Reviewerly/exHarmony huggingface dataset:
Docker Container Security Orchestration and Posture Management Tool

Docker is one of the most commonly used and popular container technologies in the modern world. And attackers are also attempting to use Docker container security issues to gain a foothold in environments and break the Confidentiality, Integrity, and Availability (CIA) of the information that relies on containerized applications. Most organizations are pursuing solutions that are capable of securing Docker container workloads and visualizing the security posture of the Docker container workload, which enables higher management to make informed decisions. Before deploying a Docker container into the production environment, developers, security professionals, or anyone else working with Docker containers can use this Docker Container Security Orchestration and Posture Management tool to identify existing security configuration issues and vulnerabilities, as well as perform automated security fixing and configuring. The product’s knowledge base on Docker container best practices, vulnerabilities, and other security issues is extensive and self-updating. Using this solution, organizations with Docker workloads can reduce their attack surface and provide security posture reports that are extremely useful for IT risk-based decision making by higher management.
Classification of points and types of disruptions for efficiency of standard operating procedures repair of distribution network suppliers

Currently, UP3 Manokwari electrical distribution network (feeder distribution network) still has problems in knowing the location of the disruption, and classifying the points and types of disruption. It also has a manual recording of disruption reports, indirect recaps of detailed disruption reports, and ineffective delivery of disruption-related information to field officers (yantek) and leaders. The purpose of this study is to obtain a pattern or node using the C4.5 algorithm in classifying points and types of feeder distribution network disruption and assist officers in recording details of disruption in real-time. Pattern evaluation using Rapidminer tools and using Confusion Matrix to test the accuracy of the classification method. The results showed that the classification of disruption points was determined by the disruption load factor and relay with 3 (three) patterns/nodes. In comparison, the classification of disruption types was determined by the damage factor and the cause of the disruption with 16 patterns/nodes. The accuracy level of the C4.5 algorithm model was 87.80. % for classification of disruption types and 96.15% for classification of disruption points.
On Topology Design for the Quantum Internet

Imagine a Quantum Internet where people can freely establish physically secure communication channels or migrate quantum programs between anywhere in the world. What would it look like? Despite the very exciting recent advances around building prototypes of quantum networks, little is known about how lab-scale prototypes can be expanded into a global infrastructure that is as capacitated, robust, and cost-efficient as the digital Internet right now. Part of the difficulty lies in our lack of understanding of how the structure of a quantum network affects its capacity and performance when serving multi-commodity quantum communication demands. This article studies the problem of designing high-performance network topologies for the quantum Internet. Utilizing abstract models of the basic quantum network operations and an optimal entanglement distribution protocol, we characterize the capacity and performance of various candidate topologies for the quantum Internet, in terms of the rate of entanglement distribution between source-destination pairs and the fidelity of entangled pairs, respectively. We discuss the implications of our preliminary results, and propose directions for further investigation. As the feasibility of largescale quantum network deployment continues to increase, we hope this article can draw attention to these macroscopic design problems, such as topology design, which potentially have a profound influence on how the entire technology evolves, just as we have observed with the digital Internet in the past decades.
ITF: A Blockchain System with Incentivized Transaction Forwarding

The blockchain is introduced as a safe and decentralized technology widely used in cryptocurrencies. It provides a distributed and disintermediation system to securely process and store transactions between peer devices. Traditionally, every transaction in the blockchain is broadcasted throughout the network, which leaves huge computational and communicational overhead to nodes. Nodes may refuse to forward transactions, thereby hindering the consensus of the blockchain. In this paper, we design a blockchain system with Incentive Transaction Forwarding (ITF). ITF allows nodes to share the revenue from transaction fees as the incentive for transaction forwarding. We propose a mechanism keeping the topology updated for computing incentive allocations. We develop an incentive allocation algorithm to distribute revenue among nodes that forward transactions. We analyze the security of ITF and prove that nodes cannot get unfair advantages in our system by common attacks. Extensive simulations show that our system can have fair incentive allocations for relay nodes and against several attacks from adversaries.
Contextual Emotion Detection in Text using Deep Learning and Big Data

Conversations on social media nowadays generate a large amount of text, which is a form of emotion. Users respond emotionally to users by communicating through text messages on various social media or other media. This work demonstrates gathering learning to identify emotions present in a sentence of messages. Printed speeches are given relevant feeling discover in our model from user text automatically such as Happy, Sad, Angry and Wow etc. When a user expresses their feelings through text, our model automatically identifies the user's feelings and sets up key related emoji like Happy, Sad, Angry, Great, etc. The LSTM model, Naive Bayes as well as SVM classifiers have been used to detect emotions.
An Event-triggered Visual Servoing Predictive Control Strategy for the Surveillance of Contour-based Areas using Multirotor Aerial Vehicles

