Reviewerly/exHarmony huggingface dataset:
Comparison of Data Classification Results in Serious Gaming for Rehabilitation of Rheumatoid Arthritis

Rheumatoid arthritis is a common disease which affects the joints of the wrist, fingers, feet and in the end the daily activities. Nowadays, gestures and virtual reality are used in many activities supporting recovery, games, learning as technology is present more and more in different fields. This paper presents results related to the grip movement detected by a Leap Motion device using binary classification and machine learning algorithms. We used 2 models to compare the results: Naïve Bayes and Random Forest Classifier. The metrics for comparison were: accuracy, precision, recall and f1-score. Also, we create a confusion matrix for a clear visualization of the results. We used 5000 data to train the algorithm and 1500 data to test. The accuracy and the precision were bigger than 97% in all the cases.
Recovery Gestures Classification Using KNN and LDA Models

Hand and joint mobility recovery involve performing a set of exercises. Gestures are often used in the hand mobility recovery process. This paper discusses the selection and the use of 2 models of neural networks for the classification of data that describe Leap Motion gestures. The gestures are: the hand opening and closing gesture and the palm rotation gesture. The purpose is the optimal selection of the neural network model to be used in the classification of the data describing the recovery gestures. The models chosen for the classification of the two gestures were: Linear Discriminant Analysis (LDA) and K-neighbors Classifier (KNN). The accuracies achieved in the classification of the gestures for each model are: 0.91 - LDA and 0.98 - KNN.
Reasoning and inference for (Maximum) satisfiability: new insights

At the heart of computer science and artificial intelligence, logic is often used as a powerful language to model and solve complex problems that arise in academia and in real-world applications. A well-known formalism in this context is the Satisfiability (SAT) problem which simply checks whether a given propositional formula in the form of a set of constraints, called clauses, can be satisfied. A natural optimization extension of this problem is Maximum Satisfiability (Max-SAT) which consists in determining the maximum number of clausal constraints that can be satisfied within the formula. In our work, we are interested in studying the power and limits of inference and reasoning in the context of (Maximum) Satisfiability. Our first contributions revolve around investigating inference in SAT and Max-SAT solving. First, we study statistical inference within a Multi-Armed Bandit (MAB) framework for online selection of branching heuristics in SAT and we show that it can further enhance the efficiency of modern clause-learning solvers. Moreover, we provide further insights on the power of inference in Branch and Bound algorithms for Max-SAT solving through the property of UP-resilience. Our contributions also extend to SAT and Max-SAT proof theory. We particularly attempt to theoretically bridge the gap between SAT and Max-SAT inference.
BiP: Bit-Phase-Flip Error Mitigation in Quantum Communications

Quantum links are inherently noisy and quantum information bits (qubits) suffer upto 13% degradation in their entangled states within time-scale of 0.5 ms. Thus, mitigating errors becomes essential for reliable end-to-end data communication in a multi-hop quantum network. Compared to the typical operations performed within the contained environment of a single quantum computer, removal of both bit- and phase-flip errors in a distributed network of such computers is challenging due to the stochastic variations in the noise at each intermediate link. This paper describes a scheme that determines both the bit-and phase-flip errors (abbreviated as ‘BiP’) and mitigates them for distributed and networked quantum systems. To achieve this, we model the environment noise using general error models and obtain error calibration matrices in different computational bases for bit-phase-flip errors. Results reveal that BiP improves the fidelity beyond 95% for the received qubits compared to the state-of-the-art error mitigation method by correcting the elevation θ and azimuthal angles φin the Bloch sphere representation.
An Analysis on Cohesive Devices of Students’ Recount Paragraphs Writing

Cohesive devices are linguistic elements or techniques used to establish connections and create coherence within a text. They serve to link ideas, sentences, and paragraphs, making the overall discourse more cohesive and facilitating the smooth flow of information. Cohesive devices contribute to the logical organization and understanding of a written or spoken text. By employing cohesive devices effectively, writers can enhance the coherence and cohesion of their texts, allowing readers to follow and understand the information more easily. This study aims to examine how cohesive devices are utilized in recount paragraphs written by students as well as to identify the challenges and underlying reasons for their difficulties in using such devices. The research involved 20 tenth-grade students from MAN 2 Jember and adopted a qualitative analysis research design. The data collection process encompassed documentation, observations, and student interviews. The analysis focused on two aspects of cohesion: lexical cohesion (reiteration and collocation) and grammatical cohesion (reference, substitution, ellipsis, and conjunction). The findings reveal the following: (a) Students employed five types of cohesive devices in their recount paragraph writing, with reference (31.29%) and conjunction (28.75%) being the most frequent, followed by substitution (12.72%), ellipsis (1.78%), as well as reiteration (13.48%) and collocation (11.95%); (b) Students exhibited varying levels of familiarity and competence with different cohesive devices. The study highlights the importance of comprehensive instruction and support to enhance students' proficiency in using cohesive devices. To address the identified challenges, pedagogical interventions are recommended.
The Influence of Reciprocal Teaching on Reading Ability Achievement

