Reviewerly/exHarmony huggingface dataset:
Enhanced Scalar Multiplication Algorithm over Prime Field Using Elliptic Net

Authors:
raw_author_name: None Norliana Muslim
raw_affiliation_string: Department of Computer and Communication Technology, Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, 31900, Kampar, Perak, Malaysia
raw_author_name: None Faridah Yunos
raw_affiliation_string: Laboratory of Cryptography, Analysis and Structure, Institute for Mathematical Research, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
raw_author_name: None Zuren Razali
raw_affiliation_string: Department of Computing, Faculty of Communication, Visual Art and Computing, Universiti Selangor, 45600, Bestari Jaya, Selangor, Malaysia

Abstract: Scalar multiplication in elliptic curve cryptography is the most expensive and time-consuming operation. The elliptic curve cryptography attracted interest due to the development of modern technology since it could offer the equivalent high level of security with a reduced length of key. Therefore, improving elliptic curve scalar multiplication performance has always been the primary goal of cryptography. In this paper, a novel scalar multiplication algorithm based on the modified double and double add via elliptic net with Karatsuba method was proposed in order to enhance the efficiency of scalar multiplication. In the experimental results, the elliptic net equivalence sequence was applied to the Twisted Edwards curve together with safe curves of numsp384t1 and numsp512t1. At the point operational level, the proposed method reduced the cost of multiplication by 46.15% and 42.30% for double and double add, respectively, when compared to elliptic net using eight blocks method. The proposed double lowered the multiplication cost by 12.5% and the squaring cost by 20% when compared to elliptic net using ten temporary variables method. Following this, proposed double add cost reductions of 6.25% and 20% were obtained to multiplication and squaring. At the field operational level, in comparison to the binary method, the eight-block elliptic net method, and the elliptic net method with ten temporary variables for the 384 bits scenario, the developed scalar multiplication algorithm obtained cost reductions of 57.6%, 31.3%, and 13.2%, respectively. On 512 bits with similar comparison, the designed algorithm exhibited better performance by averages 59.2%, 31.0% and 13.2%. The results signified that the designed algorithm over prime field performed better at the point and field operational levels with larger scalar bit size.
Security and Safety in Cyber-Physical System (CPS): An Inclusive Threat Model

Authors:
raw_author_name: None Ali Selamat
raw_affiliation_string: Malaysia-Japan Institute of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia
raw_author_name: None Ondrej Krejcar
raw_affiliation_string: Hradec Kralove Univesity, Rokitanského 62/26, 500 03 Hradec Králové 3, Czech Republic

Abstract: A Cyber-Physical System (CPS) is a combination of computational algorithms and physical processes that are integrated together. Cyber-Physical Systems (CPS) integrate computer and communication capabilities with the monitoring and management of physical parts, establishing a mutually beneficial interaction between the cyber and physical components. Industrial Control System (ICS) is one example of CPS which integrates the physical (OT) and cyber domains (IT), which makes them more vulnerable to attacks. Two essential characteristics of Cyber-Physical Systems (CPS) are safety and security. Threat models are methods for identifying, analysing, and proposing security control countermeasures for threats and their capabilities. However, the threat model methods which are used for the traditional IT systems are not sufficient as they do not include the physical interactions, consequences and impacts to the safety aspects in the Operational Technology (OT). On the other hand, a risk assessment analyses attack scenario, examines cybersecurity from the attacker's point of view, and gives cost-benefit data to support the expenditure on security measures. This study proposes an inclusive attacker’s centric threat model and pro-active risk assessment model for CPS using Mamdani Fuzzy Inference System (FIS). The outcomes of the threat model prove that the lateral propagation of the threat is possible and threat may also propagate from the CPS assets to the IT segment. The risk assessment by using FIS shown that the safety and security risk for the CPS is significant and calculated as medium level. Hence, the risk factors that are considered in calculating the overall risk for a CPS need to be immediately addressed and mitigated.
Effect of typos on text classification accuracy in word and character tokenization

Authors:
raw_author_name: None Saleh Mesbah ElKaffas
raw_affiliation_string: College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
raw_author_name: None Shawkat K Guirguis
raw_affiliation_string: Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria, Egypt

