Reviewerly/exHarmony huggingface dataset:
An E-Commerce Personalized Recommendation Algorithm Based on Multiple Social Relationships

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.
An Interval Type-2 Fuzzy Logic Approach for Dynamic Parameter Adaptation in a Whale Optimization Algorithm Applied to Mathematical Functions

In this paper, an improved whale optimization algorithm (WOA) based on the utilization of an interval type-2 fuzzy logic system (IT2FLS) is presented. The main idea is to present a proposal for adjusting the values of the r→1 and r→2 parameters in the WOA using an IT2FLS to achieve excellent results in the execution of the WOA. The original WOA has already proven itself as an algorithm with excellent results; therefore, a wide variety of improvements have been made to it. Herein, the main purpose is to provide a hybridization of the WOA algorithm employing fuzzy logic to find the appropriate values of the r→1 and r→2 parameters that can optimize the mathematical functions used in this study, thereby providing an improvement to the original WOA algorithm. The performance of the fuzzy WOA using IT2FLS (FWOA-IT2FLS) shows good results in the case study of the benchmark function optimization. An important comparative with other metaheuristics is also presented. A statistical test and the comparative with other bio-inspired algorithms, namely, the original WOA with type-1 FLS (FWOA-T1FLS) are analyzed. The performance index used is the average of the minimum errors in each proposed method.
Augmented Reality in A Sustainable Engineering Design Context: Understanding Students’ Collaboration and Negotiation Practices

This study reports on the use of augmented reality (AR) within the context of sustainable engineering design education. The objective of this study is to understand students’ collaboration and negotiation practices in a sustainable engineering design context using AR. The technology used in this study includes MERGE Cube (a physical cube that acts as a digital canvas for AR) and CoSpaces Edu (an online platform for students to build 3D creations and animate them with code). There have been 48 instances of student participation from upper elementary to middle school in four iterations of this study since February 2022. The research was conducted based on a design-based research methodology, and the data was collected through qualitative methods and analyzed using the intra-action analysis method. This study’s outcome revealed that interactions among participant dyads and with the technology shed light on distinct dynamics within collaborative sustainability-oriented learning and design processes. These diverse interactions collectively emphasize the multifaceted nature of collaborative design, where individual experiences, communication styles, and technological proficiency all play integral roles in shaping the collaborative process. In addition, the results of our research showed that the complexities inherent in negotiating ownership and collaboration dynamics are influenced by individual attitudes, timing of involvement, and previous experiences. Given the importance of collaboration in achieving sustainability-oriented goals, these findings are relevant to the field of sustainable engineering education, especially when working with younger children.
Framework for Effective Smart Contracting

Smart contracts are event-driven computer programs used to automatically execute all or parts of the agreements between two or more entities, pursuant to their specifications. The self-executing and self-enforcing attributes of smart contracts present numerous potential benefits, such as cost efficiency, accuracy, and reliability, as well as the potential to support several sustainable development goals. Smart contracts can be very efficient in many sectors, with important automation, procurement, financial, and other supply chain management features. For this study, a systematic literature review was performed, with a view to assessing, synthesizing, and critique the current state of legal and security aspects of smart contracts. The analysis of publications and reports gathered allowed the identification and mapping of the most relevant aspects and revealed numerous issues and vulnerabilities associated with the use of this technology. This paper provides the following contributions: the study and organization of a large corpus of relevant publications; the review of smart contract definitions, from several perspectives; an outline of smart contract characteristics; a framework for effective smart contracting, addressing legal and security issues and proposing several improvements.
Implementation of an Artificially Empathetic Robot Swarm

This paper presents a novel framework for integrating artificial empathy into robot swarms to improve communication and cooperation. The proposed model uses fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. By utilizing similarity measures, the model compares states, enabling empathetic reasoning for synchronized swarm behavior. The paper presents a practical application example that demonstrates the efficacy of the model in a robot swarm working toward a common goal. The evaluation methodology involves the open-source physical-based experimentation platform (OPEP), which emphasizes empirical validation in real-world scenarios. The paper proposes a transitional environment that enables automated and repeatable execution of experiments on a swarm of robots using physical devices.
Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Recently, security attacks occurring in edge computing environments have emerged as an important research topic in the field of cybersecurity. Edge computing is a distributed computing technology that expands the existing cloud computing architecture to introduce a new layer, the edge layer, between the cloud layer and the user terminal layer. Edge computing has the advantage of greatly improving the data processing speed and efficiency but, at the same time, is complex, and various new attacks occur frequently. Therefore, for improving the security of edge computing, effective and intelligent security strategies and policies must be established in consideration of a wide range of vulnerabilities. Intelligent security systems, which have recently been studied, provide a way to detect and respond to security threats by integrating the latest technologies, such as machine learning and big data analysis. Intelligent security technology can quickly recognize attack patterns or abnormal behaviors within a large amount of data and continuously respond to new threats through learning. In particular, knowledge-based technologies using ontology or knowledge graph technology play an important role in more deeply understanding the meaning and relationships between of security data and more effectively detecting and responding to complex threats. This study proposed a method for recommending strategies to respond to edge computing security incidents based on the automatic generation and embedding of security knowledge graphs. An EdgeSecurity–BERT model, utilizing the latest security vulnerability data from edge computing, was designed to extract entities and their relational information. Also, a security vulnerability assessment method was proposed to recommend strategies to respond to edge computing security incidents through knowledge graph embedding. In the experiment, the classification accuracy of security news data for common vulnerability and exposure data was approximately 86% on average. In addition, the EdgeSecurityKG applying the security vulnerability similarity improved the Hits@10 performance to identify the correct link, but the MR performance was degraded owing to the increased complexity. In complex areas, such as security, careful evaluation of the model’s performance and data selection are important. The EdgeSecurityKG applying the security vulnerability similarity provides an important advantage in understanding complex security vulnerability relationships.
Explainability and Evaluation of Vision Transformers: An In-Depth Experimental Study

