Reviewerly/exHarmony huggingface dataset:
Pengaruh Kemandirian Belajar dan Penggunaan Teknologi Terhadap Prestasi Belajar Matematika Selama Pembelajaran Daring Siswa Kelas VI SD Gugus Sinar Harapan

The purpose of this study was to (1) analyze the effect of independent learning on mathematics learning achievement during online learning for class VI students. (2) analyzing the effect of using technology on mathematics learning achievement during online learning of class VI students. (3) analyzing the effect of independent learning and the use of technology together on mathematics learning achievement during online learning for class VI students. This research took place in the Sinar Harapan Cluster, Sidoharjo District. The population includes all grade VI students at SD Gugus Sinar Harapan, totaling 7 schools. The sample in this study were 101 students, of which 30 students were for testing research instruments and the remaining 71 students were for research. Data collection techniques with questionnaires. Data analysis technique using multiple linear regression analysis. The results showed that the significance of the effect of independent learning on student achievement was obtained p-value (0.000) &lt;0.05, which means that independent learning (X1) has a positive and significant effect on student achievement (Y). The significance of the effect of the use of technology on student achievement obtained p-value (0.009) &lt;0.05, which means the use of technology (X2) has a positive and significant effect on student achievement (Y). The significance of the influence between learning independence and the use of technology on teacher learning achievement was obtained F count (73.103) &gt; F table (2.70) and a sig value (p-value) of 0.000 &lt;0.05, it can be concluded that there is a simultaneous influence the same between learning independence and the use of technology on student achievement.
Using Fuzzy Feed-Forward Neural Network for Linguistic Processing

Fuzzy sets have been implemented efficiently to manage unclear data, language terms, and vague notions.Recently, considerable work has been dedicated to merging neural-network techniques with fuzzy sets.In this study, present the structure of a fuzzy feed-forward neural network (FFFNN) with a trapezoidal fuzzy set.In addition to handling real input vectors, it is also capable of handling fuzzy input vectors.Generally, the output of a FNN is a fuzzy vector.According to the extension principle of Zadeh, each unit of a FNN has an input-output relationship.To determine the costs associated with fuzzy calculations and fuzzy objectives, developed a cost function.At that point, created a learning algorithm from the cost capacity to align the four variables of each trapezoidal fuzzy weight.In conclusion, demonstrate our methodology using numerical models.This paper discusses fuzzy generalized delta rules with distinct back propagation techniques on trapezoidal fuzzy sets.The suggested architecture offers a more compelling and distinctive feature in that every node can process fuzzy sets or verbal terms, maintaining the simplicity of the back propagation algorithm.As a result, the resulting architecture can deal with issues in which input parameters and desired outcomes are expressed verbally.Furthermore, this methodology functions at a higher level of abstraction of data by operating at the verbal level
A biobjective model for resource provisioning in multi-cloud environments with capacity constraints

Abstract Private and public clouds are good means for getting on-demand intensive computing resources. In such a context, selecting the most appropriate clouds and virtual machines (VMs) is a complex task. From the user’s point of view, the challenge consists in efficiently managing cloud resources while integrating prices and performance criteria. This paper focuses on the problem of selecting the appropriate clouds and VMs to run bags-of-tasks (BoT): big sets of identical and independent tasks. More precisely, we define new mathematical optimization models to deal with the time of use of each VMs and to jointly integrate the execution makespan and the cost into the objective function through a bi-objective problem. In order to provide trade-off solutions to the problem, we propose a lexicographic approach. In addition, we introduce, in two different ways, capacity constraints or bounds on the number of VMs available in the clouds. A global limit on the number of VMs or resource constraints at each time period can be defined. Computational experiments are performed on a synthetic dataset. Sensitivity analysis highlights the effect of the resource limits on the minimum makespan, the effect of the deadline in the total operation cost, the impact of considering instantaneous capacity constraints instead of a global limit and the trade-off between the cost and the execution makespan.
Research on machine learning-based sign language recognition using body keypoint data in video

