Reviewerly/exHarmony huggingface dataset:
Learning From Large-Scale Noisy Web Data With Ubiquitous Reweighting for Image Classification

Many important advances of deep learning techniques have originated from the efforts of addressing the image classification task on large-scale datasets. However, the construction of clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that can learn an image classification model from noisy web data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. With these challenges in mind, we assume every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags, and the labels. In this manner, the influence of bias and noise in the data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the WebVision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks first place in the image classification task.
Are Cryptocurrencies Truly Trustless?

A common narrative of cryptocurrencies presents them as "trustless," decentralized, and autonomous systems. The "trustlessness" is meant to suggest lack of need for third-party verification in blockchain technologies, but the term has been somewhat conflated with broader connotations of "trust." This chapter draws out that nuance, and critically assesses that claim, by emphasizing the human element of trust in cryptocurrencies across various contexts. It does so by highlighting four activities that require both direct human intervention and direct human participation, including: "hard forks" to directly change protocols, the management of cryptocurrency exchanges, the emission of ICOs, and investor recourse to traditional governance institutions including courts of law. The findings of the chapter therefore suggest that the cryptocurrency space is not "trustless" in every sense as it is still reliant on the trust-element in human agency and structure.
High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

Low-dose computed tomography (LDCT) imaging has attracted tremendous attention because it reduces the potential cancer risk for patients by decreasing the radiation dose.However, reducing the radiation dose may cause image quality degradation due to the introduction of noise and artifacts.The details of pathological information mainly exist in the high-frequency domain of LDCT image.Therefore, some useful details may be lost or destroyed while removing the noise and artifacts.To address this problem, we propose a high-frequency sensitive generative adversarial network (HFSGAN).The new generator includes two sub-networks.One is the high-frequency domain U-Net, which is specially designed to deal with the high-frequency components decomposed from LDCT image.The other is image space U-Net, which is used to process information from the whole image of LDCT.In addition, the discriminator in HFSGAN adopts an inception module to increase the receptive field and width of network, and to extract the multi-scale features of the true and false images.The experiments show that the proposed network preserves more texture details of denoised image while removing noise and artifacts.Compared with the state-of-the-art networks, the proposed denoising method achieves better performance both quantitatively and visually.
The Making of Continuous Colormaps

Continuous colormaps are integral parts of many visualization techniques, such as heat-maps, surface plots, and flow visualization. Despite that the critiques of rainbow colormaps have been around and well-acknowledged for three decades, rainbow colormaps are still widely used today. One reason behind the resilience of rainbow colormaps is the lack of tools for users to create a continuous colormap that encodes semantics specific to the application concerned. In this paper, we present a web-based software system, CCC-Tool (short for Charting Continuous Colormaps) under the URL https://ccctool.com, for creating, editing, and analyzing such application-specific colormaps. We introduce the notion of “colormap specification (CMS)” that maintains the essential semantics required for defining a color mapping scheme. We provide users with a set of advanced utilities for constructing CMS's with various levels of complexity, examining their quality attributes using different plots, and exporting them to external application software. We present two case studies, demonstrating that the CCC-Tool can help domain scientists as well as visualization experts in designing semantically-rich colormaps.
Detection and localization of forgery using statistics of DCT and Fourier components

In this paper, we present a comprehensive approach for investigating JPEG compressed test images, suspected of being tampered either by splicing or copy-move forgery (cmf). In JPEG compression, the image plane is divided into non-overlapping blocks of size 8 × 8 pixels. A unified approach based on block-processing of JPEG image is proposed to identify whether the image is authentic/forged and subsequently localize the tampered region in forged images. In the initial step, doubly stochastic model (dsm) of block-wise quantized discrete cosine transform (DCT) coefficients is exploited to segregate authentic and forged JPEG images from a standard dataset (CASIA). The scheme is capable of identifying both the types of forged images, spliced as well as copy-moved. Once the presence of tampering is detected, the next step is to localize the forged region according to the type of forgery. In case of spliced JPEG images, the tampered region is localized using block-wise correlation maps of dequantized DCT coefficients and its recompressed version at different quality factors. The scheme is able to identify the spliced region in images tampered by pasting uncompressed or JPEG image patch on a JPEG image or vice versa, with all possible combinations of quality factors. Alternatively, in the case of copy-move forgery, the duplication regions are identified using highly localized phase congruency features of each block. Experimental results are presented to consolidate the theoretical concept of the proposed technique and the performance is compared with the already existing state of art methods.
Deep Video Deblurring: The Devil is in the Details

