Reviewerly/exHarmony huggingface dataset:
Carry-based Differential Power Analysis (CDPA) and its Application to Attacking HMAC-SHA-2

In this paper, we introduce Carry-based Differential Power Analysis (CDPA), a novel methodology that allows for attacking schemes that use arithmetical addition. We apply this methodology to attacking HMAC-SHA-2. We provide full mathematical analysis of the method and show that under certain assumptions and with a sufficient amount of traces any key can be revealed. In the experimental part of the paper, we demonstrate successful application of the attack both in software simulation and on an FPGA board using power consumption measurements. With as few as 30K traces measured on the FPGA board, we recover the secrets that allow for forging the HMAC-SHA-2 signature of any message in 3% of the cases — while with 275K traces the success rate reaches 100%. This means that any implementation of HMAC-SHA-2, even in pure parallel hardware, is vulnerable to side-channel attacks, unless it is adequately protected. To the best of our knowledge, this is the first published full-fledged attack on pure hardware implementations of HMAC-SHA-2, which does not require a profiling stage.
cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel Multi-Scalar Multiplication Algorithm on GPUs

Zero-knowledge proof is a critical cryptographic primitive. Its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been deployed in various privacy-preserving applications such as cryptocurrencies and verifiable machine learning. Unfortunately, zkSNARK like Groth16 has a high overhead on its proof generation step, which consists of several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and multi-scalar multiplication (MSM). Therefore, this paper presents cuZK, an efficient GPU implementation of zkSNARK with the following three techniques to achieve high performance. First, we propose a new parallel MSM algorithm. This MSM algorithm achieves nearly perfect linear speedup over the Pippenger algorithm, a well-known serial MSM algorithm. Second, we parallelize the MUL operation. Along with our self-designed MSM scheme and well-studied NTT scheme, cuZK achieves the parallelization of all operations in the proof generation step. Third, cuZK reduces the latency overhead caused by CPU-GPU data transfer by 1) reducing redundant data transfer and 2) overlapping data transfer and device computation. The evaluation results show that our MSM module provides over 2.08x (up to 2.94x) speedup versus the state-of-the-art GPU implementation. cuZK achieves over 2.65x (up to 4.86x) speedup on standard benchmarks and 2.18× speedup on a GPU-accelerated cryptocurrency application, Filecoin.
Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition

Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user’s body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have varied in medium, method, and application. Modern developments in the areas of machine perception have seen the rise of single-camera, skeletal model, hand-gesture identification algorithms, such as media pipe hands (MPH). This paper evaluates the applicability of these modern HGR algorithms within the context of alternative control. Specifically, this is achieved through the development of an HGR-based alternative-control system capable of controlling of a quad-rotor drone. The technical importance of this paper stems from the results produced during the novel and clinically sound evaluation of MPH, alongside the investigatory framework used to develop the final HGR algorithm. The evaluation of MPH highlighted the Z-axis instability of its modelling system which reduced the landmark accuracy of its output from 86.7% to 41.5%. The selection of an appropriate classifier complimented the computationally lightweight nature of MPH whilst compensating for its instability, achieving a classification accuracy of 96.25% for eight single-hand static gestures. The success of the developed HGR algorithm ensured that the proposed alternative-control system could facilitate intuitive, computationally inexpensive, and repeatable drone control without requiring specialised equipment.
Divak: Non-invasive Characterization of Out-of-Bounds Write Vulnerabilities

Despite the high level of automation that fuzzing has brought into the vulnerability research process, the assessment of a discovered vulnerability’s implications mostly requires human expertise and intuition. A promising approach to reduce such a manual effort is the automatic extraction of vulnerability characteristics that provide vital clues for exploitability. In this work, we focus on out-of-bounds write vulnerabilities and investigate how to automatically distill the set of source code-level objects affected by such unintended writes. As this poses unique challenges with regard to the invasiveness of the analysis methods, we propose a novel approach that enables monitoring a compiled program for spatial memory safety violations without the need for heavy instrumentation. We implement Divak, a prototype of our design, and we evaluate it on both benchmarks and real-world vulnerabilities, showing that its detection and characterization capabilities outperform instrumentation-based tools in several scenarios, at the cost of an increased overhead.
Marketing and new technologies: The use of NFT in promotional activities of brands - an overview

