Reviewerly/exHarmony huggingface dataset:
Multi-Scale Potential Field Data Integration Using Fuzzy C-Means Clustering for Automated Geological Mapping of North Singhbhum Mobile Belt, Eastern Indian Craton

Fuzzy C-Means (FCM) clustering is an unsupervised machine learning algorithm that helps to integrate multiple geophysical datasets and provides automated objective-oriented information. This study analyzed ground-based Bouguer gravity and aeromagnetic datasets using the FCM clustering algorithm to classify lithological units in the western part of the North Singhbhum Mobile Belt, a mineralized belt in the Eastern Indian Craton. The potential field signatures of clusters obtained using FCM correlate remarkably well with the existing surface geology on a broad scale. The cluster associated with the highest gravity signatures corresponds to the metabasic rocks, and the cluster with the highest magnetic response represents the mica schist rocks. The cluster characterized by the lowest gravity and magnetic responses reflects the granite gneiss rocks. However, few geological formations are represented by two or more clusters, probably due to the close association of similar rock types. The fuzzy membership scores for most of the data points in each cluster show above 0.8, indicating a consistent relationship between geophysical signatures and the existing lithological units. Further, the study reveals that integrating multi-scale geophysical data helps to disclose bedrock information and litho-units under the sediment cover.
Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer

Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the development of computational techniques is important as diagnosing many samples can be done automatically, quickly, and efficiently, which is beneficial for medical laboratories and medical professionals. This study aims to develop automated WSI image analysis models for early diagnosis of a cervical squamous cell dataset. Several systems have been designed to analyze WSI images and accurately distinguish cervical cancer progression. For all proposed systems, the WSI images were optimized to show the contrast of edges of the low-contrast cells. Then, the cells to be analyzed were segmented and isolated from the rest of the image using the Active Contour Algorithm (ACA). WSI images were diagnosed by a hybrid method between deep learning (ResNet50, VGG19 and GoogLeNet), Random Forest (RF), and Support Vector Machine (SVM) algorithms based on the ACA algorithm. Another hybrid method for diagnosing WSI images by RF and SVM algorithms is based on fused features of deep-learning (DL) models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet). It is concluded from the systems' performance that the DL models' combined features help significantly improve the performance of the RF and SVM networks. The novelty of this research is the hybrid method that combines the features extracted from deep-learning models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet) with RF and SVM algorithms for diagnosing WSI images. The results demonstrate that the combined features from deep-learning models significantly improve the performance of RF and SVM. The RF network with fused features of ResNet50-VGG19 achieved an AUC of 98.75%, a sensitivity of 97.4%, an accuracy of 99%, a precision of 99.6%, and a specificity of 99.2%.
Features and Scope of Regulatory Technologies: Challenges and Opportunities with Industrial Internet of Things

Regulatory Technology (RegTech) is an emerging set of computing and network-based information systems and practices intended to enhance and improve regulatory compliance processes. Such technologies rely on collecting exclusive information from the environment and humans through automated Internet of Things (IoT) sensors and self-reported data. The key enablers of RegTech are the increased capabilities and reduced cost of IoT and Artificial Intelligence (AI) technologies. This article focuses on a survey of RegTech, highlighting the recent developments in various sectors. This work identifies the characteristics of existing implementations of RegTech applications in the financial industry. It examines the critical features that non-financial industries such as agriculture must address when using such technologies. We investigate the suitability of existing technologies applied in financial sectors to other industries and the potential gaps to be filled between them in terms of designing information systems for regulatory frameworks. This includes identifying specific operational parameters that are key differences between the financial and non-financial sectors that can be supported with IoT and AI technologies. These can be used by both producers of goods and services and regulators who need an affordable and efficient supervision method for managing relevant organizations.
Blockchain Payment Services in the Hospitality Sector: The Mediating Role of Data Security on Utilisation Efficiency of the Customer

