Reviewerly/exHarmony huggingface dataset:
Rapid Review: I-Voting for the 2024 General Election in Indonesia

Industry 4.0 has brought technological advances in making more contributions to human life. This progress has an impact on every aspect of human life, especially information and communication technology. The Uses and Gratifications Theory (UGT) is the basis for increasing the number of participation in the general election system that meets the needs of voters. This research was conducted using a rapid review with the aim of producing policy-making recommendations to the government quickly with a limited time. The method used in the search for coherent research is PRISMA, to find media choices that are most widely used by Indonesians and find opportunities including the threat of i-voting in the 2024 simultaneous General Elections in Indonesia. The findings show the many advantages of the i-voting system because there are more benefits large compared to conventional systems, especially i-voting is an efficient system compared to previous general election systems. The main threat to the i-voting system lies in network security, this condition already has a solution, namely the blockchain. Changes in the general election system cannot be restrained because of technology, currently changes in the general election system are still stagnant due to political interests. The development of a relevant system to implement is a combination of i-voting and e-voting, this system is able to provide solutions for voters who have resource constraints with e-voting and time constraints with i-voting
A Comparative Study on the Recognition of English and Arabic Handwritten Digits Based on the Combination of Transfer Learning and Classifier

In recent days, recognizing handwritten digits in Arabic and English has been useful for several applications. This paper presents an efficient method to recognize the unlimited variation in human handwriting. We have used freely available datasets, MNIST and MADBase, for English and Arabic handwritten digits, respectively. Each dataset involves enough number of images with ten classes from 0 to 9, so that there are 70,000 images in total, 60,000 images are used for training and 10,000 images are used for testing the models. A Deep Learning-based methodology is suggested for recognizing handwritten digits by using various transfer learning types such as; AlexNet, ResNet-18, GoogleNet, and DensNet-201 aimed at deep feature extractions. Moreover, we utilized three types of classifiers: Decision Tree (DT), k-nearest neighbors (KNN), and Support Vector Machine (SVM) and compared their performances. The results show that the AlexNet features with SVM classifiers provide the best results for both datasets, with error rates of 0.96% and 0.9997% for Arabic and English datasets, respectively.
A Design of Micromouse Based on ARM

Micromouse is one of the smart robots. Under the controlling of sensor and control system, it can self-operate and seek for settlement maze. Under the background of smart society nowadays, the Micromouse has a wide researching value. The applied technologies have been widely used in the fields as automation, smart home, robot and others.This paper designs a Micromouse that can meet the Micromouse competition requirements. The STM32F103RET6 is selected to be the main control chip, and the infrared transmitting and receiving pair tube is selected as main sensors. The gyroscope sensor is added for assisting Micromouse to correct vehicle posture, and the DC motor drives Micromouse to move. After completing the theoretical analysis, the circuit diagram, hardware PCB circuit and welding debugging are designed and completed gradually. Finally, the software design and test are carried out to this modular.
Understanding Consumer Behavior Using Market Basket Analysis

Market basket analysis (MBA) is a classic application of the association rule mining (ARM) algorithm. The objective of this unsupervised learning technique is to identify patterns and trends in consumer behavior. The algorithm is based on three fundamental concepts of support, confidence, and lift. Support indicates the percentage of occurrence of a particular transaction (percentage of times a product A is purchased), whereas confidence indicates the conditional probability of occurrence of a combination of transactions (the probability of consumers purchasing product B, whenever they purchase product A). Lift is referred to as the ‘interestingness measure.’ Lift value of greater than 1 indicates that the two products A and B are complementary and consumers prefer to purchase them together. On the other hand, a Lift value of less than 1 indicates that the two products A and B are substitutes implying that whenever the consumer purchases product A, he will not purchase product B and vice versa. The output of this algorithm is in the form of association rules represented by A, B → C indicating that whenever the consumer purchases products A and B, they will also purchase product C. Such rules provide input for store layout, product bundling, cross-selling, and customized promotions.
Designing Flex Sensor Gloves with Temperature Sensor &amp; Pulse Sensor to Help Stroke Patients

