Reviewerly/exHarmony huggingface dataset:
Learning Concept Prerequisite Relations from Educational Data via Multi-Head Attention Variational Graph Auto-Encoders

Recently, the topic of learning concept prerequisite relations has gained the attention of many researchers, which is crucial in the learning process for a learner to decide an optimal study order. However, the existing work still ignores three key factors. (1) People's cognitive differences could make a difference for annotating the prerequisite relation between resources (e.g., courses, textbooks) or concepts (e.g., binary tree). (2) The current vertex (resources or concepts) can be affected by the feature of the neighbor vertex in the resource or concept graph. (3) The feature information of the resource graph may affect the concept graph. To integrate the above factors, we propose an end-to-end graph network-based model called Multi-Head Attention Variational Graph Auto-Encoders (MHAVGAE ) to learn the prerequisite relation between concepts via a resource-concept graph. To address the first two problems, we introduce the multi-head attention mechanism to operate and compute the hidden representations of each vertex over the resource-concept graph. Then, we design a gated fusion mechanism to integrate the feature information of the resource and concept graphs to enrich concept content features. Finally, we conduct numerous experiments to demonstrate the effectiveness of the MHAVGAE across multiple widely used metrics compared with the state-of-the-art methods. The experimental results show that the performance of the MHAVGAE almost outperforms all the baseline methods.
A UAV Target Prediction and Tracking Method Based on KCF and Kalman Filter Hybrid Algorithm

When Unmanned Aerial Vehicle (UAV) follows a dynamic object as a target, it sometimes loses. In this paper, we propose a tracker switcher module to determine which algorithm is utilized for tracking. The author uses Kernelized Correlation Filter (KCF) to judge if the information from vision system is still reliable enough to leading UAV or lost. When the target is lost, we use Kalman Filter (KF) to predict the moving trajectory of the target, control the UAV movement and let UAV follow the virtual trajectory, until the target reappears and the system is in the correct tracking state again. Finally, we use OTB dataset to verify the feasibility of this method. Our tracker obtains Precision and FPS plot generator, which validate the effectiveness of this method. This method can make up for the problem under the premise of low delay, and has a wide range of application scenarios.
A robust object detection system with occlusion handling for mobile devices

Abstract Deep learning techniques help computer vision automatically learn the intrinsic patterns within complex data. This research mainly concentrates on creating a mobile application based on augmented reality for elderly mobile users that helps in identifying the traffic signals and other signboards in real‐time using deep learning techniques. TensorFlow serves as an implementation platform to build the object detection system with deep learning. The single shot multibox detector (SSD) model and the two‐stage faster‐regional convolutional neural network (RCNN) models from TensorFlow's object detection application programming interface (API) are compared in this study. The SSD model with MobileNet as a backbone network serves well for this study as it is faster than the RCNN model with comparable accuracy. However, unconstrained environments like occlusions can be an obstacle to the effective performance of an object detection system. This research provides a solution to handle occlusions by developing a robust object detection system through image segmentation techniques. The model introduced is based on the SSD MobileNet model which enables it to be deployable on mobile devices for real‐time offline detection. The developed model exhibits faster performance than the state‐of‐the‐art instance segmentation model, Mask RCNN with comparable accuracy. Elaborated implementation of this system and results are presented in further sections.
Self-Supervised Shape Alignment for Sports Field Registration

This paper presents an end-to-end self-supervised learning approach for cross-modality image registration and homography estimation, with a particular emphasis on registering sports field templates onto broadcast videos as a practical application. Rather then using any pairwise labelled data for training, we propose a self-supervised data mining method to train the registration network with a natural image and its edge map. Using an iterative estimation process controlled by a score regression network (SRN) to measure the registration error, the network can learn to estimate any homography transformation regardless of how misaligned the image and the template is. We further show the benefits of using pretrained weights to finetune the network for sports field calibration with few training data. We demonstrate the effectiveness of our proposed method by applying it to real-world sports broadcast videos where we achieve state-of-the-art results and real-time processing.
Implementation of MARS metrics and MARS charts for evaluating classifier exclusivity: The comparative uniqueness of binary classifier predictions

