Reviewerly/exHarmony huggingface dataset:
Facial Mask Detection using Semantic Segmentation

Face Detection has evolved as a very popular problem in Image processing and Computer Vision. Many new algorithms are being devised using convolutional architectures to make the algorithm as accurate as possible. These convolutional architectures have made it possible to extract even the pixel details. We aim to design a binary face classifier which can detect any face present in the frame irrespective of its alignment. We present a method to generate accurate face segmentation masks from any arbitrary size input image. Beginning from the RGB image of any size, the method uses Predefined Training Weights of VGG - 16 Architecture for feature extraction. Training is performed through Fully Convolutional Networks to semantically segment out the faces present in that image. Gradient Descent is used for training while Binomial Cross Entropy is used as a loss function. Further the output image from the FCN is processed to remove the unwanted noise and avoid the false predictions if any and make bounding box around the faces. Furthermore, proposed model has also shown great results in recognizing non-frontal faces. Along with this it is also able to detect multiple facial masks in a single frame. Experiments were performed on Multi Parsing Human Dataset obtaining mean pixel level accuracy of 93.884 % for the segmented face masks.
Electric Vehicle Power Trading Mechanism Based on Blockchain and Smart Contract in V2G Network

In order to realize peer-to-peer (P2P) transactions between electric vehicles (EVs) in vehicle-to-grid (V2G) networks, we propose an EV power trading model based on blockchain and smart contract. Firstly, based on the blockchain and smart contract technology, a decentralized power trading model is proposed to realize the information equivalence and transparent openness of power trading. Then, considering the randomness and uncertainty of EV charging and discharging, the EV trading parties use the reverse auction mechanism based on dynamic pricing strategy to complete the transaction matching, which can not only improve the profit of the less competitive power seller, but also it can reduce the cost of the electricity purchaser. Finally, in order to verify the feasibility of our proposed scheme, V2G's EV power trading smart contract was designed, and the smart contract was released to Ethereum and simulated experiments were carried out. The effectiveness of the proposed scheme is verified by simulation experiments and comparison with traditional power trading schemes.
Cross-site scripting (XSS) attacks and mitigation: A survey

The results of the Cisco 2018 Annual Security Report show that all analyzed web applications have at least one vulnerability. It also shows that web attacks are becoming more frequent, specific and sophisticated. According to this report, 40% of all attack attempts lead to a method known as Cross-Site Scripting (XSS), which was the most widely used technique. According to the OWASP Top 10 - 2017 security risk, this type of attack is ranked No. 7, and it is noted that XSS is present in approximately two thirds of all web applications. This attack occurs when a malicious user uses a web application to execute or send malicious code on another user's computer. Also, Cross Site Scripting is a type of cyber attack by which vulnerabilities are searched in a web application to introduce a harmful script. This implies that user information can be affected by stealing cookies, phishing, or attacking a company's entire network. In this context, we have analyzed a total of 67 documents to collect information of the tools and methods that the scientific community has used to detect and mitigate these type of attack. It has been hypothesized that the trend in the proposal of traditional methods to mitigate XSS attacks is greater than the proposals that use some artificial intelligence technique. Our results show that the trend is increasing in the proposals that analyze the content of web pages (13.20%), as well as those that serve as a toolkit for web browsers (16.98%). Also, we have found that there is a low tendency in the use of artificial intelligence techniques to detect or mitigate this attack, using Web Classifiers (9.43%).
Effects of steering locomotion and teleporting on cybersickness and presence in HMD-based virtual reality

