Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF WEB-BASED NAIVE BAYES ALGORITHM FOR DETERMINING DEPARTMENTS AT SMK 10 MUHAMMADIYAH KISARAN

Determination of majors is very important for the convenience of prospective students in the process and continuation of education so that they do not experience difficulties in the teaching and learning process in the future. SMK 10 Muhammadiyah Kisaran is one of the private vocational schools in Asahan that provides 3 majors including Audio Video Engineering (TAV), Computer and Network Engineering (TKJ), and Motorcycle Engineering and Business (TBSM). SMK 10 Muhammadiyah Kisaran does not yet have a special system for selecting majors so that prospective students are welcome to choose majors according to their own wishes, not a few students find it difficult because the students themselves do not understand their abilities.so that it’s not uncommon for students to choose majors in a random way or follow their friends' choices. Therefore we need a system that can help prospective students in selecting majors that match their interests and talents and reduce mistakes in choosing majors. The technique used for the classification data mining model in this study is the Naïve Bayes Algorithm. The dataset that will be used as training data and test data is data for new students for the 2021/2022 school year, to be precise, for class X SMK 10 Muhammadiyah Kisaran obtained from the results of documentation and questionnaires. The criteria used were school origin, gender, interests, major, influence of friends, parental suggestions, math scores, English grades, and science grades. The results of the classification modeling with the Naïve Bayes Algorithm produce an accuracy value of 89%.
Using Machine Learning Models to Evaluate the Performance of Website in Iraqi Universities

Academic website played a main role in giving information about academic institutions and view the services that introduced to their users. The purpose of this research evaluates the performance of Iraqi universities websites. A dynamic regression model was built. Created a dataset with 113 university websites then preprocessed executed at first then apply a correlation statistics method for determine the most features that influenced at the prediction of performance. Lastly, eight machine learning regression models was applied. After that compared these models, SGD regressor give the best result of evaluation metrics from other models were applied. For increasing the quality of a websites performance, academic institutions should take the result obtained from this research.
Right Hand

Abstract This chapter discusses creating the best, most resonant sound possible with the bow through maintaining a constant bow speed; a straight bow; creating a plane between the arm and the bow that never changes regardless of which string is being played; leading with the wrist rather than the elbow; keeping a constant “natural” bow hold that is relaxed, open, and flexible; always approaching the string from above.
Epilogue

Abstract South Korea is no "shrimp among whales" anymore as of 2022. It has one of the strongest democracies in the world, one of the ten largest economies in the world, a globally recognized culture, and a society that makes its leaders accountable. At the same time, South Korea has to adapt and respond to issues and changes affecting fellow developed countries, such as higher levels of inequality, declining birth rates and an ageing society, the changing role and expectations of women, or growing immigration and multiculturalism. Looking at the country's history, one can predict that South Korea will continue to evolve, adapt to change, and successfully strengthen its democracy, develop its economy, promote its culture, and bring social change.
Design of Intelligent Information Platform for College Students' Volunteer Service Based on Mobile Platform

With the progress of society, college students (CS)' voluntary service has become an important content in the development and reform of higher education. This paper mainly studies the design and development of intelligent information platform for mobile platform. This paper first introduces the concept of the intelligent information platform for CS' voluntary service, then studies the characteristics and advantages of the mobile platform, and also introduces the computer algorithm. Based on this research, the intelligent information platform for CS' voluntary service is designed, and the performance of the platform is tested. Finally, the test results show that the performance of the platform can basically achieve good or even excellent. This shows that the platform can meet the basic needs of users, and make it truly become the power of innovation and development in the new era of college education and teaching.
Longing for Our Conversations: Correspondence between Nikolai Zhinkin and Roman Jakobson

