Reviewerly/exHarmony huggingface dataset:
Course Classification of Online Learning Platform Based on Sentence-Bert Model

A large number of online learning platforms and the explosive growth of the types and quantity of resources on the platform greatly increase the difficulty of learning content selection. Traditional language search uses resource ranking to build index, which cannot meet the needs of professional and accurate search. An intelligent search method is proposed in this paper using Bert language model for pre-training to improve the learning and reasoning ability of the machine. Based on Sentence-Bert model and the concise and effective twin network (Siamese), the sentence vector features are generated to complete the downstream search task. Finally, experiments on the online learning resources of MOOC and the State Grid were analyzed on Google Colab platform, and results show that achieve rapid keyword matching of relevant courseware names.
Preventing drowning in information: a topic model approach to relating information on Strategic Scanning

Information overload lead managers to not adequately use the relevant information they collect in Strategic Scanning. Such information comes in small pieces of text that are dispersed in terms of time, language, and sources. These characteristics of information from Strategic Scanning usually prevent to identify connections with the previously collected information. In this paper, we propose an alternate tool for dealing with this issue by using topic analysis techniques. The tool provides a quick reading interface, in which the proximity relationship between various texts can be easily visualized. We compare our tool with two mechanisms for clustering and proximity measurement. Our tool excelled in terms of execution time and of the pertinence of results.
Challenges and Factors Affecting Cloud Computing Adoption in Higher Technical Education Institutions in Libya

Cloud computing has attracted more attention in the recent years from industry and academic as new technology paradigm. It is a model that provides services on-demand over the internet. With the spread of Corona pandemic around the world, it has restricted the movement of the people. Therefore, it becomes necessary to find alternative methods for service provisions. In spite of the apparent economic crises in Libya, there has been increasing pressure for higher technical educations sector to deliver better services using minimal resources. The aim of this study is to investigate the main factors and barriers which may affect or prohibit cloud adoption in the higher technical educational institutions of national board of technical and vocational education in Libya. A cross-sectional design method is used to answer the research questions based on Technological Organizational and Environmental (TOE) framework. In this study, the main factors that affect the cloud computing adoption in technical colleges and higher institutions where characterized and tested using SPSS software, robust analysis tool for hypothesis testing approach. The results of this study showed that the technological readiness and complexity of the technology were the most factors affecting the adoption of cloud computing in the technical institutes. The most barriers preventing the adoption of cloud are almost 59.7% the Internet connectivity, robustness and availability. Finally, the preferred cloud computing services to be adopted in the technical colleges and institutes is 52.11% IaaS.
A Comparative Approach for Email Spam Detection Using Deep Learning

The importance of electronic mail in today’s Internet-based world has been increased over the last decade. In every organizations, emails are used for communication which are more effective and convenient way. The increased amount of unwanted emails of different type creates a need to develop a robust email spam detection techniques. The machine learning (ML) techniques are suitable for this purpose due to the automatic learning process. But, due to increased the volume and dynamic nature of the emails, these technique are not adequate. In this paper, we have proposed an email spam detection technique which uses deep learning concept for the spam email detection. It provides more accurate detection rate as compared to other existing ML techniques.
Performance Analysis of DES Secured FPGA

In this paper, we will demonstrate the useful & adjustable functionality for the Data Encryption Standard (D.E.S.) algorithm. The design that we used is built in Field Programmable Gate Array (F.P.G.A.) for the VirtexEXCV400e device. As a strategy to reduce the sophisticated design approach, we used the same design that allowed eight D.E.S. S-boxes to be counted simultaneously. An examination of the design used suggests that it is practicable to produce data in 16 clock cycles while using the pipeline-free method. On the other hand, when using the pipeline method, only 17 clock signals would be required in the first stage, and one clock signal will be sufficient for every data output cycle, very high-speed integrated circuit is implemented for hardware description language (VHDL) design editing.
Code Reuse and Formative Assessment in Secondary Education

