Reviewerly/exHarmony huggingface dataset:
A Critical Assessment of Blockchain’s Application in Digital Advertising

Many individuals are unaware of the complex behind the scenes processes which facilitate the delivery of digital advertisements on web pages. One such process, known as Real-Time Bidding, involves an automated auction between advertisers, executed in the time that it takes for a user to load a web page. The Real-Time Bidding supply chain is opaque, and involves a multitude of intermediaries, causing significant issues for users, advertisers, and publishers. This thesis assesses the extent to which blockchain technology is able to help alleviate these issues caused by Real-Time Bidding. It does so predominantly through the lens of a solution by Brave Software, which combines a privacy-friendly browser with a new blockchain-based utility token. The thesis focuses on the privacy rights of users, and references a 2019 study by the UK Information Commissioner’s Office which exposed widespread violations by Real-Time Bidding participants of the EU General Data Protection Regulation, and the Privacy and Electronic Communications Regulations that implement the EU e-Privacy Directive in the UK. The thesis finds that, as evidenced by the Brave Software solution, it is possible for blockchain to help cure the harms associated with Real-Time Bidding for advertisers, publishers, and users. That said, the adequate protection of users’ privacy rights appears to also depend on the employment of additional technologies alongside the blockchain. The thesis reaches further conclusions regarding the risks of profiling by targeted advertising algorithms such as that employed by Brave Software, and the barriers to fully decentralised blockchain solutions in the digital advertising context.
Tingkat Kesulitan Dinamis Menggunakan Logika Fuzzy pada Game Musik Tradisional Jawa Tengah

The culture in Indonesia is very diverse, one of which is traditional songs. However, knowledge of traditional songs is still small. Digital Games can spread knowledge about traditional songs, one of which is Central Javanese traditional songs. However, the Game that is made still has static difficulties, so the Game cannot follow the player's ability, resulting in the player feeling bored and not wanting to continue the Game. To generate dynamic difficulties, methods in artificial intelligence can be applied to Games, one of which is Fuzzy. So in this study proposed the application of dynamic difficulties using Fuzzy Logic in music Games / Rhythm Games. Fuzzy Logic is built based on mathematical values and represents uncertainty, where this logic imitates the human way of thinking. Fuzzy Logic can convert crisp input values into fuzzy sets by performing fuzzification. After the input value is converted, the input will be entered into the set of rules provided. Each rule produces a different output. After the process is complete, the output value will be converted back to the crisp output value. Based on the research conducted, it is found that Fuzzy Logic can be applied to music Games where the Game can follow the player's ability based on the given rules.
Sparse data-based image super-resolution with ANFIS interpolation

Abstract Image processing is a very broad field containing various areas, including image super-resolution (ISR) which re-represents a low-resolution image as a high-resolution one through a certain means of image transformation. The problem with most of the existing ISR methods is that they are devised for the condition in which sufficient training data is expected to be available. This article proposes a new approach for sparse data-based (rather than sufficient training data-based) ISR, by the use of an ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation technique. Particularly, a set of given image training data is split into various subsets of sufficient and sparse training data subsets. Typical ANFIS training process is applied for those subsets involving sufficient data, and ANFIS interpolation is employed for the rest that contains sparse data only. Inadequate work is available in the current literature for the sparse data-based ISR. Consequently, the implementations of the proposed sparse data-based approach, for both training and testing processes, are compared with the state-of-the-art sufficient data-based ISR methods. This is of course very challenging, but the results of experimental evaluation demonstrate positively about the efficacy of the work presented herein.
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge

Although deep learning methods have achieved advanced video object recognition performance in recent years, perceiving heavily occluded objects in a video is still a very challenging task. To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario. OVIS consists of 296k high-quality instance masks and 901 occluded scenes. While our human vision systems can perceive those occluded objects by contextual reasoning and association, our experiments suggest that current video understanding systems cannot. On the OVIS dataset, all baseline methods encounter a significant performance degradation of about 80% in the heavily occluded object group, which demonstrates that there is still a long way to go in understanding obscured objects and videos in a complex real-world scenario. To facilitate the research on new paradigms for video understanding systems, we launched a challenge based on the OVIS dataset. The submitted top-performing algorithms have achieved much higher performance than our baselines. In this paper, we will introduce the OVIS dataset and further dissect it by analyzing the results of baselines and submitted methods. The OVIS dataset and challenge information can be found at http://songbai.site/ovis .
Application of the Promotion and Retirement Determination ProcessAt the Desktop-Based Directorate of Sabhara Polda North Sumatra