In this paper, an Event-triggered Image-based Visual Servoing Nonlinear Model Predictive Controller (ET-IBVS-NMPC) for multirotor aerial vehicles is presented. The proposed scheme is developed for the autonomous surveillance of contour-based areas with different characteristics (e.g. forest paths, coastlines, road pavements). For this purpose, an appropriately trained Deep Neural Network (DNN) is employed for the accurate detection of the contours. In an effort to reduce the remarkably large computational cost required by an IBVS-NMPC algorithm, a triggering condition is designed to define when the Optimal Control Problem (OCP) should be resolved and new control inputs will be calculated. Between two successive triggering instants, the control input trajectory is applied to the robot in an open-loop fashion, which means that no control input computations are required. As a result, the system's computing effort and energy consumption are lowered, while its autonomy and flight duration are increased. The visibility and input constraints, as well as the external disturbances, are all taken into account throughout the control design. The efficacy of the proposed strategy is demonstrated through a series of real-time experiments using a quadrotor and an octorotor both equipped with a monocular downward looking camera.
Imperceptible Digital Image Watermarking Based on Discrete Wavelet Transform and Schur Decomposition

The excessive use of multimedia content in the past decades led to threats of illegal copying and duplication of digital contents. This paper proposes one of the solutions to this problem based on an image watermarking technique using Discrete Wavelet Transform (DWT) and Schur decomposition. The Schur decomposition takes a lesser number of computations than singular value decomposition, which makes it suitable for the decomposition of the image. In the proposed algorithm, the watermark is embedded by manipulating the Schur coefficients of the LL subband of the cover image with the Schur vectors of the watermark. In the proposed work, the properties of both DWT and Schur decomposition are combined. The proposed technique performs better against, rotation, histogram equalization attacks, and various filtering attacks. The performance of the proposed technique is evaluated using PSNR and SSIM, which shows better visual imperceptibility against various attacks. The experimental result of the proposed scheme is also compared with other prevailing techniques, and the proposed method is found more effective among the other techniques.
Analysis for Sequential Frame with Facial Emotion Recognition Based on CNN and LSTM

In human interaction, emotion is a general term for a series of subjective cognitive experiences, as well as a psychological and physiological state that embodies a variety of feelings, thoughts, and behaviors. There are many ways to express emotions, such as facial expressions, tone of voice, and behaviors. Facial emotion recognition has become a popular subject in the domain of deep learning. However, most of the facial emotion recognition methods adopt static images of facial expressions to recognize emotion. These methods do not cover all variations of a facial expression. We believe that facial expressions are continuous change actions. People tend to use facial expressions to express their emotions. Therefore, we believe that it is not sufficient to recognize emotions only by static images of facial expressions. In this study, we propose a framework based on that convolutional neural network (CNN) and long short-term memory (LSTM) are combined. We extract sequential images of facial expressions from the video and input them into the CNN model individually. To solve the problem of insufficient training data, the model learns emotion-related knowledge by transfer learning on three different datasets. The model’s learning goal is to reduce the dependence on a large amount of target domain data.
WeStcoin: Weakly-Supervised Contextualized Text Classification with Imbalance and Noisy Labels

The joint problem of imbalance samples and noisy labels challenges the current text classifiers in real-world applications. Existing approaches are mostly devoted to handling either former or latter while fail to manage the fused issue. This paper introduces a novel weakly-supervised framework, dubbed WeSt-coin, to take into account the sensitivity cost on misclassifications between classes and seek seed words towards noisy-label corrections. After BERT that creates a contextualized corpus, WeStcoin learns a predicted label vector from the contextualized samples and meanwhile calculates a pseudo probability vector from seed words, and then projects the concatenated representation into an output space, followed by multiplying by a cost-sensitive matrix. WeStcoin is ultimately trained to decrease the residual between the model outputs and the noisy labels, where seed words are also updated in an iterative manner. Extensive experiments and ablation studies on two public text datasets demonstrate that the proposed model outperforms the state-of-the-art model in the text classification with imbalance samples and noisy labels. Codes are made available at https://github.com/ypzhaang.
Emerging Technology (AR, VR and MR) in Interior Design Program in the UAE: Challenges and solutions