Abstract: To train a machine to “sense” a users’ feelings through writings (sentiment analysis) has become a crucial process in several domains: marketing, research, surveys and more. Nevertheless in times of crisis like COVID. Typo is one of the underestimated challenges processing user-generated text (comments, tweets, ..etc), it affects both learning and evaluation processes. Word tokenization outcome changes drastically even with a single character change, hence as expected, experiments have shown significant accuracy decreases due to typo. Adding a spelling correction as preprocessing layer, building one for every language, is a very time and resources expensive solution, a huge challenge against large data and real-time processing. Alternatively, a CNN model consuming the same text, once tokenized on characters level and once on words level while inducing typo, showed that as the typo percentage approaches 10% of the text, the results with characters tokens surpasses words tokens. Finally, on %30 typo of the text, the model consuming characters tokenization outperformed itself with the word level by a significant %22.3 in accuracy and %24.9 in F1-Score, using the same exact model. This approach in solving the inevitable typo challenge in NLP proved to be of significant practicality, saving huge resources versus using a spelling-correction model beforehand. It also removes a blocker challenge in front of real-time processing of user-generated text while preserving acceptable accuracy results.
BiasTrap: Runtime Detection of Biased Prediction in Machine Learning Systems

Authors:
raw_author_name: None Hussaini Mamman
raw_affiliation_string: Department of Management and Information Technology, Abubakar Tafawa Balewa University, Bauchi, 740272, Nigeria
raw_author_name: None Shuib Basri
raw_affiliation_string: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
raw_author_name: None Abdullateef Oluwagbemiga Balogun
raw_affiliation_string: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
raw_author_name: None Abdullahi Abubakar Imam
raw_affiliation_string: Department of Computer Sciences, Universiti Brunei Darussalam, BE1410, Brunei Darussalam
raw_author_name: None Ganesh Kumar
raw_affiliation_string: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
raw_author_name: None Luiz Fernando Capretz
raw_affiliation_string: Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada

Abstract: Machine Learning (ML) systems are now widely used across various fields such as hiring, healthcare, and criminal justice, but they are prone to unfairness and discrimination, which can have serious consequences for individuals and society. Although various fairness testing methods have been developed to tackle this issue, they lack the mechanism to continuously monitor ML system behaviour at runtime. In this study, a runtime verification tool called BiasTrap is proposed to detect and prevent discrimination in ML systems. The tool combines data augmentation and bias detection components to create and analyse instances with different sensitive attributes, enabling the detection of discriminatory behaviour in the ML model. The simulation results demonstrate that BiasTrap can effectively detect discriminatory behaviour in ML models trained on different datasets using various algorithms. Therefore, BiasTrap is a valuable tool for ensuring fairness in ML systems in real-time.
Prediction of neural activity in connectome-constrained recurrent networks

Authors:
raw_author_name: Ashok Litwin-Kumar
raw_affiliation_string: Zuckerman Mind Brain Behavior Institute, Columbia University

Abstract: We develop a theory of connectome-constrained neural networks in which a student network is trained to reproduce the activity of a ground-truth teacher, representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here both networks have the same connectivity but they have different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently infer unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different, and provides a framework to determine when such models yield consistent dynamics.
English Teaching Assistance Employing the Multiple Intelligences Theory to Kasih Bangsa Elementary School Students

Authors:
raw_author_name: Trisnowati Tanto
raw_affiliation_string: Faculty of Languages and Cultures, Maranatha Christian University, Bandung, Indonesia
raw_author_name: Maryani Maryani
raw_affiliation_string: Faculty of Languages and Cultures, Maranatha Christian University, Bandung, Indonesia

Abstract: Considering that English is not the main language in Indonesia, an effective teaching method at school is crucial to students' ability to speak English fluently. However, in reality, many teachers still depend on conventional teaching methods. Therefore, this teaching assistance aims to facilitate Kasih Bangsa Elementary School students with a method that considers students' overall intelligence adapted from the Multiple Intelligences theory proposed by Howard Gardner, who argues that a person can have more than one intelligence. Furthermore, this teaching assistance is based on the Community-Based Participatory Research (CBPR) method, which focuses on the collaboration between researchers and communities. The program consists of three main processes: an interview with teachers of Kasih Bangsa Elementary School, a workshop on Multiple Intelligences Theory for the teachers, and a teaching demonstration by some of the students of the English Department, Maranatha Christian University. From this teaching assistance, the Multiple Intelligences theory is proven to improve the learning quality and boost students' motivation effectively.
Research on the Reform of School-Enterprise Cooperation Teaching and Education Mode for Computer Majors

