Reviewerly/exHarmony huggingface dataset:
Explainable artificial intelligence in finance: A bibliometric review

This study focuses on explainable artificial intelligence (XAI) in finance. We collected 2,733 articles published between 2013 and 2023 from the Web of Science Core Collection and analyzed trends in literature development and future prospects using an integrated CiteSpace and Natural Language Processing (NLP) bibliometric approach. Our analysis reveals an increase in publication after 2013. Early XAI research had a significant and lasting impact, shifting research focus from traditional finance research to inclusive finance. XAI has diversified financial capabilities, and non-XAI pursues interpretable solutions for improvement. We conclude with a list of prospective subjects for further study.
On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)

Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of many object detectors. The accuracy of the detection and the efficiency of the inference are always trade-offs. We modified the architecture of CenterNet and used different CNN-based backbones of ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Res2Net50, Res2Net101, DLA-34, and hourglass14. A comparison of the modified CenterNet with nine CNN-based backbones is conducted and validated using three challenging datasets, i.e., VisDrone, Stanford Drone dataset (SSD), and AU-AIR. We also implemented well-known off-the-shelf object detectors, i.e., YoloV1 to YoloV7, SSD-MobileNet-V2, and Faster RCNN. The proposed approach and state-of-the-art object detectors are optimized and then implemented on cross-edge platforms, i.e., NVIDIA Jetson Xavier, NVIDIA Jetson Nano, and Neuro Compute Stick 2 (NCS2). A detailed comparison of performance between edge platforms is provided. Our modified CenterNet combination with hourglass as a backbone achieved 91.62%, 75.61%, and 34.82% mAP using the validation sets of AU-AIR, SSD, and VisDrone datasets, respectively. An FPS of 40.02 was achieved using the ResNet18 backbone. We also compared our approach with the latest cutting-edge research and found promising results for both discrete GPU and edge platforms.
Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.
A blockchain-enabled internet of medical things system for breast cancer detection in healthcare

Intelligent and sustainable healthcare systems can considerably benefit from applying Computational Intelligence (CI) and Artificial Intelligence (AI). These technological breakthroughs can reduce the ecological footprint and raise the bar for excellence. Yet, the broad adoption of such technologies for cutting-edge Internet of Things (IoT) applications generates enormous amounts of data, which can heavily strain the available computational resources. The major motivation behind this study is to provide evidence that Gated Recurrent Units (GRUs), a sophisticated subclass of Recurrent Neural Networks (RNNs), can outperform traditional RNNs. These technologies can be effective in identifying and treating breast cancer. This study collects data from tagged IoT devices and trains a GRU-RNN classifier. The Wisconsin Diagnostic Breast Cancer (WDBC) data tests the system’s accuracy. The results show the proposed Internet of Medical Things (IoMT) is more effective than the current methods in recall, accuracy, and precision while preserving 95% of the original GRU-RNN.
Blockchain-based multi-factor authentication: A systematic literature review

Blockchain-based multi-factor authentication (BMFA) combines multi-factor authentication with blockchain technology to provide a reliable and secure authentication mechanism. In other words, it adds a layer of protection and creates a comprehensive system that protects sensitive credentials in a decentralized ledger. This makes it challenging for unauthorized users to access or modify authentication credentials. However, BMFA is still in its infancy, and its effectiveness in preventing potential security breaches and unauthorized access has yet to be explored sufficiently. Thus, its development can be further advanced by reducing complexity, improving flexibility, decreasing costs, and enhancing authenticity. Furthermore, it is becoming increasingly important to ensure robust security measures and authentication mechanisms are in place as distributed systems, such as IoT, Fog, and WSN, become more sophisticated and integrated into our daily lives. Thus, such systems must have effective authentication mechanisms to function efficiently and effectively. This paper presents a comprehensive analysis of using blockchain technology with multi-factor authentication techniques in different subject areas. Also, the paper proposes a blockchain-based multi-factor authentication-as-a- service (BMFAaaS) approach and identifies the key requirements for its effective implementation. A systematic literature review was conducted between 2019–2023 to review existing studies and analyze the results against those key requirements. The review was concluded by discussing research challenges and future works in BMFAaaS for distributed systems.
ADFL: Defending backdoor attacks in federated learning via adversarial distillation

Federated learning enables multi-participant joint modeling with distributed and localized training, thus effectively overcoming the problems of data island and privacy protection. However, existing federated learning frameworks have proven to be vulnerable to backdoor attacks, where attackers embed backdoor triggers into local models during the training phase. These triggers will be activated by crafted inputs during the prediction phase, leading to misclassification targeted by attackers. To address these issues, existing defense methods focus on both backdoor detection and backdoor erasing. However, passive backdoor detection methods cannot eliminate the effect of embedded backdoor patterns, while backdoor erasing may degenerate the model performance and cause extra computation overhead. This paper proposes ADFL, a novel adversarial distillation-based backdoor defense scheme for federated learning. ADFL generates fake samples containing backdoor features by deploying a generative adversarial network (GAN) on the server side and relabeling the fake samples to obtain the distillation dataset. Then, taking the labeled samples as inputs, knowledge distillation which employs the clean model as a teacher and the global model as a student is implemented to revise the global model and eliminate the influence of backdoored Neurons in it, thereby effectively defending against backdoor attacks while maintaining the model performance. Experimental results show that ADFL can lower the attack success rates by 95% while maintaining the main task accuracy above 90%.
On the relative value of imbalanced learning for code smell detection

