Reviewerly/exHarmony huggingface dataset:
MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees

Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesian approach to decision tree induction via maximum a posteriori inference of a posterior distribution over trees. We first demonstrate a connection between maximum a posteriori inference of decision trees and AND/OR search. Using this connection, we propose an AND/OR search algorithm, dubbed MAPTree, which is able to recover the maximum a posteriori tree. Lastly, we demonstrate the empirical performance of the maximum a posteriori tree both on synthetic data and in real world settings. On 16 real world datasets, MAPTree either outperforms baselines or demonstrates comparable performance but with much smaller trees. On a synthetic dataset, MAPTree also demonstrates greater robustness to noise and better generalization than existing approaches. Finally, MAPTree recovers the maxiumum a posteriori tree faster than existing sampling approaches and, in contrast with those algorithms, is able to provide a certificate of optimality. The code for our experiments is available at https://github.com/ThrunGroup/maptree.
Countering online hate speech: How does human rights due diligence impact terms of service?

The Internet is a global forum largely governed by private actors driven by profit concerns, often disregarding the human rights of historically marginalised communities. Increased attention is being paid to the corporate human rights due diligence (HRDD) responsibilities applicable to online platforms countering illegal online content, such as hate speech. At the European Union (EU) level, cross-sector initiatives regulate the rights of marginalised groups and establish HRDD responsibilities for online platforms to expeditiously identify, prevent, mitigate, remedy and remove online hate speech. These initiatives include the Digital Services Act, the Audiovisual Media Services Directive, the proposed Directive on Corporate Sustainability Due Diligence, the proposed Artificial Intelligence Act and the Code of conduct on countering illegal hate speech online. Nevertheless, the HRDD framework applicable to online hate speech has focused mostly on the platforms’ responsibilities throughout the course of their operations - guidance regarding HRDD requirements concerning the regulation of hate speech in the platforms’ Terms of Service (ToS) is missing. This paper employs a conceptualisation of criminal hate speech as explained in the Council of Europe Committee of Ministers’ Recommendation CM/Rec(2022)16, Paragraph 11, to develop specific HRDD responsibilities. We argue that online platforms should, as part of emerging preventive HRDD responsibilities within Europe, respect the rights of historically oppressed communities by aligning their ToS with the conceptualisation of criminal hate speech in European human rights standards.
On Learning with LAD

The logical analysis of data, LAD, is a technique that yields two-class classifiers based on Boolean functions having disjunctive normal form (DNF) representation. Although LAD algorithms employ optimization techniques, the resulting binary classifiers or binary rules do not lead to overfitting. We propose a theoretical justification for the absence of overfitting by estimating the Vapnik-Chervonenkis dimension (VC dimension) for LAD models where hypothesis sets consist of DNFs with a small number of cubic monomials. We illustrate and confirm our observations empirically.
Unraveling the Significance of the Classification Tree Algorithm in Machine Learning: A Literature Review

Machine learning, an integral component of Artificial Intelligence (AI), empowers systems to autonomously enhance their performance through experiential learning. This paper presents a comprehensive overview of the Classification Tree Algorithm's pivotal role in the realm of machine learning. This algorithm simplifies the process of categorizing new instances into predefined classes, leveraging their unique attributes. It has firmly established itself as a cornerstone within the broader landscape of classification techniques. This paper delves into the multifaceted concepts, terminologies, principles, and ideas that orbit the Classification Tree Algorithm. It sheds light on the algorithm's essence, providing readers with a clearer and more profound understanding of its inner workings. By synthesizing a plethora of existing research, this endeavor contributes to the enrichment of the discourse surrounding classification tree algorithms. In summary, the Classification Tree Algorithm plays a fundamental role in machine learning, facilitating data classification, and empowering decision-making across domains. Its adaptability, alongside emerging variations and innovative techniques, ensures its continued relevance in the ever-evolving landscape of artificial intelligence and data analysis.
THE INSTRUCTOR PARAMETERS OF TRANSITION TO FULLY ONLINE LEARNING

