Reviewerly/exHarmony huggingface dataset:
HUMAN-COMPUTER INTERACTION AND INFORMATION VISUALIZATION

This research paper explores the intersection of Human-Computer Interaction (HCI) and Information Visualization, uncovering their combined potential to enhance user experiences in the digital realm.HCI focuses on designing interactive systems that are intuitive and user-friendly, while Information Visualization deals with visually representing complex data to facilitate comprehension.By integrating these fields, researchers can create interactive interfaces and visualizations that empower users to effectively interact with and understand information.The paper begins by examining the principles of HCI, emphasizing the importance of user-centered design, usability evaluation, and user experience assessment.It then delves into the realm of Information Visualization, exploring various techniques such as charts, graphs, and interactive visualizations, along with their applications in diverse domains.By analyzing successful case studies and examples, the research demonstrates the impact of the HCI-Information Visualization synergy.It showcases how combining intuitive interface design with visually compelling representations can promote effective data exploration, decision-making, and knowledge discovery.Moreover, it highlights the role of aesthetics and interactivity in engaging users and fostering a deeper understanding of complex information.The research also addresses challenges associated with this fusion, including the need for interdisciplinary collaboration, adapting to diverse user needs, and addressing privacy and ethical considerations.It discusses the importance of user feedback and iterative design processes in refining interactive systems and visualizations.Furthermore, the paper explores emerging trends in HCI and Information Visualization, such as mobile and ubiquitous computing, immersive technologies, and data-driven storytelling.It investigates their potential to further enrich user experiences and expand the boundaries of interaction and visualization.
GESTURE RECOGNITION BASED VIRTUAL MOUSE AND KEYBOARD

In this project, an optical mouse and keyboard are created utilizing hand motions and computer vision.The computer's camera will scan the image of various hand gestures made by a user, and the computer's mouse or pointer will move in accordance with the movements of the Users can even conduct right and left clicks using various gestures.Similar to this, several gestures can be used to operate the keyboard, such as the one-finger gesture for selecting an alphabet and the four-figure motion for swiping left and right.Without a wire or other external devices, it will function as a virtual mouse and keyboard.The project's webcam is its sole piece of hardware, and Python is used to code on the Anaconda platform.Here, the Convex hull defects are first constructed, and then an algorithm is generated and maps the mouse and keyboard functions to the defects using the defect calculations.By mapping a few of them with the mouse and keyboard, the computer will recognise the user's gesture and respond appropriately.
Antitrust Economics of Cryptocurrency Mining

Abstract The development of blockchain-based applications, to date mostly virtual currencies, touches many areas of law and economics. The most well-known applications of public blockchains rely on Proof of Work as a consensus mechanism in which miners compete to solve a cryptographic puzzle. We argue that economic tools for market definition may be adapted to delineate relevant cryptocurrency mining markets. Antitrust law can help to prevent network attacks and exclusion of transactions with lower fees by large miners. When multiple blockchains are part of the same market, the role of network effects in securing the leading position of more established cryptocurrencies can potentially lead to exclusionary behaviour.
Cross-language information retrieval for poetry form of literature-based on machine transliteration using CNN

Transliteration is phonetically translating a language’s words into an international or non-native screenplay. The machine translation process now plays an essential role in scholarly research. The most crucial complement criterion of the English translation system is preserving the phonetic qualities of the language specification after English translation in the chosen language. However, a suitable bilingual text corpus is necessary for statistical models to attain improved transliteration accuracy. Marathi-to-English direct machine translation is done through a cross-language information retrieval system using the CNN classifier model in this proposed research. The proposed method considers a sequence labelling issue brought on by the split transliteration units used in the process. All half-consonant clusters in the Devanagari script are effectively mapped as half-consonant “a” s and labelled using the Modified Intermediate Phonetic Code (MIPC). After generating the phonetic units for each feature in the base and aim languages, the weight is assigned to a phonetic unit in both languages, and individual phonetic unit probabilities are computed. If the probability is zero, then segments are established and recalculated for each segment based on the target phonetic unit location in the word. Therefore, the proposed approach classifies the required phonetic unit with a high accuracy rate.
The Current Issues and Factor Determinants of User Experience (UX) using Learning Tools

