Reviewerly/exHarmony huggingface dataset:
Post-Training Dialogue Summarization using Pseudo-Paraphrasing

Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models.These features either require additional knowledge to recognize or make the resulting models harder to tune.To bridge the format gap between dialogues and narrative summaries in dialogue summarization tasks, we propose to post-train pretrained language models (PLMs) to rephrase from dialogue to narratives.After that, the model is fine-tuned for dialogue summarization as usual.Comprehensive experiments show that our approach significantly improves vanilla PLMs on dialogue summarization and outperforms other SOTA models by the summary quality and implementation costs. 1
Linked data for libraries: Creating a global knowledge space, a systematic literature review

The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services.
University Students' Learning Experiences in the Virtual Environment

The process of Teaching and Learning in times of Covid-19 brought experiences between teachers and students, seeking to improve with some teaching method to obtain satisfaction in their learning, the objective of this research is to validate the instrument of measurement of Teaching and Learning Experience Questionnaire (ETLQ) in the Peruvian context with university students, This instrument consists of three parts, the Teaching and Learning Environment which consists of 19 questions, the Student Learning Approach which consists of 9 questions and the Critical Thinking with 4 questions, each question is composed of 5-point Likert-type scales (1 point = "Strongly disagree" and 5 points = "Strongly agree". A total of 2027 university students participated throughout Peru, including students from national and private universities. Cronbach's Alpha =0.957 with 32 numbers of items was valid for its respective use, with which it will be possible to measure the Learning Experiences in university students, reinforcing this result with different tests whose results were optimal and reliability analysis. Concluding that, the validation instrument is valid in the Peruvian environment, online education is accepted in its highest percentage and that the rejection of this is only in the most vulnerable students and the feedback to students is the most outstanding. Keywords—Validation, Learning experiences, teaching, online education, Covid-19
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts.However, the most natural way that human seek or test their knowledge is via human conversations.Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows given the speech documents.In this task, our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.To this end, instead of directly adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNET, which effectively ingests cross-modal information to achieve finegrained representations of the speech and language modalities.Moreover, we propose a simple and novel mechanism, termed Dual Attention, by encouraging better alignments between audio and text to ease the process of knowledge transfer.To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.The performance of the existing state-of-the-art methods significantly degrade on our dataset, hence demonstrating the necessity of cross-modal information integration.Our experimental results demonstrate that our proposed method achieves superior performance in spoken conversational question answering tasks.$ = { !!, ! " , ⋯ , ! # } … … … … … …
Semi-Supervised 360° Depth Estimation from Multiple Fisheye Cameras with Pixel-Level Selective Loss

In this paper, we study a practical omnidirectional depth estimation with neural networks that enables effective learning on real world data obtained using wide-baseline multiple fish-eye cameras. Most previous approaches only used synthetic data providing dense and accurate depth ground truth (GT). However, it is unrealistic to acquire such high quality GT data in real world due to limitations of the existing depth sensors. We first introduce two critical problems that can reduce the accuracy of depth estimation: depth GT sparsity and sensor calibration error. We then propose a novel semi-supervised learning method using pixel-level loss that selectively uses supervised loss and unsupervised re-projection loss according to existence of GT. Empirical results demonstrate that our method efficiently reduces the performance degradation in both simulation on synthetic data and real world data using sparse depth sensor.
SOME COMMON ERRORS IN VIETNAMESE-ENGLISH TRANSLATION OF ENGLISH-MAJOR JUNIORS AT TAY DO UNIVERSITY, VIETNAM

&lt;p&gt;Most Vietnamese students who learn English often make mistakes when learning Vietnamese-English translation because there are many differences between Vietnamese and English. Therefore, translation is a difficult subject when learning English. This research “Some common errors in the Vietnamese-English translation of English majors at Tay Do University” focused on some common errors of English majors in Vietnamese-English translation. In this research, the participants were 75 English majored juniors at Tay Do University in which 55 students randomly were chosen to complete the translation tests and the remaining ones (20 students) were selected to answer the interview questions. The results of this thesis showed that the English majors made errors in Vietnamese-English translation related to word order, grammar, and lexicon. It is hoped that this research can be helpful for both students and teachers in the process of learning and teaching translation.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0938/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Controlling the Stage: A High-Level Control System for Virtual Audiences in Virtual Reality

