Reviewerly/exHarmony huggingface dataset:
Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques

The number of individuals who use credit cards has increased dramatically in recent decades, as has the volume of credit card fraud transactions. Consequently, banks and credit card companies must be able to classify fraudulent credit card transactions so that clients do not have to pay for products they did not purchase. Data Science can easily tackle such challenges, and the value of Machine Learning methodologies cannot be emphasized. The study demonstrates how to model utilizing multiple classifiers and data balance using machine learning approaches to learning about Credit Card Fraud Detection. The data has been observed as an imbalanced dataset that could have inferred not much optimal performance of models. The experimentation on the imbalanced data has been done and observed that XGBoost has yielded good performance with 0.91 precision score and 0.99 accuracy score. The different sampling techniques have been carried out in procedure so as to enhance the scores in terms of precision, recall, f1-score, and accuracy. The Random Oversampling technique has come out to be the best suited technique over the imbalance data and yields 0.99 precision and 0.99 accuracy score, when applied on the best model i.e., XGBoost. The models are then used to compare the results of all of the classifiers employed, resulting in varied conclusions and further research. While working on the study, many data balancing procedures such as oversampling, under sampling, and SMOTE are used, with XGBoost beating residual algorithms with a 99% accuracy score and precision score when Random Over-Sampling is considered. The research suggested the use of data sampling techniques to balance data over the algorithms that show best results under the imbalanced data scenarios, to conclude the best possible performance of the model for fraudulent activities classification.
Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks.
Unsupervised person re-identification via multi-domain joint learning

Deep learning techniques have achieved impressive progress in the task of person re-identification. However, how to generalize a learned model from the source domain to the target domain remains a long-standing challenge. Inspired by the fact that the enrichment of data diversity and the utilization of miscellaneous semantic features can lead to better generalization ability, we design a model that integrates a novel data augmentation method with a multi-label assignment strategy to achieve semantic features decoupling in the source domain. The pre-trained model is employed to extract several kinds of semantic features from the target dataset, and each kind of semantic features is regarded as a specific domain. We then cluster features of each domain and exploit the connection between different clustering results to perform self-distillation for generating more reliable pseudo labels. Finally, the obtained pseudo labels are used to fine-tune the pre-trained model to achieve model transfer from the source domain to the target one. Extensive experiments demonstrate that our approach outperforms some state-of-the-art methods by a clear margin and even surpass some supervised methods. Source code is available at: https://www.github.com/flychen321/MDJL.
RVGAN-TL: A generative adversarial networks and transfer learning-based hybrid approach for imbalanced data classification

Imbalanced data distribution is the main reason for the performance degradation of most supervised classification algorithms. When dealing with imbalanced learning problems, the prediction of traditional classifiers tends to favor the majority class and ignore the minority class which is often much more important. Therefore, it is necessary to balance majority data and minority data before classification. A popular strategy for balancing the two data classes is synthesising minority data. In recent years, generative adversarial networks (GAN) have shown great potential in fitting sample distributions. Based on this, this paper proposes a model combining improved GAN and transfer learning, RVGAN-TL, to solve the imbalanced learning problem of tabular data. As for the improvement of GAN, variational autoencoder (VAE) is used to generate latent variables with a posterior distribution as the input of GAN, and similarity measure loss is introduced into the generator to improve the quality of the minority data generated by GAN. In addition, a roulette wheel selection method is applied to the training data selection in GAN to rebalance data in the overlapping area. When data is balanced, the generated data is used as the source domain and the original data as the target domain, and the transfer learning method is used to train the final classifier. Experiments on 20 real datasets show that the classification performance of the proposed method is significantly improved compared with other popular methods.
A Novel Framework on Security and Energy Enhancement Based on Internet of Medical Things for Healthcare 5.0

