Reviewerly/exHarmony huggingface dataset:
Big data clustering techniques based on Spark: a literature review

A popular unsupervised learning method, known as clustering, is extensively used in data mining, machine learning and pattern recognition. The procedure involves grouping of single and distinct points in a group in such a way that they are either similar to each other or dissimilar to points of other clusters. Traditional clustering methods are greatly challenged by the recent massive growth of data. Therefore, several research works proposed novel designs for clustering methods that leverage the benefits of Big Data platforms, such as Apache Spark, which is designed for fast and distributed massive data processing. However, Spark-based clustering research is still in its early days. In this systematic survey, we investigate the existing Spark-based clustering methods in terms of their support to the characteristics Big Data. Moreover, we propose a new taxonomy for the Spark-based clustering methods. To the best of our knowledge, no survey has been conducted on Spark-based clustering of Big Data. Therefore, this survey aims to present a comprehensive summary of the previous studies in the field of Big Data clustering using Apache Spark during the span of 2010–2020. This survey also highlights the new research directions in the field of clustering massive data.
Improving University Faculty Evaluations via multi-view Knowledge Graph

University faculties generate a large amount of heterogeneous data in e-learning environments that online systems and toolkits have made widely available in all aspects of teaching and scientific researching activities. How to use the data efficiently and scientifically for faculty evaluations has recently become an important issue in university performance systems. However, it is still a challenge to comprehensively assess faculty members using multi-source and multi-modal data due to the lack of uniform representations and evaluation processes. To this end, this paper proposes a novel University Faculty Evaluation System based on a multi-view Knowledge Graph (UFES-KG) that integrates heterogeneous faculty data. Relevant data, collected both on the Internet and through university-administered internal systems, includes faculty information such as scientific research papers, patents, funds, monographs, awards, professional activities and teaching performance. Furthermore, we construct entity representations through knowledge graph embedding methods to retain their semantic information. In addition, by integrating the academic development status of scholars in the previous three years as well as student evaluation data, this paper proposes an academic development factor (ADF) for making predictions about faculty academic development. The experimental results show that this factor is closely related to the features of the knowledge graph and student evaluations. In a certain case study, this factor is superior to the traditional h-index, g-index, and RG score. Intuitively and scientifically, this multi-view approach can improve evaluations of university faculties.
Implementation of Machine Learning in Quantum Key Distributions

In the future massive applications of quantum communication network, it is crucial to realize real-time selection of the optimal quantum key distributions (QKD) protocol for the improvement of system security and optimizing resources configuration. In principle, this can be done by utilizing algorithms such as exhaustive traversal or local search algorithm, whose time cost, however, is unbearable. Here we for the first time propose to employ machine learning methods into the selecting of optimal QKD protocol and apply random forest (RF) as an example for illustration. With the help of the easy-to-train classifier of RF, we can achieve a highly efficient optimal QKD protocols selector. Besides, we also do comparisons among RF and other machine learning methods, e.g., supporting vector machine, K-nearest neighbors algorithm, multinomial naive bayes classifier, and convolutional neural networks. Results demonstrate that, besides its advantage in efficiency, the RF classifier also excels both in preciseness and robustness with an accuracy over 98% for the testing set and an enjoyable receiver operating characteristic.
The potential harms of the Tor anonymity network cluster disproportionately in free countries

Significance Measuring the proportion of Tor anonymity network users who employ the system for malicious purposes is important as this technology can facilitate child abuse, the sale of illicit drugs, and the distribution of malware. We show that only a small fraction of users globally (∼6.7%) likely use Tor for malicious purposes on an average day. However, this proportion clusters unevenly across countries, with more potentially malicious Tor users in “free” countries (∼7.8%) than in “not free” regimes (∼4.8%). These results suggest that the countries which host most of the infrastructure of the network and house the Tor Project plausibly experience a disproportional amount of harm from the Tor anonymity network.
Deep Multi Depth Panoramas for View Synthesis

