Reviewerly/exHarmony huggingface dataset:
4D Attention: Comprehensive Framework for Spatio-Temporal Gaze Mapping

This study presents a framework for capturing human attention in the spatio-temporal domain using eye-tracking glasses. Attention mapping is a key technology for human perceptual activity analysis or Human-Robot Interaction (HRI) to support human visual cognition; however, measuring human attention in dynamic environments is challenging owing to the difficulty in localizing the subject and dealing with moving objects. To address this, we present a comprehensive framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4D Attention</i> , for unified gaze mapping onto static and dynamic objects. Specifically, we estimate the glasses pose by leveraging a loose coupling of direct visual localization and Inertial Measurement Unit (IMU) values. Further, by installing reconstruction components into our framework, dynamic objects not captured in the 3D environment map are instantiated based on the input images. Finally, a scene rendering component synthesizes a first-person view with identification (ID) textures and performs direct 2D-3D gaze association. Quantitative evaluations showed the effectiveness of our framework. Additionally, we demonstrated the applications of 4D Attention through experiments in real situations. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref>
TRAK-CPABE: A novel Traceable, Revocable and Accountable Ciphertext-Policy Attribute-Based Encryption scheme in cloud computing

Ciphertext Policy Attribute Based Encryption (CPABE) offers fine-grained access control in Cloud Computing environment. It is considered as the most promising one-to-many encryption mechanism. However, a Cloud user can delegate decryption privileges to unauthorized users for profits. The key abuse issue is not the only limit with this model for the reason that when the malicious user has been caught, it cannot be revoked. To address these problems, we propose a Traceable, Revocable, Accountable and Key-escrow free CPABE scheme (TRAK-CPABE). This solution supports white-box traceability and direct revocation. The main idea of this work is to divide the original data after publishing in cloud server. In the case of user revocation, one single slice is affected. Data owner needs to retrieve, re-encrypt and re-publish it. In addition, shared keys will be traced and conflict scenario will be resolved. More, the traceability of TRAK-CPABE depends on the l-Strong Diffie–Hellman (l-SDH) and Indistinguishability under chosen-plaintext attack (IND-CPA. The security of the proposed scheme is proved formally by using the Scyther tool. Furthermore, the experimental results show the efficiency of the proposed solution.
Image-Goal Navigation via Keypoint-Based Reinforcement Learning

In this paper, we tackle the problem of image-goal navigation which is a crucial robot navigation task but also a hard problem especially when there exist obstacles and limitations on field-of-view (FoV) of the camera. Conventional visual servoing approaches require depth information and camera parameters, and are susceptible to FoV loss, while previous learning-based approaches depend on the unrealistic dense reward function to train the agent with reinforcement learning. To this end, we propose a novel reinforcement learning-based approach which simultaneously utilizes self-supervised local features and global features from an observed image and a target image. The proposed method, KeypointRL, exploits keypoint matching information and generates a self-supervised reward signal which allows the agent to be easily transferred to unseen environments. The proposed model is trained on the subset of image-goal dataset in the photo-realistic Gibson dataset together with Habitat simulator, and shown to outperform baseline algorithms and generalize better.
Beetle Antennae Search guided RRT* for path planning of GIS inspection and maintenance robot

Aiming at efficient path planning of the gas insulated switchgear (GIS) robot which is used for inspecting and maintaining the GIS equipment, this paper proposed a Beetle Antennae Search (BAS)-inspired RRT* algorithm. The new algorithm combines basic RRT* with BAS, a metaheuristic approach, to inspire the sampling, and utilizes a weighted lazy extension mechanism to contribute expanding useful nodes. BASL-RRT* is firstly compared with three improved RRT* algorithms and simulation results verify the effectiveness and the superiority of BASL-RRT*. Then, we apply this new method on path planning of GIS robot. The result shows that safe and smooth path could be obtained efficiently.
CONCEAL FACE MASK RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS

