Reviewerly/exHarmony huggingface dataset:
Analisis Alat Kohesi Gramatikal dan Alat Kohesi Leksikal dalam Teks Cerita Pendek Siswa

&lt;p&gt;&lt;em&gt;This study aims to describe the use of the types of grammatical cohesion tools and lexical cohesion tools in the short story texts of Bogor City Senior High School students, and to determine the most dominant use of cohesion tools. The method used is a qualitative descriptive method. The data source of this research is the short story text of the students of class XI SMA Negeri 4 Bogor. The data collection technique used was literature study. Checking the validity of the data using triangulation. The tools of grammatical cohesion that are the focus are references and conjunctions. The tools of lexical cohesion that are the focus are repetition, synonym, antonym, hyponym, collocation, and equivalence. From the results of the analysis found the use of the grammatical cohesion tool for 78 data. Total reference use was 32 findings (41%) and conjunction 46 findings (59%). Furthermore, the use of lexical cohesion tools totaling 140 data. The use of repetitions of 36 findings (26%), synonym 14 findings (10%), antonym 24 findings (17%), hyponym 10 findings (7%), collocation of 24 findings (17%), and equivalence of 32 findings (23%) . It can be concluded that in 25 short stories students found the use of grammatical and lexical cohesion tools, namely, 78 grammatical cohesion tools and 140 lexical cohesion tool data. The most dominant use of grammatical cohesion tools is the use of conjunctions with the findings of 46 quotations (59%) and the most dominant use of lexical cohesion tools, namely the use of repetitions as much as 36 (26%).&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: Grammatical Cohesion Tool, Lexical cohesion tool, Short story.&lt;/em&gt;&lt;/p&gt;
TRENDS IN SPECIAL EDUCATION TEACHER TRAINING IN THE USA DURING GLOBAL PANDEMIC LOCKDOWN

The issue of quality teacher training has always been recognized with international acts, but the UNESCO Global Education Monitoring Report (2021) found the current state of teacher training insufficient due to the paradigm of inclusion, lack of consistency, and ineffective professional cooperation. Among the reasons for this is the introduction of global COVID-19 pandemic. In the modern conditions the main problems of special education teachers training quality are related to lack of self-efficacy, ability to manage class activities, apply instructional strategies and involve students in educational activities. In-service teachers complain about the increase in workload, working hours, professional burnout and leave the profession. To stabilize the situation in the education system, the US government has introduced a number of financial, institutional and teaching support measures. In order to attract young people to the profession, 15 states have changed the conditions for enrolment in educational programs, including special education, simplified some organizational procedures, and proposed alternatives for field experience. However, the basic requirements of such innovations remained unchanged: maintaining the quality of field experience, the use of defined requirements and terminology for its organization and professional support of early career teachers. Analysis of researches on this issue allows us to conclude that all educational process stakeholders are not ready for the full transition to distance learning due to insufficient quality of existing tools and cyber security for effective virtual learning and the need to improve teachers training for teaching in various formats (distance, in-person, blended, hybrid) with the ICT means. Since having gained distance learning experience while obtaining their professional education, the graduates can more easily adapt to the changing and unpredictable conditions of the present situation, teacher preparation programs introduce disciplines aimed at relevant skills developing. Therefore, the scientific community should unite the efforts to develop the latest teaching methods considering the needs of all students within a school are catered for, including those with special educational needs.
Using Narrative to Improve Students’ English Reading Skill in School Literacy Movement

The objective of this research is to design a set of instructional reading material for the school literacy movement. The reading material developed is expected to support school literacy movement and to improve students’ English reading skill. The reading material designed is based on narrative. Narrative is a sequence of series of events that is fictional and nonfictional that aims to entertain the readers at once to educate the readers about attitudes and behavior through moral values that are contained in the story (Abbot, 2008). The developed material consists of three chapters. These chapters are designed based on the three kinds of narrative text, there are romance, legend, and adventure. Each chapter consists of four parts, namely preparing for the journey, starting the journey, exploring the journey, and finishing the journey. Preparing for the journey part is aimed to activate students’ prior knowledge about the learning topic and to help the students understand the learning material before the students do school literacy movement activity. Starting the journey and exploring the journey parts are designed to develop students’ reading skills and to improve students’ English. Finishing the journey part is aimed to help the students draw the conclusion and master the learning material better. The writers used the research and development method in conducting the study and designing the reading material. Then, the instruments of this research were open-ended questionnaires, close-ended questionnaires, and interview. The English reading material had been evaluated and deemed as very good after the revision. The reading material and the reading tasks are appropriate for school literacy movement activity. By mean of this research, it is expected that the instructional reading material will be beneficial to support the school literacy movement.
MONITORING AND CONTROLLING MARINE ACTIVITY USING IOT SYSTEM

