Reviewerly/exHarmony huggingface dataset:
Do you catch my drift? On the usage of embedding methods to measure concept shift in knowledge graphs

Automatically detecting and measuring differences between evolving Knowledge Graphs (KGs) has been a topic of investigation for years. With the rising popularity of embedding methods, we investigate the possibility of using embeddings to detect Concept Shift in evolving KGs. Specifically, we go deeper into the usage of nearest neighbour set comparison as the basis for a similarity measure, and show why this approach is conceptually problematic. As an alternative, we explore the possibility of using clustering methods. This paper serves to (i) inform the community about the challenges that arise when using KG embeddings for the comparison of different versions of a KG specifically, (ii) investigate how this is supported by theories on knowledge representation and semantic representation in NLP and (iii) take the first steps into the direction of valuable representation of semantics within KGs for comparison.
Advancing data sharing and reusability for restricted access data on the Web: introducing the DataSet-Variable Ontology

In response to the increasing volume of research data being generated, more and more data portals have been designed to facilitate data findability and accessibility. However, a significant portion of this data remains confidential or restricted due to its sensitive nature, such as patient data or census microdata. While maintaining confidentiality prohibits its public release, the emergence of portals supporting rich metadata can help enable researchers to at least discover the existence of restricted access data, empowering them to assess the suitability of the data before requesting access.
A Full-Fledged Framework for Combining Entity Linking Systems and Components

Named entity recognition and disambiguation, often referred to as entity linking systems, refers to the task of automatically identifying knowledge graph entities in text documents. While a variety of entity linking systems based on very different approaches exist, these systems implicitly share certain processing steps in their pipeline. Despite this fact, they have been mainly used as stand-alone solutions. In this paper, we propose a framework for combining entity linking methods. This allows multiple entity linking systems and especially their components to be used in combination to an unlimited extent, thus allowing to achieve the best possible performance. In addition, the framework allows user-developed entity linking systems or components to be easily tested and automatically evaluated against other systems without having to set up other systems first. Essentially, our framework is knowledge graph agnostic and entity linking systems can be compared across knowledge graphs. Furthermore, our framework enables entity linking method or component recommendation, supporting the goal of achieving the best performance in a given context. We demonstrate that non-domain-expert users are able to deploy the framework within minutes and integrate unknown homebrew systems into it in less than an hour. Our framework is fully open source and available on GitHub1 along with Docker containers and tutorials2 (incl. Jupyter Notebooks).
Re-Construction Impact on Metadata Representation Models

Reification in knowledge graphs has been present since the inception of RDF to allow capturing additional information in triples, usually metadata. The need of adopting or changing a metadata representation in a pre-existing graph to enhance the knowledge capture and access can lead to inducing complex structural changes in the graph, according the target representation’s schema. In these situations, it is necessary to decide whether to construct the knowledge graph again from its original sources, or to re-construct it using the current version of the graph. In this paper we conduct an empirical study to analyze which re-construction approach is more suitable for switching the representation approach from the created graph ensuring that the additional represented knowledge is preserved. We study four well-known metadata representations, using mapping languages to construct the graph, and SPARQL CONSTRUCT queries to re-construct it. With this work we aim to provide insights about the impact of re-construction on metadata representations interoperability and the implications of different approaches.
Code-Mixing Strategies for Computer Mediated Communication with Non-Native Speakers

Code-mixing, the practice of seamlessly blending multiple languages or language varieties in communication, is a common linguistic phenomenon among bilingual and multilingual individuals. However, applying code-mixing when communicating with monolingual speakers presents a challenge. This research introduces and discusses two strategies for the automated translation of Japanese sentences into code-mixed Japanese. The primary objective is to facilitate comprehension for non-native Japanese speakers who possess some level of proficiency in the language. The study is motivated by the broader goal of enhancing cross-cultural communication within a multilingual context. By enabling non-native speakers to understand messages conveyed by Japanese individuals, the research seeks to promote more inclusive and accessible interactions in intercultural chat systems. These strategies, when successfully implemented, can bridge language gaps, foster cultural exchange, and enrich cross-cultural communication.
Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond

Conversational systems are designed to offer human users social support or functional services through natural language interactions. Typical conversation researches mainly focus on the response-ability of the system, such as dialogue context understanding and response generation. In the era of large language models (LLMs), LLM-augmented conversational systems showcase exceptional capabilities of responding to user queries for different language tasks. However, as LLMs are trained to follow users’ instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In this tutorial, we will introduce the recent advances on the design of agent’s awareness of goals in a wide range of conversational systems, including proactive, non-collaborative, and multi-goal conversational systems. In addition, we will discuss the main open challenges in developing agent’s goal awareness in LLM-augmented conversational systems and several potential research directions for future studies.
Functional suitability assessment of smart contracts: A survey and first proposal

Abstract Blockchain is a cross‐cutting technology allowing interactions among untrusted entities in a distributed manner without the need for involving a trusted third party. Smart contracts (i.e., programs running on the blockchain) enabled organizations to envision and implement solutions to real‐world problems in less cost and time. Given the immutability of blockchain and the lack of best practices for properly designing and developing smart contracts, it is crucial to assure smart contract quality before deployment. With the help of an exploratory survey involving developers and researchers, this paper identifies the practices and tools used to develop, implement, and evaluate smart contracts. The survey received 55 valid responses. Such responses indicate that (i) inefficiencies may occur during the development cycle of a smart contract, especially regarding requirements specification, design, and testing phases, and (ii) the lack of a shared standard to evaluate the functional quality of implemented smart contracts. To start coping with these issues, the adoption of functional suitability assessment measures recommended by the ISO/IEC 25000 standard, widely used in software engineering, is proposed by adapting them to the context of smart contracts. Through some examples, the manuscript also illustrates how to measure the functional completeness and correctness of smart contracts. The proposed procedure to measure smart contract functional suitability brings advantages to both developers and users of decentralized finance or non‐fungible tokens platforms, data marketplaces, or shipping and real estate services, just to mention a few. In particular, it helps (i) better outline the responsibilities of smart contracts, (ii) uncover errors and deficiencies of smart contracts in the early stages, and (iii) ensure that the established requirements are met.
PENGEMBANGAN MEDIA FINGER MATH METHOD MENJADI PINBALL GAME UNTUK MELATIH KETERAMPILAN BERHITUNG PERKALIAN SISWA SEKOLAH DASAR

Each student has a different level of understanding, the teacher does not use enough teaching aids, which makes learning very everyday and monotonous or uninteresting. And the teacher does not use counting methods to help students understand, so students have a heavy memory load, such as remembering. multiplication. So the researchers developed the Jarimatika method into a pinball game to help determine the multiplication questions from 6 to 15 and design the visualization to make it more interesting. This research uses the Research and Development (RnD) methodology and the Analysis Design Development Implementation and Evaluation research model or usually shortened to the ADDIE method. From the results the percentage of data can be increased to 96.67%. According to Arikunto, 2009: 35 in the media eligibility category it is stated that it is very feasible to be a 6 to 15 multiplication pin ball game media. So that research on media development into a Pinball Game media is interpreted as valid. The ease of application helps students to improve the quality of counting. The conclusions are as follows The following: The development of Jarimatika Media in Mathematics subjects, especially in multiplication of 6 to 15 can be categorized as very suitable for use in learning at the Integrated Islamic Elementary School Insan Kamil Cepogo, according to the assessment of the pretest and posttest as well as questionnaires for class 4 user participants,
Content Analysis of an ESP Textbook; English for Vocational High School

This research aimed at describing the result of English textbook analysis. The textbook is used as a medium for teaching English for Third-Grade students of Vocational Highschool in the second. This analysis verifies the appropriateness of the material that is contained in the book and used as a medium of teaching. The development of "Kurikulum Tingkat Satuan Pendidikan (KTSP)” in the national standards of education consists of content standard (Standar Isi), process, standard of grades competence (Standar Kompetensi Lulusan), teachers standard, facilities, management, funds, and the educational evaluation. The two of education national standards, Content Standard (Standar Isi) and Standard of Grades Competence (Standar Kompetensi Lulusan) are used as the main reference in developing the curriculum (KTSP). Those two standards are implemented to achieve the goals of the learning.
TEACHING STRATEGIES USED BY ENGLISH TEACHERDURING PANDEMIC SITUATION AT EIGHTHGRADE OF SMP NEGERI 1 LAHUSA