The importance of reading comprehension is rising in the field of education. For students to read proficiently, instruction and practise are necessary. Making meaning of a text is a component of reading comprehension. Consequently, rather of focusing on understanding a particular phrase or word, readers should aim to gain a comprehensive knowledge of the material presented in the book. One of method that could improve students reading ability is reciprocal method. this study aims to investigate how reciprocal teaching can enhance students' reading comprehension of descriptive texts. A quantitative method was used to summarize the data that was obtained from the outcome. Students from SMP 1 Muhammadiyah Jember are the participant for this research. 8 grade B as the experimental class while 8 grade A as control class. These students have studied descriptive text. The class 8 B was experimental group which was taught reading text using reciprocal method. The class 8 A was control group that was taugh reading only based on the text book. The data that have been collected then analyzed using spss to find if there is any significat effect in using reciprocal method in students reading comprehension.the result of the independent sample t-test on SPSS program, The value of sig (2-tailed) was 0.001 and it was less than 0.05. It could be revealed that there was a significant difference between the experimental group and the control group after. In other words, there was a significant effect of using reciprocal on the eighth-grade students’ reading score at SMP 1 Muhammadiyah Jember.
A survey of cross-modal pre-training

Pre-training allows autonomous learning of samples inherent semantics from unlabeled data in a large scale, and then obtains a task-independent general representation. Furthermore, the generalization of model encoding across different tasks can be improved. Inspired by early high-performance pre-trained models for a single modality of text or images, scholars began to explore image-text cross-modal pre-training methods, aiming to achieve the ability of understanding both visual information and textual semantics separately and achieve effective alignment between the two. This paper aims to summarize the development of image-text pre-training. Specifically, it summarizes the feature extraction and representation methods, pre-training tasks and extended downstream tasks used by various mainstream models. This paper also introduces and compares the mainstream models into two types of architectures: single-stream and dual-stream. Through the analysis, it can be concluded that the embedding representation determines the difficulty and performance of the subsequent fusion process, and the two kinds of architectures lead to different levels of fusion capabilities.
Pengembangan Media Pembelajaran Pendidikan Agama Islam Materi Tata Cara Wudhu Berbasis Vidio Visual pada Siswa Kelas II SD Terpadu Ainul Ulum

&#x0D; &#x0D; &#x0D; &#x0D; The objectives in this ilmianh paper are: 1) To find out how to develop learning media for ablution procedures based on visual videos in grade II students of SD Terpadu Ainul Ulum Pulung. 2) To find out how the validity of learning media ablution procedures based on visual videos in grade II students of SD Terpadu Ainul Ulum Pulung. 3) To find out how effective the learning media for ablution procedures is based on visual videos in grade II students of SD Terpadu Ainul Ulum Pulung. Research methods: Research &amp; Development (R&amp;D). To develop products using a 4-D development model consisting of four stages: define, design, develop, and disseminate. In the study, only three stages were carried out, namely to the development stage because of limited time and cost. The test subjects were 10 grade II students. Data collection techniques are used validation sheets, interviews, field note sheets. Analysis of the data used to test its validity using Aiken's formula. Based on the results of the analysis, the validity of learning media of 0.78 is included in the valid category. Based on the results of the analysis, it was concluded that the audio virtual learning media developed already met the criteria for valid and effective learning tools.&#x0D; &#x0D; &#x0D; &#x0D;
Lossy Compression Approaches Based on Vector Quantization

Vector Quantization (VQ) is an effective lossy compression technology developed in the late 1970s. Its theoretical basis is Shannon's rate distortion theory. The basic principle of vector quantization is to use the index of the codeword in the codebook that best matches the input vector for transmission and storage, while decoding only requires a simple table lookup operation. Its outstanding advantages are high compression ratio, simple decoding, and the ability to preserve signal details well. In this article, several VQ approaches are introduced for lossy compression.
20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions

The ever-evolving technologies are constantly making the industrial processes more efficient and providing new opportunities for cost saving and enhanced operations. Artificial intelligence (AI)-enabled blockchain-based solutions are making significant contributions to the enhancement of industrial efficiency. AI-enabled blockchain-based solutions enable the secure and frictionless exchange of digital assets and data among members of the chain while enabling trustless interaction between them. These solutions enable faster and error-free transactions with enhanced scalability, reliability, traceability, and privacy.
Prior’s System Q and its Extensions