Summary Machine learning‐based code smell detection (CSD) has been demonstrated to be a valuable approach for improving software quality and enabling developers to identify problematic patterns in code. However, previous researches have shown that the code smell datasets commonly used to train these models are heavily imbalanced. While some recent studies have explored the use of imbalanced learning techniques for CSD, they have only evaluated a limited number of techniques and thus their conclusions about the most effective methods may be biased and inconclusive. To thoroughly evaluate the effect of imbalanced learning techniques for machine learning‐based CSD, we examine 31 imbalanced learning techniques with seven classifiers to build CSD models on four code smell data sets. We employ four evaluation metrics to assess the detection performance with the Wilcoxon signed‐rank test and Cliff's . The results show that (1) Not all imbalanced learning techniques significantly improve detection performance, but deep forest significantly outperforms the other techniques on all code smell data sets. (2) SMOTE (Synthetic Minority Over‐sampling TEchnique) is not the most effective technique for resampling code smell data sets. (3) The best‐performing imbalanced learning techniques and the top‐3 data resampling techniques have little time cost for code smell detection. Therefore, we provide some practical guidelines. First, researchers and practitioners should select the appropriate imbalanced learning techniques (e.g., deep forest) to ameliorate the class imbalance problem. In contrast, the blind application of imbalanced learning techniques could be harmful. Then, better data resampling techniques than SMOTE should be selected to preprocess the code smell data sets.
Detection of Power Poles in Orchards Based on Improved Yolov5s Model

During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to quickly and accurately detect wire poles, and proposed an improved Yolov5s deep learning object detection algorithm named Yolov5s-Pole. The algorithm enhances the model’s generalization ability and robustness by applying Mixup data augmentation technique, replaces the C3 module with the GhostBottleneck module to reduce the model’s parameters and computational complexity, and incorporates the Shuffle Attention (SA) module to improve its focus on small targets. The results show that when the improved Yolov5s-Pole model was used for detecting poles in orchards, its accuracy, recall, and mAP@50 were 0.803, 0.831, and 0.838 respectively, which increased by 0.5%, 10%, and 9.2% compared to the original Yolov5s model. Additionally, the weights, parameters, and GFLOPs of the Yolov5s-Pole model were 7.86 MB, 3,974,310, and 9, respectively. Compared to the original Yolov5s model, these represent compression rates of 42.2%, 43.4%, and 43.3%, respectively. The detection time for a single image using this model was 4.2 ms, and good robustness under different lighting conditions (dark, normal, and bright) was demonstrated. The model is suitable for deployment on agricultural UAVs’ onboard equipment, and is of great practical significance for ensuring the efficiency and flight safety of agricultural UAVs.
Assessing teachers’ digital competence in primary and secondary education: Applying a new instrument to integrate pedagogical and professional elements for digital education

Abstract Teachers’ digital competence (DC) is an important condition for the effective application of technology in education. Although several DC tools have been designed, adjustments to digital education and pedagogical or professional components are still scarce. Therefore, this study aims at developing a new instrument for assessing teachers’ DC regarding their pedagogical and professional activities in the context of digital school and digital education. The study also examines the teachers’ total DC scores and explores the differences between teacher profiles on a sample of 845 teachers in primary and secondary education in Greece. The final instrument comprises 20 items allocated in six components: 1) Teaching preparation ; 2) Teaching delivery &amp; students’ support ; 3) Teaching evaluation &amp; revision ; 4) Professional development ; 5) School’s development ; and 6) Innovating education . The PLS-SEM analysis indicated the validity and reliability of the model in respect to its factorial structure, internal consistency, convergence validity, and model fitness. The results revealed DC inefficiency among teachers in Greece. Primary school teachers reported significantly lower scores in Professional development and Teaching delivery &amp; students support . Female teachers reported significantly lower scores in Innovating education and School's development , but they reported higher scores in Professional development . The contribution and practical implications are discussed in the paper.
Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review