Stroke patients often have trouble in daily interactions, when the patient communicated with people who are guarding them. If the distance between the patient and their guard is far, this will make it difficult for the stroke patient to communicate. Therefore, this research designed a prototype glove with a flex sensor installed as a communication tool to aid stroke patients. The designed glove is paired with five flexible sensors to enable nurses easily to read the five-finger movement signals. This tool is also equipped with DS18B20 temperature and pulse sensors capable of monitoring the physical condition of stroke patients in real-time. Testing the flex sensor glove prototype was carried out by measuring temperature &amp; heart rate through pulse and temperature sensors. The output data is in the form of text and sound displayed on the LCD and heard through the speaker through the DFPlayer mini module. The body temperature was measured using the DS18B20 temperature sensor and compared with an Avico digital thermometer which has an average error of 0.1%, indicating adequacy. The heart rate test results through the pulse sensor were compared with measurements obtained using measure heart rate correctly. Instant heart rate, which has an average error of 0.7%, hence, it can
STUDY ON THE DISPERSION CHARACTERISTICS OF WOOD ACOUSTIC EMISSION SIGNAL BASED ON WAVELET DECOMPOSITION

Artificial AE sources were generated on the surfaces of Ulmus pumila, Zelkova schneideriana, Cunninghamia lanceolata, and Pinus sylvestris var.mongolicaLitv. specimens. The AE transverse wave signal was decomposed into 3-layers detail signalsby wavelet decomposition and reconstructed, and it was calculated based on correlation analysis. Then thelongitudinal wave speed was calculated according to the time-difference-of-arrival (TDOA) method, and the wood dispersion phenomenon was studied. The results showed that thedispersion phenomenon of Ulmus pumilawas obvious. The propagation speed of high-frequency signal was 2.38 times that of low-frequency signal. The ratio of high and low frequency propagation speed of soft wood was 1.72 and 1.73. The dispersion degree of Zelkova schneideriana was the weakest, and the propagation speed of the high frequency was 1.25 times of the low one. The ratios of longitudinal and transverse wave speedsof the four specimens were 4.59, 4.07, 4.24 and 4.2, respectively.
Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic

Most linguistic studies of Judeo-Arabic, the ensemble of dialects spoken and written by Jews in Arab lands, are qualitative in nature and rely on laborious manual annotation work, and are therefore limited in scale. In this work, we develop automatic methods for morpho-syntactic tagging of Algerian Judeo-Arabic texts published by Algerian Jews in the 19th--20th centuries, based on a linguistically tagged corpus. First, we describe our semi-automatic approach for preprocessing these texts. Then, we experiment with both an off-the-shelf morphological tagger and several specially designed neural network taggers. Finally, we perform a real-world evaluation of new texts that were never tagged before in comparison with human expert annotators. Our experimental results demonstrate that these methods can dramatically speed up and improve the linguistic research pipeline, enabling linguists to study these dialects on a much greater scale.
Improved reference-frame-independent quantum key distribution: erratum

We present an erratum to our Letter [Opt. Lett.47, 4219 (2022)10.1364/OL.470558]. This erratum corrects the error results of Figs. 2 and 4 due to the error in the simulation code. The corrections have no influence on the conclusions of the original Letter.
A survey on kinship verification

In this survey, kinship verification is defined as the automatic process of verifying whether two or more persons are blood relatives (kin) by analyzing images of their faces. Kinship verification is an important research field in computer vision with many applications such as finding missing persons, family album organization, and online image search. Although substantial progress has been made in kinship verification in the past decade, there are still challenges such as intrinsic (face i.e., differences in facial appearance) and extrinsic (acquisition i.e., varying imaging conditions) problems. And there is still a demand for more diverse datasets. Therefore, this paper provides a survey on kinship verification methods and datasets. The survey starts with the definition of kinship verification and its corresponding intrinsic and extrinsic challenges. Then, an overview of kinship verification methods and datasets is given. Finally, a new multi-modal dataset (Nemo-Kinship Dataset) is proposed as a benchmark dataset addressing large inter-subject age variations consisting of 4216 videos of 248 persons from 85 families. The newly collected dataset is used to systematically test and analyze state-of-the-art methods.
Image Super-Resolution via Multi-scale Channel Attention Residual Network

Abstract The refinement of existing CNN-based Super-resolution Reconstruction (SR) networks mainly focuses on deeper network architecture, which hinders the transmission of information in the networks. A deeper network is unable to make full use of intermediate correlation features and makes the training of the network difficult. To solve these problems, we propose a multi-scale channel attention residual network (MCAR). Specifically, we propose a multi-scale channel attention fusion module (MCAF) to learn local and global channels feature and capture the long-range dependencies. Furthermore, the multi-scale block is adopted to get the different scale feature representations. The experimental results on four benchmark datasets demonstrate that our models can effectively improve the visual effect of images, and outperform most of the advanced SISR methods in PSNR and SSIM.
Semantic Manipulation of Scenes with Small Visual Changes Alters Eye Movements