During the current Covid-19 pandemic, digital developments are indispensable.And what about the interaction between humans and computers?User Experience (UX) is beneficial these days because it can help people gain information quickly within their application.With the Increasing new apps developed and various apps is being used.This research used a systematic literature review approach to gain information about "What kind of learning tools have been used for User Experience (UX)" With the systematic literature review approach used for this paper, there are plenty of variables found for the Learning tools that are qualified to be used for User Experience (UX), From 51 Article Papers, There are 3 top variables that qualify the learning tools for User Experience (UX) are Usability, Ease of Use, and User Satisfaction.
Learning Application Design Using Predict Observe Explain (POE) Model for Computer Practicum in the COVID-19 Pandemic Period in Higher Education

The COVID-19 pandemic forced STIE & STMIK Jayakarta to use the distance learning process for students.Institutional policies to use the Google Workspace for Education application as a Learning Management System (LMS) are deemed appropriate, but they are still experiencing difficulties in learning computer practicums.The use of the Google Forms feature as a computer practicum worksheet is considered less effective in increasing student independence in completing their practicum assignments.This study aims to design a web-based learning application that uses the Predict-Observe-Explain (POE) learning model which has advantages, such as being used to express students' initial ideas, generate discussion, provide information to lecturers about students' thoughts and motivate students to want to explore concepts, and arouse the desire to investigate.The application development method in this study uses Extreme Programming (XP) as an agile approach model for responsive application programming, with user needs that are rapidly changing and always changing.The result of this research is the design of a web-based application that uses a POE card as a computer practicum worksheet with a web page link from the LMS so that it can accommodate the learning outcomes of computer practicum courses.
Using Deep Learning in Sign Language Translation to Text

This study is about sign language translation into text; this technology is for those experiencing difficulty in communicating, i.e., those who have speech disorders, hearing impairment, and are deaf.Sign language translation into text is done through a process when one uses sign language that could be translated into text.Deep learning is a machine learning and artificial intelligence (AI) technique that mimics how humans acquire knowledge; the complexity and abstraction of deep learning algorithms are stacked in a hierarchy.The researchers utilized Convolutional Neural Networks (CNN), Connectionist Temporal Classification (CTC), and Deep Belief Network (DBN) to collect and gather the results of their system through different methods of translating sign language into text.Through the systematic literature review of various published papers related to sign language translation using deep learning techniques, this paper discusses and systematically analyzed various studies and come up with a proposed model in translating sign language into text.
Examination Scheduling System for Mapua University

Examination timetabling/scheduling is a complex task in many academic institutions that requires intensive analysis and human scheduling expertise and experience to generate an optimal examination schedule.Without the assistance of modern computers, the accomplishment and process of this task can be tedious, time-consuming, and error-prone.The School of Information Technology (SOIT) department at Mapua University faced a technological problem with their examination scheduling process due to the dependence on manual methods and automated scheduling software with a fixed output for only the 2015 student batch.This study proposes a web-based examination scheduling system as an alternative to the school department's existing examination scheduling method and technology.It will incorporate both the concept of interactive and automated computer-based scheduling methods and is designed to have the capability to adapt and generate optimal schedules based on the dynamic scheduling variables and specified scheduling constraints.Moreover, the proposed system has been successfully demonstrated to and tested, and evaluated by the major stakeholder of the examination scheduling process of the mentioned school department, the Technical Assistant to the Dean and Proctors.Consequently, accepting and recognizing it as an alternative to their existing examination scheduling methods.
Digital Transformation Readiness in Higher Education Institutions in Indonesia: A Systematic Literature Review

Digital transformation is a process of accelerating and integrating digital technology spread across society.This process changes lifestyles by utilising a combination of information technology, communication, compilation, and connectivity.Although digital transformation has become a worldwide topic lately, little research still discusses it in Indonesia.However, indeed, digital transformation has occurred in Indonesian higher education.This study's purpose is to analyse the readiness of higher education in Indonesia to face digital transformation.This research was conducted with a systematic literature review framework with a PRISMA approach for selecting the article and thematic analysis for data analysed.The data analysed will be focused on educational stakeholders, namely students, teachers, and institutions.In students' focus, we discovered a theme, namely flexibility learning, that means students can study every time and everywhere without restriction.We also found one theme in the teacher's focus: trained teachers, which means teachers must have competencies to adapt to digital transformation.And finally, four themes were found in institutions focus, namely creating educational platforms, managing data, managing IT infrastructure, and overcoming cultural barriers.We can realise the four themes through online classes and a change in mindset at the university.
Evaluating the Accuracy of LSTM Forecasting Model and Intervention Programs in Combating Covid-19

