Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: Collaborative filtering recommendation algorithm based on Bee Colony K-means clustering model

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Multipath-Based HTTP Adaptive Streaming Scheme for the 5G Network

Fifth-generation (5G) communication has a non-line-of-sight (non-LOS) property, in that it cannot pass through obstacles due to the characteristics of ultra-high frequencies.This can lead to a high packet loss rate, which results in loss-based congestion.As the demand for high-quality video streaming services increases, multipath-based Hypertext Transfer Protocol (HTTP) adaptive streaming has been widely studied.It has the advantage of improving stability using bandwidth aggregation and alternative transmission.The request policies of existing systems degrade bandwidth utilization by the ON-OFF traffic pattern and degrade the quality-of-experience (QoE) due to incorrect bandwidth estimation.In the 5G environment, the above problems are exacerbated by severe network asymmetry, and buffer underflow occurs due to the drastic bandwidth changes in a non-LOS environment.We propose a multipath-based transmission scheme to solve the problem of HTTP adaptive streaming in a 5G environment.The proposed scheme presents a multipath-based collective segment request policy for accurate aggregated bandwidth estimation and bandwidth utilization improvement.The aggregated bandwidth estimation measures both block-based and segment-based bandwidth estimation values to improve network responsiveness and stability.We propose a rate adaptation scheme to improve bandwidth utilization and QoE.We also present a segment scheduler to solve the out-of-order problem in multipath-based transmission, and propose an offloading control scheme based on a partial-segment-based request policy to prevent buffer underflow due to sudden bandwidth reduction in the non-LOS 5G environment.Quantitative results from simulations suggest that our scheme solves the shortcomings of the existing solutions improving both bandwidth utilization and QoE.
Characterizing Failure and Repair Time of Servers in a Hyper-scale Data Center

Hyper-scale data centers are used to host cloud computing interfaces to support the increasing demand for storage and computational resources. For achieving specific service level agreements (SLA), this infrastructure demands highly available cloud computing systems. It is necessary to analyze the server failure incidents to determine the way of improving the reliability of the system since the computational interruption causes financial losses for the data center owners. Regarding the reliability analysis, it is important to characterize the time to failure and time to repair of the servers. In this paper, a publicly available data set from Google cloud-cluster data center will be analyzed to find the distribution function for the time to failure and the time to repair for the servers in a cloud based data centers.
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention

A lack of corpora has so far limited advances in integrating human gaze data as a supervisory signal in neural attention mechanisms for natural language processing(NLP). We propose a novel hybrid text saliency model(TSM) that, for the first time, combines a cognitive model of reading with explicit human gaze supervision in a single machine learning framework. On four different corpora we demonstrate that our hybrid TSM duration predictions are highly correlated with human gaze ground truth. We further propose a novel joint modeling approach to integrate TSM predictions into the attention layer of a network designed for a specific upstream NLP task without the need for any task-specific human gaze data. We demonstrate that our joint model outperforms the state of the art in paraphrase generation on the Quora Question Pairs corpus by more than 10% in BLEU-4 and achieves state of the art performance for sentence compression on the challenging Google Sentence Compression corpus. As such, our work introduces a practical approach for bridging between data-driven and cognitive models and demonstrates a new way to integrate human gaze-guided neural attention into NLP tasks.
Robust P2P Personalized Learning

Decentralized machine learning over peer-to-peer networks is very appealing for it enables to learn personalized models without sharing users data, nor relying on any central server. Peers can improve upon their locally trained model across a network graph of other peers with similar objectives. Whilst they offer an inherently scalable scheme with a very simple cost-efficient learning model, peer-to-peer networks are also fragile. In particular, they can be very easily disrupted by unfairness, free-riding, and adversarial behaviors. In this paper, we present CDPL (Contribution Driven P2P Learning), a novel Byzantine-resilient distributed algorithm to train personalized models across similar peers. We convey theoretically and empirically the effectiveness of CDPL in terms of speed of convergence as well as robustness to Byzantine behavior.
Information and Communications Technology (ICT) literacy of Hungarian English majors: A validation study

