Reviewerly/exHarmony huggingface dataset:
Selection of Cloud Service Providers for Hosting Web Applications in a Multi-cloud Environment

With the evolving data regulations and residency laws, it is becoming challenging for enterprises to serve all the geographically distributed users. To abide with the regulations and to leverage the benefits, such as good quality of service at a low cost, and avoid vendor lock-in, enterprises are seeking multiple Cloud Service Providers (CSPs) for hosting their web applications. The selection of CSPs in a multi-cloud environment is a challenging problem as it depends on multiple criteria such as security, service cost, manageability and so on.In this paper, we address the CSP selection problem for multiple applications in a multi-cloud environment comprising of multiple criteria. We provide a holistic solution methodology that ranks the CSP combinations for each web application using a multiple-criteria decision-making (MCDM) technique. The solution methodology takes virtual machine and cloud center selection into consideration for ranking the CSP combinations. However, selecting the top ranked CSP combinations for all applications may not be a viable option from the manageability and budget perspectives. Hence, we propose an optimization model to select CSPs for multiple applications of an enterprise considering budget and rank. We demonstrate our methodology through numerical experiments and provide various insights.
Intelligent Dynamic Gesture Recognition Using CNN Empowered by Edit Distance

Human activity detection and recognition is a challenging task. Video surveillance can benefit greatly by advances in Internet of Things (IoT) and cloud computing. Artificial intelligence IoT (AIoT) based devices form the basis of a smart city. The research presents Intelligent dynamic gesture recognition (IDGR) using a Convolutional neural network (CNN) empowered by edit distance for video recognition. The proposed system has been evaluated using AIoT enabled devices for static and dynamic gestures of Pakistani sign language (PSL). However, the proposed methodology can work efficiently for any type of video. The proposed research concludes that deep learning and convolutional neural networks give a most appropriate solution retaining discriminative and dynamic information of the input action. The research proposes recognition of dynamic gestures using image recognition of the keyframes based on CNN extracted from the human activity. Edit distance is used to find out the label of the word to which those sets of frames belong to. The simulation results have shown that at 400 videos per human action, 100 epochs, 234 × 234 image size, the accuracy of the system is 90.79%, which is a reasonable accuracy for a relatively small dataset as compared to the previously published techniques.
Improving transaction success rate in cryptocurrency payment channel networks

Blockchain-based cryptocurrencies has received a substantial interest in the last decade as Blockchain can ensure trust among users without relying on third parties. However, cryptocurrency adoption for micro-payments has been limited due to slow confirmation of transactions and unforeseeable high fees, especially in the case of Bitcoin. To this end, creating off-chain payment channels between users is proposed which enables instant and nearly free transactions without writing to blockchain. Off-chain channel idea is then extended to establish payment channel networks to scale the idea to allow payment routing among many users. However, due to the way these channels are designed, both sides of a channel have a fixed one-way capacity for making transactions. Consequently, if one side consumes the whole one-way capacity, the channel becomes non-transitive in that particular direction, which causes failures of payments that would like to pass through. Eventually, the network becomes partitioned with unevenly distributed funds. In this paper, we propose the adoption of three specific techniques that aim to increase the overall success rate of payments and address channel imbalance problem to keep the payment channel network sustainable in the long run. First, we show the effectiveness of balance-aware routing that better utilizes available funds in the channels. Second, we propose an efficient method for selection of the gateway (i.e., connection point) for a user by considering the gateway's inbound and outbound capacity. It exploits the fact that end-users can connect the network through multiple gateways any of which can be used to initiate the payment. Finally, we propose proportional payment splitting method to further increase success rate especially for large transactions. We implemented the three approaches for assessing their effectiveness. Compared to existing approaches such as maximum flow or greedy, the proposed approaches can achieve much higher success rates with channels balanced better.
The use of ANN and conventional solar-plant meteorological variables to estimate atmospheric horizontal extinction

In the search to optimize electricity generation systems based on renewable energy sources, solar power plants are a clear alternative for reducing pollution on the planet. In particular, concentrating solar power plants with central tower technology supply energy to large population centers and they are generally located at desert sites. Production in these plants drops due to the presence of particles in the environment. These particles are complex to measure and doing so usually requires the use of dedicated, expensive instrumentation. In this work, we present a methodology called Extinction that estimates this attenuation phenomenon utilizing meteorological variables, along with the use of artificial neural networks (ANN). Direct normal irradiance, relative humidity, temperature and pressure have been the only meteorological variables needed to estimate the Extinction. The results from the estimations presented a correlation coefficient value (R) of 0.88 (between the measured and estimated atmospheric horizontal extinction with ANN), a normalized Mean Bias Error (nMBE) of 0% and a normalized Root-Mean Square Deviation (nRMSD) of 7%.
Research data sharing behaviour of engineering researchers in Norway and the UK: uncovering the double face of Janus