This paper extends the research on the accuracy of Long Short Term Memory (LSTM) deep learning model to forecast Covid-19 daily cases in the Philippines with features affecting the transmission of virus.The study utilized a mixed method; qualitative research to analyze and understand experiences to generate ideas and quantitative research to gather measurable data that will be used for statistical analysis.The study examined three factors affecting the forecasting of Covid-19.The modeling procedure was completed using python 3.0.The research results indicate that the forecasting of Covid-19 daily cases using the LSTM forecasting model has an error of no more than 17% error which depicts as an acceptable accuracy.It also specifies that the model shows more meaningful trends in the two (2) week forecast rather than the two (2) month forecast.The study triangulated the trends of forecast with the help of focus group discussion with health experts and health administrative personnel.The results specify that 75 percent wear the necessary PPEs when going out and 60 percent always keep physical distance.Using the LSTM deep learning model, the study provides a framework that helps understand the behavior of the virus and forecast Covid-19 cases months ahead.
Online Teaching the English Language for Journalism (On the Example of Preparing a Broadcast Copy)

Thanks to the development of information and communication technologies, today it has become possible to teach a foreign language of profession in a distance format. The MGIMO Faculty of International Journalism uses educational materials based on the online course “English for Journalism” developed by the University of Pennsylvania(USA) to teach students of an intermediate level of the English language proficiency. The article deals with teaching students in an online format how to work on the copy for the radio broadcast. The training module includes listening to four six-minute video lectures, test tasks aimed at understanding the material listened to (multiple choice), as well as creative tasks representing the creation of a pitch for a radio broadcast and writing one’s own radio report.
Research on Multi-hop Transmission in Quantum Wireless Communication Networks Based on Improved Ant Colony Algorithm

Firstly, an improved ant colony algorithm (QCANT) is proposed to optimize quantum connectivity, and the entanglement example distribution node deployment in quantum wireless multi-hop networks is studied and analyzed. On this basis, this paper combined genetic algorithm with improved ant colony algorithm (GA-QCANT), which can effectively alleviate the problem of low efficiency of ant colony algorithm due to the lack of initial pheromone. Simulation results show that both QCANT and GA-QCANT improves quantum connectivity significantly, and GA-QCANT improves quantum connectivity by an average of 32.1% compared to QCANT.
Hetero-Rec: Optimal Deployment of Embeddings for High-Speed Recommendations

We see two trends emerging due to exponential increase in AI research- rise in adoption of AI based models in enterprise applications and development of different types of hardware accelerators with varying memory and computing architectures for accelerating AI workloads. Accelerators may have different types of memories, varying on access latency and storage capacity. A recommendation model’s inference latency is highly influenced by the time to fetch embeddings from the embedding tables.
Research on Road Multi-scale Target Detection Method Based on Improved YOLOv5s

In the practical application of traffic target detection, it is difficult to ensure the detection accuracy of multi-scale traffic signs in real-time detection because the scale of the target varies greatly. In this paper, an improved method based on YOLOv5s is proposed to address the accuracy problem of multi-scale targets in real-time traffic detection by adapting its data enhancement at the input and localisation loss at the output respectively. The original data enhancement method is improved to solve the problem of target loss in a small range to a certain extent. In addition, an α-FEIOU algorithm is proposed to replace the original CIoU algorithm to improve the accuracy of real-time target detection. The results show that the proposed method shows good performance in different scenarios and its effectiveness is confirmed. At the same time, it has strong robustness and provides a certain reference for the application of deep learning in the field of traffic target detection.
An Improved Particle Filter Passive Location Method Based on Differential Squirrel Search Algorithm

