Reviewerly/exHarmony huggingface dataset:
Peningkatan Hasil Belajar Matematika Materi Bilangan Prima dengan Model Pembelajaran Teams Game Tournament pada Siswa Kelas IV MI NU Manbaul Hidayah

Mathematics learning outcomes at MI NU Manbaul Hidayah are still low as evidenced by student learning outcomes that have not reached the KKM 65. This is because in the delivery of learning materials the teachers still uses the lecture method so that students become passive. The learning model can foster student activity in the process. The formulation of the problem from this research is whether the use of the teams game tournament (TGT) learning model can improve the learning outcomes of prime number mathematics in fourth grade students of MI NU Manba’ul Hidayah in the 2021/2022 school year?. The purpose of this study was to determine the use of the teams game tournament (TGT) learning model to improve the learning outcomes of prime number mathematics in fourth grade students of MI NU Manaul Hidayah in the 2021/2022 academic year. This research is a classroom action research conducted in one cycles,it consists of the four stages, namely planning, implementation, observation, and reflection. The subjects of this study were fourth grade students of MI NU Manbaul Hidayah, totaling 16 students including 9 boys and 7 girls. The research instrument includes test questions and observation sheets. Data collection methods used are tests, observations, and documentation. The data were analyzed statistically using the percentage formula, if 85% of students completed the cycle was stopped. The results showed that the teams game tournament (TGT) learning model could improve student learning outcomes for class IV MI NU Manbaul Hidayah in 2021. Increased student studies based on cycles 1 is found 87,5% student reach peak and 12,5% have not yet been close.
An Insight into Data Breaches: Challenges and Prevention

Data breach is a deliberate or desultory denudation of classified materials to proscribed bodies. In the fast- evolving technology era, data has evolved into one of the most core elements of an organization. Data spill causes grim impendence to organizations and tarnishes its solvency. As the volume of data is flowering wantonly and data breaches are happing spasmodically all the more, diagnosing and forestalling data breaches have been one of the most crying invincibility presentiments for organizations. Despite copious exploratory studies on safeguarding sensitive information from being divulged, it remains a dynamic research theme. A clear-sighted methodology for data breaches management is the crying need of the hour and, hence, there should be sincere efforts to manage the problems. Against this backdrop, this paper on the strength of secondary data is a proposition to illuminate the concept of data breaches and their classification. This paper also appraises the gravity of data breaches and the challenges that need to be cogitated in the high-technology epoch.
Kazakh-Chinese neural machine translation based on data augmentation

Machine translation is an important research field in natural language processing and artificial intelligence, which studies how to use computers to automatically convert languages. We experimented with attentional neural network machine translation for Chinese Kazakh language pairs and used data enhancement methods to alleviate the serious shortage of parallel corpus. The experimental results show that the proposed method is simple and effective, realize the mutual translation between Chinese and Kazakh with extremely low resources. At the same time, our proposed the method of two-way iterative back translation improves +4.47 BLEUs on Chinese to Kazakh and +5.97 BLEU on Kazakh to Chinese respectively.
Elderly fall detection system based on LiteOS operating system and 4G Cat1

As China enters an aging society, the problem of aging is becoming more and more serious. The elderly are often alone when their children are at work. Due to their poor physical fitness, they are prone to falls, injuries or even unaided situations during outdoor activities. It has been shown that more than 47% of elderly people who fall cannot stand up on their own. If they cannot be found in time, the best rescue time may be missed, and secondary damage and negative effects may even occur. In this regard, there are currently researches on fall detection technology for the elderly at home and abroad. The existing fall detection schemes are roughly divided into three types: wearable sensor detection, environmental sensor detection and vision-based detection. It can only be used in a specific environment to play a role. Due to the high cost of visual sensor detection, it is generally only suitable for indoor occasions, while wearable sensor detection technology is relatively low in cost and suitable for outdoor occasions and has more applicable scenarios. For example, Zigbee is used to transmit terminal data to the server for prediction, but Zigbee has the disadvantage of short communication distance for long-distance outdoors. The use of Bluetooth to transmit terminal data to the host device for prediction is also only applicable to short-distance indoor scenarios. This paper designs a fall detection system for the elderly based on the LiteOS operating system and 4G Cat1 module. It uses the high-precision and low-drift MPU6050 to collect human body posture information and relies on the Cat1 module of the high-coverage and high-speed 4G network to transmit the posture information. To the cloud server, the server runs the CNN model on the terminal data to make a decision, and finally feeds back the decision result to the terminal device for alarming. For a wide range of outdoor occasions, it is helpful to prevent the elderly from falling in outdoor activities and causing life injuries without rescue.
A method of semantic bias analysis based on SVM

