Reviewerly/exHarmony huggingface dataset:
Similarity-Based Dataset Recommendation Across Languages and Domains to Sentiment Analysis in the Electoral Domain

Traditional machine learning classifiers usually fail at predicting labels for new data when their distribution differs from the training data distribution. This is particularly true with sentiment classifiers as the vocabulary and people’s opinions rapidly evolve. Naturally, the problem aggravates when there are only a few or even none labeled instances in the target domain. In this paper, we propose a dataset recommendation method based on multilingual embeddings and similarity metrics to properly choose sentiment analysis datasets to be used as training set when labeled data is unavailable or scarce. We adopted the sentiment analysis of electoral domain as our case study, considering the complexity and difficulty for manually label millions of political social media opinions during the short period of campaigns. Our results suggest that dataset similarity may be considered, even when datasets belong to different languages, to minimize negative effects that may occur due to domain shift in sentiment classification tasks.
Voter Authentication in Remote Electronic Voting Governmental Experiences: Requirements and Practices

How to ascertain that the person voting behind a computer or smartphone screen is actually who they claim to be remains one of the key challenges in remote electronic voting. Credentials to vote online can be shared, stolen, or traded. For this reason, it is generally argued that introducing remote electronic voting from uncontrolled environments for political public elections is only feasible as long as a robust infrastructure for the digital identification of voters (e.g., based on electronic identity documents, e-ID) is already in place. But is such a digital infrastructure for voter authentication a sine qua non condition for remote electronic voting? In this paper we assess how voters are authenticated in internet voting for political public elections in nine countries: Australia, Canada, Estonia, France, Mexico, Pakistan, Panama, Switzerland, and the United States of America (USA). To the best of our knowledge, this is the broadest comparative assessment of voter authentication methods in governmental remote electronic voting experiences. Our analysis reveals that the use of solely knowledge-based factors for voter authentication is the most common practice in these experiences. In most cases, a combination of several credentials is used (e.g., in Canada and Australia). Another alternative is to rely on a different combination of knowledge and ownership-based authentication methods that does not require neither e-IDs nor digital certificates (e.g., as in France and Mexico).
SecurePrivChain: A decentralized framework for securing the global model using cryptography

VANETS (IoVs), banks, and healthcare records are the sensitive information of vehicles, clients, and patients that is stored and maintained electronically, which has historically been a popular target for privacy leakage. To address growing issues about privacy leakage in these scenarios, federated learning has been frequently accepted as a privacy-preserving framework that enables multiple users to collectively develop a global model. To preserve the privacy of the training data, the server and participants share only model parameters. However, since participants have access to modified model parameters, this solution is still prone to poisoning and label-flipping attacks. Recent research showed that the federated learning framework is highly susceptible to attacks, with poisoning and label flipping being the most powerful, as well as secluded attacks, in which attackers can poison the parameters of the global model to gain access to the clients’ confidential information or degrade the global model’s performance. To address these attacks, a decentralized framework for securing the global models in federated learning (SecurePrivChain) is proposed in this study. A permissioned private Ethereum blockchain and encryption mechanisms were adopted in the framework to hide global parameters and preserve the privacy of a client’s data as well as achieve client’s authentication. Finally, the proposed model is evaluated using metrics such as computed cost, transaction latency, loss, and cost analysis based on Ethereum gas consumption. The performance of the scheme was compared with other benchmark models which are based on different encryption mechanisms. These included RSA and algamal using publicly available MNIST, banks, vehicles, and hospital datasets. Numerical results show that the proposed framework has good performance. A formal security analysis is done to show how the system can meet its goal of protecting privacy.
INTEGRATION OF BLOCKCHAIN TECHNOLOGIES AND MACHINE LEARNING WITH DEEP ANALYSIS

