Reviewerly/exHarmony huggingface dataset:
RW-KD: Sample-wise Loss Terms Re-Weighting for Knowledge Distillation

Knowledge Distillation (KD) is extensively used in Natural Language Processing to compress the pre-training and task-specific finetuning phases of large neural language models.A student model is trained to minimize a convex combination of the prediction loss over the labels and another over the teacher output.However, most existing works either fix the interpolating weight between the two losses apriori or vary the weight using heuristics.In this work, we propose a novel samplewise loss weighting method, RW-KD.A metalearner, simultaneously trained with the student, adaptively re-weights the two losses for each sample.We demonstrate, on 7 datasets of the GLUE benchmark, that RW-KD outperforms other loss re-weighting methods for KD.
Joint demosaicing of colour and polarisation from filter arrays

This article considers the joint demosaicing of colour and polarisation image content captured with a Colour and Polarisation Filter Array imaging system. The Linear Minimum Mean Square Error algorithm is applied to this case, and its performance is compared to the state-of-theart Edge-Aware Residual Interpolation algorithm. Results show that the LMMSE demosaicing method gives statistically higher scores on the largest tested database, in term of peak signal-to-noise ratio relatively to a CPFA-dedicated algorithm.
Some aspects of methodological support of teachers in the conditions of digitalization of general education

Problem and purpose. The forced transfer of education during the COVID-19 pandemic to distance / mixed mode, demonstrating the possibilities of digitalization of education, at the same time showed that the diagnosis of teachers' difficulties, prompt and adequate support of their activities is the most important condition for ensuring the quality of implementation of basic educational programs and the educational results achieved by students. Purpose of the study: identifying the main difficulties of teachers in organizing distance / blended learning and substantiating the types of support for pedagogical activities in the context of digitalization of general education. Materials and methods. To identify the main difficulties of teachers in the implementation of distance / blended learning and popular types of support for pedagogical activities, an anonymous survey of 1,070 teachers from 76 regions of the Russian Federation was carried out using the questionnaire "Distance and blended learning: problems and opportunities". The results of the study indicate that teachers, regardless of age, work experience, qualification category, place of residence, experienced difficulties in organizing distance learning of a methodological, psychological, technical and logistical nature and needed adequate types of support. However, a significant proportion of the surveyed teachers did not receive any support (31% – material and technical, 24%-technical, 16% – methodological and 35% – psychological), or are not satisfied with the forms of its provision. Conclusion. The results of the study confirmed the need to transform the support of pedagogical activity in the process of digitalization of general education, which should become mainly personalized, complex, combining various types and forms of its implementation.
Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network

Live virtual reality (VR) streaming (a.k.a., 360-degree video streaming) has become increasingly popular because of the rapid growth of head‐mounted displays and 5G networking deployment. However, the huge bandwidth and the energy required to deliver live VR frames in the wireless video sensor network (WVSN) become bottlenecks, making it impossible for the application to be deployed more widely. To solve the bandwidth and energy challenges, VR video viewport prediction has been proposed as a feasible solution. However, the existing works mainly focuses on the bandwidth usage and prediction accuracy and ignores the resource consumption of the server. In this study, we propose a lightweight neural network-based viewport prediction method for live VR streaming in WVSN to overcome these problems. In particular, we (1) use a compressed channel lightweight network (C-GhostNet) to reduce the parameters of the whole model and (2) use an improved gate recurrent unit module (GRU-ECA) and C-GhostNet to process the video data and head movement data separately to improve the prediction accuracy. To evaluate the performance of our method, we conducted extensive experiments using an open VR user dataset. The experiments results demonstrate that our method achieves significant server resource saving, real-time performance, and high prediction accuracy, while achieving low bandwidth usage and low energy consumption in WVSN, which meets the requirement of live VR streaming.
New indifferentiability security proof of MDPH hash function

MDPH is a double-block-length hash function proposed by Naito at Latincrypt 2019. This is a combination of Hirose's compression function and the domain extender called Merkle–Damgård with permutation. When instantiated with an n-bit block cipher, Naito proved that this achieves the (nearly) optimal indifferentiable security bound of O (n − log n)-bit security. In this paper, the authors first point out that the proof of the claim contains a gap, which is related to the definition of the simulator in simulating the decryption of the block cipher. The authors then show that the proof can be fixed. The authors introduce a new simulator that addresses the issue, showing that MDPH retains its (nearly) optimal indifferentiable security bound of O (n − log n)-bit security.
Three Approaches to Handling the COVID-19 Crisis in Federal Countries