A blockchain-based unique digital identifier known as NFT is used to authenticate ownership of a particular digital asset and certain rights related to it. Although relatively new as a term, numbers in the NFT market show that this phenomenon is worth exploring. The aim of this paper was to define NFT and its application in the marketing activities of global brands. Although the importance of NFT is recognized in various industries, the focus of this paper was on examples related to the use of NFT in the marketing strategies of companies such as Cola-Cola, McDonald's, Pepsi and many other global brands. Additionally, the analysis of the promotional activities of NFT collections used in order of its popularisation and sales increase is presented.
The Effects of Technology-Assisted Listening Practice on Moodle on English-Majored Freshmen’s Motivation for Learning Listening at a University in The Mekong Delta

Purpose: This study was to investigate the effects of technology-assisted listening practice on MOODLE on English-majored freshmen's motivation for learning English listening at a university in the Mekong Delta. Approach/Methodology/Design: Two groups of sixty-four mixed-gender freshmen majoring in English language studies participated in this quasi-experimental study. To collect data, a pre-questionnaire, a post-questionnaire, and interviews were employed. Findings: The findings indicated that technology-assisted listening practice on MOODLE improved English-majored freshmen's listening motivation in specific areas for learning listening; however, this was a variance development. Practical Implications: This study was aimed to help language teachers and students benefit from technology-assisted listening teaching and learning. Originality/value: This study innovates by applying the MOODLE system to increase students’ listening motivation at a university in the Mekong Delta.
Students’ Challenges and Strategies in Extensive Listening Class

This study aims to analyze the students’ challenges in Extensive Listening class and find the strategies that students used to overcome those challenges. The researchers conducted this study to answer the questions of: What are the students’ challenges in Extensive Listening class? And What are the strategies for students to overcome those challenges in Extensive Listening class? This study used qualitative research. The participants of this study were ten students from 2019 and 2020 class year who had taken an Extensive Listening class majoring in English Language Education Program, Faculty of Language and Arts at Universitas Kristen Satya Wacana. In collecting the data, the researchers used online interviews through Zoom Meeting. The results of this study showed the students’ challenges in Extensive Listening class and strategies used to overcome the challenges in Extensive Listening class. The challenges faced by students in the Extensive Listening class were divided into internal and external factors. In the internal factors consisted of only one participant. Meanwhile, external factors consisted of six participants. Besides the challenges, this study also showed the strategies used by students to overcome the challenges and improve their listening skills, and it consisted of 10 strategies.
IOT Dashboard and Automation

Abstract: A IOT dashboard is an application that is used for retrieving real-time data from different sensors and visualizing data through different graphical and interactive techniques, and also to act on data storage and IOT devices. This paper discusses about the tool we developed followed by description of tabs/section in the dashboard.
VADER vs. BERT: A Comparative Performance Analysis for Sentiment on Coronavirus Outbreak

Nowadays, social media is getting more attention. Opinions on a wide range of topics are constantly expressed and distributed via a variety of social media platforms including Twitter. These ever-increasing subjective data are unquestionably a wealth of information for any decision-making process. The field of Sentiment Analysis has emerged to automate the analysis of such data which combines Natural Language Processing (NLP) and Machine Learning (ML). A branch of artificial intelligence (AI), machine learning uses algorithms to improve prediction and analysis for a variety of real-world issues. In this paper, initially, we crawled tweets from Twitter about COVID-19 and Omicron, and two different datasets were created. We applied VADER and BERT for finding the sentiment polarity of the tweets. Then 5 popular supervised ML algorithms were used to analyze the classification performance. From research, we found that most of the tweets expressed negative sentiments and in the classification task, Support Vector Machine outperformed other algorithms with an accuracy of 92% using BERT on the omicron dataset. We also found that BERT improves the classification performance except for the Naive Bayes algorithm.
ASSESSMENT OF ACCESS AND USAGE OF MASS MEDIA AMONG RICE FARMERS IN TARABA STATE, NIGERIA

The study examined access and usage of mass media among rice farmers in Gassol Local Government Area of Taraba State, Nigeria. It also identified the constraints to the usage of mass media tools among the farmers in the study area. Purposive random sampling methods were used to generate data from 138 respondents. Data collected were analyzed using descriptive statistics. The results indicated that the major mass media used by respondents were radio (86.2%), television (48.6%), Internet (48.6%), posters (46.4%), and leaflets (39.1%). The agricultural information medially accessed by the respondents from the mass media were credit facilities, marketing information, pests and diseases, fertilizer application, improved seeds, and improved storage facilities. The farmers identified a high need for agricultural information relating to credit, marketing, pests and diseases, fertilizer, improved seeds, and storage facilities. The high cost of mass media facilities, inadequate electricity, lack of local content, low level of education, lack of awareness of mass media use, and the inability of the farmers to access information from mass media were the major constraints faced by the respondents. The study recommended subsidizing the cost of media facilities, increasing access to electricity supply, creating awareness and skill of the farmers to take advantage provided by mass media tools for their farming benefits, and increasing the usage of other mass media outlets were made to enable the respondents to have full access/use of mass media to improve rice production.
Biomedical-named entity recognition using CUDA accelerated KNN algorithm

