Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF UTILIZATION OF GOOGLE CLASSROOM APPLICATION AS A LEARNING MEDIA IN INCREASING LEARNING MOTIVATION EVEN SEMESTER STUDENTS IN ECONOMICS COLLEGE OF INDRAGIRI RENGAT

Economics College of Indragiri Rengat, one of the universities in Riau Province located in the Indragiri Hulu district, in the center of Rengat City. Which is one of the most favorite campuses in the Indragiri Hulu district. Improving the quality of student learning must be accompanied by student learning motivation. Learning is a process of communication between learning, teachers, and teaching materials. It can be said that this form of communication will not work without the help of means to convey messages. Several technology-based applications carry out the online learning process (on a network) and can be used in the learning process, one of which is Google Classroom. The number of students in semester 2 is 459, semester 4 is 389, semester 6 is 349, and semester 8 is 369, while for the whole number of students in even semesters, Economics College of Indragiri Rengat is 1,566 students. The results of the research can be drawn by researchers to conclude that every learning media activity such as online lectures conducted by lecturers at the Economics College of Indragiri Rengat, namely using the Google Classroom Application learning media can motivate students, there are also some using the application. others such as WhatsApp, and Zoom Meeting. Because the Google Classroom Application is more effective.
Evaluating Jokowi's Policy on Alcoholic Drink Investment in Indonesia by using sentiment analysis

This paper discusses the evaluation of Jokowi's policy regarding alcoholic drink investment in Indonesia by using sentiment analysis. The source of data used as policy feedback in this study is data from the Twitter API. There were 6,963 tweets taken five days before and five days after Jokowi lifted the policy. The data was then manually annotated as many as 963 tweets by sentiment labels. This study uses NLP Techniques to process the data and uses SVM to classify it. Furthermore, using trained data, this paper labels the rest of the tweets into three groups of sentiments, namely positive, negative, and neutral, to see how much public support or rejection of policies on social media. From the analysis, the alcoholic drink investment policy was not successful. The number of negative sentiments from the public shows that this policy is against the people's will. Negative comments dominated during the period of this study, significantly before the President lifted the policy. Nevertheless, Jokowi's decision to revoke the alcohol investment permit is the right step and has succeeded in reducing negative comments and rejections on social media, especially Twitter. This paper also covers words that are interconnected and related in Twitter to give an overview of the tweet's contents. Before the policy was revoked, many tweets contained a rejection of Jokowi's policies, rejection of the Papuan government, and asked the MUI to suggest Jokowi revoke this policy. After the policy was revoked, the comments were more directed at criticism of the President's attitude, which stated that he hated foreign products but allowed investment in alcohol and had made policies that provoked a public controversy.
Enhancing Sentiment Analysis of Textual Feedback in the Student-Faculty Evaluation using Machine Learning Techniques

Sentiment Analysis has been an interesting and popular research area encouraging researchers and practitioners to adopt this tool in various fields such as the government, health care and education. In education, instruction evaluation is one of the activities that sentiment analysis has served. Though, it is a common practice that educational institutions periodically evaluate their teachers’ performance, students’ comments which are rich in insights are not easily taken into account because of lack of automated text analytics methods. In this study, supervised machine learning algorithms were used. Experiments were conducted to evaluate base models employing naïve bayes, support vector machines and logistic regression in comparison to ensemble combining the three. Random forest, an ensemble learning algorithm was also experimented. Machine learning techniques such as term-frequency – inverse document frequency (TF-IDF) and ngram were also explored to devise a model with the highest possible accuracy. Results show that in this case, tf-idf vectorization does not show significant improvement in sentiment classification. On the other hand, ngram vectorization improve performance of base models and has potential to improve ensemble models. Random forest showed higher performance measures than base models and ensemble of the three base models. However, it did not outperform ngram combined with support vector machines. In future work the model with highest accuracy found can be embedded in a sentiment analysis tool for students’ feedback on teaching performance. More advanced transformation techniques and other ensemble techniques may be explored to further improve accuracy in sentiment classification.
Correlation Power Analysis Attack to Midori-64