“Export” of intellectual culture or “transfer of ideas” is one of the most urgent problems of philosophy. The trajectories of the movement of thought are difficult to trace in historical contexts. The archive of the epoch becomes the foundation of such historical and philosophical research and requires, among other things, an un­derstanding of the epistolary heritage of intellectuals. We present the correspon­dence of two Russian humanitarians, R.O. Jakobson and N.I. Zhinkin, who be­longed to the same “sphere of conversation” in the 1920s and who resumed communication in the 1960s. They wrote letters to each other from 1958–1979, ex­changing publications and opinions on various scientific and methodological prob­lems, as well as discussing the possibility of translating Zhinkin’s “Mechanisms of speech” (1958). Their correspondence makes it possible to clarify the trajecto­ries and transformations of phenomenological ideas in Russian intellectual culture of the second half of the 20th century. Zhinkin’s book “The Mechanisms of Speech” is acquiring no less significance today. At its foundation, the author puts the idea of communication, which will allow him to reach such a complex method­ological problem as an interdisciplinary synthesis. Jakobson appreciated Zhinkin’s research because he believed that such phenomena as language, speech, and thought require a holistic structural approach, where joint physiological, psycho­logical and philosophical research can give excellent results.
A Smart LED Cane to Monitor Older People Living Alone for Considering Privacy

In recent years, the number of older people living alone has increased in Japan. Older people living alone may suddenly require urgent medical attention or treatment. However, some people in this population may not be able to monitor their physical condition; naturally, some abnormalities can only be observed by others. A wide variety of monitoring systems have been developed. This study considers cases in which older people living alone may experience a medical emergency and require urgent care. We propose a method in which an accelerometer is attached to a cane to collect behavioral data and display it with LED indicators. To avoid privacy violations, the LED display is read by a camera positioned so as not to be visible to people other than the user. We developed an implementation of the cane and confirmed the issues involved in the experiment. Research on this project is ongoing at present.
An avoiding overlap method between acoustic shadow and organ for automated ultrasound diagnosis and treatment

Ultrasound diagnosis and treatment have attracted attention because they are noninvasive and allow real-time observation of lesions. However, it is difficult to accurately estimate the location of the treatment area because of information loss due to acoustic shadows caused by the reflection of sound waves from hard tissues such as bone. In this study, we propose a probe manipulation method that uses deep learning to avoid overlap between organs and shadows for automated diagnosis.
Development of a VS ultrasound diagnostic system with image evaluation functions

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system.
Autoencoding Graph Neural Networks for Scalable Transceiver Design

Autoencoder (AE) techniques have been intensively studied for the optimization of wireless transceivers. However, fixed computational structures of existing AE models lack the flexibility to the lengths of message bits and codewords. This work proposes a versatile AE framework, termed by autoencoding graph neural network (AEGNN), where both encoder and decoder are realized by GNNs. The viability of the proposed AEGNN is demonstrated in various application scenarios.
Similarity Search with Graph Index on Bayesian Network Embedding

Similarity search on Bayesian network (BN) could retrieve K nearest neighbors (KNNs) to the query node via probabilistic inferences. To implement similarity search on BN, we first propose Bayesian Network Embedding (BNE) to map a BN into the low-dimensional vector space. Then, we propose the method of Graph Index on Bayesian Network Embedding (GIBNE) to express the neighbor relationship. Following, the greedy best-first search algorithm is adopted to perform approximate KNN search on GIBNE. Experimental results show that our proposed method outperforms the state-of-the-art competitors on both search precision and queries per second.
Hierarchical Blockchain-based Resource Access Control Architecture and Scheme for IoT Devices

Traditional access control schemes in Internet of things (IoT) systems have long been faced with security problems such as single point of failure, data tampering and cross-domain access. In this paper, we study the access control problem in an IoT system composed of multiple domains. Applying blockchain technology, we propose a hierarchical blockchain-based access control architecture which consists of two layers of blockchain networks, i.e., global blockchain network and local blockchain network. Based on the proposed architecture, the processes of inter-domain and intra-domain access control are examined, respectively. In order to achieve the secure access of user data in the proposed architecture, we further present a trust-value based access control strategy. To examine the performance of proposed architecture and strategy, we set up an access control simulation platform based on Hyperledger Fabric and verify the feasibility of the proposed architecture and strategy.
Trend Prediction of Students’ Mock Examination Results Using Matrix Completion