<h2>Abstract</h2> Traditionally, performance metrics such as accuracy, precision, recall, F-score, and ROC curve/ Area-Under-Curve (AUC) values have been used to evaluate and understand binary classifier capabilities. However, modern high-performance classifier models frequently have equivalent classification performance according to traditional metrics. We propose a novel approach – the MARS classifier evaluation method – to evaluate classifier exclusivity, using MARS ShineThrough and MARS Occlusion scores. Specifically, the MARS method vividly illustrates the extent to which classifiers spot distinct target-class observations, that other classifiers miss. In this paper, we describe the software artifact utilized to calculate MARS metrics for comparative uniqueness and MARS charts for the visualization of these calculations.
A Survey on Blockchain Technology for Network Security Applications

Massive amounts of data are being stored in cyberspace as a result of the expansion of the Internet, IoT, and various networking technologies. The privacy and security are the most essential aspects of a network. This survey analyzed the functions of blockchain in network security. The blockchain-based network security mechanism may be used to increase network security because of its decentralization, tamper-resistance, traceability, high availability, and credibility. This survey offers a review of network security studies and their contributions and limits with a critical comparison analysis based on a complete and comprehensive research of the evolution of Blockchain, architectures, working principle, security, and privacy features. This analysis examines network security applications based on blockchain technology with various networking technologies, such as IoT, Industrial IoT, WSN, MANET, VANET, Vehicular Social Network, In-vehicle networking, mobile networks (5G), and so on. For communication, the majority of these networking technologies were combined with IoT. As a result, in this study, the Internet of Things is considered as the primary network employed in important research as examined in the literature review. As a result, the application of network security utilizing blockchain was examined in this study using IoT. This research presents a comparison based on several network solutions that employ blockchain for network security. Finally, the blockchain application in various networks, as well as its difficulties, are examined.
Garbage Collecting Robot Using YOLOv3 Deep Learning Model

Due to rapid rise in population, there is an increase in the waste scattered and littered on the roads. Waste management has become an issue of concern, now more than ever around the world. There are instances where waste is deliberately left on the roads due to the lack of workers or means available. The hazardous nature of the work and the health risks it can pose for people is also another concern. With the rise of smart cities, we recognized the need to develop an autonomous garbage collecting robot that monitors, identifies and collects garbage from the environment accurately. For this purpose, we also conducted a comparative study of different object detection algorithms and evaluated them based on various parameters.
Multicriteria decision making taxonomy of code recommendation system challenges: a fuzzy-AHP analysis

Abstract The recommendation systems plays an important role in today’s life as it assist in reliable selection of common utilities. The code recommendation system is being used by the code databases (GitHub, source frog etc.) aiming to recommend the more appropriate code to the users. There are several factors that could negatively impact the performance of code recommendation systems (CRS). This study aims to empirically explore the challenges that could have critical impact on the performance of the CRS. Using systematic literature review and questionnaire survey approaches, 19 challenges were identified. Secondly, the investigated challenges were further prioritized using fuzzy-AHP analysis. The identification of challenges, their categorization and the fuzzy-AHP analysis provides the prioritization-based taxonomy of explored challenges. The study findings will assist the real-world industry experts and to academic researchers to improve and develop the new techniques for the improvement of CRS.
A Counterfactual Modeling Framework for Churn Prediction

Accurate churn prediction for retaining users is keenly important for online services because it determines their survival and prosperity. Recent research has specified social influence to be one of the most important reasons for user churn, and thereby many works start to model its effects on user churn to improve the prediction performance. However, existing works only use the data's correlational information while neglecting the problem's causal nature. Specifically, the fact that a user's churn is correlated with some social factors does not mean he/she is actually influenced by his/her friends, which results in inaccurate and unexplainable predictions of the existing methods. To bridge this gap, we develop a counterfactual modeling framework for churn prediction, which can effectively capture the causal information of social influence for accurate and explainable churn predictions. Specifically, we first propose a backbone framework that uses two separate embeddings to model users' endogenous churn intentions and the exogenous social influence. Then, we propose a counterfactual data augmentation module to introduce the causal information to the model by providing partially labeled counterfactual data. Finally, we design a three-headed counterfactual prediction framework to guide the model to learn causal information to facilitate churn prediction. Extensive experiments on two large-scale datasets with different types of social relations show our model's superior prediction performance compared with the state-of-the-art baselines. We further conduct an in-depth analysis of the prediction results demonstrating our proposed method's ability to capture causal information of social influence and give explainable churn predictions, which provide insights into designing better user retention strategies.
Effectiveness of blockchain to solve the interoperability challenges in healthcare

