Reviewerly/exHarmony huggingface dataset:
PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem

We present a new algorithm for estimating the Point Spread Function (PSF) in wide-field astronomical images with extreme source crowding. Robust and accurate PSF estimation in crowded astronomical images dramatically improves the fidelity of astrometric and photometric measurements extracted from wide-field sky monitoring imagery. Our radically new approach utilizes convolutional sparse representations to model the continuous functions involved in the image formation. This approach avoids the need to detect and precisely localize individual point sources that is shared by existing methods. In experiments involving simulated astronomical imagery, it significantly outperforms the recent alternative method with which it is compared.
What are the Features of Good Discussions for Shortening Bug Fixing Time?

Resource limitations require that bugs be resolved efficiently. The bug modification process uses bug reports, which are generated from service user reports. Developers read these reports and fix bugs. Developers discuss bugs by posting comments directly in bug reports. Although several studies have investigated the initial report in bug reports, few have researched the comments. Our research focuses on bug reports. Currently, everyone is free to comment, but the bug fixing time may be affected by how to comment. Herein we investigate the topic of comments in bug reports. Mixed topics do not affect the bug fixing time. However, the bug fixing time tends to be shorter when the discussion length of the phenomenon is short.
Automatic Detecting for Health-related Twitter Data with BioBERT

Social media used for health applications usually contains a large amount of data posted by users, which brings various challenges to NLP, such as spoken language, spelling errors, novel/creative phrases, etc. In this paper, we describe our system submitted to SMM4H 2020: Social Media Mining for Health Applications Shared Task which consists of five sub-tasks. We participate in subtask 1, subtask 2-English, and subtask 5. Our final submitted approach is an ensemble of various fine-tuned transformer-based models. We illustrate that these approaches perform well in imbalanced datasets (For example, the class ratio is 1:10 in subtask 2), but our model performance is not good in extremely imbalanced datasets (For example, the class ratio is 1:400 in subtask 1). Finally, in subtask 1, our result is lower than the average score, in subtask 2-English, our result is higher than the average score, and in subtask 5, our result achieves the highest score. The code is available online.
Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

Collaborative Filtering (CF) is a widely used technique in recommendation systems.It provides personal recommendations for users based on their preferences.However, this technique suffers from the sparsity issue which occurs due to a high proportion of missing rating scores in a rating matrix.Several factorization approaches have been used to address the sparsity issue.Such techniques have also been considered to tackle other challenges such as the overfitted predicted scores.Nevertheless, they suffer from setbacks such as drift in user preferences and items' popularity decay.These challenges can be solved by prediction approaches that accurately learn the longterm and short-term preferences integrated with factorization features.Nonetheless, the current temporal-based factorization approaches do not accurately learn the convergence of the assigned k clusters due to a lower number of short-term periods.Additionally, the use of optimization algorithms in the learning process to reduce prediction errors is time-consuming which necessitates a faster optimization algorithm.To address these issues, a new temporal-based approach named TWOCF is proposed in this paper.TWOCF utilizes the elbow clustering method to define the optimal number of clusters for the temporal activities of both users and items.This approach deploys the whale optimization algorithm to accurately learn short-term preferences within other factorization and temporal features.Experimental results indicate that TWOCF exhibits a superior CF prediction accuracy achieved within a shorter execution time when compared to the benchmark approaches.
Quantitative Spatial Analysis on Whole Slide Images Using U-Net

Advances in whole slide imaging technology have promoted a high use of digital slide images and generated a large volume of image data that is reliable and useful in determining treatment outcome.Recent technologies closely related to machine learning and deep learning algorithms have contributed to the success of digital histopathology by analyzing the digitized slide images providing quantitative information that are useful for faster turnaround times and effective treatment for the patient.The digital histopathological image analysis has received much attention due to its capability of mitigating the problem of the hand-crafted features.Features directly learned from raw data are trainable within the deep learning procedure and can be used for the histopathology image classification task.However, understanding the spatial context of cancer cells is still a challenging issue because of the heterogeneity of the tumor microenvironment which varies greatly, preventing successful diagnosis and leads to inappropriate therapeutic approaches for cancer patients.In this paper, we present a spatial analysis method for tumor microenvironment analysis using the U-Net architecture, a semantic segmentation deep-learning model, for a better understanding of the spatial relations between tissue types.We demonstrate the effectiveness of the U-Net architecture using a dataset created by an international crowdsourcing study.Moreover, we show that the quantitative estimates can be derived from the univariate spatial analysis.
Object Reconstruction Based on Attentive Recurrent Network from Single and Multiple Images

