Reviewerly/exHarmony huggingface dataset:
RTGA: Robust ternary gradients aggregation for federated learning

Federated learning is a privacy-preserving machine learning paradigm that can train a model with decentralized data. Classical federated learning systems are vulnerable to attacks from malicious clients. Although a number of efforts have been made to improve robustness of federated learning, existing solutions are either too costly or attack-specific. In this paper, we propose the Robust Ternary Gradients Aggregation (RTGA) algorithm, which can efficiently handle different attacks using two novel mechanisms. At the client-side, the ternary quantization mechanism compresses gradients using only two bits to store each coordinate of the gradient vector. We adopt the error-feedback mechanism to compensate for the difference between the compressed and actual gradients. At the server-side, the robust aggregation mechanism is designed to detect malicious clients according to the risk score of each client. The risk score is calculated by solving an optimization problem of the divergence among clients. Furthermore, we provide analysis of the compression and robustness properties of our proposed algorithm. Extensive experiments show that, compared with existing algorithms, RTGA can mitigate different types of attacks while costing much less communications.
The Role of Data Governance in Cybersecurity for E-Municipal Services

The “e-Municipality Project” in Turkey has developed information systems (IS) for electronic municipal services since May 2017. This chapter discusses the role of data governance in relation to implementing an effective cybersecurity strategy for the project. Data were collected using a workshop with information technology (IT) officials of municipalities and semi-structured interviews with eight information IS managers about their experiences in the project. The research revealed that good data governance has been a prerequisite for any organization focused on its potential to transform processes, decision making and performance. Data governance delivers the insights that organizations need to identify their high-value and high-risk data sets and allocate additional or specific resources to protect this data. Good data governance for secured municipal services is related to a number of trade-offs between the different actors and their responsibilities. The dichotomy between centralization and decentralization of data governance can help to understand the management of cyber risks.
E-Commerce and Cybersecurity Challenges

COVID-19 has accelerated the digital transformation in the business sector as many business organizations adopted electronic commerce to keep their operations running. Business organizations have also increased their participation on social networking applications to attract customers. Due to huge presence of users, social networking sites have also evolved into an emerging marketplace, which is referred as social commerce. There are many security issues involved in technological adoption in different business processes. On the other hand, social media is extensively used for product marketing, so fake information and fake product reviews can also influence consumers purchasing decision, so providing accurate marketing information is also a challenge for business organizations. In this chapter, the authors conduct a systematic literature review to understand the cybersecurity issues faced by business organizations and customers and how recent advances such as fintech, etc. provide additional cybersecurity challenges for business organization to protect themselves and their customers.
Compression of Deep Convolutional Neural Network Using Additional Importance-Weight-Based Filter Pruning Approach

The success of the convolutional neural network (CNN) comes with a tremendous growth of diverse CNN structures, making it hard to deploy on limited-resource platforms. These over-sized models contain a large amount of filters in the convolutional layers, which are responsible for almost 99% of the computation. The key question here arises: Do we really need all those filters? By removing entire filters, the computational cost can be significantly reduced. Hence, in this article, a filter pruning method, a process of discarding a subset of unimportant or weak filters from the original CNN model, is proposed, which alleviates the shortcomings of over-sized CNN architectures at the cost of storage space and time. The proposed filter pruning strategy is adopted to compress the model by assigning additional importance weights to convolutional filters. These additional importance weights help each filter learn its responsibility and contribute more efficiently. We adopted different initialization strategies to learn more about filters from different aspects and prune accordingly. Furthermore, unlike existing pruning approaches, the proposed method uses a predefined error tolerance level instead of the pruning rate. Extensive experiments on two widely used image segmentation datasets: Inria and AIRS, and two widely known CNN models for segmentation: TernausNet and standard U-Net, verify that our pruning approach can efficiently compress CNN models with almost negligible or no loss of accuracy. For instance, our approach could significantly reduce 85% of all floating point operations (FLOPs) from TernausNet on Inria with a negligible drop of 0.32% in validation accuracy. This compressed network is six-times smaller and almost seven-times faster (on a cluster of GPUs) than that of the original TernausNet, while the drop in the accuracy is less than 1%. Moreover, we reduced the FLOPs by 84.34% without significantly deteriorating the output performance on the AIRS dataset for TernausNet. The proposed pruning method effectively reduced the number of FLOPs and parameters of the CNN model, while almost retaining the original accuracy. The compact model can be deployed on any embedded device without any specialized hardware. We show that the performance of the pruned CNN model is very similar to that of the original unpruned CNN model. We also report numerous ablation studies to validate our approach.
Enabling Weakly Supervised Temporal Action Localization From On-Device Learning of the Video Stream