This study aims to explore the potential of Blockchain to transformhealthcare, build the patient-driven healthcare ecosystem ratherthan the current institutional driven and enhance the privacy, security,and interoperability of healthcare data and check the counterfeitdrugs. Interview excerpts from Industry experts/developersin the field of IT and experience from the implementation of thetechnology in the financial world will help understand its viabilityin healthcare. Consulting with domain experts is a qualitative processthat involves comprehending the perception of the experts.The research paper will help the healthcare sector lay down futurebusiness strategies to redefine its businesses by overcoming theincreasing threat on patients’ private data and resolving complexsupply chain issues. A decentralized ledger can prevent massiverevenue loss due to counterfeit drugs to track and verify eachdrug’s movement on the unchangeable record. The primary andsecondary research is original, adding its authenticity with interviewexcerpts of domain experts. The results of this research paperwill help identify the transparency, immutability, cost-saving, andstreamlining of business to be brought by Blockchain in healthcare. It would be the transformational journey from innovation tomarket the quality products to the end consumer.
Receiver-Driven RDMA Congestion Control by Differentiating Congestion Types in Datacenter Networks

The development of datacenter applications leads to the need for end-to-end communication with microsecond latency. As a result, RDMA is becoming prevalent in datacenter networks to mitigate the latency caused by the slow processing speed of the traditional software network stack. However, existing RDMA congestion control mechanisms are either far from optimal in simultaneously achieving high throughput and low latency or in need of additional in-network function support. In this paper, by leveraging the observation that most congestion occurs at the last hop in datacenter networks, we propose RCC, a receiver-driven rapid congestion control mechanism for RDMA networks that combines explicit assignment and iterative window adjustment. Firstly, we propose a network congestion distinguish method to classify congestions into two types, last-hop congestion and innetwork congestion. Then, an Explicit Window Assignment mechanism is proposed to solve the last-hop congestion, which enables senders to converge to a proper sending rate in one-RTT. For in-network congestion, a PID-based iterative delay-based window adjustment scheme is proposed to achieve fast convergence and near-zero queuing latency. RCC does not need additional innetwork support and is friendly to hardware implementation. In our evaluation, the overall average FCT (Flow Completion Time) of RCC is 4~79% better than Homa, ExpressPass, DCQCN, TIMELY, and HPCC.
Learning Cross-Channel Representations for Semantic Segmentation

Semantic segmentation is a fundamental problem in multimedia which requires delicate per-pixel predictions of object categories. Recently, many researchers strive to refine the pixel-wise feature with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial</i> -contextual information. However, many of them still neglect the invisible hand of cross- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">channel</i> information which provides inherent semantics to facilitate the segmentation performance. On the one hand, in the feature extraction stage, enhancing informative channels and suppressing trivial ones contribute to the acquisition of valuable semantic features, and thus improving the segmentation accuracy. On the other hand, in the prediction stage, we can predict the complete objects more clearly by finding the connections and complements between different channels, which can also contribute to the pixel prediction. And based on this idea, we propose a novel Channel-Adaptive Network for semantic segmentation, which is capable of enhancing the features from the perspective of channels in both feature extraction stage and prediction stage. Specifically, we propose two modules: (i) the Comprehensive Information Channel Attention (CiCA) module that addresses the shortcomings of existing channel attention by learning both low and high frequency components within each channel for emphasizing the informative channels; (ii) the Inter-Channel Relationship Reasoning (iCRR) module which is applied on the top of the feature extractor to adaptively enhance the interdependent channels by mining the complementary associations between them. Besides, our Channel-Adaptive Network is highly flexible, with a plug-and-play design. Extensive experiments have demonstrated that our method achieves the state-of-the-art segmentation performance on three challenging datasets, including Cityscapes (82.1%), ADE20K (46.51%) and PASCAL Context (55.0%).
GlowImp: Combining GLOW and GAN for Multivariate Time Series Imputation

Multivariate time series data generally contains missing values, which can confound subsequent analysis and compromise downstream applications. Commonly, an imputation method is used to estimate the missing values. The statistical and case deletion type methods can destroy the original data distribution, while RNNs contribute to a higher performance but these model may suffer from bias exploding problem. The method proposed in this paper, GlowImp is a deep sequential latent variable model that combines the advantages of both Glow-VAE with WGAN to learn the actual distribution of the hidden variable space but with high training stability and high-quality samples. Additionally, the framework integrates a variational autoencoder to reduce the dimensionality of the inputs as a way of combatting the high computational costs associated with Glow models. The result is an imputation process with state-of-the-art accuracy (ROC), as demonstrated with two public datasets.
Node Classification Using Graph Convolutional Network with Dropout Regularization

