Reviewerly/exHarmony huggingface dataset:
Decision-Making Decisions: Artificial Intelligence In Computer Games

Artificial intelligence (AI) is widely used in various applications, including computer games, to create believable and immersive experiences. This paper examines three types of AI used in computer games: Goal-Oriented Action Planning, Finite-State Machines, and Behaviour Trees. The workings, strengths, and weaknesses of each type are explained, and a comparison is made by analysing their implementation in three different games: Hitman, Skyrim, and DEFCON. The findings reveal that Goal-Oriented Action Planning is a cost-effective approach for creating believable AI in Hitman, while Finite-State Machines are suitable for the open-world setting of Skyrim, and Behaviour Trees aid in developing diverse behaviours in DEFCON.
Analysing Selected South African e-Government Failures through the Theory of Unintended Consequences

The implementation of e-government Information Systems (ISs) is important for digital transformation, facilitation of government policies and improving service delivery to citizens. Like all other technological innovations, implementation of e-governemnt IS requires the evaluation of their ethical implications. This study was aimed at analysing three selected cases from the reports of the Auditor General of South Africa (AGSA) to determine the extent to which these implemented ISs’ failures led to unintended consequences from a software engineering ethics perspective. Using Merton’s theory of unintended consequences, the study revealed that whilst the implementation of these ISs was a purpose action, ignorance, error and blindness of software practitioners and decision-makers to a hasty implementation of IS are sources of unintended ethical consequences of system failures. Such failures have led to unintended consequences such as unscrupulous people benefiting from the funds, legitimate beneficiaries losing out on benefits and the government losing lots of money. The adoption of an ethics-aware framework for ethical risk identification and impact assessment can significantly improve the prevention of unticipatable and unwanted consequences of government ISs implementation.
Modeling and Classification of EV Charging Profiles Utilizing Topological Data Analysis

The increasing amount of electric vehicles (EVs) has brought environmental benefits yet challenges to the power grid distribution system. This paper proposes a novel modeling and classification for a distribution system in the local region based on a single-house total energy demand pattern to classify houses with or without EVs. To conduct the classification, multi-dimension time series data is constructed to better represent user patterns based on the single house demand profile based on the first difference and simple moving average (SMA). To preserve all features of the high dimension dataset, the Topolog-ical Data Analysis (TDA) is adopted. The persistence diagram (PD), which represents the characteristics of a single house's demand, is constructed by constructing topology space for each house's multi-dimension demand profile. The persistence image is adopted for the final classification task based on obtained PD. The classification model is obtained by applying logistic regression on the persistence images of houses with and without EVs. Numerical results are presented using the pecan street data.
MSE Analysis of 24-Bit Fixed-Point Format in a Wide Variance Range

In this paper, we consider a fixed-point format which is one of the commonly used binary format for data representation. In particular, we focus on the 24-bit fixed-point format (FXP24) and the analysis provided here can be observed as our further research in this area. Using the relationship that exists between the structure of the fixed-point format and uniform scalar quantization, we actually use SQNR (signal-to-quantization noise ratio) to measure the performance of mentioned binary format. Assuming data modelled with a Laplacian distribution, we theoretically investigate the performance of the FXP24 format across the wide dynamic range of variances under the constraint that the number of bits in the integer part <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{n}$</tex> changes. We have shown that with an adequate selection of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{n}$</tex> , it is possible to find the range of variances in which FXP24 provides better SQNR (that is, a higher accuracy) than its floating-point counterpart FP24.
Decentralized Identifiers for IoT Systems: Current Status and Future Vision

Traditional blockchain technology poses challenges to implementing practical and sustainable decentralized identity solutions, such as scalability issues, high transaction fees, and energy consumption. However, Decentralized Identifiers (DIDs) with IOTA’s Tangle technology offer a promising solution to these challenges. This paper explores IOTA’s recent version Chrysalis, which provides a more energy-efficient and scalable infrastructure for implementing DIDs, particularly for heterogeneous IoT devices. The system enables these devices to create tamper-proof digital identities that are easily verified and used in various IoT applications. Additionally, the system is highly secure and cost-effective, with low transaction fees and minimal resource requirements. We discuss the current status of the work and provide a future vision for the IOTA-based DID system. This technology offers a more secure, efficient, and sustainable approach to decentralized identity management than traditional systems. The potential of IOTA’s DID system for IoT devices is significant, and we anticipate that it will lead to innovative use cases and solutions in the future.
Pengembangan Media E-Book Cerita Anak pada Pembelajaran Menulis Siswa Kelas V Sekolah Dasar

