Reviewerly/exHarmony huggingface dataset:
Multi-Modal Graph-Based Recommendation System: Integrating Heterogeneous Modalities for Enhanced Predictions

In the realm of modern digital applications, recommender systems have emerged as vital tools, assisting users in navigating a plethora of choices. This paper addresses the challenge of enhancing recommendation accuracy by harnessing the capabilities of Graph Neural Networks (GNNs) and multi-modal data integration. GNNs are identified as effective tools for enriching node feature representations by propagating local information. However, their efficacy wanes when dealing with data-scarce nodes. In response, the study introduces a novel approach: a multimodal graph-based recommendation system. This approach synergizes GNNs with diverse modal features, the approach excels in predicting connections within heterogeneous graphs and assessing similarity across nodes. Through the fusion of multiple modalities, including text and images, via advanced feature engineering techniques, the accuracy of node embeddings is enhanced, leading to substantial improvements in recom-mendation precision. The evaluation, conducted on the Neo4j MovieLens dataset, attests to the system's efficacy in harnessing varied data sources. The concept of a unified node representation further bolsters the system's proficiency in capturing latent semantic relationships between nodes. This integration is instru-mental in enabling content-based recommendations, showcasing the model's competence in encapsulating user-item interactions and rendering accurate movie rating predictions. Ultimately, this research sheds light on the synergy between graph-based machine learning and recommendation systems, accentuating the pivotal role of data modalities in enhancing accuracy and personalization. The proposed approach holds the potential for fostering robust recommendation systems across diverse domains.
Application of Power Flow Problem to an Open Quantum Neural Hardware

Significant progress in the construction of physical hardware for quantum computers has necessitated the development of new algorithms or protocols for the application of real-world problems on quantum computers. One of these problems is the power flow problem, which helps us understand the generation, distribution, and consumption of electricity in a system. In this study, the solution of a balanced 4-bus power system supported by the Newton-Raphson method is investigated using a newly developed dissipative quantum neural network hardware. This study presents the findings on how the proposed quantum network can be applied to the relevant problem and how the solution performance varies depending on the network parameters.
Bringing a Continuous Integration and Delivery Pipeline into MoVES

The increasing complexity of modern automotive software systems has prompted the development and adoption of various model-based methodologies. One such methodology is MoVes, which provides automated support for identifying design solutions that meet timing requirements, thus enhancing the cost-efficiency of automotive software development. To further enhance the cost-efficiency of the automotive software development process, we propose the integration of a continuous integration and delivery pipeline into MoVes. In addition to reduce manual tasks, this extension offers several improvements over traditional continuous integration and delivery pipelines with respect to the performance, parallel execution and distribution of the pipeline. We begin by discussing the overarching design of the pipeline, followed by its practical implementation using GitHub Actions. Subsequently, we validate the effectiveness of our pipeline through a use case involving brake-by-wire and wiper motor functionalities in automotive systems. Finally, we describe the research challenges encountered during this process and provide an outline of future research aimed at further enhancing the pipeline.
Hipótesis interactivas: hacia un fundamento dialógico del razonamiento sustitutivo

The aim of this paper is to propose a logical justification for the process of surrogate reasoning in modeling practice in science. To this end, we understand hypothesis generation as the creation of an interactive, formal dialogue between the model and the target system. In order to describe this idea from a logical point of view, we will rely on the pragmatic approach of Dialogic as the ideal framework to illustrate logical interactions.
Music Retrieval Algorithm Oriented to Support Vector Machine and Cloud Computing

In order to solve the existing problems in the existing music retrieval system, this article introduces a music retrieval algorithm oriented to support vector machine and cloud computing, expresses the feature vector as the vector form of cloud computing, and performs data analysis and processing in cloud computing. In order to improve the accuracy of the retrieval system, the article adopts a new music retrieval method, which firstly performs feature extraction, then performs text retrieval, and finally uses support vector machines and cloud computing to analyze the data. The experimental results show that the algorithm can effectively improve the retrieval accuracy of the music retrieval system, and its highest value is 95.6%. In the future, this method will become popular, and the majority of users will no longer have to worry about finding songs they have heard.
Revolutionizing Emergency Response: The Transformative Power of Smart Wearables Through Blockchain, Federated Learning, and Beyond 5G/6G Services