Recently, with the rapid advancement of technology, Internet of Things (IoT&amp;rlm;&amp;rlm;&amp;rlm;&amp;rlm;) security has become more efficient and more complex, especially for resource-limited devices such as embedded devices, wireless sensors and radio frequency identification tags (RFID). Lightweight block ciphers (LBCs) provide security for these technologies to protect them against adversaries, but the need for low power consumption in LBCs is one of the most important challenges for IoT&amp;rlm; technologies. Furthermore, these LBCs are subject to multiple attacks and side channel attacks (SCAs) are among the mentioned threats to these cryptosystems. A type of SCA is correlation power analysis (CPA) in which the attacker tries to reach the key using the relationship between the power consumption of the chip during the algorithm running, data processing, and operations. In this article, a CPA attack is designed to discover master key of the Midori-64 block cipher. According to the proposed method, an attack is done to the first round S-boxes to get half of the key bits. Then, the second round S-boxes were attacked to other half of key bits just use 300 plain text samples. Finally, the most important physical attacks performed on the Midori, are compared to our proposed CPA attack.
Enhanced Context Learning with Transformer for Human Parsing

Human parsing is a fine-grained human semantic segmentation task in the field of computer vision. Due to the challenges of occlusion, diverse poses and a similar appearance of different body parts and clothing, human parsing requires more attention to learn context information. Based on this observation, we enhance the learning of global and local information to obtain more accurate human parsing results. In this paper, we introduce a Global Transformer Module (GTM) via a self-attention mechanism to capture long-range dependencies for effectively extracting context information. Moreover, we design a Detailed Feature Enhancement (DFE) architecture to exploit spatial semantics for small targets. The low-level visual features from CNN intermediate layers are enhanced by using channel and spatial attention. In addition, we adopt an edge detection module to refine the prediction. We conducted extensive experiments on three datasets (i.e., LIP, ATR, and Fashion Clothing) to show the effectiveness of our method, which achieves 54.55% mIoU on the LIP dataset, 80.26% on the average F-1 score on the ATR dataset and 55.19% on the average F-1 score on the Fashion Clothing dataset.
A NOVEL APPROACH FOR DATA CONFIDENTIALITY UNDER KEY EXPOSURE IN CLOUDS

Recent surveys reveals a powerful attacker which breaks data confidentiality by acquiring cryptographic keys, by means of coercion or backdoors in cryptographic software. Once the encryption key is exposed, the only viable measure to preserve data confidentiality is to limit the attacker’s access to the ciphertext. Nevertheless, if data is encrypted with existing schemes, an adversary equipped with the encryption key, can still compromise a single server and decrypt the ciphertext blocks stored therein. In this paper, we study data confidentiality against an adversary which knows the encryption key and has access to a large fraction of the ciphertext blocks. To this end, we propose Bastion, a novel and efficient scheme that guarantees data confidentiality even if the encryption key is leaked and the adversary has access to almost all ciphertext blocks[8]. Index terms – Data Confidentiality, Key exposure, dispersed storage.
EFFICIENT AND SECURE DATA SHARING SCHEME FOR INTRUSION AVOIDANCE WITH PRIVACY PRESERVING IN CLOUDLET ENVIRONMENT

–Customary medical services framework frequently requires the conveyance of clinical information to the cloud, which includes clients' sensitiveinformation and causes correspondence energy utilization. For all intents and purposes, clinical information sharing is a basic and testing issue. Thus,in this paper, we develop a clever medical services framework by using the adaptability of cloudlet. The elements of cloudlet incorporate privacyprotection, information sharing and interruption location. In the phase of information assortment, we initially use Number Theory Research Unit technique to encode client's body information gathered by wearable gadgets. That information will be communicated to neighboring cloudlet in an energyefficient style. Besides, we present another trust model to assist clients with choosing trustable accomplices who need to share put away information in thecloudlet. The trust model additionally assists comparable patients with speaking with one another about their infections. Thirdly, we partition users'medical information put away in remote haze of clinic into three sections and give them legitimate security. At last, to shield thehealthcare framework from malevolent assaults, we foster an original cooperative interruption discovery framework (IDS) strategy in view of cloudletmesh, which can successfully forestall the far off medical care huge information cloud from assaults. Index Terms – Privacy protection, data sharing, collaborative intrusion detection system (IDS), healthcare.
Corporate Social Responsibility- A Positive Obligation on the Companies to Fulfill their Responsibility towards Country

Corporations throughout the world have adopted the notion of “Corporate Social Responsibility” (CSR). Companies in India have long been required to practice corporate social responsibility (CSR). It’s been done before and more recently by the corporate community. In order to fulfill its responsibilities, the government must incorporate it into its daily operations. In many cases, they lack a defined purpose or goal. Since recently, there has been evidence that things are beginning to change. Other things come into play, including globalization. The Millennium Development Goals and other reasons have caused people to become more concerned and work harder. There is a clear example of how the government’s objectives are being achieved through the Companies Act. Corporate social responsibility (CSR) programs have seen a huge increase in funding, and many of these programs have gone above and beyond what was expected. It is imperative that what the law requires be carried out. According to what is happening throughout the world, there have been efforts to make the country more ecologically friendly. As a crucial part of a company’s overall strategy, CSR aims to provide a competitive advantage while also benefiting the firm itself.
Summary

