Reviewerly/exHarmony huggingface dataset:
Human-technology integration with industrial conversational agents: A conceptual architecture and a taxonomy for manufacturing

Conversational agents are systems with great potential to enhance human-computer interaction in industrial settings. Although the number of applications of conversational agents in many fields is growing, there is no shared view of the elements to design and implement for chatbots in the industrial field. The paper presents the combination of many research contributions into an integrated conceptual architecture, for developing industrial conversational agents using Nickerson's methodology. The conceptual architecture consists of five core modules; every module consists of specific elements and approaches. Furthermore, the paper defines a taxonomy from the study of empirical applications of manufacturing conversational agents. Indeed, some applications of chatbots in manufacturing are available but those have never been collected in single research. The paper fills this gap by analyzing the empirical cases and presenting a qualitative analysis, with verification of the proposed taxonomy. The contribution of the article is mainly to illustrate the elements needed for the development of a conversational agent in manufacturing: researchers and practitioners can use the proposed conceptual architecture and taxonomy to more easily investigate, define, and develop all the elements for chatbot implementation.
Chatbot for communicating with university students in emergency situation

Chatbots have arrived in higher education, and professors are trying to make the most of them. Typically, chatbots are used to help students learn academic subjects. In times of crisis, such as the COVID-19 pandemic, students who were not living with their families during the course, especially international students, were isolated and in critical situations. The student services offices were in constant contact with these students to solve problems, advise them and support them during their stay, within the constraints of confinement and the guidelines dictated by the country at the time. The student services offices were overwhelmed trying to help these students because, although the students' problems were very recurrent, the government guidelines changed from one day to the next. This article proposes the use of a chatbot to provide initial support to students during crisis situations, and facilitate communication between them and the university. The chatbot was tested by more than 160 students and student services staff. The findings support the use of chatbots as a potential tool to facilitate communication with students in emerging emergency situations, and encourage universities to adopt these types of smart tools to be prepared to respond quickly and efficiently to students in times of crisis.
Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion

The use of Electronic Health Records is the most important milestone in the digitization and intelligence of the entire medical industry. AI can effectively mine the immense medical information contained in EHRs, potentially assist doctors in reducing many medical errors.This article aims to summarize the research status and trends in using AI to mine medical information from EHRs for the past thirteen years and investigate its information application.A systematic search was carried out in 5 databases, including Web of Science Core Collection and PubMed, to identify research using AI to mine medical information from EHRs for the past thirteen years. Furthermore, bibliometric and content analysis were used to explore the research hotspots and trends, and systematically analyze the conversion rate of research resources in this field.A total of 631 articles were included and analyzed. The number of published articles has increased rapidly after 2017, with an average annual growth rate of 55.73%. The US (41.68%) and China (19.65%) publish the most articles, but there is a lack of international cooperation. The extraction of disease lesions is a hot topic at present, and the research topic is gradually shifting from disease risk grading to disease risk prediction. Classification (66%), and regress (15%) are the main implemented AI tasks. For AI algorithms, deep learning (31.70%), decision tree algorithms family (26.47%), and regression algorithms family (17.43%) are used most frequently. The funding rate for publications is 69.26%, and the input-output conversion rate is 21.05%.Over the past decade, the use of AI to mine medical information from EHRs has been developing rapidly. However, it is necessary to strengthen international cooperation, improve EHRs data availability, focus on interpretable AI algorithms, and improve the resource conversion rate in future research.
Speaker Recognition Based on Dung Beetle Optimized CNN

Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has many hyperparameters that are difficult to determine, making the model easily fall into local optimum or even fail to converge during the training process. Intelligent algorithms such as particle swarm optimization and genetic algorithms are used to solve the above problems. However, these algorithms perform poorly compared to the current emerging meta-heuristic algorithms. In this study, the dung beetle optimized convolution neural network (DBO-CNN) is proposed to identify the speakers for the first time, which is helpful in finding suitable hyperparameters for training. By testing the dataset of 50 people, it was demonstrated that the accuracy of the model was significantly improved by using this approach. Compared with the traditional CNN and CNN optimized by other intelligent algorithms, the average accuracy of DBO-CNN has increased by 1.22~4.39% and reached 97.93%.
High-Resolution Remote Sensing Image Segmentation Algorithm Based on Improved Feature Extraction and Hybrid Attention Mechanism