Background: The Internet of Medical Things, often known as IoMT, is a revolutionary method of connecting medical equipment and the software that operates on it to the computer networks that are used in healthcare 5.0. The rapid development of smart medical devices on IoMT platforms has led to the adoption of major technologies in the modernization of healthcare procedures, the administration of diseases, and the improvement in patient treatment standards. The IoMT offers a variety of cloud-based applications, including data exchange, data screening, patient surveillance, information collection and analysis, and hygienic hospital attention. Wireless sensor networks (WSNs) are responsible for both the gathering and delivery of data. Method: The safety of patients and their right to privacy are the top priorities in the healthcare sector. Anyone may see and modify the patient’s health information because the data from these smart gadgets are sent wirelessly through the airways. Hence, we developed a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) to provide a high level of security and energy efficient system for healthcare 5.0. Data can be encrypted using the key-based method ECC-EERP. It employs pairs of public and private keys to decrypt and encrypts web traffic and reducse the amount of energy needed by a WSN in aggregate. Result and Discussion: The efficiency of the suggested method was evaluated in comparison with that of a variety of existing methods. The suggested method was evaluated with the use of many parameters such as security, encryption throughput, energy efficiency, network lifetime, communication overload, computation time, and implementation cost. The results showed that the proposed technique provides enhanced security and energy efficiency.
Combining convolutional and vision transformer structures for sheep face recognition

Significant progress has been made in individual livestock recognition based on convolutional neural networks (CNN), however, their performance still needs improvement. Vision transformer (ViT) emerged as a cutting-edge approach, which has been successfully applied in many tasks of the computer vision field. The superior performance of ViT motivates us to study whether ViT can provide more accurate results for sheep face recognition. In this study, we propose MobileViTFace for sheep face recognition. MobileViTFace is a lightweight sheep face recognition model which combines the convolutional and transformer structures. Compared with the standard ViT model, MobileViTFace does not require too much training data and high computational complexity and is more convenient to deploy on edge devices. Extensive benchmarking tests illustrate that MobileViTFace can secure competitive performance, which achieved 97.13% recognition accuracy on 7,434 sheep face images containing 186 sheep, significantly better than lightweight models based on convolutional structures such as MobileNet, EfficientNet, etc. Parameters and floating-point operations (FLOPs) are reduced by five times compared to ResNet-50, which has similar recognition accuracy. Real-time and accurate recognition results are obtained on the Jetson Nano-based edge computing platform, which is helpful for practical production.
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images

Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.
Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker

In existing joint detection and tracking methods, pairwise relational features are used to match previous track-lets to current detections. However, the features may not be discriminative enough for a tracker to identify a target from a large number of detections. Selecting only high-scored detections for tracking may lead to missed detections whose confidence score is low. Consequently, in the on-line setting, this results in disconnections of tracklets which cannot be recovered. In this regard, we present Sparse Graph Tracker (SGT), a novel online graph tracker using higher-order relational features which are more discriminative by aggregating the features of neighboring detections and their relations. SGT converts video data into a graph where detections, their connections, and the relational features of two connected nodes are represented by nodes, edges, and edge features, respectively. The strong edge features allow SGT to track targets with tracking candidates selected by top-K scored detections with large K. As a result, even low-scored detections can be tracked, and the missed detections are also recovered. The robustness of K value is shown through the extensive experiments. In the MOT16/17/20 and HiEve Challenge, SGT outperforms the state-of-the-art trackers with real-time inference speed. Especially, a large improvement in MOTA is shown in the MOT20 and HiEve Challenge. Code is available at https://github.com/HYUNJS/SGT.
Immersive virtual vs real office environments: A validation study for productivity, comfort and behavioural research

The use of Virtual Reality (VR) to enhance research in the building sector is currently emerging, but validation studies are still limited. This work aims to provide a contribution in VR validation on comfort, productivity, and adaptive behaviour research in offices. 104 participants performed one test session in a real or a virtual room, three cognitive tasks and surveys (on immersivity, cybersickness, comfort, and intention of interaction). The validation process was addressed by evaluating the adequacy of VR in representing real-life scenarios and the benchmark of results. Findings confirmed the ecological validity of the model by an excellent sense of presence, graphical satisfaction, involvement, realism and low cybersickness levels. The absence of significant differences between the results on comfort, productivity, and behaviour, collected in the real and virtual settings, supported the criterion validity. Results highlighted the potentialities of applying VR to support a user-centred design and investigations on multi-domain comfort.
A Collaborative Metaverse based A-La-Carte Framework for Tertiary Education (CO-MATE)

