Reviewerly/exHarmony huggingface dataset:
Comparison Among Cloud Technologies and Cloud Performance

The cloud is the best method used for the utilization and organization of data. The cloud provides many resources for us via the internet. There are many technologies used in cloud computing systems; each one uses a different kind of protocols and methods. Many tasks can execute on different servers per second, which cannot execute on their computer. The most popular technologies used in the cloud system are Hadoop, Dryad, and another map reducing framework. Also, there are many tools used to optimize the performance of the cloud system, such as Cap3, HEP, and Cloudburst. This paper reviews in detail the cloud computing system, its used technologies, and the best technologies used with it according to multiple factors and criteria such as the procedure cost, speed cons and pros. Moreover, A comprehensive comparison of the tools used for the utilization of cloud computing systems is presented.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.
Actor-Critic Reinforcement Learning for Control With Stability Guarantee

Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation. However, stability is not guaranteed in model-free RL by solely using data. From a control-theoretic perspective, stability is the most important property for any control system, since it is closely related to safety, robustness, and reliability of robotic systems. In this letter, we propose an actor-critic RL framework for control which can guarantee closed-loop stability by employing the classic Lyapunov's method in control theory. First of all, a data-based stability theorem is proposed for stochastic nonlinear systems modeled by Markov decision process. Then we show that the stability condition could be exploited as the critic in the actor-critic RL to learn a controller/policy. At last, the effectiveness of our approach is evaluated on several well-known 3-dimensional robot control tasks and a synthetic biology gene network tracking task in three different popular physics simulation platforms. As an empirical evaluation on the advantage of stability, we show that the learned policies can enable the systems to recover to the equilibrium or way-points when interfered by uncertainties such as system parametric variations and external disturbances to a certain extent.
Enhanced Hand-Oriented Activity Recognition Based on Smartwatch Sensor Data Using LSTMs

The creation of the Internet of Things (IoT), along with the latest developments in wearable technology, has provided new opportunities in human activity recognition (HAR). The modern smartwatch offers the potential for data from sensors to be relayed to novel IoT platforms, which allow the constant tracking and monitoring of human movement and behavior. Recently, traditional activity recognition techniques have done research in advance by choosing machine learning methods such as artificial neural network, decision tree, support vector machine, and naive Bayes. Nonetheless, these conventional machine learning techniques depend inevitably on heuristically handcrafted feature extraction, in which human domain knowledge is normally limited. This work proposes a hybrid deep learning model called CNN-LSTM that employed Long Short-Term Memory (LSTM) networks for activity recognition with the Convolution Neural Network (CNN). The study makes use of HAR involving smartwatches to categorize hand movements. Using the study based on the Wireless Sensor Data Mining (WISDM) public benchmark dataset, the recognition abilities of the deep learning model can be accessed. The accuracy, precision, recall, and F-measure statistics are employed using the evaluation metrics to assess the recognition abilities of LSTM models proposed. The findings indicate that this hybrid deep learning model offers better performance than its rivals, where the achievement of 96.2% accuracy, while the f-measure is 96.3%, is obtained. The results show that the proposed CNN-LSTM can support an improvement of the performance of activity recognition.
Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos

Automatically generating sentences to describe events and temporally localizing sentences in a video are two important tasks that bridge language and videos. Recent techniques leverage the multimodal nature of videos by using off-the-shelf features to represent videos, but interactions between modalities are rarely explored. Inspired by the fact that there exist cross-modal interactions in the human brain, we propose a novel method for learning pairwise modality interactions in order to better exploit complementary information for each pair of modalities in videos and thus improve performances on both tasks. We model modality interaction in both the sequence and channel levels in a pairwise fashion, and the pairwise interaction also provides some explainability for the predictions of target tasks. We demonstrate the effectiveness of our method and validate specific design choices through extensive ablation studies. Our method turns out to achieve state-of-the-art performances on four standard benchmark datasets: MSVD and MSR-VTT (event captioning task), and Charades-STA and ActivityNet Captions (temporal sentence localization task).
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection

Existing RGB-D salient object detection methods treat depth information as an independent component to complement RGB and widely follow the bistream parallel network architecture. To selectively fuse the CNN features extracted from both RGB and depth as a final result, the state-of-the-art (SOTA) bistream networks usually consist of two independent subbranches: one subbranch is used for RGB saliency, and the other aims for depth saliency. However, depth saliency is persistently inferior to the RGB saliency because the RGB component is intrinsically more informative than the depth component. The bistream architecture easily biases its subsequent fusion procedure to the RGB subbranch, leading to a performance bottleneck. In this paper, we propose a novel data-level recombination strategy to fuse RGB with D (depth) before deep feature extraction, where we cyclically convert the original 4-dimensional RGB-D into DGB, RDB and RGD. Then, a newly lightweight designed triple-stream network is applied over these novel formulated data to achieve an optimal channel-wise complementary fusion status between the RGB and D, achieving a new SOTA performance.
A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual’s awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency–Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects or low-resource languages.We systematically consider all possible language pairs.In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English.This corpus is freely available. 1 To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs.The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English.
A Declaration of the Independence of Cyberspace

