Reviewerly/exHarmony huggingface dataset:
Comparative study of fuzzy symbiotic organism search variants for pairwise test suite generation

Metaheuristic algorithms have been utilized for the past 30 years as a core in solving complex optimization problems because of their ability to explore (i.e., roaming the entire search space) and exploit (i.e., searching around the neighbourhood). Most of these algorithms rely on parameter control to balance this exploration and exploitation to find the best solution. However, tuning these parameters is problematic as they are problem-dependent, and an improper tuning of these parameters undesirably increases computational efforts and yields sub-optimal solutions. Fuzzy Symbiotic Organism Search (FSOS) is among the latest parameter-less meta-heuristics algorithm created to solve optimization problems by having an adaptive exploration and exploitation based on the search need. As this new algorithm is dependent on a Fuzzy Inference System (FIS), the interest in investigating the fuzzy design choice in FSOS has emerged to make sure the choices of the Fuzzy Inference System in FSOS are capable of solving the general optimization problem without overfitting or underfitting. In this paper, we present the effects of different versions of fuzzy rules in the FSOS Fuzzy Inference System on the performance of FSOS. Experimental results demonstrate that the original FSOS with fuzzy rules that cover most of the antecedent combinations supersedes the other combination by 0.7% (FSOS1) and 0.3% (FSOS2).
Review of &lt;ToegyeSeonsaengHwanriga(退溪先生歡理歌)&gt; and Comparison of Different Versions(異本)

This paper compares the introduction of &lt;ToegyeSeonsaengHwanriga(退溪先生歡 理歌)&gt;, one of the six lyric works included in the book 《Cheongrimdosa Jijiga(靑林道 士 知止歌)》 and examined its varlants. &lt;ToegyeSeonsaengHwanriga(退溪先生歡理歌)&gt; is an example of instructive lyrics, stating that we should never forget the grace of our parents who gave birth to us, realizing that humans are the most precious of all things. In order to steadily practice the principles of being human, the Three kang(三綱) or the five principles(五倫) are actively recommended, and it is emphasized that others are encouraged to do good deeds and build up virtue. &lt;ToegyeSeonsaengHwanriga(退溪先生歡理歌)&gt; has several different versions showing illustrating the same narrative process. These are the three pieces of &lt;LeeYulgokSeonsaenggwonseonga(李栗谷先生勸善歌)&gt;, &lt;Toegyeseonsaengkwonseongara(退溪先生勸善歌)&gt; and &lt;Kwonseonga(勸善歌)&gt;, which are housed in the Korean Gasa Literature Museum. The composition of the work is generally similar between the different versions, but there are many differences in notation and omissions or typos in phrases or vocabulary. Above all, the point of attention while examining these different versions is the issue of the title of the work and the author. &lt;Toegye SeonsaengHwanriga(退溪先生歡理歌)&gt;, &lt;LeeYulgokSeonsaenggwonseonga(李栗谷先 生勸善歌)&gt;, and &lt;ToegyeSeonsaengkwonseongara(退溪先生勸善歌)&gt;, etc. are titled after the names of two thinkers who represent Joseon Neo-Confucianism, ‘Toegye’ and ‘Yulgok’, so the original authors of the works are identified as different persons. that it does. This can be found in the distribution process of gasa in the late Joseon Dynasty that reflected the times of the time. This paper examines the authorlal intention and distribution process of these Gasa works with reference to &lt;Toegye SeonsaengHwanriga(退溪先生歡理歌)&gt; and discusses their material value.
Research on Association Rules of Train Communication Network Fault Alarm Based on Data Mining

With the continuous development of intellectualization and integration of high-speed train, the system units such as on-board equipment and lines become more and more complex, and the operation status and fault alarm data transmitted and stored in the train communication network are gradually increasing. How to obtain the correlation between fault alarms from fault alarm information data and form the relevant diagnosis knowledge is of great significance to training fault location and safe and efficient operation. FP-Growth algorithm is one of the classical algorithms for mining association rules in data mining methods, which is used to mine frequent item sets of data. Based on the analysis of real fault alarm data in a high-speed railway communication network, the association rules of fault alarm are studied by combining data mining algorithms. Experimental results show that the FP-Growth algorithm can effectively mine the association between train state information and fault information, form an effective alarm rule base, and improve the maintenance efficiency for operators.
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection

Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real world. By facilitating labeled links of the graph as the training data, numerous deep learning-based link prediction methods have been studied, which have dominant prediction accuracy compared with nondeep methods. However, the threats of maliciously crafted training graphs will leave a specific backdoor in the deep model; thus, when some specific examples are fed into the model, it will make a wrong prediction, defined as a backdoor attack. It is an important aspect that has been overlooked in the current literature. In this article, we prompt the concept of a backdoor attack on link prediction and propose Link-Backdoor to reveal the training vulnerability of the existing link prediction methods. Specifically, the Link-Backdoor combines the fake nodes with the nodes of the target link to form a trigger. Moreover, it optimizes the trigger by the gradient information from the target model. Consequently, the link prediction model trained on the backdoored dataset will predict the link with a trigger to the target state. Extensive experiments on five benchmark datasets and five well-performing link prediction models demonstrate that the Link-Backdoor achieves the state-of-the-art attack success rate under both the white box (i.e., available of the target model parameter) and the black box (i.e., unavailable of the target model parameter) scenarios. In addition, we testify the attack under the defensive circumstance, and the results indicate that the Link-Backdoor still can construct a successful attack on the well-performing link prediction methods. The code and data are available at https://github.com/Seaocn/Link-Backdoor.
Exploring Mind Powers of Rishis Inspired Optimization Algorithm for Genuine Power Loss Reduction

In this paper Exploring mind powers of Rishi inspired optimization (EMPRO) algorithm is applied for solving the Genuine Power Loss Reduction problem. In projected EMPRO algorithm how the Rishi or Yogi mind is reconnoitering and uniting in a particular region is emulated to model the EMPRO algorithm. Numerous junctures Yogi will purse the mediation by setting a particular facet. In this work two Yogis are considered in the scientific design and Procedure produces new investigational coordinates from end to end perplex; consequently, this perplex scheme is the prime. Succeeding investigational coordinates are produced in arbitrary manner Together the Migration coordinates and functional values create the Counterbalance and it gets altered rendering to specific Yogi. This computation is idiosyncratic by means of choosing migration coordinates and through that Counterbalance will be calculated. Chaotic features are integrated into the algorithm to enhance the quality of the solution.
A winter sports video classification method and system based on 3D-video Swin transformer

Videos of winter sports can be a very good resource for learning about sports in China. In this paper, we address the issues of a small winter sports video dataset and low classification accuracy by proposing a fast video classification approach that combines a transformer and a convolutional neural network. The 3D-Video Swin Transformer model is built using the resnet3D feature extraction component and the Video Swin Transformer, and it improves local and global modeling capabilities through a multi-headed self-attention mechanism. Convolutional operations are used at the network front-end to make for the Transformer's lack of inductive bias, enhancing the network's local modeling capabilities. Convolution operations are utilized at the network's front end to make for Transformer's lack of inductive bias, hence increasing the network's capacity for local modeling and decreasing the model's dependency on vast amounts of data. The experimental results indicate that the 3D-Video Swin Transformer model may achieve an accuracy of up to 76.43 percent on the winter sports video dataset developed in this paper. The classification impact is also substantially stronger, and this accuracy is also 1.15 percent higher than that of the Video Swin Transformer Network. Additionally, we develop and implement a winter sports video classification system based on the Milvus database to facilitate user interaction and enable the submission, categorization, and recommendation of winter sports movies.
Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database. We propose two different methods that greatly reduces the size of neural WSD models, with the benefit of improving their coverage without additional training data, and without impacting their precision. In addition to our method, we present a WSD system which relies on pre-trained BERT word vectors in order to achieve results that significantly outperform the state of the art on all WSD evaluation tasks.
Research Study of Thesis Writing by the Graduate Students of Education: Focused on Music Education Major, Graduate School of Education, ‘A’ University in Gyeonggi-do

