Reviewerly/exHarmony huggingface dataset:
Augmented reality, virtual reality and new age technologies demand escalates amid COVID-19

The coronavirus disease (COVID-19) pandemic is having a significant impact on healthcare, education, economics and general human well-being. Augmented reality (AR) and virtual reality (VR) have experienced a tremendous increase in demand as they play an important role in avoiding travel, social contacts and providing adequate audiovisual communication and virtual presence. AR/VR is helping in the fight of this pandemic through their deployment in various crucial areas such as telemedicine, online education and training, marketing and healthcare monitoring. AR/VR provides resolutions for the distribution of antibiotics to control the spread of the COVID-19 virus. This paper discusses the demand for AR and VR as well as other notable new technologies explored recently to support the fight against COVID-19. The same technologies are in high demand, in the form of 2D-3D visualization and tracking software, flow and dynamic wearable headsets, potentially responsible for displaying the geographic location mostly affected with COVID-19.
Private Computation of Phylogenetic Trees Based on Quantum Technologies

Individuals&#x2019; privacy and legal regulations demand genomic data be handled and studied with highly secure privacy-preserving techniques. In this work, we propose a feasible Secure Multiparty Computation (SMC) system assisted with quantum cryptographic protocols that is designed to compute a phylogenetic tree from a set of private genome sequences. This system significantly improves the privacy and security of the computation thanks to three quantum cryptographic protocols that provide enhanced security against quantum computer attacks. This system adapts several distance-based methods (Unweighted Pair Group Method with Arithmetic mean, Neighbour-Joining, Fitch-Margoliash) into a private setting where the sequences owned by each party are not disclosed to the other members present in the protocol. We theoretically evaluate the performance and privacy guarantees of the system through a complexity analysis and security proof and give an extensive explanation about the implementation details and cryptographic protocols. We also implement a quantum-assisted secure phylogenetic tree computation based on the Libscapi implementation of the Yao, the PHYLIP library and simulated keys of two quantum systems: Quantum Oblivious Key Distribution and Quantum Key Distribution. This demonstrates its effectiveness and practicality. We benchmark this implementation against a classical-only solution and we conclude that both approaches render similar execution times, the only difference being the time overhead taken by the oblivious key management system of the quantum-assisted approach.
Global Memory and Local Continuity for Video Object Detection

To deal with the challenges in video object detection (VOD), such as occlusion and motion blur, many state-of-the-art video object detectors adopt a feature aggregation module to encode the long-range contextual information to support the current frame. The main drawbacks of these detectors are three-folds: first, the frame-wise detection slows down the detection speed; second, the frame-wise detection usually ignores the local continuity of the objects in a video, resulting in temporal inconsistent detection; third, the feature aggregation module usually encodes temporal features either from a local video clip or a single video, without exploiting the features in other videos. In this work, we develop an online VOD algorithm, aiming at a balanced high-speed and high-accuracy, by exploiting the global memory and local continuity. In the algorithm, an effective and efficient global memory bank (GMB) is designed to deposit and update object class features, which enables us to exploit the support features in other videos to enhance object features in the current video frames. Besides, to further speed up the detection, we design an object tracker to perform object detection for non-key frames based on the detection results of the key frame by leveraging the local continuity property of the video. Considering the trade-off between detection accuracy and speed, the proposed framework achieves superior performance on the ImageNet VID dataset. Source codes will be released to the public via our GitHub website.
Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search

Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming (EDA-GP) algorithm that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). At each generation, the idea is to flexibly identify promising properties of the parent population and to transfer these properties to the offspring where the DAE-GP uses denoising to make the model robust to noise that is present in the parent population. Denoising partially corrupts candidate solutions that are used as input to the model. The stronger the corruption, the stronger the generalization of the model. In this work, we study how corruption strength affects the exploration and exploitation behavior of the DAE-GP. For a generalization of the royal tree problem (high-locality problem), we find that the stronger the corruption, the stronger the exploration of the solution space. For the given problem, weak corruption resulting in a stronger exploitation of the solution space performs best. However, in more rugged fitness landscapes (low-locality problems), we expect that a stronger corruption resulting in a stronger exploration will be helpful. Choosing the right denoising strategy can therefore help to control the exploration and exploitation behavior in search, leading to an improved search quality.
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