Online learning has an old background and is an efficient method if applied correctly. However, during the pandemic period, it has been faced with a negative perception due to the wrong practices brought about by the mandatory and rapid transition. During this pandemic term, most educational institutions have offered support in this process to explain the process to both their students and instructors. This study examined XXX University instructors’ perspectives regarding the emergency remote teaching period in terms of their professional experience, discipline area, online instruction experience, and whether they received training in online instruction. Quantitative research methods were used in the study. An online instructor’s emergency remote teaching perspective scale has been developed and used as a data collection tool. A significant difference has been found in the discipline areas, online instruction experience, and participation in training program. From the results of the research, the need to support the instructors according to the needs specific to the disciplines has been revealed, and it is recommended to investigate the relationships between selfcompetency for online teaching and the perception of institutional support in depth.
Solving the Shortest Total Path Length Spanning Tree Problem Using the Modified Sollin and Modified Dijkstra Algorithms

In a weighted connected graph, the shortest total path length spanning tree problem is a problem when we need to discover the spanning tree with the lowest total cost of all pairwise distances between its vertices. This problem is also known as the minimum routing cost spanning tree (MRCST). In this study, we will discuss the Modified Sollin and Modified Dijkstra Algorithms to solve that problem which implemented on 300 problems are complete graphs of orders 10 to 100 in increments of 10, where every order consists of 30 problems. The results show that the performance of the Modified Dijkstra and the Modified Sollin Algorithms are slightly similar. On orders 10, 20, 30, 60, and 80, the Modified Dijkstra Algorithm performs better than the Modified Sollin, however on orders 40, 50, 70, 90, and 100, the Modified Sollin performs better.
PENGARUH KECAKAPAN DIGITAL SISWA SMP NEGERI 1 BATANG KABUPATEN JENEPONTO TERHADAP HASIL BELAJAR SISWA PADA MATA PELAJARAN BAHASA INDONESIA

Students have smartphones and are able to use every application they have on their smartphones such as communication applications, online games, social media and the like. The aim of this research is to measure the digital abilities or skills possessed by students and how students use them, to show an overview of the implementation of the learning process involving students' digital abilities or skills in Indonesian language subjects and to find out whether there is an influence on the digital skills of students at SMP Negeri 1 Batang Jeneponto Regency on student learning outcomes in Indonesian language subjects. This research was conducted using a quantitative approach with a type of research that used a quasi-experimental type with a method that only used one treatment group and did not use a control group. The sample from this research was 25 students from class VIII. The instruments in this research use questionnaires, tests (assignments) and documentation as support for this research. The questionnaire consists of a pretest given before the learning process is carried out and a posttest given after the learning process is carried out, while the test consists of tasks containing news text and poetry text material. Data analysis uses descriptive statistical analysis and inferential statistical analysis with hypothesis testing using the t-test. Based on the results of data analysis and t-test, the average value of students' abilities and learning outcomes after the implementation of the learning process involving students' digital skills was higher than before the implementation of the learning process involving students' digital skills. All students involve digital skills by using several methods of using search engines and social media in completing assignments with news text and story text material. The results of this research show that there is an influence on the digital skills of students at SMP Negeri 1 Batang Jeneponto Regency on student learning outcomes in Indonesian language subjects.
Android-based Automatic Steak Grilling Tool

In an era of rapid technological development, technology is increasingly accessible and easily applied by humans. One of the significant developments is the Internet of Things (IoT), where physical devices such as sensors, equipment, and vehicles are equipped to communicate and interact via the Internet network. The application of IoT has expanded to various sectors, including culinary. In this regard, preparing and presenting food, especially steaks, becomes an exciting focus. There are multiple types of steaks, such as sirloin and tenderloin, and cooking involves various techniques, such as searing and grilling. However, suitability for maturity and risk during cooking is challenging for steak makers and connoisseurs. To overcome this, the application of IoT is needed in an automatic steak roaster to be a promising solution. This research is also equipped with real-time monitoring via an Android application. This aims to ensure proper doneness and consistent results in the steak cooking process. This research makes an automatic steak grill with a success rate of 83%, which shows that the tool's performance and functionality align with expectations. This tool also has an Android application to monitor and control the device remotely efficiently. This research gives confidence that this can be a solution that has been developed and provides significant benefits in roasting steaks with automatic monitoring and operation.
Pemanfaatan Media Sosial Instagram Sebagai Media Pembelajaran Bahasa Jepang