Abstract The application of traditional 3D reconstruction methods such as structure-from-motion and simultaneous localization and mapping are typically limited by illumination conditions, surface textures, and wide baseline viewpoints in the field of robotics. To solve this problem, many researchers have applied learning-based methods with convolutional neural network architectures. However, simply utilizing convolutional neural networks without taking other measures into account is computationally intensive, and the results are not satisfying. In this study, to obtain the most informative images for reconstruction, we introduce a residual block to a 2D encoder for improved feature extraction, and propose an attentive latent unit that makes it possible to select the most informative image being fed into the network rather than choosing one at random. The recurrent visual attentive network is injected into the auto-encoder network using reinforcement learning. The recurrent visual attentive network pays more attention to useful images, and the agent will quickly predict the 3D volume. This model is evaluated based on both single- and multi-view reconstructions. The experiment results show that the recurrent visual attentive network increases prediction performance in a way that is superior to other alternative methods, and our model has desirable capacity for generalization.
The NiuTrans System for the WMT20 Quality Estimation Shared Task

This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task. We participated in all tasks and all language pairs. We explored the combination of transfer learning, multi-task learning and model ensemble. Results on multiple tasks show that deep transformer machine translation models and multilingual pretraining methods significantly improve translation quality estimation performance. Our system achieved remarkable results in multiple level tasks, e.g., our submissions obtained the best results on all tracks in the sentence-level Direct Assessment task.
Cross-Lingual Transformers for Neural Automatic Post-Editing

In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source sentence and a machine-translated (MT) sentence as an input to generate the post-edited (PE) output. For further improvement, we mask incorrect or missing words in the PE output based on word-level quality estimation and then predict the actual word for each mask based on the fine-tuned cross-lingual language model (XLM-RoBERTa). Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation. When evaluated on the WMT 2020 English-German APE test dataset, our system improves the NMT output by -3.95 and +4.50 in terms of TER and BLEU, respectively.
Problems of Professional Self-Development Among Undergraduates in the Digital Space, Identified During the COVID-19 Pandemic

The article examines the problems of professional self- development among undergraduates in the digital space, identified during the COVID-19 pandemic. The results of the study showed that students have characteristics that are necessary for further personal growth. They want to know themselves and ready to change and learn new things, improve themselves. Conducted during a pandemic and distance learning in self- isolation showed that 50% of students expressed satisfaction with this form of education, explaining their opinion with a high level of independent search for the necessary information, opportunities for self-development, self-realization, and self-improvement. Among the difficulties of distance learning, all surveyed identified: search and critical analysis of digital information; communicative dissatisfaction, the need for approval. The surveyed students in their self-development need a micro- and macro- environment that would create a secure creative educational space. It is necessary to search for new solutions to overcome psychological barriers associated with the need for self-development as future highly qualified specialists in the context of modern trends in the development of digital education.
Methodology of Strategic Planning For the Development of Educational Ecosystems