Yesterday, that great invertebrate in the White House signed into the law the Telecom “Reform” Act of 1996, while Tipper Gore took digital photographs of the proceedings to be included in a book called “24 Hours in Cyberspace.” I had also been asked to participate in the creation of this book by writing something appropriate to the moment. Given the atrocity that this legislation would seek to inflict on the Net, I decided it was as good a time as any to dump some tea in the virtual harbor. After all, the Telecom “Reform” Act, passed in the Senate with only 5 dissenting votes, makes it unlawful, and punishable by a $250,000 to say “shit” online. Or, for that matter, to say any of the other 7 dirty words prohibited in broadcast media. Or to discuss abortion openly. Or to talk about any bodily function in any but the most clinical terms. It attempts to place more restrictive constraints on the conversation in Cyberspace than presently exist in the Senate cafeteria, where I have dined and heard colorful indecencies spoken by United States senators on every occasion I did. This bill was enacted upon us by people who haven’t the slightest idea who we are or where our conversation is being conducted. It is, as my good friend and Wired Editor Louis Rossetto put it, as though “the illiterate could tell you what to read.” Well, fuck them. Or, more to the point, let us now take our leave of them. They have declared war on Cyberspace. Let us show them how cunning, baffling, and powerful we can be in our own defense. I have written something (with characteristic grandiosity) that I hope will become one of many means to this end. If you find it useful, I hope you will pass it on as widely as possible. You can leave my name off it if you like, because I don’t care about the credit. I really don’t. But I do hope this cry will echo across Cyberspace, changing and growing and self-replicating, until it becomes a great shout equal to the idiocy they have just inflicted upon us. I give you... A Declaration of the Independence of Cyberspace
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List

Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index.Recent neural IR models shifts towards soft semantic matching all query document terms, but they lose the computation efficiency of exact match systems.This paper presents COIL, a contextualized exact match retrieval architecture that brings semantic lexical matching.COIL scoring is based on overlapping query document tokens' contextualized representations.The new architecture stores contextualized token representations in inverted lists, bringing together the efficiency of exact match and the representation power of deep language models.Our experimental results show COIL outperforms classical lexical retrievers and state-of-the-art deep LM retrievers with similar or smaller latency. 1
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

Contemporary works on abstractive text summarization have focused primarily on highresource languages like English, mostly due to the limited availability of datasets for low/midresource ones.In this work, we present XL-Sum, a comprehensive and diverse dataset comprising 1 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics.The dataset covers 44 languages ranging from low to high-resource, for many of which no public dataset is currently available.XL-Sum is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.We fine-tune mT5, a state-of-theart pretrained multilingual model, with XL-Sum and experiment on multilingual and lowresource summarization tasks.XL-Sum induces competitive results compared to the ones obtained using similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10 languages we benchmark on, with some of them exceeding 15, as obtained by multilingual training.Additionally, training on low-resource languages individually also provides competitive performance.To the best of our knowledge, XL-Sum is the largest abstractive summarization dataset in terms of the number of samples collected from a single source and the number of languages covered.We are releasing our dataset and models to encourage future research on multilingual abstractive summarization.The resources can be found at https://github.
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion

Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate the depth completion as a one-stage end-to-end learning task, which outputs dense depth maps directly. However, the feature extraction and supervision in one-stage frameworks are insufficient, limiting the performance of these approaches. To address this problem, we propose a novel end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i.e., a sparse-to-coarse stage and a coarse-to-fine stage. First, a coarse dense depth map is obtained by a simple CNN framework. Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with coarse depth map and color image as input. Specially, in the coarse-to-fine stage, a channel shuffle extraction operation is utilized to extract more representative features from color image and coarse depth map, and an energy based fusion operation is exploited to effectively fuse these features obtained by channel shuffle operation, thus leading to more accurate and refined depth maps. We achieve SoTA performance in RMSE on KITTI benchmark. Extensive experiments on other datasets future demonstrate the superiority of our approach over current state-of-the-art depth completion approaches.
Emergency Distance Education in the Conditions of COVID-19 Pandemic: Experience of Ukrainian Universities

