Reviewerly/exHarmony huggingface dataset:
Stabilizing Controllers for Landmark Navigation of Planar Robots in an Obstacle-Ridden Workspace

This paper essays a new solution to the landmark navigation problem of planar robots in the presence of randomly fixed obstacles through a new dynamic updating rule involving the orientation and steering angle parameters of a robot. The dynamic updating rule utilizes a first-order nonlinear ordinary differential equation for the changing of landmarks so that whenever a landmark is updated, the path followed by the robot remains continuous and smooth. This waypoints guidance is via specific landmarks selected from a new set of rules governing the robot’s field of view. The governing control laws guarantee asymptotic stability of the 2D point robot system. As an application, the landmark motion planning and control of a car-like mobile robot navigating in the presence of fixed elliptic-shaped obstacles are considered. The proposed control laws take into account the geometrical constraints imposed on steering angle and guarantee eventual uniform stability of the car-like system. Computer simulations, using Matlab software, are presented to illustrate the effectiveness of the proposed technique and its stabilizing algorithm.
Anomaly Detection With Bidirectional Consistency in Videos

The core component of most anomaly detectors is a self-supervised model, tasked with modeling patterns included in training samples and detecting unexpected patterns as the anomalies in testing samples. To cope with normal patterns, this model is typically trained with reconstruction constraints. However, the model has the risk of overfitting to training samples and being sensitive to hard normal patterns in the inference phase, which results in irregular responses at normal frames. To address this problem, we formulate anomaly detection as a mutual supervision problem. Due to collaborative training, the complementary information of mutual learning can alleviate the aforementioned problem. Based on this motivation, a SIamese generative network (SIGnet), including two subnetworks with the same architecture, is proposed to simultaneously model the patterns of the forward and backward frames. During training, in addition to traditional constraints on improving the reconstruction performance, a bidirectional consistency loss based on the forward and backward views is designed as the regularization term to improve the generalization ability of the model. Moreover, we introduce a consistency-based evaluation criterion to achieve stable scores at the normal frames, which will benefit detecting anomalies with fluctuant scores in the inference phase. The results on several challenging benchmark data sets demonstrate the effectiveness of our proposed method.
Analysing user reviews of inquiry-based learning apps in science education

The science education community is increasingly valuing the use of mobile apps in inquiry-based learning (IBL) to improve learner’ attitudes and their understanding of science concepts. Although there exists a body of research on mobile apps used for IBL in science education, limited attention has been paid to linking apps' features with their pedagogical affordances. Our study addresses this research gap by evaluating science mobile learning apps with respect to IBL pedagogy. Nine functional features of apps that support educational aspects of inquiry-based pedagogy are identified from user reviews, including: fingertip interaction, graphics visualisation, informative materials, location-based services, offline access, search by question, timeline scrolling, user tutorials, and zoom control. The information contained in the version history of the apps is analysed and four educational aspects of IBL supported by the nine functional features are identified as: motivation, conceptualisation, exploration, and conclusion. We have further compared the evolution of the functional features of apps to the educational aspects of inquiry-based pedagogy identified from different versions of apps. The findings of this study show the trend of updated functional features that support IBL and inform practitioners seeking to improve their use of mobile apps to support students' learning in science. We conclude by proposing areas of future research in this field.
Teaching American Sign Language in Mixed Reality

This paper presents a holistic system to scale up the teaching and learning of vocabulary words of American Sign Language (ASL). The system leverages the most recent mixed-reality technology to allow the user to perceive her own hands in an immersive learning environment with first- and third-person views for motion demonstration and practice. Precise motion sensing is used to record and evaluate motion, providing real-time feedback tailored to the specific learner. As part of this evaluation, learner motions are matched to features derived from the Hamburg Notation System (HNS) developed by sign-language linguists. We develop a prototype to evaluate the efficacy of mixed-reality-based interactive motion teaching. Results with 60 participants show a statistically significant improvement in learning ASL signs when using our system, in comparison to traditional desktop-based, non-interactive learning. We expect this approach to ultimately allow teaching and guided practice of thousands of signs.
Data Augmentation for Insider Threat Detection with GAN