Purpose In a context of growing policy pressures to increase the societal impact of Higher Education Institutions (HEI), open access to research data has gained increased significance, in spite of the limited availability of standard procedures and protocols, particularly in the engineering disciplines. In this article, we explore how engineering researchers' engagement with such external environment pressures impacts the conventional dimensions of engineering research work, and how engineering researchers within engineering HEI resolve potential tensions and make sense of their research data sharing practices. Design/methodology/approach We use an institutional logics theoretical perspective to qualitatively examine research data sharing behaviours of researchers in Norway and the UK as leading engineering research centres, through the use of policy analysis and narrative interviews. Findings The findings indicate that research data sharing behaviours are heavily mediated by institutional rules and rationalities that inform researchers' attitudes, but the logics that prize openness and sharing co-exist with logics that favour control and self-interest. Originality/value Our findings suggest that logics-specific variations in engineering researchers' identities and goals are of paramount importance to policymakers, research funders and academic leaders striving to support HEI in their efforts to augment the societal impact of research.
Towards accurate HDR imaging with learning generator constraints

High dynamic range (HDR) image generation is a useful technology in various applications. An easy way to obtain an HDR image is the multiple exposures fusion technique which fuses a set of sequential exposures. However it suffers from the ghosting artifacts for scenes with significant motion objects, which is the key challenge in HDR imaging. Detecting and removing ghosting artifacts is a crucial issue for automatically generating HDR images of dynamic scenes. Although previous methods align the low dynamic range (LDR) images or detect motion regions to remove the ghosting artifacts in the final HDR, they still cannot generate a well-pleasing HDR image. In this paper, we propose a novel deep neural network with learning generator constraints called GHDRNet that blends information from all the exposures. Different from previous methods which only use ground truth to learn parameters of the network for HDR image reconstruction, our method is based on a novel successive network which not only estimates HDR results from dynamic scenes but also restores the static LDR images from the estimated HDR image. This special designed network restrains the estimated HDR image by restoring the corresponding LDR images. To capture deep hierarchical features and increase the receptive field for recovering the abundant details, we also propose an enhancement block (EBlock) that is a topology structure to aggregate several scale features. Furthermore, considering the cumbersome networks have a redundant parameter, we introduce a light-weight residual module to EBlock which effectively reduces the network parameters. Extensive evaluations show the advantages of our method over related state-of-the-art methods on three benchmarks.
Are public makerspaces a means to empowering citizens? The case of Ateneus de Fabricació in Barcelona

Since the beginning of the 2010s, the existence of widespread shared workshops has enabled access to digital manufacturing tools. Mainly located in urban areas, these facilities seek to make means of production, to design, prototype and repair all kinds of objects available to the citizens. These practices have been supported by activists of the “democratization” of digital manufacturing spearheaded by the maker movement. Urban authorities have gradually been won over by the these activits’ promises to encourage entrepreneurship, generate new knowledge, mobilize citizens and even transform the perceptions of and the way in which manufacturing is conducted in the city. However, these activists’ attractive discourse overly mechanically equates the accessibility of these digital tools to the idea of empowerment. Under what conditions can these practices of making lead to an increase in citizens’ individual and collective power to act, or even to social change? This article aims to answer this question by firstly constructing an analytical framework of makerspaces empowerment, and secondly by analysing the case of the public network of makerspaces in Barcelona: the Ateneus de Fabricació. To do so, our qualitative research will be based on the documentary analysis of official publications, direct observations carried out at the aforementioned workshops, and in-depth interviews with key players Barcelona makerspaces’ scene. Our results will demonstrate that in the context of smart cities, the public acceptance of these places leads to the transformation of administrative practices. Free access to public makerspaces is accompanied by a change in the relationship between public organizations and users in order to promote the empowerment of the inhabitants of Barcelona thanks to these fabrication practices.
Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep Neural Network classifier