Segmentation of high-resolution remote sensing images is one of the hottest topics in deep learning. Compared to ordinary images, high-resolution remote sensing images possess characteristics such as higher intra-class diversity and lower inter-class separability. Additionally, the objects in these images are complex and have smaller sizes. Aiming at the classical segmentation network in remote sensing images, there are some problems, such as inaccurate edge object segmentation, inconsistent segmentation of different types of objects, low detection accuracy, and a high false detection rate. This paper proposes a new hybrid attention model (S-CA), a new coordinate efficient channel attention module (C-ECA), and a new small-target feature extraction network (S-FE). The S-CA model enhances important spatial and channel features in shallow layers, allowing for more detailed feature extraction. The C-ECA model utilizes convolutional layers to capture complex dependencies between variations, thereby better capturing feature information at each position and reducing redundancy in feature channels. The S-FE network can capture the local feature information of different targets more effectively. It enhances the recognition and classification capabilities of various targets and improves the detection rate of small targets. The algorithm is used for segmentation in high-resolution remote sensing images. Experiments were conducted on the public dataset GID-15 based on Gaofen-2 satellite remote sensing images. The experimental results demonstrate that the improved DeepLabV3+ segmentation algorithm for remote sensing images achieved a mean intersection over union (mIoU), mean pixel accuracy (mPA), and mean precision (mP) of 91.6%, 96.1%, and 95.5%, respectively. The improved algorithm is more effective than current mainstream segmentation networks.
Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies

Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrated exceptional performance in generating high-quality synthetic images that closely resemble the original imprinted characters required for augmenting the limited datasets. And the evaluation metric, Fréchet inception distance, further validated the outstanding performance of the WGAN-GP and WGANDIV models, establishing them as optimal choices for dataset augmentation to enhance the accuracy and reliability of recognition systems.
An Empirical Survey on the Prevalence of Technical Debt in Systems Engineering

Abstract The technical debt metaphor is used within software engineering to describe technical concessions that produce a short‐term benefit but result in long‐term consequences. Systems engineering is subject to these concessions, yet there is a limited amount of research associating technical debt with systems engineering. This paper provides the results of an empirical survey investigating the prevalence of technical debt in systems engineering, including the occurrence of technical debt, the use of the metaphor, and the distribution of technical debt within the systems engineering lifecycle. The results of the survey show that while technical debt is common in systems engineering and occurs throughout the lifecycle, the metaphor and terminology of technical debt is not consistently applied. These results emphasize the need to enrich the usage of the technical debt metaphor within systems engineering to enable the management of technical debt and to reduce the risk of technical bankruptcy.
The Potential Impact of Central Bank Digital Currencies (CBDCs) on Economic and Financial Sector Development

Central bank digital currency (CBDC) is a central-bank issued digital currency or “digital banknote” which is programmable and could be transferred peer-to-peer via the blockchain technology. This paper studies several design types of CBDCs according to their implementation model (retail vs wholesale), underlying format (account-based vs token-based) or their distribution model (direct, indirect or hybrid). The analysis focuses on the potential impacts of the different CBDCs design types on the major financial sector actors, as well as on the financial sector development in general. The programmability of CBDCs to include smart contracts is found to be a powerful factor that could foster innovation and efficiency improvements in the financial sector. Major concerns are related to a potential disintermediation in the financial sector as a result of the introduction of CBDCs.
Semantic Search Engine Optimization in the News Media Industry: Challenges and Impact on Media Outlets and Journalism Practice in Greece

In today’s digital media environment, search engines constitute a significant gateway to news and an important source of traffic for online news outlets worldwide, identifying the significance of top rankings in search results. In this context, search engine optimization (SEO) has entered newsrooms as an essential element of disseminating the content of online media organizations, creating new practices and challenges for media professionals. However, the transition to the Semantic Web (SW) and the development of major search engines in recent years have led to the emergence of Semantic SEO as a new approach to old SEO practices, creating at the same time new challenges for the news media industry. This study investigates the application of Semantic SEO inside newsrooms and its impact on journalism and news media organizations. Drawing upon a series of semi-structured interviews with Greek SEO experts and a systematic review of the notion of semantic search and the corresponding semantic SEO technologies, the objective of this work is to present an analysis of how Semantic SEO affects news media and journalism content as well as to identify some of the technological practices that could contribute to enhancing the discoverability of news content in the changing context of online search.
Modular Models of Monoids with Operations