Due to the covid-19 pandemic, online platform has made learning more flexible. However, in its implementation, there are obstacles that hinder learning to achieve its goals. Instagram, a social media platform for sharing photos and videos, is one of the most popular social media among millennials. The existence of Instagram as an alternative learning tool makes it possible to supports the achievement of learning objectives, especially language learning. The purpose of this study was to identify the use of social media Instagram as a medium for learning Japanese. This study uses the method of observation and documentation. The data objects are learning content and Instagram features utilization of five content accounts of Japanese language learning creators in Indonesia, namely @auliasenpai, @kejepangan, @harumon.japanese, @jepangeasy and @nihongo.monggo accounts. The results of the study indicate that Instagram social media can be an alternative medium for learning Japanese by utilizing existing Instagram features to create learning content. The types of material themes found in the data are Japanese linguistic, historical and cultural materials. Besides, the introduced Japanese language learning material was diverse, containing linguistic materials which introduce idioms, expressions and several others. These accounts also address JLPT or Noryokushiken test units.
Systematic Literature Mapping: Studies Related to ESL/EFL Oral Communication Skills (2018-2022)

In our interconnected world, English has become the language used for communication in different contexts. It is used as the language employed to facilitate exchange of information in different fields such as business, academia, science, technology, and culture, among others. This paper describes the development of a systematic literature mapping (SLM) using 68 studies from the Scopus and WoS databases from 2018 to 2022 related to English language oral communication with the purpose of analyzing recent publications on the topic, the thematic lines that researchers have focused on, the methodology and tools used to carry out their research, the contexts in which investigations take place, the journals that publish these articles, and the recommendations for future studies. The results show the interest in EFL/ESL oral communication in different environments, the strategies used by teachers and learners, some of the cognitive and affective processes that impact oral proficiency, as well as the use of technology and how it contributes to the development of these investigations. The search for articles was limited to articles written in the English language that referred to oral communication in English as a second or foreign language. This work is of value for researchers and teachers interested in exploring the trends in this topic.
Acculturation Theory and its Implementation in Foregn Language Teaching in Indonesia

Mixing cultures in an area can be a positive thing or a negative thing. Therefore, society must be wise in responding to the mixing of cultures. In Indonesia, it is known as acculturation. By recognizing complex relationships In this way, language teachers can design what treatment is suitable for their student's class, both in terms of approach and technique. research questions were formed, namely 1). What is the acculturation theory and 2). What about research in the acculturation theory as scientific work.. The type of data used is secondary data. The method that will be used for this study is literary studies. The data obtained were compiled, analyzed, and reconciled so that conclusions were obtained regarding the acculturation theory. acculturation is the presence of a foreign culture that enters its own culture so that it will slowly be accepted by members of the community without having to lose the character of the culture itself. acculturation cannot be viewed negatively because these two things have different effects on society and its environment. In this way, language teachers can design what treatment is suitable for their student''s classes, both in terms of approach and technique. Teachers can also provide understanding to students about the importance of understanding the culture of the target language.
Can Artificial Intelligence be conscious?

&lt;p&gt;In this paper, I propose some new approach to determine the possibility of introducing artificial consciousness into information systems. The new rigorous formulations of the basic cognitive concepts outlined in the article are in themselves a valuable result. &lt;/p&gt; &lt;p&gt;The implementation of basic cognitive concepts in the real AI software is impossible without adapting well-known, but verbose and vague formulations of such concepts to the requirements of Information Technology. In this article I propose possible "codable" formulations of such cognitive concepts, including "consciousness".&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
MANAJEMEN KEAMANAN INFORMASI UNTUK TRANSFORMASI DIGITAL INSURCO BERBASIS COBIT 2019 FOCUS AREA INFORMATION SECURITY

Previous research has shown the contribution of IT governance to digital transformation. However, further exploration is needed regarding the role of information security management in overseeing such changes through a case study of InsurCo insurance company. This is necessary in line with the latest guidance provided by PER-2/MBU/03/2023 on corporate governance and the Indonesian Financial Services Sector Master Plan 2021–2025 regarding the acceleration of digital transformation. This research method utilizes Design Science Research based on interview data. DSS05 Managed Security Service, APO13 Managed Security, and APO12 Managed Risk were selected as the priority implementation areas of the COBIT 2019 Information Security Focus Area. Capability assessments, gap analysis, recommendation development, solution design, and roadmap have been conducted on the seven related governance components. An estimated average maturity improvement of 0,8 (30,77%) was observed in these three processes. This study contributes to the knowledge base on the concept of prioritizing information security management to support organizational digital transformation, and it is particularly beneficial for InsurCo in ensuring the success of its strategic programs, as well as for the insurance industry in general.
Efficient face detection and replacement in the creationofsimple fake videos