Blockchain technology has the potential to completely transform the hospitality sector by offering a safe, open, and effective method of payment. Increased customer utilisation efficiency may result from this. This study looks into how blockchain payment methods affect hotel customers’ intentions to stay loyal by devising four hypotheses. A questionnaire was specifically created and self-administered for this study as a data-gathering tool and distributed to hotel customers. The I.B.M. SPSS and Amos software packages were used to analyse the data of the 301 valid responses. Findings show that hospitality customers may use blockchain payment services if the customer is satisfied with the data security of this payment system. The study also highlighted that customer data security mediated the association between utilisation efficiency and blockchain payment systems. Blockchain payment services can affect visitors’ intentions to stay loyal by impacting data security and consumer happiness. Results suggest that blockchain payment systems can be useful for hospitality firms looking to increase client utilisation efficiency. Blockchain can simplify visitor booking and payment processes by providing a safe, open, and effective transacting method. This may result in a satisfying encounter that visitors are more inclined to recall and repeat.
Recent Trends in Real-Time Photovoltaic Prediction Systems

Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations.
Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models

Deep learning researchers believe that as deep learning models evolve, they can perform well on many tasks. However, the complex parameters of deep learning models make it difficult for users to understand how deep learning models make predictions. In this paper, we propose the specific-input local interpretable model-agnostic explanations (LIME) model, a novel interpretable artificial intelligence (XAI) method that interprets deep learning models of tabular data. The specific-input process uses feature importance and partial dependency plots (PDPs) to select the “what” and “how”. In our experiments, we first obtain a basic interpretation of the data by simulating user behaviour. Second, we use our approach to understand “which” features deep learning models focus on and how these features affect the model’s predictions. From the experimental results, we find that this approach improves the stability of LIME interpretations, compensates for the problem of LIME only focusing on local interpretations, and achieves a balance between global and local interpretations.
Spatial Attention Mechanism and Cascade Feature Extraction in a U-Net Model for Enhancing Breast Tumor Segmentation

In the field of medical imaging, the accurate segmentation of breast tumors is a critical task for the diagnosis and treatment of breast cancer. To address the challenges posed by fuzzy boundaries, vague tumor shapes, variation in tumor size, and illumination variation, we propose a new approach that combines a U-Net model with a spatial attention mechanism. Our method utilizes a cascade feature extraction technique to enhance the subtle features of breast tumors, thereby improving segmentation accuracy. In addition, our model incorporates a spatial attention mechanism to enable the network to focus on important regions of the image while suppressing irrelevant areas. This combination of techniques leads to significant improvements in segmentation accuracy, particularly in challenging cases where tumors have fuzzy boundaries or vague shapes. We evaluate our suggested technique on the Mini-MIAS dataset and demonstrate state-of-the-art performance, surpassing existing methods in terms of accuracy, sensitivity, and specificity. Specifically, our method achieves an overall accuracy of 91%, a sensitivity of 91%, and a specificity of 93%, demonstrating its effectiveness in accurately identifying breast tumors.
How to mend the dormant user in Q&amp;A communities? A social cognitive theory-based study of consistent geeks of StackOverflow

ABSTRACTLow user participation and less knowledge contribution seriously threaten the sustainability of online question and answers communities. Although researchers studied the different aspects of knowledge contribution and proposed useful suggestions, there is still no thorough study on the knowledge contribution pattern of consistent geeks’ that can help improve low participation. According to social cognitive and self-determination theory, peers follow credible sources or role models in their participation patterns and are influenced by the community environment. Based on social cognitive and self-determination theory, we have studied the most consistent geeks of StackOverflow for the period between 2010–2020 to employ the results to activate dormant users. Two-step system GMM results revealed that most users take a free ride and hesitate to reciprocate; knowledge-seeking negatively influences the quantity of contributed knowledge. Peer recognition and repudiation positively influence the knowledge contribution of active geeks, whereas reputation scores and badges negatively influence the contributed knowledge's quantity and quality. Social interaction's role as moderator is also different for quantity and quality of knowledge contributed. Study results improve the existing literature and provide comprehensive managerial implications to improve low participation and create a progressive knowledge contribution environment.KEYWORDS: Consistent geeks; low participationknowledge contributionQ&A communitiesGMM Disclosure statementNo potential conflict of interest was reported by the author(s).Data availabilityThe dataset used in this study is publically available at the following address data.stackexchange.com.Conflict of interest disclosureThe authors who contributed to the study have no financial or professional conflict of interest to disclose.Notes1 https://stackexchange.com/leagues/1/alltime/stackoverflow2 https://stackexchange.com/sites#trafficAdditional informationFundingThis work was supported by the National Natural Science Foundation of China [Grant Number 72174018, 71932002]; the Philosophy and sociology science fund from Beijing Municipal Education Commission [Grant Number SZ202110005001], and Beijing Natural Science Fund [Grant Number 9222001].
Constructing a Knowledge Graph for the Chinese Subject Based on Collective Intelligence