We propose a learning-based approach for novel view synthesis for multi-camera 360 $$^\circ $$ panorama capture rigs. Previous work constructs RGBD panoramas from such data, allowing for view synthesis with small amounts of translation, but cannot handle the disocclusions and view-dependent effects that are caused by large translations. To address this issue, we present a novel scene representation—Multi Depth Panorama (MDP)—that consists of multiple RGBD $$\alpha $$ panoramas that represent both scene geometry and appearance. We demonstrate a deep neural network-based method to reconstruct MDPs from multi-camera 360 $$^\circ $$ images. MDPs are more compact than previous 3D scene representations and enable high-quality, efficient new view rendering. We demonstrate this via experiments on both synthetic and real data and comparisons with previous state-of-the-art methods spanning both learning-based approaches and classical RGBD-based methods.
Inducing Optimal Attribute Representations for Conditional GANs

Conditional GANs (cGANs) are widely used in translating an image from one category to another. Meaningful conditions on GANs provide greater flexibility and control over the nature of the target domain synthetic data. Existing conditional GANs commonly encode target domain label information as hard-coded categorical vectors in the form of 0s and 1s. The major drawbacks of such representations are inability to encode the high-order semantic information of target categories and their relative dependencies. We propose a novel end-to-end learning framework based on Graph Convolutional Networks to learn the attribute representations to condition the generator. The GAN losses, the discriminator and attribute classification loss, are fed back to the graph resulting in the synthetic images that are more natural and clearer with respect to the attributes generation. Moreover, prior-arts are mostly given priorities to condition on the generator side, not on the discriminator side of GANs. We apply the conditions on the discriminator side as well via multi-task learning. We enhanced four state-of-the-art cGANs architectures: Stargan, Stargan-JNT, AttGAN and STGAN. Our extensive qualitative and quantitative evaluations on challenging face attributes manipulation data set, CelebA, LFWA, and RaFD, show that the cGANs enhanced by our methods outperform by a large margin, compared to their counter-parts and other conditioning methods, in terms of both target attributes recognition rates and quality measures such as PSNR and SSIM.
Violence Detection and Localization in Surveillance Video

Automatic violence detection in video surveillance is crucial for social and personal security. Due to the massive video data produced by surveillance cameras installed in different environments like airports, trains, stadiums, schools, etc., traditional video monitoring by humans operators becomes inefficient. In this context, develop systems capable of detect automatically violent actions is a challenging task. This study describes a method to detect and localize violent acts in video surveillance using dynamic images, CNN's, and weakly supervised localization methods. Experimental results demonstrate the effectiveness of our approach when applied to three public benchmark datasets: Hockey Fight [1], Violent Flows [2], and UCFCrime2Loca1 [3].
Augmented Reality for Manual Assembly in Industry 4.0: Gathering Guidelines

The manufacturing industry always strugle to maintain competitiveness and lowering its production costs. With the significant improvement in the reliability and stability of facilities, human error has become one of the most critical factors for quality assurance. About 70% to 90% of quality defects in assembly production systems are directly or indirectly caused by human errors. In the last decades, the interest in using Augmented Reality (AR) in many application areas, including the industry, has increased. Although AR is recognized as an important technology, there is an evident gap in design guidelines and methodologies in this field. The objective of this paper is to find guidelines to help in designing AR-based manual assembly systems. We did a systematic literature mapping over scientific review papers, from 2015 to 2020, and found 14 review papers. From 14 papers, 11 of them (78,6%) presented guidelines topics. The guidelines found were classified into four groups: usability, cognitive, ergonomics, and corporate-related. The suggested group of general guidelines based on previous work may be useful as a starting point when designing AR-based manual assembly systems. We conclude that a comprehensive set, that encompasses general and specific guidelines, depends on the characteristics of the assembly line being implemented. Limitations of this work are the low number of review papers found and the probability of having new guideline categories not found in this paper.
Leveraging conversational technology to answer common COVID-19 questions