The importance of reliable and secure wireless communication cannot be overstated, especially in emergency situations that require the use of smart wearables. To ensure the provision of robust and secure communication services for emergency applications, we propose a decentralized and real-time approach to accessing data from wearable technology. This approach involves integrating blockchain technology and federated-learning-enabled beyond 5G/6G (B5G/6G) network architecture. We also discuss the potential benefits of using massively populated and real-time-streaming, data-intensive, federated-learning-enabled B5G/6G systems to provide emergency services with real-time access to relevant information. By integrating blockchain and responsible AI frameworks, we aim to ensure the security and trustworthiness of the communication channels. We believe that our proposed solution will address sovereignty concerns and have the potential to transform the way emergency responses are handled, resulting in faster and more efficient responses that ultimately save lives.
IT Empowering Rescuers and First Responders in Saving Lives

There’s no better application of digital technology than technology designed to help first responders (FRs). Some of theapplication areas include data, architectures, wearables, communications and situation awareness. The articles in this Special Issue represent a package that describes how IT empowers rescuers and FRs in saving lives. They understand FR requirements and propose technology solutions that are more than workable, and in many cases, ready for field testing.
Semantic Closure and Classicality

The semantic paradoxes show that semantic theories that internalize their semantic concepts, such as truth and validity, cannot validate all classical logic. That is, it is necessary to weaken some connective of the object language, taken as the guilty of the paradoxes, or give up some property of the consequence relation of the logical theory. Both strategies may distance us from classical logic, the logic commonly used in our current mathematical theories. So, a desirable solution to semantic paradoxes cannot distance us from classical logic. This paper analyzes two interesting proposals that aim to maintain classical logic as most as possible. The first strategy, the Barrio &amp; Pailos &amp; Szmuc-approach (2017) (BPS-approach), proposes the paraconsistent logic MSC that contains in its object language a connective capable of recovering classical inference whenever the sentences at issue are consistent. So, they show that it is possible to build a semantic theory over this logic that is immune to the semantic paradoxes. The second approach is based on the hierarchy STω of non-transitive systems STn, proposed by Pailos (2020a). This hierarchy recovers classical metainferences as many as possible in higher levels of the hierarchy. We argue in favor of the second approach by arguing that the first strategy must adopt weak self-referential procedures to avoid the paradoxes.
RCS Information Aided PMBM Filter

In clutter environment, multi-target tracking (MTT) often faces challenges such as false tracks, lose tracks and cardinality overestimation. This paper incorporates target's radar cross section (RCS) information into the Poisson multi-Bernoulli mixture (PMBM) filter to distinguish the target from false alarms better. The algorithm presents the Bayesian RCS estimation strategy and proposes an RCS aided PMBM (RCSI-PMBM) filter. Then an efficient implementation is developed based on the Gamma Gaussian mixture (GGM) form. Simulations reveal that the proposed RCSI-PMBM filter is superior to existing filters in terms of cardinality estimation and MTT performance evaluation methods.
The Perception of UTAR Ed &amp; El Undergraduates on the Impacts of Facebook in Enhancing their ESL Writing Skills

The advancement of information, communications, and technology (ICT) has made social media an indispensable tool for language learning, especially second language learning. This study aims to analyze the perception of undergraduates on the positive impacts of social media particularly Facebook by utilizing Facebook in improving ESL writing skills. Using TAM as the theoretical lens, this quantitative-method research involved questionnaire survey. The sample consisted of 50 undergraduates. The questionnaires were distributed in a Google form to the 30 English Education undergraduates. By doing this, the researcher was able to gather and analyse their perception on the impacts of Facebook in enhancing ESL writing skills. Results of the analysis showed that the perception of participants towards the usage of Facebook that significantly influenced their ESL writing skills. The positive impacts of Facebook concerning ESL writing skills were also depicted via the statistical analysis. The findings provide an insight to stakeholders in suggesting ways to encourage the use of Facebook in language learning specifically writing skills. Similar research can be done in the future by involving a larger number of participants from different private higher education institutions.
Improving the Performance of the General Course Scheduling System at UPN Veteran Jawa Timur through the Application of the IWCFPSO Algorithm

