Reviewerly/exHarmony huggingface dataset:
Temporal information oriented motion accumulation and selection network for RGB-based action recognition

Numerous studies have highlighted the crucial role of motion information in accurate action recognition in videos. However, current methods heavily rely on temporal differences of features extracted by convolutional neural networks (CNNs) to represent motion, which may have two potential limitations: (1) incomplete representation of the moving target contour due to the difference operation, and (2) equal treatment of all extracted motion features, regardless of their relevance to the classification task, which may negatively impact performance. To address these limitations, we propose a novel approach called the Motion Accumulation and Selection Network (MAS-Net). Although our new approach also considers spatial attributes, it draws inspiration from the cumulative and selective nature of human visual attention, with a primary focus on capturing the temporal attributes of actions for recognition. Further, an motion selection module is exploited to prioritize relevant temporal features while filtering out irrelevant ones. Currently, there is a growing demand for action recognition with strong temporal information, as opposed to conventional scene-related datasets such as UCF-101 and HMDB-51. Therefore, we evaluated MAS-Net on benchmark video datasets that primarily emphasize temporal information, including Something-Something V1&V2, Diving48, and Kinetics-400. Our experimental results demonstrate that MAS-Net achieves state-of-the-art performance on Something-Something V1&V2 and Diving48 datasets. Furthermore, when compared to other 2D CNN-based models, MAS-Net exhibits competitive results on the Kinetics-400 dataset while maintaining computational efficiency. These findings highlight the effectiveness and efficiency of MAS-Net for temporal modeling in video analysis tasks.
Blockchain-based electronic health record system with patient-centred data access control

This study examines the application of blockchain technology in addressing the increasing prevalence of mental health issues among young adults in the UK. Despite the recognition of the potential of digital health solutions and mental health apps by healthcare organizations and professionals, many individuals are hesitant to share sensitive information due to security concerns. To address this, the study proposes utilizing blockchain technology to create a patient-centred, transparent system for data access control that adheres to the General Data Protection Regulation (GDPR) and established security protocols. The research includes the development of a proof-of-concept decentralized web application, which incorporates a modified scrum methodology, test-driven development, clearly defined security requirements, and design patterns for security and gas optimization. The study concludes with an evaluation of the application, insights from automated security analytics, and recommendations for future research.
DeepPatch: A Patching-Based Method for Repairing Deep Neural Networks

Deep neural networks (DNNs) have achieved competitive performance in many fields, but they also have defects to be repaired. For example, when a DNN encounters images with some corruption patterns (e.g. fog, Gaussian noise and glass blur) that it has never seen in the training dataset, its classification accuracy may decrease. A typical repair method is to add these corrupted images to the original training dataset and retrain the network. However, in many DNN repair scenarios, the original training dataset is not available, and retraining the network with only corrupted images may significantly decrease its accuracy on clean images. Therefore, without using the original training dataset, how to improve the network's accuracy on corrupted images while avoiding too much impact on clean images becomes an important issue. In this paper, we propose DeepPatch, a patching-based DNN repair method to tackle this issue. We retrain the original network with corrupted images to obtain a new network. In most cases, the new network performs better on corrupted images than the original network, and the original network performs better on clean images than the new network. To combine their advantages, we train an arbiter classifier. For an input image, the arbiter judges whether it is clean or corrupted. If it is clean, the original network is used for classification, otherwise the new network is used. We conduct large-scale experiments on 15 corruption patterns, four DNN models and three baselines. Experimental results show that our method achieves better performance than the baseline methods in most cases.
Modeling of Blockchain and Application Concerns in Blockchain Applications

This paper addresses a modeling approach to describe blockchain concerns and application concerns in blockchain applications. Those concerns are modeled using the meta-model of the functional, static, and dynamic views for blockchain applications. The meta-model was developed by extending the meta-model for UML models. The meta-model for blockchain applications also defined the relationships between blockchain and application concerns. An online shopping blockchain application is modeled in line with the meta-model to demonstrate the approach. In addition, the online shopping blockchain application is implemented with the Hyperledger Fabric framework to validate this research.
Exploring distributed ledger technology for severely constrained environment