This paper proposes a method of semantic bias analysis and discrimination based on SVM. This paper adopts the word frequency coefficient analysis method based on a single word. Through the analysis of function words (auxiliary words, conjunctions, etc.), it studies the language differences between different forum users. After training the SVM classifier, carry out the test. The accuracy of the semantic bias test results reaches 77.5%.
Research on emotion recognition based on multimodal fusion

In order to solve the problem that the recognition accuracy of a single mode model depends on the emotion type, this paper proposes a multimodal emotion recognition model based on the Di-directional Gated Recurrent Unit (BiGRU) network and attention mechanism. In this paper, BiGRU neural networks are used to extract high level features from the original features of text, audio and video, and then high-level features are carried out. When high level features are fused, the attention mechanism is used to adjust the weight of emotional features, and finally emotional recognition is carried out to obtain emotional classification. The model is trained and tested on the CH-SIMS dataset, with an accuracy of 75.15%. Experimental results show that the proposed model has lower computational complexity and higher recognition accuracy.
An attention mechanism and GAN based low-light image enhancement method

The low-light image has degradation problems such as low contrast, noise artifact, loss of details and color distortion, which cause poor visual perception quality of the image, and reduce the accuracy of subsequent image recognition, classification, and detection tasks. To solve the above problems, a low-light image enhancement method is proposed, which combines attention mechanism and Generative Adversarial Networks (GAN). A generator network model with convolutional attention module (CBAM) and residual block is designed. Three loss functions of content, color and texture are proposed for image features. The experiment is compared with the four advanced methods on LOL dataset. In this dataset, the experimental results are at least 5% better than those of other methods. The experimental results show that the saliency model proposed in this paper can effectively enhance the low-light image and obtain a good visual effect.
Evaluation of Clustering Techniques to Estimate the Effective Bandwidth of a Markovian Fluid from Traffic Traces

Integrated services digital networks, designed to transport data in real time, are modeled by a multiplexer system, where several data fluids share a single output. At the time of admission of a new connection, in order to maintain the quality of service (QoS), it is important to know the amount of available resources required by the connections sharing the channel. It is therefore important to have models for the sources and techniques that provide accurate estimates of the resources required for each of them. It is assumed that the network sources are modeled using the Generalized Markovian Fluid Model (GMFM) because of its versatility in describing traffic fluctuations. This is a Markovian fluid where the transfer rate is a random variable whose range and probability distribution are determined by the state of the modulating chain. To measure resource allocation, the concept of Effective Bandwidth (EB) is used, since it allows expressing this magnitude as a function of the model parameters, which will be estimated from traffic traces. As the size of the data to describe the behavior of a source is clearly enormous, they are studied using clustering techniques. In this work, different methods, supervised and unsupervised, are presented to estimate the parameters involved in the calculation of the EB. Finally, the performance of the estimators is analyzed by calculating partition comparison indices, corresponding to the dispatch intervals, which are based on the confusion matrix and the notion of mutual information entropy between partitions, on simulated data.
Investigation of Machine Learning Models for Human Activity Recognition: A Comparative Study

Human Activity Recognition (HAR) has shown enormous potential for facilitating older adults’ daily lives. Many models have been developed for recognizing human activities in Ambient Assisted Living (AAL) environments. But most models are conceptual and need to focus on the practical aspects. This paper discusses the use of learning models for human activity detection. The article focuses on the practical implementation of different machine learning models for HAR. This work investigates eight learning algorithms for recognizing human activities on a common dataset. This paper presents the performance analysis of different algorithms on the raw dataset comprising all features and on a dataset containing only promising features selected based on tree-based classifiers. The results present superior performance of deep learning models compared to others. The results, based on evaluation metrics, show that the Long Short Term Memory (LSTM) algorithm outperformed all the other algorithms while some algorithms, such as Decision Tree, Naive Bayes, and others. Under-performed while recognizing human activities.
Polynomials taking integer values on primes in a function field

Let Fq[x] be the ring of polynomials over a finite field Fq and Fq(x) its quotient field.Let P be the set of primes in Fq[x] , and let I be the set of all polynomials f over Fq(x) for which f (P) ⊆ Fq[x] .The existence of a basis for I is established using the notion of characteristic ideal; this shows that I is a free Fq[x] -module.Through localization, explicit shapes of certain bases for the localization of I are derived, and a well-known procedure is described as to how to obtain explicit forms of some bases of I .
Research on Fast Encryption of Electronic Health Record Data Based on Privacy Protection