Marine activities at sea are very popular with the people of the world. Various marine activities such as swimming, boating, fishing and even shipping are often done on the coast and sea. However, loose control and monitoring cause various problems such as accidents, marine life threats and even pollution. The current method uses a vessel monitoring system (VMS) as well as a geographic information system (GIS). This method of course uses high costs because the information obtained is international. There are also barriers to obtaining sensitive information. This makes it difficult for local authorities to obtain uniform information on centralized water activities. This study proposes a smart and centralized system of control and monitoring of water activities. The novelty of this study is to recognise and detect a foreign object on the surface of the sea. In addition, this study made a virtual barrier between safe and dangerous zone. This system can control all marine activities with the use of mobile equipment installed on the body of the user or even the vessel used. The system will control and monitor all activities centrally. In the event of an emergency or violation of the law, this system will deliver an immediate message to the authorities. The system is evaluated based on user distribution and control environment. System performance audit shows that this system method successfully controls and monitors water activity within 1 kilometre from the coast
Object Recognition Using the Deformation Dynamics of Polyurethane Foam

For the purpose of developing a versatile skin for soft robots, we propose a sensing method based on physical reservoir computing (PRC) in which we applied conductive polyurethane foam (CPF) to classify the state of a contacted object. Inspired by PRC, we exploited the natural deformation dynamics of CPF as an information processing device. By monitoring the deformation through the resistance change of the cellular foam, we demonstrate that the foam's deformation dynamics are innately capable of recognizing the different states of objects (such as shape, angle, and position). Thus, the foam acts as a natural classifier that facilitates separability through its high-dimensional dynamics. This feature is useful in practical applications and would reduce the electrical components and power consumption required to work with other systems. In this study, we examined proof-of-concept experiments using simple object conditions: three object shapes (circle, triangle, and square) at different rotations and positions. As a result, high classification accuracy was demonstrated in a number of experiments, and the possibility for enhancing the method's generalization capability was investigated. Our approach is potentially applicable to not only the foam but also to other various soft materials with large internal degrees of freedom, suggesting its universality to soft robotic platforms in general.
HotFed: Hot Start through Self-Supervised Learning in Federated Learning

Federated Learning learns a shared model for prediction from the rich and privacy-sensitive training data distributed on the clients which are resource-constrained edge devices or isolated cloud clusters with high-security protection. The training data collected by separated clients faces the non-IID (not Independent and Identically Distributed) challenge, which requires more communication rounds to achieve an acceptable precision and hence costs considerable computing and network resources. The bias of the dataset requires more communication rounds to achieve an acceptable precision which costs a lot of computing and network resources. Existing works propose sharing a small amount of dataset among clients to mitigate the non-IID issue, bringing a new communication burden for shared data transition and the new distribution statistical challenge that the model will inevitably over-fit the shared data. Inspired by the hot start-up in system design, this paper designs a novel HotFed method in which each client starts its training with a representation model for better precision and communication efficiency. This paper analyzes the value of HotFed and different methods to warm up the client&#x0027;s Federated Learning. The conclusion turns out that HotFed through Self-Supervised Learning (SSL) can efficiently solve the non-IID issue and receive the training efficiency improvement.
Enabling Auction-based Cross-Blockchain Protocol for Online Anonymous Payment

Nowadays, online medical services have been greatly developing. Cryptocurrencies like Bitcoin and Ethereum are very suitable for online medical electronic payment scenarios that require identity privacy protection because of their good anonymity and financial payment attributes. However, cryptocurrencies varies widely, the need of cryptocurrencies exchange is urgent for patients to pay different doctors and platforms with diverse cryptocurrencies. Exchanging cryptocurrencies through centralized exchanges has problems such as high fees and cumbersome operations. The decentralized exchanges mainly focus on cross-blockchain connectivity but high intermediate fees charged by connectors are ignored. In order to minimize the exchange fees, we propose a cross-blockchain connector selection scheme utilizing the reverse Vickrey auction along with Interledger. Our scheme abstracts the connector nodes selection into a service provider bidding process, throught which we can find the very node with the lowest bid, namely, the least exchange fees, as the ideal cross-blockchain service provider. Our scheme implements cross-blockchain payment of different cryptocurrencies conveniently, quickly and cheaply, which can provide patients with better identity protection of personal privacy information. Security analysis and performance evaluations show that our scheme can effectively promote the applications of cryptocurrencies in the field of medical care.
Author Identification with Machine Learning Algorithms