The successful development of the digital economy, which we can observe since the advent of the internet, is closely related to progress in several "frontier technologies" (frontier technologies), among which the most important, according to the scientific community and international organizations, are such software-oriented technologies as blockchain, Big Data Analytics (Big Data), Artificial Intelligence (AI) and cloud Computing (Cloud Computing), as well as specialized machine-oriented equipment: 3D printers, internet of Things devices (Internet of things Things, IoT), automation and robotics. Significant progress in the application of these technologies contributes to the growth of production capabilities, labor productivity, and capital return of both digital companies and enterprises of the non-digital economy while transforming their established business models and principles of generating income and expenses of companies. This makes it necessary to study the above technologies in detail from the point of view of analyzing their essence, role, and potential for use in various spheres of economic life. Although the term "blockchain" has recently entered scientific and public use, the idea of the technology appeared in the late 1980s, namely in 1989. Lamport proposed "a model for achieving consensus on results in a network of computers, where computers or the network itself can be unreliable". In 2008, Satoshi Nakamoto proposed the concept of using a decentralized computer network to operate a P2P electronic money system. In the article "Bitcoin: a Peer-to-Peer Electronic Cash System" published on the internet, the innovator described the algorithm of functioning of the Bitcoin cryptocurrency as a completely independent electronic cash system from a single issue Center, which does not require the trust (mediation) of a third party, but relies on direct operations between the parties to the transaction, protected by cryptographic encryption.
Information Visualization Dashboard to Proctor Test-Takers During an Online Language Proficiency Test

Abstract As a result of the COVID-19 pandemic, the learning and evaluation processes have been moved to an online modality to keep social distance and reduce the spreading of the virus. The strategies implemented for assessment and proctoring in this online remote teaching and assessment emergency are no exception when proctoring test-takers. This problem is addressed from a practical context of study: the English Language Proficiency Tests of a University in southeast Mexico. Considering an iterative user-centered mixed methodology, a set of dashboards was designed, implemented and evaluated to visualize the information generated by test-takers during the administration process. An increase in the Usability of the dashboards is observed in all heuristic categories, with visual design being greater. The use of the mixed methodology and the constant user feedback during the process helped us to reduce development time compared with other works found in the literature. Moreover, it is possible to use the proposed dashboards in other application domains like medicine, or care facilities where user activity monitoring is needed to make informed decisions. categoryHuman-centered computing; Information visualization
The Innovative use of social media for teaching and learning: A case study at the University of Johannesburg

Covid 19 forced lecturers and students to accept that inspirational and beneficial teaching and learning does not necessarily rest within the four walls of a lecture hall. When higher education institutions had to transition rapidly from face-to-face to online education, social media platforms became a powerful catalyst for effective teaching and learning. Undoubtedly social media platforms have influenced our lives in an unprecedented manner. It has also altered the teaching and learning landscape by taking on an innovative role in online education. This investigation explored students' experiences of learning module content via TikTok. TikTok was adopted as a teaching tool because it is considered an appealing social media platform for Gen Z users, with its central attraction being short video content. This investigation primarily relied on Bandura’s social learning theory as the theoretical lens applied to the data as it points to the importance of an individual’s self-regulation and the external situation in terms of learning. It also offers an alternative view of social learning by seeing it as situated in communities of practice. Through a qualitative inquiry, teacher education students' reflections on being taught using TikTok were gathered. Evidence from the survey administered to 80 students strongly suggests that TikTok offered an efficient, fun, and exciting way of gaining unlimited access to learning content. It also enhances retention and creativity, making it a suitable 21st-century teaching strategy that promotes self-directed learning. It is a perfectly suited social media platform for a generation that prefers to consume content via rich video formats that act as confidence boosters. Hence making it an innovative teaching tool that encourages a new form of social learning.
MITIGATING RAPPORT THREATS ON SOCIAL MEDIA

This study aims to find out the strategies applied by people on social media in mitigating rapport threat behaviors and tries to formulate the rapport building behaviors from the applied strategies. The research is done to see whether the strategies work well in rapport threatening atmosphere in various situations taken from social media interactions. The data for this study were obtained from a number of online chats and discussion on several social media apps such as, Facebook, Whatsapp, Youtube and Instagram. The interactions were some chats, comments, discussion and arguments in Bahasa Indonesia. The data were taken from the interactions that cause offenses or threat on the interpersonal rapport of the people chatting on the social media. The method applied for this study is descriptive qualitative to identify the people’s strategies on social media to mitigate some rapport threat behaviors in the social media interactions. The study shows that people apply a number of strategies to mitigate the threats to the interpersonal rapport. The strategies are apologizing, complimenting, expressing gratitude, using emoji, using honorifics to address others, choosing the appropriate words, choosing the relevant topic and organizing information to be told to others. It is clearly shown from the study that there are a number of rapport threat behaviors in social media interactions but the people interacting on the social media have certain strategies which in turn become the rapport building behaviors to mitigate the threat.
FIGURE OF SPEECH: IT’S ROLE IN LITERARY WORKS

