Reviewerly/exHarmony huggingface dataset:
Spatio-Temporal Player Relation Modeling for Tactic Recognition in Sports Videos

Tactic recognition in sports videos is a challenging task. To address this, we present a novel spatio-temporal relation modeling approach, which captures both detailed player interactions and long-range group dynamics in tactics. In spatial modeling, we propose an Adaptive Graph Convolutional Network (A-GCN), and it represents individual and common patterns of data through local and global graphs to learn diverse player interactions. In temporal modeling, we propose an Attentive Temporal Convolutional Network (A-TCN) and with spatial configurations as input, it builds group dynamics and is robust to redundant content by considering sequence dependencies. Due to adaptive interaction and attentive dynamics modeling, our approach is able to comprehensively describe team cooperation over time in a tactic. We extensively evaluate the proposed approach on the Volleyball dataset and a newly collected VolleyTactic dataset, and the experimental results show its advantage.
Implementation and Early Adoption of an Ethereum-Based Electronic Voting System for the Prevention of Fraudulent Voting

Today’s online voting systems pose security concerns and cannot be used for public elections, while offline voting costs significantly more. As a result, a decentralized electronic voting system is emerging, backed by blockchain technology. With blockchain technology applied to online voting, the system can guarantee transparency and confidentiality because individual voter information and aggregate information are stored in a distributed fashion. Due to its decentralized nature, a blockchain-based voting system is more secure than the existing central server-based online voting system. In this study, an Ethereum-based electronic voting system was developed. This system resolves the issue of fraudulent voting by enhancing the safety and reliability of the electronic voting system.
Execution of synthetic Bayesian model average for solar energy forecasting

Accurate photovoltaic (PV) forecasting is quite crucial in planning and in the regular operation of power system. Stochastic habit along with the high risks in PV signal uncertainty and a probabilistic forecasting model is required to address the numerical weather prediction (NWP) underdispersion. In this study, a new synthetic prediction process based on Bayesian model averaging (BMA) and Ensemble Learning is developed. The proposed model is initiated by the improved self-organizing map (ISOM) clustering K-fold cross-validation for the training process. To provide desirable learning model for different input samples, three learners including long short-term memory (LSTM) network, general regression neural network (GRNN), and non-linear auto-regressive eXogenous NN (NARXNN) are employed. The proposed BMA approach is combined with the output of the learners to obtain accurate and desirable outcomes. Different models are precisely compared with the obtained numerical results over real-world engineering test site, that is, Arta-Solar case study. The numerical analysis and recorded results validate the performance and superiority of the proposed model.
Whole Slide Image Analysis and Detection of Prostate Cancer using Vision Transformers

Prostate cancer (PCa) is the most frequently diagnosed non-skin malignancy in men and the second leading cause of fatality from cancer. The most prognostic marker for PCa is the Gleason grading system on histopathology images. Pathologists examine the Gleason grade on stained tissue specimens of Hematoxylin and Eosin (H&E) based on tumor structural growth patterns from whole slide image (WSI). According to the Gleason grading system, prostate cancers are scaled into five grades based on glandular patterns of differentiation. It varies from grade 1 (normal tumor) to grade 5 (abnormal tumor). Cancer cells that look similar to healthy cells receive a low score. Recent developments in Computer-Aided Detection (CAD) using Artificial Intelligence (AI), mainly Deep learning (DL) have brought the immense scope of automatic detection and recognition at better accuracy in adenocarcinoma like other medical diagnoses. Automated DL systems have delivered promising results from histopathological images to accurate grading of prostatic adenocarcinoma. This study aims to classify multiple patterns of images extracted from the WSI of a prostate biopsy based on the Gleason grading system. First, extract patches from the detected region of interest (ROI), then applying Vision Transformers (ViT) model for classification. Finally, the classified patches are scored and graded. The proposed deep learning model in this research will be able to assist the pathologist and other researchers to identify and treat of prostate cancer.
High-capacity reversible data hiding in encrypted images based on adaptive block encoding

