Reviewerly/exHarmony huggingface dataset:
The Causality between Offensive Language Use and Negative Emotions in the Public Event— An Empirical Study Using Convergent Cross Mapping

ABSTRACT The mutual causal interdependence between offensive language use and negative emotions has been largely underexplored in the public event. Using 784,179 posts about the Tangshan violence event collected from Sina Weibo, nine themes were recognized based on framing theory. The mutual causal relationship between offensive language and negative emotions under each theme was examined through Convergent Cross Mapping. Results suggested that the mutual causal relationships between offensive language and negative emotion intensity under various themes were different with bidirectional causality under moral judgement, emotional venting, and power conflict, unidirectional causality under the vulnerable framework and the trust framework, and no causality under other themes. More detailed examination revealed special bidirectional or unidirectional causality between offensive language and some fine‐grained negative emotions under the vulnerable framework, the trust framework, and secondary opinion. This study provides insight into the interaction between offensive language and negative emotions and helps emergency managers make targeted strategies to solve the problems of offensive language use and negative emotions.
Pendampingan Pembelajaran Membaca AlQuran Menggunakan Metode Tartil di Taman Pendidikan Quran Jamiatul Muksinin

The tartil method is considered a practical and fast Alquran learning method to help students read the Alquran well. The aim of this assistance is to provide learning pedagogy for teaching staff at TPQ Jamiatul Muksisnin in accordance with the methods applied, and to provide supporting facilities for teaching staff at TPQ Jamiatul Muksisnin. The service partners are all students at TPQ Jamiatul Muksisnin. The method used in this assistance is through a Participatory Action Research (PAR) approach oriented towards community empowerment where this assistance is carried out to solve problems that exist in the community, precisely at TPQ Jamiatul Muksinin, by applying learning through the tartil method it can provide satisfaction and increasing students' ability to read the Koran. The results of this assistance show that students are very enthusiastic and enjoy this activity because it brings other benefits in training their Alquran reading skills. It can be seen that students are able to understand the tartil method so they can read the Alquran well, correctly and fluently. These results also show that the tartil method can improve students' ability to read the Alquran precisely and accurately.
Composing Radiographic Dictionary for Radiology Students and Radiographers

The rapid development of science and technology on radiology requires radiology students in particular and radiographers in general to be able to understand and master the science and technology through books or scientific articles, which are mostly written in English. To help them understand the radiological scientific information easily, a complete basic terminologies dictionary on radiology is needed. This study aimed to analyze the vocabularies contained in radiology textbooks linguistically which then are compiled into a radiological pocket dictionary. This research used a qualitative descriptive method. Data were taken in the form of vocabulary list resulted from previous research conducted by the author on radiology textbooks in 2019. This research had 7 stages of activities which include syntactic, morphological, semantic, and phonetic analysis. In addition, the vocabularies then went through 2 stages of evaluation to check the perfection of words regarding to their writing, meaning, and pronunciation. Furthermore, each vocabulary was given an example of its use in the form of sentences in the context of radiology. Results of the study in the form of a pocket dictionary consisting list of basic radiological vocabulary/terms accompanied by the syntactic categories, the meaning of words in Indonesian language that are in accordance with the scientific context of radiology, phonetic transcription, and examples of the use of words in sentences in the context of radiology are expected to contribute to improving the quality of human resources of radiographers in Indonesia.
Adopting NLP Techniques to Analyze Twitter Social Discourses around The Good Doctor

The Good Doctor (ABC, 2017-) is a US medical drama centered upon the life of Shaun Murphy, a surgeon with Autism Spectrum Disorder (ASD) and savant syndrome.While the serial product has been widely investigated from a linguistic and a medical humanities approach, little has been said about its reception.Based on this lack of research and considering the ability of medical dramas to promote information about health issues, the aim of this paper is twofold: first, to analyze Twitter social discourses around The Good Doctor with a focus on the representation of ASD and, second, to explore the potential of Natural Language Processing (NLP) techniques to investigate the reception of TV series on Twitter.Two different methodologies were implemented to analyze the tweets: a topic modeling technique to detect the main topics, carried out using BERTopic, and a sentiment analysis approach to mine the audience's opinion on a subset of tweets related to ASD, conducted using RoBERTa.Findings have shown that that The Good Doctor's audience mostly discuss narrative and fruition-related topics, specifically throughout its airing time period, and have mainly positively received the representation of ASD contained in it.
Comparing the Writing Styles of Multiple Disciplines: A <scp>Large‐Scale</scp> Quantitative Analysis