Author identification is one of the application areas of text mining.It deals with the automatic prediction of the potential author of an electronic text among predefined author candidates by using author specific writing styles.In this study, we conducted an experiment for the identification of the author of a Turkish language text by using classical machine learning methods including Support Vector Machines (SVM), Gaussian Naive Bayes (GaussianNB), Multi Layer Perceptron (MLP), Logistic Regression (LR), Stochastic Gradient Descent (SGD) and ensemble learning methods including Extremely Randomized Trees (ExtraTrees), and eXtreme Gradient Boosting (XGBoost).The proposed method was applied on three different sizes of author groups including 10, 15 and 20 authors obtained from a new dataset of newspaper articles.Term frequency-inverse document frequency (TF-IDF) vectors were created by using 1-gram and 2-gram word tokens.Our results show that the most successful method is the SGD with a classification performance accuracy of 0.976% by using word unigrams and most successful method is the LR with a classification performance accuracy of 0.935% by using word bigrams.
PENGEMBANGAN E-MODUL BERBASIS FLIPBOOK UNTUK MENINGKATKAN MOTIVASI BELAJAR SISWA SMP TEMA CAHAYA

The study aims to analyze the worthiness, the effectiveness, and the practical science e-module flipbook-based as the learning material to improve students’ learning motivation in light topic and its benefits for life. The writer decided to develop teaching material to support online class such as e-module flipbook-based. This study is research and development (R&amp;D) using ADDIE model. This study used Likert Scale with particular interval as data analysis technique. To know the worthiness of the data, the writer used validation sheets which had been validated by e-module expert and material expert and its result is categorized as worthy. Then, the effectiveness of the data used motivation questionnaires which are given before and after the learning process. The practical level had been done from the questionnaire of students and teachers’ response. The results of this study showed that e-module can be categorized as worthy, effective, and practical to use. Therefore, it can be concluded that the development of e-module flipbook-based to improve students’ learning motivation achieves the expected objectives and help the teachers as a reference in developing an interesting learning material for students to improve their learning motivation when they do online class.
Analisis Sentimen Pada Isu Vaksin Covid-19 di Indonesia dengan Metode Naive Bayes Classifier

The Covid-19 pandemic that has occurred in Indonesia and even in the world has not yet ended. Various efforts have been made by the Indonesian government to minimize the spread of this virus, such as the implementation of a lockdown, Large-Scale Social Restrictions (PSBB), a ban on going home during the Eid al-Fitr holiday, and so on. One of the new policies issued by the government is the vaccination program, where the government has started implementing the program since early 2021 for the people of Indonesia, which aims to increase antibodies to avoid exposure to the Covid-19 virus. To find out opinions, comments, or feedback given by the public on this new policy, sentiment analysis can be done. The process of this sentiment analysis includes data collection, namely the crawled tweet data originating from the Twitter social media. The data is then selected for further pre-processing stage so that the data is clean and ready for classification. Furthermore, sentiment weighting is carried out for data labeling using a lexicon dictionary and negative words. Then after that, the terms or words are weighted with tf-idf and followed by the feature selection process using Information Gain. Furthermore, the classification process is carried out using the Naive Bayes Classifier algorithm to classify the data into 3 classes, namely positive, negative, and neutral sentiments. The results of this study are to produce a model accuracy rate of 78%, recall 80%, and an AUC score of 0.904.
Opportunities and Challenges in Immersive Entertainment

The next major shift in digital entertainment will be the wide adoption of mixed reality platforms. Although owners of mixed reality devices are still a minority today, immersive systems are quickly getting better and cheaper. We identity eight research topics on immersive entertainment and discuss possible research directions that can lead to advances in the field
Methods of Multilanguage Question Answering

In this paper we explore different approaches to solving the question-answering task. The task is approached with the goal of targeting multiple natural languages at once in mind. Presently, Ukrainian and English languages are considered, and training data in both languages is sourced. The research considers different machine learning models of natural language processing, based on the state-of-the-art models presented by researchers in the last few years. We compare the outputs of different models fine-tuned on different data to improve the precision of the predictions. We show how fine-tuning a language model on one language may increase the precision of that model's predictions in other languages on the example of fine-tuning a model on English in order to increase performance on Ukrainian. At last, we point out the drawbacks of such an approach and emphasize the need for a large dedicated Ukrainian language dataset, that would be assembled specifically targeting the question-answering task.
Estimation of the Key Segments of the Cyber Crime Economics