The tremendous potential of quantum computers, which this book attempts to explore and explain, lies hidden in their smallest components, the qubits. Qubits can be physically realized in different ways, for example, using ions, superconductors or anyons. The phenomena of entanglement and superposition allow qubits to interact with each other and share information at an enormous speed.
Abstractive Document Summarization Using Divide and Conquer Approach

Abstract: Document summarising is a multi-step process with numerous subtasks. Each subtask has the ability to provide highquality summaries. Identifying necessary paragraphs from the given document is a vital aspect of abstractive document summarization. In this paper, we describe an abstractive text summary strategy based on a statistically innovative sentence rating technique, with the summarizer selecting the sentences. The abstraction sentences are generated as a summary text and then transformed to audio. In terms of accuracy, the proposed model outperforms the standard technique
Automated Multi Label Classification on Fertilizer Themed Patent Documents in Indonesia

&#x0D; Patent literature research has a high scientific value for the industrial, commercial, legal, and policymaking communities. Therefore, patent analysis has become crucial. Patent topic classification is an important process in patent topic modeling analysis. However, the classification process is time-consuming and expensive, as it is usually carried out manually by an expert. Moreover, a patent document may be categorised in more than one category or label, further complicating the task. As the number of patent documents submitted increases, creating an automated patent classification system that yields accurate results becomes increasingly critical. Therefore, in this paper, we analyse the performance of two algorithms with regard to multi-label classification in patent documents: multi-label k-nearest neighbor (ML-KNN) and classifier chain k-nearest neighbor (CC-KNN), combined with latent Dirichlet allocation (LDA). These two methods have a considerable advantage in handling the continuously updated dataset; they also exhibit superior performance compared to other multi-label learning algorithms. This study also compares these two algorithms with the term frequency (TF)-weighting measure. The optimal value obtained is based on the following evaluation parameters: micro F1, accuracy, Hamming loss, and one error. The result shows that the ML-KNN method is better than the CC-KNN method and that the multi-label classification based on topics (patent LDA) is better than the TF-weighting technique.
Internet of Medical Things Application in Encryption of Medical Images Based on Synchronization of Multi-state Chaotic Systems

Introduction: Studies on the security and interoperability of Internet of medical things (IoMT)in health are still scarce. The most important effects of the IoMT on health care are the ability to exchange information and reduce hospitalization and health care costs. The main challenges of the IoMT in healthcare are security and protection of privacy, among which encryption of images is very important in the field of communication and security. The main purpose of this study was to design a secure channel for encrypting and sending medical data. Methods: This study presents a new method for encrypting medical images to preserve patient information by synchronizing two chaotic systems. The use of chaotic signals as carriers of medical images greatly enhances security and greatly reduces the possibility of detection. The idea of synchronization of chaotic systems was presented in multi states with fixed time delay. In this idea, with the help of Lyapunov’s theory of stability, a controller was designed to establish the stability of the closed-loop system. Then, according to the synchronization idea and its error, a chaotic masking method was proposed to encrypt patient images. The simulation and accuracy of the results of the presented method were investigated in MATLAB software version 2020.Results: The simulations were performed on medical color images and CT, MRI, and X-ray images. Encrypted and recovered images were obtained by the proposed system. To evaluate the performance of the proposed method, various criteria including image histogram, signal-to-noise rate, correlation, and entropy of information were evaluated. The results showed the excellent image encrypting efficiency of the proposed method.Conclusion: Using the proposed method, medical images were encrypted based on a secure protocol, and then with satisfactory quality, images were recovered after transmission.
‘Taking jokes seriously’: Establishing a normative place for satire within the freedom of expression analysis of the European Court of Human Rights

Satire is a form of expression which tackles complex, contemporary issues in a nuanced, jesting spirit. Vitally, it does so through humour and provocation. This article demonstrates why we should not place extensive limits on controversial satire. At the European Court level, this requires a robust proportionality assessment of each satirical expression, rather than granting a wide margin of appreciation to national authorities which are more likely to uphold restrictions. The definition of satire in Vereinigung Bildender Künstler (VBK) v Austria did not go far enough to ensure that satire is treated as a distinct category worthy of higher protection at the European Court level. Moreover, the European Court’s worrying approach to controversial expression allows offence to feelings to justify restrictions, thus satire rests in a precarious position. This article remedies this deficiency by elucidating clearer reasoning for protecting satire than that offered in VBK, while providing a rigorous framework for assessing the proportionality of restrictions on satire, in order to provide limited, well-reasoned exceptions.
Translating with technology