In insider threat detection domain, the datasets are highly imbalanced, where the number of user's normal behavior is higher than that of insider's anomalous behavior. A direct approach to handle the class imbalance problem is using data augmentation on the minority class. Existing data augmentation methods mainly produce synthetic samples according with the linear operation based on samples of the minority class. Hence, these methods just focus on local information which leads to the unitarily of the synthetic samples, resulting in overfitting. To enrich the diversity of the synthetic samples, we propose a deep adversarial insider threat detection (DAITD) framework using the Generative Adversarial Networks (GAN) to approximate the true anomalous behavior distribution. Specifically, we first obtain anomalous user behavior representations from the anomalous behavior data (minority class), and then use the generator of the GAN to model the actual anomalous behavior distribution, use the discriminator of the GAN to distinguish whether the synthetic sample from the generator is real or not. In this way, our method is able to generate high quality synthetic samples that are close to the anomalous user behavior. Experimental results show that the DAITD framework outperforms other comparative inside threat detection algorithms.
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images

The inherent imbalance in the data distribution of X-ray security images is one of the most challenging aspects of computer vision algorithms applied in this domain. Most of the prior studies in this field have ignored this aspect, limiting their application in the practical setting. This paper investigates the effect of employing Generative Adversarial Networks (GAN)-based image augmentation, or image synthesis, in improving the performance of computer vision algorithms on an imbalanced X-ray dataset. We used Deep Convolutional GAN (DCGAN) to generate new X-ray images of threat objects and Cycle-GAN to translate camera images of threat objects to X-ray images. We synthesized new X-ray security images by combining threat objects with background X-ray images, which are used to augment the dataset. Then, we trained various Faster (Region Based Convolutional Neural Network) R-CNN models using different augmentation approaches and evaluated their performance on a large-scale practical X-ray image dataset. Experiment results show that image synthesis is an effective approach to combating the imbalance problem by significantly reducing the false-positive rate (FPR) by up to 15.3%. The FPR is further improved by up to 19.9% by combining image synthesis and conventional image augmentation. Meanwhile, a relatively high true positive rate (TPR) of about 94% was maintained regardless of the augmentation method used.
BorderChain: Blockchain-Based Access Control Framework for the Internet of Things Endpoint

The Internet of Things (IoT) providers serve better IoT services each year while producing more IoT gateways and devices to expand their services.However, the security of the IoT ecosystem remains an afterthought for most IoT providers.This action results in many cybersecurity breaches in the field, most likely due to the lack of access control mechanisms.In this paper, we propose BorderChain, an access control framework based on blockchain for IoT endpoints.The security protocol guarantees two properties.First, our proposal assures IoT users and services that they communicate with approved IoT gateways as endpoints, holding verified IoT devices that they need.Second, BorderChain also generates access tokens that the IoT service and users can use to query IoT resources legitimately inside the IoT domains.As a result, the protocol can convince IoT domain owners that the system will only authorize IoT requests that they approve.We realize our protocol in the form of a smart contract to allow many IoT entities such as IoT domain owners, IoT devices, IoT gateways, IoT vendors, IoT services, IoT users, and Internet Service Provider (ISP) to collaborate in a unified environment.We then implement entities in BorderChain as Node JS applications connecting to the Ethereum blockchain as our peer-to-peer platform.Based on our performance evaluation using several Raspberry Pi hardware and our private server, we show that BorderChain can process entities' authentication and authorization requests efficiently using all hardware resources.Finally, we release BorderChain for public use.
PERT: Payload Encoding Representation from Transformer for Encrypted Traffic Classification

Traffic identification becomes more important yet more challenging as related encryption techniques are rapidly developing nowadays. In difference to recent deep learning methods that apply image processing to solve such encrypted traffic problems, in this paper, we propose a method named Payload Encoding Representation from Transformer (PERT) to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique. Based on this, we further provide a traffic classification framework in which unlabeled traffic is utilized to pre-train an encoding network that learns the contextual distribution of traffic payload bytes. Then, the downward classification reuses the pre-trained network to obtain an enhanced classification result. By implementing experiments on a public encrypted traffic data set and our captured Android HTTPS traffic, we prove the proposed method can achieve an obvious better effectiveness than other compared baselines. To the best of our knowledge, this is the first time the encrypted traffic classification with the dynamic word embedding alone with its pre-training strategy has been addressed.
Optimized Polynomial Multiplier Architectures for Post-Quantum KEM Saber