Knowledge graphs are a valuable tool for intelligent tutoring systems and are typically constructed with a focus on objectivity and accuracy. However, they may not effectively capture the subjectivity and complex relationships often present in the humanities. To address this issue, a dynamic visualization of subject matter knowledge graph was developed using a collective intelligence approach that integrates the individual intelligence of learners and considers cognitive diversity to construct and evolve the knowledge graph. The approach resulted in the construction of 722 knowledge associations and the evolution of 584 triples. A survey assessed the effectiveness and user-friendliness, revealing that this approach is effective, easy to use, and can improve subject matter knowledge ontology. In conclusion, combining individual and collective intelligence is a promising approach for building effective knowledge graphs in subject areas with subjectivity and complexity.
Crystalline Quantum Circuits

Random quantum circuits continue to inspire a wide range of applications in quantum information science and many-body quantum physics, while remaining analytically tractable through probabilistic methods. Motivated by an interest in deterministic circuits with similar applications, we construct classes of \textit{nonrandom} unitary Clifford circuits by imposing translation invariance in both time and space. Further imposing dual-unitarity, our circuits effectively become crystalline spacetime lattices whose vertices are SWAP or iSWAP two-qubit gates and whose edges may contain one-qubit gates. One can then require invariance under (subgroups of) the crystal's point group. Working on the square and kagome lattices, we use the formalism of Clifford quantum cellular automata to describe operator spreading, entanglement generation, and recurrence times of these circuits. A full classification on the square lattice reveals, of particular interest, a "nonfractal good scrambling class" with dense operator spreading that generates codes with linear contiguous code distance and high performance under erasure errors at the end of the circuit. We also break unitarity by adding spacetime-translation-invariant measurements and find a class of such circuits with fractal dynamics.
DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance

Unmanned aerial vehicle (UAV) object detection technology is widely used in security surveillance applications, allowing for real-time collection and analysis of image data from camera equipment carried by a UAV to determine the category and location of all targets in the collected images. However, small-scale targets can be difficult to detect and can compromise the effectiveness of security surveillance. In this work, we propose a novel dual-backbone network detection method (DB-YOLOv5) that uses multiple composite backbone networks to enhance the extraction capability of small-scale targets’ features and improve the accuracy of the object detection model. We introduce a bi-directional feature pyramid network for multi-scale feature learning and a spatial pyramidal attention mechanism to enhance the network’s ability to detect small-scale targets during the object detection process. Experimental results on the challenging UAV aerial photography dataset VisDrone-DET demonstrate the effectiveness of our proposed method, with a 3% improvement over the benchmark model. Our approach can enhance security surveillance in UAV object detection, providing a valuable tool for monitoring and protecting critical infrastructure.
Sarcasm detection framework using context, emotion and sentiment features

Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. In this paper, we propose a model, that incorporates different features to capture sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. In our architecture, sentiment and emotion models were used only as feature extractors. Other blocks (pre-trained transformer and CNN) were fine-tuned. We run experiments on four datasets from different domains. Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.
Opportunities and Challenges for AI-Assisted Qualitative Data Analysis: An Example from Collaborative Problem-Solving Discourse Data

Recent advances in the usability of generative AI platforms, such as ChatGPT, suggest that artificial intelligence can seemingly capture many of the rules and meanings underlying human language. As a result, AI can potentially automate many tasks requiring human-like understanding and generation of natural language. Qualitative data analysis tends to be a time-consuming task which is susceptible to human bias and mistakes. In this study, we explored the use of ChatGPT and the GPT-4 model to assist with analyzing qualitative data from a previous study by Rannastu-Avalos, Mäeots, and Siiman (2022). In that prior study, pairs of adults collaborated via a free-form, text-based chat interface to solve a computer simulation problem about balancing a seesaw. To re-analyze the data using AI assistance, both deductive and inductive approaches were applied and the results compared to human coding and human interpretation of the data. The results show that it is important to structure and phrase prompts so that AI responses best align with human interpretation. Deductive analysis performed better than inductive analysis, presumably because prompts with richer contextual information referring to specific theoretical perspectives could be crafted. Our results suggest that AI-assisted qualitative analysis has the potential to improve transparency in the coding of qualitative data by encouraging human analysts to report AI prompts that agree with their interpretations of the data, and in turn can be reused by other researchers.
Adaptive Edge-Aware Semantic Interaction Network for Salient Object Detection in Optical Remote Sensing Images

