Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI AUGMENTED REALITY SEBAGAI MEDIA PENGENALAN AKSARA ARAB MELAYU BERBASIS ANDROID

Indonesia's diverse cultural heritage includes regional scripts and traditional writings, which currently face the risk of extinction due to limited data and declining use as a means of communication. Introducing the Arab-Malay script has mainly relied on print media, making it less appealing to the millennial generation, who prefer modern technology. This script holds cultural significance, originating from Riau Province and spreading across Indonesia, necessitating preservation for future generations. Augmented Reality (AR) technology is proposed as a solution, integrating 3D computer graphics with the real world to introduce and conserve the Arab-Malay script's richness. Using Unity and the Vuforia library, an AR application can project script images into the real environment using markers and Android smartphones, facilitating easier learning and engagement for younger generations in preserving this heritage.
Impact of Faculty Development Programs in ELT &amp; Professional Development

FDPs and job-embedded professional development could improve teaching and learning, as they provide knowledge, skills, and beliefs to teach effectively; opportunity to participate with other professionals in creating role models that contributed to the improvement of teaching; strengthened commitment to teaching by embedding it within their personal lives; connected what they teach with real-world experiences that were important to them both professionally and personally. The paper also focuses on the challenges related to Faculty Development Programs.
The Way We Were: Structural Operational Semantics Research in Perspective

This position paper on the (meta-)theory of Structural Operational Semantic (SOS) is motivated by the following two questions: (1) Is the (meta-)theory of SOS dying out as a research field? (2) If so, is it possible to rejuvenate this field with a redefined purpose? In this article, we will consider possible answers to those questions by first analysing the history of the EXPRESS/SOS workshops and the data concerning the authors and the presentations featured in the editions of those workshops as well as their subject matters. The results of our quantitative and qualitative analyses all indicate a diminishing interest in the theory of SOS as a field of research. Even though `all good things must come to an end', we strive to finish this position paper on an upbeat note by addressing our second motivating question with some optimism. To this end, we use our personal reflections and an analysis of recent trends in two of the flagship conferences in the field of Programming Languages (namely POPL and PDLI) to draw some conclusions on possible future directions that may rejuvenate research on the (meta-)theory of SOS. We hope that our musings will entice members of the research community to breathe new life into a field of research that has been kind to three of the authors of this article.
MAPPING OF MANAGERIALISM AND SCHOOL MANAGERIZATION: FROM METHOD DESCRIPTION TO SUBJECT UNDERSTANDING

The methodological basis of&nbsp; the research. Content analysis and comparative analysis of scientific publications on managerial topics. Research results. The general boundaries and dynamics of managerial discourse are revealed as simultaneously, from the point of view of disciplinary affiliation, part of the sociological and interdisciplinary research fields. The main directions of research in the managerial methodology are revealed, as well as those social institutions in which the processes of managerization can be recorded. The characteristics and parameters according to which a comprehensive Russian school can be studied with the help of a managerial methodology are determined. Prospects of the study. Further theoretical and methodological elaboration of managerism as a way of studying specific practices in the field of institutions related to the creation of public goods and access to them, as well as a more active and widespread application of this conceptual apparatus to empirical reality can allow not only to identify cause-and-effect relationships among a variety of social problems. Scientific work in this direction in the future will make it possible to better understand the boundaries and differences of the processes of formation and management of public goods, the improvement of the latter and the development of new and effective for all social actors management strategies and decisions in the public sector, including for the field of general education.
Bayesian Statistical Inference

Learn about probability as it is used in computer science with this rigorous, yet highly accessible, undergraduate textbook. Fundamental probability concepts are explained in depth, prerequisite mathematics is summarized, and a wide range of computer science applications is described. Throughout, the material is presented in a “question and answer” style designed to encourage student engagement and understanding. Replete with almost 400 exercises, real-world computer science examples, and covering a wide range of topics from simulation with computer science workloads, to statistical inference, to randomized algorithms, to Markov models and queues, this interactive text is an invaluable learning tool whether your course covers probability with statistics, with stochastic processes, with randomized algorithms, or with simulation. The teaching package includes solutions, lecture slides, and lecture notes for students.
Hardware Security and Privacy: Threats and Opportunities

Welcome to the 2023 September/October issue of IEEE Micro! This month, we bring the Special Issue on Security and Privacy-Preserving Execution Environments and the Special Issue on Commercial Products 2023 to IEEE Micro’s readers.
What Is the Role of Information Technology in Helping Enhance Gender Equality in Developing Countries and Improving Long-Term Economic Growth?