ABSTRACT Investigating the writing styles of different disciplines is crucial for comprehending the scientific language in scholarly communication. This study examines the variations in scientific writing styles across disciplines using a large‐scale analysis on 14 million abstracts from the Microsoft Academic Graph database. Writing styles are quantified in four aspects: symbolic, lexical, syntactic, and readability. The t‐tests were employed to explore potential trends in the evolution of writing style and investigate linguistic differences across disciplines. Results indicate an overall trend towards increasing complexity and informativeness in writing, with significant changes observed in Biology, Chemistry, Computer Science, and Psychology. Disparities of linguistic features in writing styles were observed across disciplines. The findings shed light on the distinct language use in scholarly communication across disciplines.
Pengaruh Pembelajaran Daring di Masa Covid 19 terhadap Motivasi Belajar Siswa Kelas X SMK TIK-MM Swasta Rakyat Sei Glugur

Online learning is an electronic learning system, using electronic media, internet, computer, gedget (HP), and multimedia files (sound, pictures and video). Learning motivation is the overall driving force in the student that moves the student and gives rise to the learning activity so that the desired goal of the learning subject can be achieved. This research is quantitative research that examines phenomena objectively by using statistical calculations to test hypotheses in research. The sample of this study amounted to 30 people with total sampling technique. The test result of product moment r value is positive 0.632 and perolehan p count = 0.000 &lt; 0.05 which indicates that online learning variables (variable X) and learning motivation variable (variable Y) have a significant influence. Anova regression analysis test obtained an F value of 18,628 and a sig value. That is 0.000 &lt; 0.05, so it can be concluded that Ha was accepted and Ho rejected. The result of the determinant coefficient, known determinant cofisien value of 0.399 which means variable X (Online Learning) affects variable Y (Learning Motivation) by 39.9%.
Analisis Kebijakan Peraturan Daerah Kabupaten Tebo Tentang Pelayanan Pendidikan

Quality education services can develop the potential of students to the fullest. The purpose of this study was to analyze the regional regulations of Tebo Regency. This study uses a qualitative method with a literature study approach. The data sources in this study used primary data sources in the form of policy Number 1 of 2013 concerning Education Services and secondary data sources from various sources that support both journals and books related to policies. The data analysis technique used is in the form of content analysis. The results of this study indicate that the Education Service Policy has been regulated in the regional regulation policy of Tebo Regency Number 1 of 2013 concerning Education Services, as has been stipulated in the policy of the regulation regarding educational services, namely through: 1) 12 -Year Compulsory Education Pilot Program, 2) Children's Education Early Age, 3) Educators and Education Personnel
Digital Visual Grammar Concept Map Facilitated EFL Holistic Grammar Comprehension

Form-focused deductive grammar learning approach is an effective way to facilitate language learners to memorize atomistic grammatical rules. However, beyond retaining isolated grammar rules, learners can benefit from using web based concept map, a visualized utility, to overlap grammar rules and achieve holistic comprehension. Therefore, this research looks into detail (total nodes, depth, and breadth) of how visual grammar concept map affects student’s examination score and aims to assist students to gain a holistic visual grammar learning through utilizing an online concept map software (CoCoing.info). One hundred and thirty-two college students were involved in this study for 18 weeks. The quantitative results revealed that students who created a more developed and holistic visual grammar concept map gained a better understanding of the grammatical rules. This was however not true for low achieving students. Therefore, more detailed analysis showed students with deeper depth and wider breadth achieved higher score. However, the high achieving students and low achieving students’ comparison study indicated that the depth of the visual grammar concept map is harder for students to construct and is a better indicator of achievement compared to breadth. The findings show that visual grammar concept map can facilitate traditional form-focused classroom.
Students’ Critical Thinking Skills in Writing Analytical Exposition Text

