Reviewerly/exHarmony huggingface dataset:
Towards a Threat Model and Security Analysis of Video Conferencing Systems

Video Conferencing has emerged as a new paradigm of communication in the age of COVID-19 pandemic. This technology is allowing us to have real-time interaction during the social distancing era. Even before the current crisis, it was increasingly commonplace for organizations to adopt a video conferencing tool. As people adopt video conferencing tools and access data with potentially less secure equipment and connections, meetings are becoming a target to cyber attackers. Enforcing appropriate security and privacy settings prevents attackers from exploiting the system. To design the video conferencing system's security and privacy model, an exhaustive threat model must be adopted. Threat modeling is a process of optimizing security by identifying objectives, vulnerabilities, and defining the plan to mitigate or prevent potential threats to the system. In this paper, we use the widely accepted STRIDE threat modeling technique to identify all possible risks to video conferencing tools and suggest mitigation strategies for creating a safe and secure system.
Addressing architectural distortion in mammogram using AlexNet and support vector machine

To address the architectural distortion (AD) which is an irregularity in the parenchymal pattern of breast. The nature of AD is extremely complex; still, the study is very much essential because AD is viewed as a primitive sign of breast cancer. In this study, a new convolutional neural network (CNN) based system is developed that performs classification of AD distorted mammograms and other mammograms. In the first part, mammograms undergo pre-processing and image augmentation techniques. In the other half, learned and handcrafted features are retrieved. The AlexNet Pretrained CNN is utilized for extraction of learned features. The support vector machine (SVM) validates the existence of AD. For improved classification, the scheme is tested for various conditions. A sophisticated CNN based system is developed for stepwise analysis of AD. The maximum accuracy, sensitivity and specificity yielded as 92%, 81.50% and 90.83% respectively. The results outperform the conventional methods. Based on the overall study, it is recommended that a combination of CNN pre-trained network and support vector machine is a good option for identification of AD. The study will motivate researchers to find improved methods of high performance. Further, it will also help the radiologists. The AD can develop up to two years before the growth of any anomaly. The proposed system will play an essential role in the detection of early manifestations of breast cancer. The system will aid society to go for better treatment options for women all over the world and curtail the mortality rate.
Compressive imaging for defending deep neural networks from adversarial attacks

Despite their outstanding performance, convolutional deep neural networks (DNNs) are vulnerable to small adversarial perturbations. In this Letter, we introduce a novel approach to thwart adversarial attacks. We propose to employ compressive sensing (CS) to defend DNNs from adversarial attacks, and at the same time to encode the image, thus preventing counterattacks. We present computer simulations and optical experimental results of object classification in adversarial images captured with a CS single pixel camera.
The impact of the soft errors in convolutional neural network on GPUs: Alexnet as case study

Convolutional Neural Networks (CNNs) have been increasingly deployed in many applications, including safety critical system such as healthcare and autonomous vehicles. Meanwhile, the vulnerability of CNN model to soft errors (e.g., caused by radiation induced) rapidly increases, thus reliability is crucial especially in real-time system. There are many traditional techniques for improve the reliability of the system, e.g., Triple Modular Redundancy, but these techniques incur high overheads, which makes them hard to deploy. In this paper, we experimentally evaluate the vulnerable parts of Alexnet mode (e.g., fault injector). Results show that FADD and LD are the top vulnerable instructions against soft errors for Alexnet model, both instructions generate at least 84% of injected faults as SDC errors. Thus, these the only parts of the Alexnet model that need to be hardened instead of using fully duplication solutions.
Quality-driven deep active learning method for 3D brain MRI segmentation