Recently, and as happens from time to time in New Zealand, a typescript of Prior’s turned up. This one was in the personal collection of Oliver Sutherland. Prior typed it in November 1957 and used copies in his senior logic group, an informal research group at Canterbury University. A terse and relentlessly compressed couple of pages, it concerns Prior’s system Q, which even towards the end of his life he was still describing as ‘the true modal logic’. We analyse Prior’s typescript and the issues underlying it, as well as providing an exposition of Q, and an examination of Łukasiewicz’s objections to Q. The article also includes an interview with Prior’s student Robert Bull concerning Q.
4. Scientific Hoaxes and the Predatory Paradox: Past, Present, and Future

In Chapter 4, ‘Scientific Hoaxes and the Predatory Paradox: Past, Present, and Future,’ Amy Koerber examines scientific hoax articles with a focus on the weaknesses and flaws that such hoaxes can expose in the larger information ecosystem of scholarly publishing. The chapter thus reveals that scientific hoaxes further complicate any neat distinction between journals that are predatory and those that are not. Hoaxes have, in some cases, exposed specific journals as predatory. But in other cases, they have had effects beyond those that the author anticipated, exposing major weaknesses or fraudulent practices not only at journals or publishers suspected to be predatory but also at the most prestigious and well-respected journals. More importantly, publishing hoaxes have unintentionally exposed weaknesses in the mechanisms that we have long relied on to ensure research quality. For example, hoaxes have exposed flaws in even the best journals’ peer review systems, and when hoax articles continue to get cited in subsequent literature—sometimes even after retraction—they lead us to question our habit of relying on citation counts as a measure of research quality. Partly in response to hoaxes, industries have emerged around the desire to pin down the legitimacy of a particular author or publication in an environment that makes it increasingly easy for fakes to be mistaken as the real thing. For example, we now have ORCID identifiers to help us establish the identity of authors and Digital Object Identifiers (DOI) to help us pinpoint the location and verify the identity of published texts. These identifiers are becoming commonplace in academic lingo, but it is easy to overlook the fact that each of these markers emerged as a commercial development with its own complexities, nuances, and shortcomings. As we argue, these innovations reflect our desire to pin down something that is certain and real in a landscape where it is increasingly easy for fakes to circulate as the real thing.
Utilizing the K-Means Algorithm for Breast Cancer Diagnosis: A Promising Approach for Improved Early Detection

Breast cancer is a pressing non-communicable disease, especially affecting women, with its incidence on the rise. In 2020, it ranked among the most common cancers in Indonesia. Timely detection and precise diagnosis are pivotal for effective breast cancer management. To enhance diagnostic accuracy, the K-means clustering method is applied to group patients based on shared attributes. This research aims to contribute significantly to breast cancer diagnosis by leveraging the K-means method, potentially improving patient survival rates.The research process involves data collection, preprocessing, K-means application, evaluation, and visualization. A dataset of 569 breast cancer patient records with 32 attributes from Kaggle is utilized. The K-Means algorithm is assessed using accuracy, yielding a value of 0.8457, signifying good performance. Malignant cases (211) and benign cases (301) are visualized in a scatter plot, distinguishing between them.In conclusion, this study presents an initial step in utilizing the K-means algorithm for breast cancer diagnosis, offering promising results. Further research and the development of more advanced models are imperative to address the global health challenge posed by breast cancer among women.Index Terms—breast cancer; clustering; K-Means Algorithm
Preventing and Combating Cybercrimes: Case of Cybercrimes Investigation Unit of Tanzania Police

The study was conducted at the cybercrimes investigation unit of Tanzania Police Force. The study investigates the current situation of Tanzania Police Force on combating and preventing cybercrimes in Tanzania and examine the effect of the current legal framework governing them on preventing and combating cybercrime. The study used a sample size of 86 respondents from cybercrimes investigation unit at Tanzania Police Force. Data collection was done by using interview guide and questionnaire. The data was analysed by using multiple regression model and thematic analysis for quantitative and qualitative data respectively. The findings show that, Cybercrimes in Tanzania is on high rate as justified by the respondents regarding on number of reported cases of cybercrimes in Dar es Salaam despite of effort shown by Police Force to battle this problem. It was also found that a unit increases in technological capability of Police Force lead to significant decreases in preventing and combating cybercrimes. Furthermore, the results show that, other things remain constant, a unit increase in the current legal framework governing the Police Force lead to significant decrease of effort on preventing and combating cybercrimes. Therefore, it is recommended that there is a need for the Police Force to invest more in acquiring new technologies well as providing continuous public awareness of cybercrimes. Also cybercrime act of 2015 as well as legal and regulatory compliances governing all cybercrimes stakeholders to be reviewed so that can enable Police Force to perform their duties of preventing and combating cybercrimes efficiently.
INTERROGATING THE IMPACT OF INTERNATIONAL MONETARY FUND (IMF) POLICIES ON ECONOMIC DEVELOPMENT IN NIGERIA: LENDING PRECONDITIONS, 1999–2022 PERSPECTIVE