This paper proposes a new high-capacity reversible data hiding scheme in encrypted images. The content owner first divides the cover image into blocks. Then, the block permutation and the bitwise stream cipher processes are applied to encrypt the image. Upon receiving the encrypted image, the data hider analyzes the image blocks and adaptively decides an optimal block-type labeling strategy. Based on the adaptive block encoding, the image is compressed to vacate the spare room, and the secret data are encrypted and embedded into the spare space. According to the granted authority, the receiver can restore the cover image, extract the secret data, or do both. Experimental results show that the embedding capacity of the proposed scheme outperforms state-of-the-art schemes. In addition, security level and robustness of the proposed scheme are also investigated.
Fairness-aware configuration of machine learning libraries

This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs. Data-driven software is increasingly applied in social-critical applications where ensuring fairness is of paramount importance. The existing approaches focus on addressing fairness bugs by either modifying the input dataset or modifying the learning algorithms. On the other hand, the selection of hyperparameters, which provide finer controls of ML algorithms, may enable a less intrusive approach to influence the fairness. Can hyperparameters amplify or suppress discrimination present in the input dataset? How can we help programmers in detecting, understanding, and exploiting the role of hyperparameters to improve the fairness?
A Format-compatible Searchable Encryption Scheme for JPEG Images Using Bag-of-words

The development of cloud computing attracts enterprises and individuals to outsource their data, such as images, to the cloud server. However, direct outsourcing causes the extensive concern of privacy leakage, as images often contain rich sensitive information. A straightforward way to protect privacy is to encrypt the images using the standard cryptographic tools before outsourcing. However, in such a way the possible usage of the outsourced images would be strongly limited together with the services provided to users, like the Content-Based Image Retrieval (CBIR). In this article, we propose a secure outsourced CBIR scheme, in which an encryption scheme is designed for the widely used JPEG-format images, and the secure features can be directly extracted from such encrypted images. Specifically, the JPEG images are encrypted by the block permutation, intra-block permutation, polyalphabetic cipher, and stream cipher. Then secure local histograms are extracted from the encrypted DCT blocks and the Bag-Of-Words (BOW) model is further used to organize the encrypted local features to represent the image. The proposed image encryption gets all of the image data protected and the experimental results show that the proposed scheme achieves improved accuracy with a small file size expansion.
DeXAR

The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments is an active research area, with relevant applications in healthcare and ambient assisted living. The application of Explainable Artificial Intelligence (XAI) to ADLs recognition has the potential of making this process trusted, transparent and understandable. The few works that investigated this problem considered only interpretable machine learning models. In this work, we propose DeXAR, a novel methodology to transform sensor data into semantic images to take advantage of XAI methods based on Convolutional Neural Networks (CNN). We apply different XAI approaches for deep learning and, from the resulting heat maps, we generate explanations in natural language. In order to identify the most effective XAI method, we performed extensive experiments on two different datasets, with both a common-knowledge and a user-based evaluation. The results of a user study show that the white-box XAI method based on prototypes is the most effective.
Online human action detection and anticipation in videos: A survey

To meet the demand for powerful models for practical applications in real time, the focus of research on human actions has shifted from offline detection to online and real-time understanding, such as driver-assistance systems, surveillance analysis, and robot services. In recent years, with the development of video recording acquisition technology and deep learning, online action analysis has made significant progress. However, there is a lack of comprehensive online surveys for online human action detection. In this survey, we discuss two hot real-time concerns: online action detection and action anticipation. Online action/activity detection aims to determine whether an action is currently taking place and what kind of action it is in untrimmed videos. Action anticipation aims to anticipate human actions under limited observation of videos. Online action detection and anticipation require accuracy and low latency of detection when the video is partly observed. We present a comprehensive study that includes the definition, taxonomy, comparison of state-of-the-art techniques, datasets, metrics, challenges, and future directions. We hope that it will provide readers with a detailed understanding of the topic and inspiration for new research directions.
Blockchain-based distributed power transaction mechanism considering credit management

The market-oriented transaction of distributed energy resources is regarded as an important way to solve the problem of local distributed energy consumption and improve the operation economy and security of the distribution network. However, the market for distributed power transactions in China is still in the early stage, and there is a lack of effective and reliable transaction mechanisms for standardizing users' credit behaviors. Therefore, a blockchain-based distributed power transaction mechanism considering credit management is proposed. First, the framework of the distributed power transaction based on blockchain is constructed. On this basis, the process of distributed power transaction considering credit management is designed, which can manage users' credit behaviors comprehensively from the aspects of market access, bidding mechanism, and trading settlement. Then, the smart contract of distributed power transactions is customized. The effectiveness of the proposed mechanism is illustrated by the simulation results on the platform of Remix IDE.
LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL

Images captured in low-brightness environments often lead to poor visibility and exhibit artifacts such as low brightness, low contrast, and color distortion.These artifacts not only affect the visual perception of the human eye but also decrease the performance of computer vision algorithms.Existing deep learning-based image enhancements studies are quite slow and usually require extensive hardware specifications.Conversely, lightweight enhancement approaches do not provide satisfactory performance as compared to state-of-the-art methods.Therefore, we proposed a fast and lightweight deep learning-based algorithm for performing low-light image enhancement using the light channel of Hue Saturation Lightness (HSL).LiCENt stands for Light Channel Enhancement Network that uses a combination of an autoencoder and convolutional neural network (CNN) to train a low-light enhancer to first improve the illumination and later improve the details of the low-light image in a unified framework.This method used a single channel lightness 'L' of HSL color space instead of traditional RGB color channels which helps in reducing the number of learnable parameters by a factor of 8.92, at the most.LiCENt also has significant advantages for the Brilliance Perception Adjustment, which enables the model to avoid issues including over-enhancement and color distortion.The experimental results demonstrate that our approach generalizes well in synthetic and natural low-light images and outperforms other methods in terms of qualitative and quantitative metrics.
Automated pancreas segmentation and volumetry using deep neural network on computed tomography

Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the cancer imaging archive pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.
Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Digital watermarking is considered one of the most promising techniques to verify the authenticity and integrity of digital data. It is used for a wide range of applications, e.g., copyright protection, tamper detection, traitor tracing, maintaining the integrity of data, etc. In the past two decades, a wide range of algorithms for relational database watermarking has been proposed. Even though a number of surveys exist in the literature, they are unable to provide insightful guidance to choose the right watermarking technique for a given application. In this paper, we provide an exhaustive empirical study and thorough comparative analysis of various relational database watermarking techniques in the literature. Our work is different from the existing survey papers as we consider both distortion-based and distortion-free techniques along with a rigorous experimental analysis demonstrating a detailed comparison on robustness, data usability, and computational cost with considerable empirical evidence.
Recognizing the groundwater related to chronic kidney disease of unknown etiology by humic-like organic matter

Abstract Chronic kidney disease of unknown etiology (CKDu) becomes a health concern in developing countries. It is urgent to recognize CKDu-related groundwater in CKDu-prevalent areas. Here, spectral indices showed that DOM from CKDu groundwater was characterized by higher molecular weight, stronger exogenous feature, and greater degree of humification and unsaturation than from non-CKDu groundwater. Parallel factor analysis of fluorescence spectra showed that DOM from CKDu groundwater contained significantly more humic-like substances (C1%) and less protein-like substances than from non-CKDu groundwater. Furthermore, C1% was correlated with concentrations of inorganic chemicals associated with CKDu, indicating the feasibility of using C1% for probing CKDu groundwater. According to our self-developed method, both the non-CKDu probability of groundwater with C1% less than the recognizing threshold (RT, 28.8%) and the CKDu probability of groundwater with C1% larger than RT are 70.1%. This indicates that the C1%-based method is a feasible tool for recognizing CKDu groundwater.
Adaptive evolution strategy with ensemble of mutations for Reinforcement Learning

Evolving the weights of learning networks through evolutionary computation (neuroevolution) has proven scalable over a range of challenging Reinforcement Learning (RL) control tasks. However, similar to most black-box optimization problems, existing neuroevolution approaches require an additional adaptation process to effectively balance exploration and exploitation through the selection of sensitive hyper-parameters throughout the evolution process. Therefore, these methods are often plagued by the computation complexities of such adaptation processes which often rely on a number of sophisticatedly formulated strategy parameters. In this paper, Evolution Strategy (ES) with a simple yet efficient ensemble of mutation strategies is proposed. Specifically, two distinct mutation strategies coexist throughout the evolution process where each strategy is associated with its own population subset. Consequently, elites for generating a population of offspring are realized by co-evaluation of the combined population. Experiments on testbed of six (6) black-box optimization problems which are generated using a classical control problem and six (6) proven continuous RL agents demonstrate the efficiency of the proposed method in terms of faster convergence and scalability than the canonical ES. Furthermore, the proposed Adaptive Ensemble ES (AEES) shows an average of 5 - 10000x and 10 - 100x better sample complexity in low and high dimension problems, respectively than their associated base DRL agents.
Uncertainty-aware Prediction Validator in Deep Learning Models for Cyber-physical System Data