Algorithm selection is the main key in producing a system, especially an artificial intelligence-based system, for example, a course scheduling system that involves many constraints in producing an optimal schedule. This research develops a scheduling system using the ICWFPSO algorithm which is a development from previous research, namely the development of a scheduling system using the MIPSO algorithm. The use of inertia weight in the MIPSO algorithm which is used as a scheduling algorithm in previous research can be optimized by using a combination of inertia weight and constriction factor. Scheduling system development is carried out using the waterfall method with research steps namely problem and needs analysis, data collection and literature study, system design, system implementation, and ends with testing the performance of the algorithms that are applied to the scheduling system that is built. Based on the tests carried out, it can be concluded that the ICWFPSO algorithm provides 2 times better performance compared to the MIPSO algorithm in terms of optimal schedule generation time. Meanwhile, tests carried out on CPU and RAM usage showed that there was no significant impact on these two parameters through the implementation of MIPSO and ICWFPSO in the course scheduling system created.&#x0D; &#x0D; Keywords: best scheduling, ICWFPSO, number of iterations
Analysis of Nuri course teacher's guide: Focusing on physical activity and nutrition education

This study aimed to examine the content categories related to physical activity and nutrition education by area and analyze how much related activities are included in the early Nuri course at the national level related to physical health of young children, focusing on the Nuri course commentary by age and the teacher's guide. In order to analyze the contents and activities related to physical health in the Nuri course, the subjects of the study were the Nuri course commentary and the Nuri course teacher's guide distributed by the Ministry of Education, Science and Technology and the Ministry of Health and Welfare in 2013, and the frequency was calculated. As a result of the study, content related to physical activity was included in the physical exercise/health area and the art experience area, and the content categories directly related to physical activity and exercise were ‘body recognition’, ‘body control and basic exercise’, and ‘body control’. ‘Participating in activities’ and ‘Expressing through movement and dance’ among ‘Artistic expression’ in the field of art experience. In addition, the contents of nutrition education were covered in the physical exercise and health area, and it was related to ‘healthy living’ content category. Second, there were a total of 101 activities dealing with physical activity in the 5-year-old Nuri course, 147 activities in the 4-year-old and 120 activities in the 3-year-old, and the most physical activity-related activities were covered in the 4-year-old course. As for nutrition education activities, 24 activities were presented for 3 years old, 29 for 4 years old, and 22 activities for 5 years old. This study is significant in that it provided basic data on what parts are lacking and need to be improved to improve the physical health of young children when revising the national level curriculum for young children.
The main content, prospects and legislative implications of the EU AI Libility Directive

This paper was written for the purpose of examining the background and main contents of the proposal of the EU artificial intelligence liability directive and confirming the future prospects and their legislative implications. Academics, consumer organizations, AI-related businesses, and religious organizations are expected to continue to express various opinions on ethical debates about artificial intelligence, the scope of legal regulations, and remedies for infringement of legal interests. Legislative efforts need to be strengthened to reconcile the conflicting interests of promoting the AI industry and protecting consumers. In addition, the improvement of laws and regulations that can respond appropriately to the pace of development of AI technology and industry will be an important task. In addition, given that the size of the AI-related industry in the global market is expanding significantly and competition between countries is intensifying, it is necessary to enact domestic laws that can harmonize with international norms and AI-related laws in the EU and the United States. The principles of civil liability stipulated in the European Artificial Intelligence Liability Directive, and legal measures such as disclosure of evidence or presumption of causality to protect consumers from damage caused by high-risk AI systems, are expected to be important legislative data for enacting the domestic civil liability law related to artificial intelligence.
How can Meaning be Quantified?: For the Development of a Language Model Reflecting the Linearity and Structure of Language.

The purpose of this paper is to introduce various attempts to quantify meaning, review their significance, and then find ways to supplement methods to quantify meaning. Until now, meaning has been quantified based on the word frequency in the document and the word frequency of search words and their collocates. The former is a method that does not consider the order of appearance of words, and the latter considers only adjacent words. To elaborate the quantification of meaning, efforts are needed to reduce the importance of relationships between adjacent but syntactically unrelated words and to pay attention to relationships between distant but syntactically related words. If meaning is quantified in this way, vectorization of words or sentences can become more sophisticated, and based on this, more efficient language models can be developed.
Online Undercover Investigations and the Role of Private Third Parties

This chapter compares the use of covert online investigative methods by state agencies, with those undertaken by individuals and institutions in civil society. The focus is primarily on intrusive investigations of online child exploitation. In particular, it deals with two types of investigative activity-- a) an investigator’s online deceptive impersonation of a child or of a facilitator of child exploitation; and b) techniques of accessing and compromising information systems used for the purpose of child exploitation. While these investigative methods may have a legitimate place in contemporary crime control, they do pose problems: First, they pose a significant risk of abuse by state agencies, and remedies available to the targets of illegal or otherwise questionable state practices may be limited. Next, the discussion of non-state investigators suggests that the targets of private investigation have even less protection than the targets of state agencies. The chapter concludes by articulating some standards by which the propriety of state and non-state covert online investigative activity may be evaluated.
Explicitando quantitativamente ações de avaliação por pares aberta na política editorial da revista, considerando preprints