Coronavirus disease (Covid-19) Pandemic has spread in many countries included Indonesia. As consequence that it was not only activities in the economic sector are limited, world of education was also restricted because of the Covid-19, so the teaching and learning activities cannot be carried out as usual. This was challenge for all of the teachers. There was changed in the teaching and learning situation, teaching and learning system, and the way of convey the material. Therefore, the objectives of this research were to describe teaching strategies used by English teacher during pandemic situation at Eighth Grade of SMP Negeri 1 Lahusa. This research was designed by applying qualitative descriptive. The instruments of data collection were observation and interview. After analyzing the data, researcher found the result. The teaching strategies used by English teacher during pandemic situation, namely: (1) teachers adjusted learning objectives to basic competencies and the students level, (2) teachers selected teaching and learning approach by having good communication and gave motivation for the students, (3) teachers adjusted teaching and learning method to the duration of time and the availability of text books, (4) teachers adjusted norm and minimal success or criteria by finding how many students are interested in the lesson and how many students are capable of learning.
Pengunaan Formula Haversine pada Sistem Informasi Pencarian Guru Mengaji

The search for private reciting teachers is mainly carried out through contacts with colleagues of the same profession, recommendations from previous reciting teacher service users so that broadcasts through social media problems that then arise through searches like this, including searching for prospective private reciting teachers who take a long time, find it challenging to find a match The location and time between the prospective Koran Kaji and the students, cannot know the competence, achievements and track-records of the future Koran teachers in private. This research is qualitative research where the strategy used is Design and Creation—the method used for data collection using the form of observation, literature study, questionnaires and interviews. This research aims to design a system that can make it easier for students to find Koran teachers. In contrast, the design of this system uses the Java and MySQL programming languages for databases and the programming method using the Haversine Formula method. This research produces an Android-based private apprenticeship teacher information system application for the conclusions drawn from this study. To design a system for ordering private Koran teachers to make it easier for the community, especially in Makassar.
Sistem Informasi Pendistribusian Hasil Ternak Dan Pengolahan Data Pakan Ternak Berbasis Web

The development of technology is speedy and accurate, making us live side by side with information and technology itself. Many people have abandoned searching for information manually and switched to using computerized systems so that they can access information more quickly. The animal market is one of the regional assets in improving the community's economy. This animal market also distributes cattle. This distribution requires a fast, precise and accurate time. Animal markets also carry out the processing of animal feed which is used as its animal feed and for sale. In processing data on animal feed ingredients, there are also obstacles; for example, from the production side, errors often occur in recording data for feed ingredients and the amount of feed production and its processed products. This study aims to design and build an information system to distribute livestock products and data processing for animal feed in the animal market.
Enhancing Cybersecurity Resilience: A Comprehensive Analysis of Human Factors and Security Practices Aligned with the NIST Cybersecurity Framework

Although effective technical countermeasures play a pivotal role in safeguarding organizations' digital assets, the persistent challenge of human factors in cybersecurity cannot be underestimated. This study aims to identify the human factors employed within the cybersecurity research community and the relevant human-centric security practices. These human factors and security practices are subsequently mapped to the functions, categories, and sub-categories of the NIST Cybersecurity Framework (NIST-CSF). The methodology for this research comprises a literature review and qualitative mapping techniques. The findings show the identification of 20 distinct human factors and 12 security practices. Additionally, the mapping reveals that 3 of the NIST-CSF functions, 8 categories, and 19 sub-categories are directly related with human aspects of cybersecurity. By aligning human factors and security practices with established NIST-CSF guidelines, organizations can strengthen their overall security posture. Moreover, it helps identify gaps in cybersecurity related to human factors to address vulnerabilities and mitigate risks associated with human errors, reducing the likelihood of security incidents and data breaches. Ultimately, this study provides valuable insights, presents conclusions, and suggests directions for future work.
Khmer Calligraphy Style Transfer Using SkelGAN

Font style transfer is a challenging task in computer vision, aimed at extracting the visual characteristics such as stroke contrast and apply them to the content image. In this article, we focus on utilizing SkelGAN, a modified version of the U-Net architecture, to transfer font styles from English to Khmer characters. For our experiment, we collected a dataset of 15 calligraphy fonts containing both Khmer and English characters. To assess the performance of the generated images, we used the Structural Similarity Index Measure (SSIM). We compare the generated images with the ground truth images, providing a quantitative assessment of their similarity. Our experimental results indicate that SkelGAN successfully performs style transfer from English calligraphy to Khmer calligraphy, achieving an average SSIM score of 0.93. This score signifies a significant resemblance between the generated and target styles.
Adaptive Background Music According to the Player’s Arousal for DareFightingICE