Detecting actions in videos have been widely applied in on-device applications, such as cars, robots, etc. Practical on-device videos are always untrimmed with both action and background. It is desirable for a model to both recognize the class of action and localize the temporal position where the action happens. Such a task is called temporal action location (TAL), which is always trained on the cloud where multiple untrimmed videos are collected and labeled. It is desirable for a TAL model to continuously and locally learn from new data, which can directly improve the action detection precision while protecting customers’ privacy. However, directly training a TAL model on the device is nontrivial. To train a TAL model which can precisely recognize and localize each action, tremendous video samples with temporal annotations are required. However, annotating videos frame by frame is exorbitantly time consuming and expensive. Although weakly supervised temporal action localization (W-TAL) has been proposed to learn from untrimmed videos with only video-level labels, such an approach is also not suitable for on-device learning scenarios. In practical on-device learning applications, data are collected in streaming. For example, the camera on the device keeps collecting video frames for hours or days, and the actions of nearly all classes are included in a single long video stream. Dividing such a long video stream into multiple video segments requires lots of human effort, which hinders the exploration of applying the TAL tasks to realistic on-device learning applications. To enable W-TAL models to learn from a long, untrimmed streaming video, we propose an efficient video learning approach that can directly adapt to new environments. We first propose a self-adaptive video dividing approach with a contrast score-based segment merging approach to convert the video stream into multiple segments. Then, we explore different sampling strategies on the TAL tasks to request as few labels as possible. To the best of our knowledge, we are the first attempt to directly learn from the on-device, long video stream. Experimental results on the THUMOS’14 dataset show that the performance of our approach is comparable to the current W-TAL state-of-the-art (SOTA) work without any laborious manual video splitting.
Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification

Abstract Deep autoencoder (AE) networks show a powerful ability for geochemical anomaly identification. Because of little contribution to the AE network, small probability samples (again, please check this) having comparatively high reconstructed errors can be recognized by the trained model as anomalous samples. However, different autoencoder networks have different abilities for anomaly identification. To test these methods for geochemical anomaly identification, we based our study on stream sediment data of the Cu‐Zn‐Ag metallogenic area in southwest Fujian province as samples. Three unsupervised deep learning models: the autoencoder (AE), multi‐convolutional autoencoder (MCAE), and fusion convolutional autoencoder (FCAE), were used to extract the combined structural, spatial distribution, and mixed features of multiple‐elements. The results showed that the anomalous area delineated by the FCAE model had the best consistency with the known copper mineral occurrences, followed by the MCAE and AE models, with area under the curve values (AUC) of 0.80, 0.78, and 0.61, respectively. FCAE and AE were insensitive to changes in convolution window size, while MCAE extracted more spatial distribution or mixed features. Overall, FCAE focused more on structural distribution or mixed features, combining the advantages of both MCAE and AE. Therefore, FCAE performed best among the three deep learning methods. This study provides a practical basis for selecting and constructing geochemical anomaly recognition models based on deep learning algorithms.
Describe like a pathologist: Glomerular immunofluorescence image caption based on hierarchical feature fusion attention network

Clinically, the histopathological assessment of glomerular immunofluorescence (IF) images is widely-used and indispensable for diagnosis of most chronic kidney diseases, however the histopathological assessment always requires human’s judgements and suffers from the inter-observer variability. In this paper, firstly, a hierarchical feature fusion attention network (HFANet) is proposed to enhance the feature extraction capability for glomerular IF images, which concatenates the weighted feature maps extracted from different depths. Secondly, a proposed intensity equalization (IE) algorithm improves the qualities of images and generated IF descriptions by adjusting all images to a uniform and appropriate intensity level. Thirdly, an encoder–decoder architecture with the HFANet as the core is utilized to generate descriptions that emulate the reading habits of clinicians. At last, we construct a large dataset containing 11,506 glomerular IF images and their corresponding IF descriptions to support the model training. In the testing phase, the proposed framework achieves the BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE-L, and CIDEr of 0.704, 0.563, 0.444, 0.387, 0.760, and 3.594, respectively. Extensive comparative experiments and ablation studies reveal the effectiveness of proposed modules and the generalization performance of the framework for various cases and IF descriptive indicators.
No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.
BEERL: Both Ends Explanations for Reinforcement Learning