Introduction: Eye Movements during scene viewing are not just directed to areas with low-level saliency but to regions that are task-relevant (Hayhoe, 2005; Koehler et al., 2015) and/or semantically meaningful (Henderson et al., 2017). A recent study (Henderson et al., 2017 ) defined semantic informativeness by measuring subjective human ratings of the meaningfulness of different regions of scenes. We propose a novel method to measure semantic content of a scene region based on its influence on the scene description. We demonstrate how small visual changes of images that greatly change the scene description can result in large changes in fixational eye movements. Methods: Twenty pairs of images were photographed with small changes in object positions or actor/s posture that altered the scene description while minimally changing the visual content. Twenty four participants were split to provide descriptions for only one set from the pair of images. Presentation time was 3 sec and eye position was measured using an Eyelink 1000. Heat maps for the fixations were generated for each observer and image. Results: Correlation of fixational heat maps across observers for the same images averaged 0.76 +- 0.05. In contrast, the correlation of fixational heat maps of the observers across image pairs (an image and its visually similar but semantically manipulated pair) was significantly lower r = 0.3 +- 0.15 (p<0.001). Conclusion: Our findings show the extent to which semantic scene properties can influence eye movements even with minimal visual changes to the images.
A Study of Online Database Servers: The Case of SQL - Injection, How Evil that could be?

SQL injection attack is one of the most serious security vulnerabilities in many Databases Managements systems. Most of these vulnerabilities are caused by lack of input validation and SQL parameters used particularity at this time of technology revolution. The results of a SQL injection attack (SQLIA) are unpleasant because the attacker could wipe the entire contents of the victim's database or shut it down. As such, SQLIA can be used as important weapons in cyber warfare. As an attempt of breaching of number of application data bases systems two SQL injection techniques were used to successful locating vulnerable points during this research which are Blind Text Injection Differential and Error based Exploitation. The motivations behind were to find out where the databases systems are most likely to face an attack and proactively shore up those weaknesses before exploitation by hackers. The success of both techniques is a result of poor web server (online database server) design especially in the selection of error messages (or answers) they display to website users if something goes wrong. The approach through examination of error messages (error codes) did enable to precisely know the backend Database Management System (DBMS) type and version and what exactly are parameters (variables) which can allow “illegally” injecting codes (a SQL query). Additionally, the paper presents SQLIA cases and their impact in Tanzania cyber space as well as it suggests the possible mitigation ways while reflecting the collected data with what currently existing in cyberworld as far as SQL injection attack is concern to present the reality.
Towards a Federated Learning Framework on a Multi-Cloud Environment

This paper proposes Multi-FedLS, a Cross-silo Federated Learning (FL) framework for a multi-cloud environment aiming at minimizing financial cost as well as execution time. It comprises four modules: Pre-Scheduling, Initial Mapping, Fault Tolerance, and Dynamic Scheduler. Given an application and a multi-cloud environment, the Pre-Scheduling module runs experiments to obtain the expected execution times of the FL tasks and communication delays. The Initial Mapping module receives these computed values and provides a scheduling map for the server and clients’ VMs. Finally, Multi-FedLS deploys the selected VMs, starts the FL application, and monitors it. The Fault Tolerance (FT) module includes fault tolerance strategies in the FL application, such as checkpoint and replication, and detects some anomalous behaviors. In case of an unexpected increase in the communication delay or a VM failure, the FT module triggers the Dynamic Scheduler Module in order to select a new VM and resume the concerned tasks of the FL application. Some preliminary experiments are presented, confirming that some proposed strategies are crucial to efficiently execute an FL application on a multi-cloud environment.
1921. Leveraging Bluetooth Low-Energy Technology to Improve Contact Tracing in Healthcare Settings during the COVID-19 Pandemic