Learning media plays a crucial role in the learning process, serving as a source of learning that helps teachers enrich students' knowledge. The objective of this research is to develop a suitable E-Book media for children's stories that can be used effectively in teaching and to describe students' responses to the media. This study adopts the Research and Development method, employing the ADDIE model. The developed media has been evaluated by subject matter experts and media experts, who deemed it "appropriate" for instructional use. This indicates that the E-Book media for children's stories meets the criteria of high quality for implementation in fifth-grade classrooms.&#x0D; Keywords: Learning media, Children`s stories, Indonesian.
Market Adoption of Cybersecurity: A Dynamic Analysis

This paper presents a continuous-time dynamic model of market adoption of cybersecurity. Individuals choose whether and when to make a precautionary investment in self-protection against the evolving security risk of direct attack and indirect contagion. The equilibrium adoption path has a ``tipping point'': individual users will invest to get protected all at once when a critical mass of the infected has been reached. Perhaps surprisingly, in the presence of externalities, the market equilibrium path is found to be exactly the socially optimal path. This welfare theorem is robust under several extensions of the model, including heterogeneous valuation, software piracy, weak targets, security upgrade, and ex-post recovery. The findings cast doubt on the government interventions that are justified by externalities in the market for information security.
Towards Smart Open Education Ecosystems through Generative AI, Blockchain, DAO, MMLA and NFT

The aim of this research was to explore how DAOs, blockchain, NFTs, generative AI, and multimodal learning analytics technologies can be integrated to create a smart open education ecosystem representing a global, seamless, personalized, and democratic education system. The integration of these technologies can help to overcome the challenges faced by today's education systems and enhance accessibility, affordability, and quality of education worldwide. Educational technologies have never been closer to overcoming the issues of democracy and accessible education, which are among the biggest handicaps of contemporary education systems. Moreover, the successful integration of these technologies can create unique opportunities for personalized learning. Initiatives exemplifying this timely paradigm shift, such as The Open Campus project, have already begun to emerge. In this study, the potential uses of these technologies for a smart education ecosystem were examined with a scoping review of 30 most related studies. Afterwards, contributions, possible task distributions, and integration of these technologies were explained using authentic examples and real-world scenarios. At the end of the study, 12 key benefits of smart open education ecosystem were explained: accessibility, personalization, educational quality, security and transparency, democratic governance, intellectual property and content value, open collaboration and integration, equality and equal opportunity, reusability of educational resources, preservation and promotion of cultural and linguistic diversity, innovation and experiential learning in education, and support for student success and career planning. This article was produced within the scope of Anadolu University BAP Project No. 2207E125.
A Novel Method for Constructing Spatiotemporal Knowledge Graph for Maritime Ship Activities

This study focused on the construction of a spatiotemporal knowledge graph for ship activities. First, a ship activity ontology model was proposed to describe the entities and relations of ship activities. Then, maritime event text data were utilized as the ship activity dataset, where entities and relations were extracted to form triplets. Thus, the data layer was populated, completing the construction of the ship activity spatiotemporal knowledge graph. The process of extracting triplets involved initially inputting the text sentences into the Bidirectional Encoder Representations from Transformers (BERT) model for pretraining to obtain vector representations of characters. These representations were then fed into a lattice long short-term memory network (Lattice-LSTM) for further processing. The resulting hidden vectors h1,h2,⋯,hn were input into the conditional random field (CRF) to perform named entity recognition. The recognized entities were then labeled in the original sentences and input into another BERT-Lattice-LSTM network. The resulting hidden vectors h′1,h′2,⋯,h′n were fed into a relation classifier, which output the relation between the two labeled entities, completing the extraction of entity–relation triplets. In experiments, the proposed method achieved triplet extraction performance exceeding 90% for three different evaluation metrics: Precision, Recall, and F1-measure.
Towards Building Cybersecurity Culture in TVET Colleges in South Africa