Electronic health records data has the characteristics of massive, multi-modal, heterogeneous, but electronic health records data is easy to be invaded, leading to the disclosure of patient personal information. Health record management systems often ignore the security problems when patients interact with other roles, and the traditional encryption methods have been difficult to effectively meet the security needs of modern privacy data. Therefore, a fast encryption method of electronic health record data based on privacy protection is proposed. Mainly through the combination of privacy protection and homomorphic encryption technology to achieve distributed user privacy data protection, the mining effect of privacy protection is effectively improved, and the data security is guaranteed. The effectiveness and practicability of this method in data privacy security protection are verified by experiments.
Development of Android-Based Mathematics Learning Media on Three Dimensional Subject Materials

This study aims to develop learning media products in the form of an android application called Geometry Learning to help students understand the concept of three dimensions. This research is included in the Research and Development (R&amp;D) type using the ADDIE research development model (Analysis, Design, Development, Implementation, and Evaluation). The development of this research is based on several assessments of the developed Android-based learning media. The assessment results given by material experts have an average percentage of 87% (very feasible), and the assessment given by media experts has an average percentage of 91% (very feasible). In addition, the students were also assessed with an average percentage of 96.6% (very decent), and the average percentage of the teacher's assessment was 81% (very decent). Thus, the application of learning media in the form of an android application is feasible to be used in the learning process.
The Effect of Problem Based Instruction Modelusing Telegram Quiz Bot on Students’ Learning Motivation

This research is a quantitative research with the quasi experimental research type. The population in this study were all Semester V students of the Mathematics Education Department (TMTK) IAIN Kerinci and the samples were 30 students of VA class through purposive sampling technique. Data collection techniques used in this study were observation sheets and questionnaires. The purpose of this study was to find out how the application of the Problem Based Instruction (PBI) learning model using Telegram Quiz Bot, to find out how the students’ mathematics learning motivation, and to find out how the relationship between the PBI learning model using the Telegram Quiz Bot and the students’ mathematics learning motivation. The findings show that the use of the PBI learning model using Telegram Quiz Bot in the VA class obtained an average value of 3.1 which is included in the "Good" category. The mathematics learning motivation before applying the PBI learning model using Telegram Quiz Bot has an average value of 57.30 so that it is included in the "less good" category and the results for the average value of the mathematics learning motivation after applying the PBI learning model using Telegram Quiz Bot is 81.33 in the "very good" category. There is a significant effect between the application of the PBI learning model using Telegram Quiz Bot and students' mathematics learning motivation in the VA class of 0.607 which is included in the "strong" category.
A Machine Learning Approach for Solution Space Reduction in Aircraft Disruption Recovery

Aircraft recovery, a critical step in airline operations recovery, aims to minimize costs from disrupted aircraft schedules. Exact methods for aircraft recovery are computationally expensive and operationally infeasible in practice. Heuristics offer faster solutions but have inconsistent solution quality, often leading to large losses. We propose a supervised machine learning approach to accelerate aircraft recovery by pruning the solution space of the optimization problem. It leverages similarities with previously solved instances through an offline model-training phase, identifies optimal solution components for new problem instances in the online phase, and links them to the optimization model to rapidly generate high-quality solutions.Computational results from multiple historical and representative disruption instances, for a large US airline with over 1200 daily flights, demonstrate that this approach significantly outperforms exact methods on computational runtime while producing similarly high quality solutions. It also outperforms existing heuristics due to its ability to prune solution spaces in a more principled manner, leading to higher quality solutions in similarly short runtimes. For a runtime budget of two minutes, our approach provides a solution within 1.4% of the true optimal cost, resulting in an average daily saving of over $390,000 compared to all existing approaches. The main drivers of these improvements are explainable in terms of key airline operational metrics and are further validated in multiple sensitivity and robustness tests.
'It is Really Interesting how that Small Robot Impacts Humans.' The Exploratory Analysis of Human Attitudes Toward the Social Robot Vector in Reddit and Youtube Comments"

We present the results of an exploratory analysis of human attitudes toward the social robot Vector. The study was conducted on the natural language data (2,635 comments) retrieved from Reddit and YouTube. We describe the tag-set used and the (manual) annotation procedure. We present and compare attitudes structure revealed from Reddit and YouTube data. Two main findings are described and discussed: almost 20% of comments from both Reddit and YouTube consist of various manifestations of attitudes toward Vector (mainly attribution of autonomy and declaration of feelings toward Vector); Reddit and YouTube comments differ when it comes to revealed attitudes structure -- the data source matters for attitudes studies.
AN EFFICIENT E-CERTIFICATE MANAGEMENT SYSTEM IN E-LEARNING USING BLOCKCHAIN