Abstract Issue Despite their geographical and cultural proximity, Germany, Austria, and Switzerland can teach very different lessons on how to handle the COVID-19 pandemic. Timing and severity of outbreaks during the first wave were fairly similar in Germany and Austria, whereas Switzerland faced a higher infection rate at the peak of the crisis (although far from rates in France or Italy). Description The aim of this presentation is to examine the outbreak responses of the three countries in the spring of 2020 and give insight into the dynamics and rationales behind these responses. Results Although the three countries are all prime examples of federalism, Austria and Switzerland showed a decisive, uniform public health response resulting from epidemic laws granting the central level precedence under such circumstances. In Germany, by contrast, individual state governments played a central role. Response measures eventually taken by the three countries were not too different but how decisions were made and subsequently communicated to the public varied considerably. The three countries fared well in reducing transmission rates and never came close to reaching capacity limits in their health systems during the first wave. However, while all of them experienced a resurgence of infections in fall, the epidemiological development took a diverging course across the three countries in the following months. Lessons While all countries were successful in the first wave, differences in the paths chosen are likely to impact adherence to measures and may help explain divergent developments in the subsequent waves. Main messages Swift and decisive actions seem to have been successful answers to the first wave of the pandemic, but approaches to implementation and communication of these approaches to the public may make the difference in the further course of the pandemic.
Adversarial training for supervised relation extraction

Most supervised methods for relation extraction (RE) involve time-consuming human annotation. Distant supervision for RE is an efficient method to obtain large corpora that contains thousands of instances and various relations. However, the existing approaches rely heavily on knowledge bases (e.g., Freebase), thereby introducing data noise. Various relations and noisy labeling instances make the issue difficult to solve. In this study, we propose a model based on a piecewise convolution neural network with adversarial training. Inspired by generative adversarial networks, we adopt a heuristic algorithm to identify noisy datasets and apply adversarial training to RE. Experiments on the extended dataset of SemEval-2010 Task 8 show that our model can obtain more accurate training data for RE and significantly outperforms several competitive baseline models. Our model has an Fi score of 89.61%.
Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method

Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which causes difficulties in object detection. For objects with a large aspect ratio and that are oblique and densely arranged, using an oriented bounding box can help to avoid deleting some correct detection bounding boxes by mistake. The classic rotational region convolutional neural network (R2CNN) has advantages for text detection. However, R2CNN has poor performance in the detection of slender objects with arbitrary directivity in remote sensing images, and its fault tolerance rate is low. In order to solve this problem, this paper proposes an improved R2CNN based on a double detection head structure and a three-point regression method, namely, TPR-R2CNN. The proposed network modifies the original R2CNN network structure by applying a double fully connected (2-fc) detection head and classification fusion. One detection head is for classification and horizontal bounding box regression, the other is for classification and oriented bounding box regression. The three-point regression method (TPR) is proposed for oriented bounding box regression, which determines the positions of the oriented bounding box by regressing the coordinates of the center point and the first two vertices. The proposed network was validated on the DOTA-v1.5 and HRSC2016 datasets, and it achieved a mean average precision (mAP) of 3.90% and 15.27%, respectively, from feature pyramid network (FPN) baselines with a ResNet-50 backbone.
A NEW MODEL OF PARALLEL PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING NUMERICAL PROBLEMS

Evolutionary algorithms are suitable methods for solving complex problems. Many changes have thus been made on their original structures in order to obtain more desirable solutions. Parallelization is a suitable technique to decrease the runtime of the algorithm, and therefore, to obtain solutions with higher quality. In this paper, a new algorithm is proposed with two approaches, which is based on a parallelization technique with shared memory architecture. In the proposed algorithm, the search space is firstly decomposed into multiple equal and independent subspaces. Then, a subtask is performed on each subspace simultaneously in a parallel manner which leads to providing more qualified solutions. Splitting the search space into smaller subspaces causes the algorithm to find optimal solutions in each region in an easier way. The algorithm RAPSO is improved with applying a new acceleration coefficient which has been named IRAPSO. In the proposed algorithm, the IRAPSO is used as the subtask. For the sake of testing the proposed algorithm, fourteen well-known benchmarks of numerical optimizing problems are inspected. Then, the proposed algorithm is compared with algorithms PPBO and PSOPSO that were both based on the island model. The results of the proposed algorithm are much better than those of the other two algorithms.
Explainable Natural Language to Bash Translation using Abstract Syntax Tree