Distributed ledger technology (DLT) is the basic building block of Web3, which is gradually surpassing Web 2.0. Organizations worldwide whose key business requirements are immutability, traceability, and transparency are transforming their business to provide solutions that are built on DLT. Recently, the usage of it to achieve sustainable development goals (SDGs) are extensively talked about in the industry as well as academia. However, the current focus is on solving the problem assuming reasonable constraints. But there are scenarios where the participating user base is severely constrained. In this paper, we explore the existing standard and non-standard techniques and provide recommendations that can be useful when building solution architecture for such extreme scenarios.
A novel method based on log files for Smart Contract testing

Smart Contracts have enabled Distributed Ledger Technologies and Blockchains to execute business logic over decentralized and distributed networks. Consequently, Blockchain technology boosted through Smart Contracts has become increasingly widespread in decentralized finance and industrial applications in recent years. Although Blockchain-based development systems have experienced significant growth in adoption areas, Smart Contract testing is still an immature aspect of the Blockchain development life cycle, even if it is involved in mission-critical financial and non-financial applications. To contribute to remedying this state of affairs, we provide the design guidelines for implementing a Smart Contract testing approach based on log analysis and programming language independence. We thus describe the key aspects of this testing strategy, such as the integration of the Elastic Search (ELK) stack and the feasibility of its use in distributed and decentralized applications.
A Notarization System for Water Quality Parameters

The thrust on research and development of blockchain technologies is closely related to the possibility of broadening the horizons of its use. In this regard, it is worthwhile to look into the possibilities of digitally notarizing data and documents on blockchain as a guarantee for the involved parties. In a context of distributed, reliable, innovative and intelligent monitoring, it is important to be able to take advantage of this technology to make the data relating to the analyses carried out on the water to be introduced into the city water network permanent, secure and unalterable. This paper describes in detail the development of a blockchain-based system for the notarization of water quality data, produced by sensing devices disseminated in the water plant and connected to the central administration system. The proposed system provides for the automatic notarization into a permissioned blockchain of a daily report of water quality parameters through a notarization oracle and for manual notarization. The proposed system is an example of how private and state-owned companies may boost their reputation by providing a service to the community.
The Study of Malay’s Prosodic Features Impact on Classical Arabic Accents Recognition

Modeling individual’s variation in speech pattern can be challenging in Automatic Speech Recognition (ASR). In Classical Arabic (CA) language, 20 Quranic accents are permitted for Quranic recitation. An ASR system for CA with accent detection requires a modeling method that can capture speech pattern changes. Here, we study the accentual influences on Malay speakers’ pronunciation and its prosodic impacts towards ASR system for CA language with seven Quranic accents identification. The proposed ASR system was developed over three stages. First, a dataset of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Surah Al-Fatihah</i> recitation was recorded from 14 Malay speakers in seven Quranic accents, forming a total of 5,684 words. Second, various spectral and prosodic features are extracted from the dataset for further classification process. The final stage includes training and testing the classification model. The existing ASR systems are often enabled by Gaussian Mixture Models (GMM) because of its capability to represent a wide range of sample distributions. However, GMM is susceptible to overfitting when the model complexity is high, due to the presence of singularities. To support identification of seven Quranic accents, Universal Background Model (UBM) is adapted to GMM using Maximum A Posteriori (MAP) estimation method. The UBM models were trained over each of Quranic accents, and combined to establish final UBM with 512 mixture components. The proposed ASR system utilizing the GMM-UBM outperformed k-NN, GMM, and GMM-iVector in identifying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Al-Fatihah</i> recitation to the corresponding Quranic accents. The GMM-UBM yields a testing accuracy of 86.148%, which is an increment of 4.435% from utilizing GMM alone.
Multiple Projector Camera Calibration by Fiducial Marker Detection