Saber is one of the four finalists in the ongoing NIST post-quantum cryptography standardization project. A significant portion of Saber's computation time is spent on computing polynomial multiplications in polynomial rings with powers-of-two moduli. We propose several optimization strategies for improving the performance of polynomial multiplier architectures for Saber, targeting different hardware platforms and diverse application goals. We propose two high-speed architectures that exploit the smallness of operand polynomials in Saber and can achieve great performance with a moderate area consumption. We also propose a lightweight multiplier that consumes only 541 LUTs and 301 FFs on a small Artix-7 FPGA.
Improving Scalable K-Means++

Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for the initialization. The proposed methods are scalable and can be run in parallel, which make them suitable for initializing large-scale problems. In the experiments, comparison of the proposed methods to the K-means++ and K-means‖ methods is conducted using an extensive set of reference and synthetic large-scale datasets. Concerning the latter, a novel high-dimensional clustering data generation algorithm is given. The experiments show that the proposed methods compare favorably to the state-of-the-art by improving clustering accuracy and the speed of convergence. We also observe that the currently most popular K-means++ initialization behaves like the random one in the very high-dimensional cases.
Validation of Image-Based Neural Network Controllers through Adaptive Stress Testing

Neural networks have become state-of-the-art for computer vision problems because of their ability to efficiently model complex functions from large amounts of data. While neural networks can be shown to perform well empirically for a variety of tasks, their performance is difficult to guarantee. Neural network verification tools have been developed that can certify robustness with respect to a given input image; however, for neural network systems used in closed-loop controllers, robustness with respect to individual images does not address multi-step properties of the neural network controller and its environment. Furthermore, neural network systems interacting in the physical world and using natural images are operating in a black-box environment, making formal verification intractable. This work combines the adaptive stress testing (AST) framework with neural network verification tools to search for the most likely sequence of image disturbances that cause the neural network controlled system to reach a failure. An autonomous aircraft taxi application is presented, and results show that the AST method finds failures with more likely image disturbances than baseline methods. Further analysis of AST results revealed an explainable cause of the failure, giving insight into the problematic scenarios that should be addressed.
Technical Q8A Site Answer Recommendation via Question Boosting

Software developers have heavily used online question and answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q&A sites is "answer hungriness" i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel DeepAns neural network-based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given a post, we first generate a clarifying question as a way of question boosting. We automatically establish the positive, neutral+, neutral- and negative training samples via label establishment. When it comes to answer recommendation, we sort answer candidates by the matching scores calculated by our neural network-based model. To evaluate the performance of our proposed model, we conducted a large scale evaluation on four datasets, collected from the real world technical Q&A sites (i.e., Ask Ubuntu, Super User, Stack Overflow Python and Stack Overflow Java). Our experimental results show that our approach significantly outperforms several state-of-the-art baselines in automatic evaluation. We also conducted a user study with 50 solved/unanswered/unresolved questions. The user study results demonstrate that our approach is effective in solving the answer hungry problem by recommending the most relevant answers from historical archives.
Blockchain and Aadhaar based Electronic Voting System

The frequency of election has been increased in multiple parts of the country. Being the largest democracy, India takes several months for accomplishing elections. Additionally, the elections demand government works, development, cost, tenure, etc. Since 1999, Electronic Voting Machine [EVM]have deployed widely to ensure that the voting can also be made tamper-proof. India has introduced Electronic Voting Machines (EVM) way back in 2004 for its parliamentary election with 380 million networked voters. EVM, being an electronic system was used to record votes. Only Bharat Electronics Limited and Electronics Corporation of India Limited conduct EVM and VVPATs (Voter Verifiable Paper Audit Trail) checks. The sole solution to the issue is introduction of Aadhaar; a unique identification number. The proposed research work introduces a novel concept that provides tamperless and paperless election in India. In summary, this work provides a simple, reliable concept using Blockchain technology and Aadhaar for deploying paperless electronic voting in India.
A Comparative Approach to Naïve Bayes Classifier and Support Vector Machine for Email Spam Classification

Spam or unsolicited emails that are used by spammers can cause huge loss to both the email users and the email server. Therefore, in order to detect spam emails not to enter into our mailbox, a developed email spam classification system is required. This paper proposes two popular machine learning methods, Naïve Bayes Classifier and Support Vector Machine, to classify the emails into spam or ham based on the body or content of the emails. In Naïve Bayes Classifier, independent words are considered as features. Support Vector Machine can be used to represent an email in vector space in which each feature means one dimension. Finally, two methods are compared in terms of precision, recall, F-measure performance metrics with the aim of finding the best method.
Data Trading Certification Based on Consortium Blockchain and Smart Contracts

