Reviewerly/exHarmony huggingface dataset:
Task Scheduling in Cloud Computing Environment by Grey Wolf Optimizer

Cloud computing is an emerging computer technology, that provides distributed, scalable, elastic computer resources to the end-user over the Internet. One of the most challenging tasks in the cloud computing environment is task scheduling. The main objectives of the task scheduling are to identify the appropriate resources for scheduling a specific task on time, utilize the resources more efficiently, and reduce the total completion time of all input tasks to be executed. The task scheduling problem belongs to the class NP-hard. Since metaheuristic algorithms are proven to be efficient in the NP hard optimization, in this paper, we propose a task scheduling algorithm using metaheuristics approach. The proposed scheduler is based on the grey wolf optimizer nature-inspired algorithm. The experimental results prove the quality and robustness of the proposed method.
A systematic analysis of meteorological variables for PV output power estimation

While the large-scale deployment of photovoltaics (PV) for generating electricity plays an important role to mitigate global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological variables at the PV site. In this paper, we present a systematic approach to perform an analysis on different meteorological variables, namely temperature, dew point temperature, relative humidity, visibility, air pressure, wind speed, cloud cover, wind bearing and precipitation, and assess their impact on PV output power estimation. The study uses three years of input meteorological data and PV output power data from multiple prosumers in two case studies, one in the U.S. and one in the Netherlands. The analysis covers the correlation and interdependence among the meteorological variables. Then, by using machine learning-based regression methods, we identify the primary meteorological variables for PV output power estimation. Finally, the paper concludes that the impact of using a lower-dimensional subspace of meteorological variables per location, as input for the regression methods, results in a similar estimation accuracy in the two case studies.
Thin MobileNet: An Enhanced MobileNet Architecture

In the field of computer, mobile and embedded vision Convolutional Neural Networks (CNNs) are deep learning models which play a significant role in object detection and recognition. MobileNet is one such efficient, light-weighted model for this purpose, but there are many constraints or challenges for the hardware deployment of such architectures into resource-constrained micro-controller units due to limited memory, energy and power. Also, the overall accuracy of the model generally decreases when the size and the total number of parameters are reduced by any method such as pruning or deep compression. The paper proposes three hybrid MobileNet architectures which has improved accuracy along-with reduced size, lesser number of layers, lower average computation time and very less overfitting as compared to the baseline MobileNet v1. The reason behind developing these models is to have a variant of the existing MobileNet model which will be easily deployable in memory constrained MCUs. We name the model having the smallest size (9.9 MB) as Thin MobileNet. We achieve an increase in accuracy by replacing the standard non-linear activation function ReLU with Drop Activation and introducing Random erasing regularization technique in place of drop out. The model size is reduced by using Separable Convolutions instead of the Depthwise separable convolutions used in the baseline MobileNet. Later on, we make our model shallow by eliminating a few unnecessary layers without a drop in the accuracy. The experimental results are based on training the model on CIFAR-10 dataset.
Very Long Natural Scenery Image Prediction by Outpainting

Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between generated images and original input. The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input. To solve the two problems, we devise some innovative modules, named Skip Horizontal Connection and Recurrent Content Transfer, and integrate them into our designed encoder-decoder structure. By this design, our network can generate highly realistic outpainting prediction effectively and efficiently. Other than that, our method can generate new images with very long sizes while keeping the same style and semantic content as the given input. To test the effectiveness of the proposed architecture, we collect a new scenery dataset with diverse, complicated natural scenes. The experimental results on this dataset have demonstrated the efficacy of our proposed network.
An incentive mechanism for data sharing based on blockchain with smart contracts

Data sharing techniques have progressively drawn increasing attention as a means of significantly reducing repetitive work. However, in the process of data sharing, the challenges regarding formation of mutual-trust relationships and increasing the level of user participation are yet to be solved. The existing solution is to use a third party as a trust organization for data sharing, but there is no dynamic incentive mechanism for data sharing with a large number of users. Blockchain 2.0 with smart contract has the natural advantage of being able to enable trust and automated transactions between a large number of users. This paper proposes a data sharing incentive model based on evolutionary game theory using blockchain with smart contract. The smart contract mechanism can dynamically control the excitation parameters and continuously encourages users to participate in data sharing.
Examining E-Government development effects on corruption in Africa: The mediating effects of ICT development and institutional quality