While head-mounted display-based virtual reality (VR) can produce compelling feelings of presence (or “being there”) in its users, it also often induces motion sickness. This study compared the presence, cybersickness and perceptions of self-motion (or “vection”) induced when using two common methods of virtual locomotion: steering locomotion and teleporting. In four trials, conducted over two separate days, 25 participants repeatedly explored the “Red Fall” virtual environment in the game Nature Treks VR for 16 min at a time. Although steering locomotion was found to be more sickening on average than teleporting, 9 participants reported more severe sickness while teleporting. On checking their spontaneous postural activity before entering VR, these “TELEsick” participants were found to differ from “STEERsick” participants in terms of their positional variability when attempting to stand still. While cybersickness was not altered by having the user stand or sit during gameplay, presence was enhanced by standing during virtual locomotion. Cybersickness was found to increase with time in trial for both methods of virtual locomotion. By contrast, presence only increased with time in trial during steering locomotion (it did not vary over time when teleporting). Steering locomotion was also found to generate greater presence for female, but not male, participants. While there was not a clear advantage for teleporting over steering locomotion in terms of reducing cybersickness, we did find some evidence of the benefits of steering locomotion for presence.
HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation

Estimating 3D human pose from a single image is a challenging task. This work attempts to address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an intermediate state - Part-Centric Heatmap Triplets (HEMlets), which shortens the gap between the 2D observation and the 3D interpretation. The HEMlets utilize three joint-heatmaps to represent the relative depth information of the end-joints for each skeletal body part. In our approach, a Convolutional Network(ConvNet) is first trained to predict HEMlests from the input image, followed by a volumetric joint-heatmap regression. We leverage on the integral operation to extract the joint locations from the volumetric heatmaps, guaranteeing end-to-end learning. Despite the simplicity of the network design, the quantitative comparisons show a significant performance improvement over the best-of-grade method (by 20% on Human3.6M). The proposed method naturally supports training with "in-the-wild'' images, where only weakly-annotated relative depth information of skeletal joints is available. This further improves the generalization ability of our model, as validated by qualitative comparisons on outdoor images.
Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification

In this paper, we present a new deep learning model to classify hematoxylin-eosin-stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas). Our model uses a parallel structure consist of a convolutional neural network (CNN) and a recurrent neural network (RNN) for image feature extraction, which is greatly different from the common existed serial method of extracting image features by CNN and then inputting them into RNN. Then, we introduce a special perceptron attention mechanism, which is derived from the natural language processing (NLP) field, to unify the features extracted by the two different neural network structures of the model. In the convolution layer, general batch normalization is replaced by the new switchable normalization method. And the latest regularization technology, targeted dropout, is used to substitute for the general dropout in the last three fully connected layers of the model. In the testing phase, we use the model fusion method and test time augmentation technology on three different datasets of hematoxylin-eosin-stained breast biopsy images. The results demonstrate that our model significantly outperforms state-of-the-art methods.
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (e.g., articles with citation link tend to be in the same class), graph neural networks could be more sensitive to the perturbations, since the perturbations from connected examples exacerbate the impact on a target example. Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization. However, existing AT methods focus on standard classification, being less effective when training models on graph since it does not model the impact from connected examples. In this work, we explore adversarial training on graph, aiming to improve the robustness and generalization of models learned on graph. We propose Graph Adversarial Training (GraphAT), which takes the impact from connected examples into account when learning to construct and resist perturbations. We give a general formulation of GraphAT, which can be seen as a dynamic regularization scheme based on the graph structure. To demonstrate the utility of GraphAT, we employ it on a state-of-the-art graph neural network model - Graph Convolutional Network (GCN). We conduct experiments on two citation graphs (Citeseer and Cora) and a knowledge graph (NELL), verifying the effectiveness of GraphAT which outperforms normal training on GCN by 4.51 percent in node classification accuracy. Codes are available via: https://github.com/fulifeng/GraphAT.
The impact of slippage on the data quality of head-worn eye trackers

Abstract Mobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant’s head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs’ Pupil in 3D mode, and (iv) Pupil-Labs’ Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8–3.1 ∘ increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup.
Defining organisational information security culture—Perspectives from academia and industry