Video deblurring for hand-held cameras is a challenging task, since the underlying blur is caused by both camera shake and object motion. State-of-the-art deep networks exploit temporal information from neighboring frames, either by means of spatio-temporal transformers or by recurrent architectures. In contrast to these involved models, we found that a simple baseline CNN can perform astonishingly well when particular care is taken w.r.t. the details of model and training procedure. To that end, we conduct a comprehensive study regarding these crucial details, uncovering extreme differences in quantitative and qualitative performance. Exploiting these details allows us to boost the architecture and training procedure of a simple baseline CNN by a staggering 3.15dB, such that it becomes highly competitive w.r.t. cutting-edge networks. This raises the question whether the reported accuracy difference between models is always due to technical contributions or also subject to such orthogonal, but crucial details.
Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD).
Primary Quantization Matrix Estimation of Double Compressed JPEG Images via CNN

Available model-based techniques for the estimation of the primary quantization matrix in double-compressed JPEG images work only under specific conditions regarding the relationship between the first and second compression quality factors, and the alignment of the first and second JPEG compression grids. In this paper, we propose a single CNN-based estimation technique that can work under a wide range of settings. We do so, by adapting a dense CNN network to the problem at hand. Particular attention is paid to the choice of the loss function. Experimental results highlight several advantages of the new method, including: i) capability of working under very general conditions, ii) improved performance in terms of MSE and Accuracy, especially in the non-aligned case, iii) better spatial resolution due to the ability of providing good results also on small image patches.
Enhanced Memory Network for Video Segmentation

This paper proposes an Enhanced Memory Network (EMN) for semi-supervised video object segmentation. Space-Time Memory Networks has proven the effectiveness of the abundant use of guidance information. To further improve the accuracy of unknown and small targets, we propose to perform fined-grained segmentation based on the correlation attention map. We introduce a siamese network to obtain the semantic similarity and relevance between the tracking objects and the whole image. The feature map extracted from the siamese network on the cropped image is multiplied onto the whole feature map as the attention of proposal objects. Also, an ASPP module is employed to increase the semantic receptive filed to further improve the segmentation accuracy on different scale. Based on the multi-object combination and multi-scale ensemble, the proposed algorithm achieves the first place on the YouTube-VOS 2019 Semi-supervised Video Object Segmentation Challenge with a J&F mean score of 81.8%.
A Cost-sensitive weighted Random Forest Technique for Credit Card Fraud Detection

The act of fraudulent credit card transaction has been increased over the past recent years, as the era of digitization hits our day-to-day life, with people are getting more involved in online banking and online transaction system. Machine learning algorithms have played a significant role in detection of credit card frauds. However, the unbalanced nature of the real-life datasets causes the traditional classification algorithms to perform low in detection of credit card fraud. In this work, a cost-sensitive weighted random forest algorithm has been proposed for effective credit card fraud detection. A cost-function has been defined in the training phase of each tree, in bagging which emphasizes to assign more weight to the minority instances during training. The trees are ranked according to their predictive ability of the minority class instances. The proposed work has been compared with two existing random-forest based techniques for two binary credit card datasets. The efficiency of the model has been evaluated in terms G-mean, F-measure and AUC values. The experimental results have established the proficiency of the proposed model, than the existing ones.
Deep Semantic Feature Learning with Embedded Static Metrics for Software Defect Prediction