Natural language processing for program synthesis has been widely researched.In this work, we focus on generating Bash commands from natural language invocations with explanations.We propose a novel transformer based solution by utilizing Bash Abstract Syntax Trees and manual pages.Our method incorporates tree structure information in the transformer architecture and provides explanations for its predictions via alignment matrices between user invocation and manual page text.Our method performs on par with the state of the art performance on Natural Language Context to Command task and performs better than fine-tuned T5 and Seq2Seq models.
Wireless ultraviolet scattering channel estimation method based on deep learning

Due to the strong scattering characteristics, there are serious problems of inter-symbol interference (ISI) and transmission attenuation in the none-line-of-sight (NLOS) wireless ultraviolet communication system. In this paper, a wireless ultraviolet scattering channel estimation method based on deep learning is presented. The learning model structure is designed by combining the one-dimensional convolutional neural network (1D-CNN) and the deep neural network (DNN). In the training stage, the network optimization process is improved by the differential evolution (DE) algorithm. The computer simulation results show that the proposed deep learning channel estimation scheme has better mean square error (MSE) performance and bit error rate (BER) performance compared with the traditional algorithms. Furthermore, we verify the stability of this scheme in different communication environments, and the constructed neural network model has good generalization ability.
Knowledge Graph Representation Learning using Ordinary Differential Equations

Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space.The capability of KGEs in preserving graph characteristics including structural aspects and semantics, highly depends on the design of their score function, as well as the inherited abilities from the underlying geometry.Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models.To address this problem, we propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs).To this end, we represent each relation (edge) in a KG as a vector field on several manifolds.We specifically parameterize ODEs by a neural network to represent complex manifolds and complex vector fields on the manifolds.Therefore, the underlying embedding space is capable to assume the shape of various geometric forms to encode heterogeneous subgraphs.Experiments on synthetic and benchmark datasets using state-of-the-art KGE models justify the ODE trajectories as a means to enable structure preservation and consequently avoiding wrong inferences.
Impact of Open Access Publication Requirements on a Traditional, Society-Sponsored Geochemistry Journal and Potential Future Outcomes

2016, likely reflecting the substantial article publishing changes (APCs).An additional, major impact on GCA from the move towards open access has been the termination of subscription contracts between Elsevier and Projekt DEAL, the University of California system, and other entities.This has led many authors, reviewers, and associate editors from these communities to end their association with GCA.Despite these challenges, total submissions to GCA have increased 23% over the last five years, largely driven by an increasing number of manuscripts from China.These and other impacts on GCA from the transition to open access publishing are outside of the journal's control but raise valid questions regarding its future sustainability.APCs for GCA (3250 USD/2850 EUR) are not viable expenses for most researchers; many other geochemistry journals have similar APCs.Attempts by Elsevier to create mirror, fully-open access journals simply circumvent the central problem: the cost of author-supported open access is not viable.Geochemists have little interest in or ability to pay APCs yet have no desire to place their work behind a paywall with increasingly limited access.Preprints are one mechanism for authors to increase access to their research, but deposition of accepted manuscript versions in repositories (i.e., green open access) is accompanied by a 24 month embargo period.A possible future outcome is the negotiation of contracts between institutions and publishers that include blanket open access fees for authors at the covered organizations.Widespread adoption of such agreements would provide near-complete open access without draining the limited research funds of authors.However, if a new access model is not forthcoming then the geochemistry community faces the prospect of eventually abandoning foundational journals like GCA in favor of alternative, low-cost or free open access venues.
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text