There has been increased development in e-government initiatives in countries across Africa and these initiatives have been linked to reduced corruption levels. Although some studies provide evidence of how e-government development contributes to reduced corruption, few studies have analyzed the mediating effects of ICT development and institutional quality on the e-government development—corruption linkage. Relying on secondary data from multiple sources, we used a hypothesized model and Partial Least Squares Structural Equation Modelling (PLS-SEM) to empirically examine the mediating role of these two factors on e-government development on corruption. Our results show that a significant relationship exists between e-government development and ICT development, e-government development and institutional quality as well as ICT development and corruption. Whilst these were all supported in confirming the effects of ICT development on corruption. The findings demonstrate the significant role ICT development and institutional quality play as mediators of e-government effects on corruption. However, the study's direct effects of e-government on corruption was not significant. The research suggests that the ICT development of countries be strengthened. Furthermore, there is the need for an improved quality of a country's institutions as measures to ensure stronger effects of e-government on corruption.
Dynamic ReLU

Rectified linear units (ReLU) are commonly used in deep neural networks. So far ReLU and its generalizations (non-parametric or parametric) are static, performing identically for all input samples. In this paper, we propose Dynamic ReLU (DY-ReLU), a dynamic rectifier of which parameters are generated by a hyper function over all input elements. The key insight is that DY-ReLU encodes the global context into the hyper function, and adapts the piecewise linear activation function accordingly. Compared to its static counterpart, DY-ReLU has negligible extra computational cost, but significantly more representation capability, especially for light-weight neural networks. By simply using DY-ReLU for MobileNetV2, the top-1 accuracy on ImageNet classification is boosted from 72.0% to 76.2% with only 5% additional FLOPs.
The importance of digital literacy on the labour market

Purpose The purpose of this paper is to examine the relationship between digital skills and employment and in this way accentuate importance of policy interventions for improving digital literacy. Design/methodology/approach Using Eurostat data, this paper demonstrates statistically significant correlation between digital skills and employment rates in EU. Findings This implies why it is so important for governments and employers to seek, propose and implement new strategies to promote digital inclusion, literacy and the training not only for new ICT professionals but also for the whole workforce. Social implications Digital skills, particularly digital literacy, are an important factor for socio-economic development of the society and employability of the labour force. Without adequate digital literacy, it is not possible to participate in the economy and the digital society, particularly having in mind the digital transformation that the world of work is experiencing in the content and the organisation. Beyond the work environment, it also affects the way people live and communicate. Originality/value In this paper, the importance and definition of digital literacy are elaborated, explaining the measurement of digital literacy and skills, as well as the relation between digital literacy and employability.
Blockchain-enabled logistics finance execution platform for capital-constrained E-commerce retail

As one of the most prevailing retail channels, E-commerce has nowadays facilitated retailers to sell goods to customers worldwide and tremendously increased the supply chain efficiency by removing most intermediate links. The broaden business scope and accelerated goods circulation, nevertheless, have generally led to capital shortages for retailers, especially small and medium enterprises (SMEs). Given the SMEs' difficulty in acquiring capital from financial institutions such as banks, logistics finance (LF) has emerged as an alternative, which is the combination of logistics and financial service. However, the frequent order fulfillment and diversity of pledges in E-commerce hinder SMEs in meeting LF's financing requirements. Furthermore, the current LF relies more on large and reputable third-party logistics (3PLs) to alleviate financing risks, which in turn raises the entry threshold for other 3PLs. Hence, this paper has proposed a blockchain-enabled logistics finance execution platform (BcLFEP) as an integrated solution to facilitate LF for E-commerce retail. A cross-layered architecture is proposed to organize and manage involved resources, workflows and decisions based on the object-oriented methodology (OOM). A hybrid finite state machine-based smart contract (HFSM-SC) is designed to associate and coordinate with all kinds of agents for LF operations throughout its lifecycle. Moreover, blockchain is integrated with agent technology to construct a blockchain-enabled multi-agent system (BcMAS), providing a trusted runtime environment to more autonomously and efficiently execute smart contract. Finally, a case study is conducted to implement BcLFEP-enabled dynamic pledge management for verification and evaluation.
ZEXE: Enabling Decentralized Private Computation