본 연구는 교육대학원생의 학위논문 작성에 관한 조사 연구로서 보다 개선된 학위논문 관련 운영방침을 수립할 목적으로 진행되었다. 이를 위해 경기도 소재 A대학 교육대학원의 음악교육전공 학위논문 작성자 23명을 대상으로 집단심층면접(FGI)및 설문조사를 실시한 결과 다음과 같은 결론이 도출되었다. 첫째, 원생들은 주로 자발적 동기에서 학문적인 성취 및 향후 커리어를 위해 학위논문을 작성한 것으로 나타났다. 둘째, 논문작성법/연구방법론 교과 수강과 관련한 조사에서는 수업의 내용 및 실용성 측면에서 비교적 낮은 만족도를 나타내고 있었다. 셋째, 원생들은 지도교수가 논문 작성에 큰 영향을 주는 것으로 인식하는 가운데 1:1 개별지도 방식 및 지도교수와의 소통 측면에서 긍정적인 경험을 한 것으로 나타났다. 넷째, 논문작성 과정에서 원생들이 경험한 가장 큰 어려움은 막막함, 자신감 하락과 같은 심리적 요인이었으며 작성의 실제에서는 ‘연구주제 선정’을 가장 큰 난제로 꼽았다. 향후 교육대학원 원생들의 입학 목적, 특성 및 심리적 측면 등을 고려한 논문 지도와 논문 교과의 설계, 운영이 필요하며 대학 당국은 학위논문과 관련된 기타 행정 서비스 및 관리에 관심을 가지고 환경을 개선해 나가야 할 것이다.This study is a survey study of thesis writing by the graduate students of education, conducted aiming to establish a more improved thesis-related operational policy. For this purpose, this study conducted a Focus Group Interview (FGI) and survey with 23 music education majors in the Graduate School of Education at A. University in Gyeonggi-do, who wrote a thesis, and the following results were drawn. First, the students wrote the thesis to help academic achievement and career from voluntary motivation. Second, in the curriculum, their satisfaction with taking the thesis writing guide/study methodology was at a relatively low level. Third, while the students recognized that the academic advisers had a great impact on thesis writing, they had a positive experience in terms of 1:1 individual instruction and communication with the academic advisers. Fourth, the biggest difficulties experienced by the students in the thesis writing process were psychological aspects, like not knowing what to do and the loss of self-confidence, and they picked ‘the selection of a research topic’ as the biggest challenge in the actual writing. It will be necessary to operate and guide the thesis subjects, considering the purpose, characteristics, and psychological aspects of entering the graduate school of education, and the university authorities should be interested in other administrative services and management concerning the thesis to improve the education environment.
Estimate Gaze Point of Table-Meeting Participants by Spherical Camera

In this paper, we propose a method of estimating the gaze point of table-meeting participants by a spherical camera. Assuming participants are sitting around a table and observed by a spherical camera, multiple persons and their gaze points can be observed by a single camera simultaneously. In contrast with the previous research on estimating gaze points using a spherical camera based on a spherical model, we develop a deeper neural network and add a normalization term to the cost function which constrains estimated gaze points onto a unit sphere. As shown in the results of the comparative experiments, the proposed method improves the accuracy of gaze point estimation significantly.
A High-Dynamic-Range Mesh Screen VR Display by Combining Frontal Projection and Retinal Projection

We proposed a high-dynamic-range virtual reality (VR) display that can represent a glossy material by combining a conventional frontal projection mapping and a retinal projection light passing through a screen of a microporous plate. In the prototype system, the retinal projection could be superimposed on the frontal projection and increase the luminance of only the specular highlight in the image. This paper also reports approximately six hundred times brighter presentation of the retinal projection than that of the frontal projection.
TRENDS AND DIRECTIONS OF DEVELOPMENT OF UNIVERSITIES IN THE DIGITAL SCIENTIFIC AND EDUCATIONAL ENVIRONMENT

The digitalization trend has fundamentally changed the operating conditions of modern universities and contributed to the formation of new market segments. The use of modern information technologies has made it possible to improve the manufacturability and individualization of educational and research processes, ensuring the achievement of a new quality of the digital scientific and educational environment. New areas of development of the digital scientific and educational environment based on the use of information technology capabilities include gamification technologies, virtual and augmented reality, robotics, cloud technologies, artificial intelligence, blockchain, virtual assistants and the use of social networks in education, research and innovation and other fields of activity. universities. At the same time, these technologies can be applied in the context of developing a software package and information and methodological support for the process of forming and developing sustainable competitiveness of employees of modern universities.
GSWIN: Gated MLP Vision Model with Hierarchical Structure of Shifted Window

Following the success in language domain, the self-attention mechanism (Transformer) has been adopted in the vision domain and achieving great success recently. Additionally, the use of multi-layer perceptron (MLP) is also explored in the vision domain as another stream. These architectures have been attracting attention recently to alternate the traditional CNNs, and many Vision Transformers and Vision MLPs have been proposed. By fusing the above two streams, this paper proposes gSwin, a novel vision model which can consider spatial hierarchy and locality due to its network structure similar to the Swin Transformer, and is parameter efficient due to its gated MLP-based architecture. It is experimentally confirmed that the gSwin can achieve better accuracy than Swin Transformer on three common tasks of vision with smaller model size.
Optimising Different Feature Types for Inpainting-Based Image Representations

