Reviewerly/exHarmony huggingface dataset:
Evaluating end‐users' digital competencies and ethical perceptions of <scp>AI</scp> systems in the context of sustainable digital banking

Authors:
raw_author_name: Anup Pradhan
raw_affiliation_string: Department of Quality and Operations Management University of Johannesburg Faculty of Engineering and the Built Environment Johannesburg South Africa

Abstract: Abstract In the pursuit of fostering sustainable digital banking (SDB), this study investigates the nexus between end‐users' digital competencies, perceptions of artificially intelligent systems, and sustainable practices. With a focus on addressing inclusivity barriers and promoting ethical considerations, expert interviews were employed to provide nuanced insights. The study's foundation rests on the significance of sustainable development goals, digital competencies, and artificial intelligence (AI) ethics in shaping the future of digital banking. An interview schedule was developed and distributed online to IT experts and researchers in the field of AI. The instrument examined the major challenges faced by customers in accessing digital banking, the expected timeline for the complete digitalization of the South African banking sector, customers' anticipated competency, and comfort levels with a 100% digital banking interface, and their perspectives on the overall impact of such a transition on humanity. The interview gathered participants' views on AI‐based banking systems that determine credit scores and creditworthiness. The study also included questions to obtain expert opinions on customers' digital capabilities, access to resources, and the impact of AI trust issues on financial service customers. The findings of this study provided valuable insights for stakeholders, including governments, NGOs, and financial institutions. Implications derived from expert perspectives illuminate strategies for enhancing digital competencies, addressing inclusivity hurdles, and ensuring ethical AI integration. This study contributes to the sustainable development discourse by offering actionable recommendations for stakeholders in the pursuit of a more inclusive, digitally competent, and ethically responsible digital banking landscape.
Navigating the technical analysis in stock markets: Insights from bibliometric and topic modeling approaches

Authors:
raw_author_name: Sarveshwar Kumar Inani
raw_affiliation_string: Ph.D., Assistant Professor, Finance and Accounting Area, Jindal Global Business School, OP Jindal Global University, Sonipat, Haryana, India
raw_author_name: Surender Kumar
raw_affiliation_string: Ph.D., Assistant Professor, Jaipuria Institute of Management, Noida, Uttar Pradesh, India
raw_author_name: Baidyanath Biswas
raw_affiliation_string: Ph.D., Assistant Professor, Business Analytics Area, Trinity Business School, Trinity College Dublin, Ireland

Abstract: In stock markets, technical analysis plays a vital role by offering valuable insights into price trends, patterns, and anticipated market movements, aiding investors in making well-informed decisions. This study employs bibliometric and topic modelling approaches on 589 English-language journal articles indexed in Scopus in the last two decades (from 2003 to 2023), exclusively focusing on technical analysis in stock markets. The keyword co-occurrence analysis identifies five topic clusters. The application of structural topic modelling also unravels five prominent thematic clusters, namely pattern-based forecasting, rule-based trading, algorithmic trading, techno-fundamental trading, and machine learning &amp;amp;amp; sentiment analysis. The topic of pattern-based forecasting involves researching the application of various patterns or models to predict stock prices. Rule-based trading concentrates on utilizing technical analysis tools to generate buy and sell signals, aiming for profitability. The algorithmic trading cluster explores the use of algorithms to systematically execute buy and sell actions, especially in high-frequency trading scenarios. Techno-fundamental trading investigates the integration of both fundamental and technical analysis in trading and investment decisions. Lastly, machine learning &amp;amp;amp; sentiment analysis focus on applying advanced machine learning techniques and sentiment analysis for predicting stock prices, highlighting the use of sophisticated methods in this domain. The three predominant topics in the dataset are &amp;quot;rule-based trading,&amp;quot; &amp;quot;machine learning &amp;amp;amp; sentiment analysis,&amp;quot; and &amp;quot;algorithmic trading&amp;quot; constituting 26.79%, 23.52%, and 21.11% of the dataset, respectively. These findings underscore the prominence and significance of these themes within the context of the research domain.
Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning

Authors:
raw_author_name: Misbah Ali
raw_affiliation_string: Department of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, Pakistan
raw_author_name: Tehseen Mazhar
raw_affiliation_string: Department of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, Pakistan
raw_author_name: Amal Al-Rasheed
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
raw_author_name: Tariq Shahzad
raw_affiliation_string: Department of Computer Sciences, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, Pakistan
raw_author_name: Yazeed Yasin Ghadi
raw_affiliation_string: Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, UAE
raw_author_name: Muhammad Amir Khan
raw_affiliation_string: School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

Abstract: Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data’s integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, F-measure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction.
Like a human: The social facilitation/inhibition effect in presence of a virtual observer depends on arousal