<h2>Abstract</h2> The paper aims to propose a futuristic educational and learning framework called CO-MATE (Collaborative Metaverse-based A-La-Carte Framework for Tertiary Education). The architectural framework of CO-MATE was conceptualized in a four-layered approach which depicts various infrastructure and service layer functionalities. CO-MATE is a technologically driven educational metaverse environment involving loosely coupled building blocks to provide an a-la-carte model for platform designers. For this, the authors had undertaken a systematic mapping study of the pre/post-COVID period to review the application of various emerging technologies. Further, the paper also discusses the core attributes and component offerings of CO-MATE for a technology-driven and automated immersive-learning environment and exemplifies the same through various use cases.
MFGAD: Multi-fuzzy granules anomaly detection

Unsupervised anomaly detection is an important research direction in the process of unsupervised knowledge acquisition. It has been successfully applied in many fields, such as online fraud identification, loan approval, and medical diagnosis. Multi-granularity thinking is an effective information fusion method for solving problems in a multi-granular environment, which allows people to understand and analyze problems from multiple perspectives. However, there are few studies on building anomaly detection models using the idea of multi-fuzzy granules. To this end, this paper constructs a multi-fuzzy granules anomaly detection method by using a fuzzy rough computing model. In this method, a hybrid metric is first used to calculate the fuzzy relations. Then, two ranking sequences are constructed based on the significance of attributes. Furthermore, forward and reverse multi-fuzzy granules are constructed to define anomaly scores based on the ranking sequences. Finally, a multi-fuzzy granules-based anomaly detection algorithm is designed to detect anomalies. The experimental results compared with existing algorithms show the effectiveness of the proposed algorithm.
Strategic Leadership and Its Role on Implementing Public Policies in the Government Departments in Karak Governorate

The major aim of the study was to examine the impact of strategic leadership on implementing public policies in government departments in Karak Governorate. Therefore, it was focused on government departments in Karak Governorate in Jordan. Data were primarily gathered through questionnaires which were distributed to a random sample of (175) respondent. In total, (175) responses were received including (5) invalid to statistical analysis due to uncompleted or inaccurate. Hence, the final sample contained (170) responses. Structural equation modeling (SEM) was conducted to test hypotheses. The study reached the presence of a statistically significant effect of strategic leadership on the implementation of public policies. The study recommended the necessity of paying attention to human capital in government departments in Karak governorate and enhancing interest in ethical practices from major implications in planning activities and processes and participation with stakeholders in providing services and preserving government departments’resources in Karak governorate.
YOLO-S: A Lightweight and Accurate YOLO-like Network for Small Target Detection in Aerial Imagery

Small target detection is still a challenging task, especially when looking at fast and accurate solutions for mobile or edge applications. In this work, we present YOLO-S, a simple, fast, and efficient network. It exploits a small feature extractor, as well as skip connection, via both bypass and concatenation, and a reshape-passthrough layer to promote feature reuse across network and combine low-level positional information with more meaningful high-level information. Performances are evaluated on AIRES, a novel dataset acquired in Europe, and VEDAI, benchmarking the proposed YOLO-S architecture with four baselines. We also demonstrate that a transitional learning task over a combined dataset based on DOTAv2 and VEDAI can enhance the overall accuracy with respect to more general features transferred from COCO data. YOLO-S is from 25% to 50% faster than YOLOv3 and only 15-25% slower than Tiny-YOLOv3, outperforming also YOLOv3 by a 15% in terms of accuracy (mAP) on the VEDAI dataset. Simulations on SARD dataset also prove its suitability for search and rescue operations. In addition, YOLO-S has roughly 90% of Tiny-YOLOv3's parameters and one half FLOPs of YOLOv3, making possible the deployment for low-power industrial applications.
Federated User Modeling from Hierarchical Information