This paper presents a system that adapts the background music of a fighting game according to the player's arousal. The system uses a model to estimate the player's arousal based on events in the game and accordingly changes the pitch, tempo, and volume of the music. The system does not use AI music generation or arrangement but modifies existing music in real time. We experiment to evaluate the impact of different arousal durations on the player's immersion and music preference. The results show that a duration of 10 seconds is the most suitable for fighting games, as it increases the immersion and matches the music with the game situation.
Adaptive Decentralized Federated Gossip Learning for Resource-Constrained IoT Devices

With the increasing number of internet of things devices and the ability to enhance data privacy using federated learning (FL), there is a growing interest in exploring FL on resource-constrained devices. Centralized FL uses a central server (CS), which can lead to privacy issues, poor scalability, high communication costs. To address these challenges, decentralized federated learning (DFL) has been proposed, which allows FL without a CS. To keep the communication cost low in DFL the gossip protocol can be used, where random clients exchange their models. However, this may result in slower convergence and lower accuracy. To overcome these limitations, we present AdaStair, a static method, and AdaLoss, a dynamic method that adaptively adjusts the learning rate of each client in a decentralized manner. Additionally, we modify the communication protocol of GossipFL and add quantization to further reduce the communication cost. We demonstrate the robustness of these methods in both simulations and real-world evaluations using microcontroller units in a partial WiFi ad-hoc mesh network. Our experimental results show that AdaStair and AdaLoss can improve accuracy up to 19% and reduce communication costs by up to 57% using a quantization scheme compared to a segmented gossip method.
Predicting Blood Drop Height and Volume using Physics Equations, VGG-19, and XGBoost

Blood Pattern Analysis is a technique in forensic science that focuses on leftover bloodstains from the crime to recreate the event. However, the fluctuation in air resistance and drop deformity causes the calculations to deviate from the exact values. Therefore, machine learning models were constructed to overcome this limitation of calculations. A series of experiments was conducted by dropping porcine blood on paper across nine distinct heights: 20, 40, 60, 80, 100, 120, 140, 160, and 180 cm with four different drop volumes: 13, 16, 25, and 30 μL resulting in 36 classes. A simple simulation of a free-fall spherical object was also created to convert any drop height into impact velocity. Regarding both the empirical data and simulation, the correlation between the spreading factor and modified Reynold number, along with the number of spines and modified Weber number, were expressed as equations that can be used to determine drop height and drop volume. Concurrently, the same dataset used in physics calculations was used to train machine learning models that implement VGG-19 and XGBoost. For VGG-19, the inputs are images of bloodstains, while for XGBoost, the inputs are stain area, stain perimeter, and the number of spines. As a result, the accuracy for physics equations VGG-19 and XGBoost were 0.26, 0.56, and 0.49, respectively.
Framework for Choosing a Supervised Machine Learning Method for Classification Based on Object Categories : Classifying Subjectivity of Online Comments by Product Categories

The core objective of this research is to develop a methodology for selecting a supervised machine learning classification technique based on the specific categories of objects that need to be classified. The study focuses on product categories extracted from Amazon's Product Reviews database, which are utilized to evaluate the subjectivity of post-purchase feedback. The primary supervised machine learning methods are utilized to efficiently perform the classification task. The resulting insights will enable the prioritization and choice of the best approach based on the selected categories. In the context of accelerated technological adoption due to the COVID-19 pandemic, this research contributes by showcasing how AI/ML can play a pivotal role in enhancing decision-making processes across various sectors and highlighting the significance of adapting to emerging technologies for sustainable growth.
Minecraft Video Aesthetics Quality Assessment Model

We propose the Minecraft Video Aesthetic Quality Assessment Model (MCVQA), a tool designed to assess the aesthetic quality of Minecraft videos (MC videos) comprehensively. We explain the meticulous processes, including creating purpose-built datasets, implementing sophisticated feature extraction techniques, and fine-tuning model hyperparameters. We validate MCVQA's efficacy through a two-step approach: accuracy validation and an information recommendation system. In conclusion, first, using the datasets created in this study, an impressive 63.6% alignment was observed between the "best" shot types assigned by the user and the model's predictions. Subsequently, the usefulness of MCVQA as a recommendation system was validated, and a remarkable accuracy of 58.3% was confirmed.