리 법령비교연구

Weapons of mass destruction (WMD) in the possession of malicious terrorists and/or terrorist groups and states represents one of the most dangerous security challenges to most of nations. Among the WMD, the possession of nuclear and radioactive materials by terrorists and terrorists group may poses the greatest security threats because the risk of using these material as a weapons of mass destruction is high. Therefore, states and governments pursue an effective strategy to counter this threat in their domestic countries. The most effective strategy regarding WMD attacks by terrorists is to prevent them from possession of these materials that can be potentially used as weapons. That is a deterrence. To achieve such a goal, states and governments adopted rigorous legal and political measures to strength the security of WMD materials. Such attempts has been implemented in our nation and the Congress passed Counter Terrorism Law in 2016 to protect Koreans from attacks of nuclear and radioactive weapons. However, there has been criticism that the law of Counter Terrorism of Korea need to be amended to protect Koreans better. Therefore, this current study aims to examine the current legal and political issues surrounded to the Courter Terrorism Law in Korea. Specifically, the focus of this study is to identify and assesses the current and emerging security threats regarding radioactive materials to prevent anty potential WMD attacks using radioactive materials in our homeland. Furthermore, this study identify the better way to improve the current security laws, regulations and operational measurers regarding to the control of radioactive materials in an comparison with the legal approaches and operational measures adopted in the United States. In concision and discussion, the postretinal policy implications and suggestions for the legal revisions for the Counter Terrorism Law of South Korea.
Video Retrieval Using Residual Networks

With the growing size of data across various different forms in today’s world, a lot of meaningful information needs to be extracted from huge amount of data. Specially the multimedia content on web is increasing rapidly; thus, the demand for searching and retrieval of the required multimedia data is also increasing. Hence, there is a need for a faster retrieval of required data for different queries such as image, video, audio and text. In this paper, we propose a video retrieval framework using residual networks (ResNet-34) based on the query image or video clip and retrieve the relevant or similar videos from the video dataset. The ResNet-34 with locality sensitive hashing algorithm provides a faster retrieval of the relevant or similar videos from the dataset. The retrieval efficiency is improved from quadratic to logarithmic efficiency class. We demonstrate the proposed method for nine different categories of you tube videos and obtain an overall precision rate of 84% which is comparable with the state of the art.
Development of Teaching Materials for Experiments on Power Electronics and Application Effects of Teaching Materials for Distance Education

We developed teaching materials of step-down chopper circuit experiments for efficient learning of power electronics. In this experiment, students learn the principle of the step-down chopper circuit and PWM control, and the relationship between transient response and current ripple. On the other hand, in order to prevent the spread of COVID-19 infection, the distance education was carried out in many technical colleges and many on-demand teaching materials were developed. In this study, we investigated the effect of applying on-demand teaching materials developed for distance education to engineering experiments. From confirmation tests, questionnaire surveys, and student reports, we confirmed that it is effective to apply on-demand teaching materials to engineering experiments.
On the Renewal of Basketball Concepts Based on the Reform of Basketball Teaching in Colleges

The purpose of the research is to explore the updating of basketball teaching concepts in colleges and universities. The research method adopts literature and expert interview method. The results of the study believe that although basketball is a highly technical ball game, basketball teaching in colleges and universities is not only limited to the technical level, but also puts basketball on a high ground for educating people, through physical fitness and technical and tactical training, fully shaping Personality and individuality, spread basketball cultural knowledge, develop survivability, cultivate a good attitude, let students learn to live, learn to survive, have the courage to assume social responsibility, and cultivate the spirit of hard work and enterprising.
PENILAIAN KEPUASAN PELANGGAN DENGAN APLIKASI SURVEI PADA PDAM KOTA BENGKULU

Customer satisfaction is a major reason in the service or goods business. The company will get a big profit if the customer is satisfied with the ideal service provided. The purpose of this study was to find the level of satisfaction of the PDAM's performance on customer service by making an online satisfaction survey application. The research method used is system requirements analysis, designing system requirements, coding translating the system with a programming language, implementing the system and maintaining any changes to the system. The results of the study show that the satisfaction survey application can make it easier for customers to send complaints about the service quality of PDAM Kota Bengkulu. With the PDAM polling application, it is faster to take action according to complaints from customers.
Hybrid Extreme Learning Machine and Backpropagation with Adaptive Activation Functions for Classification Problems