Abstract The rapidly evolving science about the Coronavirus Disease 2019 (COVID-19) pandemic created unprecedented health information needs and dramatic changes in policies globally. We describe a platform, Watson Assistant (WA), which has been used to develop conversational agents to deliver COVID-19 related information. We characterized the diverse use cases and implementations during the early pandemic and measured adoption through a number of users, messages sent, and conversational turns (ie, pairs of interactions between users and agents). Thirty-seven institutions in 9 countries deployed COVID-19 conversational agents with WA between March 30 and August 10, 2020, including 24 governmental agencies, 7 employers, 5 provider organizations, and 1 health plan. Over 6.8 million messages were delivered through the platform. The mean number of conversational turns per session ranged between 1.9 and 3.5. Our experience demonstrates that conversational technologies can be rapidly deployed for pandemic response and are adopted globally by a wide range of users.
ES2ISL: An Advancement in Speech to Sign Language Translation using 3D Avatar Animator

This work proposes a model and an initial implementation of a robust system, which converts English Speech into Indian Sign Language (ES2ISL) animations. Such system may considerably enhance the lives of hearing-impaired people, especially in interaction and information exchange between concerned parties. The core purpose of the system is to bridge the communication gap between hearing-impaired people in India and others. It exploits and integrates the semantics of the Natural Language Processing (NLP), Google cloud speech recognizer API, and a predefined sign language database. The experimental results show that the proposed system outperforms existing models with an average accuracy of 77%. Hence, it overshadows the existing systems in terms of processing time by taking about 0.85s.
Poking a Hole in the Wall: Efficient Censorship-Resistant Internet Communications by Parasitizing on WebRTC

Many censorship circumvention tools rely on trusted proxies that allow users within censored regions to access blocked Internet content by tunneling it through a covert channel (e.g,. piggybacking on Skype video calls). However, building tools that can simultaneously (i) provide good bandwidth capacity for accommodating the typical activities of Internet users, and (ii) be secure against traffic analysis attacks has remained an open problem and a stumbling block to the practical adoption of such tools for censorship evasion.
A real-world service mashup platform based on data integration, information synthesis, and knowledge fusion

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Learning dynamic regression with automatic distractor repression for real-time UAV tracking

With high efficiency and efficacy, the trackers based on the discriminative correlation filter have experienced rapid development in the field of unmanned aerial vehicle (UAV) over the past decade. In literature, these trackers aim at solving a regression problem in which the circulated samples are mapped into a Gaussian label for online filter training. However, the fixed target label for regression makes trackers lose adaptivity in uncertain tracking scenarios. One of the typical failure cases is that the distractors, e.g., background clutter, camouflage, and similar object, are prone to confuse these trackers. In this work, an efficient approach to instantly monitor the local maximums of the response map for discovering distractors automatically is proposed. In addition, the regression target is accordingly learned, i.e., the location possessing local maximum indicates latent distractor and thus should be repressed by reducing its target response value in filter training. Qualitative and quantitative experiments performed on three challenging well-known benchmarks demonstrate that the presented method not only outperforms the state-of-the-art handcrafted feature-based trackers but also exhibits comparable performance compared to deep learning-based approaches. Specifically, the presented tracker has phenomenal practicability in real-time UAV applications with an average speed of ∼50 frames per second on an affordable CPU.
A deep bi-directional prediction model for live streaming recommendation

Live streaming becomes very popular in recent years. An accurate live streaming recommendation system is key to enhance user experience. As both viewer and anchor change their preferences dynamically, using existing recommendation approaches cannot fully capture their preferences. In this paper, we study how to model both viewer and anchor’s dynamic behaviors and predict their behaviors from two angles to improve recommendation accuracy. Existing works mainly focus on prediction from one angle, neglecting the prediction from another angle. How to simultaneously model the predictions from two angles to enhance their mutual learning and make recommendation based on them are not well studied in literature. To solve this problem, we propose a deep bi-directional prediction model to perform two prediction tasks: predicting viewer’s next favorite anchor and prediction anchor’s future audience simultaneously, based on which different recommendation methods are also developed. In the model, we develop multiple mechanisms such as shared embedding layer, cross attention and cross loss to help the two prediction tasks to learn from each other. Experiments conducted on real world datasets demonstrate that the proposed model performs better than state-of-the-art recommendation models and prediction from the two sides improves recommendation performance.
Preliminary Findings about DevSecOps from Grey Literature