In this work, we propose a fully automated calibration method for a multi-projector-camera system.The projectors and cameras are un-calibrated, and we consider an arbitrary geometric shape of the projection surface.Without using checkboards or user-provided parameters, our proposed method can automatically estimate calibration parameters for the cameras and projectors and generate compensated content for projection without distortion within a reasonable amount of time.The proposed method utilizes AprilTag markers and modified YOLO v8 with deformable convolution for robust marker detection and correspondence estimation between the projectors and cameras, providing an automatic process for completing calibration and distortion correction.Various experiments have demonstrated that the proposed method outperforms existing methods using checkerboards in terms of calibration accuracy and processing time across various camera-projector configurations.The proposed method can minimize the difficulty of projection mapping, allowing it to be used in everyday situations without requiring a certain level of knowledge about projection mapping theory and related hardware.
Attribute-Aware Deep Hashing With Self-Consistency for Large-Scale Fine-Grained Image Retrieval

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods. Moreover, qualitative results demonstrate that not only the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects, but also our self-consistency designs can effectively overcome simplicity bias in fine-grained hashing.
Predicting Short-Term Variations in End-to-End Cloud Data Transfer Throughput Using Neural Networks

Predicting data transfer throughput of cloud networks plays an important role in several resource optimization applications, such as auto-scaling, replica selection, and load balancing. However, constant short-term variations in cloud networks make the prediction of end-to-end data transfer throughput a very challenging task. The parameters that affect the throughput can be categorized into three different areas: end-system characteristics (e.g., disk I/O bandwidth, CPU utilization), network characteristics (e.g., network bandwidth, latency, background traffic, bandwidth shaping mechanisms), and dataset characteristics (e.g., average file size, dataset size). Although there are promising results in the literature using neural networks, the datasets are collected from network layer devices and memory-to-memory data transfers where end-system and dataset characteristics are not considered as part of the problem. Also, very few studies use multivariate time series data collected from cloud networks, and the variables differ from study to study. In this project, we collected multivariate time series data from Amazon Web Services (AWS) by conducting intra- and inter-region transfers between storage systems and compute resources using monitoring services. This dataset is unique in the sense that end-system metrics in addition to network throughput are collected from both source and destination systems. Different average file size, instance type and regionality parameters provide various settings making the dataset applicable to various types of prediction models. Our multivariate neural network models predict the one-step ahead network throughput with ~ 3.7% and disk throughput with ~ 6.1% error rate, outperforming multivariate models with least-correlated variables and univariate models empowered by transfer learning.
Dynamic Adaptive Attention-Guided Self-Supervised Single Remote-Sensing Image Denoising

Optical remote sensing images are widely used in many fields, and local complex texture details in images usually play a critical role in downstream tasks. However, noise interference will destroy the complex texture in the image, thus reducing the accuracy of downstream tasks. The current attention mechanism usually focuses on the global high-level features in the image, so it cannot effectively focus on the high-frequency information in the local complex texture in the remote sensing image, and obtaining clean remote sensing images to train neural networks is difficult. Therefore, applying the current depth learning based natural image denoising methods directly to optical remote sensing images is challenging. To solve these problems, we propose a dynamic adaptive attention guided self-supervised single remote sensing image denoising network (DAA-SSID). We construct a dynamic adaptive attention module (DAAM) by dynamically calculating the activation intensity of each neuron and combining the spatial feature information extracted from remote sensing images. It can effectively extract complex texture features from remote sensing images when only a single remote sensing image participates in training. And we use independent random Bernoulli sampling in the training and inference stages respectively to prevent over-fitting caused by single-image training. Therefore, compared with other self-supervised denoising methods, our proposed model can denoise remote sensing images with more complex textures when only a single image destroyed by noise is used as the training input. Experiments on synthetic additive gaussian noise data and authentic noise data have shown that the proposed model achieves satisfactory results.
Modeling Noisy Annotations for Point-Wise Supervision