Wireless channels can be inherently privacy preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing these natural distortions and superpositions by wireless channels, we propose a novel privacy-preserving machine learning (ML) framework at the network edge, coined over-the-air mixup ML (AirMixML). In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and-superpositioned samples. AirMixML coincides with model training using mixup data augmentation achieving comparable accuracy to that with raw data samples. From a privacy perspective, AirMixML is a differentially private (DP) mechanism limiting the disclosure of each worker's private sample information at the server, while the worker's transmit power determines the privacy disclosure level. To this end, we develop a fractional channel-inversion power control (PC) method, a-Dirichlet mixup PC (DirMix(a)-PC), wherein for a given global power scaling factor after channel inversion, each worker's local power contribution to the superpositioned signal is controlled by the Dirichlet dispersion ratio a. Mathematically, we derive a closed-form expression clarifying the relationship between the local and global PC factors to guarantee a target DP level. By simulations, we provide DirMix(α)-PC design guidelines to improve accuracy, privacy, and energy-efficiency. Finally, AirMixML with DirMix(a)-PC is shown to achieve reasonable accuracy compared to a privacy-violating baseline with neither superposition nor PC.
Secure Medical Data Sharing Through Blockchain and Decentralized Models

The global health system has been strongly impacted by the events caused by the Covid-19 pandemic, highlighting and amplifying several critical issues. To contrast a global pandemic, it is necessary to have tools that can offer an integral vision of the situation to act in a timely manner on any possible case. In this regard, technologies based on decentralized systems, such as Blockchain, can be helpful, as they are global, real-time, and can be implemented to respect people's privacy. This work aims to investigate the use of decentralized networks to develop a system that can solve the problem of management and collaborative analysis of sensitive data. A general framework has been proposed that is mainly dedicated to healthcare organizations, such as hospitals and research centres, which first had to face the difficult situation caused by the Covid-19 pandemic. It has been highlighted how decentralized systems can effectively improve communications between these entities, promoting the fair exchange of knowledge in real-time without compromising citizens’ privacy.
A fresh approach to evaluate performance in distributed parallel genetic algorithms

This work proposes a novel approach to evaluate and analyze the behavior of multi-population parallel genetic algorithms (PGAs) when running on a cluster of multi-core processors. In particular, we deeply study their numerical and computational behavior by proposing a mathematical model representing the observed performance curves. In them, we discuss the emerging mathematical descriptions of PGA performance instead of, e.g., individual isolated results subject to visual inspection, for a better understanding of the effects of the number of cores used (scalability), their migration policy (the migration gap, in this paper), and the features of the solved problem (type of encoding and problem size). The conclusions based on the real figures and the numerical models fitting them represent a fresh way of understanding their speed-up, running time, and numerical effort, allowing a comparison based on a few meaningful numeric parameters. This represents a set of conclusions beyond the usual textual lessons found in past works on PGAs. It can be used as an estimation tool for the future performance of the algorithms and a way of finding out the upper limit of the performance if the number of used cores increases.
AdaFed: Performance-based Adaptive Federated Learning

Federated Learning is a distributed and privacy-preserving machine learning technique that allows local clients to learn a model without sharing their own data by coordinating with a global server. In this work, we present the Adaptive Federated Learning (AdaFed) algorithm, which aims at improving the training performance of deep neural networks in Federated Learning settings by: (i) dynamically weighting the local models in the model averaging procedure; (ii) by adapting the loss function used by the federation at every communication round. We discuss the specialisation of AdaFed for both classification and regression tasks, providing several validation examples. Due to its adaptive design, the AdaFed algorithm showed a robust behaviour against unbalanced data distributions and adversarial clients.
RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges

Recommendation (recommender) systems have played an increasingly important role in both research and industry in recent years. In the area of publication data, for example, there is a strong need to help people find the right research information through recommendations and scientific reports. The difference between search engines and recommendation systems is that search engines help us find something we already know, while recommendation systems are more likely to help us find new items. An essential function of recommendation systems is to support users in their decision making. Recommendation systems are information systems that can be categorized into decision support systems, as long as they are used for decision making and are intended to support people instead of replacing them. This paper deals with recommendation systems for research information, especially publication data. We discuss and analyze the challenges and peculiarities of implementing recommender systems for the scientific exchange of research information. For this purpose, data mining techniques are examined and a concept for a recommendation system for research information is developed. Our aim is to investigate to what extent a recommendation system based on a collaborative filtering approach with cookies is possible. The data source is publication data extracted from cookies in the Web of Science database. The results of our investigation show that a collaborative filtering process is suitable for publication data and that recommendations can be generated with user information. In addition, we have seen that collaborative filtering is an important element that can solve a practical problem by sifting through large amounts of dynamically generated information to provide users with personalized content and services.
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs

Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. GCNs incorporate the graph structure information and the node (or edge) features through message passing and computes ‘deep’ node representations. Despite significant progress in the field, designing GCN architectures for heterogeneous graphs still remains an open challenge. Due to the schema of a heterogeneous graph, useful information may reside multiple hops away. A key question is how to perform message passing to incorporate information of neighbors multiple hops away while avoiding the well-known over-smoothing problem in GCNs. To address this question, we propose our GCN framework Deep Heterogeneous Graph Convolutional Network (DHGCN), which takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away. It first computes representations of the target nodes based on their schema-derived ego-network (SEN). It then links the nodes of the same type with various pre-defined metapaths and performs message passing along these links to compute final node representations. Our design choices naturally capture the way a heterogeneous graph is generated from the schema. The experimental results on real and synthetic datasets corroborate the design choice and illustrate the performance gains relative to competing alternatives.
Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning

In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture.We differentiated breast mass lesions from gray-scale X-ray mammography images based on regions of interest (ROIs). Our dataset comprised breast mammogram images for 150 cases of malignant masses from which we extracted the mass ROI, and we composed a CNN-based deep learning model trained on this dataset to identify ROI mass lesions. The test dataset was created by shifting some of the training data images. Thus, although both datasets were different, they retained a deep structural similarity. We then applied our trained deep-learning model to detect masses on 8-bit mammogram images containing malignant masses. The input images were preprocessed by applying a scaling parameter of intensity before being used to train the CNN model for mass differentiation.The highest area under the receiver operating characteristic curve was 0.897 (Î 20).Our results indicated that the proposed patch-wise detection method can be utilized as a mass detection and segmentation tool.
Conversion paths of online consumers: A sequential pattern mining approach

• New data mining technique, so that it can consider the sequence of media. • Enabling more in-depth analysis and insight which cannot be found in the data used. • Channels can be correctly judged, advertisers can make efficient marketing mix. As the number and variety of online channels increase, businesses and advertisers must realize efficient marketing strategies and marketing mix. Customers' online channel usage patterns can be heterogeneous depending on various variables. If they understand this behavior correctly and use them for channel inflow or advertisement toward customers, they can establish an effective marketing strategy. In this paper, we utilize sequential pattern mining to find homogeneous patterns in customer's conversion paths by using conversion path data of customers who purchase diapers and find related insights. Furthermore, by analyzing the customer's channel movement pattern by dividing it into demographic information, we examine how demographic factors affect customer's online channel usage behavior.
Reconfigurable Hybrid Model Convolutional Stage – Infinity Laplacian Applied to Depth Completion

Convolutional networks are the current approach that presents the best performance in many applications. The principal critic of classical models is that they are hand-crafted by the designer. Still, the proposed architecture of a convolutional network is also hand-crafted, for example, the number of layers. Depth map completion is crucial for computer vision due to its applications in different fields such as video games or autonomous vehicles. Depth maps are acquired by a sensor or obtained by a stereo algorithm and present a lack of information due to occlusions or sensor misinterpretation. In this paper, we offer a reconfigurable hybrid model to interpolate depth maps. This model consists of a convolutional stage (SC1) pipeline, interpolation model, and convolutional stage (SC2). The convolutional stage input is a color reference image of the scene, creating a color features map as input for the next step. The interpolation model is the infinity Laplacian. We interpolated the incomplete depth map solving the Infinity Laplacian in a Manifold. Then, the completed depth map is processed again by the last convolutional stage. In this pipeline, we used a fixed number of convolutional filters, but we can interchange convolutional steps, i.e., interchange SC1 by SC2, reconfiguring the computing sequence. We estimated the parameters of the convolutional filter and the Infinity Laplacian using Particle Swarm Optimization (PSO). Our proposal obtained MSE=1.315 in the KITTI depth completion suite outperforming some contemporaneous methods.
Incentive Mechanism for Edge Computing-Based Blockchain: A Sequential Game Approach