Context: Emerging from the agile culture, DevOps particularly emphasizes development and deployment speed to achieve rapid value delivery, which however brings some security risks to the software development process. DevSecOps is an extension of DevOps, which is considered as a means to intertwine development, operation and security. Some companies with security concerns begin to take DevSecOps into consideration when it comes to the application of DevOps. Objective: The goal of this study is to report the state-of-the-practice of DevSecOps as well as calling for academia to pay more attention to DevSecOps. Method: Using Google search engine to collect articles on DevSecOps, we conducted a Grey Literature Review (GLR) on the selected articles. Results: Whilst there exists three major software security risks in DevOps, the establishment of DevOps pipeline provides opportunities for software security activities. Based on the preliminary consensus that DevSecOps is an extension of DevOps, it is observed that the interpretations of DevSecOps can be classified into three core aspects, which are: DevSecOps capabilities, cultural enablers, and technological enablers. Furthermore, to materialize the interpretations into daily software production activities, the recommended DevSecOps practices we obtain from Grey Literature (GL) can be categorized in terms of process, infrastructure and collaboration. Conclusion: Although DevSecOps is getting increasing attention by industry, it is still in its infancy and needs to be promoted by both academia and industry.
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search

Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, performance gains can be achieved by designing network architectures specifically for detection and segmentation, as shown by recent neural architecture search (NAS) research for detection and segmentation. One major challenge though is that ImageNet pre-training of the search space representation (a.k.a. super network) or the searched networks incurs huge computational cost. In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e.g., an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently. In our experiments, we apply FNA++ on MobileNetV2 to obtain new networks for semantic segmentation, object detection, and human pose estimation that clearly outperform existing networks designed both manually and by NAS. We also implement FNA++ on ResNets and NAS networks, which demonstrates a great generalization ability. The total computation cost of FNA++ is significantly less than SOTA segmentation and detection NAS approaches: 1737× less than DPC, 6.8× less than Auto-DeepLab, and 8.0× less than DetNAS. A series of ablation studies are performed to demonstrate the effectiveness, and detailed analysis is provided for more insights into the working mechanism. Codes are available at https://github.com/JaminFong/FNA.
Influence of community structure on misinformation containment in online social networks

With the emergence of Online Social Networks (OSNs) as an effective medium of information dissemination, its abuse in spreading misinformation has become a great concern to its users. Hence, the misinformation containment problem in various forms has emerged as an important topic of research. In general, given a snapshot of an online social network with a set of misinformed nodes and a budget limiting the maximum number of seed nodes, the goal is to determine a set of seed nodes with the correct information, to contain the misinformation at the earliest. In this paper, we leverage the community structure of the online social network to select the seed nodes statically, independent of the distribution of misinformed nodes for faster misinformation containment with simple one-time computation. We extend the work to include OSNs with overlapped community as well. To the best of our knowledge, so far, ours is the first work where the topology of the OSN has been exploited to combat the spread of misinformation faster. Experiments on real OSNs reveal that the proposed techniques outperform state-of-the-art algorithms significantly in terms of maximum and average infected time, and the point of decline, manifesting the key role of community structure on misinformation containment in a social network. Moreover, the parallel implementations of the proposed algorithms achieve around 10× speed-up over the sequential ones enhancing the scalability of the proposed approach.
Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems

The recent surge of interest in explainability in artificial intelligence (XAI) is propelled by not only technological advancements in machine learning but also by regulatory initiatives to foster transparency in algorithmic decision making. In this article, we revise the current concept of explainability and identify three limitations: passive explainee, narrow view on the social process, and undifferentiated assessment of explainee’s understanding. In order to overcome these limitations, we present explanation as a social practice in which explainer and explainee co-construct understanding on the microlevel. We view the co-construction on a microlevel as embedded into a macrolevel, yielding expectations concerning, e.g., social roles or partner models: typically, the role of the explainer is to provide an explanation and to adapt it to the current level of explainee’s understanding; the explainee, in turn, is expected to provide cues that direct the explainer. Building on explanations being a social practice, we present a conceptual framework that aims to guide future research in XAI. The framework relies on the key concepts of monitoring and scaffolding to capture the development of interaction. We relate our conceptual framework and our new perspective on explaining to transparency and autonomy as objectives considered for XAI.