Blockchain is a cutting-edge information technology that allows users to create ever-growing lists of data sequences.Individual higher education organizations or groupings of educational institutions can use blockchain in their automated management systems.Because there is no efficient anti-forge system in place, occurrences that result in the falsification of academic credentials are frequently recognized.The digital certificate system based on blockchain technology will be offered to overcome the problem of certificate counterfeiting.
Circuit implementation of a four-dimensional multi-scroll hyper-chaotic system and its applications 

In this paper, a hyper-chaotic system with a controllable number of scrolls and multiple pairs of coexistence attractors is proposed based on the Sprott-C system. By introducing a multi-segmental family of square functions, the system is verified to have a complex dynamical behavior by analyzing the dissipation, equilibrium point, Lyapunov exponent, dimension and bifurcation, Poincaré cross- section, power spectrum, and 0-1 test of the system. The system is then further characterized in terms of the chaotic bursting oscillations present in the system, the coexistence attractor of the system, the system complexity, and the effect of the parameter change on the system. Verified the implementability of the system through circuit design and simulation. Finally, the hyper-chaotic system is combined with the DNA encryption algorithm and applied to image encryption, and the feasibility and security of the encryption method are verified by comprehensive analysis of the key space and sensitivity, histogram, correlation, information entropy, and robustness of the encrypted image. The system can be used in the field of image encryption in the future.
Menguasai Bahasa Inggris dalam Satu Menit: Pengembangan Bahasa Inggris di STAIBA Kediri

Due to the fact that students will eventually enter the workforce, English proficiency plays a crucial role in their education. The world of work requires workers who have the ability to speak English orally and in writing. Students of the PIAUD study program in the pesantren environment who carry out their daily routines in the pesantren environment must be ready to enter the world outside the pesantren with a variety of chosen jobs. With the routine of Islamic boarding school activities which are very minimal in the opportunity to learn English, students who graduate from boarding schools feel that they are lacking in mastering the international language. In addition, the lack of motivation also greatly affects them in learning English. Based on the problems faced by students in Islamic boarding schools, it is necessary to provide motivation and learn English that is able to change their mindset in learning English easily and quickly. The development of English in the "Mastering English in One Minutes" activity by presenting natives from Canada together with English language study students was carried out face to face after previously students received online classes 3 times in meetings. This development uses the question and answer method, where PIAUD study program students can directly ask and practice English natively and will provide a golden opportunity to study briefly and increase their motivation in learning English. So it is necessary to organize Community Service activities to improve students' English mastery in the Islamic boarding school environment
3D First-Person Shooter Game Development Using Unreal Engine 5

Abstract: This research paper will explore the development of a 3D first-person shooter game using Unreal Engine 5. The paper will cover the basics of game development using Unreal Engine 5 and how it can be used to create a 3D first-person shooter game. Unreal Engine has changed significantly over the years and thus this paper will also explore the new features and techniques introduced in recent years. Additionally, the paper will discuss the challenges faced during the game development process and how they were overcome.
Design and Fabrication of Stroke Therapy Chair

Abstract: Stroke is usually the sudden death of brain cells due to lack of oxygen, caused by blockage of blood flow or rupture of an artery to the brain. A stroke occurs when part of the brain loses its blood supply and stops working. This causes the part of the body that it controls to stop working as well. There are several treatments for the stroke like endovascular treatments, surgical treatments etc. but the best treatment for the paralysis, strokes, is the physiotherapy. Usually, the therapy is done manually with an assistant. We propose a method in which the physiotherapy is done automatically with the help of simple mechanisms. This consists of a chair setup in which the patients are made to sit and the arms and the legs of the chair is given motion, so that the patients arms and legs move at a relative speed so that the treatment is done effectively.
Students' Self-Efficacy in Public Speaking: A Case Study in Indonesian EFL Context

This study aims to investigate students’ self-efficacy in public speaking. This study involved four participants. The data collection method used is in the form of semi-structured interviews, then the data will be analyzed using thematic analysis. The research results are divided into four sources of self-efficacy from Bandura, namely Enactive Mastery Experience, Verbal Persuasion, Vicarious Experience, and Physiological and Affective States. This finding discusses the final level of students' confidence in their self-efficacy. Therefore, the results of this study provide empirical insight for students about self-efficacy beliefs of Enactive Mastery Experience (EME), Vicarious Experience (VE), Verbal Persuasion (VP), and Physiological and Affective State (PAS) and also provide information for teachers to optimize future learning, especially regarding self-efficacy.Keywords: Self-Efficacy, Source Self-Efficacy, Public Speaking