The ideal or strong information security culture can aid in minimising the threat of humans to information protection and thereby aid in reducing data breaches or incidents in organisations. This research sets out to understand how information security culture is defined from an academic and industry perspective using a mixed-method approach. The definition, factors necessary to instil the ideal information security culture and the potential impact of the ideal information security culture were investigated from both perspectives. A survey approach was implemented to obtain the views from industry and 512 respondents from organisations, many of which operate at an international level, participated in the survey. The research presents a description of information security culture, integrating the existing literature and expanding on it with the views of industry, thereby giving clarity to the concept. The ideal information security culture was identified with the top traits relating to aspects such as an aware and knowledgeable workforce implementing conscientious, caring behaviour to comply with policies as guided by management. The factors that could positively influence an information security culture were identified, consolidated and expanded to five external factors and twenty internal factors. Organisations that have a strong information security culture were identified as achieving mutual trust and integrity through the protection of their information. The description of an information security culture can be used as a baseline to define and understand the concept, identify a single, comprehensive set of factors to be implemented, comprehend the traits of such a culture, as well as what an organisation can achieve by having a strong information security culture. The analysis showed that scientific interpretations of the definitions and factors of information security culture are much wider than their understanding of the industry. Both the results from the scoping review of papers and the feedback from the industry experts are synthesised visually to provide an organisational information security culture model (OISCM). The definition, factors, and model that influence the organisational culture of information security, have prognostic value for industry. For scientists, this is an important topic of research on methods and forms of increasing the level of this knowledge.
C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion

We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images. We do so by learning a deep network that reconstructs a 3D object from a single view at a time, accounting for partial occlusions, and explicitly factoring the effects of viewpoint changes and object deformations. In order to achieve this factorization, we introduce a novel regularization technique. We first show that the factorization is successful if, and only if, there exists a certain canonicalization function of the reconstructed shapes. Then, we learn the canonicalization function together with the reconstruction one, which constrains the result to be consistent. We demonstrate state-of-the-art reconstruction results for methods that do not use ground-truth 3D supervision for a number of benchmarks, including Up3D and PASCAL3D+.
Towards Automated Reentrancy Detection for Smart Contracts Based on Sequential Models

In the last decade, smart contract security issues lead to tremendous losses, which has attracted increasing public attention both in industry and in academia.Researchers have embarked on efforts with logic rules, symbolic analysis, and formal analysis to achieve encouraging results in smart contract vulnerability detection tasks.However, the existing detection tools are far from satisfactory.In this paper, we attempt to utilize the deep learning-based approach, namely bidirectional long-short term memory with attention mechanism (BLSTM-ATT), aiming to precisely detect reentrancy bugs.Furthermore, we propose contract snippet representations for smart contracts, which contributes to capturing essential semantic information and control flow dependencies.Our extensive experimental studies on over 42,000 real-world smart contracts show that our proposed model and contract snippet representations significantly outperform state-of-the-art methods.In addition, this work proves that it is practical to apply deep learning-based technology on smart contract vulnerability detection, which is able to promote future research towards this area.
A Blockchain-Based Secure Image Encryption Scheme for the Industrial Internet of Things

Smart cameras and image sensors are widely used in industrial processes, from the designing to the quality checking of the final product. Images generated by these sensors are at continuous risk of disclosure and privacy breach in the industrial Internet of Things (IIoT). Traditional solutions to secure sensitive data fade in IIoT environments because of the involvement of third parties. Blockchain technology is the modern-day solution for trust issues and eliminating or minimizing the role of the third party. In the context of the IIoT, we propose a permissioned private blockchain-based solution to secure the image while encrypting it. In this scheme, the cryptographic pixel values of an image are stored on the blockchain, ensuring the privacy and security of the image data. Based on the number of pixels change rate (NPCR), the unified averaged changed intensity (UACI), and information entropy analysis, we evaluate the strength of proposed image encryption algorithm ciphers with respect to differential attacks. We obtained entropy values near to an ideal value of 8, which is considered to be safe from brute force attack. Encrypted results show that the proposed scheme is highly effective for data leakage prevention and security.
A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine

Intrusion detection is a challenging technology in the area of cyberspace security for protecting a system from malicious attacks. A novel accurate and effective misuse intrusion detection system that relies on specific attack signatures to distinguish between normal and malicious activities is therefore presented to detect various attacks based on an extreme learning machine with a hybrid kernel function (HKELM). First, the derivation and proof of the proposed hybrid kernel are given. A combination of the gravitational search algorithm (GSA) and differential evolution (DE) algorithm is employed to optimize the parameters of HKELM, which improves its global and local optimization abilities during prediction attacks. In addition, the kernel principal component analysis (KPCA) algorithm is introduced for dimensionality reduction and feature extraction of the intrusion detection data. Then, a novel intrusion detection approach, KPCA-DEGSA-HKELM, is obtained. The proposed approach is eventually applied to the classic benchmark KDD99 dataset, the real modern UNSW-NB15 dataset and the industrial intrusion detection dataset from the Tennessee Eastman process. The numerical results validate both the high accuracy and the time-saving benefit of the proposed approach.
Efficient treatment of outliers and class imbalance for diabetes prediction

Learning from outliers and imbalanced data remains one of the major difficulties for machine learning classifiers. Among the numerous techniques dedicated to tackle this problem, data preprocessing solutions are known to be efficient and easy to implement. In this paper, we propose a selective data preprocessing approach that embeds knowledge of the outlier instances into artificially generated subset to achieve an even distribution. The Synthetic Minority Oversampling TEchnique (SMOTE) was used to balance the training data by introducing artificial minority instances. However, this was not before the outliers were identified and oversampled (irrespective of class). The aim is to balance the training dataset while controlling the effect of outliers. The experiments prove that such selective oversampling empowers SMOTE, ultimately leading to improved classification performance.
A Survey on Canonical Correlation Analysis

In recent years, the advances in data collection and statistical analysis promotes canonical correlation analysis (CCA) available for more advanced research. CCA is the main technique for two-set data dimensionality reduction such that the correlation between the pairwise variables in the common subspace is mutually maximized. Over 80-years of developments, a number of CCA models have been proposed according to different machine learning mechanisms. However, the field lacks an insightful review for the state-of-art developments. This survey targets to provide a well-organized overview for CCA and its extensions. Specifically, we first review the CCA theory from the perspective of both model formation and model optimization. The association between two popular solution methods, i.e., eigen value decomposition (EVD) and singular value decomposition (SVD), are discussed. Following that, we present a taxonomy of current progresses and classify them into seven groups: 1) multi-view CCA, 2) probabilistic CCA, 3) deep CCA, 4) kernel CCA, 5) discriminative CCA, 6) sparse CCA and 7) locality preserving CCA. For each group, we demonstrate two or three representative mathematical models, identifying their strengths and limitations. We summarize the representative applications and numerical results of these seven groups in real-world practices, collecting the data sets and open-sources for implementation. In the end, we provide several promising future research directions that can improve the current state of the art.
A blockchain-based smart home gateway architecture for preventing data forgery

Abstract With the advancement of Information and Communication Technology (ICT) and the proliferation of sensor technologies, the Internet of Things (IoT) is now being widely used in smart home for the purposes of efficient resource management and pervasive sensing. In smart homes, various IoT devices are connected to each other, and these connections are centered on gateways. The role of gateways in the smart homes is significant, however, its centralized structure presents multiple security vulnerabilities such as integrity, certification, and availability. To address these security vulnerabilities, in this paper, we propose a blockchain-based smart home gateway network that counters possible attacks on the gateway of smart homes. The network consists of three layers including device, gateway, and cloud layers. The blockchain technology is employed at the gateway layer wherein data is stored and exchanged in the form blocks of blockchain to support decentralization and overcome the problem from traditional centralized architecture. The blockchain ensures the integrity of the data inside and outside of the smart home and provides availability through authentication and efficient communication between network members. We implemented the proposed network on the Ethereum blockchain technology and evaluated in terms of standard security measures including security response time and accuracy. The evaluation results demonstrate that the proposed security solutions outperforms over the existing solutions.