In this paper, the first data trading certification blockchain solution based on consortium blockchain and smart contracts is proposed to solve the certification data security problem in data trading, so as to realize the auditability, accountability and integrity of data trading.By the proof-of-authority algorithm, a cheap-and-quick consortium blockchain is built.By the consortium blockchain, smart contracts can be deployed safely and conveniently.By the Solidity language, a concise-and-effective certification data smart contract and data trading smart contract are constructed to ensure the certification data security.By deploying the certification data smart contract and the data trading smart contract on the consortium blockchain, the security, transparency and supervisability of certification data is carried out.By utilizing the consortium blockchain and smart contracts, the data trading certification model is established to ensure the certification data security in data trading.By the experiments, the consortium blockchain is successfully established, and the certification data smart contract and the data trading smart contract are successfully deployed, so that the certification data security is effectively guaranteed.Finally, by the Github, the source code of the certification data smart contract and the data trading smart contract is uploaded.
On the Robustness of Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively take actions as a team to maximize a total team reward.We analyze the robustness of c-MARL to adversaries capable of attacking one of the agents on a team.Through the ability to manipulate this agent's observations, the adversary seeks to decrease the total team reward.Attacking c-MARL is challenging for three reasons: first, it is difficult to estimate team rewards or how they are impacted by an agent mispredicting; second, models are non-differentiable; and third, the feature space is low-dimensional.Thus, we introduce a novel attack.The attacker first trains a policy network with reinforcement learning to find a wrong action it should encourage the victim agent to take.Then, the adversary uses targeted adversarial examples to force the victim to take this action.Our results on the StartCraft II multi-agent benchmark demonstrate that c-MARL teams are highly vulnerable to perturbations applied to one of their agent's observations.By attacking a single agent, our attack method has highly negative impact on the overall team reward, reducing it from 20 to 9.4.This results in the team's winning rate to go down from 98.9% to 0%.
A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion

Prompt diagnosis of benign and malignant breast masses is essential for early breast cancer screening. Convolutional neural networks (CNNs) can be used to assist in the classification of benign and malignant breast masses. A persistent problem in current mammography mass classification via CNN is the lack of local-invariant features, which cannot effectively respond to geometric image transformations or changes caused by imaging angles. In this study, a novel model that trains both texton representation and deep CNN representation for mass classification tasks is proposed. Rotation-invariant features provided by the maximum response filter bank are incorporated with the CNN-based classification. The fusion after implementing the reduction approach is used to address the deficiencies of CNN in extracting mass features. This model is tested on public datasets, CBIS-DDSM, and a combined dataset, namely, mini-MIAS and INbreast. The fusion after implementing the reduction approach on the CBIS-DDSM dataset outperforms that of the other models in terms of area under the receiver operating curve (0.97), accuracy (94.30%), and specificity (97.19%). Therefore, our proposed method can be integrated with computer-aided diagnosis systems to achieve precise screening of breast masses.
Video transcoding for adaptive bitrate streaming over edge-cloud continuum

Video transcoding is to create multiple representations of a video for content adaptation. It is deemed as a core technique in Adaptive BitRate (ABR) streaming. How to manage video transcoding affects the performance of ABR streaming in various aspects, including operational cost, streaming delays, Quality of Experience (QoE), etc. Therefore, the problems of implementing video transcoding in ABR streaming must be systematically studied to improve the overall performance of the streaming services. These problems become more worthy of investigation with the emergence of the edge-cloud continuum, which makes the resource allocation for video transcoding more complicated. To this end, this paper provides an investigation of the main technical problems related to video transcoding in ABR streaming, including designing a rate profile for video transcoding, providing resources for video transcoding in clouds, and caching multi-bitrate video contents in networks, etc. We analyze these problems from the perspective of resource allocation in the edge-cloud continuum and cast them into resource and Quality of Service (QoS) optimization problems. The goal is to minimize resource consumption while guaranteeing the QoS for ABR streaming. We also discuss some promising research directions for the ABR streaming services.