Reviewerly/exHarmony huggingface dataset:
Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework

Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering.This can be primarily attributed to the absence of standard benchmark datasets.In this paper we meticulously create a large amount of data connected with E-manuals and develop suitable algorithm to exploit it.We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals and pretrain RoBERTa on this large corpus.We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc.We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices.Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section.For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over the most competitive baseline.We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances.
The role of communication and dialogue during studies in higher education institutions

The new paradigm of education development actualizes the problem of the formation of highly qualified teaching staff who masterfully mastered communicative competence, which is based on communicative skills. In the face of growing competition in the higher education market, effective communications management is becoming increasingly important. Among the various ways to attract consumers of educational services to study at higher educational institutions, communications are becoming more and more relevant - one of the most important components of the activities of higher educational institutions, provides the process of transferring the necessary information about the range of educational services to consumers. The main participants in the educational services market are: the state represented by executive authorities at different levels, higher education institutions, organizations and enterprises, households, and individual citizens.
Data Analysis of Athletes’ Physiological Indexes in Training and Competition Based on Wireless Sensor Network

The application of physiological and biochemical indicators in athlete training and competition has become a hot research topic in related fields at home and abroad. Both coaches and scientific researchers hope to use quantitative physiological and biochemical indicators to study the load, fatigue, and recovery of athletes in training competitions and use them to scientifically guide athletes in training competitions, improve sports performance, and reduce injuries. This article introduces in detail the development status of wireless sensor network technology, energy consumption detection system, and ZigBee technology. On this basis, the focus is on the design of the detection terminal (coordinator and router node), the routing protocol of the ZigBee network, and the algorithm for the detection of human energy consumption. This subject proposes a design plan for the human exercise energy consumption detection system and researches and designs the wireless sensor network coordinator, router node, and host computer monitoring system. The microprocessors of the two types of network nodes use the single-chip microcomputer. Among them, the router node is composed of sensor modules, data transmission modules, and power modules; the software part is transplanted to ZigBee protocol Z-Stack, combined with the routing algorithm, and we add the corresponding node function code to achieve them. Based on the introduction of the development status and development points of the single-chip-based motion wireless sensor, this article focuses on the analysis of the single-chip-based motion wireless sensor network products. The common features of the single-chip microcomputer are wireless, huge low power consumption, and simple development. Engineering practice shows that the designed system is relatively good in terms of reliability and stability of data transmission; even in the case of severe noise interference and electromagnetic interference, the probability of network nodes malfunctioning is still very small. The router node processes and analyzes the collected motion data, calculates the energy consumption and motion state of human motion based on the acceleration value of each axis and extracts data characteristics, and transmits the obtained results to the coordinator for real-time display.
AN APPRAISAL ANALYSIS OF NARRATIVE TEXT FROM THE 11TH GRADE ENGLISH TEXTBOOK

This present study explain the appraisal devices especially the attitude types. Appraisal theory is the development of Systemic Functional Linguistics theory in the realm of interpersonal meaning initiated by Martin and White. This study used qualitative research with discourse analysis (DA) techniques. The object of analysis in this study is narrative text of the English textbooks for senior high school students published by the Ministry of Education and Culture. This study employed a document analysis as a technique for collecting data. The findings showed that there were 65 systems of attitude which consist of 19 positive affect, 25 negative affect, 8 positive judgment, 8 negative judgment, 1 positive appreciation, and 4 negative appreciation.
The development of m-LKPD project-based assisted by smart apps creator 3 to stimulate science process skills