In recent years, the task of salient object detection in optical remote sensing images (RSI-SOD) has received extensive attention. Benefiting from the development of deep learning, much progress has been made in RSI-SOD field. However, existing methods still face challenges in addressing various issues present in optical RSI, including uncertain numbers of salient objects, cluttered backgrounds, and interference from shadows. To address these challenges, we propose a novel approach, Adaptive Edge-aware Semantic Interaction Network (AESINet) for efficient salient object detection. Specifically, to improve the extraction of complex edge information, we design a Local Detail Aggregation Module (LDAM). This module can adaptively enhance the edge information of salient objects by leveraging our proposed difference perception mechanism. Notably, our difference perception mechanism is a novel edge enhancement method without the supervision of edge groundtruth. Additionally, to accurately locate salient objects of varying numbers and scales, we design a Multi-scale Feature Enhancement Module (MFEM), which effectively captures and utilizes multi-scale information. Moreover, we design the Deep Semantic Interaction Module (DSIM) to identify salient objects amidst cluttered backgrounds and effectively mitigate the interference of shadows. We conduct extensive experiments on three well-established optical RSI datasets and the results demonstrate that our proposed model outperforms 14 state-of-the-art methods. All codes and detection results are available at https://github.com/xumingzhu989/AESINet-TGRS.
Applying Detection Leakage on Hybrid Cryptography to Secure Transaction Information in E-Commerce Apps

Technology advancements have driven a boost in electronic commerce use in the present day due to an increase in demand processes, regardless of whether goods, products, services, or payments are being bought or sold. Various goods are purchased and sold online by merchants (M)s for large amounts of money. Nonetheless, during the transmission of information via electronic commerce, Ms’ information may be compromised or attacked. In order to enhance the security of e-commerce transaction data, particularly sensitive M information, we have devised a protocol that combines the Fernet (FER) algorithm with the ElGamal (ELG) algorithm. Additionally, we have integrated data leakage detection (DLD) technology to verify the integrity of keys, encryptions, and decryptions. The integration of these algorithms ensures that electronic-commerce transactions are both highly secure and efficiently processed. Our analysis of the protocol’s security and performance indicates that it outperforms the algorithms used in previous studies, providing superior levels of security and performance.
Preliminary Results from Norway, Slovenia, Portugal, Turkey, Ukraine, and Jordan: Investigating Pre-Service Teachers’ Expected Use of Digital Technology When Becoming Teachers

Digital technology has become an important part of society and deserves attention and in-depth research, which is still lacking. The study presented in this paper includes international perspectives from six countries and examines which factors influence the use of digital technologies in future pedagogical work in primary schools. Specifically, we investigated how pre-service teachers assess their attitudes, knowledge, and skills towards digital technology, and we determined these factors’ role in the pre-service teachers’ future use of digital technologies in pedagogical work. For the purpose of the research, an online questionnaire was used, which contained open-ended questions, optional questions, and five-point Likert-type scales, and Spearman correlation and Stepwise linear regression statistical methods were used in the data analysis. The sample consisted of 573 full-time and part-time undergraduate pre-service teachers from the Faculties of Education’s higher professional programmes in Norway, Slovenia, Portugal, Turkey, Ukraine, and Jordan. Data were collected between June 2021 and May 2022 and processed using IBM SPSS. The present research showed the significant importance of knowledge and skills about digital technologies and professional attitude towards digital tool factors for pre-service teachers’ future professional use of digital technologies. The demonstrated intention of the future use of digital technologies showed the readiness of pre-service teachers regarding the inclusion of digital technologies in pedagogical work. Opportunities for further research are in the implementation of focus groups after surveying pre-service teachers, as well as regular measurements and the inclusion of other important constructs in the regression model.