Reviewerly/exHarmony huggingface dataset:
What Makes People Feel Empathy for AI Chatbots? Assessing the Role of Competence and Warmth

AbstractAbstractWith the growth of contactless shopping, artificial intelligence (AI) chatbot services have become an effective tool for brands to interact with consumers. We investigate the effects of AI chatbots’ humanlike characteristics on consumers’ acceptance of chatbot services by mediating competence, warmth, and empathy based on the AI device use acceptance (AIDUA) model. Using structural equation modeling, we tested how the relationship between these variables differs depending on one’s need for human interaction. Findings suggest that AI chatbots’ personalization and anthropomorphism significantly increase perceived competence and warmth, leading to empathy. This positively affects consumers’ willingness to use AI chatbots. Consumers’ need for interaction moderates the relationship between anthropomorphism and competence. We applied a valid theoretical model to explore how the humanlike characteristics of AI chatbots affect consumers’ cognitive and emotional reactions, determining their service acceptance. The findings have practical value for online retailers who use AI chatbots as marketing communication tools.Keywords: AI chatbotshumanlike AI characteristicsperceptionempathyservice acceptanceneed for human interaction AcknowledgmentsWe would like to thank Editage (www.editage.co.kr) for English language editing.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe datasets used and analyzed during the current study are available from the first author upon reasonable request.Additional informationNotes on contributorsWoo Bin KimWoo Bin Kim is a post-doctoral researcher at Chungnam National University in Daejeon, South Korea. She earned her Ph.D. in fashion marketing at the Department of Fashion and Textiles, Seoul National University. Dr. Kim’s current research interests lie in consumers’ creative experience in technology-driven retail settings.Hee Jin HurHee Jin Hur is an assistant professor at the Department of Fashion Design, Konkuk University Glocal Campus in Chungju, South Korea. She earned her Ph.D. in fashion marketing at the Department of Fashion and Textiles, Seoul National University. Her research interests include retail technology and tourism shopping.
An adaptive artificial electric field algorithm for continuous optimization problems

Abstract The comprehensive learning strategy is a meticulous method for enhancing the optimization ability of population‐based optimization algorithms. This article proposes an adaptive artificial electric field algorithm (iAEFA), which is developed by embedding a comprehensive learning strategy into AEFA. The proposed algorithm utilizes a novel adaptive approach for developing a better learning strategy in which an agent's velocity is updated using the comprehensive influence of the entire population. The developed scheme has shown a stronger potential to discover better candidate solutions in each iteration. The objective of the proposed method is to develop an efficient optimizer for continuous optimization problems. The performance of the proposed iAEFA is evaluated using a set of 13 classical benchmark test problems and the CEC 2019 (100‐digit challenge) benchmark functions. The experimental results are compared to seven state‐of‐the‐art optimization algorithms. Using the Wilcoxon signed‐rank test, the statistical significance of the results is confirmed. This article also discusses the theoretical convergence of the proposed algorithm, along with other significant findings about the proposed scheme. The experimental results and the theoretical analysis shows that the proposed scheme can be an excellent choice for the function optimization task compared to other existing algorithms.
An enhanced recommender system based on heterogeneous graph link prediction

Graph-based data has gained popularity in various applications, including social networks, recommendation systems, and knowledge graphs. Detecting missing links between nodes is a critical challenge in processing graph-based data, as it directly impacts the performance of these applications. The framework for inductive representation learning on large graphs called GraphSAGE method is commonly used for identifying node embeddings, but it has limitations in terms of sensitivity to hyperparameters and difficulties in processing large graphs and capturing global graph information. To address these issues, we propose an improved GraphSAGE architecture for link prediction. Our approach leverages advanced aggregation functions and diverse neural network architectures. Through extensive experimentation on benchmark datasets, we demonstrate the superiority of our method in terms of link prediction accuracy compared to standard GraphSAGE. Additionally, by integrating clustering techniques, we develop a robust recommender system that outperforms state-of-the-art techniques. Our findings highlight the practical potential of our methodology for recommendation tasks and underline its significance in graph-based data processing.
Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

This paper presents a dataset containing automatically collected source codes solving unique programming exercises of different types. The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers or benchmarking mutation testing frameworks, and more applications are yet to be discovered. We describe the architecture of the DTA system, aiming to provide detailed insight regarding how and why the dataset was collected. In addition, we describe the algorithms responsible for source code analysis in the DTA system. These algorithms use vector representations of programs based on Markov chains, compute pairwise Jensen–Shannon divergences of programs, and apply hierarchical clustering algorithms in order to automatically discover high-level concepts used by students while solving unique tasks. The proposed approach can be incorporated into massive programming courses when there is a need to identify approaches implemented by students.
Auto-attention mechanism for multi-view deep embedding clustering