Software defect prediction, which locates defective code snippets, can assist developers in finding potential bugs and assigning their testing efforts. Traditional defect prediction features are static code metrics, which only contain statistic information of programs and fail to capture semantics in programs, leading to the degradation of defect prediction performance. To take full advantage of the semantics and static metrics of programs, we propose a framework called Defect Prediction via Attention Mechanism (DP-AM) in this paper. Specifically, DPAM first extracts vectors which are then encoded as digital vectors by mapping and word embedding from abstract syntax trees (ASTs) of programs. Then it feeds these numerical vectors into Recurrent Neural Network to automatically learn semantic features of programs. After that, it applies self-attention mechanism to further build relationship among these features. Furthermore, it employs global attention mechanism to generate significant features among them. Finally, we combine these semantic features with traditional static metrics for accurate software defect prediction. We evaluate our method in terms of F1-measure on seven open-source Java projects in Apache. Our experimental results show that DP-AM improves F1-measure by 11% in average, compared with the state-of-the-art methods.
On Clinical Event Prediction in Patient Treatment Trajectory Using Longitudinal Electronic Health Records

Healthcare process leaves patient treatment trajectory (PTT), described as a sequence of interdependent clinical events affiliated with a large volume of longitudinal therapy and treatment information. Predicting the future clinical event in PTT, as a vital and essential task for providing insights into the entire treatment trajectory, can serve as an efficient and proactive altering service for health service delivery. However, it is challenging because there are long-term dependencies between clinical events, which are irregularly distributed along the temporal axis with varying time intervals. This characteristic inevitably impedes the performance of clinical event prediction (CEP) using the existing approaches. To address this challenge, we propose a novel approach to learn representative and discriminative PTT features for CEP. In detail, multivariate Hawkes process (HP) is adopted to uncover the mutual excitation intensities between clinical event pairs in an interpretable manner. Thereafter, the captured spontaneous and interactional intensities of events are incorporated into recurrent neural networks (RNN) to encode PTT in latent representations, while jointly performing the CEP task based on the extracted trajectory representations. We evaluate the performance of the proposed approach on a real clinical dataset consisting of 13,545 visits of 2,102 heart failure patients. Compared to state-of-the-art methods, our best model achieves 6.4% and 4.1% AUC performance gains on three-months and one-year CEP tasks, respectively. The experimental results demonstrate that the proposed approach outperforms state-of-the-art models in CEP, and can be profitably exploited as a basis for PTT analysis and optimization.
Attention guided neural network models for occluded pedestrian detection

Occluded pedestrian detection has been a research difficulty in computer vision for a long time. The conventional approach to solve this problem is to learn partial detectors, which can be properly integrated for occlusion handling. However, the efficiency of this type of methods is limited in practical applications since the partial detectors can not cover all occlusion patterns. In this paper, an attention guided neural network model (AGNN) is proposed for the occlusion handling of pedestrian detections, which is inspired by the approaches of sentiment classification. Firstly, a fixed-size window slides on a still image without overlapping to generate a set of sub-images. Secondly, a convolutional neural network is employed to extract the high-level features from resulting sub-images. Then, the attention network performs local feature weighting, from which the features representing the body parts of pedestrians are selected. Finally, the feature sequences are classified by recurrent neural network in proper order based on the weighted results. In addition, we explore different mechanisms of attention guidance on the detector for the detections. Compared with the state-of-the-art methods on two standard pedestrian datasets, experimental results demonstrate the comparable performance of our approach in terms of miss rates.
Clustering Facial Attributes: Narrowing the Path From Soft to Hard Biometrics