Abstract Information and communications technology (ICT) inclusion has long been at the forefront of professional language pedagogy discourse. It has been argued that ICT implementation is globally advocated but depends on local variables. ICT literacy nowadays does not only include owning and operating devices, but also the ability to create content, solve problems and minding digital safety. The aims of this study were to validate a questionnaire mapping the ICT literacy of one particular group of adult learners: Hungarian English majors ( N = 45) and to offer some preliminary results. After two rounds of reliability analysis, all nine constructs of the questionnaire proved to be reliable, each above a minimum Cronbach's alpha value of 0.60. Based on the questionnaire results, it can be said that Hungarian English majors have good digital competences, ICT devices are generally available for them, but their ICT acceptance is lower than hypothesised, and devices emerge as learning tools for students rather than substitutes for face-to-face interaction. Since the questionnaire was piloted on a small sample size ( N = 45), results are only preliminary; therefore, this article outlines plans for future administration of the questionnaire.
The entropy power inequality with quantum conditioning

The conditional entropy power inequality is a fundamental inequality in information theory, stating that the conditional entropy of the sum of two conditionally independent vector-valued random variables each with an assigned conditional entropy is minimum when the random variables are Gaussian. We prove the conditional entropy power inequality in the scenario where the conditioning system is quantum. The proof is based on the heat semigroup and on a generalization of the Stam inequality in the presence of quantum conditioning. The entropy power inequality with quantum conditioning will be a key tool of quantum information, with applications in distributed source coding protocols with the assistance of quantum entanglement.
Next-Generation Blockchain-Enabled Virtualized Cloud Security Solutions: Review and Open Challenges

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective IT solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from the grid and parallel computing models. It uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better IT solutions for large-scale computational data centres. It encompasses different services for supporting data storage, networking, and computing for facilities and amenities for businesses and multinational corporations. The enormous elastic on-demand cloud provisioning resources and services and datasets are processed and stored in tier-level virtualized cloud data centres operated by third-party service providers called cloud owners. The primary issue with these cloud service providers is to provide and maintain data security, privacy, and confidentiality and service availability and data support for end-users. This paper reviews, highlights, and discusses some of the common cloud computing vulnerabilities primarily related to virtualization platforms and their implementations while outsourcing services and resources to different end-users and business enterprises. We then provided blockchain-enabled solutions for virtualized cloud platforms involving both the end-users and cloud service providers (CSP) to address and solve various security and privacy-related vulnerabilities. These solutions will help the data centre industry to improve its virtualized cloud services and resource provisioning facilities. Finally, we discussed different blockchain-related implementation challenges in cloud infrastructures.
Analysis on the Number of Linear Regions of Piecewise Linear Neural Networks

Deep neural networks (DNNs) are shown to be excellent solutions to staggering and sophisticated problems in machine learning. A key reason for their success is due to the strong expressive power of function representation. For piecewise linear neural networks (PLNNs), the number of linear regions is a natural measure of their expressive power since it characterizes the number of linear pieces available to model complex patterns. In this article, we theoretically analyze the expressive power of PLNNs by counting and bounding the number of linear regions. We first refine the existing upper and lower bounds on the number of linear regions of PLNNs with rectified linear units (ReLU PLNNs). Next, we extend the analysis to PLNNs with general piecewise linear (PWL) activation functions and derive the exact maximum number of linear regions of single-layer PLNNs. Moreover, the upper and lower bounds on the number of linear regions of multilayer PLNNs are obtained, both of which scale polynomially with the number of neurons at each layer and pieces of PWL activation function but exponentially with the number of layers. This key property enables deep PLNNs with complex activation functions to outperform their shallow counterparts when computing highly complex and structured functions, which, to some extent, explains the performance improvement of deep PLNNs in classification and function fitting.
Progressive Rain Removal via a Recurrent Convolutional Network for Real Rain Videos

Rain removal in videos is a problem that has attracted tremendous interest of researchers within the field of deep learning.Although deep-learning-based rain removal methods outperform large number of conventional vision methods, some technical issues that need to be resolved remain.In this article, we propose a new deep learning method for video rain removal based on recurrent neural network (RNN) architecture.Pseudo groundtruth was generated from real rainy video sequence by temporal filtering for supervised learning.Instead of focusing on various shapes of rain streaks similar to conventional methods, in this article, we focused on the changing behaviors of rain streaks.To accomplish this, images of progressive rain streaks were generated from the real rain videos and are sequentially fed to the network in a decreasing rain order.Multiple images with different amounts of rain streaks were used as RNN inputs to more efficiently identify rain streaks and then remove them.Experimental results demonstrate that our method is suitable for a wide range of rainy images.Moreover, experiments performed on both real-world and synthetic images demonstrate that our proposed method can achieve competitive results in comparison with the benchmarked and conventional approaches for rain streak removal from images.
A novel <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4804" altimg="si218.svg"><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math>-threshold two-in-one secret image sharing scheme for multiple …