The end of 2019 saw the flare-up of Covid Disease 2019 (COVID-19), which has kept on being the reason for predicament for a great many lives and organizations even in 2020. As the world recuperates from the pandemic and plans to re-visitation of a condition of routineness, there is a wave of nervousness among all people, particularly the individuals who mean to continue in person action. By and by, it isn't attainable to really follow the execution of this technique. Advancement holds the key here. Attestation from faces is a norm and indispensable improvement of late. Face changes and the presence of various cover make it an excess of testing. Truth be told, when an individual is uncooperative with the designs, for example, in video perception by then covering is further typical conditions. We present a Deep Learning based framework that can identify cases where face veils are definitely not utilized appropriately. Our framework comprises of a dual stage Convolutional Neural Network (CNN) design equipped for distinguishing veiled and exposed faces and can be coordinated with pre-introduced CCTV cameras. The crucial pressure to this work is over facial covers, and particularly to improve the attestation precision of various secret appearances. A potential procedure has been proposed that contains first particular the facial areas. The hindered face affirmation issue has been progressed toward utilizing Multi-Task Cascaded Convolutional Neural Network (MTCNN). By then facial highlights extract is performed utilizing the Google FaceNet presenting model. At long last, a correlative report besides made here for a predominant insight. We present one of the interesting algorithms in Deep Learning based construction.
Digital hostages: Leveraging ransomware attacks in cyberspace

Global institutions face costly infrastructure disruption from cyberthreats such as ransomware attacks. The global threat of ransomware attacks has continued to increase over time. Although it is important to conduct a cost-benefit evaluation and to weigh the risks prior to engaging, a new emerging option of dealing with ransomware attacks should be considered. I outline an interdisciplinary approach to cybernegotiation combining features of online dispute resolution and terrorist hostage theory. In this novel cyber-based dispute system design, the Wade and seek method is the application of traditional hostage negotiation theory used to help businesses mitigate the costs of ransomware attacks.
Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning

Humans learn to imitate by observing others. However, robot imitation learning generally requires expert demonstrations in the first-person view (FPV). Collecting such FPV videos for every robot could be very expensive.Third-person imitation learning (TPIL) is the concept of learning action policies by observing other agents in a third-person view (TPV), similar to what humans do. This ultimately allows utilizing human and robot demonstration videos in TPV from many different data sources, for the policy learning. In this paper, we present a TPIL approach for robot tasks with egomotion. Although many robot tasks with ground/aerial mobility often involve actions with camera egomotion, study on TPIL for such tasks has been limited. Here, FPV and TPV observations are visually very different; FPV shows egomotion while the agent appearance is only observable in TPV. To enable better state learning for TPIL, we propose our disentangled representation learning method. We use a dual auto-encoder structure plus representation permutation loss and time-contrastive loss to ensure the state and viewpoint representations are well disentangled. Our experiments show the effectiveness of our approach.
An Adaptive Resolution Scheme for Performance Enhancement of a Web-based Multi-User VR Application

Over the last few years, several frameworks have been introduced to help developers build virtual reality (VR) experiences for the web. One such open-source web framework is the A-Frame framework, which is built on top of WebXR and Three.js. A-Frame can be used to create 3D scenes that can be rendered using compatible browsers such as Chrome and Firefox. The performance of web-based VR applications, however, can be affected due to the limitations of the CPU and GPU, especially in multi-user applications. In this paper, an A-Frame-based multiuser VR application is developed and the performance is analyzed under different scenarios, demonstrating how an increase in the number of users affects metrics related to VR quality of experience (QoE). Then, an Adaptive Resolution Scheme for VR (ARS-VR) is proposed, which improves the VR performance in terms of frame rate and frame latency on remote devices with limited processing and display features.
Efficient detection of adversarial, out-of-distribution and other misclassified samples

Deep Neural Networks (DNNs) are increasingly being considered for safety–critical approaches in which it is crucial to detect misclassified samples. Typically, detection methods are geared towards either the detection of out-of-distribution or adversarial data. Additionally, most detection methods require a significant amount of parameters and runtime. In this contribution we discuss a novel approach for detecting misclassified samples suitable for out-of-distribution, adversarial and additionally real world error-causing corruptions. It is based on the Gradient’s Norm (GraN) of the DNN and is parameter and runtime efficient. We evaluate GraN on two different classification DNNs (DenseNet, ResNet) trained on different datasets (CIFAR-10, CIFAR-100, SVHN). In addition to the detection of different adversarial example types (FGSM, BIM, Deepfool, CWL2) and out-of-distribution data (TinyImageNet, LSUN, CIFAR-10, SVHN) we evaluate GraN for novel corruption set-ups (Gaussian, Shot and Impulse noise). Our experiments show that GraN performs comparable to state-of-the-art methods for adversarial and out-of-distribution detection and is superior for real world corruptions while being parameter and runtime efficient.
Unsupervised Two-Stage Root-Cause Analysis for Integrated Systems