Automatic segmentation of the brain Magnetic Resonance Imaging (MRI) plays a crucial role in many brain MRI processing algorithms, which is effective for the prevention, detection, monitoring, and treatment planning of brain disease. Currently, deep learning algorithms have shown outstanding performance in brain segmentation. Most algorithms train models with fully annotated brain MRI datasets. However, full annotation of 3D brain MRI is laborious and time-consuming. Using sparsely annotated datasets to train models may be one appropriate solution to reduce annotation cost. However, the approach of 3D dense segmentation with sparse annotation has not been fully studied. In this paper, we develop a segmentation framework combined with the quality-driven active learning (QDAL) module for suggestive annotation. In the proposed Active Learning module, attention mechanism and deep supervision mode are used to improve the segmentation accuracy and feedback segmentation quality information. Meanwhile, we observe a high correlation coefficient between the proposed two surrogate metrics and the real segmentation accuracy of per slice in one scan. We validate our framework on two public brain MRI datasets for brain region extraction and brain tissue segmentation. The comparative experiments demonstrate that the QDAL method outperforms the other four popular sampling strategies. The segmentation network with the guidance of the QDAL method only needs 15–20% annotated slices in brain extraction task, and 30–40% annotated slices in tissue segmentation task to achieve competitive results compared with training with full supervision.
The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage

We propose a reversible face de-identification method for video surveillance data, where landmark-based techniques cannot be reliably used. Our solution generates a photorealistic de-identified stream that meets the data protection regulations and can be publicly released under minimal privacy concerns. Notably, such stream still encapsulates the information required to later reconstruct the original scene, which is useful for scenarios, such as crime investigation, where subjects identification is of most importance. Our learning process jointly optimizes two main components: 1) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">public</i> module, that receives the raw data and generates the de-identified stream; and 2) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private</i> module, designed for security authorities, that receives the public stream and reconstructs the original data, disclosing the actual IDs of the subjects in a scene. The proposed solution is landmarks-free and uses a conditional generative adversarial network to obtain synthetic faces that preserve pose, lighting, background information and even facial expressions. Also, we keep full control over the set of soft facial attributes to be preserved/changed between the raw/de-identified data, which extends the range of applications for the proposed solution. Our experiments were conducted in three visual surveillance datasets (BIODI, MARS and P-DESTRE) plus one video face data set (YouTube Faces), showing highly encouraging results. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hugomcp/uu-net</uri> .
An Intelligent Data-Driven Learning Approach to Enhance Online Probabilistic Voltage Stability Margin Prediction

This letter presents a self-adaptive data-driven learning method for enhanced probabilistic prediction of voltage stability margin (VSM). An online probabilistic extreme learning machine (ELM) algorithm based on the power transformation technique is developed. The prediction interval (PI) estimation for VSM is formulated as a Box-Cox transformation (BT) model to take into account uncertainties associated with predictions. The parameters in the transformed model are determined by the maximum likelihood estimator. The proposed PI-based VSM estimation method is applied to power grids with high proliferation of renewable energy generation. It enables to update the prediction model online and adapt to changing operating conditions. Numerical studies along with comparative results demonstrate the efficacy and robustness of the proposed method.
Object Detection Using Improved Bi-Directional Feature Pyramid Network

Conventional single-stage object detectors have been able to efficiently detect objects of various sizes using a feature pyramid network. However, because they adopt a too simple manner of aggregating feature maps, they cannot avoid performance degradation due to information loss. To solve this problem, this paper proposes a new framework for single-stage object detection. The proposed aggregation scheme introduces two independent modules to extract global and local information. First, the global information extractor is designed so that each feature vector can reflect the information of the entire image through a non-local neural network (NLNN). Next, the local information extractor aggregates each feature map more effectively through the improved bi-directional network. The proposed method can achieve better performance than the existing single-stage object detection methods by providing improved feature maps to the detection heads. For example, the proposed method shows 1.6% higher average precision (AP) than the efficient featurized image pyramid network (EFIPNet) for the MicroSoft Common Objects in COntext (MS COCO) dataset.
Collecting data on textiles from the internet using web crawling and web scraping tools