In this paper, node classification using graph convolutional network (GCN) is studied. First, problem formulation of node classification is described. Then, the graph convolutional operator (GCO) is constructed and it is combined with nonlinear activation function to obtain the two-layer GCN for tackling the node classification problem. Next, the dropout regularization is incorporated into the GCN for solving the overfitting problem. Because input feature data is very sparse, sparse dropout is used in the first layer and general dropout is employed in the second layer. Finally, citation network datasets, t-SNE data visualization, ablation study, and classification accuracy are used to evaluate the performance of the GCN with dropout regularization.
A Semi-supervised Generalized VAE Framework for Abnormality Detection using One-Class Classification

Abnormality detection is a one-class classification (OCC) problem where the methods learn either a generative model of the inlier class (e.g., in the variants of kernel principal component analysis) or a decision boundary to encapsulate the inlier class (e.g., in the one-class variants of the support vector machine). Learning schemes for OCC typically train on data solely from the inlier class, but some recent OCC methods have proposed semi-supervised extensions that also leverage a small amount of training data from outlier classes. Other recent methods extend existing principles to employ deep neural network (DNN) models for learning (for the inlier class) either latent-space distributions or autoencoders, but not both. We propose a semi-supervised variational formulation, leveraging generalized-Gaussian (GG) models leading to data-adaptive, robust, and uncertainty-aware distribution modeling in both latent space and image space. We propose a reparameterization for sampling from the latent-space GG to enable backpropagation-based optimization. Results on many publicly available real-world image sets and a synthetic image set show the benefits of our method over existing methods.
Gaze Estimation via Modulation-Based Adaptive Network With Auxiliary Self-Learning

Given a face image, most of previous works in gaze estimation infer the gaze via a well-trained model with supervised training. However, the distribution of test data may be very different compared to that of training data since samples might be corrupted in real-world scenarios ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., taking a photo in strong light). This will lead to a gap between source domain ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., training data) and target domain ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., test data). In this paper, we first introduce self-supervised learning into our method for addressing challenging situations in gaze estimation. Moreover, existing appearance-based gaze estimation methods focus on directing towards the development of powerful regressors, which mainly utilize face and eye images simultaneously or face (eye) images only. However, the problem of inter cues between face and eye features has been largely overlooked. To this end, we propose a novel Modulation-based Adaptive Network (MANet) for gaze estimation, which uses high-level knowledge to filter the distractive information and bridges the intrinsic relationship between face and eye features. Further, we combine self-supervised learning and MANet to learn to adapt to challenging cases, such as abnormal lighting conditions and poor-quality images, by minimizing a self-supervised loss and a supervised loss jointly. The experimental results on several datasets demonstrate the effectiveness of our proposed approach with a real-time speed of 900 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fps</i> on a PC with an NVIDIA Titan RTX GPU.
Student’s t-Kernel-Based Maximum Correntropy Kalman Filter

The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is sensitive to large outliers. In many areas of engineering, the noise may be non-Gaussian and outliers may arise naturally. Therefore, the performance of the Kalman filter may deteriorate significantly in non-Gaussian noise environments. To improve the accuracy of the state estimation in this case, a novel filter named Student's t kernel-based maximum correntropy Kalman filter is proposed in this paper. In addition, considering that the fixed-point iteration method is used to solve the optimal estimated state in the filtering algorithm, the convergence of the algorithm is also analyzed. Finally, comparative simulations are conducted and the results demonstrate that with the proper parameters of the kernel function, the proposed filter outperforms the other conventional filters, such as the Kalman filter, Huber-based filter, and maximum correntropy Kalman filter.
An ensemble approach to anomaly detection using high- and low-variance principal components

With the recent proliferation of cyber physical systems (CPSs), there is a growing demand for reliable anomaly detection systems. In this paper, we propose a new ensemble learning approach for anomaly detection that utilizes the extraction of specific features tailored to anomaly detection problems. Whereas typical principal component analysis (PCA) selects principal components (PCs) associated with high variances, our proposed method also leverages PCs with low variances to account for unexpressed variations in the training data. The extracted features are then fed into conventional learning models such as support vector machines or recurrent neural networks. Since each PC can be particularly good at detecting certain types of attacks, classifiers based on different combinations of selected PCs are further combined as an ensemble. Our results show that the ensemble approach improves the overall accuracy and helps detect diverse types of unknown attacks as well. Furthermore, our simple yet effective and flexible approach can easily be deployed to various CPS environments of increasing complexity.