Authors:
raw_author_name: Zichen Xu
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong 226000, Jiangsu Province, China

Abstract: This paper discusses the innovative methods of school-enterprise cooperation education mode in computerapplied talent training. An innovative training model based on school-enterprise cooperation is proposed to promote the cultivation of students’ practical and innovative skills, so as to better adapt to the needs of society. By analyzing the key links and influencing factors of the training mode, this paper puts forward some concrete suggestions and measures to provide guidelines for universities and enterprises in personnel training.
Research on the PDSIPE Teaching Mode for English Teaching in Applied Undergraduate Colleges

Authors:
raw_author_name: Xiaopeng Wang
raw_affiliation_string: Heilongjiang University of Finance and Economics, Harbin 150025, Heilongjiang Province, China

Abstract: PDSIPE (Planning, Design, Scene, Implementation, Product, Evaluation) is the teaching concept and teachingoperation mode implemented by Professor Jungang Duo, which is promoted and tested in Heilongjiang University of Finance and Economics since 2022. The teaching mode meets the needs of application-oriented professional personnel training, and is compatible with local undergraduate colleges in terms of educational positioning, development strategic objectives, and teaching reform. It plays an important role in promoting the teaching reform of application-oriented undergraduate colleges, which is discussed from three aspects: teaching idea, practical operation, and overall evaluation mechanism.
An Emergency Plan Selection Method for Emergency Latency

Authors:
raw_author_name: Huajun Liu
raw_affiliation_string: General Office of Sichuan Provincial Government, Chengdu 610000, Sichuan Province, China

Abstract: Drawing upon a characteristic analysis of the latency period in emergencies, this paper proposes an emergency plan selection method based on interval language variables and information entropy to address the challenge of acquiring critical information during this crucial stage. Initially, decision-makers employ interval language variables to express the preference information regarding emergency plans, while also introducing an enhanced information entropy theory to derive the weight coefficients of key indicators. Subsequently, the weighted arithmetic average operator of interval language is applied twice to aggregate the preference information alongside the relative importance of decision-makers and the weight coefficients of key indicators. Finally, the ranking coefficients of each emergency plan are sorted to determine the optimal scheme. The feasibility and effectiveness of this method are demonstrated through a case study involving the selection of an emergency plan for a flood disaster in a specific location.
Multi-modal Emotion Recognition Using Neural Networks: Deploy on Cloud

Authors:
raw_author_name: IJSREM Journal

Abstract: This paper explores recent developments in emotion recognition, focusing on methodologies employing Multilayer Perceptron (MLP) classifiers. Emotion recognition is crucial for human-machine interaction across various domains, including healthcare, surveillance systems, and intelligent learning environments. There are many deep learning models that have been employed for emotion detection, this emphasizes the effectiveness of MLP classifiers. The project at hand centers on predicting emotions such as happiness, calmness, fear, and disgust solely utilizing MLP classifiers. The system is trained on audio data, with recordings matched in frequency, time, and speed to the raw data used for training. Additionally, Convolutional Neural Network (CNN) models are used here to analyze facial expressions in real-time video streams by providing neat and complete approach to emotion recognition. Upon deployment on the AWS cloud platform, the system provides real-time emotion predictions in textual format via a web interface created using Flask. Furthermore, the system offers the capability to analyze emotions from live video streams, presenting continuous feedback on emotions through a face-outlined box updated every three seconds. This functionality serves to confirm individuals' emotions and provide self-awareness regarding their emotional states. This project focuses on giving efficiency by using MLP classifiers in conjunction with CNN models for emotion recognition tasks, thereby contributing to the advancement of human-machine interaction. Key Words: MLP Classifier, CNN, AWS Cloud, Flask, emotion, audio, video
Implementing Interdisciplinary Teaching Approaches: Challenges and Strategies in Teacher Education under NEP 2020

Authors:
raw_author_name: Pragatika Singh -

Abstract: This paper explores the challenges and strategies associated with implementing interdisciplinary teaching approaches in teacher education, aligning with the objectives of the National Education Policy (NEP) 2020 in India. Emphasizing the holistic development of students, NEP 2020 advocates for interdisciplinary education to nurture critical thinking, creativity, and problem-solving skills.
Making new technology understandable through multimodal orientations: A digital exercise stick in customer training interaction

Authors:
raw_author_name: Niina Hynninen
raw_affiliation_string: University of Helsinki