This article presents a novel method for controlling a virtual audience system (VAS) in Virtual Reality (VR) application, called STAGE, which has been originally designed for supervised public speaking training in university seminars dedicated to the preparation and delivery of scientific talks. We are interested in creating pedagogical narratives : narratives encompass affective phenomenon and rather than organizing events changing the course of a training scenario, pedagogical plans using our system focus on organizing the affects it arouses for the trainees. Efficiently controlling a virtual audience towards a specific training objective while evaluating the speaker’s performance presents a challenge for a seminar instructor: the high level of cognitive and physical demands required to be able to control the virtual audience, whilst evaluating speaker’s performance, adjusting and allowing it to quickly react to the user’s behaviors and interactions. It is indeed a critical limitation of a number of existing systems that they rely on a Wizard of Oz approach, where the tutor drives the audience in reaction to the user’s performance. We address this problem by integrating with a VAS a high-level control component for tutors, which allows using predefined audience behavior rules, defining custom ones, as well as intervening during run-time for finer control of the unfolding of the pedagogical plan. At its core, this component offers a tool to program, select, modify and monitor interactive training narratives using a high-level representation. The STAGE offers the following features: i) a high-level API to program pedagogical narratives focusing on a specific public speaking situation and training objectives, ii) an interactive visualization interface iii) computation and visualization of user metrics, iv) a semi-autonomous virtual audience composed of virtual spectators with automatic reactions to the speaker and surrounding spectators while following the pedagogical plan V) and the possibility for the instructor to embody a virtual spectator to ask questions or guide the speaker from within the Virtual Environment. We present here the design, and implementation of the tutoring system and its integration in STAGE, and discuss its reception by end-users.
Normal Forms of Conditional Belief Bases Respecting Inductive Inference

Normal forms of syntactic entities play an important role in many different areas in computer science. In this paper, weaddress the question of how to obtain normal forms and minimal normal forms of conditional belief bases in order to,e.g., ease reasoning with them or to simplify their comparison. We introduce notions of equivalence of belief basestaking nonmonotonic inductive inference operators into account. Furthermore, we also consider renamings of beliefbases induced by renamings of the underlying signatures. We show how renamings constitute another dimension of normalforms. Based on these different dimensions, we introduce and illustrate various useful normal forms and show their properties, advantages, and interrelationships.
RL-SSI Model: Adapting a Supervised Learning Approach to a Semi-Supervised Approach for Human Action Recognition

Generally, the action recognition task requires a vast amount of labeled data, which represents a time-consuming human annotation effort. To mitigate the dependency on labeled data, this study proposes Semi-Supervised and Iterative Reinforcement Learning (RL-SSI), which adapts a supervised approach that uses 100% labeled data to a semi-supervised and iterative approach using reinforcement learning for human action recognition in videos. The JIGSAWS and Breakfast datasets were used to evaluate the RL-SSI model, because they are commonly used in the action segmentation task. The same applies to the performance metrics used in this work-F-Score (F1) and Edit Score-which are commonly applied for such tasks. In JIGSAWS tests, we observed that the RL-SSI outperformed previously developed state-of-the-art techniques in all quantitative measures, while using only 65% of the labeled data. When analysing the Breakfast tests, we compared the effectiveness of RL-SSI with the results of the self-supervised technique called SSTDA. We have found that RL-SSI outperformed SSTDA with an accuracy of 66.44% versus 65.8%, but RL-SSI was surpassed by the F1@10 segmentation measure, which presented an accuracy of 67.33% versus 69.3% for SSTDA. Despite this, our experiment only used 55.8% of the labeled data, while SSTDA used 65%. We conclude that our approach outperformed equivalent supervised learning methods and is comparable to SSTDA, when evaluated on multiple datasets of human action recognition, proving to be an important innovative method to successfully building solutions to reduce the amount of fully labeled data, leveraging the work of human specialists in the task of data labeling of videos, and their respectives frames, for human action recognition, thus reducing the required resources to accomplish it.
Computer Assisted Language Learning (CALL) and Mobile Assisted Language Learning (MALL); Hefty Tools for Workplace English Training: An Empirical Study