This research intends to develop m-LKPD (mobile lembar kerja peserta didik) based on a project assisted by smart apps creator 3 in the diffraction of light lesson validly and practically. m-LKPD develops to stimulate scientific processes skill as multimedia to support online learning in this Covid-19 pandemic. This research uses Design and Development Research (DDR), consisting of 4 stages: analysis, design, development, and evaluation. The instruments used in this research are interview guidelines, validation test questionnaire, practicality test questionnaire, teacher perception questionnaire, student response questionnaire, and assessment rubrics of scientific processes skill. The result of the validation test results 3,62, with a very valid category. The outcome of the practicality test is 90% as a percentage average, with a very practical as a category. The result of stimulating scientific processes skills through a small team test gets 88% as an average percentage with stimulated as a category. This shows that m-LKPD develops very valid and efficient ways to stimulate processes skills. Besides, m-LKPD creates the potential to be implemented in online learning and face-to-face learning, proven by students’ responses gaining 94% with very good as a category and teachers’ perceptions that achieve 96% average percentage with very good as a category. Therefore, the developed m-LKPD can be used as multimedia to support online learning in this Covid-19 pandemic.
Learning From and Learning with Technology

Technology supports exploration of key concepts and their interdisciplinary applications, providing students a wider vista in which to recognize those concepts and a broader canvas on which to convey their understandings. This chapter explores ways educators of the gifted and talented can use technology to enhance their students' gifts and talents, both by building their knowledge of and skill with technological tools and by using such tools to promote learning and creative productivity across all content areas. Today's students live in a world in which being plugged into technology is a way of life. Almost every part of their lives has some root in the technological revolution. Some studies have shown that a blended approach to professional development for technology integration is most successful, combining professional development workshops, curricular examples, and a peer-support system. The chapter describes tools and resources that can help educators purposefully incorporate technology to enhance student learning and encourage talent development.
Smart Body Posture Guidance System

Body posture plays a crucial role in the measurement of body vitals. Accurate measurement of blood pressure, body weight, etc. requires correct posture which greatly affects the result. To achieve the correct posture the person has to depend on the lab technician, who helps the person to sit in the correct pose, to place the cuff in the right place to measure blood pressure, to stand in the correct pose to measure body weight, etc. To eliminate the need for lab technician, we develop a system, to capture image and recognize the pose using a camera, image processing using pose estimation algorithm, guide the person to correct if the recognized pose is incorrect using a User Interface developed using pyqt. The pose estimation algorithm works by estimating and connecting the body keypoints. Hence we are creating a prototype model for real time pose estimation and guidance.
Product Configuration in Answer Set Programming

This is a preliminary work on configuration knowledge representation which serves as a foundation for building interactive configuration systems in Answer Set Programming (ASP).The major concepts of the product configuration problem are identified and discussed with a bike configuration example.A fact format is developed for expressing product knowledge that is domainspecific and can be mapped from other systems.Finally, a domain-independent ASP encoding is provided that represents the concepts in the configuration problem.
Domain-Agnostic Context-Aware Framework for Natural Language Interface in a Task-Based Environment

Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only.In this paper, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by creating a customizable mapping between the language constructs and the system primitives. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation and machine learning models for language processing tasks.
COULD THE EMA ARTIFICIAL NEURAL NETWORK GRADE BLASTOCYSTS AS AN EMBRYOLOGIST?

To validate the accuracy of the EMA automated embryo grading system compared to senior embryologists. Time-lapse sequences of 10,000 individual blastocysts were graded by 20 senior embryologists according to ASEBIR criteria (based on the morphology of the inner cell mass and trophectoderm, which defined 3 categories of viable blastocyst from A to C), and subsequently used to train artificial neural network (ANN) model. This ANN model was used to develop the EMA automated multi-class blastocyst grading method. The ANN EMA model was trained to distinguish between grades of blastocysts; A, B and C. After training on 8,000 sequences, the model was tested on 2,000 sequences, to determine accuracy, sensitivity and specificity values. The area under the receiver operating characteristic (ROC) curves (AUC) was used to calculate performance evaluations. The accuracy, sensitivity and specificity values for the automated EMA ANN blastocyst grading model (A, B, C) compared to the ASEBIR grading by the embryologists were 0.92, 0.94 and 0.95, respectively. The EMA ANN model had an extremely high accuracy for distinguishing between the three grades of blastocysts, with a corresponding AUCs of 0.99 for A versus B; 0.97 for B versus C; and 0.98 for A versus C. The EMA ANN model showed remarkable consistency with embryologist grading. Moreover, the model was highly accurate in differentiating between different grades of blastocysts. This study demonstrates that the EMA ANN model can serve as an efficient and objective tool for blastocyst evaluation.
SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification

Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances. Improvement upon the x-vector has been an active research area, and enormous neural networks have been elaborately designed based on the x-vector, eg, extended TDNN (E-TDNN), factorized TDNN (F-TDNN), and densely connected TDNN (D-TDNN). In this work, we try to identify the optimal architectures from a TDNN based search space employing neural architecture search (NAS), named SpeechNAS. Leveraging the recent advances in the speaker recognition, such as high-order statistics pooling, multi-branch mechanism, D-TDNN and angular additive margin softmax (AAM) loss with a minimum hyper-spherical energy (MHE), SpeechNAS automatically discovers five network architectures, from SpeechNAS-1 to SpeechNAS-5, of various numbers of parameters and GFLOPs on the large-scale text-independent speaker recognition dataset VoxCeleb1. Our derived best neural network achieves an equal error rate (EER) of 1.02% on the standard test set of VoxCeleb1, which surpasses previous TDNN based state-of-the-art approaches by a large margin. Code and trained weights are in https://github.com/wentaozhu/speechnas.git
Let the CAT out of the bag: Contrastive Attributed explanations for Text

Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse.In this paper, we propose a method Contrastive Attributed explanations for Text (CAT) which provides contrastive explanations for natural language text data with a novel twist as we build and exploit attribute classifiers leading to more semantically meaningful explanations.To ensure that our contrastive generated text has the fewest possible edits with respect to the original text, while also being fluent and close to a human generated contrastive, we resort to a minimal perturbation approach regularized using a BERT language model and attribute classifiers trained on available attributes.We show through qualitative examples and a user study that our method not only conveys more insight because of these attributes, but also leads to better quality (contrastive) text.Quantitatively, we show that our method outperforms other state-ofthe-art methods across four data sets on four benchmark metrics.
Multi-resolution Graph Neural Networks for PDE Approximation

Deep Learning algorithms have recently received a growing interest to learn from examples of existing solutions and some accurate approximations of the solution of complex physical problems, in particular relying on Graph Neural Networks applied on a mesh of the domain at hand. On the other hand, state-of-the-art deep approaches of image processing use different resolutions to better handle the different scales of the images, thanks to pooling and up-scaling operations. But no such operators can be easily defined for Graph Convolutional Neural Networks (GCNN). This paper defines such operators based on meshes of different granularities. Multi-resolution GCNNs can then be defined. We propose the MGMI approach, as well as an architecture based on the famed U-Net. These approaches are experimentally validated on a diffusion problem, compared with projected CNN approach and the experiments witness their efficiency, as well as their generalization capabilities.
LIMIT REPRESENTATIONS OF IMPRECISE RANDOM FIELDS

In order to describe spatially uncertain parameters by random fields, the underlying autocorrelation structure in engineering structures is usually not known..The idea of imprecise random fields is to acknowledge this lack of knowledge by adding epistemic uncertainties.Within this contribution the influence of the correlation length is studied.In particular, it is shown that there exist bounds that limit the case of having no idea at all.This "absolutely no idea p-box" is defined by white noise and the random variable corresponding to the mean value and standard deviation of the imprecise random field.By this, the limits of having "absolutely no idea" can be described without the need of Karhunen-Loève expansion and random field propagation.Then, at least for linear problems, every response in between can be estimated by linear interpolation without any need for sampling.82
New Center Folding Strategy Encoding for Reversible Data Hiding in Dual Stego Images

A new center folding encoding technique used for dual stego image is proposed. This proposed encoding technique aims to minimize distortion caused due to the changes made in the pixel while embedding. As the modification in pixel intensity reduces, the distortion of these stego images also reduces. This outline, first, combines the data bits and converts to decimal to obtain the decimal intensity. The index is generated from the decimal intensity using the intensity reduction process. The index is further converted to an encoded index, and it is hidden on the cover image. During the extraction stage, the encoded index is extracted after the dual stego images and it is renewed to index. The decimal intensity is reconstructed from the index using the intensity reconstruction process. Finally, the decimal intensity values are transformed to binary to estimate the secret image. The performance evaluation of the proposed dual stego reversible data hiding (RDH) was done using the factors such as peak signal-to-noise ratio (PSNR) and embedding rate.
Student-Initiated Action Advising via Advice Novelty