Authors:
raw_author_name: Radosław Sterna
raw_affiliation_string: Emotion and Perception Lab, Institute of Psychology, Faculty of Philosophy, Jagiellonian University in Krakow, Romana Ingardena 6, Kraków, Lesser Poland Voivodeship, 30-060, Poland
raw_author_name: Joanna Pilarczyk
raw_affiliation_string: Emotion and Perception Lab, Institute of Psychology, Faculty of Philosophy, Jagiellonian University in Krakow, Romana Ingardena 6, Kraków, Lesser Poland Voivodeship, 30-060, Poland
raw_author_name: Agnieszka Siry
raw_affiliation_string: Doctoral School in the Social Sciences, Jagiellonian University in Krakow, Kraków, Poland
raw_author_name: Michał Kuniecki
raw_affiliation_string: Emotion and Perception Lab, Institute of Psychology, Faculty of Philosophy, Jagiellonian University in Krakow, Romana Ingardena 6, Kraków, Lesser Poland Voivodeship, 30-060, Poland

Abstract: Abstract Better (social facilitation) or worse (social inhibition) performance in a task while being observed has repeatedly been demonstrated with real human observers, yet it has not been fully tested with virtual observers. We tested this effect in a virtual environment using a visual search task to verify if the mere presence of a virtual observer would boost participants’ performance in the easy variant of the task and hinder it in the difficult one. We used electrodermal (EDA) activity measurement to test whether physiological arousal would moderate this effect. The presence of a virtual character affected search performance, which was moderated by arousal, with lower arousal leading to better performance. These results confirm that the mere presence of virtual characters can evoke a social facilitation/inhibition effect similar to that of human beings; this could confirm Zajonc’s theory and serve as encouragement for further development of VR-based training &amp; educational tools with virtual agents.
Enhancing Deep Learning and Computer Image Analysis in Petrography through Artificial Self-Awareness Mechanisms

Authors:
raw_author_name: Paolo Dell’Aversana
raw_affiliation_string: Eni S.p.A., San Donato Milanese, 20097 Milan, Italy

Abstract: In this paper, we discuss the implementation of artificial self-awareness mechanisms and self-reflection abilities in deep neural networks. While the current limitations of research prevent achieving cognitive capabilities on par with natural biological entities, the incorporation of basic self-awareness and self-reflection mechanisms in deep learning architectures offers substantial advantages in tackling specific problems across various scientific fields, including geosciences. In the first section, we outline the foundational architecture of our deep learning approach termed Self-Aware Learning (SAL). The subsequent part of the paper highlights the practical benefits of this machine learning methodology through synthetic tests and applications addressed to automatic classification and image analysis of real petrological data sets. We show how Self-Aware Learning allows enhanced accuracy, reduced overfitting problems, and improved performances compared to other existing methods.
A Systematic Evaluation of Recurrent Neural Network Models for Edge Intelligence and Human Activity Recognition Applications

Authors:
raw_author_name: Varsha S. Lalapura
raw_affiliation_string: Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, Karnataka, India
raw_author_name: J. Amudha
raw_affiliation_string: Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru 560035, Karnataka, India
raw_author_name: Hariram Selvamurugan Satheesh
raw_affiliation_string: ABB Global Industries and Services Private Limited, Bengaluru 560048, Karnataka, India

Abstract: The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local or cloud-based GPU machines are used to train them. However, inference is now shifting to miniature, mobile, IoT devices and even micro-controllers. Due to their colossal memory and computing requirements, mapping RNNs directly onto resource-constrained platforms is arcane and challenging. The efficacy of edge-intelligent RNNs (EI-RNNs) must satisfy both performance and memory-fitting requirements at the same time without compromising one for the other. This study’s aim was to provide an empirical evaluation and optimization of historic as well as recent RNN architectures for high-performance and low-memory footprint goals. We focused on Human Activity Recognition (HAR) tasks based on wearable sensor data for embedded healthcare applications. We evaluated and optimized six different recurrent units, namely Vanilla RNNs, Long Short-Term Memory (LSTM) units, Gated Recurrent Units (GRUs), Fast Gated Recurrent Neural Networks (FGRNNs), Fast Recurrent Neural Networks (FRNNs), and Unitary Gated Recurrent Neural Networks (UGRNNs) on eight publicly available time-series HAR datasets. We used the hold-out and cross-validation protocols for training the RNNs. We used low-rank parameterization, iterative hard thresholding, and spare retraining compression for RNNs. We found that efficient training (i.e., dataset handling and preprocessing procedures, hyperparameter tuning, and so on, and suitable compression methods (like low-rank parameterization and iterative pruning) are critical in optimizing RNNs for performance and memory efficiency. We implemented the inference of the optimized models on Raspberry Pi.
Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

Authors:
raw_author_name: Sudha Prathyusha Jakkaladiki
raw_affiliation_string: Faculty of Informatics and Management, University of Hradec Králové, Hradec Kralove, Hradec Kralove,  Czech Republic
raw_author_name: Filip Maly
raw_affiliation_string: Faculty of Informatics and Management, University of Hradec Králové, Hradec Kralove, Hradec Kralove,  Czech Republic