This paper presents an active learning approach that aims to reduce the human effort required during the annotation of natural language corpora composed of entities and semantic relations.Our approach assists human annotators by intelligently selecting the most informative sentences to annotate and then pre-annotating them with a few highly accurate entities and semantic relations.We define an uncertainty-based query strategy with a weighted density factor, using similarity metrics based on sentence embeddings.As a case study, we evaluate our approach via simulation in a biomedical corpus and estimate the potential reduction in total annotation time.Experimental results suggest that the query strategy reduces by between 35% and 40% the number of sentences that must be manually annotated to develop systems able to reach a target F 1 score, while the pre-annotation strategy produces an additional 24% reduction in the total annotation time.Overall, our preliminary experiments suggest that as much as 60% of the annotation time could be saved while producing corpora that have the same usefulness for training machine learning algorithms.An open-source computational tool that implements the aforementioned strategies is presented and published online for the research community.
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs

An open-domain knowledge graph (KG) has entities as nodes and natural language relations as edges, and is constructed by extracting (subject, relation, object) triples from text.The task of open-domain link prediction is to infer missing relations in the KG.Previous work has used standard link prediction for the task.Since triples are extracted from text, we can ground them in the larger textual context in which they were originally found.However, standard link prediction methods only rely on the KG structure and ignore the textual context that each triple was extracted from.In this paper, we introduce the new task of opendomain contextual link prediction which has access to both the textual context and the KG structure to perform link prediction.We build a dataset for the task and propose a model for it.Our experiments show that context is crucial in predicting missing relations.We also demonstrate the utility of contextual link prediction in discovering context-independent entailments between relations, in the form of entailment graphs (EG), in which the nodes are the relations.The reverse holds too: contextindependent EGs assist in predicting relations in context.
Sentiment Analysis in Code-Mixed Telugu-English Text with Unsupervised Data Normalization

In a multilingual society, people communicate in more than one language, leading to Code-Mixed data.Sentimental analysis on Code-Mixed Telugu-English Text (CMTET) poses unique challenges.The unstructured nature of the Code-Mixed Data is due to the informal language, informal transliterations, and spelling errors.In this paper, we introduce an annotated dataset for Sentiment Analysis in CMTET.Also, we report an accuracy of 80.22% on this dataset using novel unsupervised data normalization with a Multilayer Perceptron (MLP) model.This proposed data normalization technique can be extended to any NLP task involving CMTET.Further, we report an increase of 2.53% accuracy due to this data normalization approach in our best model.
Privacy-aware examination results ranking for the balance between teachers and mothers

As the main parent and guardian, mothers are often concerned with the study performance of their children. More specifically, most mothers are eager to know the concrete examination scores of their children. However, with the continuous progress of modern education systems, most schools or teachers have now been forbidden to release sensitive student examination scores to the public due to privacy concerns, which has made it infeasible for mothers to know the real study level or examination performance of their children. Therefore, a conflict has come to exist between teachers and mothers, which harms the general growing up of students in their study. In view of this challenge, we propose a Privacy-aware Examination Results Ranking (PERR) method to attempt at balancing teachers' privacy disclosure concerns and the mothers' concerns over their children's examination performance. By drawing on a relevant case study, we prove the effectiveness of the proposed PERR method in evaluating and ranking students according to their examination scores while at the same time securing sensitive student information.
The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis Resolution in Dialogue: A Cross-Team Analysis

The CODI-CRAC 2021 shared task is the first shared task that focuses exclusively on anaphora resolution in dialogue and provides three tracks, namely entity coreference resolution, bridging resolution, and discourse deixis resolution.We perform a cross-task analysis of the systems that participated in the shared task in each of these tracks.
A Proposed Fuzzy Model for Diseases Diagnosis

Decision making has become a problem in environments full of uncertain, vague and imprecise information. They face many problems to train computer systems to simulate human thinking to make the right decision. Different methodologies and approaches have been used to train computers to understand and mimic human thinking. This paper proposes a fuzzy model for a bone disease to have the right diagnosis answer, as a human expertise doctor. and to prove that using fuzzy logic has a significant ability to mimic human thinking. The model accepts inputs in the different forms as physiological and clinical parameters and all data based on medical expertise, using a rule-based fuzzy system approach applied with fourteen rules to have final accurate output decisions. it has been tested in the orthopedic unit against the real existing diagnosis answer from expertise doctor and found that is capable of assisting medical experts in diagnosing diseases and provide good health services to their patients.