This paper proposes a hybrid approach of Extreme Learning Machine with Backpropagation with adaptive activation functions for classification problems. In general, machine learning research seeks to find algorithms that can learn specific parameters through data to create increasingly accurate generalist predictive models. In some scenarios, these models become very complex, requiring great computational power for both the training stage and the predictive stage. Adaptive activation functions emerged intending to increase models’ predictive capacity, thus generating better models without increasing their complexity. We evaluate the performance of the proposal in a benchmark of ten classification problems. The results obtained show that the hybrid approach with adaptive activation functions, in general, surpasses the standard functions with the same architecture.
Distance-based Human-Object Interaction Detection

Abstract In our lives, the interactions between humans and objects around them are happening all the time. The purpose of human-object interaction(HOI) detection is to locate humans and objects in a visual scene and infer the type of interaction between the two. Most of the HOI detection works infer the interaction type from two aspects: spatial features and visual features. We adopt the same strategy as them, but in the stage of extracting spatial features, we believe that the relationships between human body parts and objects are very important. We choose distance as a standard to measure the relationship between body parts and objects, and add it to the process of extracting spatial features, then use the refined spatial features to further extract visual features. Our experimental results on the V-COCO dataset show that our new model based on distance is effective. Compared with other methods, the accuracy of our model is significantly improved.
Dynamic Search Paths for Visual Object Tracking

The long-term sub-track of visual object tracking challenge comprises of some of the most challenging scenarios like occlusion and target disappearance and reappearance. To this end, many deep learning solutions with multiple levels of detection have been proposed. Most of these solutions tend to re-identify a wrong target during the occlusion or disappearance as they start looking for the target in the entire frame. Instead, through this work, we intend to prove that predicting a probable search region for the target by understanding its trajectory and searching for a target in it will help in reducing the misidentifications and also aid in the increase of IoU. For this, we have utilized the trajectory modeling capabilities of the Kalman filter. With this proof of concept work, we achieved an average improvement of 37.37% in IoU in the sequences where we overperformed MBMD.
Swarm Robotic Interactions in an Open and Cluttered Environment: A Survey

Recent population migrations have led to numerous accidents and deaths. Little research has been done to help migrants in their journey. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction. This article presents a review of techniques that can be used in a robots swarm to find, locate, protect and help migrants in hazardous environment such as militarized zone. The paper presents a swarm interaction taxonomy including a detailed study on the control of swarm with and without interaction. As the interaction mainly occurs in cluttered or crowded environment (with obstacles) the paper discussed the algorithms related to navigation that can be included with an interaction strategy. It focused on comparing algorithms and their advantages and disadvantages.
Human-Like Storyteller: A Hierarchical Network with Gated Memory for Visual Storytelling

Different from the visual captioning that describes an image concretely, the visual storytelling aims at generating an imaginative paragraph with a deep understanding of the given image stream. It is more challenging for the requirements of inferring contextual relationships among images. Intuitively, humans tend to tell the story around a central idea that is constantly expressed with the continuation of the storytelling. Therefore, we propose the Human-Like StoryTeller (HLST), a hierarchical neural network with a gated memory module, which imitates the storytelling process of human beings. First, we utilize the hierarchical decoder to integrate the context information effectively. Second, we introduce the memory module as the story’s central idea to enhance the coherence of generated stories. And the multi-head attention mechanism with a self adjust query is employed to initialize the memory module, which distils the salient information of the visual semantic features. Finally, we equip the memory module with a gated mechanism to guide the story generation dynamically. During the generation process, the expressed information contained in memory is erased with the control of the read and write gate. The experimental results indicate that our approach significantly outperforms all state-of-the-art (SOTA) methods.
The <i>modus operandi</i> of perpetrators for credit card fraud in the Vaal Region, South Africa