Information technology is a significant part of modern life and has changed society in many aspects. This study aims to investigate the relationship between developing informational technology and the economic growth within developing countries with female status by using data from various sources including the National Bureau of Statistics of PRC, World Bank, and research statistics from companies and papers. In the work, individual data is analyzed and connections between them are discovered and concluded. The data in the work show strong correlations between developing information technology and decreasing gender inequality with economic growth. The data from the EU report further confirms that point. Therefore, it is safe to conclude that information technology helps enhance gender equality and improve long-term economic growth in developing countries.
Evaluation of a decision support system using Bayesian network modeling in an applied Multi-INT surveillance environment

ABSTRACTSensemaking and decision-making are fundamental components of applied Intelligence, Surveillance, and Reconnaissance (ISR). Analysts acquire information from multiple sources over a period of hours, days, or even over the scale of months or years, that must be interpreted and integrated to predict future adversarial events. Sensemaking is essential for developing an appropriate mental model that will lead to accurate predictions sooner. Decision Support Systems (DSS) are one proposed solution to improve analyst decision-making outcomes by leveraging computers to conduct calculations that may be difficult for human operators and provide recommendations. In this study, we tested two simulated DSS that were informed by a Bayesian Network Model as a potential prediction-assistive tool. Participants completed a simulated multi-day, multi-source intelligence task and were asked to make predictions regarding five potential outcomes on each day. Participants in both DSS conditions were able to converge on the correct solution significantly faster than the control group, and between 36–44% more of the sample was able to reach the correct conclusion. Furthermore, we found that a DSS representing projected outcome probabilities as numerical, rather than using verbal ordinal labels, were better able to differentiate which outcomes were extremely unlikely than the control group or verbal-probability DSS.KEYWORDS: Decision support systemssurveillancedecision-making Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data from this study are available upon reasonable request.Additional informationFundingThis work was supported by the Air Force Research Laboratory [FA8650-22-F-6401].
The ADM TOGAF Framework: Analysis and Design of Sales Information System on PO. Bunga Tani

Objective – To help MSME businesses, especially PO. Bunga Tani needs the use of information system technology.Methodology – This research using qualitative method and TOGAF ADM framework can provide an overview of sales information system design on PO. Bunga Tani, by using the preliminary phase to phase E, namely opportunity and solutions.Findings – The results of the study found MSMEs PO. Bunga Tani has problems in its business activities, so it is necessary to implement information system technology. Therefore, analysis and design of information systems to produce a blueprint for the design of sales information systems in their business processes in accordance with the needs of MSMEs PO. Bunga Tani.Novelty – By knowing the feasibility test in analyzing and designing sales information systems using the enterprise architecture scorecard method, to know the proposed analysis and design is feasible or not.Keywords: Enterprise Architecture, Information System Technology, PO. Bunga Tani, Sales, TOGAF ADM Framework
Designing graph neural networks training data with limited samples and small network sizes

Machine learning is a data-driven domain, which means a learning model's performance depends on the availability of large volumes of data to train it. However, by improving data quality, we can train effective machine learning models with little data. This paper demonstrates this possibility by proposing a methodology to generate high-quality data in the networking domain. We designed a dataset to train a given Graph Neural Network (GNN) that not only contains a small number of samples, but whose samples also feature network graphs of a reduced size (10-node networks). Our evaluations indicate that the dataset generated by the proposed pipeline can train a GNN model that scales well to larger networks of 50 to 300 nodes. The trained model compares favorably to the baseline, achieving a mean absolute percentage error of 5-6%, while being significantly smaller at 90 samples total (vs. thousands of samples for the baseline).
Generating Semantic Graph Corpora with Graph Expansion Grammar

We introduce LOVELACE, a tool for creating corpora of semantic graphs.The system uses graph expansion grammar as a representational language, thus allowing users to craft a grammar that describes a corpus with desired properties.When given such grammar as input, the system generates a set of output graphs that are well-formed according to the grammar, i.e., a graph bank.The generation process can be controlled via a number of configurable parameters that allow the user to, for example, specify a range of desired output graph sizes.Central use cases are the creation of synthetic data to augment existing corpora, and as a pedagogical tool for teaching formal language theory.
ART AND RIGOUR: CREATING METHODOLOGIES FOR ARTISTIC RESEARCH IN MUSIC