This chapter examines questions of copyright, authorship, and textual ownership in the context of literary translation, and particularly the effects of the increasing use of translation technology. Overall, translation is an anomaly in terms of copyright, which seeks to protect the expressions of original works rather than the ideas contained in them. Translations, however, are derivative works, where the ideas of the source text are expressed in a new form. Since the nineteenth century, copyright regimes have struggled to accommodate the moral and economic rights of the translator and the author of the original text. More recently, new complications have been introduced by technologies such as translation memories and machine translation, which are increasingly used in various domains. Such technological tools enable the reuse of translations as data, and machine translation in particular has the potential to extend this reuse to new contexts, including literary translation where translation technology has so far had less impact. The question of textual ownership becomes ambiguous in a technology-assisted translation process where a translator employs suggestions based on previous works. We examine the notions of textual ownership and authorship from legal, technical, and ethical perspectives, and we suggest possible solutions to the open questions.
Introduction

Machine Translation has come a long way since the first “mechanical translation” experiments of the post-war period, an era that also marked the birth of Translation Studies. In this introduction, we trace the histories of both areas of study, which are interconnected in many ways, even though they have often been perceived as opposite camps. In the second decade of the twenty-first century, Machine Translation and Translation Studies have started to converge. An area in which this convergence can be seen is in the application of Machine Translation (and other translation technologies) to the translation of creative texts, together with the study of the implications thereof, e.g., societal, ethical, and legal. This book is the result of cross-fertilisation between scholars in Machine Translation and Translation Studies, and Literary Studies. It is the first of its kind in creating a snapshot of the state of the art in the use of technology in translating creative texts.
Bilingual e-books via neural machine translation and their reception

This chapter discusses the application of neural machine translation (NMT) to works of fiction that have been published as e-books in a bilingual setting. These bilingual e-books are translated using NMT for readers with an intermediate or high level of a foreign language who want to read novels originally written in English. NMT is used to provide a translation in their mother tongue that the readers can refer to on demand, e.g., to help them understand difficult passages in the original. The main hypothesis of this research is that the quality provided by tailored NMT systems is sufficient to meet this goal and that using this methodology will enhance the readers’ experience. We expect the users of these bilingual e-books to read most of the book in the original language and to use the translation only sporadically when encountering difficult parts. This could help the reading experience become more intense, since the reader does not need to consult external sources such as dictionaries or use external public NMT engines. In this chapter, we first discuss our motivation and present the goals of the project. Then the previous work in this field is presented. After that, we explain the process of customizing an NMT engine and creating the bilingual e-books. We follow with the design of the survey and the results of a reading experiment using an English short story set up for Catalan native speakers with a between-subjects design. Finally, we reflect on the results and propose avenues for future work. We are mainly interested in describing a useful methodology for creating bilingual books with NMT and assessing their usability by involving L2 readers.
A Fixpoint Characterization of Three-Valued Disjunctive Hybrid MKNF Knowledge Bases

The logic of hybrid MKNF (minimal knowledge and negation as failure) is a powerful knowledge representation language that elegantly pairs ASP (answer set programming) with ontologies. Disjunctive rules are a desirable extension to normal rule-based reasoning and typically semantic frameworks designed for normal knowledge bases need substantial restructuring to support disjunctive rules. Alternatively, one may lift characterizations of normal rules to support disjunctive rules by inducing a collection of normal knowledge bases, each with the same body and a single atom in its head. In this work, we refer to a set of such normal knowledge bases as a head-cut of a disjunctive knowledge base. The question arises as to whether the semantics of disjunctive hybrid MKNF knowledge bases can be characterized using fixpoint constructions with head-cuts. Earlier, we have shown that head-cuts can be paired with fixpoint operators to capture the two-valued MKNF models of disjunctive hybrid MKNF knowledge bases. Three-valued semantics extends two-valued semantics with the ability to express partial information. In this work, we present a fixpoint construction that leverages head-cuts using an operator that iteratively captures three-valued models of hybrid MKNF knowledge bases with disjunctive rules. This characterization also captures partial stable models of disjunctive logic programs since a program can be expressed as a disjunctive hybrid MKNF knowledge base with an empty ontology. We elaborate on a relationship between this characterization and approximators in AFT (approximation fixpoint theory) for normal hybrid MKNF knowledge bases.