The objective of this study is to assess the proficiency of students' critical thinking skills in composing analytical exposition texts, as well as to examine their perceptions regarding the development of critical thinking skills in this particular writing genre. The research employed a descriptive qualitative methodology, utilizing data gathering instruments such as observations and interviews. A study was undertaken at SMA Negeri 4 Gowa Regency during the academic year of 2021/2022. The participants in this study consisted of 30 students enrolled in the class XI MIA 3 during the second semester. However, for the purpose of data collection, the researchers selected a subset of 15 students by purposive sampling for interviews. The findings of this study suggest that students frequently commit errors when it comes to utilizing generic structures and language elements in their written analytic exposition texts. Based on the identified faults, the researcher saw that a majority of the students exhibited deficiencies in adhering to the prescribed guidelines of the analytical exposition text. Furthermore, it was noted that critical thinking skills were not adequately incorporated in the writing sessions conducted within this particular class. The observed phenomenon can be attributed to the insufficient comprehension and instruction provided to students within the educational setting, hindering the development of critical skills. Additionally, the absence of thought-provoking inquiries posed by educators may impede students' progress in enhancing their critical thinking abilities. Based on the analysis and observations conducted, the researcher has reached the conclusion that the students of class XI MIA 3 at SMA Negeri 4 Kabupaten Gowa demonstrate a moderate degree of critical thinking skills, as assessed by the critical thinking characteristics outlined by Facione (Facione, 2013).
EFL Higher Education Students’ Psychological Factor in Speaking Classroom Activities at Universitas Muslim Indonesia: A Research Report

The aim of this study is to determine what types of psychological factors sixth-semester EFL higher education students confront when speaking English and how to overcome these psychological factors. This study employs qualitative descriptive methods with two variables. This research used the technique of purposive sampling to select participants, in which volunteers are chosen based on the criteria and objectives of the study. In this study, twenty 2019 Academic Foreign Language Student of the Sixth Semester participated. This research employed qualitative data analysis techniques (questionnaire). The score assigned to each student as a result. Using the Likert Scale Procedure, the data obtained from this study were analyzed. The findings revealed that the majority of students are affected by the same psychological factors, namely Anxiety, Lack of Self-Confidence, Fear, and Shyness. The majority of students are terrified of speaking English. They experience discomfort and agitation if they are required to answer or ask questions in English. Also, the results demonstrated that the sixth-semester ABA UMI class of 2019 possessed an extremely high degree of motivation. This is where they feel that learning English is crucial, since they are constantly asking their professors and peers about subjects they do not understand, and their desire to learn in English is quite strong. The study suggests that educators should focus on enhancing students' psychological factors, particularly motivation, anxiety, and self-confidence, to improve their speaking performance in the EFL classroom. The findings of this study contribute to the body of knowledge on the impact of psychological factors on EFL learners' speaking skills, and can help educators develop effective strategies to improve their students' speaking proficiency in the EFL classroom.
The Vehicle Tutorial: Neural Network Verification with Vehicle

Machine learning components, such as neural networks, gradually make their way into software; and, when the software is critically safe, the machine learning components must be verifiably safe. This gives rise to the problem of neural network verification. The community has been making rapid progress in developing methods for incorporating logical specifications into neural networks, both in training and verification. However, to truly unlock the ability to verify real-world neural network-enhanced systems we believe the following is necessary: 1. The specification should be written once and should automatically work with training and verification tools. 2. The specification should be written in a manner independent of any particular neural network training/inference platform. 3. The specification should be able to be written as a high-level property over the problem space, rather than a property over the input space (of the neural network). 4. After verification the specification should be exportable to general interactive theorem provers so that its proof can be incorporated into proofs about the larger systems around the neural network. In this tutorial we presented Vehicle, a tool that allows users to do all of this. We provided an introduction to the Vehicle specification language, and then walked attendees through using it to express a variety of famous and not-so-famous specifications. Subsequently we demonstrate how a specification can be compiled down to i) queries for network verifiers, ii) Tensorflow graphs to be used as loss functions during training and iii) cross-compiled to Agda, a main-stream interactive theorem prover. Finally we discussed some of the technical challenges in the implementation as well as some of the outstanding problems.
Quantum Optimization in Engineering: A Brief Review

Since its beginning, quantum computing has opened up a plethora of new perspectives and advantages., including its ability to exponentially accelerate computing processes. A large amount of research has been devoted to developing innovative methods for solving a variety of engineering problems by designing quantum versions of numerous mathematical processes. In this paper, we review the state of the art in these new techniques from the standpoint of quantum computer creation and efficiency enhancement, with an emphasis on the most widespread mathematical optimization techniques for engineering applications. It also emphasizes the obstacles and constraints associated with the use of quantum computing, as well as the major prospects for future contributions. This paper is providing a useful resource for engineering researchers who are likely to seek solutions from quantum computing.
Energy-Efficient and Privacy-Preserved Incentive Mechanism for Federated Learning in Mobile Edge Computing