Particle filtering is a standard method for parameter estimation in passive location and has great application value in nonlinear and non-Gaussian systems. The standard particle filter (PF) is prone to the problem of particle weight degradation and particle dilution as the number of iterations increases, which affects the overall performance of the location algorithm. Aiming at this problem, a PF algorithm based on differential squirrel search algorithm (DSSA) optimization is proposed. The particles are divided into optimal individuals, sub-optimal individuals, and ordinary individuals according to the weight of the particles. The low-weight particles are moved closer to the position of the high-weight particles by simulating the predation behavior of squirrels, so that the location information of most particles can be retained. And seasonal monitoring condition is used to avoid the algorithm falling into local optimal. The simulation results show that the improved algorithm has a lower root mean square error (RMSE) than the standard PF algorithm and the improved PF algorithms of other intelligent optimization algorithms under non-Gaussian noise. The improved algorithm can accurately achieve the passive location of the moving target.
Cross-modal retrieval based on fusion lightweight network

With the increase in data diversity, how to solve the multiple semantic problems under modality becomes the key to cross-modal retrieval. Most of the existing cross-modal methods often accompany many parameters and large models, and they have low operation efficiency. In this paper, we propose a cross-modal retrieval method based on fusion lightweight networks to address the aforementioned problem. Our goal is to extract the features of different modalities separately through two lightweight networks. Additionally, we propose a fusion strategy for local and global features to better preserve local fine-grained information. Finally, we use the generative adversarial training to preserve the contextual semantic information between different modalities and improve the generalization ability of the model. Experiments show that the algorithm in this paper achieves excellent cross-modal retrieval performance on the MSCOCO dataset.
Joint MLP and Pseudo-label Unsupervised Hash for Image Retrieval

The increasing growth of image data has made the implementation of image retrieval in big data environment a hot research topic. Traditional hashing technique uses hand-designed features for learning, which can't obtain high-level semantic features. Deep hashing has become a powerful tool for solving multimedia data retrieval in recent years with the powerful feature learning capability and the inherent advantage of small hash code storage/fast retrieval. Most of these algorithms rely on manual tagging, which is very expensive. In this paper, we propose the Joint MLP and Pseudo-label unsupervised hash (JMPH). In this approach, object features in the image are extracted with the target detection method as pseudo-labels to train the deep hash. In particular, we additionally devise the MLP network to conduct the encoding process. Experimental results on two datasets demonstrate that our unsupervised hashing method outperforms the state-of-art unsupervised hashing methods.
Towards 3D Virtual Dressing Room Based User-Friendly Metaverse Strategy

This paper applies a meta-verse strategy as an intelligent application works as a virtual reality for the dressing room. The proposed website is designed as a virtual fashion store, such that the women and/or men may foumd their fashion with a privacy and convenience characteristics based on the E-commerce and meta-verse strategy. The proposed website is a real-time virtual dressing room that eliminates a lot of the trouble from shopping by which no more long time required in the fitting room with an armful of clothing or the time-consuming process of getting dressed and undressed multiple times. Stand a few feet in front of a webcam to utilize this device, which uses motion-sensing technology. On the computer screen, a live image of you will display, along with various categories such as trousers, shirts, and dresses. By waving your hand over Translation Controls, Scale Controls, and Selection Controls, you may select a category, such as shirts and the preferred colors. Therefore, the clothing you wish to try on will be on, and it will be digitally overlaid over your live image. A quick size algorithm to assist the user in determining the prices and appropriate size is proposed. Moreover, we present RSA for user authentication to make the system more secure especially for mobility clients. As a result, the primary rule is that the proposed meta-verse strategy should generate pictures in real-time and respond to user interaction in real-time. It should also be a low-cost, user-friendly system.
The boundaries of lying

Abstract The Holy Scriptures can be considered a specific kind of normative texts, whose use to assess practical moral cases requires interpretation. In the field of ethics, this interpretative problem results in the necessity of bridging the gap between the normative source – moral precepts – and the specific cases. In the history of the Church, this problem was the core of the so-called casuistry, namely the decision-making practice consisting in applying the Commandments and other principles of the Holy Scriptures to specific cases or moral problems. By taking into account the sin of lying, this paper argues that casuistic texts reveal an extremely sophisticated interpretative method, grounded on “pragmatic” contextual and communicative considerations and argumentative structures that resemble the ones used in legal interpretation. These works show how the underspecified biblical text expressing an abstract norm was enriched pragmatically by completing it and modulating its meaning so that it could be used to draw a conclusion in a specific context on a specific case. The mutual interdependence between biblical interpretation, pragmatics, and argumentation sheds light on a much broader phenomenon, namely the pragmatic nature of argumentation.