This study aims at analysing the role of figure of speech and its meaning in literary works. The use of figure of speech in literary works is not only to create layers of meaning but also sense of beauty, complexity, power, etc. Figure of speech, among others, metaphor, simile, personification, hyperbole refer to a group of words that deviate from literal interpretation that must be taken in a non literal sense or figurative way in order to understand the meaning. This study is a descriptive qualitative research in the semantic field which uses a short story in Balinese Language Da Nakonang Adan Tiyange ‘Jangan Tanya Namaku’ as the data source. The descriptive qualitative method will be applied in data collection, data analysis, and analysis presentation. The theory of meaning entitled Semantic: The Study of Meaning proposed by Geoffrey Leech will be used as the main theory for the needs of data analysis. The result of the study will be expected to be useful either in realizing the important role in using figure of speech in literary works and how its meanings are interpreted.
Physical and Augmented Reality based Playful Activities for Refresher Training of ASHA Workers in India

Recent health surveys in India highlight the alarming child malnutrition levels and lower rates of complete child immunization in many parts of India. Previous researches report that the conventional training pedagogy of the CHWs (Community Healthcare Workers) or the ASHAs (Accredited Social Health Activists) in India is ineffective in enhancing their capacity. Considering that the CHWs are getting equipped with smartphones, it calls for a rethinking of their training pedagogy using the ICT approach. Two refresher training tools were developed to make learning the child immunization schedule more exciting and conceptually engaging for ASHAs. The physical and AR (Augmented Reality) versions of designed card games were compared for effectiveness and knowledge retention, pre, and post-intervention through questionnaire tests conducted immediately before and after playing multiple sessions. The AR-based play was found to be better in learning and knowledge retention with more engagement, mainly due to its interactive and intuitive nature of play.
HCI Education and UX Practice: Highlights from Singapore

This position paper highlights trends in education, practice, and support of HCI/UX in Singapore, a small city-state island in Southeast Asia. The paper was prepared for the 2022 Southeast Asia Computer-Human Interaction (SEACHI'22) virtual workshop on Apr 14, 2022, as part of the ACM CHI Conference on Human Factors in Computing Systems (CHI'22) international conference.
EFEKTIFITAS APLIKASI CANVA UNTUK MENINGKATKAN MOTIVASI BELAJAR SISWA MASA KINI

Competition in the world of education makes educators compete to improve the quality of students in the academic and non-academic fields. One way to trigger students' seriousness to learn is to create interesting ideas in the process of delivering learning. The use of diverse learning media has many functions to deliver educators to channel the knowledge that students will learn in order to achieve the goals of the learner. One of the interesting and accessible media for creating learning media with various images is the Canva application which is designed to make it easier for educators In creating between the material to be provided with various features packaged in the Canva application.
*Shknowing: Constraints on the Semantics of Clause-Embedding Predicates

In the previous chapters, I have motivated the question-oriented theory for clause-embedding predicates based on a range of empirical facts. Throughout these chapters, we have seen that the question-oriented theory can adequately account for prima facie puzzling behaviors exhibited by various classes of clause-embedding predicates, such as rogative predicates (e.g., wonder), predicates of relevance (e.g., care), predicates with false-answer sensitivity (e.g., know), and non-veridical preferential predicates (e.g., hope). The proposition-oriented theory, on the other hand, is not equipped with adequate resources to account for the behaviors of these predicates.
Knowing: The Initial Puzzle

In the tradition of epistemic logic, knowledge is a relationship between an agent and a proposition, i.e., a piece of information that can be true or false in a given situation.
Caring: Question Orientation in Responsive Predicates

In the previous chapter, we have discussed the semantics of rogative predicates—predicates that are compatible only with interrogative complements (e.g., wonder, ask)—and how it bears on the comparison between the proposition-oriented theory and the question-oriented theory. In this section, I will shift our focus back to responsive predicates—predicates that are compatible with declarative and interrogative complements. In particular, within responsive predicates, I will identify three classes of predicates that possess a semantic property that is hard to capture under the proposition-oriented theory. I will then demonstrate that these predicates can be adequately analyzed under the question-oriented theory.
Characterization of a robust probabilistic framework for brain magnetic resonance image data distributions