This paper focuses on the construction of a methodology for artistic researchers, in particular practising musicians. Artistic research is a steadily growing field, gaining increasing relevance in academic discourse and flourishing in universities and music academies. Due to its relatively recent history there is still a lack of standardized widespread research methods, and the advice to be creative and borrow from other disciplines, though reasonable, can be confusing for young researchers. The adaptation process can be problematic, with the need to mediate between the rigour of academic research and the openness and creativity of the artistic side. How can methods from different disciplines be adapted to the needs of artistic research, without hindering creative practice? In this paper I will present, in meta-research terms, the personal adaptation of three qualitative methods: autobiographical design, borrowed from human computer interaction; thematic analysis, borrowed from psychology; and autoethnography, borrowed from social sciences. These were used to design an accountable research process to investigate differences and similarities in the musical affordances of the 13-keyed period clarinet and the modern clarinet. The three methods intertwine to form a complete methodology that could be applied by other researchers investigating similar topics. Adapting methods proved to be a fruitful process, bearing in mind that the goal is not objectivity, unreachable in the arts, but accountability and organization.
Guitar recognizer - an artificial intelligence-based teaching software

This article describes a preliminary idea for guitar teaching software, briefly describing its several components: sound recording, sound recognition, assessment methods, and a feedback system. The voice entry system is as uncomplicated as possible to avoid difficulties in user input. Users can use the most commonly used tool - microphone - to input. Two approaches to voice recognition are presented to suggest possible future implementations. This system is more difficult to implement because of the actual need to consider more external factors, such as noise, volume level, etc. The handling of these issues is the biggest challenge for this system. Three main aspects are considered in the assessment system, and more assessment elements are to be further explored. Some help methods are mentioned in the feedback system and how to test their help efficiency. At the same time, the feedback system turns some difficult techniques that may hinder the learning of beginners into bonus content, so as not to discourage them from learning. The conclusion presents some of the necessary databases for the software and other system issues that still need to be addressed. Some possible methods of implementation are discussed as well as a preliminary plan for future research that could be conducted.
Security analysis of blockchain-based service network

Recently, the extensive applications of blockchain technology in fields like financial exchanges, insurance, Logistics and healthcare has proven to be pragmatic and revolutionary. In order to enable the blockchain to have a more complete development space, Blockchain-based Service Network (BSN) was proposed. It integrates the developers, portal, cloud resources and blockchain framework to provide the basic environment for blockchain applications. Researches on its security have kept place with the advent of blockchain technology, while potential issues on BSN security remain largely unexplored. In this paper, we are devoted to diving deep into BSN security-related problems, specifically the security of BSN is analysed, and corresponding real attacks are summarised and investigated by checking the blockchain system adopted by BSN, which will provide a good reference for the future research. Finally, we reach the conclusion that although security issues still exist, there are fewer security issues compared with traditional blockchain.
Spam/Ham email classification using BERT

Email is a popular method for communicating with each other. However, as sending email is free of charge as long as an email server and a domain name are available, spam mail is becoming a critical problem in the email network. Conventionally, the industry uses spam filters based on rules and Bayesian inference to counteract spam mail, reaching an accuracy of 98.76%, which is far from satisfactory. Hence, to better protect email users from unsolicited messages containing advertisements, sensitive content, phishing content, and viruses, a new approach is proposed, in which email content is filtered by a spam detector using bidirectional encoder representations from transformers (BERT). BERT is a new language representation model published by Google that has achieved great success because of its powerful capabilities in understanding natural language. After the model is trained on a corpus from Kaggle, the spam detector equipped with the BERT model reaches a binary accuracy of 99.40% when classifying spam mail.
SYMBOLIC MUSICAL RESYNTHESIS AS AN EKPHRASTIC COMPOSITIONAL PRACTICE USING COMPUTATIONAL METHODS