Inpainting-based image compression is a promising alternative to classical transform-based lossy codecs. Typically it stores a carefully selected subset of all pixel locations and their colour values. In the decoding phase the missing information is reconstructed by an inpainting process such as homogeneous diffusion inpainting. Optimising the stored data is the key for achieving good performance. A few heuristic approaches also advocate alternative feature types such as derivative data and construct dedicated inpainting concepts. However, one still lacks a general approach that allows to optimise and inpaint the data simultaneously w.r.t. a collection of different feature types, their locations, and their values. Our paper closes this gap. We introduce a generalised inpainting process that can handle arbitrary features which can be expressed as linear equality constraints. This includes e.g. colour values and derivatives of any order. We propose a fully automatic algorithm that aims at finding the optimal features from a given collection as well as their locations and their function values within a specified total feature density. Its performance is demonstrated with a novel set of features that also includes local averages. Our experiments show that it clearly outperforms the popular inpainting with optimised colour data with the same density.
End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles

Autonomous driving vehicles rely on sensors for the robust perception of surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor systems bring up the requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype combines camera and range sensors LiDAR and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications, also suitable for quick and large-scale practical deployment.
Pedestrian action recognition in infrared image based on improved AlexNet

Human action recognition is one of the core tasks in the field of computer aided driving. Considering that the auxiliary driving system requires high real-time, and the hardware requirements can not be too high, it is proposed to identify human behavior in a single image. Considering that the night illumination is insufficient, and the infrared camera receives the infrared radiation of the object, it can work at night without the influence of visible light. Therefore, we focus on the human behavior recognition in infrared images. According to the scale of the problem, we first use AlexNet with moderate network depth as the backbone network, then improve the network, modify the classification output layer of the network according to the classification number. After preprocessing the dataset to adapt to improve AlexNet, we trained and tested the network. The experimental results quantify the classification performance of the network. Experimental results show that the proposed algorithm mean average precision, average recall and F1 score are better than traditional methods.
The wider context of ERL Journal

The experiencing of language, which was the eponymous issue of the first volume and which acquires both an individual and social character, laid the foundations for ERL Journal. It has prompted us, meaning academics involved in the informal so-called “ERL circle”, to seek what can be referred to as the glottodidactic paradigm, the essence of which is consideration of educational phenomena through the prism of language. Its scope is very wide and implies far-reaching interdisciplinarity and assistance of a large community of researchers including theoreticians of education, applied linguists, psycho-, socio- and neuro-linguists, to mention just a few subgroups. ERL Journal remains open to papers by all of them due to the simple fact that only through their systematic and well-informed cooperation can we get “to the heart” of the educational role of language. By referring to the general goal of those whose interests intersect language and education as “a paradigm”, we convey the view that owing to the salience of language across multiple subjects and disciplines, the world of education can well be studied and advanced by application of terms and methods traditionally associated with language and (especially second) language learning.
Professional Characteristics and Liberal Arts Character of Classical Literature Education Text: Focusing on Its Status as a University’s Major Education

Traditionally, there has been a general perception that universities are immersed in specialized research that creates new knowledge, and the contents of education established hereby are spread through secondary education. However, as Korean language education itself moved away from academic-centered knowledge education and moved to a communication-centered function center, classical literature education also has had a gradual change from the education content focusing on the existing literature theory and history of literature to education centered on the linguistic attributes of literature. Classical literature has two educational properties. One is a property that is constantly enjoyed and reinterpreted while being handed down over a long time, which has a certain emotional influence even today. This can be conceptualized as a liberal arts character. The other is the fact that it shows the original form of culture as a material that contains the appearance of life at the time. This gave it an academic status of acquiring literacy for classical texts. I attempt to conceptualize this in terms of professional characteristics. Every classical literature text has its own inherent value. However, even if the original text is valuable, it does not immediately acquire educational value. ‘Liberal arts’ refers to creative understanding or knowledge that is embodied through acquiring a certain cultural ideal, and its content changes according to the changing times or the cultural ideology of a nation. As an educational philosophy of classical literature, I emphasize that ‘culture’ has a meaning encompassing history and philosophy. Here, the concept that responds well to culture is considered liberal arts. At this time, in the case of Korea, attention is paid to the concept term of ‘culture’ in the composition of the curriculum. In the case of Japan, the concept term of ‘liberal arts’ is highlighted as important in the composition of the curriculum. Here, I will not make a value-based evaluation of the difference between the two countries. The difference in perception between the two countries is because each has its own historical background and reason. This trend comes to affect the university curriculum in Korea, and even in undergraduate major classes at universities, there has been a situation where we are thinking about new teaching methods. As a result, it shows the characteristics that flow from the existing professional characteristics to the strengthening of liberal arts characteristics.