Fibre population surveys are a necessary part of the forensic fibres examination field. They provide valuable information as to which fibres are the most popular and help estimate the likelihood of observing similar properties in a fibre unrelated to the event. The time needed to carry these types of studies is however a major obstacle to wider use. With the advent of e-commerce and digital computation, collecting information from digital sources and structuring it in a convenient way may provide meaningful information on fibres population. It has become more affordable for researchers who can now devote most of their time to extracting meaningful information from the structured data. In this article, we have used a scrapy and kibana/elastic search interface to crawl and scrape a major online clothes retailer. In less than 24 h we have extracted 68 text-based field describing a total of 24,701 clothes to help provide precise estimations of fibres types and color frequencies. We were able to provide data that cotton, polyester, viscose and elastane are the 4 main types of fibres used in the textile industry. Elastane, while being very popular in garments, rarely accounts for more than 10% of the mass while cotton accounts for up to 80% of content. The most common colors are white, black, and blue, with important dependencies to the fibre type. Through further statistics and examples we demonstrate that web scraping techniques have the potential to provide near real-time population studies that can greatly benefit forensic practitioners.
Evaluating pixel-based vs. object-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared (VNIR) bands of WorldView-3 (WV-3) satellite imagery. The study area is Hormuz Island, southern Iran, a salt dome composed of dominant sedimentary and igneous rocks. When performing the object-based image analysis (OBIA) approach, the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine (SVM) algorithm. However, in the pixel-based image analysis (PBIA), the spectra of lithological end-members, extracted from imagery, were used through the spectral angle mapper (SAM) method. Several test samples were used in a confusion matrix to assess the accuracy of classification methods quantitatively. Results showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54% which was 19.33% greater than the accuracy of PBIA. OBIA also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological borders. This research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 imagery. It is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.
Adv-Depth: Self-Supervised Monocular Depth Estimation With an Adversarial Loss

Loss function plays a key role in self-supervised monocular depth estimation methods. Current reprojection loss functions are hand-designed and mainly focus on local patch similarity but overlook the global distribution differences between a synthetic image and a target image. In this paper, we leverage global distribution differences by introducing an adversarial loss into the training stage of self-supervised depth estimation. Specifically, we formulate this task as a novel view synthesis problem. We use a depth estimation module and a pose estimation module to form a generator, and then design a discriminator to learn the global distribution differences between real and synthetic images. With the learned global distribution differences, the adversarial loss can be back-propagated to the depth estimation module to improve its performance. Experiments on the KITTI dataset have demonstrated the effectiveness of the adversarial loss. The adversarial loss is further combined with the reprojection loss to achieve the state-of-the-art performance on the KITTI dataset.
Research on Target Tracking Algorithm Based on Siamese Neural Network

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.
AID: An Automated Detector for Gender-Inclusivity Bugs in OSS Project Pages

The tools and infrastructure used in tech, including Open Source Software (OSS), can embed "inclusivity bugs"- features that disproportionately disadvantage particular groups of contributors. To see whether OSS developers have existing practices to ward off such bugs, we surveyed 266 OSS developers. Our results show that a majority (77%) of developers do not use any inclusivity practices, and 92% of respondents cited a lack of concrete resources to enable them to do so. To help fill this gap, this paper introduces AID, a tool that automates the GenderMag method to systematically find gender-inclusivity bugs in software. We then present the results of the tool's evaluation on 20 GitHub projects. The tool achieved precision of 0.69, recall of 0.92, an F-measure of 0.79 and even captured some inclusivity bugs that human GenderMag teams missed.
Contrasting Contrastive Self-Supervised Representation Learning Pipelines

In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning. Despite the success and adoption of representations learned through this paradigm, much is yet to be understood about how different training methods and datasets influence performance on downstream tasks. In this paper, we analyze contrastive approaches as one of the most successful and popular variants of self-supervised representation learning. We perform this analysis from the perspective of the training algorithms, pre-training datasets and end tasks. We examine over 700 training experiments including 30 encoders, 4 pre-training datasets and 20 diverse downstream tasks. Our experiments address various questions regarding the performance of self-supervised models compared to their supervised counterparts, current benchmarks used for evaluation, and the effect of the pre-training data on end task performance. Our Visual Representation Benchmark (ViRB) is available at: https://github.com/allenai/virb.
Indonesian Sign Language Recognition using YOLO Method