본 논문에서는 실시간 영상 내의 신체 움직임을 머신러닝 기반으로 추론하여 수어를 인식하는 시스템을 제안한다. 제안 시스템은 움직이는 신체부위의 핵심 좌표 데이터로 구성되는 모션 벡터를 머신러닝에 활용함으로써 인공신경망 모델의 훈련 시간을 줄이고 동작 인식의 정확도를 높이는 특징을 갖는다. 이러한 모션 벡터는 표현 시간이 상이한 다양한 낱말들에 대해 동일한 규격으로 구성되므로 추론의 신뢰도를 더욱 높이는 효과를 제공한다. 그리고 수어문장을 표현한 영상 중 각 낱말에 해당되는 부분 영상을 정확하게 분할해 내기 위해 점진적으로 추론 구간을 조정하면서 반복적인 신뢰도 평가를 수행하는 방법을 제시한다. 실험을 통해 영상 자체를 학습 및 추론에 이용하는 일반적인 기존 방식과 비교했을 때 훈련 시간, 추론 속도 및 신뢰도, 부분 영상의 분할 정확도 측면에서 제안 시스템이 더욱 우수한 성능을 보임을 알 수 있었다. This paper proposes a sign language recognition system based on machine learning, which can infer the meaning of body movement in realtime video. It has a feature that motion vectors constructed with keypoint data of moving body are used for machine learning. This enables the proposed system to reduce the training time of an artificial neural network model and to enhance the recognition accuracy. Since those motion vectors have the same size and format regardless of the different expression times of words, the confidence level of an inference could be further increased. To correctly divide a part of video corresponding to each word in a sentence, we propose an iterative evaluation method of confidence levels for inferences with the gradual length adjustment of video fragment. The experimental results showed that our system outperforms a conventional method using the video itself for training and inference, in training time, inference speed, confidence level of inference and accuracy of video fragmentation.
IMPLEMENTATION OF LEARNING-BASED GAME METHOD TO IMPROVE ELEMENTARY STUDENTS’ LEARNING ACTIVENESS

Students’ learning activeness is crucial in the ongoing learning process. In fact, there are still students who do not have active in learning. Based on the observation, it was found that there were still students who did not have active in learning. Students never expressed opinions and questions, students were not enthusiastic, students played or talked with friends during the learning process, students hesitated to express opinions, students often got permission to leave class, and students did not contribute to group discussions. The solution to these problems is to apply the game method in learning. The aim is to explain that the application of the game method can increase the students’ activeness in learning during the learning process in the classroom. The method used is a descriptive qualitative method. The result is that the application of the game method can increase students’ learning activeness. It is marked by a change in the response showed by students in which students become enthusiastic while participating in learning, students actively ask and answer questions, and students are actively involved in discussions and some of them are in accordance with indicators of student’s learning activeness. Nevertheless, to obtain significant results, a teacher can apply this method repeatedly. As a Christian teacher, of course, the teacher must be able to use their abilities and intellect to see the problems that students are experiencing in class, then the teacher seeks the right solution based on Bible truth. The suggestion that can be given to teachers is to evaluate the problems of students’ learning activeness in order to be able to determine other methods that are more effective for the improvement of students’ learning activeness.
Test of NLP models with various news datasets

Natural Language Processing is one of the fields that attracts a lot of attention in deep learning, and with the introduction of transformer-based GPT[1] and BERT[2], it is showing tremendous performance improvement. In this paper, we compared and analyzed the performance of word embedding, neural network, and pre-trained language model, dependent othe model and data type of the news. ISOT, Kaggle, and Politifact datasets were used for fake news dataset as a result, BERT showed best performance in this study, however in Politifact Dataset, it showed relatively poor performance. We analyzed the structure of dataset and from the model perspectives to find out the reason why the performance differences were occurred.
Howl: The Tip of the Obscene Iceberg

My essay on Allen Ginsberg’s Howl is actually a chapter of a bigger research paper I’m working&nbsp; on. While performing research on literary censorship, I found a source from the UC Merced&nbsp; Library search engine. It was a book called Howl on Trial. Thus, I went down the rabbit hole. My objective was to actually argue against a commonly-viewed idea regarding Howl. Thanks to&nbsp; the information provided by Howl on Trial, it was evident that many scholars had specific takes&nbsp; on why Howl was initially banned. I decided to assert that many authors don’t really consider the&nbsp; idea that the people who banned Howl, mainly police officers from the 1950s, never understood&nbsp; the point of the poem. It was actually thanks to Professor Garcia from the UC Merced English&nbsp; Department who gave me the idea to create a thesis that contested a previously established point.&nbsp; Thus, thanks to the UC Merced Library, I chose to discuss American Scream: Allen Ginsberg's&nbsp; Howl and the Making of the Beat Generation by Jonah Raskin.
An Effective Security Comparison Protocol in Cloud Computing

With the development of cloud computing technology, more and more data owners upload their local data to the public cloud server for storage and calculation. While this can save customers’ operating costs, it also poses privacy and security challenges. Such challenges can be solved using secure multi-party computation (SMPC), but this still exposes more security issues. In cloud computing using SMPC, clients need to process their data and submit the processed data to the cloud server, which then performs the calculation and returns the results to each client. Each client and server must be honest. If there is cooperation or dishonest behavior between clients, some clients may profit from it or even disclose the private data of other clients. This paper proposes the SMPC based on a Partially-Homomorphic Encryption (PHE) scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof (ZKP) is used to limit the client's malicious behavior. In addition, the introduction of Oblivious Transfer (OT) technology also ensures that the semi-honest cloud server knows nothing about private data, so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each client. Finally, the security analysis shows that the scheme not only ensures the privacy of participants, but also ensures the fairness of the comparison protocol data.
Facilitating Cyber Security inContemporary Life