Biomedical named entity recognition (Bio-NER) is a highly complex and time-consuming research domain using natural language processing (NLP). It’s widely used in information retrieval, knowledge summarization, biomolecular event extraction, and discovery applications. This paper proposes a method for the recognition and classification of named entities in the biomedical domain using machine learning (ML) techniques. Support vector machine (SVM), decision trees (DT), K-nearest neighbor (KNN), and its kernel versions are used. However, recent advancements in programmable, massively parallel graphics processing units (GPU) hold promise in terms of increased computational capacity at a lower cost to address multi-dimensional data and time complexity. We implement a novel parallel version of KNN by porting the distance computation step on GPU using the compute unified device architecture (CUDA) and compare the performance of all the algorithms using the BioNLP/NLPBA 2004 corpus. Results demonstrate that CUDA-KNN takes full advantage of the GPU’s computational capacity and multi-leveled memory architecture, resulting in a 35× performance enhancement over the central processing unit (CPU). In a comparative study with existing research, the proposed model provides an option for a faster NER system for higher dimensionality and larger datasets as it offers balanced performance in terms of accuracy and speed-up, thus providing critical design insights into developing a robust BioNLP system.
The Impact of Manufacturing, Investment, Labor Force and Technology on Economic Growth in Palestine

The study aims to explore the impact of manufacturing, investment, labor force, and technology on the economic growth of Palestine using Kaldor’s first law of growth and the neoclassical growth model. The study used secondary data from the World Bank Database covering the period from 2000-2020. Correlation analysis and VIF tests were conducted to explore correlations and assess multicollinearity among the variables. A linear regression model was used to measure the relationship between manufacturing output, Investment, labor force, technology, and economic growth in Palestine. The analysis showed that an increase in manufacturing output leads to an increase in economic growth. Labor force participation and technology advancement have a significantly positive impact on economic growth, while investment has a significantly negative relation to economic growth. Based on these findings, policymakers are recommended to prioritize inclusive and sustainable industrialization, enhance the business environment, reduce unemployment rates, and promote competitiveness and innovation.
Electronic Health Records Design in the Nutrition Care Center of Teaching Factory at Politeknik Negeri Jember, Indonesia

The Nutrition Care Center (NCC) is one of the teaching factories (TEFA) at Politeknik Negeri Jember that provides community nutrition services. Preliminary data collection found that the factory did not have complete health database of their staff so that nutritionists find it difficult to screen for non-communicable disease risk factors. In this study, the use of Electronic Health Record (EHR) equipped with Clinical Decision Support System (CDSS) was incorporated to record, retrieve, archive, and update patients and other medical records in the NCC. This system provides easier health record management for the nutritionist team at the NCC TEFA Politeknik Negeri Jember. Medical records help them conduct screening on non-communicable disease (NCD) risk factors and make clinical decision support for doctors/counselors which have missions to reduce NCD cases in the productive age. This study used a waterfall method model and collected data through a literature review, observation, and interviews. The results produced an application of a clinic database management in which all data and information could be integrated well to improve the quality of services and facilitate data storage and access. This study concludes that the EHRs generates quick and accurate information to support operations and decision-making in the NCC.
Measuring school social climate in Latin America: the need for multidimensional and multi-informant tests – A systematic review

Introduction School social climate is central to understanding learning experiences in education environments. Previous studies describe various conceptual and operational definitions around the construct; however, there are no records of reviews focused on Latin America. Aim This study sought to analyze the available evidence and the quality of school social climate measures in Latin America through a systematic review of the literature based on the PRISMA methodology and the COSMIN checklist to assess the psychometric properties of the instruments. Methodology The Web of Science, Scopus, Psycinfo, and SciELO databases were consulted. A total of 582 records was identified, of which 27 fulfilled the inclusion criteria and methodological quality to be included in the systematization. Results The results show that the country with the greatest scientific production on the topic is Chile, the measures are centered mainly on the students’ perspective and the most used instrument is the CECSCE. In addition, a common aspect to all the records is that they were not sufficient to capture the complexity of school social climate. Conclusion Multidimensional and multi-informant measures are needed to adequately assess the construct.
Improve individual fairness in federated learning via adversarial training