Code reuse is a widely accepted practice in software engineering. Not surprisingly, the practice is becoming more and more widespread also in the context of computing education. This paper explores the use of code reuse in secondary education, focusing on the challenges that are connected to formative assessment. The paper presents the results of an interview study with 12 teachers teaching computing in secondary schools. Teachers have been asked about their attitudes towards code reuse in education, challenges with formative assessment and code reuse, and best practices to address the identified challenges. The study shows that attitude and approaches towards code reuse in the classroom span wide. Some teachers are open and positive to include it as a practice, while others are more restrictive. Most of the reported challenges revolve around the dilemma between learners reusing code to be efficient and create complex programs, but at the expense of adequately learning and understanding what they do and practicing general and specific programming skills. The paper also presents some lessons learned that can be directly relevant for educators. some lessons learned that can be directly relevant for educators.
러시아문학 강좌의 온라인 수업 모델 연구

Most of the undergraduate education was converted to non-face-to-face distance learning in the unprecedented situation of Corona 19, but before that, cyber universities, K-MOOC, and flip-running level distance learning were virtually the only form of distance learning in the country. It was generally provided in the form of teacher's lecture video content. Among Corona 19 undergraduate education institutes that defaulted to these limited experiences resulted in considerable dissatisfaction among students. In fact, with regard to online education, not a few previous studies have been accumulated in the country, but the studies so far have generally been based on meta-research utilizing domestic and foreign research cases or cases of cyber universities. Therefore, empirical research on non-face-to-face distance learning at general universities has recently emerged based on the experience of the first semester of 2020. Before Corona 19, the author had the experience of running a major course centered on student participation by breaking out of the professor's lecture-centered class. And the author had an improvement plan for the same course that will be opened in 2021, based on this course management experience, student satisfaction surveys and questionnaire surveys. However, depending on the situation of Corona 19, the course may be operated in the form of non-face-to-face distance learning in 2021, so it was decided to proceed with this research due to a practical need. The author reviewed previous research on the theoretical foundations of effective online education and research on implementation based on it, and combined his course management improvement plan with it. The core of this paper is how to effectively manage the teaching and learning components that make up it. In many school settings, real-time lessons and non-real-time lessons, zoom lessons and lecture video content-based lessons are often confused by choosing only one of these. As lessons in universities progressed for nearly a year among Corona 19, the so-called distance learning style, which is a mixture of real-time and non-real-time, zoom and video lectures, has already been instinctively adopted by some of advanced teachers. Through this paper, the author designed and presented the grounds for the need for hybrid distance learning and the lesson model of educational content distribution methods such as real-time and non-real-time. As I presented, how effective the lesson model is in actual lesson management, and whether this can be a lesson model that can be continuously managed without the overwork of teachers, will be discussed in the future. It should be verified through practice studies.
Fusion Based Approach for Enhancement of Low - Illumination Imagery

Photographs captured in dim-light conditions experience low visibility and low distinctness. Due to the poor visual aesthetics of photographs, the output of many algorithms' degenerates. Due to their poor visibility, low-light images are not favorable for human examination. While several strategies for image enhancement have been suggested for low light photography, these approaches introduce under-enhancement or over-enhancement of contrast. In this paper a hybrid model has been proposed that focuses on low light photography exposure and contrast enhancement. Experiments demonstrated that compared with several different approaches, the proposed approach results in less contrast and less distortion. In turn, to improve low-light images, a comparison between them draws conclusions unique to creating a method. The proposed method works comfortably better than the existing system with respect to brisque and entropy as comparable metrics.
Leveraging Unstructured Text Within the Context of Conversational Agents

We propose exploring alternative designs for a conversational agent developed as a tool to provide feedback within the education domain for pre-service teachers, students pursuing their teaching certificate, to practice their questioning skills in a given scenario. We utilize a component-based approach in the design of our conversational agent and this research focuses on proposing methods within the knowledge base component specifically leveraging unstructured text as the foundation of the knowledge base. Through leveraging unstructured text we intend to explore the possibilities of improving conversational agent response quality while minimizing resources required of domain experts in scenario development.
On the Utility of Combining Topic Models and Recurrent Neural Networks