In mobile edge computing (MEC)-assisted federated learning (FL), the MEC users can train data locally and send the results to the MEC server to update the global model. However, the implementation of FL may be prevented by the selfish nature of MEC users, as they need to contribute considerable data and computing resources while scarifying certain data privacy for the FL process. Therefore, it is of great importance to design an efficient incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission and data privacy, we design an energy-efficient and privacy-preserved incentive scheme to facilitate the FL process by investigating interactions between the MEC server and MEC users in a MEC-assisted FL system. Using a Stackelberg game model, we explore the transmit power allocation and privacy budget determination of MEC users and reward strategy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results demonstrate the effectiveness of our proposed scheme.
Sequential Semantic Generative Communication for Progressive Text-to-Image Generation

This paper proposes new framework of communication system leveraging promising generation capabilities of multimodal generative models. Regarding nowadays’ smart applications, successful communication can be made by conveying the perceptual meaning, which we set as text prompt. Text serves as a suitable semantic representation of image data as it has evolved to instruct an image or generate image through mutli-modal techniques, by being interpreted in a manner similar to human cogitation. Utilizing text can also reduce the overload compared to transmitting the intact data itself. The transmitter converts objective image to text through multi-model generation process and the receiver reconstructs the image using reverse process. Each word in the text sentence has each syntactic role, responsible for particular piece of information the text contains. For further efficiency in communication load, the transmitter sequentially sends words in priority of carrying the most information until reaches successful communication. Therefore, our primary focus is on the promising design of a communication system based on image-to-text transformation and the proposed schemes for sequentially transmitting word tokens. Our work is expected to pave a new road of utilizing state-of-the-art generative models to real communication systems.
Sparse Federated Training of Object Detection in the Internet of Vehicles

As an essential component part of the Intelligent Transportation System (ITS), the Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in the IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns. To mitigate such privacy leakage, we first propose a federated learning-based framework, where well-trained local models are shared in the central server. However, since edge devices usually have limited computing power, plus a strict requirement of low latency in IoVs, we further propose a sparse training process on edge devices, which can effectively lighten the model, and ensure its training efficiency on edge devices, thereby reducing communication overheads. In addition, due to the diverse computing capabilities and dynamic environment, different sparsity rates are applied to edge devices. To further guarantee the performance, we propose, FedWeg, an improved aggregation scheme based on FedAvg, which is designed by the inverse ratio of sparsity rates. Experiments on the real-life dataset using YOLO show that the proposed scheme can achieve the required object detection rate while saving considerable communication costs.
An Incremental Gray-Box Physical Adversarial Attack on Neural Network Training

Neural networks have demonstrated remarkable success in learning and solving complex tasks in a variety of fields including cognitive cities. Nevertheless, the rise of those networks in modern computing has been accompanied by concerns regarding their vulnerability to adversarial attacks. In this work, we propose a novel gradient-free, gray box, incremental attack that targets the training process of neural networks. The proposed attack, which implicitly poisons the intermediate data structures that retain the training instances between training epochs acquires its high-risk property from attacking data structures that are typically unobserved by professionals. Hence, the attack goes unnoticed despite the damage it can cause. Moreover, the attack can be executed without the attackers' knowledge of the neural network structure or training data, making it more dangerous. The proposed attack was tested under a sensitive application of secure cognitive cities, namely, biometric authentication. The conducted experiments showed that the proposed attack is effective and stealthy. Finally, the attack effectiveness property was concluded from the fact that it was able to flip the sign of the loss gradient in the conducted experiments to become positive, which is noisy and unstable training. Moreover, the attack was able to decrease the inference probability in the poisoned networks compared to their unpoisoned counterparts by 15.37%, 14.68%, and 24.88% for the Densenet, VGG, and Xception, respectively. Finally, the attack retained its stealthiness despite its high effectiveness. This was demonstrated by the fact that the attack did not cause a notable increase in the training time, in addition, the Fscore values only dropped by an average of 1.2%, 1.9%, and 1.5% for the poisoned Densenet, VGG, and Xception, respectively.