Traditional presentation methods like sketching, 2D illustration, 3D images, animation and drawings in interior design program have been widely used for many decades. This study focuses on the potential application of emerging technology in the interior design program in the United Arab Emirates (UAE). We are interested in the potential of technology such as virtual reality (VR), augmented reality (AR), and mixed reality (MR) to provide an innovative solution in interior design program. This research covers a study on the impact, reasons, applications, challenges and recommended solutions to deploy in interior design program. A short survey of interior design students in the UAE was used to gather student feedback on this technology. According to the feedback, this technology has a promising future, but most program did not incorporate it into their curriculum. Thus, it left students with no choice other than using the same methods for presenting their work. Challenges and solutions are included in this study. Finally, the paper predicts future potential of this emerging technology in interior design program.
Hybrid Blockchain Security Mechanism for protection of data related to Precision Agriculture

There is a new study looking at the safety of the blockchain technology. Precision agricultural data security will be the focus of this study. It is believed that blockchain technology would be able to offer additional security for data sent between sensors in precision agriculture, given the rising need and strength of this technology. There have been a number of studies that have looked at security, but the problem with these studies is that they're not very effective. Furthermore, these investigations fell short of offering adequate protection.
Towards Real-Time Visual Tracking in Mobile Robots

This paper presents a robust and real-time target detection and tracking method under the framework of Meanshift for autonomous robot applications. Meanshift, because of its simplicity and efficiency, has been widely used in tracking tasks, especially in embedded environments. But, the traditional MS algorithm can be effective only when the target regions located in the previous frame are very similar to the candidate regions of the current frame. Therefore, the tracking performance will deteriorate when the target moves fast, or the target is blocked, because at this time, the overlapping area in the image will be greatly reduced. In this paper, we integrate the background models and the graded color features of moving targets to solve the above challenges. In addition, our method is tested on 10 video streams with different scenarios on the ARMv7 platform. Experimental results show the effectiveness of this method in the above scenarios.
Preliminary insights to enable automation of the software development process in software StartUps

Artificial Intelligence (AI) and Machine Learning (ML) tools and techniques have increasingly effectively supported Software Engineering (SE) tasks, whether for requirements classification, software refactoring, defect prediction, and many others. In the context of software StartUps, where innovative and scalable software products are developed, dealing with the pressure of fast delivery of a working solution becomes a challenging factor. We aim to investigate AI and ML techniques used by SE practitioners and entrepreneurs to support their Software Development Processes (SDP) and thus enable their use by software StartUps. We seek to identify this information through the application of an online Survey instrument, mainly disseminated in Brazil and Finland. This preliminary study provides insights that can support improving the SDP in StartUps.
Machine learning-based performance comparison of breast cancer detection using support vector machine

The most common disease is breast cancer among all women in the world. The death rate of breast cancer is higher due to unawareness of cancer’s initial symptoms. There are methods already existed to classify the disease using various real-time tools and algorithms. Machine learning has emerged as a fast boom in all fields of training and classification. The traditional computer programming techniques for the classification of breast cancer disease utilize the Deep Learning (DL) technique for training the model using Convolutional Neural Network (CNN) for dominant feature extraction to identify the given breast cancer test image samples. The datasets of the test samples are fed to the automated detection model for classifying the mammography images into ‘Malignant’ and ‘Benign’ breast tumors using Kernel SVM classification. In this paper, the breast cancer recognition accuracy results are compared for different kernels like linear, poly, Radial Basis Function, and sigmoid function. The linear Kernel gives better accuracy of “95.78%”. The training set is varied, and their recognition performance is observed until the recognition accuracy is better.
Hyperspectral Image Restoration Using 3-D Hybrid Higher Degree Total Variation Regularized Nonconvex Local Low-Rank Tensor Recovery

The degradation of spaceborne hyperspectral images (HSIs) usually results from various types of noise. In this letter, we propose a 3D hybrid higher degree total variation regularized nonconvex local low-rank tensor recovery (H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTV-NLRTR) model to restore the HSIs. Inspired by the good performance of the higher degree total variation penalty in image denoising, we first develop a 3D hybrid higher degree total variation penalty term, which is able to capture the fine image details and edges along the spatial dimensions and spectral dimension. The tensor multi-Schatten- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> norm is chosen as the relaxation of the low-rank tensor constraint, which can not only separate the low-rank clean HSI patches from noisy images effectively but also improve the computational efficiency. The proposed H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTV-NLRTR model can simultaneously characterize the spectral correlation and the spatial structure of the HSI dataset by incorporating the H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTV penalty in the nonconvex local low-rank tensor recovery problem. In addition, we adopt a fast iterative majorize-minimize algorithm to efficiently solve the corresponding optimization problem. The numerical experiments on both simulated and real HSI datasets demonstrate that the proposed algorithm provides consistently improved restoration results compared with the state-of-the-art algorithms.
SAR-AD-BagNet: An Interpretable Model for SAR Image Recognition Based on Adversarial Defense

