Reviewerly/exHarmony huggingface dataset:
The landscape of academic literature in quantum technologies

In this study, we investigated the academic literature on quantum technologies (QT) using bibliometric tools. We used a set of 49,823 articles obtained from the Web of Science (WoS) database using a search query constructed through expert opinion. Analysis of this revealed that QT is deeply rooted in physics, and the majority of the articles are published in physics journals. Keyword analysis revealed that the literature could be clustered into three distinct sets, which are (i) quantum communication/cryptography, (ii) quantum computation, and (iii) physical realizations of quantum systems. We performed a burst analysis that showed the emergence and fading away of certain key concepts in the literature. This is followed by co-citation analysis on the highly cited articles provided by the WoS, using these we devised a set of core corpus of 34 publications. Comparing the most highly cited articles in this set with respect to the initial set we found that there is a clear difference in most cited subjects. Finally, we performed co-citation analyses on country and organization levels to find the central nodes in the literature. Overall, the analyses of the datasets allowed us to cluster the literature into three distinct sets, construct the core corpus of the academic literature in QT, and to identify the key players on country and organization levels, thus offering insight into the current state of the field. Search queries and access to figures are provided in the appendix.
Folding BIKE: Scalable Hardware Implementation for Reconfigurable Devices

Contemporary digital infrastructures and systems use and trust Public-Key Cryptography to exchange keys over insecure communication channels. With the development and progress in the research field of quantum computers, well established schemes like RSA and ECC are more and more threatened. The urgent demand to find and standardize new schemes &#x2013; which are secure in a post-quantum world &#x2013; was also realized by the National Institute of Standards and Technology which announced a Post-Quantum Cryptography Standardization Project in 2017. Recently, the round three candidates were announced and one of the alternate candidates is the Key Encapsulation Mechanism scheme BIKE. In this article, we investigate different strategies to efficiently implement the BIKE algorithm on Field-Programmable Gate Arrays (FPGAs). To this extend, we improve already existing polynomial multipliers, propose efficient strategies to realize polynomial inversions, and implement the Black-Gray-Flip decoder for the first time. Additionally, our implementation is designed to be scalable and generic with the BIKE specific parameters. All together, the fastest designs achieve latencies of 2.69 ms for the key generation, 0.1 ms for the encapsulation, and 1.89 ms for the decapsulation considering the lowest security level.
Development of the Cuhk Elderly Speech Recognition System for Neurocognitive Disorder Detection Using the Dementiabank Corpus

Early diagnosis of Neurocognitive Disorder (NCD) is crucial in facilitating preventive care and timely treatment to delay further progression. This paper presents the development of a state-of-the-art automatic speech recognition (ASR) system built on the Dementia-Bank Pitt corpus for automatic NCD detection. Speed perturbation based audio data augmentation expanded the limited elderly speech data by four times. Large quantities of out-of-domain, non-aged adult speech were exploited by cross-domain adapting a 1000-hour LibriSpeech corpus trained LF-MMI factored TDNN system to DementiaBank. The variability among elderly speakers was modeled using i-Vector and learning hidden unit contributions (LHUC) based speaker adaptive training. Robust Bayesian estimation of TDNN systems and LHUC transforms were used in both cross-domain and speaker adaptation. A Transformer language model was also built to improve the final system performance. A word error rate (WER) reduction of 11.72% absolute (26.11% relative) was obtained over the baseline i-Vector adapted LF-MMI TDNN system on the evaluation data of 48 elderly speakers. The best NCD detection accuracy of 88%, comparable to that using the ground truth speech transcripts, was obtained using the textual features extracted from the final ASR system outputs.
RCEA-360VR: Real-time, Continuous Emotion Annotation in 360° VR Videos for Collecting Precise Viewport-dependent Ground Truth Labels

Precise emotion ground truth labels for 360° virtual reality (VR) video watching are essential for fine-grained predictions under varying viewing behavior. However, current annotation techniques either rely on post-stimulus discrete self-reports, or real-time, continuous emotion annotations (RCEA) but only for desktop/mobile settings. We present RCEA for 360° VR videos (RCEA-360VR), where we evaluate in a controlled study (N=32) the usability of two peripheral visualization techniques: HaloLight and DotSize. We furthermore develop a method that considers head movements when fusing labels. Using physiological, behavioral, and subjective measures, we show that (1) both techniques do not increase users’ workload, sickness, nor break presence (2) our continuous valence and arousal annotations are consistent with discrete within-VR and original stimuli ratings (3) users exhibit high similarity in viewing behavior, where fused ratings perfectly align with intended labels. Our work contributes usable and effective techniques for collecting fine-grained viewport-dependent emotion labels in 360° VR.
Gauge-invariant quantum circuits for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>U</mml:mi></mml:math> (1) and Yang-Mills lattice gauge theories