In the administrative process for information on the rank and retirement of personnel from the Sabhara Directorate of the North Sumatra Regional Police, they still apply a manual system, where personnel must know when they will be promoted or retired, resulting in personnel neglect due to too much work. The promotion and retirement process takes a long time, because it requires consideration and approval from the leadership. From this problem, the author applies a system for determining the promotion and retirement process at the North Sumatra directorate automatically with notifications (alarms) for personnel who will be promoted and retired based on a desktop with Windows operating system. This application was created to assist the North Sumatra Regional Police Directorate in processing rank and pension data from members of the police quickly and accurately, to realize smooth operations..
Exploring Mandarin Chinese Vocabulary that Children in Kindergarten Should Learn in Indonesia

This study aimed to explore what Mandarin Chinese vocabulary should be designed and delivered for young second language learners in kindergarten when they learn Mandarin Chinese language in Indonesia. A Mandarin Chinese vocabulary list designed by language teachers at a trilingual kindergarten (Indonesian, Mandarin, and English) in Indonesia was examined and revised by the researcher, and then the new revised Mandarin Chinese vocabulary list was examined by the Committee of the Kindergarten. The study found: (1) The Mandarin Chinese vocabulary list designed by language kindergarten teachers was lack of some concepts that children should have from perspectives of child development. For example, Mandarin direction words were not included in the list. (2) From the perspectives of parts of speech, the original vocabulary list focused mainly on nouns, partly on adjectives and verbs. Other parts of speech were not in the list. For example, pronouns (e.g., “我 [I]”, “你 [you]”, “他 [he/she]”) was not in it. (3) Some Mandarin words of the vocabulary list were categorized mistakenly or confusedly. For example, “浇花 (watering flowers)”, was categorized as plants. (4) The Mandarin meanings of “公公” (pronounced as [gōng-gōng]) and “婆婆” (pronounced as [po-po]) in the vocabulary list were different from the general meanings in China. (5) Some objects may have different Mandarin names and may confuse children to learn Mandarin. For example, pineapple can be called as “凤梨” (pronounced as [feng-li]), “菠萝” (pronounced as [bō-luo]), or “黄梨” (pronounced as [huang-li]). The study suggests: (1) Mandarin vocabulary should be delivered to children in kindergarten based on principles of child development and linguistics. (2) Mandarin vocabulary list should be designed based on children’s daily life and their local environment. (3) Mandarin vocabulary list should be categorized based not only on children’s development of language skills, but also on their cognitive, physical, social-emotional skills. (4) Based on child development, the trilingual kindergarten may define a standard vocabulary list from all languages they teach, including Indonesian language, English language, and Mandarin language.
Ontology-Based Data Mining Workflow Construction

Currently, Data mining is applied in various domains. Many data science researchers are confused on which algorithms are suitable for the context. Hundreds of the operators/algorithms are combined within complex data mining workflows. A data mining assistant can significantly improve the efficiency of workflow construction. In our previous work, we constructed data mining ontologies based on “semantic meta mining” to support the selection of algorithms and operators for solving data mining tasks. But the use of such ontologies is still unfriendly. Strict query syntax still plagues many users. This paper proposes an interactive interface based on the reasoning mechanism to help users generate queries and build suitable data mining workflows.
Verified functional programming of an IoT operating system's bootloader

The fault of one device on a grid may incur severe economical or physical damages. Among the many critical components in such IoT devices, the operating system's bootloader comes first to initiate the trusted function of the device on the network. However, a bootloader uses hardware-dependent features that make its functional correctness proof difficult. This paper uses verified programming to automate the verification of both the C libraries and assembly boot-sequence of such a, real-world, bootloader in an operating system for ARM-based IoT devices: RIoT. We first define the ARM ISA specification, semantics and properties in F* to model its critical assembly code boot sequence. We then use Low*, a DSL rendering a C-like memory model in F*, to implement the complete bootloader library and verify its functional correctness and memory safety. Other than fixing potential faults and vulnerabilities in the source C and ASM bootloader, our evaluation provides an optimized and formally documented code structure, a reasonable specification/implementation ratio, a high degree of proof automation and an equally efficient generated code.
Application of Bayesian Regression Model in Financial Stock Market Forecasting