Since the 1980s, Nigeria’s economy has witnessed severe stagnation. While Eurocentric literature pinpoints the Nigerian civil war and her leaders’ corruptive tendency as the prima facie, Afrocentric literature traces the country’s economic woes to her historical processes of colonial domination and economic exploitation. Nevertheless, none of the above arguments underpin more firmly as being the catalyst to the country’s economic dysfunction especially when compared to the International Monetary Fund (IMF) policies in the country. This paper demonstrates that IMF policies in Nigeria vis-a-vis its Structural Adjustment Program (SAP) through its Loan Conditionality is “the crux impediment facing the country.” As such, the paper argues that the acceptance of IMF loans during General Ibrahim Babangida’s among other misgovernance administrations perpetuated the economic woes and fostered the backwardness of development in the country. The paper employs primary and secondary data to examine the above concerns. It concludes that a more inclusive economic system devoid of the current extractive economic policies would revitalize its fortunes.
Sign Language Recognition System using Flex Sensor Network

This journal work on sign language recognition system using flex sensor network aims to x-ray the implementation of a sign language recognition system using smart gloves, raspberry pi, python and C language. Therefore, research into sign language interpretation using gestures has been explored progressively during recent decades to serve as an auxiliary tool for deaf and mute people to blend into society without barriers. In this work, a smart sign language interpretation system using a wearable hand device. This wearable system utilizes five flex-sensors, then using a PIC microcontroller, raspberry pi, python and C language to create a real-time Sign Language Recognition system. In this journal work, a sensor-based sign language recognition can serve as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. This work provides a thorough technique in recent hand gesture and sign language recognition. After implementation, the system was able to output up to thirty-one phrases successfully. This paper was focused on improving the accuracy and usability of the system by optimizing the sensor placement and developing a user-friendly interface.
A Robust Adversary Detection-Deactivation Method for Metaverse-oriented Collaborative Deep Learning

Metaverse is trending to create a digital circumstance that can transfer the real world to an online platform supported by large quantities of real-time interactions. Pre-trained Artificial Intelligence (AI) models are demonstrating their increasing capability in aiding the metaverse to achieve an excellent response with negligible delay, and nowadays, many large models are collaboratively trained by various participants in a manner named collaborative deep learning (CDL). However, several security weaknesses can threaten the safety of the CDL training process, which might result in fatal attacks to either the pre-trained large model or the local sensitive data sets possessed by an individual entity. In CDL, malicious participants can hide within the major innocent and silently uploads deceptive parameters to degenerate the model performance, or they can abuse the downloaded parameters to construct a Generative Adversarial Network (GAN) to acquire the private information of others illegally. To compensate for these vulnerabilities, this paper proposes an adversary detection-deactivation method, which can limit and isolate the access of potential malicious participants, quarantine and disable the GAN-attack or harmful backpropagation of received threatening gradients. A detailed protection analysis has been conducted on a Multiview CDL case, and results show that the protocol can effectively prevent harmful access by heuristic manner analysis and can protect the existing model by swiftly checking received gradients using only one low-cost branch with an embedded firewall.
SALES PREDICTION OF ANUGERAH JAYA PANCING 99 SHOP WITH BACKPROPAGATION NEURAL NETWORKS

Artificial Neural Network (Artificial Neural Network) is an information processing paradigm inspired by the workings of biological nervous systems, such as the performance of the brain, which processes information. Toko Anugerah Jaya Pancing 99 faces challenges in predicting product sales that fluctuate according to customer demand, and external factors can also affect sales of their products. To determine store sales predictions using the backpropagation method. From the research results with data training experiments it was found that the iterations process stopped when the epoch reached 6, time 0.00.01, performance 0.317, Gradient 0.359, and Validation Checks 6. Keywords: Prediction, Backpropagation, Neural Networks, Sales
Analysis of Online Learning Applications During The Covid-19 Pandemic

This study aims to analyze the perceptions of English language education students towards applications that are used effectively and the most effective applications for online learning in the era of the Covid-19 pandemic. This research uses the SEM (Structural Equation Modeling) method. The subjects of this study were 105 sixth-semester students of English Language Education at UIN Sultan Maulana Hasanudin Banten. The instrument used in this study was a questionnaire. Analyze data using descriptive statistics and structural equation modeling in inferential statistics. The findings showed that students had a positive or positive perception of effective applications for online learning during the Covid-19 pandemic. Based on the findings, it can be seen that the application used by students in online learning in this pandemic era is very effective because the distance learning process has become easier with the application. Then 77% of respondents said that the most efficient application used in online learning is Google Meet because, technically, Google Meet is easier to use, does not require much internet costs, does not interfere when online learning, and is convenient to use.