Deep Reinforcement Learning (RL) is a black-box method and is hard to understand because the agent employs a neural network (NN). To explain the behavior and decisions made by the agent, different eXplainable RL (XRL) methods are developed; for example, feature importance methods are applied to analyze the contribution of the input side of the model, and reward decomposition methods are applied to explain the components of the output end of the RL model. In this study, we present a novel method to connect explanations from both input and output ends of a black-box model, which results in fine-grained explanations. Our method exposes the reward prioritization to the user, which in turn generates two different levels of explanation and allows RL agent reconfigurations when unwanted behaviors are observed. The method further summarizes the detailed explanations into a focus value that takes into account all reward components and quantifies the fulfillment of the explanation of desired properties. We evaluated our method by applying it to a remote electrical telecom-antenna-tilt use case and two openAI gym environments: lunar lander and cartpole. The results demonstrated fine-grained explanations by detailing input features’ contributions to certain rewards and revealed biases of the reward components, which are then addressed by adjusting the reward’s weights.
A Multi-Head Convolutional Neural Network with Multi-Path Attention Improves Image Denoising

Recently, convolutional neural networks (CNNs) and attention mechanisms have been widely used in image denoising and achieved satisfactory performance. However, the previous works mostly use a single head to receive the noisy image, limiting the richness of extracted features. Therefore, a novel CNN with multiple heads (MH) named MHCNN is proposed in this paper, whose heads will receive the input images rotated by different rotation angles. MH makes MHCNN simultaneously utilize features of rotated images to remove noise. To integrate these features effectively, we present a novel multi-path attention mechanism (MPA). Unlike previous attention mechanisms that handle pixel-level, channel-level, or patch-level features, MPA focuses on features at the image level. Experiments show MHCNN surpasses other state-of-the-art CNN models on additive white Gaussian noise (AWGN) denoising and real-world image denoising. Its peak signal-to-noise ratio (PSNR) results are higher than other networks, such as BRDNet, RIDNet, PAN-Net, and CSANN. The code is accessible at https://github.com/JiaHongZ/MHCNN.
Generating High-Resolution CT Slices from Two Image Series Using Deep-Learning-Based Resolution Enhancement Methods

Medical image super-resolution (SR) has mainly been developed for a single image in the literature. However, there is a growing demand for high-resolution, thin-slice medical images. We hypothesized that fusing the two planes of a computed tomography (CT) study and applying the SR model to the third plane could yield high-quality thin-slice SR images. From the same CT study, we collected axial planes of 1 mm and 5 mm in thickness and coronal planes of 5 mm in thickness. Four SR algorithms were then used for SR reconstruction. Quantitative measurements were performed for image quality testing. We also tested the effects of different regions of interest (ROIs). Based on quantitative comparisons, the image quality obtained when the SR models were applied to the sagittal plane was better than that when applying the models to the other planes. The results were statistically significant according to the Wilcoxon signed-rank test. The overall effect of the enhanced deep residual network (EDSR) model was superior to those of the other three resolution-enhancement methods. A maximal ROI containing minimal blank areas was the most appropriate for quantitative measurements. Fusing two series of thick-slice CT images and applying SR models to the third plane can yield high-resolution thin-slice CT images. EDSR provides superior SR performance across all ROI conditions.
Gold and Bitcoin Optimal Portfolio Research and Analysis Based on Machine-Learning Methods

In recent years, the bitcoin market has developed rapidly and has been recognized as a new type of gold by many investors. It may replace gold as a hedge against inflation and become a new investment asset for financial management. The investment relationship with gold has increasingly important research value and practical significance. This paper modeled daily price flow data from 11 September 2016 to 10 September 2021 to help market traders determine whether they need to buy, hold, or sell assets in their portfolios daily. The model predicts price fluctuations through linear regression prediction of machine learning, K-Nearest Neighbor (KNN) algorithm. In the linear regression prediction, the goodness of fit of gold is 89.44%, and the goodness of fit of Bitcoin is 98.43%. In the test set prediction of KNN algorithm, the goodness of fit of gold is 97.25%, and the goodness of fit of Bitcoin is 95.06%. Based on this, the optimal investment strategy and the initial investment value are obtained. Empirical analysis shows that bitcoin price volatility and gold price volatility have a strong substitution effect; gold and currency used will be a suitable combination of hedging, which will bring momentum for the development of the market economy and become an important force in the sustainable development of a high-quality-driven economy.
Ground Truth Dataset: Objectionable Web Content