Quantum computation represents an emerging framework to solve lattice gauge theories (LGT) with arbitrary gauge groups, a general and long-standing problem in computational physics. While quantum computers may encode LGT using only polynomially increasing resources, a major openissue concerns the violation of gauge-invariance during the dynamics and the search for groundstates. Here, we propose a new class of parametrized quantum circuits that can represent states belonging only to the physical sector of the total Hilbert space. This class of circuits is compact yet flexible enough to be used as a variational ansatz to study ground state properties, as well as representing states originating from a real-time dynamics. Concerning the first application, the structure of the wavefunction ansatz guarantees the preservation of physical constraints such as the Gauss law along the entire optimization process, enabling reliable variational calculations. As for the second application, this class of quantum circuits can be used in combination with timedependent variational quantum algorithms, thus drastically reducing the resource requirements to access dynamical properties.
AraCust: a Saudi Telecom Tweets corpus for sentiment analysis

Comparing Arabic to other languages, Arabic lacks large corpora for Natural Language Processing (Assiri, Emam &amp; Al-Dossari, 2018; Gamal et al., 2019). A number of scholars depended on translation from one language to another to construct their corpus (Rushdi-Saleh et al., 2011). This paper presents how we have constructed, cleaned, pre-processed, and annotated our 20,0000 Gold Standard Corpus (GSC) AraCust, the first Telecom GSC for Arabic Sentiment Analysis (ASA) for Dialectal Arabic (DA). AraCust contains Saudi dialect tweets, processed from a self-collected Arabic tweets dataset and has been annotated for sentiment analysis, i.e.,manually labelled (k=0.60). In addition, we have illustrated AraCust’s power, by performing an exploratory data analysis, to analyse the features that were sourced from the nature of our corpus, to assist with choosing the right ASA methods for it. To evaluate our Golden Standard corpus AraCust, we have first applied a simple experiment, using a supervised classifier, to offer benchmark outcomes for forthcoming works. In addition, we have applied the same supervised classifier on a publicly available Arabic dataset created from Twitter, ASTD (Nabil, Aly &amp; Atiya, 2015). The result shows that our dataset AraCust outperforms the ASTD result with 91% accuracy and 89% F1avg score. The AraCust corpus will be released, together with code useful for its exploration, via GitHub as a part of this submission.
Similarity Analysis of Self-Supervised Speech Representations

Self-supervised speech representation learning has recently been a prosperous research topic. Many algorithms have been proposed for learning useful representations from large-scale unlabeled data, and their applications to a wide range of speech tasks have also been investigated. However, there has been little research focusing on understanding the properties of existing approaches. In this work, we aim to provide a comparative study of some of the most representative self-supervised algorithms. Specifically, we quantify the similarities between different self-supervised representations using existing similarity measures. We also design probing tasks to study the correlation between the models’ pre-training loss and the amount of specific speech information contained in their learned representations. In addition to showing how various self-supervised models behave differently given the same input, our study also finds that the training objective has a higher impact on representation similarity than architectural choices such as building blocks (RNN/Transformer/CNN) and directionality (uni/bidirectional). Our results also suggest that there exists a strong correlation between pre-training loss and downstream performance for some self-supervised algorithms.
Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning

The generation of electricity through renewable energy sources increases every day, with solar energy being one of the fastest-growing. The emergence of information technologies such as Digital Twins (DT) in the field of the Internet of Things and Industry 4.0 allows a substantial development in automatic diagnostic systems. The objective of this work is to obtain the DT of a Photovoltaic Solar Farm (PVSF) with a deep-learning (DL) approach. To build such a DT, sensor-based time series are properly analyzed and processed. The resulting data are used to train a DL model (e.g., autoencoders) in order to detect anomalies of the physical system in its DT. Results show a reconstruction error around 0.1, a recall score of 0.92 and an Area Under Curve (AUC) of 0.97. Therefore, this paper demonstrates that the DT can reproduce the behavior as well as detect efficiently anomalies of the physical system.
MultiScaler: A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> , to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.
CyberSense: A Closed-Loop Framework to Detect Cybersickness Severity and Adaptively apply Reduction Techniques