Dueto its distributed characteristics, the development and deployment of the blockchain framework are able to provide feasible solutions for a wide range of Internet of Things (IoT) applications. While the IoT devices are usually resource-limited, how to make sure the acquisition of computational resources and participation of the devices will be the driving force to realize blockchain at the network edge. In this article, an edge computing-based blockchain framework is considered, where multiple edge service providers (ESPs) can provide computational resources to the devices for mining. We mainly focus on investigating the trading between the devices and ESPs in the computational resource market, where ESPs act as the sellers and devices act as the buyers. Accordingly, a sequential game model is formulated and by exploring the sequential Nash equilibrium (SE), the existence of the optimal solutions of selling and buying strategies can be proved. Then, a deep Q-network-based algorithm with modified experience replay update method is applied to find the optimal strategies. Through theoretical analysis and simulations, we demonstrate the effectiveness of the proposed incentive mechanism on forming the blockchain via the assistance of edge computing.
Enhancing distributed sensing with imperfect error correction

Entanglement has shown promise in enhancing information processing tasks in a sensor network, via distributed quantum sensing protocols. As noise is ubiquitous in sensor networks, error correction schemes based on Gottesman, Kitaev and Preskill (GKP) states are required to enhance the performance, as shown in [New J. Phys. 22, 022001 (2020)] assuming homogeneous noise among sensors and perfect GKP states. Here, we extend the analyses of performance enhancement to finite squeezed GKP states in a heterogeneous noise model. To begin with, we study different concatenation schemes of GKP-two-mode-squeezing codes. While traditional sequential concatenation schemes in previous works do improve the suppression of noise, we propose a balanced concatenation scheme that outperforms the sequential scheme in presence of finite GKP squeezing. We then apply these results to two specific tasks in distributed quantum sensing -- parameter estimation and hypothesis testing -- to understand the trade-off between imperfect squeezing and performance. For the former task, we consider an energy-constrained scenario and provide an optimal way to distribute the energy of the finite squeezed GKP states among the sensors. For the latter task, we show that the error probability can still be drastically lowered via concatenation of realistic finite squeezed GKP codes.
A CNN-Based Quality Assessment Method for Pseudo 4K Contents

Recently, there has been a growing interest in Ultra High-Definition (UHD) content, which brings a better visual experience for end-users. However, quite a few contents with 4K resolution are upscaled from High-Definition (HD) contents and suffer degradations in quality, such as blur, texture shift, etc. These pseudo 4K contents can not deliver the expected quality of experience (QoE) to end-users while requiring a high transmission bit rate in the meantime, which inevitably results in a waste of bandwidth resources. Hence, we develop a novel deep learning-based no reference (NR) image quality assessment (IQA) model for recognition and quality evaluation of real and fake 4K images. To reduce the computational overhead for a 4K image, we first select three representative patches with high texture complexity by the Grey-Level Co-occurrence Matrix (GLCM) based measure. Next, the convolutional neural network (CNN) is adopted to extract the quality-aware features of three representative patches. Specifically, we extract different levels of features from intermediate layers of CNN and concatenate them into a more effective quality-aware feature representation. Finally, the shallow fully connected (FC) network is utilized to aggregate the features into the quality score and the overall quality score of the 4K image is calculated as the average value of three patches’ quality scores. The experimental results show that the proposed method outperforms all compared NR IQA metrics on the 4K IQA database.
Improved Real-Time Traffic Obstacle Detection and Classification Method Applied in Intelligent and Connected Vehicles in Mixed Traffic Environment

Mixed traffic is a common phenomenon in urban environment. For the mixed traffic situation, the detection of traffic obstacles, including motor vehicle, non-motor vehicle, and pedestrian, is an essential task for intelligent and connected vehicles (ICVs). In this paper, an improved YOLO model is proposed for traffic obstacle detection and classification. The YOLO network is used to accurately detect the traffic obstacles, while the Wasserstein distance-based loss is used to improve the misclassification in the detection that may cause serious consequences. A new established dataset containing four types of traffic obstacles including vehicles, bikes, riders, and pedestrians is collected under different time periods and different weather conditions in urban environment in Wuhan, China. Experiments are performed on the established dataset on Windows PC and NVIDIA TX2, respectively. From the experimental results, the improved YOLO model has higher mean average precision than the original YOLO model and can effectively reduce intolerable misclassifications. In addition, the improved YOLOv4-tiny model has a detection speed of 22.5928 fps on NVIDIA TX2, which can basically realize the real-time detection of traffic obstacles.