The growing economic, social and digital inequality in the world, turbulence of economic and social processes lead to disorientation of educational organizations in development priorities. There are threats of stagnation and lag in the development of educational organizations, ineffective use of resources. The active development of ideas for sustainable development and the digital transformation of socio-economic relations induce educational organizations to revise long-term development programs. The study is aimed at solving the urgent problem of uncertainty in the strategic planning of the development of educational organizations in the context of digital transformation, socio-economic instability, challenges of the 21st century and new requirements for a specialist. The analysis of the studies showed that the professional community is actively discussing educational ecosystems of different types, based on the relationship between the participants and digital educational technologies. The methodology of strategic planning for the development of educational ecosystems presented in the work is considered as a complex of scientific methods: system analysis, classification, personality-oriented and ecosystem approaches. In the context of the concept of continuous, sustainable development, the educational ecosystem should be considered as a complex self-organizing, self-regulating and self-developing community — a group of people united by common interests, using common resources and a system of connections for learning and development. Using the example of the RANEPA North-West Institute of Management, it is shown that the use of the ecosystem approach for strategic development planning allows you to get the most complete and holistic view of the educational organization, its environment and role in the development of society. The methodology of strategic planning for the development of educational ecosystems allows: to create better models for the development than the generally accepted models of managing the educational process; develop an “ecosystem” strategy for the development of an educational organization; avoid costs and waste of resources in the context of digital transformation and dynamic changes in social relations.
[Paper] Sports Camera Calibration using Flexible Intersection Selection and Refinement

Sports scene analysis is an important technology to quantify a player's action and visualize game statistics. To realize such technology, camera calibration is required to recognize the player's position from a video. In this paper, we propose an automatic camera calibration method by using intersection resorting and refinement. Our contributions are 1) flexible intersection selection and 2) intersection refinement to improve accuracy in calibration. A homography matrix is used to convert world coordinates to image coordinates for the calibration. Sports scenes can be estimated by using a priori information such as length and position of field lines and their intersections. Conventional methods using the field lines and intersections cannot realize sufficient calibration accuracy because the intersections are selected from the combination of horizontal and vertical lines. Moreover, displacement at the intersections occurs between the detected position and a real one on the input image. Our proposed method can solve these problems by flexible intersection selection and refinement. As a result, a player's position in the real world is identified from the video by using the estimated homography matrix. Our experimental results show that the proposed method achieves higher accuracy than that by conventional methods.
Extension of Direct Citation Model Using In-Text Citations

Citations based relevant research paper recommendations can be generated primarily with the assistance of three citation models: (1) Bibliographic Coupling, (2) Co-Citation, and (3) Direct Citations.Millions of new scholarly articles are published every year.This flux of scientific information has made it a challenging task to devise techniques that could help researchers to find the most relevant research papers for the paper at hand.In this study, we have deployed an in-text citation analysis that extends the Direct Citation Model to discover the nature of the relationship degree-ofrelevancy among scientific papers.For this purpose, the relationship between citing and cited articles is categorized into three categories: weak, medium, and strong.As an experiment, around 5,000 research papers were crawled from the CiteSeerX.These research papers were parsed for the identification of in-text citation frequencies.Subsequently, 0.1 million references of those articles were extracted, and their in-text citation frequencies were computed.A comprehensive benchmark dataset was established based on the user study.Afterwards, the results were validated with the help of Least Square Approximation by Quadratic Polynomial method.It was found that degreeof-relevancy between scientific papers is a quadratic increasing/decreasing polynomial with respect to-increase/decrease in the in-text citation frequencies of a cited article.Furthermore, the results of the proposed model were compared with state-of-the-art techniques by utilizing a well-known measure, known as the normalized Discount Cumulative Gain (nDCG).The proposed method received an nDCG score of 0.89, whereas the state-of-the-art models such as the Content, Bibliographic-coupling, and Metadata-based Models were able to acquire the nDCG values of 0.65, 0.54, and 0.51 respectively.These results indicate that the proposed mechanism may be applied in future information retrieval systems for better results.
Spatio-Temporal Self-Attention Weighted VLAD Neural Network for Action Recognition