Despite the success obtained in face detection and recognition over the last ten years of research, the analysis of facial attributes still represents a trend topic. Keeping the full face recognition aside, exploring the potentials of soft biometric traits, i.e. singular facial traits like the nose, the mouth, the hair and so on, is yet considered a fruitful field of investigation. Being able to infer the identity of an occluded face, e.g. voluntary occluded by sunglasses or accidentally due to environmental factors, can be useful in a wide range of operative fields where user collaboration cannot be considered as an assumption. This especially happens when dealing with forensic scenarios in which is not unusual to have partial face photos or partial fingerprints. In this paper, an unsupervised clustering approach is described. It consists in a neural network model for face attributes recognition based on transfer learning whose goal is grouping faces according to common facial features. Moreover, we use the features collected in each cluster to provide a compact and comprehensive description of the faces belonging to each cluster and deep learning as a mean for task prediction in partially visible faces.
Towards a knowledge based Explainable Recommender Systems

Most current Machine Learning based recommender systems act like black boxes, not offering the user any insight into the system logic or justification for the recommendations. Thus, risking losing trust with users and failing to achieve acceptance. The goal of this work is to improve the explainability of recommender systems by using a knowledge extraction method.
Review of Cybersecurity Research Topics, Taxonomy and Challenges: Interdisciplinary Perspective

As cybersecurity is a growing field of science, there is not complete agreement across the scope of cybersecurity research topics. This paper proposes a grouping and classifications of cybersecurity researches to introduce an easily referenceable taxonomy of the cybersecurity research topics. A literature survey was conducted to collect published literature review papers about various cybersecurity researches during the past five years periods. Taxonomic analysis of 99 selected papers, which were grouped based on research topic similarities, produced sets of research categories. It categorized the cybersecurity research topics into 8 areas: (1) Applied cybersecurity, (2) Cybersecurity data science, (3) Cybersecurity education and training, (4) Cybersecurity incidents, (5) Cybersecurity management and policy, (6) Cybersecurity technology, (7) Human and social cybersecurity and (8) Theories in cybersecurity. Although cybersecurity grew out of the computer science field, this paper argues that its interdisciplinary nature (not only technical computer security, but also data, system/technology, and human/social) can attract researcher from various disciplines, such as management, policy, psychology, and so on, to contribute to cybersecurity advancement.
Symbiotic Organisms Search Algorithm for multilevel thresholding of images

Thresholding is a frequently used method in image processing because of its consistency and low computational cost. Otsu's and Kapur's methods are two important techniques that were proved to be best thresholding methods. However, they have high computational complexity when extended to multilevel thresholding because of their exhaustively search. Recently, meta-heuristic algorithms have been successfully applied for thresholding problems. In this study, six different meta-heuristic algorithms based on Otsu's and Kapur's functions; Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Symbiotic Organisms Search (SOS), Artifical Bee Colony (ABC), Genetic Algorithm (GA) and grey Wolf Optimizer (GWO) were used for multilevel thresholding problem and compared. Experimental results suggest that SOS, PSO and FA algorithms often have higher fitness values than other algorithms. Especially when more than two threshold values are determined, SOS algorithm mostly gives higher fitness values. PSNR and SSIM results of the algorithms are similar. In terms of computational complexity, the GWO algorithm has the fastest convergence. For standard deviations of objective functions; more stable results were obtained with SOS based on Kapur's function, SOS and PSO based on Otsu's function. Also, SOS based on Kapur's function was found to be the most successful algorithm in the Friedman test. As a result, although the GWO approached faster, the SOS algorithm produced more consistent results for both objective functions.
Data poisoning attacks on neighborhood‐based recommender systems

Abstract Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighborhood‐based collaborative filtering (CF) is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighborhood‐based recommender systems be easily fooled? To this end, we shed light on the robustness of neighborhood‐based recommender systems and propose a novel data poisoning attack framework, encoding the purpose of attack and constraint against them. We first illustrate how to calculate the optimal data poisoning attack, namely, UNAttack. We inject a few well‐designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real‐world datasets to validate the effectiveness and the transferability of our proposed method. In addition, some interesting phenomena can be found. For example, (i) neighborhood‐based recommender systems with Euclidean distance‐based similarity have strong robustness and (ii) the fake users can be transferred to attack the state‐of‐the‐art CF recommender systems such as neural CF and Bayesian personalized ranking matrix factorization.
Bayesian network models for incomplete and dynamic data

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper, we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.