With the adoption of digital technologies, cyber-attacks are on the increase, and it is likely to grow in coming years. In other words, with the increased use of new technologies and systems with more connectivity and less isolation from the outside world, the risk of cyber-attacks is high. This pioneering study addresses the cybersecurity protection of TVET colleges through the development of a cybersecurity culture in the two participating institutions. In that regard, this paper presents the first of the four phases of this study, i.e. the development of the cybersecurity culture conceptual framework, which was one of the objectives of this study. The study found that the main elements of cybersecurity culture include cybersecurity culture dimensions, layers, factors, practices, strategy guidelines, education and training curriculum, forms of delivery, and measuring cybersecurity culture. The implementation phase of this study is planned to be executed in the near future in the two already selected TVET colleges in the KwaZulu Natal province of South Africa. It is perceived that this study will benefit the participating and similar higher learning institutions in the country.
Training neural networks from an ergodic perspective

In this research, we viewed the weights of a neural network as points in a metric space. We proposed that the process of training a neural network can be seen as an iterated function system on this space. Additionally, we found that the most effective training method involves starting with initial weights that are very close to the minimum error. We also provided an ergodic characterization of this efficient training. Our findings suggest that there is potential for further optimization advancements through numerical experimentation and the study of dynamical systems theory.
Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction using a Hybrid Semantic Knowledge Base - Machine Learning Approach

Relation extraction from unstructured Arabic text is especially challenging due to the Arabic language complex morphology and the variation in word semantics and lexical categories. The research documented in this paper presents a hybrid Semantic Knowledge base - Machine Learning (SKML) approach for extracting complex Arabic relations from unstructured Arabic documents; the proposed approach exploits the principles of Functional Discourse Grammar (FDG) to emphasise the semantic and pragmatic properties of the language and facilitate the identification of relation elements. At the initial phase, the novel FDG-SKML relation extraction approach deploys a lexical-based mechanism that utilises a purposely built domain-specific Semantic Knowledge to encode the semantic association between the identified relations’ elements. The evaluation of the initial stage evidenced improved accuracy for extracting most complex Arabic relations. The initial relation extraction mechanism was further extended by integrating its output into a Machine Learning classifier that facilitated extracting especially complex relations with significant disparity in the relation elements’ presence, order, and correlation. Using Economics as the problem domain, experimental evaluation evidenced the high accuracy of our FDG-SKML approach in complex Arabic relation extraction task and demonstrated its further improvement upon integration with machine learning classifiers.
A Gentle Introduction to ReSTIR Path Reuse in Real-Time

In recent years, reservoir-based spatiotemporal importance resampling (ReSTIR) algorithms appeared out of nowhere to take parts of the realtime rendering community by storm, with sample reuse speeding direct lighting from millions of dynamic lights [1], diffuse multi-bounce lighting [2], participating media [3], and even complex global illumination paths [4]. Highly optimized variants (e.g. [5]) can give 100x efficiency improvement over traditional ray- and path-tracing methods; this is key to achieve 30 or 60 Hz framerates. In production engines, tracing even one ray or path per pixel may only be feasible on the highest-end systems, so maximizing image quality per sample is vital.
Brief Introduction to Artificial Intelligence and Machine Learning

Artificial intelligence (AI) has made it possible for machines to perform human tasks. Healthcare is one of the main fields where AI and machine learning have been successfully employed. There have been many articles presenting the various applications of AI and its favorable outcomes in dentistry and maxillofacial surgery. It may be difficult for dental researchers to understand and interpret these studies since they are different in methodology. In addition, they are unfamiliar with the definitions and terminology used in these articles. The purpose of this chapter is to provide an explanation of the terms and concepts frequently used in AI articles and, specifically, in the following two chapters where we discussed its current application in maxillofacial surgery and its future.
E-commerce Platform for Sustainable Thrift Fashion Industry Powered by Blockchain Technology

The fashion industry is a massive industry whose impact on the environment and economy cannot be understated. ‘Fast Fashion’ endeavors to make trendy and good quality garments increasingly available to the masses. Currently, textile production and agricultural production are the top freshwater-polluting industries. It is high time to give serious consideration to the future of the fashion industry going forward and hold the various stakeholders accountable in order to ensure that fashion is sustainable. There is a shift happening in the consumer base as people are becoming aware of their contribution to the environment and society. There is an increase in demand for use in high-cost garments at discounted prices resulting in a boom in thrift fashion culture. Eventually, it supports sustainability in the fashion industry. The study aimed to gain insight into the existing fashion supply chain and measures to make it more sustainable. The user-centric design process has been followed to propose a design solution. In this paper, various solutions like a blockchain-powered tracking and green rating system for brands, and a circular fashion-driven digital platform for educating and allowing consumers to access more sustainable clothing options are proposed. Accessible sustainable fashion doesn’t have to be a choice only for the rich few, but a choice that millions of common buyers can make a part of their daily lives.
Research on Road Sign Detection and Visual Depth Perception Technology for Mobile Robots