The use of Deep learning in Cyber-Physical Systems (CPSs) is gaining popularity due to its ability to bring intelligence to CPS behaviors. However, both CPSs and deep learning have inherent uncertainty. Such uncertainty, if not handled adequately, can lead to unsafe CPS behavior. The first step toward addressing such uncertainty in deep learning is to quantify uncertainty. Hence, we propose a novel method called NIRVANA (uNcertaInty pRediction ValidAtor iN Ai) for prediction validation based on uncertainty metrics. To this end, we first employ prediction-time Dropout-based Neural Networks to quantify uncertainty in deep learning models applied to CPS data. Second, such quantified uncertainty is taken as the input to predict wrong labels using a support vector machine, with the aim of building a highly discriminating prediction validator model with uncertainty values. In addition, we investigated the relationship between uncertainty quantification and prediction performance and conducted experiments to obtain optimal dropout ratios. We conducted all the experiments with four real-world CPS datasets. Results show that uncertainty quantification is negatively correlated to prediction performance of a deep learning model of CPS data. Also, our dropout ratio adjustment approach is effective in reducing uncertainty of correct predictions while increasing uncertainty of wrong predictions.
DNS tunnels detection via DNS-images

DNS tunneling is a typical attack adopted by cyber-criminals to compromise victims’ devices, steal sensitive data, or perform fraudulent actions against third parties without their knowledge. The fraudulent traffic is encapsulated into DNS queries to evade intrusion detection. Unfortunately, traditional defense systems based on Deep Packet Inspection cannot always detect such traffic. As a result, DNS tunneling is one problem that has worried the cybersecurity community over the past decade. In this paper, we propose a robust and reliable Deep Learning-based DNS tunneling detection approach to mine valuable insight from DNS query payloads. More precisely, several features are first extracted by the DNS flow, and then they are arranged as bi-dimensional images. A Convolutional Neural Network is used to automatically and adaptively learn spatial hierarchies of features to be used in a fully connected neural network for traffic classification. The proposed approach may result in an extremely interesting task in predictive security approaches to attack detection. The effectiveness of the proposal is evaluated in several experiments using a real-world traffic dataset. The obtained results show that our approach achieves 99.99% of accuracy and performs better than state-of-the-art solutions.
Application Analysis of the Machine Learning Fusion Model in Building a Financial Fraud Prediction Model

Financial data fraud by listed companies has brought an extremely bad impact on the market and society. Predicting the financial data fraud of listed companies in advance may reduce losses. Therefore, the key to solving the problem is to build a financial fraud prediction model. This paper analyzes the prediction and identification models of financial fraud at home and abroad in detail, and finds the problems existing in these prediction models. In view of these shortcomings, this paper proposes to build a financial fraud prediction model based on a machine learning fusion model. The first is the unbalanced processing of data samples. The oversampling method is used to improve the model prediction effect by setting a reasonable sampling ratio. Then, four machine learning models (GBDT, random forest, support vector machine, and decision tree) are selected suitable for financial data. The training set is used to optimize the hyperparameters of the four machine learning models separately. This paper proposes integrating the random search and grid search mechanisms to adjust the parameters to the optimum. Finally, a financial fraud prediction model is constructed based on the multimodel fusion of the integrated learning framework. First, the base learner integrates the predicted results of the four models and performs five-fold crossvalidation on the training set. The meta-learner then uses the GBDT model to train integrated data from the first layer, resulting in a fusion model. The experimental results show that the AUC value of the fusion model is significantly higher than that of the single model. Therefore, the fusion model proposed in this paper can effectively improve the prediction effect.