Inspired by algebraic effects and the principle of notions of computations as monoids, we study a categorical framework for equational theories and models of monoids equipped with operations. The framework covers not only algebraic operations but also scoped and variable-binding operations. Appealingly, in this framework both theories and models can be modularly composed. Technically, a general monoid-theory correspondence is shown, saying that the category of theories of algebraic operations is equivalent to the category of monoids. Moreover, more complex forms of operations can be coreflected into algebraic operations, in a way that preserves initial algebras. On models, we introduce modular models of a theory, which can interpret abstract syntax in the presence of other operations. We show constructions of modular models (i) from monoid transformers, (ii) from free algebras, (iii) by composition, and (iv) in symmetric monoidal categories.
Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency Transactions

Cryptocurrency has emerged as a decentralized transaction to overcome the problems of the centralized transaction system. Although it has become a popular trend in online cryptocurrency transactions and mobile wallets, this method has increased the number of fraudulent transactions instead of physically transferring money. Because the shared data and the history of online transactions may lead to fraudulent transactions. The preprocess identification of fraudulent cryptocurrency transactions is becoming an urgent research question. With the exponential blossoming of Artificial Intelligence, the employing of deep learning in predicting social issues has been achieved in many disciplines. From this perspective, this paper proposes an ensemble learning approach for fraudulent cryptocurrency transactions by integrating two deep learning methods: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The off-the-shelf CNN and LSTM, ensemble CNN, and ensemble LSTM with the bagged and boosted approach are compared in terms of accuracy and losses from training and test datasets. Moreover, the 10-fold cross-validation approach is employed for the evaluation of the proposed approach. The evaluation results indicate that the bagged LSTM ensembled approach is significant with 96.4% accuracy and outperforms the other approaches.
Critical Minerals Map Feature Extraction Using Deep Learning

Critical minerals play a significant role in various areas such as national security, economic growth, renewable energy development, and infrastructure. The assessment of critical minerals requires examining historical scanned maps. The traditional processes of analyzing these scanned maps are labor-intensive, time-consuming, and prone to errors. In this study, we introduce a deep learning technique to help assess critical minerals by automatically extracting digital features from scanned maps. The polygon features extraction is essential for evaluating the concentration and abundance of critical minerals. The extracted polygon features can be used to update existing geospatial databases, conduct further analysis, and support decision-making processes. The proposed U-Net model takes a 6-channel array as input, where legend feature is concatenated with the map image and serves as a prompt, and the model can generate image segmentation based on arbitrary prompts at test time. Our study shows that the modified U-Net model can effectively extract the mining-related polygon regions based on features listed in legends from historic topographic maps. The model achieves a median F1-score of 0.67. This study has the potential to significantly reduce the time and effort involved in manually digitizing geospatial data from historical topographic maps, thus streamlining the overall assessment process.
Test-Time Adaptation for Optical Flow Estimation Using Motion Vectors

Due to the prohibitive cost as well as technical challenges in annotating ground-truth optical flow for large-scale realistic video datasets, the existing deep learning models for optical flow estimation mostly rely on synthetic data for training, which in turn may lead to significant performance degradation under test-data distribution shift in real-world environments. In this work, we propose the methodology to tackle this important problem. We design a self-supervised learning task for adjusting the optical flow estimation model at test time. We exploit the fact that most videos are stored in compressed formats, from which compact information on motion, in the form of motion vectors and residuals, can be made readily available. We formulate the self-supervised task as motion vector prediction, and link this task to optical flow estimation. To the best of our knowledge, our Test-Time Adaption guided with Motion Vectors (TTA-MV), is the first work to perform such adaptation for optical flow. The experimental results demonstrate that TTA-MV can improve the generalization capability of various well-known deep learning methods for optical flow estimation, such as FlowNet, PWCNet, and RAFT.
Management of Digital Twin-Driven IoT Using Federated Learning