The increasing complexity and high cost of integrated systems have placed immense pressure on root-cause analysis and diagnosis. In light of artificial intelligence and machine learning, a large amount of intelligent root-cause analysis methods have been proposed. However, most of them need historical test data with root-cause labels from repair history, which are often difficult and expensive to obtain. We propose a two-stage unsupervised root-cause-analysis method in which no repair history is needed. In the first stage, a decision-tree model is trained with system test information to cluster the data in a coarse-grained manner. In the second stage, frequent-pattern mining is applied to extract frequent patterns in each decision-tree node to precisely cluster the data so that each cluster represents only a small number of root causes. The proposed method can accommodate both numerical and categorical test items. A combination of the L-method, cross validation, and Silhouette score enables us to automatically determine all hyperparameters. Two industry case studies with system test data demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised root-cause-analysis method.
A Hybrid Dimension Reduction Based Linear Discriminant Analysis for Classification of High-Dimensional Data

Linear discriminant analysis (LDA) is a notable classification algorithm thanks to its major success in many applications of the real-world. In spite of its successfulness for low-dimensional data, a dimension reduction is inevitable for its achievement with high-dimensional data, especially in which the number of features is more than the training sample size or close to the training sample size. Principal component analysis (PCA) plus LDA (PCA+LDA), a quite popular technique, is widely used for raising the classification performance of LDA over high-dimensional data. However, PCA ignores the label information in the data. On the other hand, the reduced dimensional data through PCA still includes indiscriminate (i.e., irrelevant) features. To cope with the dimensionality problem of LDA, a hybrid dimension reduction approach of supervised and unsupervised algorithms is proposed in this study. In the supervised part of the proposed hybrid dimension reduction method, called DBDERF+PCA, we propose to combine an ensemble classifier (i.e., random forest) with dichotomous binary differential evolution (DBDE), a recently proposed variant of binary differential evolution, by introducing a robust wrapper feature selection. On the other hand, unsupervised part of the proposed hybrid dimension reduction method utilizes PCA. The experimental results show that DBDERF+PCA outperforms PCA in terms of dimension reduction. Thereupon, this hybrid dimension reduction based LDA, called DBDERF+PCA+LDA, performs better than PCA+LDA and LDA in terms of run-time and classification performances.
Multi-task Learning with Riemannian Optimization

Multi-task learning (MTL) is a promising research field of machine learning, in which the training process of the neural network is equivalent to multi-objective optimization. On one hand, MTL trains all the network weights simultaneously to converge the multi-task loss. On the other hand, multi-objective optimization aims to find the optimum solution, which satisfies the constraints and optimizes the vector of objective functions. Therefore, the performance of MTL is dominated by the computation of the multi-objective solution. This paper proposes a method based on Riemannian optimization to solve the multi-objective optimization in MTL. Firstly, multi-objective optimization is reduced to its Karush-Kuhn-Tucker (KKT) condition as the optimum solution of constrained quadratic optimization. Secondly, by mapping the Euclidean space of the constraint into manifold, the quadratic optimization is transformed to an unconstrained problem. Finally, Riemannian optimization algorithm is used to compute the solution of this problem, which gives a Pareto direction towards the KKT condition. We perform experiments on the MultiMNIST and Fashion MNIST datasets, and the experimental results demonstrate the efficiency of our method.
The first named entity recognizer in Maithili: Resource creation and system development

In this paper, we present our effort on the development of a Maithili Named Entity Recognition (NER) system. Maithili is one of the official languages of India, with around 50 million native speakers. Although various NER systems have been developed in several Indian languages, we did not find any openly available NER resource or system in Maithili. For the development, we manually annotated a Maithili NER corpus containing around 200K words. We prepared a baseline classifier using Conditional Random Fields (CRF). Then we ran many experiments using various recurrent neural networks (RNN). We collected larger raw corpus to obtain better word embedding and character embedding. In our experiments, we found, neural models are better than CRF; a CRF layer is effective for the prediction of the final output in the RNN models; character embedding is effective in Maithili language. We also investigated the effectiveness of gazetteer lists in neural models. We prepared a few gazetteer lists from various web resources and used those in the neural models. The incorporation of the gazetteer layer caused performance improvement. The final system achieved an f-measure of 91.6% with 94.9% precision and 88.53% recall.
Whole Slide Pathology Image Patch Based Deep Classification: An Investigation of the Effects of the Latent Autoencoder Representation and the Loss Function Form

The analysis of whole-slide pathological images is a major area of deep learning applications in medicine. The automation of disease identification, prevention, diagnosis, and treatment selection from whole-slide images (WSIs) has seen many advances in the last decade due to the progress made in the areas of computer vision and machine learning. The focus of this work is on patch level to slide image level analysis of WSIs, popular in the existing literature. In particular, we investigate the nature of the information content present in images on the local level of individual patches using autoencoding. Driven by our findings at this stage, which raise questions about the us of autoencoders, we next address the challenge posed by what we argue is an overly coarse classification of patches as tumorous and non-tumorous, which leads to the loss of important information. We showed that task specific modifications of the loss function, which take into account the content of individual patches in a more nuanced manner, facilitate a dramatic reduction in the false negative classification rate.
Incorporating Translation Quality Estimation into Chinese-Korean Neural Machine Translation