The Bayesian method is a statistics field targeting the Bayes theorem in interpreting probabilities.The Bayesian formula provides an insight into conditional probability based on present data and prior information.Due to the efficiency of the Bayesian model in predicting future outcomes, the model is integrated with regression analysis which is a set of statistical methods utilized for estimating relationships between dependent and independent variables.Bayesian regression analysis is a reliable model for investigating variables having a significant impact on the output of a particular process, such as financial stock market forecasting considered in this research.To fulfill the study's aim, the research adopts secondary research on published journals, case studies, and reports documented by scholars in the field.Due to the stochastic nature of stock market variables, inadequate data or poorly dispersed data can be addressed using Bayesian linear regression allowing investors to make better decisions and cut larger profit margins.The vector autoregression and the classical frequentist approach achieve a higher probability accuracy than non-Bayesian methods such as the Auto-Regressive and Moving Average Model time series models.The author found that by studying the vector Bayesian autoregressive prediction model, it is possible to analyze how investors use the Bayesian model to predict stock market volatility.
reproducing "ner and pos when nothing is capitalized"

Capitalization is an important feature in many NLP tasks such as Named Entity Recognition (NER) or Part of Speech Tagging (POS). We are trying to reproduce results of paper which shows how to mitigate a significant performance drop when casing is mismatched between training and testing data. In particular we show that lowercasing 50% of the dataset provides the best performance, matching the claims of the original paper. We also show that we got slightly lower performance in almost all experiments we have tried to reproduce, suggesting that there might be some hidden factors impacting our performance. Lastly, we make all of our work available in a public github repository.
Peningkatan Literasi Masyarakat Melalui Pojok Baca Sekaput di Warung Kopi

The establishment and socialization of Sekaput Reading Corner to increase public interest in reading at coffee shops in Pangkalpinang City is a joint effort to increase literacy and reading interest among coffee lovers since coffee shops do not usually provide a reading corner. This program aimed to improve people’s literacy skills with the support of the Sekaput Reading Corner. At the same time, this program brought reading materials closer to the community while being at the coffee shop. The case observation method was used during the implementation of this program. Accordingly, the program involved 15 coffee shops by providing a reading corner stimulus to 5 selected coffee shops. The results showed approximately 80% of coffee shop customers gave a good response to the Sekaput Reading Corner in the coffee shops. Reflecting on the results, in the future, Sekaput Reading Corner can be applied in every coffee shop by moving from place to place.
An Assessment of the Seventh-Day Adventist Church Teacher Recruitment Process on the Achievement of the Church’s goals. A Study of Adventist Schools in Bulawayo

This paper aimed at unearthing practices driven by the Adventist-Teacher recruitment process in order to determine achievement of the organization’s goals. The study was conducted in Adventist Schools in Bulawayo. The schools have a population of about 218 teachers. The study followed a mixed method of research, where quantitative-descriptive statistics were used to discuss findings. A sample of 106 teachers was chosen using the stratified sampling to respond to a self-administered questionnaire. Simple random was used to choose teachers from within each stratum among the three Secondary and Five Primary Schools. In the schools selected, the teachers’ list was used, and either odd numbers or even numbers were used as respondents. Through purposive sampling, three Secondary and five Primary School heads were chosen to respond to interview questions indicated in this study. It is important to note that teachers and school heads did not agree on issues regarding induction and mentoring. Primary data was collected and analyzed using SPSS. Tables and graphs were used in the presentation and discussion of findings. The study established the link between the Recruitment process- which aims at the integration of faith and learning- and Redemptive practices to be positive at Pearson’s r 0,696. The interaction of the Recruitment process and the cutting-edge practices recorded a Pearson’s r of 0,589. The recruitment process appeared to be operating below the ideal standard. The study revealed a poor teacher induction and mentoring process in the schools studied.
Validación de la Escala de Cyberbullying y prevalencia en juventudes mexicanas norteñas

Cyberbullying, coupled with a high prevalence of violence in northern Mexico, becomes a threat to the integrity and future identity of youth. Its violent modalities trigger various learning problems, school dropouts, substance abuse and psychopathologies in the victims, among others. A scale focused on the prevalence of cyberbullying behaviors was validated, with contextualization to social particularities, prevalence and predominant typologies in northern Mexico. The psychometric properties were analyzed with the exploratory factor analysis, confirmatory factor analysis, Cronbach's alpha analysis, measurement invariance analysis and discriminative item analysis where the scale showed acceptable values. It is intended to use this tool for future research in northern youth, applicable to the social and educational field.
TransVae:A Novel Variational Sequence-to-Sequence Framework for Semi-supervised Learning and Diversity Improvement