As characterizing videos simultaneously from spatial and temporal cues have been shown crucial for video processing, with the shortage of temporal information of soft assignment, the vector of locally aggregated descriptor (VLAD) should be considered as a suboptimal framework for learning the spatio-temporal video representation. With the development of attention mechanisms in natural language processing, in this work, we present a novel model with VLAD following spatio-temporal self-attention operations, named spatio-temporal self-attention weighted VLAD (ST-SAWVLAD). In particular, sequential convolutional feature maps extracted from two modalities i.e., RGB and Flow are receptively fed into the self-attention module to learn soft spatio-temporal assignments parameters, which enabling aggregate not only detailed spatial information but also fine motion information from successive video frames. In experiments, we evaluate ST-SAWVLAD by using competitive action recognition datasets, UCF101 and HMDB51, the results shcoutstanding performance. The source code is available at:https://github.com/badstones/st-sawvlad.
Posterior impropriety of some sparse Bayesian learning models

Sparse Bayesian learning models are typically used for prediction in datasets with significantly greater number of covariates than observations. Such models often take a reproducing kernel Hilbert space (RKHS) approach to carry out the task of prediction and can be implemented using either proper or improper priors. In this article we show that a few sparse Bayesian learning models in the literature, when implemented using improper priors, lead to improper posteriors.
Private Sequential Learning

Can we learn privately and efficiently through sequential interactions? A private learning model is formulated to study an intrinsic tradeoff between privacy and query complexity in sequential learning. The formulation involves a learner who aims to learn a scalar value by sequentially querying an external database and receiving binary responses. In the meantime, an adversary observes the learner’s queries, although not the responses, and tries to infer from them the scalar value of interest. The objective of the learner is to obtain an accurate estimate of the scalar value using only a small number of queries while simultaneously protecting his or her privacy by making the scalar value provably difficult to learn for the adversary. The main results provide tight upper and lower bounds on the learner’s query complexity as a function of desired levels of privacy and estimation accuracy. The authors also construct explicit query strategies whose complexity is optimal up to an additive constant.
Hybrid semantic recommender system for chemical compounds in large-scale datasets

Abstract The large, and increasing, number of chemical compounds poses challenges to the exploration of such datasets. In this work, we propose the usage of recommender systems to identify compounds of interest to scientific researchers. Our approach consists of a hybrid recommender model suitable for implicit feedback datasets and focused on retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares and Bayesian Personalized Ranking) and a new content-based algorithm, using the semantic similarity between the chemical compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of chemical compounds, CheRM-20, with more than 16.000 items (chemical compounds). The hybrid model was able to improve the results of the collaborative-filtering algorithms, by more than ten percentage points in most of the assessed evaluation metrics.
Architectural Supports for Block Ciphers in a RISC CPU Core by Instruction Overloading

We propose a novel computer architectural concept of instruction overloading to support block ciphers. Instead of adding new instructions, we extend only the execution of some existing instructions. The proposed method allows a central processing unit core to execute different operations for the same instructions, depending on the address of the data, similar to operator overloading in object-oriented languages. We first present an extension for the AES algorithm, then we demonstrate its enhanced applicability with two further extensions supporting multiple block ciphers and hardware masking. The first extension for AES is also applicable to add/AND-rotate-XOR-based block ciphers such as SIMON. The AES and SIMON encryption speed, on this extended core, is at least doubled and is significantly less affected by memory latency. In addition, the AES encryption code requires only 18% of the memory of the previous software implementation. The second extension can further support various block ciphers defined over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ), and the SM4 encryption speed is increased by at least 182%. The third extension provides correlation power analysis (CPA) resistance with a 66.6% area overhead but almost no speed overhead, whereas a typical software anti-CPA AES implementation requires at least hundreds of times the execution time.
A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems’ Worldviews

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot's abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi-robot system that uses the shared-world coordination paradigm autonomously schedules which robot should perform a given task, and when, using its worldview-the robot's internal representation of its belief about both its own state, and other robots' states. A key problem for operators is that robots' worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots' scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots' scheduling decisions, detecting de-synchronizations, and pinpointing the cause by comparing every robot's worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots' schedules and (ii) detect and conduct a root cause analysis of the robots' desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, we conduct a formative study to identify the necessary system design requirements and a qualitative evaluation with 12 roboticists. We find that MOSAIC Viewer is faster- and easier-to-use than the users' current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots' schedules and detect and pinpoint the cause of the desynchronized worldviews.