The COVID-19 pandemic has brought significant changes in education worldwide throughout 2020. In Ukraine, the preparedness for this process was different. There were various technical problems such as the absence of Internet connection, computers, or educational materials on the network, and most importantly, the unpreparedness of teachers to perform distance learning. From 28 April to 8 May 2020 at the Oles Honchar Dnipro National University (Dnipro, Ukraine), students were surveyed on the quality of distance learning organized during the quarantine period. A total of 1224 students from almost all faculties and centers took part in the survey and answered 19 questions in an online form developed in Office 365 (Microsoft Corporation, Redmond, WA, USA). The survey algorithm is based on the principle of cross-validation. According to the survey, the most important opportunities of distance (digital) learning are as follows: the opportunity to study in a convenient place, in a comfortable and familiar environment—28.57%; the opportunity to combine work with study—16.97%; the development of self-control skills and motivation to self-educate—16.43%; and the technical level of the learning process (use of information technologies)—13.83%. The aspects that negatively affect the organization of full-scale distance learning are as follows: the large volume of tasks—16.80%; the rapid fatigue due to prolonged work in front of a computer—16.35%; the absence of necessary equipment and/or constant (stable) access to the Internet—15.33%. In conclusion, ways to overcome the difficulties of distance learning, both by universities and by the government, are suggested.
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them.Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation.In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl.We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites.Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets.To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals.Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.
Can teachers’ digital competence influence technology acceptance in vocational education?

Acceptance of technology in educational contexts is a relevant factor in determining teachers' intention to use digital tools in their teaching practice. However, the mechanism through which teachers' digital competence can influence or enhance their technology acceptance and use intention remains relatively unexplored, especially in the context of vocational education and training. The aim of this study is twofold: to evaluate the fit of the technology acceptance model (TAM) in the context of vocational education, and to examine the relationship between self-assessed teachers' digital competence belief and their acceptance of and intention to use technology in their classroom. Data were collected via a self-administered questionnaire and the responses of 2011 vocational teachers were analysed. Applying structural equation modelling, the results show that the TAM adequately explains teachers' intention to use digital tools in vocational education; further, there are positive and significant relationships between teachers' beliefs about their digital competence and their beliefs about technology ease of use and the perceived usefulness of technology in teaching; this latter, positively correlates with technology use intention. Understanding the factors interplaying with teachers’ acceptance of technology and use intention is important for designing teacher training to enhance the successful integration of technology and to foster the connectivity between the different learning locations of vocational education.
Summarizing User-Contributed Comments

User-contributed comments are one of the hallmarks of the Social Web, widely adopted across social media sites and mainstream news providers alike. While comments encourage higher-levels of user engagement with online media, their wide success places new burdens on users to process and assimilate the perspectives of a huge number of user-contributed perspectives. Toward overcoming this problem we study in this paper the comment summarization problem: for a set of n user-contributed comments associated with an online resource, select the best top-k comments for summarization. In this paper we propose (i) a clustering-based approach for identifying correlated groups of comments; and (ii) a precedence-based ranking framework for automatically selecting informative user-contributed comments. We find that in combination, these two salient features yield promising results.
Robust Anomaly Detection in Videos Using Multilevel Representations

Detecting anomalies in surveillance videos has long been an important but unsolved problem. In particular, many existing solutions are overly sensitive to (often ephemeral) visual artifacts in the raw video data, resulting in false positives and fragmented detection regions. To overcome such sensitivity and to capture true anomalies with semantic significance, one natural idea is to seek validation from abstract representations of the videos. This paper introduces a framework of robust anomaly detection using multilevel representations of both intensity and motion data. The framework consists of three main components: 1) representation learning using Denoising Autoencoders, 2) level-wise representation generation using Conditional Generative Adversarial Networks, and 3) consolidating anomalous regions detected at each representation level. Our proposed multilevel detector shows a significant improvement in pixel-level Equal Error Rate, namely 11.35%, 12.32% and 4.31% improvement in UCSD Ped 1, UCSD Ped 2 and Avenue datasets respectively. In addition, the model allowed us to detect mislabeled anomalies in the UCDS Ped 1.
Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation

We present a gradient-based optimal-control technique for open quantum systems that utilizes quantum trajectories to simulate the quantum dynamics during optimization. Using trajectories allows for optimizing open systems with less computational cost than the regular density matrix approaches in most realistic optimization problems. We introduce an improved-sampling algorithm which minimizes the number of trajectories needed per optimization iteration. Together with employing stochastic gradient descent techniques, this reduces the complexity of optimizing many realistic open quantum systems to the complexity encountered with closed systems. Our optimizer harnesses automatic differentiation to provide flexibility in optimization and to suit the different constraints and diverse parameter regimes of real-life experiments. We utilize the optimizer in a variety of applications to demonstrate how the use of quantum trajectories significantly reduces the computation complexity while achieving a multitude of simultaneous optimization targets. Demonstrated targets include high state-transfer fidelities despite dissipation, faster gate times, and maximization of qubit-readout fidelity while maintaining the quantum nondemolition nature of the measurement and allowing for subsequent fast resonator reset.