In recent years, numerous explainable artificial intelligence (XAI) use cases have been developed, to solve numerous real problems in industrial applications while maintaining the explainability level of the used artificial intelligence (AI) models to judge their quality and potentially hold the models accountable if they become corrupted. Therefore, understanding the state-of-the-art methods, pointing out recent issues, and deriving future directions are important to drive XAI research efficiently. This paper presents a systematic literature review of local explanation techniques and their practical applications in various industrial sectors. We first establish the need for XAI in response to opaque AI models and survey different local explanation methods for industrial AI applications. The number of studies is then examined with several factors, including industry sectors, AI models, data types, and XAI-based usage and purpose. We also look at the advantages and disadvantages of local explanation methods and how well they work in practical settings. The difficulties of using local explanation techniques are also covered, including computing complexity and the trade-off between precision and interpretability. Our findings demonstrate that local explanation techniques can boost industrial AI models’ transparency and interpretability and give insightful information about them. The efficiency of these procedures must be improved, and ethical concerns about their application must be resolved. This paper contributes to the increasing knowledge of local explanation strategies and offers guidance to academics and industry professionals who want to use these methods in practical settings.
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. To be specific, we regard the original input graph as the first view and generate the second view by graph augmentation with edge modifications. With the guidance of maximizing the similarity of the subgraph pairs, the proposed subgraph-subgraph contrast contributes to more robust subgraph embeddings despite of the structure variation. Moreover, the introduced subgraph-subgraph contrast cooperates well with the widely-adopted node-subgraph and node-node contrastive counterparts for mutual GAD performance promotions. Besides, we also conduct sufficient experiments to investigate the impact of different graph augmentation approaches on detection performance. The comprehensive experimental results well demonstrate the superiority of our method compared with the state-of-the-art approaches and the effectiveness of the multi-view subgraph pair contrastive strategy for the GAD task. The source code is released at https://github.com/FelixDJC/GRADATE.
Chatbot or human? The impact of online customer service on consumers' purchase intentions

Abstract Artificial intelligence (AI) chatbots and human employees have emerged as the dominant forms of online customer service. However, existing research rarely connects the service differences between them in terms of product type, ignoring the interactivity between the two. This study reveals the effect of matching customer service type (AI chatbot vs. human) to product type (search vs. experience) on consumers' purchase intentions through four experiments, revealing the psychological mechanism and boundary condition for the existence of this effect. It shows that (1) the match between customer service type and product type positively affects consumers' purchase intentions; (2) this matching effect is mediated by processing fluency and perceived service quality; and (3) the matching effect works only when consumers' demand certainty is low. These findings enrich the theoretical study of online customer service, and provide marketing insights for companies to improve the adoption of AI chatbots and human employees.
Use of blockchain in the agri-food value chain: State of the art in Spain and some lessons from the perspective of public support.

The Common Agricultural Policy (CAP) of the European Union, in effect since January 2023, seeks to differentiate products on quality, provide greater transparency on food origin, and on the transactions and the actors involved in the supply chain. At the same time, in Spain, the adoption of new technologies for the digitization of the agri-food sector has been proposed as a solution to address structural issues such as lack of competitiveness and innovation. In fact, systems using blockchain-related technologies for food control and traceability have seen great progress in recent years and, currently, the use of blockchain in supply chain management is almost doubling year on year. In this context, this paper investigates the level of development of blockchain technology in the agri-food sector in Spain and its applications for certifying food production conditions within the supply chain, and how it is supported by public policies. It identifies several challenges that need to be addressed for a widespread adoption could take place, such as data recorded on the blockchain, lack of standards, limited scope of projects, and integration of data capture automatically or with other technologies like RFID and AI. The document proposes to reorient public development policies to address these challenges, such as reusing data from, already in place, European data collections for production control and food traceability, educating users and stakeholders about the use of blockchain technology, and fostering legal and technical provisions which ensure system transparency to facilitate a successful implementation of blockchain.
Cyberbullying Detection in Social Networks: A Comparison Between Machine Learning and Transfer Learning Approaches

Information and Communication Technologies fueled social networking and facilitated communication.However, cyberbullying on the platform had detrimental ramifications.The user-dependent mechanisms like reporting, blocking, and removing bullying posts online is manual and ineffective.Bagof-words text representation without metadata limited cyberbullying post text classification.This research developed an automatic system for cyberbullying detection with two approaches: Conventional Machine Learning and Transfer Learning.This research adopted AMiCA data encompassing significant amount of cyberbullying context and structured annotation process.Textual, sentiment and emotional, static and contextual word embeddings, psycholinguistics, term lists, and toxicity features were used in the conventional Machine Learning approach.This study was the first to use toxicity features to detect cyberbullying.This study is also the first to use the latest psycholinguistics features from the Linguistic Inquiry and Word (LIWC) 2022 tool, as well as Empath's lexicon, to detect cyberbullying.The contextual embeddings of ggeluBert, tnBert, and DistilBert have alike performance, however DistilBert embeddings were elected for higher F-measure.Textual features, DistilBert embeddings, and toxicity features that struck new benchmark were the top three unique features when fed individually.The model's performance was boosted to F-measure of 64.8% after feeding with a combination of textual, sentiment, DistilBert embeddings, psycholinguistics, and toxicity features to the Logistic Regression model that outperforms Linear SVC with faster training time and efficient handling of high-dimensionality features.Transfer Learning approach was by fine-tuning optimized version Pre-trained Language Models namely, DistilBert, DistilRoBerta, and Electra-small which were found to have speedier training computation than their base form.The fine-tuned DistilBert resulted with the highest F-measure of 72.42%, surpassing CML.Our research concluded that Transfer Learning was the best for uplifted performance and lesser effort as feature engineering and resampling was omitted.