In my artistic work, I explore the affordances of computational methods from the discipline of artificial intelligence for music composition. Grounded in philosophical perspectives that revolve around the notions of trans- and post-humanism and cybernetics, I understand the mediating role of computers in music composition as having the potential to expand a composer’s creative process by providing him with novel ways of exploring relationships between musical material and structure. Under this premise, music composition becomes a process that occurs through the assemblage between human actors and technological artifacts, and this association should result in new, interesting, and valuable artistic works. In this text, I will discuss a personal compositional practice that I understand as ekphrastic, based on the notion of symbolic resynthesis of musical symbolic information employing computational methods, as means for composing an original piece in response to the madrigal “Io Mi Son Giovinetta” by Claudio Monteverdi.
Evaluasi Pembelajaran Pjok pada Siswa Kelas XI SMK Roudhlotul Mubtadiin Balekambang

The purpose of this study is to find out and describe the evaluation of the process of implementing PJOK learning in class XI students of SMK Roudhlotul Mubtadiin Balekambang in 2021. The results show that teacher learning planning activities do not plan well, only stop at the syllabus, lesson plans and teaching materials. Learning implementation activities are still focused on teachers, learning has not led to analytical activities according to basic competencies. In the evaluation activities the teacher does not compile an assessment instrument, the teacher's assessment is carried out in a modest manner without being guided by the assessment instrument. The conclusion of the research is that the evaluation of the learning implementation process has not run optimally. Starting from the planning, implementation and evaluation of learning activities, it has not been carried out properly&#x0D;
An Industrial Framework for Personalized Serendipitous Recommendation in E-commerce

Classical recommendation methods typically face the filter bubble problem where users likely receive recommendations of their familiar items, making them bored and dissatisfied. To alleviate such an issue, this applied paper introduces a novel framework for personalized serendipitous recommendation in an e-commerce platform (i.e., JD.com), which allows to present user unexpected and satisfying items deviating from user’s prior behaviors, considering both accuracy and novelty. To achieve such a goal, it is crucial yet challenging to recognize when a user is willing to receive serendipitous items and how many novel items are expected. To address above two challenges, a two-stage framework is designed. Firstly, a DNN-based scorer is deployed to quantify the novelty degree of a product category based on user behavior history. Then, we resort to a potential outcome framework to decide the optimal timing to recommend a user serendipitous items and the novelty degree of the recommendation. Online A/B test on the e-commerce recommender platform in JD.com demonstrates that our model achieves significant gains on various metrics, 0.54% relative increase of impressive depth, 0.8% of average user click count, 3.23% and 1.38% of number of novel impressive and clicked items individually.
User Behavior Modeling with Deep Learning for Recommendation: Recent Advances

User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems. The exploration of key interactive patterns between users and items has yielded significant improvements and great commercial success across a variety of recommendation tasks. This tutorial aims to offer an in-depth exploration of this evolving research topic. We start by reviewing the research background of UBM, paving the way to a clearer understanding of the opportunities and challenges. Then, we present a systematic categorization of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. To provide an expansive understanding, we delve into each category, discussing representative models while highlighting their respective strengths and weaknesses. Furthermore, we elucidate on the industrial applications of UBM methods, aiming to provide insights into the practical value of existing UBM solutions. Finally, we identify some open challenges and future prospects in UBM. This comprehensive tutorial serves to provide a solid foundation for anyone looking to understand and implement UBM in their research or business.
VideoRecSys 2023: First Workshop on Large-Scale Video Recommender Systems

The demand for personalized video recommendations has grown exponentially with the widespread use of video content across various domains, including entertainment, e-commerce, education and social media. The explosive growth of video content on the internet, combined with the ubiquitous availability of high-speed internet and advancements in mobile camera technology have made it easier than ever for users to create, access and consume videos. With the proliferation of online social media applications like Instagram, YouTube, Facebook and TikTok, the need for large-scale video recommendation systems which can provide users with personalized and relevant recommendations has increased. However, building effective and scalable video recommender systems for large-scale applications is a challenging task due to several technical and operational factors such as the huge volume of video content, the diversity of user preferences, noise and biases in underlying data and the need for real-time recommendations. At the same time, building a fair recommendation system which can distribute content from niche interests and emerging creators besides more popular content is very important to balance the demand and supply side of the ecosystem. This makes large-scale video recommendations an interesting, rapidly evolving and challenging problem which requires more discussions around various nuanced topics. The goal of this workshop is to provide a platform for researchers, practitioners, and industry experts to discuss and share insights on the latest trends, challenges and opportunities in the field of large-scale video recommendations, where the corpus of content to recommend from is in the order of hundreds of millions or even tens of billions. We hope that this workshop will foster collaborations in the growing recommender systems community and spark new ideas for future research in this exciting and rapidly evolving field.