The generation of large amounts of personal data provides data centers with sufficient resources to mine idiosyncrasy from private records. User modeling has long been a fundamental task with the goal of capturing the latent characteristics of users from their behaviors. However, centralized user modeling on collected data has raised concerns about the risk of data misuse and privacy leakage. As a result, federated user modeling has come into favor, since it expects to provide secure multi-client collaboration for user modeling through federated learning. Unfortunately, to the best of our knowledge, existing federated learning methods that ignore the inconsistency among clients cannot be applied directly to practical user modeling scenarios, and moreover, they meet the following critical challenges: 1) Statistical heterogeneity . The distributions of user data in different clients are not always independently identically distributed (IID), which leads to unique clients with needful personalized information; 2) Privacy heterogeneity . User data contains both public and private information, which have different levels of privacy, indicating that we should balance different information shared and protected; 3) Model heterogeneity . The local user models trained with client records are heterogeneous, and thus require a flexible aggregation in the server; 4) Quality heterogeneity . Low-quality information from inconsistent clients poisons the reliability of user models and offsets the benefit from high-quality ones, meaning that we should augment the high-quality information during the process. To address the challenges, in this paper, we first propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL), with a primary goal of serving federated learning for user modeling in inconsistent clients. More specifically, the client train and deliver the local user model via the hierarchical components containing hierarchical information from privacy heterogeneity to join collaboration in federated learning. Moreover, the client updates the personalized user model with a fine-grained personalized update strategy for statistical heterogeneity. Correspondingly, the server flexibly aggregates hierarchical components from heterogeneous user models in the case of privacy and model heterogeneity with a differentiated component aggregation strategy. In order to augment high-quality information and generate high-quality user models, we expand HPFL to the Augmented-HPFL (AHPFL) framework by incorporating the augmented mechanisms, which filters out low-quality information such as noise, sparse information and redundant information. Specially, we construct two implementations of AHPFL, i.e., AHPFL-SVD and AHPFL-AE, where the augmented mechanisms follow SVD (singular value decomposition) and AE (autoencoder), respectively. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of both HPFL and AHPFL frameworks.
E-Branchformer: Branchformer with Enhanced Merging for Speech Recognition

Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR). Several other studies have explored integrating convolution and self-attention but they have not managed to match Conformer's performance. The recently introduced Branchformer achieves comparable performance to Conformer by using dedicated branches of convolution and self-attention and merging local and global context from each branch. In this paper, we propose E-Branchformer, which enhances Branchformer by applying an effective merging method and stacking additional point-wise modules. E-Branchformer sets new state-of-the-art word error rates (WERs) 1.81% and 3.65% on LibriSpeech test-clean and test-other sets without using any external training data.
Digital finance and corporate financial fraud

Corporate financial fraud harms the interests of investors and affects the healthy development of the capital market. Understanding corporate financial fraud has important academic value and practical significance. Digital finance been rapidly developing over the past few years and scholars are investigating strategies for using digital finance as a tool to curb corporate financial fraud. This paper empirically examines the direct effect, intrinsic mechanism, and heterogeneous effect of digital finance on corporate financial fraud based on panel data of A-share listed corporations in China from 2011 to 2020. Results show that digital finance significantly inhibits corporate financial fraud. The breadth of coverage and depth of usage within digital finance show inhibitory effects on corporate financial fraud. This suggests that a combination of coverage and depth is needed to improve the success of digital finance on corporate financial fraud. The internal mechanisms suggest that digital finance inhibits corporate financial fraud by alleviating financing constraints, reducing corporate leverage, and decreasing agency costs. The heterogeneity analysis shows digital finance has a greater inhibitory effect for large-scale corporates, state-owned corporates, and corporates in areas with low degree of marketization. Our findings can provide reference for financial institutions, investors, analysts, and regulators to improve the quality of decision-making.