Ledger-based systems that support rich applications often suffer from two limitations. First, validating a transaction requires re-executing the state transition that it attests to. Second, transactions not only reveal which application had a state transition but also reveal the application's internal state.We design, implement, and evaluate ZEXE, a ledger-based system where users can execute offline computations and subsequently produce transactions, attesting to the correctness of these computations, that satisfy two main properties. First, transactions hide all information about the offline computations. Second, transactions can be validated in constant time by anyone, regardless of the offline computation.The core of ZEXE is a construction for a new cryptographic primitive that we introduce, decentralized private computation (DPC) schemes. In order to achieve an efficient implementation of our construction, we leverage tools in the area of cryptographic proofs, including succinct zero knowledge proofs and recursive proof composition. Overall, transactions in ZEXE are 968 bytes regardless of the offline computation, and generating them takes less than 1min plus a time that grows with the offline computation.We demonstrate how to use ZEXE to realize privacy-preserving analogues of popular applications: private user-defined assets and private decentralized exchanges for these assets.
Augmented Reality for Learning of Children and Adolescents With Autism Spectrum Disorder (ASD): A Systematic Review

This paper presents a systematic review of relevant primary studies on the use of augmented reality (AR) to improve various skills of children and adolescents diagnosed with autism spectrum disorder (ASD) from years 2005 to 2018 inclusive in eight bibliographic databases. This systematic review attempts to address eleven specific research questions related to the learing skills, participants, AR technology, research design, data collection methods, settings, evaluation parameters, intervention outcomes, generalization, and maintenance. The social communication skill was the highly targeted skill, and individuals with ASD were part of all the studies. Computer, smartphone, and smartglass are more frequently used technologies. The commonly used research design was pre-test and post-test. Almost all the studies used observation as a data collection method, and classroom environment or controlled research environment were used as a setting of evaluation. Most of the evaluation parameters were human-assisted. The results of the studies show that AR benefited children with ASD in learning skills. The generalization test was conducted in one study only, but the results were not reported. The results of maintenance tests conducted in five studies during a short-term period following the withdrawal of intervention were positive. Although the effect of using AR towards the learning of individuals was positive, given the wide variety of skills targeted in the studies, and the heterogeneity of the participants, a summative conclusion regarding the effectiveness of AR for teaching or learning of skills related to ASD based on the existing literature is not possible. The review also proposes the research taxonomy for ASD. Future research addressing the effectiveness of AR among more participants, different technologies supporting AR for the intervention, generalization, and maintenance of learning skills, and the evaluation in the inslusive classroom environment and other settings is warranted.
How will service robots redefine leadership in hotel management? A Delphi approach

Purpose Using the Delphi technique, this paper aims to investigate how human resource experts perceive service robots will impact leadership and human resource management in the hospitality industry. Design/methodology/approach A three-stage Delphi study with hotel industry human resource experts was conducted to identify the key trends and major challenges that will emerge in the next ten years and how leaders should deal with the challenges brought about by service robot technologies. Findings The results show that while service robots are anticipated to increase efficiency and productivity of hotel activities, they may also pose challenges such as high costs, skill deficits and significant changes to the organizational structure and culture of hotels. Therefore, the anticipated applications and integration of robotic technology will require leaders of the future to carefully consider the balance between the roles of service robots and human employees in the guest experience and to nurture a work environment that embraces open-mindedness and change. Originality/value This is the first type of study to examine hospitality leadership and human resource management in the context of robotized hotels. This study has taken an important step to understand the leadership role in robotized hotels from a human resource perspective and brings clarity as to how robotic technology can influence leadership in the future workplace.
Defending network intrusion detection systems against adversarial evasion attacks

Intrusion Detection and the ability to detect attacks is a crucial aspect to ensure cybersecurity. However, what if an IDS (Intrusion Detection System) itself is attacked; in other words what defends the defender? In this work, the focus is on countering attacks on machine learning-based cyberattack detectors. In principle, we propose the adversarial machine learning detection solution. Indeed, contemporary machine learning algorithms have not been designed bearing in mind the adversarial nature of the environments they are deployed in. Thus, Machine Learning solutions are currently the target of a range of attacks. This paper evaluates the possibility of deteriorating the performance of a well-optimised intrusion detection algorithm at test time by crafting adversarial attacks with the four of the recently proposed methods and then offers a way to detect those attacks. The relevant background is provided for both artificial neural networks and four ways of crafting adversarial attacks. The new detection method is explained in detail, and the results of five different classifiers are compared. To the best of our knowledge, detecting adversarial attacks on artificial neural networks has not yet been widely researched in the context of intrusion detection systems.
MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings

The efficiency of an accelerator depends on three factors-mapping, deep neural network (DNN) layers, and hardware-constructing extremely complicated design space of DNN accelerators. To demystify such complicated design space and guide the DNN accelerator design for better efficiency, we propose an analytical cost model, MAESTRO. MAESTRO receives DNN model description and hardware resources information as a list, and mapping described in a data-centric representation we propose as inputs. The data-centric representation consists of three directives that enable concise description of mappings in a compiler-friendly form. MAESTRO analyzes various forms of data reuse in an accelerator based on inputs quickly and generates more than 20 statistics including total latency, energy, throughput, etc., as outputs. MAESTRO's fast analysis enables various optimization tools for DNN accelerators such as hardware design exploration tool we present as an example.
Double-Factor-Regularized Low-Rank Tensor Factorization for Mixed Noise Removal in Hyperspectral Image

As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many subsequent applications. Recently, based on the framework of subspace representation and low-rank matrix/tensor factorization (LRMF/LRTF), many single-factor-regularized methods add various regularizations on the spatial factor to characterize its spatial prior knowledge. However, these methods neglect the common characteristics among different bands and the spectral continuity of HSIs. To tackle this issue, this article establishes a bridge between the factor-based regularization and the HSI priors and proposes a double-factor-regularized LRTF model for HSI mixed noise removal. The proposed model employs LRTF to characterize the spectral global low rankness, introduces a weighted group sparsity constraint on the spatial difference images (SpatDIs) of the spatial factor to promote the group sparsity in the SpatDIs of HSIs, and suggests a continuity constraint on the spectral factor to promote the spectral continuity of HSIs. Moreover, we develop a proximal alternating minimization-based algorithm to solve the proposed model. Extensive experiments conducted on the simulated and real HSIs demonstrate that the proposed method has superior performance on mixed noise removal compared with the state-of-the-art methods based on subspace representation, noise modeling, and LRMF/LRTF.
Simultaneous Global and Local Graph Structure Preserving for Multiple Kernel Clustering

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids selecting and tuning predefined kernel. By integrating the self-expression learning framework, the graph-based MKL subspace clustering has recently attracted considerable attention. However, the graph structure of data in kernel space is largely ignored by previous MKL methods, which is a key concept of affinity graph construction for spectral clustering purposes. In order to address this problem, a novel MKL method is proposed in this article, namely, structure-preserving multiple kernel clustering (SPMKC). Specifically, SPMKC proposes a new kernel affine weight strategy to learn an optimal consensus kernel from a predefined kernel pool, which can assign a suitable weight for each base kernel automatically. Furthermore, SPMKC proposes a kernel group self-expressiveness term and a kernel adaptive local structure learning term to preserve the global and local structure of the input data in kernel space, respectively, rather than the original space. In addition, an efficient algorithm is proposed to solve the resulting unified objective function, which iteratively updates the consensus kernel and the affinity graph so that collaboratively promoting each of them to reach the optimum condition. Experiments on both image and text clustering demonstrate that SPMKC outperforms the state-of-the-art MKL clustering methods in terms of clustering performance and computational cost.
What makes immersive virtual reality the ultimate empathy machine? Discerning the underlying mechanisms of change

Recent research has emphasized the promise of Immersive Virtual Reality (IVR) to bolster empathy. The underlying mechanisms of such an intriguing outcome are, however, underexplored. The present study examines the effect of distinct but interrelated user experiences (UX) dimensions underlying the bolstering effect of IVR on empathy. Sixty-five young adults participated in five 1-h weekly programs encompassing a total of 25 IVR experiences of different nature. Following each session, participants rated their UX of immersion-presence, illusion of body ownership, illusion of agency, engagement, and IVR-triggered mind-wandering for each IVR experience. Baseline empathy, operationalized in a multifaceted fashion, was measured both before and after the IVR program, spanning a total of seven weeks. Empathy significantly increased after the program with a moderate effect size (d = .52). Further, 27.6% of the variance in empathy change was explained by the combined effect of the UX variables. The largest predictors were illusion of body ownership and agency. The pattern of results was the same across types of IVR experience, suggesting that media content (whether empathy-evoking or not), did not moderate the relationship between UX and empathy. Together, this study highlights that IVR is more an effective “perspective taking-machine” than “empathy machine” per se.