Abstract Sign language is a form of communication commonly used by people with hearing impairment or people with speech impediments. Not all ordinary people understand the language. The translation of sign language into the alphabet/text automatically will facilitate the communication of the deaf with ordinary people. This research aims to develop a sign language recognition system that can process input from video data using You Only Look Once (YOLO) in real-time. YOLO is an object detection method based on Convolutional Neural Network (CNN), which is accurate and fast. Retraining the Yolov3 pre-trained model is performed with adjustments to the number of channels and classes according to the sign language recognition requirement. In this research, we collect datasets independently based on the Indonesian Sign Language (BISINDO). In the experiment using image data, the system achieves 100% precision, recall, accuracy, and F1 score. While using video data, the system’s performance gets precision 77.14%, recall 93.1%, accuracy 72.97%, and F1 score 84.38%, with a speed of 8 fps.
Channel-exchanged feature representations for person re-identification

Benefiting from convolutional neural networks, person re-identification (Re-ID) has achieved great improvements with most state-of-the-art studies. However, person Re-ID in real scenarios is still confronted by intricate challenges, especially regarding the color problem. Scenarios in which different people wear clothes with the same or similar colors will make person Re-ID more difficult. In this work, we formulate a novel two-stream channel-exchanged multi-layer network (CENet), which can not only learn color-robust features to alleviate color interference but also essentially be regarded as a data augmentation method. For the first stream, we use the original RGB image as the input to extract global pedestrian features. Furthermore, we can obtain six images with different colors by exchanging the three channels of the RGB image, and then, we randomly select one image, which is fed to the second stream to alleviate the interference of colors. Moreover, by swapping RGB channels, we can obtain six types of images that still retain the same identities, increasing the training sets immensely. Extensive experiments conducted on three benchmark datasets, Market-1501, DukeMTMC-reID and CUHK03, demonstrate that our proposed method achieves the best Rank-1/mAP of 95.6%/88.1% and 89.2%/77.5% on the Market-1501 and DukeMTMC-reID datasets, respectively, outperforming current state-of-the-art methods significantly.
An improved K-means algorithm for underwater image background segmentation

Abstract Conventional algorithms fail to obtain satisfactory background segmentation results for underwater images. In this study, an improved K-means algorithm was developed for underwater image background segmentation to address the issue of improper K value determination and minimize the impact of initial centroid position of grayscale image during the gray level quantization of the conventional K-means algorithm. A total of 100 underwater images taken by an underwater robot were sampled to test the aforementioned algorithm in respect of background segmentation validity and time cost. The K value and initial centroid position of grayscale image were optimized. The results were compared to the other three existing algorithms, including the conventional K-means algorithm, the improved Otsu algorithm, and the Canny operator edge extraction method. The experimental results showed that the improved K-means underwater background segmentation algorithm could effectively segment the background of underwater images with a low color cast, low contrast, and blurred edges. Although its cost in time was higher than that of the other three algorithms, it none the less proved more efficient than the time-consuming manual segmentation method. The algorithm proposed in this paper could potentially be used in underwater environments for underwater background segmentation.
Efficient streaming subgraph isomorphism with graph neural networks

Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do not scale to a dynamic setting of a continuous stream of queries. In this paper, we address the scalability challenges induced by a stream of subgraph isomorphism queries by caching and re-use of previous results. We first present a novel subgraph index based on graph embeddings that serves as the foundation for efficient stream processing. It enables not only effective caching and re-use of results, but also speeds-up traditional algorithms for subgraph isomorphism in case of cache misses. Moreover, we propose cache management policies that incorporate notions of reusability of query results. Experiments using real-world datasets demonstrate the effectiveness of our approach in handling isomorphic subgraph search for streams of queries.