Learning under deep neural network models has shown interesting abilities to generate semi-structured sequences. Such generated fragments can appear to be remarkably like real data, yet applications of these generative techniques are generally lacking. This is partly due to the inability of recurrent neural network sequences to be semantically connected amongst long related fragments. One way to increase the semantics of these fragments is to combine features learned from topic modeling with those of recurrent neural networks. This combination ensures a higher correlation between generated sequences, much like real data. In this paper we investigate existing approaches to adding topics into the recurrent neural network. We then develop a novel approach called Topic-RNN, which represents the state-of-the-art technique of integrating topics into the RNN. Our results show this method outperforms existing approaches substantially. Additionally, we show that in such cases where topic information is available, Topic-RNN surpasses word level RNNs for the task of word prediction.
Data Structure for Faster Graph Processing

The performance of graph algorithms can vary greatly depending on the data structure used to store the graph. This is happen due to the fact that the graph is an inherently irregular data structure, which makes it hard to deal with. In particular, such operations like checking nodes connectivity and adding or deleting nodes and edges, in some cases, can be computationally expensive and require iterating over most of the structure elements in the graph. This paper proposes a new data structure that allows many of such operations to be performed with approximately linear computational complexity. The new data structure is based on hash maps and linked list which makes it operates better when need to arbitrary navigates through the graph and modify it. Performance comparison of max flow algorithm and construction of random graph when using the proposed data structure, as well as using general data structures such as compressed sparse rows and adjacency matrix is presented. The new data structure in both cases shows better performance over its counterparts.
A Terahertz Radar Feature Set for Device-Free Gesture Recognition

This paper proposes a set of simple but effective features using a terahertz radar, specifically for device-free gesture recognition based on high resolution range profiles. Three types with seven features are extracted, including the tracking features, directional features, and behavioural features. The proposed method is evaluated on a dataset based on 0.34 THz radar, which contains 10 kinds of 5 pairs of frequently-used gestures. These features are demonstrated to be effective to encode the morphological differences among various gestures and be sensitive to the moving direction in a short period of time. The results show that the proposed method achieves 95.5% accuracy on frame-level gesture recognition.
A Multicultural Approach at the Early Stage of Teaching a Foreign language

The article presents the experience of developing an elective course program for future foreign language teachers studying at Tyumen State&#x0D; University. The content of the course is based on the principles of communication and variability and is focused on the development of the&#x0D; communicative, psychological, pedagogical and technological competencies of the future teacher. The creation by the teacher of favorable&#x0D; pedagogical conditions for a positive cultural identification of the student’s personality allows the child to become aware of the multicultural&#x0D; space of the world. The professional portfolio of a novice teacher includes techniques, methods and technologies that can be used for further&#x0D; pedagogical modeling.
Trends and Advancements in Genome Data Compression and Processing Algorithms

The healthcare industry is moving toward drug development, which involves the use of human genetic information, so that medical treatment can be adjusted to the individuals specific characteristics. Modern healthcare study provides access to biological samples, including a patient’s genome sequences and other medical documents. Personalized medicine also requires the capacity to store and interpret genome sequences and patients medical records. Genomics is the study of an organism by extracting its genome’s secret (hidden) features. There are trillions of cells in the human body, which are the essential building frameworks of most living things. They provide the body with structure, take nutrients from the meal, turn certain nutrients into energy, and perform particular functions. With modern possibilities for new medical knowledge, the field of genomics is entering a new era. In order to incorporate heterogeneous information, such data requires highly distributed acquisitions, enormous storage requirements, broad distribution bandwidth, and highly involved analyses. To facilitate the storage and dissemination of genomic data and to facilitate its access and analysis, genome data compression and processing algorithms are used. The state-of-the-art algorithms to the compression and processing of genomic data are discussed in this paper. The ever-increasing production of genome data presents many storage, processing, and transmission challenges. The cost of storage, processing, and transmission is greater than the cost of the DNA sequence being produced. The main solutions that can solve these problems by minimizing the storage size and the cost and time of processing are compression and efficient processing.Kumar, Sanjeev
Implementation of the fisher yates shuffle algorithm in the randomization of department recommendation examinations at PMB FKOM UNIKU