Action advising is a budget-constrained knowledge exchange mechanism between teacher-student peers that can help tackle exploration and sample inefficiency problems in deep reinforcement learning (RL). Most recently, student-initiated techniques that utilise state novelty and uncertainty estimations have obtained promising results. However, the approaches built on these estimations have some potential weaknesses. First, they assume that the convergence of the student's RL model implies less need for advice. This can be misleading in scenarios with teacher absence early on where the student is likely to learn suboptimally by itself; yet also ignore the teacher's assistance later. Secondly, the delays between encountering states and having them to take effect in the RL model updates in presence of the experience replay dynamics cause a feedback lag in what the student actually needs advice for. We propose a student-initiated algorithm that alleviates these by employing Random Network Distillation (RND) to measure the novelty of a piece of advice. Furthermore, we perform RND updates only for the advised states to ensure that the student's own learning does not impair its ability to leverage the teacher. Experiments in GridWorld and MinAtar show that our approach performs on par with the state-of-the-art and demonstrates significant advantages in the scenarios where the existing methods are prone to fail.
Cluster-based mention typing for named entity disambiguation

An entity mention in text such as "Washington" may correspond to many different named entities such as the city "Washington D.C." or the newspaper "Washington Post." The goal of named entity disambiguation is to identify the mentioned named entity correctly among all possible candidates. If the type (e.g. location or person) of a mentioned entity can be correctly predicted from the context, it may increase the chance of selecting the right candidate by assigning low probability to the unlikely ones. This paper proposes cluster-based mention typing for named entity disambiguation. The aim of mention typing is to predict the type of a given mention based on its context. Generally, manually curated type taxonomies such as Wikipedia categories are used. We introduce cluster-based mention typing, where named entities are clustered based on their contextual similarities and the cluster ids are assigned as types. The hyperlinked mentions and their context in Wikipedia are used in order to obtain these cluster-based types. Then, mention typing models are trained on these mentions, which have been labeled with their cluster-based types through distant supervision. At the named entity disambiguation phase, first the cluster-based types of a given mention are predicted and then, these types are used as features in a ranking model to select the best entity among the candidates. We represent entities at multiple contextual levels and obtain different clusterings (and thus typing models) based on each level. As each clustering breaks the entity space differently, mention typing based on each clustering discriminates the mention differently. When predictions from all typing models are used together, our system achieves better or comparable results based on randomization tests with respect to the state-of-the-art levels on four defacto test sets.
A Proposal for Multiple Instance Learning Framework Application to Protect Data Access Rights under General Data Protection Regulation (GDPR)

Multiple Instance Learning (MIL) framework (supervised learning) is a sub-framework of machine learning, it is a powerful tool that could be used in demonstrating General Data Protection Regulation (GDPR) compliance to regulators or any interested independent reviewer. Whilst there are a number of examples of various topics under GDPR, demonstrating the ‘right-to-be-forgotten’ has been chosen as a topic to explore how MIL could be used as a framework. Email communication is chosen as a use-case to be studied given its ubiquity and potential to advertently or inadvertently collect personally identifiable information (PII) which may lead to privacy issues. We concluded this section by proposing how the MIL framework could be enhanced through combination with visualisation to enhance visual analysis of data identified as GDPR relevant.
Single Page Reactive Application using Angular Spring MVC and Rest API

Single Page Application (SPA) in web technology has the potential to compete with the seamless performance of app. Single Page Applications are highly reactive as they load the JavaScript bundles just once and then according to the required DOM changes the elements are downloaded. So, it is very fast and consumes less resources. In this paper Angular and Spring MVC are discussed to provide a methodology for creating SPA.