Text generation tasks require that the generated text have certain diversity while ensuring the relevance. Traditional Seq2Seq models usually use cross entropy as the objective function. It demands the results keep strictly consistent with the ground truth texts, which easily leads to the lack of variability in generated texts. In this paper, we propose a novel framework, TransVAE, which applies Variational Auto-Encoder (VAE) to improve the Seq2Seq architecture. We design the Translator module to transform the latent variable spaces of origin input to target output, thus enhancing the diversity of generated texts and supporting semi-supervised learning. Moreover, we add attention and copy mechanisms to the TransVAE model to balance the relevance and diversity. Abundant experiments are carried out on three different string transduction tasks: dialogue generation, machine translation, and text summarization. The experiment results verify the effectiveness of our method.
STUDENTS LEARNING PROBLEMS IN EARLY READING AND WRITING AT GRADE 1 OF ELEMENTARY SCHOOL

Characteristics of students who have learning difficulties are minimum IQ levels, tend to have lower academic self-esteem than able students, cannot be good listeners, think, speak, read and write, write spelling, and even calculate the calculation of natural mathematics. This research is a qualitative study with the participants of grade 1 students in an elementary school in Bandung. The focus of this research is on students' difficulty in reading and writing initially. The results of the study found a way to deal with students who have difficulty learning to read and write initially by connecting the initial knowledge students have with the material to be taught, structuring a comfortable environment for learning, using interesting resources, learning material and media, provide lessons for improvement or improvement, provide repetition of material or enrichment, provide learning motivation, and build good learning attitudes and habits. The involvement of parents in overcoming student learning difficulties is to be able to establish communication with class teachers to find out their children's learning development, provide free time with children to discuss lessons that have been given at school, provide learning facilities, understand children's learning difficulties and provide guidance, and provide motivation in learning.
Parallel Constraint-Driven Inductive Logic Programming

Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limitation, we introduce parallel techniques based on constraint-driven ILP where the goal is to accumulate constraints to restrict the hypothesis space. Our experiments on two domains (program synthesis and inductive general game playing) show that (i) parallelisation can substantially reduce learning times, and (ii) worker communication (i.e. sharing constraints) is important for good performance.
Creating Indigenous Knowledge Spaces in Physics Learning Environments: Postcolonial Views and Insights from High School Physics Teachers

The study explores teachers' perspectives on the possibility and necessity of creating indigenous knowledge spaces (IKS) in physical learning environments (PLE) for learning physics in high schools in Zimbabwe.Traditional PLE such as laboratories for learning physics are still dominated by colonized western pedagogical designs and features but can be redesigned in light of the recent debates on Indigenous Knowledge (IK) and Science integration decolonizing agenda.IKS intends to make classrooms physics more accessible and meaningful for both indigenous teachers and learners.In many African countries including Zimbabwe, educational policies indicate the need to integrate IK and IKS in the school curriculum.However, these policies do not provide guidelines on what IK to integrate, where and how to do it in the existing curriculum as well as how to create and facilitate IKS.There are limited studies on IKS and advice on how to transform current educational facilities spaces.This study therefore provides insights on how IK can be integrated in physics through the creation of IKS in the PLE.In this regard, data were gathered from 10 male and 10 female teachers using quantitative (Likert-scale questionnaires) and qualitative data (observations and interviews).The findings suggest that the existing physics PLE are limited in providing IK resources for the teachers.The teachers confirm that IKS can be created in the PLE where indigenous artefacts are incorporated, traditional homestead are modelled, etc. integrated together with existing physics laboratory and school resources.The study recommends a variety of IK resources and strategies in the creation of IKS but it will also require a team effort from teachers, community, and educational authorities in effecting the decolonizing agenda.
Analysis on the Status Quo and Paths of Informatization Teaching Ability of Teachers in Rural Primary and Middle Schools in Hotan Area

This study conducted an in-depth analysis of the informatization teaching ability of teachers in rural primary and secondary schools in Hotan area through the literature method and questionnaire survey method. The investigation is mainly conducted in the aspects of teachers’ informatization awareness, teachers’ informatization ability, informatization application status, and existing problems, etc., analyzing the constraints existing in the informatization teaching of rural elementary and middle schools teachers, and making targeted improvements Strategies for teachers’ informatization teaching ability in rural elementary and middle schools: raising awareness of informatization teaching; informatization teaching training; informatization evaluation system; establishing a systematic and complete management system.
Emerging AI Security Threats for Autonomous Cars -- Case Studies

Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning. However, AI models require a large amount of sensitive training data and are usually computationally intensive to build. The commercial value of such models motivates attackers to mount various attacks. Adversaries can launch model extraction attacks for monetization purposes or step-ping-stone towards other attacks like model evasion. In specific cases, it even results in destroying brand reputation, differentiation, and value proposition. In addition, IP laws and AI-related legalities are still evolving and are not uniform across countries. We discuss model extraction attacks in detail with two use-cases and a generic kill-chain that can compromise autonomous cars. It is essential to investigate strategies to manage and mitigate the risk of model theft.