Abstract Background COVID-19 rapidly evolved into a global pandemic. Contact tracing with isolation and quarantine contribute to epidemic control but they are time consuming, costly and may be incomplete. We set out to assess the usability and performance characteristics of Bluetooth Low-Energy (BLE) wireless technology for indoor localization applied to contact tracing in healthcare settings. Methods Consented healthcare workers (HCW) from 2 designated COVID-19 wards (one intensive care unit (ICU) and one medical ward) were equipped with coin-sized BLE- emitting beacons. The signal was captured by small embedded computers (anchors) placed at designated locations, time-stamped and transmitted to an edge server via secure Wi-Fi where data were stored and real time contact algorithms were run (Fig.1). We developed experiments mimicking clinical scenarios and tested indoor localization during observed clinical activity for 6 months. We constructed our algorithms based on room structure (e.g. open spaces vs computer rooms) and activity characteristics (e.g. rounding in a large group vs 2 healthcare workers sitting together). We used 1) radio fingerprint localization where an initial virtual radio map was developed, 2) semantic localization which carries additional information such as proximity to a computer to define indirect transmission via fomites, and 3) clustering contact tracing to identify individuals rounding together. Close contact was defined as per the CDC guidelines. Fig. 1System configuration Results Consent rate was 43.3% with 187 HCW enrolled in the study. Consent rate was higher in the ICU and among attendings. All participants were compliant with wearing the beacons for the duration of the study. The performance characteristics for contact tracing using fingerprinting methods were AUROC 0.93, AUPRC 0.96, sensitivity 0.9, specificity 0.77 with F1 score of 0.89 and overall accuracy of 0.85. The clustering contact tracing registered a sensitivity of 0.86, specificity 0.89, F1 score 0.91 and accuracy 0.87. Computation time necessary to generate a list of close contacts as per specified criteria was less than 30 minutes. Conclusion We have developed and tested a reliable and accurate, low-cost and easily deployable system based on BLE technology to improve contact tracing among healthcare workers. Disclosures M Cristina Vazquez Guillamet, MD, AUPH: Stocks/Bonds|BNGO: Stocks/Bonds|OCGN: Stocks/Bonds|SESN: Stocks/Bonds.
IS AN IDEAL SCIENTIFIC TRAINING TEXTBOOK POSSIBLE?&#x0D; Review of the textbook by I.V. Ponkin and A.I. Lapteva «Methodology of Scientific Research and Practical Analytics» 2022 (3rd edition)

A scientific review of the 3rd edition of the textbook by I.V. Ponkin and A.I. Lapteva «Methodology of Scientific Research and Practical Analytics» is published. The author of the review raises fundamental questions about the training of future scientists, the educational programs and manuals necessary for this.
The Effectiveness of Mobile Learning Technology at the Tertiary Level During Conflicts

The paper investigates the effectiveness of personal smartphones and other mobile devices for learning English at the tertiary level during hostility in Ukraine. The research methods included 1) general theoretical methods (analysis and synthesis); 2) empirical methods (quasi-experiment, observation, interpretation); 3) methods of mathematical statistics (Pearson’s criterion). The material of the research involved 80 students of the first year of Yaroslav Mudryi National Law University (Kharkiv, Ukraine) during spring semester of 2021/2022 academic year. There were two samples: the experimental group (N = 40) that learned the course of “Legal English” exclusively through mobile devices and the control group (N = 40) that had only traditional forms of learning. The results revealed that academic progress of the students of the experimental group have improved by 15%, while achievements of the students of the control group have deteriorated by 10%. The reliability of the results is confirmed by the methods of mathematical statistics (p-value = 0.25; the significance level was chosen as 0.05). The conclusion is that mobile learning technology, which is able to provide students with access to systematic online training; platforms containing different textbooks and other local and remote educational content; as well as various educational services may become a great teaching and learning tool.
Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions

In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user’s sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings

Privacy-preserving machine learning in data-sharing processes is an ever-critical task that enables collaborative training of Machine Learning (ML) models without the need to share the original data sources. It is especially relevant when an organization must assure that sensitive data remains private throughout the whole ML pipeline, i.e., training and inference phases. This paper presents an innovative framework that uses Representation Learning via autoencoders to generate privacy preserving embedded data. Thus, organizations can share the data representation to increase machine learning models’ performance in scenarios with more than one data source for a shared predictive downstream task.
A Study on the Current Status of Universities’ Character Education: Focusing on 5 Major Universities

In this study, the trends of character education related curriculum and non-curricular programs of five major universities representing university character education were presented. These universities have won the Grand Prize for Character Education and the National Grand Prize for Sharing, and are well-known for their substantial character education. For the progress of this study, previous studies related to university character education were explored, and various data were collected and analyzed focusing on the university’s curriculum, rulebook, and website. As a result of the study, character education for each university was opened and operated in various ways, including character education subjects and non-curricular programs that reflect the ideology and goals of each university based on the relevant departments and professional manpower under the support of the university level. Through this, the main characteristics and specializations of universities that emphasize character education were confirmed. Based on the research results, discussions, conclusions, and suggestions for future research were drawn. Through this study, it will be possible to comprehensively grasp the trends of major universities leading university character education and to seek realistic and effective university character education methods.