Our everyday world is rapidly changing with the help of digital transformation and is increasingly dependent on information technology, and because of that our society needs to be prepared for new challenges and threats that come with the change. The composition of the latter is constantly shifting due to the development and improvement of computer technology, software, and methods for collecting, processing, and storing information and data. In these conditions, the relevance of scientific works on topics related to research in the field of cybercrime as an economic phenomenon increases drastically. The digital transformation of all aspects of daily activity turns the user's devices into the target of attacks aimed at taking possession of their private information (accounts, banking details, etc.) and recently even the devices' computing power. The authors see an immense necessity in studying of shadow (criminal) business models based on the development, debugging, and commercial distribution of criminal software.
Hateful Meme Prediction Model Using Multimodal Deep Learning

With the emergence of deep neural networks along with high-end computers that can process deep architectures, there has been a lot of research when Computer Vision and Natural Language Processing has been fused into a single problem. To enable students and researchers to deep dive into multimodal deep learning Facebook AI Research team published a dataset on hateful meme classification &#x201C;The Hateful Meme Challenge Dataset&#x201D; in May 2020 that gave us the motivation to test ourselves and an opportunity to learn more about the dataset. The rise of communication on the internet with memes as a medium, they have been used to convey incorrect information, political agendas and also has led to cyberbullying, trolling etc. This results in the need of creating an automated tool that can detect such hateful content published on the internet and remove it at the root level before it does any harm. This paper intends to adopt Unimodal Text and Image models using Bert, LSTM and VGG16, Resnet50, SE-Resnet50, XSE-Resnet architectures and combining them into Multimodal models for effective prediction of a hateful meme. The paper compares various architectures both unimodal models and multimodal models on the evaluation metrics AUC-ROC score, F1 score and accuracy score.)
Concept-Based Approach to Word-Sense Disambiguation

The task of automatically determining the correct sense of a polysemous word has remained a challenge to this day. In our research, we introduce Concept-Based Disambiguation (CBD), a novel framework that utilizes recent semantic analysis techniques to represent both the context of the word and its senses in a high-dimensional space of natural concepts. The concepts are retrieved from a vast encyclopedic resource, thus enriching the disambiguation process with large amounts of domain-specific knowledge. In such concept-based spaces, more comprehensive measures can be applied in order to pick the right sense. Additionally, we introduce a novel representation scheme, denoted anchored representation, that builds a more specific text representation associated with an anchoring word. We evaluate our framework and show that the anchored representation is more suitable to the task of word-sense disambiguation (WSD). Additionally, we show that our system is superior to state-of-the-art methods when evaluated on domain-specific corpora, and competitive with recent methods when evaluated on a general corpus.
Counter-framing design: Politics of the 'new normal'

In this paper, we introduce the concept of counterframes in relation to discourses of sustainability, and elaborate on it in correspondence with participatory design practices.We present our analysis through the lens of the 'new normal' in the wake of the pandemic, to demonstrate and unpack the complex and conflictual nature of emergent frames and counter-frame debates, evident within the field of sustainability.The paper draws on participatory activities and interviews with social movements and grassroots organisations.We present initial reflections on the ways in which design can productively engage with and address counter-frames, as they both fill in and open up spaces for political debate in which new paradigms may be carved out of obsolete discourses and worldviews.A core contribution of paper is a re-articulation of how we understand frames in design and the acknowledgement that any counter-/framing is doing political work.A frame is a description, a 'take' on a social or political problem or issue, that identifies the originators of the problem and implies solutions, e.g.'climate change'.Frames present a way of viewing issues that are 'constructed products' (Snow and Benford, 2000), that are linked to the culture of a given context and its
Blowfish cryptography implementation by using Microcontroller

The main task of paper studied new ciphering and deciphering techniques reported previously.Computer programs are designed in C language to perform theses algorithms and to assess their performance.Out of these algorithms, it is found that the BLOWFISH is the more sophisticated one.Therefore, a development for this algorithm is introduced to enhance its operation.This has been carried out by using the Microcontroller in the decrypting operation to support fast and more accurate operation.From the results of simulation, it is found that the expansion and permutation operations require most of the computing time.Therefore, software was designed and implemented to execute the expansion and permutation operations.A series of experiments was connected using the new developed algorithm as applied to different types of data (e.g.text, graphics and geographic maps).In all paper experiment, the encrypted and decrypted files were exactly the same.