Probabilistic characterisation of image data for accurate prognosis and treatment planning remains a long‐standing problem in medical research, especially when the data distribution depicts flat‐top and high‐order contact. Such flat‐top distributions are quite common in brain magnetic resonance (MR) image data, where the density drops sharply beyond the flat interval. Intuitively, it would indicate a bipartition of data into positive region containing observations definitely belonging to the image class and boundary region with observations possibly belonging to it. The flat peak would also imply that multiple values are equally most likely to belong to that class. However, the popular probability distributions used in such cases are unimodal, creating ambiguity about the positive region. In this work, we study the statistical properties and develop likelihood‐based iterative estimation method for the parameters of a novel class of platykurtic probability distributions containing normal, called the stomped normal distribution, that provides more accurate modelling to the flat‐top data distributions. The robustness of the proposed stomped normal model has been illustrated with six simulated and nine real brain MR volumes. Our analysis shows substantial improvement in explaining a variety of shapes of data distributions using the proposed probability model.
Feasibility of emulating Singapore's experience during the Omicron wave in China

•We reproduced the multiple Omicron waves in Singapore using a multi-dimensional model. •Simulation showed the feasibility and existence of successful control in mainland China.
Cognitive artifacts of the metuniverse

The article describes the cognitive artifacts of the metaverse within the framework of digital anthropology. Their role in the formation of a new social environment is investigated. Conceptual definitions of a cognitive artifact based on the theory of L.S. Vygotsky and his follower M. Cole. The forms of response by users of digital ecosystems to the spread of the influence of technology in society have been studied and the difference in this response has been stated. The reasons for this different response, due to structural changes in society, are shown. Attention is paid to the influence of cognitive artifacts of the metaverse on the perception of the consequences of emerging technological trends. It is stated that the modification of technological determinism in the form of metaverses contains different intentions of individual consciousness. This is due to the fact that the classical Internet is gradually giving way to new communication technologies, in which avatars, digital twins and immersive space plays an important role.
Enhancing Robustness of Malware Detection Model Against White Box Adversarial Attacks

Deep Neural Networks(DNNs) have made remarkable breakthroughs in several fields such as computer vision, autonomous vehicles etc. Due to its adaptability to malware evolution, security analysts heavily utilise end-to-end DNNs in malware detection systems. Unfortunately, security threats such as adversarial samples cause these classifiers to output erroneous results. These adversarial samples pose major security and privacy risks since a malware detection model will mistakenly label a malware sample as benign. In this paper, we assess the resilience and reliability of our deep learning-based malware detection algorithm. We employed Malconv architecture for malware detection and classification, which was trained using the Microsoft Malware Dataset. We used the Fast Gradient Sign Method (FGSM), a white-box gradient-based attack, to generate adversarial samples for our malware detection model. Based on the performance of our model against this attack, we draw a comparative study between various mitigation techniques such as adversarial training, ensemble methodologies, and defensive distillation in order to analyse how capable they are at solving the problem at hand. Finally, we propose a novel approach - Iterative Distilled Adversarial Training - that combines two of these defence mechanisms, namely adversarial training and defensive distillation, in order to make our model more resilient to an adversarial attack in a white box setting. As a result, we drastically reduced the FGSM attack success rate by around 75% with only a small increase in training time. Additionally, unlike other multi-model defence strategies like ensemble learning, our technique uses one architecture while offering stronger defensive capabilities by relatively decreasing the success rate of attacks by 15%.
A Novel Image Steganography Technique Using AES Encryption in DCT Domain

Steganography is a process to conceal the existence of a message. Image Steganography involves hiding the message onto a cover image to obtain a resultant stego image. This paper discusses a procedure for hiding coloured message images onto coloured cover images using a lossy Image Steganography technique. The secret images undergo Discrete Cosine Transformation, quantization and AES encryption before embedding using the Least Significant Bit embedding (LSB) technique onto the cover image. Finally, Peak signal-to-noise ratio (PSNR), Structural Similarity Index measurement (SSIM), zero normalized cross-correlation (ZNCC) are used for comparing the cover and stego images as well as the extracted and original images.
Research on the Internet of Things and Application of Artificial Intelligence

Later on, with the assistance of man-made reasoning advancements, huge information innovations, 5G, and other new advances, and with the expansion in Internet use, the focal point of the Internet association will move from a group- focused association with the Internet of Things. In light of this, this article investigates the set of experiences and present status of man-made brainpower in-game turn of events and presents potential changes and effects of machine-put together man-made consciousness innovation with respect to future game turn of events. This paper sums up the connected substance of the activity, working on man-made brainpower and huge information totally investigates the advancement of the monetary field and presents the use of computerized reasoning and large information in the field of money and expenses. This article assesses the profound coordination of man-made reasoning and unknown dialect educating and afterward from the displaying of unknown dialect understudies and educators in the man-made brainpower climate further investigates the utilization of man- made consciousness in unknown dialect instructing and anticipates the advancement of man-made brainpower later on.