Abstract: With a variety of smart technologies on the market, health technologies have become an increasingly everyday phenomenon. This paper focuses on customer training in the use of a new technological device, a digital mobility stick, which is an exercise stick with a built-in haptic component. It can be used as a measuring and training tool to analyze the body’s ability to balance, bend, and rotate and to guide the trajectory of one’s exercise movements. We focus on the mobility stick as a training tool, investigating how the haptic technology is introduced to the customer in instructional interaction, and what roles the technology obtains in the process. The paper uses video-recorded customer training interaction data from a health technology company. It draws on multimodal conversation analytic research on instructions and instructed actions, objects, technologies, and touch in interaction. We show that the company representative’s specific orientation to the mobility stick was consequential to the instructed actions of the customers and their learning. The analyzed cases also illustrate that the mobility stick gained different roles in the interaction, ranging from a technological and sensorial object to a technology representing information to an active participant in interaction guiding human action. The study thus contributes to our understanding of the multimodality of instructional interaction and the potentially varied roles of technology in such interaction.
Application of Web 2.0 to Reduce Failure in English

Authors:
raw_author_name: Kristian Armando Pineda Castillo
raw_author_name: Rubén Jerónimo Yedra

Abstract: The purpose of the intervention was to reduce the failure rate in English with the help of a website built to support the learning experience. Action research was employed in a mixed-methods approach with an exploratory sequential design. Techniques such as documentary analysis, semistructured interviews, and participant observation were employed with 255 high schoolers. Findings revealed that the website, together with follow-up from the teacher-researchers, helped most students achieve a passing grade, empowered the learning experience, and enhanced communicative competence and linguistic awareness. Additionally, the website’s user-friendly nature and well-suited interface simplified the learning dynamic. Furthermore, according to website data, this digital space was also used in other settings with similar objectives. Even so, apathy remained a problem as some learners chose not to take part in the strategy and did not pass. We believe that the research contributes to the state of the art and generates knowledge, especially given the lack of research addressing failure in language education.
PELATIHAN APLIKASI MICROSOFT EXCEL PEMBUATAN SLIP UPAH KARYAWAN PADA UMKM PENGRAJIN TAHU RUMAHAN KECAMATAN CIMANGGIS DEPOK

Authors:
raw_author_name: None Rahmadani

Abstract: Small, Micro and Medium Enterprises are one of the backbones of the community economy in the Cimanggis sub-district, Depok. One of the known products is tofu which is produced by MSME business actors in the Cimanggis sub-district, Depok. MSME bag craftsmen in the region still use simple systems and technology in running their business including production, administration,marketing, financial recording and payroll. The aim of this service activity is to provide and introduce how to make salary slips to help prepare and transparency salaries/wages for employees in these MSMEs. The service was carried out online using Zoom Meeting media in the form of presentations and practice (tutorials) in making pay slips, presentations using the Microsoft Office Powerpoint and Microsoft Office Excel applications. Participants in service activities are representatives of MSME business actors in Cimanggis sub-district, Depok and fellow Community Service (PKM) participants. The implementation of the service received enthusiasm from the participants because it was in accordance with the conditions of business actors and could be implemented immediately, easily and cheaply.
Parametric Taxonomy of Educational Texts

Authors:
raw_author_name: Roman Kupriyanov
raw_author_name: Marina Solnyshkina

Abstract: The article is aimed at considering the issue of the discursive text typology and developing a parametric model of the elementary school texts for the ontological domain by employing a corpus-based approach and methods of linguistic statistics. The research corpus of over 90,000 tokens comprises texts of 13 textbooks acknowledged in the 2 nd grade of Russian schools. The applied multifactor discriminant analysis enabled identification and validation of typological characteristics of the texts under study, offering the formula for referring educational texts to a subject domain on Philology, Mathematics, and Natural Sciences. The discriminant analysis results confirmed the hypothesis that each type of text corresponds to a parametric model, which includes six constants: the average number of words in a sentence, the average number of nouns, the average number of verbs and the average number of adjectives per sentence, local noun overlap, global argument overlap. The assessment of linguistic parameters was performed by an automatic Russian text analyzer RuLingva. The classification accuracy of the parametric model was identified as 80%, which ensures its high reliability and allows for the data obtained to be employed in linguistic expertise, as well as for in automated linguistic profiling of texts. The prospect of the research implies installation of the model in RuLingva and development of similar models for texts of other subject domains.