Advanced techniques in the recognition and classification of seismo-volcanic events are transcendental when studying active volcanoes, not only for their importance as an accurate real time seismic monitoring procedure but also for the use of their results in modeling the dynamics of the volcanic environment. It is well known that real time seismic monitoring deals with such a large amount of data that it would become an overwhelming job for an operator to do manually. Therefore the use of automatic detection and classification techniques based on the Machine Learning approach are suitable in meeting such a challenge. The aim of this work is to test the capability of the Deep Neural Network (DNN) by using different event parametrization as a confident classifier tool that could permit a reliable seismic catalog to be built in a new and un-analyzed volcanic scenario. We tested different configurations in order to build an approach that was as simple as possible to use this classifier with a limited number of events. In this regard, the feature space was explored in order to select the most significant parameters of the seismic signals. The data used for this analysis corresponds to the Planchon Peteroa Volcanic Complex (PPVC) located in the Transitional Southern Volcanic Zone (TSVZ) between Chile and Argentina, South America. The most significant result of this work was not only that it provided an analysis in terms of performance of this algorithm, especially when the training, validation and test dataset is reliable although definitely reduced, but it also gave an insight of into how an optimal event parametrization can significantly improve the automatic detection and classification of seismo-volcanic events.
An Improved Proxy Re-Encryption Scheme for IoT-Based Data Outsourcing Services in Clouds

IoT-based data outsourcing services in clouds could be regarded as a new trend in recent years, as they could reduce the hardware and software cost for enterprises and obtain higher flexibility. To securely transfer an encrypted message in the cloud, a so-called proxy re-encryption scheme is a better alternative. In such schemes, a ciphertext designated for a data aggregation is able to be re-encrypted as one designated for another by a semi-trusted proxy without decryption. In this paper, we introduce a secure proxy re-encryption protocol for IoT-based data outsourcing services in clouds. The proposed scheme is provably secure assuming the hardness of the bilinear inverse Diffie–Hellman problem (BIDHP). In particular, our scheme is bidirectional and supports the functionality of multi-hop, which allows an uploaded ciphertext to be transformed into a different one multiple times. The ciphertext length of our method is independent of the number of involved IoT nodes. Specifically, the re-encryption process only takes one exponentiation computation which is around 54 ms when sharing the data with 100 IoT devices. For each IoT node, the decryption process only requires two exponentiation computations. When compared with a related protocol presented by Kim and Lee, the proposed one also exhibits lower computational costs.
Real-Time Detection of Simulator Sickness in Virtual Reality Games Based on Players' Psychophysiological Data during Gameplay

Virtual Reality (VR) technology has been proliferating in the last decade, especially in the last few years. However, Simulator Sickness (SS) still represents a significant problem for its wider adoption. Currently, the most common way to detect SS is using the Simulator Sickness Questionnaire (SSQ). SSQ is a subjective measurement and is inadequate for real-time applications such as VR games. This research aims to investigate how to use machine learning techniques to detect SS based on in-game characters’ and users' physiological data during gameplay in VR games. To achieve this, we designed an experiment to collect such data with three types of games. We trained a Long Short-Term Memory neural network with the dataset eye-tracking and character movement data to detect SS in real-time. Our results indicate that, in VR games, our model is an accurate and efficient way to detect SS in real-time.
VMAF Based Rate-Distortion Optimization for Video Coding

Video Multi-method Assessment Fusion (VMAF) is a machine-learning based video quality metric. It is experimentally shown to provide higher correlation with human visual system as compared to conventional metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in many scenarios and has drawn considerable interest as an alternative metric to evaluate the perceptual quality. This work proposes a systematic approach to improve the video compression performance in VMAF. It is composed of multiple components including a pre-processing stage with a complement automatic filter parameter selection, and a modified rate-distortion optimization framework tailored for VMAF metric. The proposed scheme achieves on average 37% BD-rate reduction in VMAF, as compared to conventional video codec optimized for PSNR.
Adversarial Adaptive Neighborhood With Feature Importance-Aware Convex Interpolation