In several fields, deep learning has achieved tremendous success. Multi-view learning is a workable method for handling data from several sources. For clustering multi-view data, deep learning and multi-view learning are excellent options. However, a persistent challenge is a need for the current deep learning approach to independently drive divergent neural networks for different perspectives while working with multi-view data. The current methods use the number of viewpoints to calculate neural network statistics. Consequently, as the number of views rises, it results in a considerable calculation. Furthermore, they vainly try to unite various viewpoints at the training. Incorporating a triple fusion technique, this research suggests an innovative multi-view deep embedding clustering (MDEC) model. The suggested model can jointly acquire the specific knowledge in each view as well as the information fragment of the collective views. The main goal of the MDEC is to lower the errors made when learning the features of each view and correlating data from many views. To address the optimization problem, the MDEC model advises a suitable iterative updating approach. In testing modern deep learning and non-deep learning algorithms, the experimental study on small and large-scale multi-view data shows encouraging results for the MDEC model. In multi-view clustering, this work demonstrates the benefit of the deep learning-based approach over the non-ones. However, future work will address a variety of issues related to MDEC including the speed.
Artificial emotions for charity collection: A serial mediation through perceived anthropomorphism and social presence

Despite the broad application of chatbot agents in online interactions, an ongoing debate persists regarding their persuasive role and human-like emotional disclosure. Our study adds to this debate by exploring the effect of chatbot agents’ emotional disclosure on people’s willingness to donate to a charitable cause, and by examining individual and serial mediation between the main effects of perceived anthropomorphism and social presence. To this end, two types of artificial intelligence chatbot agents—one disclosing factual information and another disclosing human-like emotion—were developed and trained using Dialogflow, a natural language processing engine. A total of 619 US residents were recruited through Amazon Mechanical Turk, an online crowdsourcing platform. Of these, 593 participants completed the required conversation with either version of the chatbot agent (factual vs. emotional), as well as the survey questionnaire, and therefore, were included in the final analysis. The participants exhibited a higher willingness to donate when they interacted with a chatbot disclosing human-like emotions than when they were only exposed to factual information. Moreover, this study found both individual and serial mediating roles of perceived anthropomorphism and social presence. Concerning the implications, theoretically, this study adds to the understanding of applying the notion of human interaction to that involving humans and chatbots. Practically, our findings can be of great help in increasing willingness to donate thereby enhancing fund-raising activities.
UAV Path Planning Based on Improved Artificial Potential Field Method

The obstacle avoidance system of a drone affects the quality of its flight path. The artificial potential field method can react quickly when facing obstacles; however, the traditional artificial potential field method lacks consideration of the position information between drones and obstacles during flight, issues including local minima, unreachable targets, and unreasonable obstacle avoidance techniques that lengthen flight times and consume more energy get encountered. Therefore, an improved artificial potential field method is proposed. First, a collision risk assessment mechanism was introduced to avoid unreasonable obstacle avoidance actions and reduce the length of unmanned aerial vehicle flight paths. Then, to solve the problem of local minimum values and unreachable targets, a virtual sub-target was set up and the traditional artificial potential field model was modified to enable the drone to avoid obstacles and reach the target point. At the same time, a virtual sub-target evaluation factor was set up to determine the reasonable virtual sub-target, to achieve a reasonable obstacle avoidance path compared to the traditional artificial potential field method. The proposed algorithm can plan a reasonable path, reduce energy consumption during flight, reduce drone turning angle changes in the path, make the path smoother, and can also be applied in complex environments.
Exploring English language learning via Chabot: A case study from a self determination theory perspective

This study applied Self Determination Study to understand 25 undergraduate students' motivation to learn the English language via chatbot. The data collected from interviews were categorized based on three psychological needs of learners: autonomy, competence and relatedness. The interview data were categorized based on the thematic analysis suggested by Braun and Clarke. The findings revealed that chatbots support competence, autonomy, and relatedness. However, the findings also revealed that chatbots lack an emotional environment and give inaccurate English language learning information. To address these problems, students suggested that chatbots should be used solely for assessment during teaching. They also recommended a blended learning approach or a traditional classroom teaching that will clear their doubts after the use of chatbots. Overall, this study adds to the body of knowledge on chatbots and English language learning by highlighting their potential as useful teaching aids and providing guidance for researchers, educators, and developers on how to further improve chatbot-based language learning.
A Novel Image Encryption Scheme Using Chaotic Maps and Fuzzy Numbers for Secure Transmission of Information