Abstract: Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system’s performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study’s findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.
A Multi-Task Network Based on Dual-Neck Structure for Autonomous Driving Perception

Authors:
raw_author_name: Chao Wang
raw_affiliation_string: Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Handan 056038, China; School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China
raw_author_name: Zhihua Li
raw_affiliation_string: Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Handan 056038, China; School of Information & Electrical Engineering, Hebei University of Engineering, Handan 056038, China

Abstract: A vision-based autonomous driving perception system necessitates the accomplishment of a suite of tasks, including vehicle detection, drivable area segmentation, and lane line segmentation. In light of the limited computational resources available, multi-task learning has emerged as the preeminent methodology for crafting such systems. In this article, we introduce a highly efficient end-to-end multi-task learning model that showcases promising performance on all fronts. Our approach entails the development of a reliable feature extraction network by introducing a feature extraction module called C2SPD. Moreover, to account for the disparities among various tasks, we propose a dual-neck architecture. Finally, we present an optimized design for the decoders of each task. Our model evinces strong performance on the demanding BDD100K dataset, attaining remarkable accuracy (Acc) in vehicle detection and superior precision in drivable area segmentation (mIoU). In addition, this is the first work that can process these three visual perception tasks simultaneously in real time on an embedded device Atlas 200I A2 and maintain excellent accuracy.
DeepMCGCN: Multi-channel Deep Graph Neural Networks

Authors:
raw_author_name: Lei Meng
raw_affiliation_string: College of Computer, Qinghai Normal University, Xining, 810001, Qinghai, China
raw_author_name: Zhonglin Ye
raw_affiliation_string: College of Computer, Qinghai Normal University, Xining, 810001, Qinghai, China
raw_author_name: Yanlin Yang
raw_affiliation_string: College of Computer, Qinghai Normal University, Xining, 810001, Qinghai, China
raw_author_name: Haixing Zhao
raw_affiliation_string: The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining, 810008, Qinghai, China

Abstract: Abstract Graph neural networks (GNNs) have shown powerful capabilities in modeling and representing graph structural data across various graph learning tasks as an emerging deep learning approach. However, most existing GNNs focus on single-relational graphs and fail to fully utilize the rich and diverse relational information present in real-world graph data. In addition, deeper GNNs tend to suffer from overfitting and oversmoothing issues, leading to degraded model performance. To deeply excavate the multi-relational features in graph data and strengthen the modeling and representation abilities of GNNs, this paper proposes a multi-channel deep graph convolutional neural network method called DeepMCGCN. It constructs multiple relational subgraphs and adopts multiple GCN channels to learn the characteristics of different relational subgraphs separately. Cross-channel connections are utilized to obtain interactions between different relational subgraphs, which can learn node embeddings richer and more discriminative than single-channel GNNs. Meanwhile, it alleviates overfitting issues of deep models by optimizing convolution functions and adding residual connections between and within channels. The DeepMCGCN method is evaluated on three real-world datasets, and the experimental results show that its node classification performance outperforms that of single-channel GCN and other benchmark models, which improves the modeling and representation capabilities of the model.
Using of the Weibull distribution in developing global solar radiation forecasting models

Authors:
raw_author_name: Ayşe Gül Kaplan
raw_affiliation_string: Department of Mathematics Osmaniye Korkut Ata University Osmaniye Turkey
raw_author_name: Yusuf Alper Kaplan
raw_affiliation_string: Department of Energy Engineering Osmaniye Korkut Ata University Osmaniye Turkey

Abstract: Abstract In this study, a solar radiation prediction model was developed using the Weibull distribution function (WDF). The main purpose of this study is to develop a new model for solar radiation (SR) estimation using the WDF and to bring this model to the literature. Although the WDF is widely used in wind energy forecasting due to its compatibility with wind speed data, it has not been used before in solar radiation forecasting model development. In this study, it is aimed to make SR estimation with the WDF for the first time. SR data for all regions where this new model was developed and its performance was examined were obtained from the General Directorate of Meteorology. In order to decide the success of these developed models, five different statistical metrics (RPE, MPE, MAPE, SSRE, and t ‐stat) were discussed in the study. When the results are evaluated in general, it is seen that the new developed model has an acceptable performance. According to all test results in the region where the model was developed, it is seen that the new developed model showed the best performance with 0.0353, 0.4068, 7.1851, 0.1144, and 0.0162 values, respectively. The performance of this new developed model was observed in three different regions. When the statistical test results in these regions were examined, it was seen that the new model developed had a similar performance to the popular solar forecasting models widely used in the literature.
A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Authors:
raw_author_name: Rohit Salgotra
raw_affiliation_string: Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, Poland
raw_author_name: Amir H. Gandomi
raw_affiliation_string: Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia

Abstract: Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.