To accomplish the task of detecting and avoiding road signs by mobile robots for autonomous running, in this paper, we propose a method of road sign detection and visual depth perception based on improved Yolov5 and improved centroid depth value filtering. First, the Yolov5 model has a large number of parameters, a large computational volume, and a large model size, which is difficult to deploy to the CPU side (industrial control computer) of the robot mobile platform. To solve this problem, the study proposes a lightweight Yolov5-SC3FB model. Compared with the original Yolov5n model, the Yolov5-SC3FB model only loses lower detection accuracy, the parameter volume is reduced to 0.19 M, the computational volume is reduced to 0.5 GFLOPS, and the model size is only 0.72 MB, making it easy to deploy on mobile robot platforms. Secondly, the obtained depth value of the center point of the bounding box is 0 due to the influence of noise. To solve this problem, we proposed an improved filtering method for the depth value of the center point in the study, and the relative error of its depth measurement is only 2%. Finally, the improved Yolov5-SC3FB model is fused with the improved filtering method for acquiring centroid depth values and the fused algorithm is deployed to the mobile robot platform. We verified the effectiveness of this fusion algorithm for the detection and avoidance of road signs of the robot. Thus, it can enable the mobile robot to correctly perceive the environment and achieve autonomous running.
Digital Transformation of the South Asia Region: Challenges and Prospects

The digital economy is on the agenda all over the world today, it represents a new paradigm for the development of cooperation between countries and can become a driver of their economic growth. The implementation of the key digitalization agenda is one of the main priorities for the development of the economies of South Asia. The purpose of the study was to determine the impact of the digital economy, and the introduction of R&amp;D on the development of the economies of South Asian countries. Exploring the development of the transformation of the digital economy in the countries of South Asia, we came to the conclusion that this process can be significantly facilitated by the introduction of blockchain technology and cryptocurrencies. The use of a mathematical correlation and regression model led to the conclusion that the growth of R&amp;D funding in South Asian countries has a positive effect on the development of their economies.
Productivity Analysis of Original Character (OC) Research in Open Access Journal Indexed by Scopus

The research conducted to do an analysis of research productivity related to original character (OC) research based on open access journal indexed in Scopus from 2013 to 2022. The methods used in this research is bibliometric. The results of this research found the number of OC research publications in open access journal from 2013 to 2022 shows a dynamic trend. Publication data with the keyword original character with the highest amount of documents is Plos One with 44 documents. CNRS Centre National de la Recherche Scientifique with 90 documents as an affiliation with the most documents. Subject area in the field of arts and humanities dominates with 722 documents. The most occurrences from the graph shows “species” as the most related item with the original character research trend from 2013 to 2022 in Scopus open access journal database.
Information Technology Skills Extractor for Job Descriptions in vku-ITSkills Dataset Using Natural Language Processing

The IT skills extractor is convenient and efficient for recent job recommendation systems and job seekers to find suitable jobs. In this paper, we design an efficient SpaCy pipeline for extracting IT skills based on Natural Language Processing (NLP) and Named Entity Recognition (NER) methods from the job description. The main proposed method helps to extract potential hard-soft skills and later could provide to job recommender and job seekers. As the state-of-the-art open-source NLP framework, we first construct a new IT skills dictionary based on ChatGPT and perform automatic labeling for scrapped job description dataset, named vku-ITSkills dataset. Using this dataset, the quality of labels could be improved by the Part-of-Speech (POS) function and additional rules. We then fine-tune the pre-trained RoBERTa-base model for Transformer based word embedding in training NER model to extract skills. Thereafter, we define additional logical rules to enhance the extracted results that could further find out more skills based on syntactic such as the comma rule. In this language pipeline, RoBERTa embedding, NER, and additional rules play important roles to cope with unseen and new IT skills that are non-existed in vku-ITSkills dataset and are missed from NER. Throughout the evaluation, we test the proposed pipeline with 200 job descriptions manually labeled by our team and demonstrate the efficiency of each step in the pipeline.