A two-in-one secret image sharing scheme (TiOSISS) is the combination of two different secret image sharing schemes (SISSs), which has advantages of both schemes, such as the simple stacking-to-see property and precise recovery with computing devices available. Since most of current TiOSISSs depend on steganography techniques, it results in several drawbacks, such as large pixel expansion and poor visual quality with shares stacking. Besides, researchers ignore the independence between two different SISSs, that is, both SISSs should deal with irrelevant secret images with different thresholds. By controlling the randomness of the sharing phase according to constraints from both SISSs, we combine polynomial-based SISS (PSISS) and random-grid-based visual cryptography scheme (RGVCS) together, and propose an ideal (k1,k2,n)-threshold TiOSISS for multiple secrets. The proposed TiOSISS not only overcomes drawbacks above, but also has high scalability to improve its performances by utilizing different RGVCSs. Sufficient analyses and experiments are provided to verify its security and effectiveness.
Rethinking Motivation of Deep Neural Architectures

Nowadays, deep neural architectures have acquired great achievements in many domains, such as image processing and natural language processing. In this paper, we hope to provide new perspectives for the future exploration of novel artificial neural architectures via reviewing the proposal and development of existing architectures. We first roughly divide the influence domain of intrinsic motivations on some common deep neural architectures into three categories: information processing, information transmission and learning strategy. Furthermore, to illustrate how deep neural architectures are motivated and developed, motivation and architecture details of three deep neural networks, namely convolutional neural network (CNN), recurrent neural network (RNN) and generative adversarial network (GAN), are introduced respectively. Moreover, the evolution of these neural architectures are also elaborated in this paper. At last, this review is concluded and several promising research topics about deep neural architectures in the future are discussed.
3D vibrations reconstruction with only one camera

In this paper, we propose a new method to measure the vibrations of a 3D mechanical structure. It requires only a video sequence taken by a single camera and a Finite Element model of the structure. First, the intrinsic and extrinsic parameters of the camera are identified. Then, a collection of numerical deflection shapes and normal modes are projected in the image frame of the camera. This is done thanks to a linearization of the perspective projection model. Comparing the motion of targets seen by the camera and the motion of Finite Element model at the same location gives the time evolution of modes amplitude. Finally the motion of the model is reconstructed in 2D in the image frame and in 3D in the world frame. By following this procedure, vibration amplitude can be magnified applying a scale factor on a modal basis covering a large frequency bandwidth.
Social Interaction and Pain Threshold in Virtual Reality

This experiment examined the effects of social presence and perceived location of a virtual environment on participants' pain thresholds in a preregistered, within-subjects experiment. First, we examined the effects of social interaction versus being alone in a virtual environment. Second, we compared a virtual environment representing a remote location to a replication of the laboratory environment. Social interaction predicted increased pain tolerance, but there was no effect of the "location" of the virtual environment. To our knowledge, this research project is the first to use real-time social interaction in virtual reality as a distractor for experimental thermal pain, and the first to examine the potential interaction between social interaction and transportation to different virtual locations. While this task is not directly analogous to the experience of pain in a medical setting, this preliminary study indicates future avenues for patient treatment. Clinical Trial Registration number: 1701006910.
Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy

A known technique to obtain subpixel resolution by using object tracking through cross-correlation consists of interpolating the obtained correlation function and then refining peak location. Although the technique provides accurate results, peak location is usually biased toward the closest integer coordinate. This effect is known as the peak-locking error and it strongly limits this calculation technique's experimental accuracy. This error may differ depending on the scene and algorithm used to fit and interpolate the correlation peak, but in general, it may be attributed to a sampling problem and the presence of aliasing. Many studies in the literature analyze this effect in the Fourier domain. Here, we propose an alternative analysis on the spatial domain. According to our interpretation, the peak-locking error may be produced by a non-symmetrical sample distribution, thus provoking a bias in the result. According to this, the peak interpolant function, the size of the local domain and low-pass filters play a relevant role in diminishing the error. Our study explores these effects on different samples taken from the DIC Challenge database, and the results show that, in general, peak fitting with a Gaussian function on a relatively large domain provides the most accurate results.
Skeleton-based explainable human activity recognition for child gross-motor assessment