To be above board, English Language is playing a gigantic role in terms of workplace communication. Workplace English training has a humongous demand to make employees proficient in terms of the four basic skills 'Listening', 'Speaking', 'Reading', 'Writing' (LSRW) of English language to a tee. To make the grade in terms of workplace communication, effective and professional training is highly needed. To call a spade a spade, workplace English training has got a strategic ponderosity as it helps different organizations, companies to come out with triumphant in the international market before one can say Jack Robinson. This paper also zeroes in on how to hone communication skills in English in terms of workplace training through Computer Assisted Language Learning (CALL) and Mobile Assisted Language Learning (MALL). Computer Assisted Language Learning (CALL) and Mobile Assisted Language Learning (MALL) are helpful for the employees to continue their workplace English Training through online learning or E-learning along with their Professional work in a balanced manner. To talk Turkey, Computer Assisted Language Learning (CALL) and Mobile Assisted Language Learning (MALL) are the two ballistic missiles of developing the communicative competence, pronunciation, fluency, grammatical competence, learning proper applications of vocabularies, developing Cognitive skills, Written Communication in terms of workplace English Training.
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning

Early detection and analysis of calcifications in mammogram images is crucial in a breast cancer diagnosis workflow. Management of calcifications that require immediate follow-up and further analyzing its benignancy or malignancy can result in a better prognosis. Recent studies have shown that deep learning-based algorithms can learn robust representations to analyze suspicious calcifications in mammography. In this work, we demonstrate that randomly equalizing the histograms of calcification patches as a data augmentation technique can significantly improve the classification performance for analyzing suspicious calcifications. We validate our approach by using the CBIS-DDSM dataset for two classification tasks. The results on both the tasks show that the proposed methodology gains more than 1% mean accuracy and F1-score when equalizing the data with a probability of 0.4 when compared to not using histogram equalization. This is further supported by the t-tests, where we obtain a p-value of p<0.0001, thus showing the statistical significance of our approach.
Non-Repudiation-based Network Security System using Multiparty Computation

Security has always been a prominent concern over the network, and various essential requirements are required to cater to an efficient security system. Non-repudiation is a requirement about the non-deniability of services acting as a bridge between seamless relaying of service/data and efficient security implementation. There have been various studies carried out towards strengthening the non-repudiation system. There are certain pitfalls that render inapplicability on dynamic cases of vulnerability. The conventional two-party non-repudiation schemes have been widely explored in the existing literature. But this paper also advocates the adoption of multi-party computation, which has better feasibility toward strengthening a distributed security system. The current work presents a survey on the existing approaches of non-repudiation to investigate its effectiveness in the multi-party system. The prime aim of the proposed work is to analyze the current research progress and draw a research gap as the prominent contribution of the proposed study. The manuscript begins by highlighting the issues concerning multi-party strategies and cryptographic approaches, and the security requirements and standardization are briefly discussed. It then describes the essentials of non-repudiation and examines state-of-the-art mechanisms. Finally, the study summarizes and discusses research gaps identified through the review analysis.
Impact of Accumulated Dust on Performance of Two Types of Photovoltaic Cells: Evidence from the South of Jordan

This paper examines the impact of accumulated dust on two types of photovoltaic (PV) cells in the performance of solar panels facility located in the southern part of Jordan between January to August 2020. To determine the performance of the solar PV panel system, two elements have been considered: sun radiation total efficiency and output power generated from the two types of the PV panel. Results of the study revealed that the mass of dust accumulated on the polycrystalline panel accumulated faster than on the cadmium telluride panel at a rate of 10.5 g/m2 for polycrystalline panels and 8.4g/m2 for cadmium telluride panel. Furthermore, results indicated that the projected drop in the efficiency of washed and unwashed polycrystalline panels decreased monthly by 5% and 16% respectively, while the efficiency of washed and unwashed cadmium telluride panels decreased monthly by 5% and 11.5% respectively. In the same context, results indicated that the wind speed, concentration rate, and relative humidity increased by 3%, 5%, and 8% respectively whereas the ambient temperature decreased by 4% monthly. On the other hand, the size and charge of accumulated dust on the cadmium telluride panel surface were larger than the size and charge of dust on the polycrystalline panel surface with a high percent of (Si) and low percent of (Ca) and (Fe). This research contributes to the literature by providing empirical evidence for the impact of accumulated dust on PV panels applied on a dusty-weather such as the one in the southern part of Jordan.
Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation

The citation intent extraction and classification has long been studied as it is a good measure of relevancy. Different approaches have classified the citations into different classes; including weak and strong, positive and negative, important and unimportant. Others have gone further from binary classification to multi-classes, including extension, use, background, or comparison. Researchers have utilized various elements of the information, including both meta and contents of the paper. The actual context of any referred article lies within the citation context where a paper is referred. Various attempts have been made to study the citation context to capture the citation intent, but very few have encoded the words to their contextual representations. For automated classification, we need to train deep learning models, which take the citation context as input and provides the reason for citing a paper. Deep neural models work on numeric data, and therefore, we must convert the text information to its numeric representation. Natural languages are much complex than computer languages. Computer languages have a pre-defined fixed syntax where each word has a unique meaning. In contrast, every word in natural language may have a different meaning and may well be understood by understanding the position, previous discussion, and neighboring words. The extra information provides the context of a word within a sentence. We have, therefore, used contextual word representation, which is trained through deep neural networks. Deep models require massive data for generalizing the model, however, the existing state-of-the-art datasets don&#x2019;t provide much information for the training models to get generalized. Therefore, we have developed our own scholarly dataset, Citation Context Dataset with Intent (C2D-I), an extension of the C2D dataset. We used a transformers based model for capturing the contextual representation of words. Our proposed method outperformed the existing benchmark methods with F1 score of 89&#x0025;.
Decentralized Statistical Inference with Unrolled Graph Neural Networks

In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples without centralized coordination. Existing optimization-based algorithms suffer from issues of model mismatches and poor convergence speed, and thus their performance would be degraded provided that the number of communication rounds is limited. This motivates us to propose a learning-based framework, which unrolls well-noted decentralized optimization algorithms (e.g., Prox-DGD and PG-EXTRA) into graph neural networks (GNNs). By minimizing the recovery error via end-toend training, this learning-based framework resolves the model mismatch issue. Our convergence analysis (with PG-EXTRA as the base algorithm) reveals that the learned model parameters may accelerate the convergence and reduce the recovery error to a large extent. The simulation results demonstrate that the proposed GNN-based learning methods prominently outperform several state-of-the-art optimization-based algorithms in convergence speed and recovery error.
C-P Map: A Novel Evaluation Toolkit for Speaker Verification

Evaluation trials are used to probe performance of automatic speaker verification (ASV) systems. In spite of the clear importance and impact, evaluation trials have not been seriously treated in research and engineering practice. This paper firstly presents a theoretical analysis on evaluation trials and highlights potential bias with the most popular cross-pairing approach used in trials design. To interpret and settle this problem, we define the concept of trial config and C-P map derived from it. The C-P map measures the performance of an ASV system on various trial configs in a 2-dimensional map. On the map, each location represents a particular trial config and its corresponding color represents the system performance. Experiments conducted on representative ASV systems show that the proposed C-P map offers a powerful evaluation toolkit for ASV performance analysis and comparison. The source code for C-P map has been release at https://gitlab.com/csltstu/sunine.
S-transform application in phase extraction of spectrally resolved interferometry measuring step height

Spectrally resolved interferometry is widely used in the measurement of distance, displacement, film thickness, and surface morphology in micro/nano-scale geometric measurement. The core of spectrally resolved white-light interferometry is phase extraction. Temporal phase shifting, Fourier transform, wavelet transform, and other methods are commonly used in phase extraction of spectrally resolved interferometry. The S-transform, providing frequency-dependent resolution and having good time-frequency characteristics, is widely used in power quality disturbance analysis, seismic wave analysis, and phase recovery in profilometry. S-transform is used to extract the phase of the spectrally resolved white-light interferometry signal measuring step height. Compared with Fourier transform and wavelet transform, it is proved that S-transform is a feasible method in phase extraction of spectrally resolved interferometry measuring step height.