Abstract The new student admission test is a routine activity carried out by the Faculty of Computer Science, University of Kuningan every year. This activity is the start of the process of searching for qualified new student candidates. In the current system, there are interview sessions that aim to see the interests and talents of prospective students so that the majors chosen by these prospective students are right according to their interests and talents, but because they are still carried out face-to-face it results in less efficiency in terms of time. So we need an application that can provide recommendations for the right majors for prospective students of the Faculty of Computer Science, University of Kuningan efficiently. To avoid cheating in the new student admissions test, the application must be able to randomize the questions optimally by using the Fisher Yates Shuffle Algorithm. In this exam application, there are 300 multiple choice question banks that represent the competences of each department, namely questions with competency categories consisting of basic computer questions, logic, mathematics, programming languages, patterns, graphic design, business processes. The system will randomize using the fisher yates shuffle algorithm and display the randomized questions per category to the test takers. To see the level of effectiveness of the Fisher Yates Shuffle algorithm, 50 questions were randomized per category, displayed as many as 20 questions per examinee with a total of 30 iterations (for 30 exam participants). As a result, the position of the randomized questions is optimal and the time required for randomization is very efficient +0.01 seconds with the appearance rate of the most numbered questions being 3.17% so that the Fisher Yates Shuffle algorithm is one of the optimal algorithms for randomization.
Schmidt rank constraints in quantum information theory

Can vectors with low Schmidt rank form mutually unbiased bases? Can vectors with high Schmidt rank form positive under partial transpose states? In this work, we address these questions by presenting several new results related to Schmidt rank constraints and their compatibility with other properties. We provide an upper bound on the number of mutually unbiased bases of $\mathbb{C}^m\otimes\mathbb{C}^n$ $(m\leq n)$ formed by vectors with low Schmidt rank. In particular, the number of mutually unbiased product bases of $\mathbb{C}^m\otimes\mathbb{C}^n$ cannot exceed $m+1$, which solves a conjecture proposed by McNulty et al. Then we show how to create a positive under partial transpose entangled state from any state supported on the antisymmetric space and how their Schmidt numbers are exactly related. Finally, we show that the Schmidt number of operator Schmidt rank 3 states of $\mathcal{M}_m\otimes \mathcal{M}_n\ (m\leq n)$ that are invariant under left partial transpose cannot exceed $m-2$.
Information Usefulness as a Strategy for Action Selection in Health Dialogues

The design of effective strategies for managing information within the healthcare domain presents more complex challenges with respect to the classic conversation paradigm. One of such challenges is the capability of an agent of adapting its behavior based on the context in which it is deployed. For example, while some agents have to be able to acquire enough knowledge to reduce as much as possible its uncertainty on the situation (e.g. diagnosis), an agent that deals with risky situations (e.g. asthma self-management) has to be able to reduce as much as possible the number of interactions with the patient to quickly detect those situations. In this paper, we propose a framework that supports the development of intelligent conversational agents within the healthcare domain. We introduce our framework through the use of a running example, showing how an agent acts in the conversation based on its background knowledge and on the information that it acquires from the patient. Then, we describe the framework by providing the definition and the rationale of each component. Finally, we evaluated the metric we proposed for selecting the next action with respect to a set of baselines.
Review on Automatic Image Homogenization for Panoramic Images

Image Homogenization is an essential part in image processing environment, which allows stitching of multiple images with the same background. Homogenization is a basic idea which combines various multiple images having similar characteristics. The software of the system will help to homogenize multiple images by taking them as input images that depicts similar matching features specifically the background and it will combine the images to form a single panoramic image. However, Image processing is a domain that will help in homogenizing multiple images automatically with the help of various homogenizing algorithms. Mainly, homogenization concept is used for the “Image Stabilization” that is featured in camcorders which uses frame-rate image alignment and for the high-resolution images. We aimed for creating an architecture for homogenizing multiple images which results in a panoramic image as an output which is executed using python. Various images with similar key points are collected from various image data sources. The data collected from different sources is aggregated, processed and compared and further analyzation will take place through image processing. The proposed architecture will be able to handle multiple images, efficiently. Also, from the existing previous image homogenizing process, it is capable of stitching a greater number of images with matching the features, correlation and basic details of the image obtained from different sources, identifying patterns which may help in homogenizing of the image automatically. Besides an architecture description, we present feasibility results based on the experiments performed on rendering and overlapping of image. Image homogenizing are essential feature in image processing environment, allowing for stitching of two or more than two most similar images. By image homogenizing it is even possible to homogenize medical images as well as satellite images due to these high-resolution images can be captured automatically through this technique.