Point-wise supervision is widely adopted in computer vision tasks such as crowd counting and human pose estimation. In practice, the noise in point annotations may affect the performance and robustness of algorithm significantly. In this paper, we investigate the effect of annotation noise in point-wise supervision and propose a series of robust loss functions for different tasks. In particular, the point annotation noise includes spatial-shift noise, missing-point noise, and duplicate-point noise. The spatial-shift noise is the most common one, and exists in crowd counting, pose estimation, visual tracking, etc, while the missing-point and duplicate-point noises usually appear in dense annotations, such as crowd counting. In this paper, we first consider the shift noise by modeling the real locations as random variables and the annotated points as noisy observations. The probability density function of the intermediate representation (a smooth heat map generated from dot annotations) is derived and the negative log likelihood is used as the loss function to naturally model the shift uncertainty in the intermediate representation. The missing and duplicate noise are further modeled by an empirical way with the assumption that the noise appears at high density region with a high probability. We apply the method to crowd counting, human pose estimation and visual tracking, propose robust loss functions for those tasks, and achieve superior performance and robustness on widely used datasets.
Fin-EMRC: An Efficient Machine Reading Comprehension Framework for Financial Entity-Relation Extraction

Extracting entities and their relationships from financial documents is crucial for analyzing and predicting future market trends. However, the current state of the art in this field faces two major challenges: multiple sentences between related entities and poor few-shot performance caused by the vast amount of knowledge required in the financial domain. To address these challenges, we propose a framework for entity-relation extraction in financial documents that leverages multi-turn machine reading comprehension (MRC) and a Longformer model to handle long text dependencies. Furthermore, we proposed a knowledge-enhanced method that incorporates structured knowledge from the financial domain. We also propose a new query template scheme that reduces the computational complexity of inferring complicated entity relations, thus improving the inference efficiency of the MRC framework. We evaluated our proposed framework on both general domain datasets and a real-world annotated Financial Entity-Relation (FinER) dataset. The results demonstrate that our Fin-EMRC framework outperforms currently available state-of-the-art methods in terms of entity and relation extraction accuracies. Moreover, the proposed efficient framework requires only 1.8 to 2.7 times less time for inference compared to the standard MRC framework.
Three-Dimensional Animation Generation and Image Enhancement Technology Based on Multi-Column Convolutional Neural Network Model

This paper aims to improve the quality and fidelity of three-dimensional (3D) animation. Firstly, the application model of Multi-Column Convolutional Neural Network (MCNN) in 3D animation generation and image enhancement is proposed. Aiming at the generation of 3D animation, the MCNN algorithm suitable for this field is selected, and its working principle is explained in detail. Meanwhile, the theoretical basis of 3D animation generation is introduced, which provides a theoretical basis for subsequent experiments. Secondly, for image enhancement, the MCNN is also selected as the key technology, and its application model in image enhancement is explained. Finally, a simulation experiment is carried out to evaluate the effect of the proposed MCNN model in 3D animation generation and image enhancement. By collecting appropriate data sets and setting parameters in the corresponding experimental environment, the performance of the proposed model is evaluated. The results show that, compared with the traditional methods, the MCNN model shows better performance and effect in animation generation and image enhancement tasks. Specifically, this method can still maintain good performance under the conditions of shorter training time, faster reasoning time and lower memory occupation, and this method has advantages in computational efficiency. 3D animation generation and image enhancement technology with MCNN model can significantly improve the animation quality and image fidelity, and satisfactory experimental results have been obtained. The experimental results in this paper verify the application potential of MCNN in 3D animation generation and image enhancement, and provide new ideas and directions for further research and application.
PRUS: Product Recommender System Based on User Specifications and Customers Reviews

The rising popularity of online shopping has led to a steady stream of new product evaluations.Consumers benefit from these evaluations as they make purchasing decisions.Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity while evaluating products for client needs.The main contribution of this research is the inclusion of negative polarity in the analysis of product rankings alongside positive polarity.To account for reviews that contain many sentiments and different elements, the suggested method first breaks them down into sentences.This process aids in determining the polarity of products at the phrase level by extracting elements from product evaluations.The next step is to link the polarity to the review's sentence-level features.Products are prioritized following user needs by assigning relative importance to each of the polarities.The Amazon review dataset has been used in the experimental assessments so that the efficacy of the suggested approach can be estimated.Experimental evaluation of PRUS utilizes rank score (RS) and normalized discounted cumulative gain (nDCG) score.Results indicate that PRUS gives independence to the user to select recommended list based on specific features with respect to positive or negative aspects of the products.