Internet of Things (IoT), Digital Twin (DT), and Federated Learning (FL) are redefining the future vision of globalization. While IoT is about sensing data from physical devices, DTs reflect their digital representation and enable optimized decision-making by tightly integrating Artificial Intelligence (AI). Although swiftly growing, DTs are raising new challenges in privacy concerns, which are nowadays addressed by FL. However, the limited IoT resources, the communication overhead, and the lack of trust among clients are major obstacles that hinder the effectiveness of learning systems. In this paper, we design a new IoT-based architecture empowered by DT to improve the efficiencies of limited-resources devices. On top of this architecture, we leverage FL to construct the DT models. We further propose CISCO-FL, a Clustered FL with Intelligent Selection and Computation Offloading. Particularly, we study the computing resources of the clients and the quality of their models, and we embed in the proposed approach an intelligent offloading model, where the clients with high computational resources can assist and optimize the model of those struggling with limited resources. As such, both communication cost and computation resources are reduced and optimized. Finally, thorough experimental results are presented to support our findings and validate our model.
ARTriViT: Automatic Face Recognition System Using ViT-Based Siamese Neural Networks with a Triplet Loss

Computer-based face recognition and other biometric techniques are now mature and trustworthy technology that plays a crucial role in many access control scenarios. Face recognition undergoes a variety of difficulties, including those related to angle, lighting, position, facial expression, noise, resolution, occlusion, and the scarcity of samples from each class. In this study, we proposed a triplet loss-based Siamese network with a vision transformer as a feature extractor instead of traditional convolution. Our Siamese analyzes a pair of face images as input, extracts the characteristics from these pairs, and uses similarity indexes to evaluate them for face recognition using the Celeb-DF (version 2) dataset. As a result, the suggested model performs well compared to the state-of-the-art (SOTA) on the Celeb-DF version 2 dataset. The trained model and code will be available at: https://github.com/MuhammadSaeedMBZUAINiTBased-Siamese.
Toward quality education: Contributions of <scp>EdTech</scp> to the achievement of the fourth <scp>United Nations</scp> sustainable development goal

Abstract Quality Education (QE) is the fourth Sustainable Development Goal (SDG 4) established by the United Nations, which seeks to ensure quality, inclusive, and equitable education for all. Despite its importance in facing the challenges of society, QE is not offered to the population equitably and democratically, especially to the most vulnerable people and those living in remote areas. In this regard, the use of Educational Technology (EdTech) has been encouraged to improve the teaching‐learning process and promote the achievement of SDG 4. This work proposes a framework containing recommendations for EdTech to contribute to the development of QE. The framework was developed from the systematization of pillars related to the main EdTech categories identified through the content analysis method. The main scientific contribution of this work is the deepening and broadening of the theory that associates EdTech with QE, providing new bases for the further development of research on the subject. This work also contributes to the development and achievement of SDG 4 through the framework with recommendations for democratizing access to education and improving students' academic performance.
Design and Implementation of a Dual-Axis Solar Tracking System

A dual-axis solar tracking system with a novel and simple structure was designed and constructed, as documented in this paper. The photoelectric method was utilized to perform the tracking. The solar radiation values of the designed system and a fixed panel system were theoretically estimated and compared, showing that the proposed system is more efficient in collecting solar energy than a fixed solar panel with a 30° tilted fixed surface facing south. The experimental results verified the validity of the prediction as well as the efficiency of the proposed solar tracking system. In a comparison of the data obtained from the measurements, 24.6% more energy was seen to have been obtained in the dual-axis solar tracking system compared to the fixed system. This study possesses potential value in small- and medium-sized photovoltaic applications.
Sat-Mesh: Learning Neural Implicit Surfaces for Multi-View Satellite Reconstruction

Automatic reconstruction of surfaces from satellite imagery is a hot topic in computer vision and photogrammetry. State-of-the-art reconstruction methods typically produce 2.5D elevation data. In contrast, we propose a one-stage method directly generating a 3D mesh model from multi-view satellite imagery. We introduce a novel Sat-Mesh approach for satellite implicit surface reconstruction: We represent the scene as a continuous signed distance function (SDF) and leverage a volume rendering framework to learn the SDF values. To address the challenges posed by lighting variations and inconsistent appearances in satellite imagery, we incorporate a latent vector in the network architecture to encode image appearances. Furthermore, we introduce a multi-view stereo constraint to enhance surface quality. This constraint minimizes the similarity between image patches to optimize the position and orientation of the SDF surface. Experimental results demonstrate that our method achieves superior visual quality and quantitative accuracy in generating mesh models. Moreover, our approach can learn seasonal variations in satellite imagery, resulting in texture mesh models with different and consistent seasonal appearances.