Card payments in South Africa continue to be a predominant part of the National Payments System in an evolving payments ecosystem. Due to the growing volume of electronic payments, the monetary strain of credit card fraud is turning into a substantial challenge for financial institutions and service providers, thus forcing them to continuously improve their fraud detection systems. This article attempts to explain the Modus Operandi (MO) of perpetrators of credit card fraud in the Vaal Region in South Africa. The article begins with an examination of the extent of the challenge and response by the relevant stakeholders, especially the Criminal Justice System (CJS). This study was carried out utilising a qualitative research approach with a convenience, purposive and snowball sampling techniques. Thirty-nine (39) interviews were conducted to solicit the views of the participants and police investigators from Vanderbijlpark, Sebokeng, Sharpeville and Vereeniging police stations, members of the community, and victims of credit card fraud were interviewed. These interviews were analysed according to the phenomenological approach, aided with the inductive Thematic Content Analysis (TCA) to identify the participants’ responses and themes. The findings indicated that the extent of credit card fraud in Vaal region is reaching alarming rates. Based on the findings, the authors provided recommendations such as: police investigators being taken for regular workshops and training on how to investigate sophisticated methods used by perpetrators such as technology, awareness in the society about credit card fraud should be prioritised and enhanced.
Research on the Mechanism of Data Visualization Analysis in the Cultivation of Foreign-related Legal Talents

This article conducts in-depth research on the mechanism of foreign-related legal personnel training, visually analyzes relevant data, based on the practical teaching model, formulates reasonable teaching training goals based on the theoretical context of foreign-related legal management and the characteristics of industry practice, and implements field research technology, by sending teaching questions to students in advance, letting students conduct project investigations in groups on the spot, trying to let students gain an in-depth understanding of the operation of foreign-related laws through cooperation and practice, and explore the mechanism of practical infiltration teaching mode for professional talent training. Build a more open pattern of legal services, and at the same time increase the training of foreign-related legal talents, the cooperation, and exchanges within and outside the lawyer industry, and improve the ability of foreign-related legal services.
The Development of English Language Education in the Internet Age

With the continuous development of the current Internet age, while bringing great convenience to people's lives, it also has a certain impact on the teaching career. The development of English language education is also constantly changing with the Internet age. This article takes English language education as an entry point, analyzes the current situation of college English language education in the Internet age, and proposes some ways to optimize education models. This article combines the types of English language education commonly used by college students, analyzes the advantages of English language education, and proposes effective countermeasures to the problems in English language education, that is, to create a new model for teachers and students to participate in the use of English language education in the Internet age. To play a role in the autonomous learning of college students, it is even more necessary for teachers to use online teaching methods in and out of class to help online education become an effective tool for enhancing the effectiveness of English language education in the classroom. Through the guidance and participation of teachers, students will be more interested in English mobile learning, promote online and offline activities of teachers and students, enhance students' autonomous learning and lifelong learning capabilities, and further promote the reform of new models of English language education. The experimental research results show that in the environment of the Internet age, English teachers should change the traditional teaching mode, recognize the advantages of current Internet technology, introduce it into teaching, and change the teaching method in accordance with the actual needs of contemporary college students.
Identifying and Recommending Experts Using a Syntactic-Semantic Analysis Approach

Finding experts that can help address critical elements or problems in a project is a challenging task. This is especially true in global software development where there is often a need to identify developers with specific skill sets and expertise. It is also essential to identify developers that can help move the project forward. To address this, we propose an expert recommendation system to help identify and classify individuals with the expertise and skills that could collaborate on a project. To achieve this goal, we model a popular project on GitHub as a contribution network. Our approach analyzes both syntactic and semantic aspects of the data by exploring the network with machine learning algorithms and considering an ontology that can be used to extract topics from the project's key terms. Our approach looks to recommend individuals with the necessary skills, have been strong contributors in the past, and have a positive interest trend.
How does that name sound? Name representation learning using accent-specific speech generation

Searching for information about a specific person is a frequent online activity. In most cases, users are aided in the search process by queries containing a name in Web search engines. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the synonym suggestion problem by utilizing automated speech generation, and deep learning to produce novel spoken name embeddings. These embeddings capture the way people pronounce names in a particular language and accent. Utilizing a name’s pronunciation can help detect names that sound alike, but are written differently. We demonstrated the proposed approach on a large-scale dataset with more than 250,000 forenames and surnames and evaluated it on two ground truth datasets containing 7400 forenames and 25,000 surnames (including their verified synonyms). The performance of SpokenName2Vec was found superior to the 10 other algorithms evaluated, including phonetic encoding, string similarity, and machine learning algorithms. The results obtained emphasize the potential of spoken name embeddings for improved synonym suggestion.
Visual roughened sensing for private human pose recognition