Human activity recognition (HAR) is a basic technology for understanding human behavior and motion, and is used in various fields and products related in the industrial informatics. This paper focuses on child gross motor (GM) skills as the target of explainable HAR and proposes a visualization method of recognition reason. Subsequently, a motional time series image conversion method and its data augmentation are proposed, and the recognition accuracy to thirteen type of GMs was extremely improved to 99.5% using presented deep network. Finally, a skeleton-based method for visualizing the reason of discrimination using Grad-CAM was proposed, and its usefulness was investigated by an agreement analysis. As a result, the similarities / differences between humans and machines were found, and a network structure of explainable HAR suitable for obtaining a high consent by human was confirmed.
Corruption and banks’ non-performing loans: empirical evidence from MENA countries

Corruption has long been a serious problem in most countries in the Middle East and North Africa (MENA). This research aims to investigate the impact of country-wide corruption on banks' credit risk across 16 countries in this region over the period 2011–2019. Applying the interactive fixed effects estimation technique on a model consisting of both macro and bank-specific variables and utilizing data from 197 banks, the results show a positive significant association between corruption and banks non-performing loans (NPL). Corruption was found to have a positive relation with credit risk even in banks with high risk aversion.
Knowledge, Skills, and Abilities for Specialized Curricula in Cyber Defense

More specialized cybersecurity education programs are needed to address workforce needs, but it is unclear which knowledge, skills, and abilities (KSAs) fulfil industry needs. We interviewed 48 professionals within four cyber defense specialty areas: (1) Cyber Network Defense Analysis, (2) Cyber Network Defense Infrastructure Support, (3) Incident Response, and (4) Vulnerability Assessment and Management. The professionals rated a number of specialized KSAs along two dimensions: how important the KSA was to their job and how difficult the KSA was to learn. Overall, communication and other non-technical skills were rated as being very important for all cyber defense jobs. Findings indicated that, for some specialty areas, technical knowledge and skills vary considerably between jobs and so the ability to teach oneself is more valuable than proficiency in any one KSA. Findings may be used to inform the development of general cybersecurity curricula, as well as curricula that focus on Cyber Network Defense Analysis, Cyber Network Defense Infrastructure Support, or Vulnerability Assessment and Management.
Joint Reducer Placement and Coflow Bandwidth Scheduling for Computing Clusters

Reducing Coflow Completion Time (CCT) has a significant impact on application performance in data-parallel frameworks. Most existing works assume that the endpoints of constituent flows in each coflow are predetermined. We argue that CCT can be further optimized by treating flows' destinations as an additional optimization dimension via reducer placement. In this article, we propose and implement RPC, a joint online Reducer Placement and Coflow bandwidth scheduling framework, to minimize the average CCT in cloud clusters. We first develop a 2-approximation algorithm to minimize the CCT of a single coflow, and then schedule all the coflows following the Shortest Remaining Time First (SRTF) principle. We use real testbed experiments and extensive large-scale simulations to demonstrate that RPC can reduce the average CCT by 64.98% compared with the state-of-the-art technologies.
The virtual frame technique: ultrafast imaging with any camera

Many phenomena of interest in nature and industry occur rapidly and are difficult and cost-prohibitive to visualize properly without specialized cameras.Here we describe in detail the Virtual Frame Technique (VFT), a simple, useful, and accessible form of compressed sensing that increases the frame acquisition rate of any camera by several orders of magnitude by leveraging its dynamic range.VFT is a powerful tool for capturing rapid phenomenon where the dynamics facilitate a transition between two states, and are thus binary.The advantages of VFT are demonstrated by examining such dynamics in five physical processes at unprecedented rates and spatial resolution: fracture of an elastic solid, wetting of a solid surface, rapid fingerprint reading, peeling of adhesive tape, and impact of an elastic hemisphere on a hard surface.We show that the performance of the VFT exceeds that of any commercial high speed camera not only in rate of imaging but also in field of view, achieving a 65MHz frame rate at 4MPx resolution.Finally, we discuss the performance of the VFT with several commercially available conventional and high-speed cameras.In principle, modern cell phones can achieve imaging rates of over a million frames per second using the VFT.