Face detection and facial recognition technologies are among the most intensively studied topics within the field of computervision, owing to their vast application potential across a multitude of industries. These technologies have demonstrated practicalap-plicability in varied contexts such as identifying suspicious individuals in crowded urban spaces, real-time recognition of smartphone owners, creating compelling deepfakes for entertainment applications, and specialized applications that modify the movements of facial features such as the lips or eyes. With the current state-of-the-art advancements in hardware and software technology, today's technological infrastructure provides more resources than are necessary for video streaming. As a result, simpleface recognition systems can be implemented without the need for high-cost server instances that require specified pre-trained models. This abun-dance of resources is changing the landscape of face recognition, and the discussion within this paper will revolve around these emerging paradigms.The primary focus of this article is an in-depth analysis of the key concepts of face detection in streaming video data using prominent pre-trained models. The models under discussion include HRNet, RetinaFace, Dlib, MediaPipe, and KeyPoint R-CNN. Each of these models has its strengths and weaknesses, and the article discusses these attributes in the context of real-world case studies. This discussion provides valuable insights into the practical applications of these models and the trade-offs involved in their utilization.Moreover, this paper presents a comprehensive overview of image transformation techniques. It introduces an ab-stract method for affine image transformation, animportanttechnique in image processing that changes the geometric properties of an image without affecting its pixel intensity. Additionally, the article discusses image transformation operations executed through the OpenCV library, one of the leading libraries in the field of computer vision, providing a highly flexible and efficient toolset for image manipulation.The culmination of this research is presented as a practical standalone system for image replacement in video. This system leverages the RetinaFace model for inference and employs OpenCV for affine transformations, demonstrating the con-cepts and technologies discussed in the paper. The work outlined in this article thereby advances the field of face detectionand recognition, presenting an innovative approach that makes full use of contemporary hardware and software advances
Demystifying Quantum Computing: A Holistic Non-Technical Overview of the NISQ Paradigm and its Opportunities and Obstacles

&lt;p&gt;Quantum Computing is a field that is gaining recognition at a rapid rate around the world. This review shall address the unique elements of quantum computing with respect to classical computing, the current situation of quantum computing (i.e. Noisy Intermediate Scale Quantum or NISQ era computers) and the potential of such technology for the future. The review further seeks to discuss the problems that need to be overcome to attain quantum computing feasibility and the use of hybrid computing as an alternative that can derive from the benefits of the quantum world.&lt;/p&gt;
Knowledge Graphs for Data Integration - UHasselt 2023

In June 2023 I attended a workshop in Hasselt, Belgium, organized by a research network on Knowledge Graphs and Data Integration. Linked Data-based systems and declarative SPARQL queries somehow magically do all the work efficiently, but how? For many people, presentations about &lt;em&gt;using&lt;/em&gt; Linked Data and SPARQL already seem very technical.
Learned Image Compression Using Cross-Component Attention Mechanism

Learned image compression methods have achieved satisfactory results in recent years. However, existing methods are typically designed for RGB format, which are not suitable for YUV420 format due to the variance of different formats. In this paper, we propose an information-guided compression framework using cross-component attention mechanism, which can achieve efficient image compression in YUV420 format. Specifically, we design a dual-branch advanced information-preserving module (AIPM) based on the information-guided unit (IGU) and attention mechanism. On the one hand, the dual-branch architecture can prevent changes in original data distribution and avoid information disturbance between different components. The feature attention block (FAB) can preserve the important information. On the other hand, IGU can efficiently utilize the correlations between Y and UV components, which can further preserve the information of UV by the guidance of Y. Furthermore, we design an adaptive cross-channel enhancement module (ACEM) to reconstruct the details by utilizing the relations from different components, which makes use of the reconstructed Y as the textural and structural guidance for UV components. Extensive experiments show that the proposed framework can achieve the state-of-the-art performance in image compression for YUV420 format. More importantly, the proposed framework outperforms Versatile Video Coding (VVC) with 8.37% BD-rate reduction on common test conditions (CTC) sequences on average. In addition, we propose a quantization scheme for context model without model retraining, which can overcome the cross-platform decoding error caused by the floating-point operations in context model and provide a reference approach for the application of neural codec on different platforms.