Federated learning (FL) has been widely investigated these years. Since FL will be universally applied in the real world, the fairness issue involved is worthy of attention, while there are few relevant studies. Unlike previous work on group fairness in FL or fairness in centralized machine learning, this paper firstly considers both privacy and individual fairness and proposes promoting individual fairness in FL through distributionally adversarial training without violating data privacy. Specifically, we assume a model satisfying individual fairness as one robust to certain sensitive perturbations, which aligns with the goal of adversarial training. Then we transform the task of training an individually fair FL model into an adversarial training task. To obey the FL requirement of keeping data on clients privately, we execute the adversarial training task on the client side distributionally. Extensive experimental results on two real datasets collectively demonstrate the effectiveness of our proposed method, which not only improves individual fairness significantly but improves group fairness at the same time.
Implementation of the PBL Model to Improve Learning Outcomes in Establishing the Experience of Elementary Students

The learning outcomes of class VI students in the Indonesian language subject matter of experience framing are still low, which is indicated by the fact that many students have not reached the minimum KKM. The low learning outcomes are caused by the learning process, which has not increased students' learning motivation. This classroom action research aims to improve the learning of class VI students by implementing the PBL model on student learning outcomes. This research involved 20 grade VI students at SDN Junrejo 2 Batu who were positioned as research subjects. The instruments used in this research are observation and tests. The results of this study indicate an increase in the learning outcomes of Indonesian language students in class VI at SDN Junrejo 02 Batu in constructing experiences after using the PBL model. This can be seen from comparing the mastery of student learning outcomes in pre-cycle conditions of 25%, in cycle I increased to 30% and in cycle II increased to 85% with KKM 70. So it can be concluded that the PBL model can improve student learning outcomes in the Indonesian language subject assembling the experiences of class VI SD
Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography

IntroductionEarly breast cancer detection with screening mammography has been shown to reduce mortality and improve breast cancer survival.This study aims to evaluate the ability of an artificial intelligence computeraided detection (AI CAD) system to detect biopsy-proven invasive lobular carcinoma (ILC) on digital mammography. MethodsThis retrospective study reviewed mammograms of patients who were diagnosed with biopsy-proved ILC between January 1, 2017, and January 1, 2022.All mammograms were analyzed using cmAssist ® (CureMetrix, San Diego, California, United States), which is an AI CAD for mammography.The AI CAD sensitivity for detecting ILC on mammography was calculated and further subdivided by lesion type, mass shape, and mass margins.To account for the within-subject correlation, generalized linear mixed models were implemented to investigate the association between age, family history, and breast density and whether the AI detected a false positive or true positive.Odds ratios, 95% confidence intervals, and pvalues were also calculated. ResultsA total of 124 patients with 153 biopsy-proven ILC lesions were included.The AI CAD detected ILC on mammography with a sensitivity of 80%.The AI CAD had the highest sensitivity for detecting calcifications (100%), masses with irregular shape (82%), and masses with spiculated margins (86%).However, 88% of mammograms had at least one false positive mark with an average number of 3.9 false positive marks per mammogram. ConclusionThe AI CAD system evaluated was successful in marking the malignancy in digital mammography.However, the numerous annotations confounded the ability to determine its overall accuracy and this reduces its potential use in real-life practice.
Using EEG Data to Detect Eye Movement

In this paper, we show that it is possible to use EEG data to detect eye movement using machine learning. By recognizing eye movement through EEG results, our goal is to help individuals with disabilities better control object movement and perform daily activities independently. This is especially important as many disabled individuals rely on assistance from others for their daily needs, which can be burdensome for the person providing help. To achieve these objectives, we trained different machine learning models using a data set of eye-state classification from Kaggle. We analyzed the results to assess the accuracy of a KNN (K Nearest Neighbors) model. With the model achieved an accuracy of 95.23% in detecting eye movement in patients. These findings suggest that the model could be effectively utilized in the future, with further research to assist individuals with disabilities. Overall, our research suggests that it is possible to recognize eye movement through EEG results reliably. Further research in this area could lead to the development of more effective and personalized interventions for individuals with poor hand-eye coordination.