Exposure bias and poor translation diversity are two common problems in neural machine translation (NMT), which are caused by the general of the teacher forcing strategy for training in the NMT models. Moreover, the NMT models usually require the large-scale and high-quality parallel corpus. However, Korean is a low resource language, and there is no large-scale parallel corpus between Chinese and Korean, which is a challenging for the researchers. Therefore, we propose a method which is to incorporate translation quality estimation into the translation process and adopt reinforcement learning. The evaluation mechanism is used to guide the training of the model, so that the prediction cannot converge completely to the ground truth word. When the model predicts a sequence different from the ground truth word, the evaluation mechanism can give an appropriate evaluation and reward to the model. In addition, we alleviated the lack of Korean corpus resources by adding training data. In our experiment, we introduce a monolingual corpus of a certain scale to construct pseudo-parallel data. At the same time, we also preprocessed the Korean corpus with different granularities to overcome the data sparsity. Experimental results show that our work is superior to the baselines in Chinese-Korean and Korean-Chinese translation tasks, which fully certificates the effectiveness of our method.
Creating Immersive Learning Experiences: A Pedagogical Design Perspective

Advances in information technology have enabled the development of audio-visual, interactive experiences in education, often realized in the form of virtual or augmented reality. However, simply adopting these technologies cannot guarantee successful teaching and learning experiences. Success requires the design of a pedagogy that consists of techniques, strategies, and styles, which a teacher can employ to drive the learning process. We propose a pedagogy through which a teacher can infuse immersive technologies in the course of teaching to stimulate students' learning experiences, thereby improving their enjoyment and learning outcomes. Readers will gain both a research background concerning the use of immersive technologies in teaching and learning, and practical advice on how to effectively design and deliver pedagogically sound, learner-focused, and accessible learning activities.
Automating Conflict Detection and Mitigation in Large-Scale IoT Systems

In this paper we examine the problem of conflict detection and mitigation across multiple independently designed IoT subsystems deployed in a shared environment. The desired behavior of the system is codified in terms of predefined "safety properties". We allow both the operational rules and safety properties to include time and temporal logic operations and detect their potential violation proactively via a "look ahead" mechanism. The problematic operational rules are then perturbed within the allowable range for mitigation. We show that our mitigation approach, based on intelligent combinatorial optimization, can resolve the conflicts via perturbation in 100% of the cases where such a resolution is feasible.
Extracting features related to bug fixing time of bug reports by deep learning and gradient-based visualization

A bug report is a document indicating when a bug occurs. Developers discuss and resolve the bug through comments in the report. The time required to fix a bug can depend on the bug report. Although many studies have researched bug reports, few have examined bug report comments. Herein we adopt a convolutional neural network (CNN), which is a class of deep neural networks, to classify bug reports into those with short and long fixing times based on the data collected from a bug tracking system. Then we extract the features related to the bug fixing time by visualizing the decision basis that the CNN model uses in the prediction process. We employ a gradient-based visualization technique called Grad-cam to visualize the word sequence that the CNN model uses in the prediction. We use the top ten word sequences as the decision basis to extract the features of the bug report. An experiment confirmed that our method classified more than 36,000 actual bug reports taken from Bugzilla by short and long fixing times with 75-80% accuracy. Further visualization using Grad-cam shows the difference in the stack trace and the degree of abstraction of the words used. Bug reports with a short bug fixing time are specific and informative with regard to stack trace descriptions. In contrast, those with a long bug fixing time are abstract.
Survey and Analysis on AI Based Phishing Detection Techniques

From the emergence of networking technologies and the access to websites, there are always threats associated in one way or the other to a user. Cyber Attacks has been a major problem. Even during the current pandemic situation of Covid-19 we have seen an escalation in the number of cyberattacks. There are many kinds of attacks, phishing being one of them. There has been a rise in the number of phishing attacks recently and many prestigious companies like Facebook have been victims of such attacks. Thus, it is observable that phishing attacks have been a major issue in the IT sector and it must be taken into account seriously. In this paper we try to efficiently study and analyse the detection of phishing sites that have been haunting people since the emergence of the internet era.Three types of detection techniques have been discussed in this paper: An HTML detection method, URL detection methods and lastly the visual similarity based approach.