Researchers often collect subjective measurements before, after, and during the immersive experience to measure cybersickness severity. However, collecting data before and after the immersive experience does not provide a sufficiently granular understanding of cybersickness during the immersion. Thus, no preventive measures can be taken during immersion. This research presents CyberSense - an automated framework for cybersickness severity detection during immersion. The framework collects the users' physiological data at user-defined intervals. It uses a pre-trained neural network to detect cybersickness severity on the experience with a root mean square error of 2.61 and adaptively adjusts the cybersickness reduction techniques.
What Makes Civic Tech Initiatives To Last Over Time? Dissecting Two Global Cases

Civic tech initiatives dedicated to environmental issues have become a worldwide phenomenon and made invaluable contributions to data, community building, and publics. However, many of them stop after a relatively short time. Therefore, we studied two long-lasting civic tech initiatives of global scale, to understand what makes them sustain over time. To this end, we conducted two mixed-method case studies, combining social network analysis and qualitative content analysis of Twitter data with insights from expert interviews. Drawing on our findings, we identified a set of key factors that help the studied civic tech initiatives to grow and last. Contributing to Digital Civics in HCI, we argue that the civic tech initiatives’ scaling and sustaining are configured through the entanglement of (1) civic data both captured and owned by the citizens for the citizens, (2) the use of open and accessible technology, and (3) the initiatives’ public narrative, giving them a voice on the environmental issue.
EnsembleFool: A method to generate adversarial examples based on model fusion strategy

Deep neural networks have been shown vulnerable to adversarial attacks launched by adversarial examples. These examples’ transferability makes an attack in the real-world feasible, which poses a security threat to deep learning. Considering the limited representation capacity of a single deep model, the transferability of an adversarial example generated by a single attack model would cause the failure of attacking other different models. In this paper, we propose a new adversarial attack method, named EnsembleFool, which flexibly integrates multiple models to enhance adversarial examples’ transferability. Specifically, the model confidence concerning an input example reveals the risk of a successful attack. In an iterative attacking case, the result of a previous attack could guide us to enforce a new attack that possesses a higher probability of success. Regarding this, we design a series of integration strategies to improve the adversarial examples in each iteration. Extensive experiments on ImageNet indicate that the proposed method has superior attack performance and transferability than state-of-the-art methods.
Cross-Model Attention-Guided Tumor Segmentation for 3D Automated Breast Ultrasound (ABUS) Images

Tumor segmentation in 3D automated breast ultrasound (ABUS) plays an important role in breast disease diagnosis and surgical planning. However, automatic segmentation of tumors in 3D ABUS images is still challenging, due to the large tumor shape and size variations, and uncertain tumor locations among patients. In this paper, we develop a novel cross-model attention-guided tumor segmentation network with a hybrid loss for 3D ABUS images. Specifically, we incorporate the tumor location into a segmentation network by combining an improved 3D Mask R-CNN head into V-Net as an end-to-end architecture. Furthermore, we introduce a cross-model attention mechanism that is able to aggregate the segmentation probability map from the improved 3D Mask R-CNN to each feature extraction level in the V-Net. Then, we design a hybrid loss to balance the contribution of each part in the proposed cross-model segmentation network. We conduct extensive experiments on 170 3D ABUS from 107 patients. Experimental results show that our method outperforms other state-of-the-art methods, by achieving the Dice similarity coefficient (DSC) of 64.57%, Jaccard coefficient (JC) of 53.39%, recall (REC) of 64.43%, precision (PRE) of 74.51%, 95th Hausdorff distance (95HD) of 11.91 mm, and average surface distance (ASD) of 4.63 mm. Our code will be available online (https://github.com/zhouyuegithub/CMVNet).
Evaluating the VNIR-SWIR datasets of WorldView-3 for lithological mapping of a metamorphic-igneous terrain using support vector machine algorithm; a case study of Central Iran