In this paper, we propose a method for predicting scores of mock examinations taken by high school students using the matrix completion method. In the proposed method, we adopt the matrix completion method to predict the scores from the results of the past mock examinations. The matrix completion method can solve the problem of missing information due to different mock examinations taken by each student. In practice, we adopt singular value decomposition and use the estimated pseudo-information to solve the problem. This is the first attempt to realize accurate prediction of scores of mock examinations using the matrix completion method with those of the past mock examinations, and this is the main contribution of this paper. The evaluation is performed by measuring the difference between the original information and the information predicted by using the proposed method.
Fall Prediction Based on Head-Mounted IMU Sensor System

Owing to the danger of falls, many fall detection systems have been proposed. However, fall detection cannot avoid the injuries and discover the falls in advance. This work proposes the algorithm of the fall prediction, which alarms before the impact from the fall. In order to identify the forecast of the fall, this algorithm, based on the preset threshold, analyzes the inertial information from the accelerometer and the gyroscope. In the experiment, 5 subjects carried out the simulated falls and the daily activities, which were predicted correctly by the proposed algorithm with the sensitivity of 81.4% and accuracy of 79.4%. Moreover, the time interval between the alarm and the impact was 0.287 seconds in average from the experiment.
Development of a Mixed Reality System Based on IoT and Augmented Reality

The Internet of Things or IoT describes the network of physical terminals, the “objects” that integrate sensors, software, and other technologies to connect to different terminals and systems on the Internet and exchange data with them. These terminals can be simple household appliances and industrial tools of great complexity. Augmented Reality (AR) provides operators with information directly in their field of vision, using a helmet or adapted glasses. The obstacles to the adoption of Augmented Reality are related to several factors. First, the devices do not yet offer efficient and discreet ergonomics and are still expensive. While IoT and sensors are focused on the productivity of machines, Augmented Reality makes it possible to increase the performance of a human. Industrial Augmented Reality aims to improve the factory’s economic performance. This article proposes a hybrid solution between mixed Reality and IIoT for industrial applications.
Proposal of a Transportation Routing Method for Swarm Robots in WLAN Environment

The Vehicle Routing Problem (VRP) is an optimization problem that satisfies various constraints and minimizes the total route cost by multiple vehicles. Solving VRP can optimize transportation routes by swarm robots for pick-up and delivery of the loads in the warehouse or factories. In this paper, we propose a transportation routing method based on an A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm and a Hill Climbing (HC) for the transportation with a WLAN connected Swarm Robot (WSR). The proposed method optimizes the transportation sequence and decides the transportation route of each WSR considering WLAN connectivity.
PEP 8 Compliance Analysis for Python Open Source Projects

he use of the Python language in large projects in combination with modern development methodologies entails a number of tasks for checking and standardizing program code in order to improve its quality. For the Python language, PEP 8 is the current standard. Verification procedures are performed by specialized software tools — linters that use static analysis methods. This paper investigates the main violations of the PEP 8 standard in Python open source projects. In addition, an analysis was made of the effectiveness of automatic formatting of program code using the Black and YAPF formatters.
Problems of Automatic Processing of Scientific Texts based on Extraction of Information from Encyclopedias of Relevant Domain Areas

The article discusses the problems arising in the automatic processing of scientific texts and presents the results of work on creating a combined method for aspect-oriented analysis of scientific texts in the field of fundamental disciplines, taking into account both knowledge of the subject area and statistical methods of text processing. Thematic encyclopedias, which are not only a source of professional scientific terminology, but are considered to be an information resource for extracting knowledge about the subject area, are proposed to be used as training data. The work offers the structure of templates designed to extract information from the partially structured text of the encyclopedia, considers the structure of extracted sets of professional terms, offers the algorithm of formation of semantic relationships between special terms. The process of knowledge extraction in this paper is demonstrated on the example of processing four encyclopedias: mathematical, physical, chemical, medical. The general principles of the formation of domain scientific terminology are highlighted, and statistical data on the terminological composition in each of the examined areas is given. Within the framework of the conducted research on the basis of the texts of encyclopedias the basic semantic graphs of the corresponding scientific fields with the relations between the professional terms introduced on them are constructed. Basic graphs accumulate knowledge about the scientific field and are intended for the subsequent thematic analysis of unstructured texts of scientific articles. The Implemented algorithm of extraction of semantics of the given scientific text is based both on amplification of weights of nodes — terms of the applied domain, and on the correction of semantic relations between the nodes of the graph according to the processed text. The results of experiments on automatic construction of the list of keywords of the article are given. The results were compared with the list of keywords specified by the author of the article. It should be noted that the relevance of correctly extracted terms is mainly determined by semantic links in the basic domain graph, and depends significantly less on the number of keywords in the original article, which demonstrates the advantage of the proposed combined method compared with a simple frequency analysis. The sample analysis of the texts of the articles on mathematics showed good accuracy in the extraction of key terms compared to the list of keywords specified by the author of the article.
A study on the impact of demonetisation on cryptocurrency in India