The use of technology within global societies has increased with the rapid rate of technological advancements in contemporary life. The Internet has successfully facilitated social connectedness and enhanced business opportunities in numerous ways. Our entire social and industrial history has been re-written and re-catalogued in over 25years. A vast number of the population has access to a smartphone, while most households have internet enabled devices. There is a growth in the digital revolution with many opportunities for economic growth, contributing to culture and wellbeing. However, with such rapid technological growth, there are digital concerns pertaining to individual and organisational risk, particularly when inputting and storing sensitive data. This study aims to understand the reasons why cyber threats are faced in contemporary life. There is an endeavour to facilitate cyber security through recommendations. A literature review is carried out within this study. Results have revealed the following reasons for cyber threats: Economy; Malware a business; Smaller organisations without cyber insurance; Human error. This study has deduced that cyber security is a vital component of contemporary life that requires global societies to educate themselves as to limit cyber- threats and attacks. Criminals wait for opportunities through online-interaction and seek to exploit societal vulnerabilities. Being vigilant while raising cyber awareness and taking responsibility is important. Therefore, it is essential for global societies to harbour a sense of cyber maturity and understand how digital technologies can cause harm. Preventative methods must be implemented to facilitate cyber security in contemporary life.
Boundedness of the discrete Hilbert transform in discrete Hölder spaces

The Hilbert transform plays an important role in the theory and practice of signal processing operations in continuous system theory. The Hilbert transform was the motivation for the development of modern harmonic analysis. Its discrete version is also widely used in many areas of science and technology and plays an important role in digital signal processing. The essential motivation behind thinking about discrete transforms is that experimental data are most frequently not taken in a continuous manner but sampled at discrete time values. Since much of the data collected in both the physical sciences and engineering are discrete, the discrete Hilbert transform is a rather useful tool in these areas for the general analysis of this type of data. The Hilbert transform has been well studied on classical function spaces such as H¨older, Lebesgue, Morrey, etc. But its discrete version, which also has numerous applications, has not been fully studied in discrete analogues of these spaces. In this paper, we discuss the discrete Hilbert transform in discrete H¨older spaces and obtain its boundedness in these spaces.
Non-EFL Students’ Experience of Speaking English: A Project Based Learning in Making Video

The objective of this research is to know the non-EFL students’ experience in making a project of English Speaking video as Project-Based Learning (PBL). This research is descriptive qualitative. The analysis shows that: students feel that speaking is a difficult skill in English, but they agree that the topic of the speaking project about ‘Introduction Myself &amp; Others is “interesting”. In making an English project video, students make an Indonesian script then translate it into English, students read scripts when recording videos and almost do 5 times repetitions of making an English video project. The duration of making an English video project about more than 1 minute and 2 minutes duration. During making an English project video, the students face difficulties, such as doing pause (pauses the message while thinking about what to say), doing filler (filling the gap in speaking with a certain voice (eeeeee, emmmmm, hmmm, and others), doing a mispronunciation/spelling error, doing a grammatical error, having a limited vocabulary, having trouble using accents in English, and having difficulty when expressing ideas. While, external students’ difficulties such as having mental problems, being easily distracted by their surroundings, having problems related to media (the audio/image is not clear), and having problems with lighting. By using video projects, students can know the vocabularies and how to read it in English, can know better English grammar, students can express ideas in speaking English, students feel courageous and confident in speaking English and they can improve their English speaking skills.
Topic-Oriented Dialogue Summarization

A multi-turn dialogue often contains multiple discussion topics. In several scenarios (e.g., customer service dispute, public opinion monitoring), people are only interested in the gist of a specific topic in the dialogue. Therefore, we propose a novel summarization task, i.e., Topic-Oriented Dialogue Summarization (TODS). Given a dialogue with a topic label, TODS aims to produce a summary covering the main content of the given topic in the dialogue. To model the relationship between dialogues and topics, three key abilities are needed for TODS: (1) Learning the semantic information of different topics. (2) Locating the topic-related content in the dialogue. (3) Distinguishing summaries for different topics in the same dialogue. Thus, we propose three topic-related auxiliary tasks to make the summarization model learn the three abilities above. First, the topic identification task aims at generating all the topics in the dialogue. Second, the topic attention restriction task tries to constrain the attention distribution on topic-related utterances. Third, the topic summary distinguishing task focuses on increasing the difference of summaries for different topics in the same dialogue. Experimental results on two public TODS datasets show that all auxiliary tasks are critical for TODS and help generate high-quality summaries. We also point out the expansions and challenges in TODS for future research.
“Coming here you should speak English”: performing creativity on a YouTube channel for teaching speaking