The rapid development of the Internet has made people increasingly dependent on networks for information transmission. Visual media such as digital images and videos have gradually become two of the most important forms of information exchange because they can be disseminated very quickly. However, images and videos often contain private data, such as corporate secrets and personal identity information and the harm caused by leakage of such data cannot be underestimated. Therefore, the security of images and videos has attracted widespread attention from the public and related researchers. At present, the processing of images and videos tends to include the following steps: sampling, compression, encryption, transmission, decryption, decompression, reconstruction, and intelligent processing. During these steps, images and videos are encrypted and decrypted by the sender and the receiver respectively to avoid potential leakage of private data by interception during online transmission. This processing mode is mainly aimed at preventing information-leakage problems during transmission, but it ignores the risks caused by the intelligent application of images and videos after their reconstruction. For the contradiction between existing data processing methods and actual social needs, a novel visual roughened sensing is proposed for typical intelligent applications such as private human pose recognition.
Knowledge Graphs in Recommender Systems

Recommender systems are used to overcome the information overload problem and provide a personalized recommendation to the user. Recommender systems suffer from several challenges, like data sparsity and cold start problems. Knowledge graphs are proven to benefit recommender systems in multiple ways, like elevating cold start problems, tackling data sparsity problems, increasing the accuracy of recommendations, and can also be used to provide explanations for recommendations. Knowledge graphs are heterogeneous information graph that is made up of entities as nodes and relationships as edges; they store rich semantic information. Many researchers have used a knowledge graph for recommendations of movies, news, music, fashion, etc. Knowledge graph embedding or knowledge graph representational learning is the trending approach for the usage of knowledge graphs in recommender systems. Translational models like TransE, TransH are widely used and neural network models like graph convolutional network and graph attention network are seen to perform efficiently. The article, gives a background of recommender systems and knowledge graphs and then talks about some different approaches available for their integration and also presents a unique classification of available methods.
Path Index-Enhanced Incremental Subgraph Matching Algorithm for Dynamic Graph

A dynamic graph is defined by an initial graph and a graph update stream consisting of edge insertions and deletions. Given a query graph and a dynamic data graph, the problem of incremental subgraph matching is to conduct all subgraph isomorphic matchings for edge insertion. In this paper, an incremental subgraph matching, IncISM, is proposed hierarchical path index-based incremental subgraph matching algorithm for dynamic graph is proposed. Firstly, design a positive-negative-possible path index (PNPC-PI) to index the intermediate results and final results. Then, propose a incremental strategy to maintaining the intermediate results and conducting the subgraph matchings for edge insertion. Compared with SJ-Tree and GraphFlow, experimental results show that IncISM is more efficient.
Essay Scoring Tool by Employing RoBERTa Architecture

The automated essay scoring (AES) has significant importance in machine grading of student essays particularly in standardized exams like the Graduate Record Examination (GRE). However, some issues in AES have remained unsolved over the past several years. The current approaches have scrutinized AES from both classification and regression perspectives. This study discusses the cutting edge architectures such as RoBERTa, XLNet, and BERT and compares their automated essay scoring performance. ASAP, a publicly accessible dataset is used for this purpose. The obtained results indicate that the natural language understanding (NLU) model proposed in this paper depicts significantly improved performance than all the other existing approaches.
Hybrid Algorithm Based on DWT-DCT-RSA with Digital Watermarking for Secure Image Transfer

The accelerated expansion of digitalized information in terms of multimedia has created the requirement for secure encryption enforcement techniques to protect data from any illegal manipulations. Digital image watermarking is one of the technologies which is developed to preserve any digital image from any strangers or 3rd party authorities. The aim of this research paper is to develop a new technique to encrypt digital images and decrypt it with higher PSNR value. This article proposes a new algorithm that combines three techniques (DWT, DCT, and RSA) to boost the performance and security of image watermarking. Our proposed algorithm is tested against a wide range of previously proposed algorithms and the results are reported. We have also tested this proposed algorithm using different type of image formats like png, jpeg, gif, tiff, etc. This study definitively answers the question regarding watermarking and encryption of digital images.