Cyber parental control aims to filter objectionable web content and prevent children from being exposed to harmful content. Succeeding in detecting and blocking objectionable content depends heavily on the accuracy of the topic model. A reliable ground truth dataset is essential for building effective cyber parental control models and validation of new detection methods. The ground truth is the measurement for labeling objectionable and unobjectionable websites of the cyber parental control dataset. The lack of publicly accessible datasets with a reliable ground truth has prevented a fair and coherent comparison of different methods proposed in the field of cyber parental control. This paper presents a ground truth dataset that contains 8000 labelled websites with 4000 objectionable websites and 4000 unobjectionable websites. These websites consist of more than 2 million web pages. Creating a ground truth objectionable web content dataset involved a few phases, including data collection, extraction, and labeling. Finally, the presence of bias, using kappa coefficient measurement, is addressed. The ground truth dataset is available publicly in the Mendeley repository.
Accelerate adversarial training with loss guided propagation for robust image classification

Adversarial training is effective to train robust image classification models. To improve the robustness, existing approaches often use many propagations to generate adversarial examples, which have high time consumption. In this work, we propose an efficient adversarial training method with loss guided propagation (ATLGP) to accelerate the adversarial training process. ATLGP takes the loss value of generated adversarial examples as guidance to control the number of propagations for each training instance at different training stages, which decreases the computation while keeping the strength of generated adversarial examples. In this way, our method can achieve comparable robustness with less time than traditional training methods. It also has good generalization ability and can be easily combined with other efficient training methods. We conduct comprehensive experiments on CIFAR10 and MNIST, the standard datasets for several benchmarks. The experimental results show that ATLGP reduces 30% to 60% training time compared with other baseline methods while achieving similar robustness against various adversarial attacks. The combination of ATLGP and ATTA (an efficient adversarial training method) achieves superior acceleration potential when robustness meets high requirements. The statistical propagation in different training stages and ablation studies prove the effectiveness of applying loss guided propagation for each training instance. The acceleration technique can more easily extend adversarial training methods to large-scale datasets and more diverse model architectures such as vision transformers. • We propose an novel formulation to define the efficient adversarial training problem. • ATLGP considers the difference of adversarial attributes among training instance. • Extensive experiments are conducted to prove the outstanding performance of ATLGP.
Anchor-free temporal action localization via Progressive Boundary-aware Boosting

Enormous untrimmed videos from the real world are difficult to analyze and manage. Temporal action localization algorithms can help us to locate and recognize human activity clips in untrimmed videos. Recently, anchor-free temporal action localization methods have gained increasing attention due to small computational costs and no complex hyperparameters of pre-set anchors. Although the performance has been significantly improved, most existing anchor-free temporal action localization methods still suffer from inaccurate action boundary predictions. In this paper, we want to alleviate the above problem through boundary refinement and temporal context aggregation. To this end, a novel Progressive Boundary-aware Boosting Network (PBBNet) is proposed for anchor-free temporal action localization. The PBBNet consists of three main modules: Temporal Context-aware Module (TCM), Instance-wise Boundary-aware Module (IBM), and Frame-wise Progressive Boundary-aware Module (FPBM). The TCM aggregates the temporal context information and provides features for the IBM and the FPBM. The IBM generates multi-scale video features to predict action results coarsely. Compared with IBM, the FPBM focuses on instance features corresponding to action predictions and uses more supervision information for boundary regression. Given action results from IBM, the FPBM uses a progressive boosting strategy to refine the boundary predictions multiple times with supervision from weak to strong. Extensive experiments on three benchmark datasets THUMOS14, ActivityNet-v1.3 and HACS show our PPBNet outperforms all existing anchor-free methods. Further, our PPBNet achieves state-of-the-art performance (72.5% mAP at tIoU = 0.5) on THUMOS14 dataset. • We focus on instance features for anchor-free temporal action localization. • A novel temporal context-aware module is designed to improve the capability. • The state-of-the-art performances are achieved on three public datasets.