The complexity of chaotic systems, if used in information encryption, can determine the status of security. The paper proposes a novel image encryption scheme that uses chaotic maps and fuzzy numbers for the secure transmission of information. The encryption method combines logistic and sine maps to form the logistic sine map, as well as the fuzzy concept and the Hénon map to form the fuzzy Hénon map, in which these maps are used to generate secure secret keys, respectively. Additionally, a fuzzy triangular membership function is used to modify the initial conditions of the maps during the diffusion process. The encryption process involves scrambling the image pixels, summing adjacent row values, and XORing the result with randomly generated numbers from the chaotic maps. The proposed method is tested against various attacks, including statistical attack analysis, local entropy analysis, differential attack analysis, signal-to-noise ratio, signal-to-noise distortion ratio, mean error square, brute force attack analysis, and information entropy analysis, while the randomness number has been evaluated using the NIST test. This scheme also has a high key sensitivity, which means that a small change in the secret keys can result in a significant change in the encrypted image The results demonstrate the effectiveness of the proposed scheme in ensuring the secure transmission of information.
CNTR-YOLO: Improved YOLOv5 Based on ConvNext and Transformer for Aircraft Detection in Remote Sensing Images

Aircraft detection in remote sensing images is an important branch of target detection due to the military value of aircraft. However, the diverse categories of aircraft and the intricate background of remote sensing images often lead to insufficient detection accuracy. Here, we present the CNTR-YOLO algorithm based on YOLOv5 as a solution to this issue. The CNTR-YOLO algorithm improves detection accuracy through three primary strategies. (1) We deploy DenseNet in the backbone to address the vanishing gradient problem during training and enhance the extraction of fundamental information. (2) The CBAM attention mechanism is integrated into the neck to minimize background noise interference. (3) The C3CNTR module is designed based on ConvNext and Transformer to clarify the target’s position in the feature map from both local and global perspectives. This module is applied before the prediction head to optimize the accuracy of prediction results. Our proposed algorithm is validated on the MAR20 and DOTA datasets. The results on the MAR20 dataset show that the mean average precision (mAP) of CNTR-YOLO reached 70.1%, which is a 3.3% improvement compared with YOLOv5l. On the DOTA dataset, the results indicate that the mAP of CNTR-YOLO reached 63.7%, which is 2.5% higher than YOLOv5l.
Predictive Intelligence for Healthcare Outcomes: An AI Architecture Overview

This research aims to analyze the continuous structure involved in developing Artificial Intelligence (AI)-based healthcare systems to ensure sustainable outcomes. A comparison is made between the organizational objectives in the healthcare system to determine the proper use of AI in architectural design. The study will delve into the results obtained from using AI in architectural design and highlight the activities found to perform tasks. The research structure will provide insights into the potential of AI in improving the healthcare sector. The results of this study will be helpful for organizations and individuals interested in incorporating AI into their healthcare system. This work focuses on understanding the role of AI in maintaining sustainable development in healthcare and its impact on the design and architecture of healthcare facilities.
Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware Recommendations

In this paper, we present a strategy to provide users with knowledgeaware recommendations based on the combination of graph neural networks and sentence encoders.In particular, our approach relies on the intuition that different data sources (i.e., structured data available in a knowledge graph and unstructured data, such as textual content) provide complementary information and can equally contribute to learn an accurate item representation.Accordingly, we first exploited graph neural networks to encode both collaborative features, such as the interactions between users and items, and structured properties of the items.Next, we used a sentence encoder that relies on transformers to learn a representation based on textual content describing the items.Finally, these embeddings are combined by exploiting a deep neural network where both self-attention and cross-attention mechanisms are used to learn the relationships between the initial embeddings and to further refine the representation.Such a neural network provides as output a prediction of users' interest in the items, which is used to return a top-k recommendation list.In the experimental evaluation, we carried out an experiment against two datasets, and the results showed that our approach overcame several competitive baselines.
Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Abstract Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system.
Day-ahead photovoltaic power forecasting using hybrid K-Means++ and improved deep neural network

The relationship between environmental factors with the power output of photovoltaic (PV) stations is unclear due to the non-linear characteristics of PV systems, which is challenging for PV power forecasting technology. To cope with these challenges, a hybrid forecasting approach called hybrid K-Means++ and Deep Neural Network with input and output adjusting structures (K-IAOA-DNN) is proposed to accurately predict PV power output. The proposed forecasting approach designs several features for K-Means++ to search for similar samples, reducing the complexity of the following forecasting model. Due to changeable environmental conditions and characteristics of PV systems like degradation, the prediction result of a forecasting model may deviate from the expected one. Therefore, IAOA-DNN model is developed by using a DNN adopting the two proposed structures e.g., the input adjusting structure and the output adjusting structure. The input adjusting structure uses several trainable parameters to determine the importance of each meteorological input for further feature extracting by DNN. The output structure in the prediction model is used for analyzing features extracted from DNN to generate a scalar factor fine-tuning the output of DNN. Additionally, the proposed method provides more accurate forecasting results with average RMSE, NRMSE, and MAE values of 14.43, 0.048, and 9.53 kW, respectively.
Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.
AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning

Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework, AutoPrognosis 2.0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques. Software: https://github.com/vanderschaarlab/AutoPrognosis.