• Evaluating the WV-3 data in lithological mapping of an area through confusion matrix. • Successfully discrimination of lithological units and boundaries with high accuracy. • Verifying the produced map by field observations, petrography, and spectroscopy. • Defining effect of each band of WV-3 in classification accuracy of individual class. • Finding SWIR-7 as the most significant band in raising the classification accuracy. WorldView-3 (WV-3) is the first high spatial resolution and super-spectral commercial satellite which has the capability in improvement of geological mapping. The VNIR-SWIR data of this satellite are evaluated in this study for mapping the lithological units of a metamorphic-igneous terrain in Chadormalu area, Central Iran, by the use of support vector machine (SVM) classification method. Applying principal component analysis (PCA) as an image transformation technique on the WV-3 data, and interpretation of its results, supported by field observations, petrography, and spectroscopy, lead to identifying training areas which are input to the SVM algorithm. This method not only produces a detailed lithological map of all exposed rock units, but also well discriminates diorite-gabbro diorite from granite, and gneissic granite from green schist and garnet mica schist, which are not revealed in 1:100,000 geological map of the area, published by Geological Survey of Iran (GSI), with overall accuracy of 88.36% and the Kappa coefficient of 0.86. Furthermore, the efficacy of each applied band of WV-3 is assessed in promoting classification accuracy using SVM method. Results show that band 7 of SWIR region has increased the overall accuracy 4.81% relative to SWIR dataset, and improves the classification accuracy of green schist and diorite. Moreover, bands 5, 6, and 8 of SWIR dataset are more efficient in improvement of the overall accuracies than other bands. This study concludes that WV-3 data provides a good facility to generate large scale geological maps owing to its high spatial and radiometric resolution and appropriate SWIR bands.
Seamless Variability Management with the Virtual Platform

Customization is a general trend in software engineering, demanding systems that support variable stakeholder requirements. Two opposing strategies are commonly used to create variants: software clone&own and software configuration with an integrated platform. Organizations often start with the former, which is cheap, agile, and supports quick innovation, but does not scale. The latter scales by establishing an integrated platform that shares software assets between variants, but requires high up-front investments or risky migration processes. So, could we have a method that allows an easy transition or even combine the benefits of both strategies? We propose a method and tool that supports a truly incremental development of variant rich systems, exploiting a spectrum between both opposing strategies. We design, formalize, and prototype the variability management framework <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">virtual</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">platform</i> . It bridges clone&own and platform-oriented development. Relying on programming language independent conceptual structures representing software assets, it offers operators for engineering and evolving a system, comprising: traditional, asset-oriented operators and novel, feature-oriented operators for incrementally adopting concepts of an integrated platform. The operators record meta-data that is exploited by other operators to support the transition. Among others, they eliminate expensive feature-location effort or the need to trace clones. Our evaluation simulates the evolution of a real-world, clone-based system, measuring its costs and benefits.
Sentence Similarity Based on Contexts

Abstract Existing methods to measure sentence similarity are faced with two challenges: (1) labeled datasets are usually limited in size, making them insufficient to train supervised neural models; and (2) there is a training-test gap for unsupervised language modeling (LM) based models to compute semantic scores between sentences, since sentence-level semantics are not explicitly modeled at training. This results in inferior performances in this task. In this work, we propose a new framework to address these two issues. The proposed framework is based on the core idea that the meaning of a sentence should be defined by its contexts, and that sentence similarity can be measured by comparing the probabilities of generating two sentences given the same context. The proposed framework is able to generate high-quality, large-scale dataset with semantic similarity scores between two sentences in an unsupervised manner, with which the train-test gap can be largely bridged. Extensive experiments show that the proposed framework achieves significant performance boosts over existing baselines under both the supervised and unsupervised settings across different datasets.
Developmental trajectories of blockchain research and its major subfields

Unlike previous reviews, most of which focus on a field and use qualitative methods to analyze blockchain technology, herein main path analysis was used to determine the key route of citations and identify the developmental trajectories of blockchain technology and its major themes. A total of 3159 blockchain-related articles published through August 5, 2019, were gathered from the Web of Science. In all, 2478 articles formed the citation network, 27 of which were on the key route. These 27 key papers on blockchain represented the four developmental stages. Stage 1 focused on challenges to bitcoin. Stage 2 focused on smart contract issues. Stage 3 focused on opportunities, challenges, and development in blockchain. Finally, Stage 4 focused on smart contract applications, especially in e-healthcare, where blockchain technology is paving the way to a quicker, more secure, and more efficient means of storing and accessing medical data. The Girvan-Newman clustering technique was used to classify articles, and keyword analysis was used to analyze the top six major themes. These were bitcoin security, bitcoin financial applications, e-healthcare, Ethereum smart contracts, security and privacy in IoT, and energy. Main path analysis was also used to analyze their developmental trends. The results have significant value for both academics and practitioners.