Open Peer Review (OPER) is one of the Open Science movements whose widespread adoption has faced great difficulties, both practical and conceptual in nature. In this work, we briefly discuss OPER, highlight its advantages, present the more than 100 possibilities for its adoption, and offer a quantitative, freely accessible tool, composed of hierarchically organized scored items, which allows editors to identify whether and how much OPER is present in the editorial policy of the journal they publish. Furthermore, the tool serves to guide a gradual opening of the process and could be useful for managers, indexers, and journal evaluation committees to establish progressive goals for the adoption of OPER among their criteria.
Identifying Barriers Hindering the Application of Blockchain in the Energy Sector: Pestle and SWOT Analyses

The global energy market is in a period of radical change. De­centralized energy production, energy storage and consumer-producers of electricity constitute the future shape of the electricity system. Distributed Ledger Technology, known as blockchain, is an emerging technology that can play an important role in green energy transition. Although blockchain can be applied to a wide range of applications in the energy sector, there is a lack of sound understanding. This paper aims to provide a market anal­ysis with the use of PESTLE and SWOT, resulting in a list of barriers that af­fect the successful application of blockchain technology in the energy sec­tor. The findings could assist related stakeholders, such as energy market ac­tors and policymakers to acquire a clearer view of blockchain use in the en­ergy sector and draw strategies that will overcome the barriers leading to the establishment of this technology in the energy sector.
Virtual Histology Staining of Skin Tissue using Ex Vivo Confocal Microscopy and Deep Learning

Background: The use of Hematoxylin-and-Eosin (H&E) staining is widely accepted as the most reliable method for diagnosing pathological tissues. However, the conventional H&E staining process for tissue sections is time-consuming and requires significant labor. In contrast, Confocal Microscopy (CM) enables quick and high-resolution imaging with minimal tissue preparation by fluorescence detection. However, it seems harder to interpret images from CM than H&E-stained images. Objective: This study aimed to modify an unsupervised deep-learning model to generate H&E-like images from CM images. Material and Methods: This analytical study evaluated the efficacy of CM and virtual H&E staining for skin tumor sections related to Basal Cell Carcinoma (BCC). The acridine orange staining, combined with virtual staining techniques, was used to simulate H&E dyes; accordingly, an unsupervised CycleGAN framework, trained to virtually stain CM images was implemented. The training process incorporated adversarial and cycle consistency losses to ensure a precise mapping between CM and H&E images without compromising image content. The quality of the generated images was assessed by comparing them to the original images. Results: The CM images, specifically focusing on subtyping BCC and evaluating skin tissue characteristics, were qualitatively assessed. The H&E-like images generated from CM using the CycleGAN model exhibited both qualitative and quantitative similarities to real H&E images. Conclusion: The integration of CM with deep learning-based virtual staining provides advantages for diagnostic applications by streamlining laboratory staining procedures.
Optimizing AWS Lambda Performance: Unveiling the Relationship between Memory Allocation, CPU Power and Cost Efficiency

This technical paper examines, emphasizing AWS Lambda, the crucial relationship between memory use, cost-effectiveness and performance in server less applications. We highlight the significance of memory allocation, look into factors that affect consumption, and offer methods for locating and resolving memory-related problems. An essential part of our research is the relationship between CPU power assignment and Lambda execution time. We illustrate the significant influence of CPU power on execution time and operating expenses using a Lambda function from the real world. We present a sample Lambda function and examine its performance with different memory setups. The AWS Lambda Power Tuning framework results, intended to identify the most economical memory configuration automatically are compared against manual memory determination in the article. Empirical evidence evaluates the framework's success in maximizing performance and cost. The report concludes by synthesizing significant findings and providing practitioners with practical suggestions. This study adds significant knowledge to server less computing by offering real-world examples, empirical support, and contrasts between automated and human memory tuning. By addressing the direct relation of memory allocation to cost-effectiveness and its implications on server less application performance, the work advances our understanding of the complex balance necessary for optimal resource management.