The present study aims to explore strategies in teaching speaking using YouTube as media as well as the advantages and disadvantages of this platform in teaching speaking. Nested in a narrative case study approach, data were garnered through interviews using WhatsApp and Google Forms. An EFL teacher who is also a YouTuber and six students in tenth grade of a senior high school in Purwokerto, Central Java, Indonesia volunteered to participate. Study findings demonstrate that the EFL teacher integrated technology, i.e., YouTube, as a learning medium, and she provided video learning videos according to the material needed by students. The teacher designed a learning video with herself as a role model. Before uploading it on YouTube, the teacher watched the video first and edited some errors. The study also reveals several advantages of using YouTube in teaching speaking. YouTube videos promoted the teacher’s creativity in employing technology as learning media. Students could learn anywhere and anytime through videos on YouTube and practiced from videos of native speakers. Conversely, adopting YouTube videos in a speaking class had disadvantages, such as the selection of YouTube videos relevant to learning materials, restricted opportunities to have feedback from their teachers, and poor internet connection.
Analysis of Questioning Skills through Science Learning Practices in Microteaching Courses

Study aims to describe the mastery of the questioning skills of prospective elementary school teacher students at the Purworejo Muhammadiyah UniversitySources of data in this study were student teacher candidates for elementar school in Muhammadiyah University. The data collection technique used is observation. The analysis used in this research is qualitative data analysis. The indicators of mastery of questioning skills include basic questioning skills and advanced questioning skills. The results showed that Prospective elementary school teacher students at Purworejo Muhammadiyah University had more mastery of basic questioning skills compared to advanced questioning skills. The data were obtained by using the observation method on micro learning activities by Prospective elementary school teacher students at Purworejo Muhammadiyah University. The basic teaching skills, especially the questioning skills, were analyzed and observed directly in the implementation of microteaching activities. Based on the research that has been done, it is found that the mastery of basic skills in teaching is very necessary, especially the skills in asking questions. Asking is a verbal utterance that asks for a response from someone you know. Responses can be in the form of knowledge or things that are the result of consideration.
Meningkatkan Kehandalan Safety dengan Menciptakan Alat Coupling Spool Pieces di Conventional Buoy Mooring IT Ampenan

Hose connect/disconnect activities to Manifold Tankers in the CBM area have a very high risk (spill overboard, fire, etc.). Therefore, one of the things that can be used as a solution is to create a tool called a Coupling Spool Piece which is made of stainless steel which is anti-rust, seawater resistant, not easily broken, and non-welded. It is designed with an ergonomic size and can be used as a rope tie hook. So that with the solution created, it is hoped that the quality of materials and materials will be more reliable and safety, save costs because they have succeeded in preventing fuel spills and reducing operational losses as well as the effectiveness of tanker operations, and can improve operational safety reliability
OBJECT DETECTION IN AN IMAGE

This project aims to develop an object detection system using TensorFlow.js, a JavaScript library for building and training machine learning models in the browser or Node.js environment. Object detection is a critical task in computer vision, with various applications such as image and video analysis, robotics, and autonomous driving. The proposed system uses a pre-trained deep learning model such as MobileNet or ResNet as the feature extractor, and applies the Single Shot MultiBox Detector (SSD) algorithm for object detection. The system is trained and fine-tuned on a custom dataset, and the resulting model is integrated with a web-based user interface for real-time object detection. The performance of the system is evaluated on a test set and compared with existing object detection models
Pondering About Task Spatial Misalignment: Classification-Localization Equilibrated Object Detection

Object detection is a fundamental task in computer vision, consisting of both classification and localization tasks. Previous works mostly perform classification and localization with shared feature extractor like Convolution Neural Network. However, the tasks of classification and localization exhibit different sensitivities with regard to the same feature, hence the "task spatial misalignment" issue. This issue can result in a hedge issue between the performances of localizer and classifier. To address these issues, we first propose a novel Dynamic Coefficient Loss to simultaneously consider and balance the performances of classification and localization tasks. To well address anchor label misjudgment issue in irregular- shaped object detection, we define a new classification-aware IoU metric to assign anchors intelligently. Finally, we further introduce the localization factor into NMS by proposing a Classification-Localization balanced NMS. Extensive experiments on MS COCO and PASCAL VOC demonstrate that our proposals can improve RetinaNet by around 1.5% AP with various backbones.