Although deep neural networks (DNNs) have achieved good results on some common datasets of synthetic aperture radar (SAR) automatic target recognition (ATR), DNNs are opaque and difficult to interpret, which limits its practical application. In view of this, many interpretable models have been proposed in recent years. However, the previous interpretable models only represent transparent network structures and cannot be called real “interpretable” models. We think that the interpretability of a model contains two meanings: on the one hand, the decision process of the model is transparent, and on the other hand, the decision-making basis of the model should be reasonable and conform to human cognition. For this reason, we propose a new SAR image recognition model based on adversarial defense, namely SAR-AD-BagNet. Not only does it have a transparent decision-making process, but it also has a more reasonable basis for decision-making. In addition, the model also has high SAR image recognition accuracy and strong adversarial robustness.
Quantum Walk Random Number Generation: Memory-based Models

The semi-source independent quantum walk random number generator (SI-QW-QRNG) is a cryptographic protocol that extracts a string of true random bits from a quantum random walk with an adversary controls a randomness source, but the dimension of the system is known. This paper analyzes SI-QW-QRNG protocols with a memory-based quantum walk state. The new protocol utilizes a generalized coin operator with various parameters to optimize the randomness of the quantum walk state. We focus on evaluations of the protocols in multiple scenarios and walk configurations. Moreover, we show some interesting behavior of the system depending on the size of the memory space and the number of quantum coins.
Evaluating the Robustness of Object Detection in Autonomous Driving System

Autonomous vehicles are facing many challenges because of various types of scenarios with different conditions. Object detection is a computer vision task and the foundation of high-level tasks of autonomous driving operation, such as object tracking and path planning. Object detection model performance can vary due to various weather conditions and extreme scenarios. In this article, we propose a method to evaluate the robustness of object detection in autonomous vehicles with three metrics. The result of experiment shows that as the level of image corruptions increase, the performance of standard object detection trained on large-scale datasets prominently drop in our robustness metrics.
DyCoDa: A Multi-modal Data Collection of Multi-user Remote Survival Game Recordings

In this paper, we present a novel multi-modal multi-party conversation corpus called DyCoDa (Dynamic Conversational Dataset). It consists of remote intensive conversations among three interaction partners within a collaborative problem-solving scenario. The fundamental aim of building up this corpus is to investigate how humans interact with each other online via a video conferencing tool in a cooperation setting and which audio-visual cues are conveyed and perceived during this interaction. Apart from the high-quality audio and video recordings, the depth and infrared information recorded using the Microsoft Azure Kinect is also provided. Furthermore, various self-evaluation questionnaires are used to get socio-demographic information as well as the personality structure and the individual team role. In total, 30 native German-speaking participants have taken part in the experiment carried out at Magdeburg University. Overall, the DyCoDa consists of 10 h of recorded interactions and will be beneficial for researchers in the field of human-computer interaction.
Enhancing Quality of Experience for Collaborative Virtual Reality with Commodity Mobile Devices

Virtual Reality (VR), together with the network infrastructure, can provide an interactive and immersive experience for multiple users simultaneously and thus enables collaborative VR applications (e.g., VR-based classroom). However, the satisfactory user experience requires not only high-resolution panoramic image rendering but also extremely low latency and seamless user experience. Besides, the competition for limited network resources (e.g., multiple users share the total limited bandwidth) poses a significant challenge to collaborative user experience, in particular under the wireless network with time-varying capacities. While existing works have tackled some of these challenges, a principled design considering all those factors is still missing. In this paper, we formulate a combinatorial optimization problem to maximize the Quality of Experience (QoE), defined as the linear combination of the quality, the average VR content delivery delay, and variance of the quality over a finite time horizon. In particular, we incorporate the influence of imperfect motion prediction when considering the quality of the perceived contents. However, the optimal solution to this problem can not be implemented in real-time since it relies on future decisions. Then, we decompose the optimization problem into a series of combinatorial optimization in each time slot and develop a low-complexity algorithm that can achieve at least 1/2 of the optimal value. Despite this, the trace-based simulation results reveal that our algorithm performs very close to the decomposed optimal offline solution. Furthermore, we implement our proposed algorithm in a practical system with commercial mobile devices and demonstrate its superior performance over state-of-the-art algorithms. We open-source our implementations on https://github.com/SNeC-Lab-PSU/ICDCS-CollaborativeVR.