Unaccounted wealth (UW) is a large unsolved problem in India. The demonetisation exercise in November 2016 was an attempt to address it. As regulatory measures are taken to tackle UW, criminals identify newer ways to avoid detection, e.g., cryptocurrency usage for illicit activities, as it bypasses formal banking channels. This article examines evidence of change in cryptocurrency usage in the context of demonetisation. This is done by examining four datasets viz. keyword search for bitcoin (BTC), volume of BTC traded in India, price variation between USD/BTC/INR and BTC blockchain transaction dataset. The data indicates heightened awareness of cryptocurrency, increase in volume traded and increase in price spread between BTC/USD/INR, though unable to discern deviation in blockchain dataset due to India led transactions. Increasing BTC usage, given inability to regulate these transactions, supports move by Government of India to ban its usage till the technology and its implications are understood better.
A decision tree-based method for detecting middle school students' behaviour characteristics in online English learning

In order to solve the problem of low accuracy and long detection time caused by poor feature extraction effect of online English learning students' behavioural characteristics detection, this paper proposes a method of online English learning students' behaviour characteristics detection based on decision tree. Firstly, the concept and structure of decision tree are analysed, and the classification steps are designed. Secondly, weighted principal component analysis was used to extract the behaviour characteristics of students. Then, the characteristic data is standardised. Finally, the C4.5 decision tree algorithm is used to construct a student behaviour feature detection model to detect students' behaviour characteristics in online English learning. The experimental results show that the feature detection rate of the proposed method is as high as 99.5%, the accuracy is 96.2%, and the detection time is 19.8 s. Therefore, the feature detection effect of the proposed method is good, the accuracy is high, and the detection time is effectively shortened.
Image detail enhancement of two-dimensional animation scene based on dual domain decomposition

In order to improve the performance of traditional image detail enhancement methods in distortion, real-time and detail information error, a new two-dimensional animation scene image detail enhancement method based on dual domain decomposition is proposed in this paper. Firstly, the two-dimensional animation scene image is divided into basic image domain and image detail domain by double domain decomposition. Then, the basic image domain is reconstructed and the image detail domain is denoised. Finally, the image detail enhancement is realised by fusing the results of information reconstruction and denoising. The simulation results show that the enhancement process of this method takes at least 11 minutes, and the maximum local standard deviation can reach 0.975, which is closer to 1.000, indicating that this method has the advantages of low distortion, high real-time performance and small detail information error.
Rectangular honeycomb structure as absorber structure for road crashed barrier application

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Awangku Muhammad Asyraf bin Pengiran Haji Mat Rais, Yulfian Aminanda; Rectangular honeycomb structure as absorber structure for road crashed barrier application. AIP Conference Proceedings 10 January 2023; 2643 (1): 050068. https://doi.org/10.1063/5.0110748 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
3D non-immersive VR game for process safety education

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Salwa Sofri, Reddy Prasad D. M, Hazwan Azri, Aisah Timbang; 3D non-immersive VR game for process safety education. AIP Conference Proceedings 10 January 2023; 2643 (1): 060011. https://doi.org/10.1063/5.0110924 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The role of disruptive technologies on accounting

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Mayamin Adibah Muhd Masa Masdi, Siti Wardah Hadanan, Nur Syahmina Afiqah Zamain, Aziz Hanafi Hasnan, Muhd Hazim Syakiran Julaihi, Bazilah Nuni Qurratu’Aini Sani, Fadzliwati Mohiddin, Ulaganathan Subramanian, Norazillah Abdullah; The role of disruptive technologies on accounting. AIP Conference Proceedings 10 January 2023; 2643 (1): 050053. https://doi.org/10.1063/5.0110329 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search