Adversarial Examples threaten to fool deep learning models to output erroneous predictions with high confidence. Optimization-based methods for constructing such samples have been extensively studied. While being effective in terms of aggression, they typically lack clear interpretation and constraint about their underlying generation process, which thus hinders us from leveraging the produced adversarial samples for model protection in the reverse direction. Hence, we expect them to repair bugs in the pre-trained models by produced additional training data equipped with strong attack ability rather than time-consuming full re-training from scratch. To address these issues, we first study the black-box behaviors and the intrinsic deficiency of neighborhood information in previous optimization-based adversarial attacks and defenses, respectively. Then we introduce a new method dubbed FeaCP, which uses correct predicted samples in disjoint classes to guide the generation of more explainable adversarial samples in the ambiguous region around the decision boundary instead of uncontrolled “blind spots”, via convex combination in a feature component-wise manner which takes the individual importance of feature ingredients into account. Our method incorporates the prior fact that for well-separated samples, the path connecting them would go through model's decision-boundary that lies in a low-density region, however, wherein adversarial examples are spread with high probability, thus having an impact on the ultimate trained model. In our work, the path is constructed by proposed inhomogeneous feature-wise convex interpolation rather than operating on sample-wise level, limiting the search space of FeaCP to obtain an adaptive neighborhood. Finally, we provide detailed insights and extend our method to adversarial fine-tuning using vicinity distribution to optimize the approximated decision boundary, and validate the significance of our FeaCP to model performance. The experimental results show that our method provides competitive performance on various datasets and networks.
Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence

Review-based recommendation algorithms can alleviate the data sparsity issue in collaborative filtering by combining user ratings and reviews in model learning. However, most existing methods simplify the feature extraction process from reviews by assuming that different granularities of information (e.g., word, review, and feature) are equally important, which cannot optimally leverage the most important information and thus achieves suboptimal recommendation accuracy. Besides, many existing works directly regard text features as users or items representations, which may not be enough to make precise representations due to the large amount of redundant information in reviews. To tackle the two problems mentioned above, we propose a deep learning-based method named Hierarchical Attention Network Oriented Towards Crowd Intelligence (HANCI). First, HANCI replaces the commonly-used topic models or CNN text processor with an RNN text processor in review feature extraction, which can fully exploit the advantages of the sequential dependencies of reviews by using the whole hidden layers of the bidirectional LSTM as outputs. Second, HANCI weighs the importance of features guided by crowd intelligence to more accurately represent each user on each item, and vice versa. Third, HANCI utilizes a hierarchical attention network based on multi-level review text analysis to extract more precise user preferences and item latent features, so that HANCI can explore the importance of words, the usefulness of reviews and the importance of features to achieve more accurate recommendation. Extensive experiments on three public datasets show that HANCI outperforms the state-of-the-art review-based recommendation algorithms in accuracy and meanwhile provides insightful explanations.
Is This Phishing? Older Age Is Associated With Greater Difficulty Discriminating Between Safe and Malicious Emails

As our social worlds become increasingly digitally connected, so too has concern about older adults falling victim to "phishing" emails, which attempt to deceive a person into identity theft and fraud. In the present study, we investigated whether older age is associated with differences in perceived suspiciousness of phishing emails.Sixty-five cognitively normal middle-aged to older adults rated a series of genuine and phishing emails on a scale from definitely safe to definitely suspicious.Although older age was not related to a shift in overall perception of email safety, older age was related to worse discrimination between genuine and phishing emails, according to perceived suspiciousness.These findings suggest that cognitively normal older adults may be at particular risk for online fraud because of an age-associated reduction in their sensitivity to the credibility of emails.
A Selection Metric for semi-supervised learning based on neighborhood construction

The present paper focuses on semi-supervised classification problems. Semi-supervised learning is a learning task through both labeled and unlabeled samples. One of the main issues in semi-supervised learning is to use a proper selection metric for sampling from the unlabeled data in order to extract informative unlabeled data points. This is indeed vital for the semi-supervised self-training algorithms. Most self-training algorithms employ the probability estimations of the underlying base learners to select high-confidence predictions, which are not always useful for improving the decision boundary. In this study, a novel self-training algorithm is proposed based on a new selection metric using a neighborhood construction algorithm. We select unlabeled data points that are close to the decision boundary. Although these points are not high-confidence based on the probability estimation of the underlying base learner, they are more effective for finding an optimal decision boundary. To assign the correct labels to these data points, we propose an agreement between the classifier predictions and the neighborhood construction algorithm. The proposed approach uses a neighborhood construction algorithm employing peak data points and an Apollonius circle for sampling from unlabeled data. The algorithm then finds the agreement between the classifier predictions and the neighborhood construction algorithm to assign labels to unlabeled data at each iteration of the training process. The experimental results demonstrate that the proposed algorithm can effectively improve the performance of the constructed classification model.