In the era of artificial intelligence (AI), the deployment of intelligent systems for autonomous decision making has surged across diverse fields. However, the widespread adoption of AI technology is hindered by the risks associated with ceding control to autonomous systems, particularly in critical domains. Explainable artificial intelligence (XAI) has emerged as a critical subdomain fostering human understanding and trust. It addresses the opacity of complex models such as vision transformers (ViTs), which have gained prominence lately. With the expanding landscape of XAI methods, selecting the most effective method remains an open question, due to the lack of a ground-truth label for explainability. To avoid subjective human judgment, numerous metrics have been developed, with each aiming to fulfill certain properties required for a valid explanation. This study conducts a detailed evaluation of various XAI methods applied to the ViT architecture, thereby exploring metrics criteria like faithfulness, coherence, robustness, and complexity. We especially study the metric convergence, correlation, discriminative power, and inference time of both XAI methods and metrics. Contrary to expectations, the metrics of each criterion reveal minimal convergence and correlation. This study not only challenges the conventional practice of metric-based ranking of XAI methods but also underscores the dependence of explanations on the experimental environment, thereby presenting crucial considerations for the future development and adoption of XAI methods in real-world applications.
SAR Image Ship Target Detection Based on Receptive Field Enhancement Module and Cross-Layer Feature Fusion

The interference of natural factors on the sea surface often results in a blurred background in Synthetic Aperture Radar (SAR) ship images, and the detection difficulty is further increased when different types of ships are densely docked together in nearshore scenes. To tackle these hurdles, this paper proposes a target detection model based on YOLOv5s, named YOLO-CLF. Initially, we constructed a Receptive Field Enhancement Module (RFEM) to improve the model’s performance in handling blurred background images. Subsequently, considering the situation of dense multi-size ship images, we designed a Cross-Layer Fusion Feature Pyramid Network (CLF-FPN) to aggregate multi-scale features, thereby enhancing detection accuracy. Finally, we introduce a Normalized Wasserstein Distance (NWD) metric to replace the commonly used Intersection over Union (IoU) metric, aiming to improve the detection capability of small targets. Experimental findings show that the enhanced algorithm attains an Average Precision (AP50) of 98.2% and 90.4% on the SSDD and HRSID datasets, respectively, which is an increase of 1.3% and 2.2% compared to the baseline model YOLOv5s. Simultaneously, it has also achieved a significant performance advantage in comparison to some other models.
Fuzzy Model for Detection of Fraudulent Financial Statements: A Case Study of Lithuanian Micro and Small Enterprises

90 per cent of enterprises in the European Union (EU), including Lithuania, are small enterprises that prepare the abridged financial statements. Verifying the fairness of these reports for stakeholders is challenged due to the lack of data. The aim of this research is to develop a novel model based on fuzzy logic for detecting fraudulent financial statements in micro and small enterprises by using financial ratios suitable for abridged financial statements. The results have shown that the developed fuzzy model enables estimation of the level of fraud in each individual element of accounting. Identifying each fraudulent accounting element allows us to gain insights into the areas where the enterprise has committed fraud. The proposed model has been designed to help small businesses reduce the risk, but it may also be used by public authorities as a tool for achieving greater business transparency.
A Data-Driven Approach to Team Formation in Software Engineering Based on Personality Traits

Collaboration among individuals with diverse skills and personalities is crucial to producing high-quality software. The success of any software project depends on the team’s cohesive functionality and mutual complementation. This study introduces a data-centric methodology for forming Software Engineering (SE) teams centred around personality traits. Our study analysed data from an SE course where 157 students in 31 teams worked through four project phases and were evaluated based on deliverables and instructor feedback. Using the Five-Factor Model (FFM) and a variety of statistical tests, we determined that teams with higher levels of extraversion and conscientiousness, and lower neuroticism, consistently performed better. We examined team members’ interactions and developed a predictive model using extreme gradient boosting. The model achieved a 74% accuracy rate in predicting inter-member satisfaction rankings. Through graphical explainability, the model underscored incompatibilities among members, notably those with differing levels of extraversion. Based on our findings, we introduce a team formation algorithm using Simulated Annealing (SA) built upon the insights derived from our predictive model and additional heuristics.
Internet-of-Things Traffic Analysis and Device Identification Based on Two-Stage Clustering in Smart Home Environments

Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices and challenges in replacing or the updating their firmware, adequate security measures have not been implemented. Instead, IoT device identification methods based on traffic analysis have been proposed. Since conventional methods process and analyze traffic data simultaneously, bias in the occurrence rate of traffic patterns has a negative impact on the analysis results. Therefore, this paper proposes an IoT traffic analysis and device identification method based on two-stage clustering in smart home environments. In the first step, traffic patterns are extracted by clustering IoT traffic at a local gateway located in each smart home and subsequently sent to a cloud server. In the second step, the cloud server extracts common traffic units to represent IoT traffic by clustering the patterns obtained in the first step. Two-stage clustering can reduce the impact of data bias, because each cluster extracted in the first clustering is summarized as one value and used as a single data point in the second clustering, regardless of the occurrence rate of traffic patterns. Through the proposed two-stage clustering method, IoT traffic is transformed into time series vector data that consist of common unit patterns and can be identified based on time series representations. Experiments using public IoT traffic datasets indicated that the proposed method could identify 21 IoTs devices with an accuracy of 86.9%. Therefore, we can conclude that traffic analysis using two-stage clustering is effective for improving the clustering quality, device identification, and implementation in distributed environments.
Educational Data Mining for Mapping Student Ability Based on School Location Using Apriori Method Case Study : SMK YPM Sidoarjo

With the advancement of information technology today, the need for accurate information is needed in everyday life, so that information will become an important element in the development of society today and in the future. However, the high need for information is sometimes not matched by the presentation of adequate information, often the information still has to be re-excavated from very large amounts of data. Traditional methods of analyzing existing data, cannot handle large amounts of data. Basically Senior High School is programmed for those who continue to a higher level, while the provision of skills can be said to be non-existent. Vocational High Schools can produce quality graduates in terms of work skills, therefore currently many companies require graduates from Vocational Schools. The purpose of this study is to create an application to obtain useful information about mapping the value of subjects, especially English at YPM Vocational School in Sidoarjo with data mining techniques and Apriori method. From the results of system testing, it shows that there is still a lack of National Examination Scores for English in most YPM Vocational Schools in Sidoarjo.
Predicting Software Defects in Hybrid MPI and OpenMP Parallel Programs Using Machine Learning

High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems.
Facial Beauty Prediction Combined with Multi-Task Learning of Adaptive Sharing Policy and Attentional Feature Fusion

Facial beauty prediction (FBP) is a leading research subject in the field of artificial intelligence (AI), in which computers make facial beauty judgments and predictions similar to those of humans. At present, the methods are mainly based on deep neural networks. However, there still exist some problems such as insufficient label information and overfitting. Multi-task learning uses label information from multiple databases, which increases the utilization of label information and enhances the feature extraction ability of the network. Attentional feature fusion (AFF) combines semantic information and introduces an attention mechanism to reduce the risk of overfitting. In this study, the multi-task learning of an adaptive sharing policy combined with AFF is presented based on the adaptive sharing (AdaShare) network in FBP. First, an adaptive sharing policy is added to multi-task learning with ResNet18 as the backbone network. Second, the AFF is introduced at the short skip connections of the network. The proposed method improves the accuracy of FBP by solving the problems of insufficient label information and overfitting issues. The experimental results based on the large-scale Asia facial beauty database (LSAFBD) and SCUT-FBP5500 databases show that the proposed method outperforms the single-database single-task baseline and can be applied extensively in image classification and other fields.
Model View Controller Method For Animal Care (Petcare) Information System At Niz Petcare Lawang

Pet Care is a company that offers animal care services that are equipped with a variety of pet shop products such as food, vitamins and care. In addition to Daily Pet Care selling pet supplies and also offering animal medical and treatment services. on one computer. Pet sitting services are one of the best solutions for the majority of Indonesian people who have pets but have to travel far and cannot bring their pets with them. The owners of Niz Petcare see this as a great business opportunity. However, currently they still use manual recording in their business processes, sometimes resulting in loss of material and customer confidence. The purpose of this research is to solve this problem by designing a flow and design of a petcare application prototype that is integrated and more organized using information technology that is developing rapidly and makes it easier for customers to access online. Given these needs, the authors apply the prototyping method to accommodate these needs. The implementation of making software uses the model view controller (MVC) method which adopts the CodeIgniter framework. In this study, an evaluation was carried out using UAT and Usability Testing with ISO/IEC 9126. The evaluation results show that the application of the MVC method can produce good software products that work according to their functionality. Based on the ISO/IEC 9126 percentage, the criteria with the greatest value for understanding on P05, P07, and P09 is 89%, usability on P05 is 89%, and learning on P07 is 89% and 88% power handling at P10. From the results of the summary of the evaluation, it can be concluded that there are if the users of the system are owners, customers, administrators, fully aware of the ease of understanding, learning, use and attractiveness of the NIZ pet care information system created