Reviewerly/exHarmony huggingface dataset:
Compression of XML and JSON API Responses

Web services are the de-facto standard for implementing web-based systems today, and comprise message-based interactions involving XML and JSON documents. These formats can be quite verbose, especially XML, and therefore compression of such documents can potentially improve the communication efficiency and performance of service-oriented systems. In this paper, we review the various formulations of XML compression and propose a novel technique for the same, wherein large section of the documents are substituted by numerical representations. The approach is simple yet effective, especially on small documents that constitute the bulk of communicated content in web-based systems. We conduct experiments with several datasets and demonstrate that the proposed technique for XML compression outperforms the existing state-of-the-art techniques.
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language.

The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing knowledge interaction between the two tasks. Our MTL model’s architecture consists of a Bidirectional Encoder Representation from Transformers (BERT) model, a multi-task attention interaction module, and two task classifiers. The overall obtained results show that our proposed model outperforms its single-task and MTL counterparts on both sarcasm and sentiment detection subtasks.
Post-earthquake warning for Vrancea seismic source based on code spectral acceleration exceedance

Post-earthquake crisis management is a key capability for a country to be able to recover after a major seismic event. Instrumental seismic data transmitted and processed in a very short time can contribute to better management of the emergency and can give insights on the earthquake`s impact on a specific area. Romania is a country with a high seismic hazard, mostly due to the Vrancea intermediate-depth earthquakes. The elastic acceleration response spectrum of a seismic motion provides important information on the level of maximum acceleration the buildings were subjected to. Based on new data analysis and knowledge advancements, the acceleration elastic response spectrum for horizontal ground components recommended by the Romanian seismic codes has been evolving over the last six decades. This study aims to propose a framework for post-earthquake warning based on code spectrum exceedances. A comprehensive background analysis was undertaken using strong motion data from previous earthquakes corroborated with observational damage, to prove the method`s applicability. Moreover, a case-study for two densely populated Romanian cities (Focsani and Bucharest) is presented, using data from a 5.5 MW earthquake (October 28, 2018) and considering the evolution of the three generations of code-based spectral levels for the two cities. Data recorded in free-field and in buildings were analyzed and has confirmed that no structural damage occurred within the two cities. For future strong seismic events, this tool can provide useful information on the effect of the earthquake on structures in the most exposed areas.
Unsupervised Occlusion-Aware Stereo Matching With Directed Disparity Smoothing

When handling occlusion in unsupervised stereo matching, existing methods tend to neglect the supportive role of occlusion and to perform inappropriate disparity smoothing around the occlusion. To address these problems, we propose an occlusion-aware stereo network that contains a specific module to first estimate occlusion as an additional depth cue. In the occlusion inference module, a pixel is classified with a three-category label based on whether an area is occluded by an object on the left, occluded by an object on the right, or unoccluded. After the occluders are detected, we introduce a directed disparity smoothing loss that allows valid disparity estimates to be propagated to fill the occluded region, while ambiguous matches in the occluded region do not affect other regions. Disparity and occlusion are trained alternately in an unsupervised manner with detached backpropagation to enable the directed smoothness. Experiments show that our method achieves 3-pixel threshold error rates of 6.51% and 5.69% on the KITTI 2015 and KITTI 2012 validation sets, state-of-the-art results among unsupervised learning networks at the time of submission.
A Shared Ontology for Logistics Information Management in the Construction Industry

Logistics management plays an essential role in supporting the primary activities in manufacturing industries.Similarly, in the construction industry, logistics operations are a crucial part that directly influence the construction operations.Construction operations management requires various stakeholders to collaborate through effective communication and prompt information sharing.However, logistics management in construction is often challenged by insufficient information management, lack of formalized information standards and poor information interoperability among heterogeneous systems.Semantic Web technologies advance information management support and improve information interoperability.In this research, we present a domain-level ontology as a common information reference for standardizing and integrating construction logistics information, and finally to improve the efficiency and transparency of logistics information management.The proposed ontology provides information interoperability between logistics management and construction workflow management.The ontology was evaluated by automatic consistency checking and answering the competency questions (CQs) via SPARQL queries.Furthermore, we used actual schedule and material delivery data of a construction project to evaluate the proposed ontology to see if it could support the material kitting logistics practice.We provide a valid ontology that is able support the logistics information management for the construction.The research is limited to providing a single example application of the ontology.Future research should focus on extending the ontology for different specific solutions to yield standardized information management for construction operations.
Parameters' Confidence Intervals Evaluation for Heterogeneous Strain Field Specimen Designs by Using Digital Image Correlation

This paper aims to compare different heterogeneous test designs from the perspective of the confidence interval quantification of inversely identified parameters, where the influence of a DIC optical system systematic and random error are taken into account. Because the errors in optical measurement can arise from many reasons and sources, our methodology relies on the system's errors determined from initial sets of pictures acquired at the load-free state for hundreds of specimens (over 850 tests over the past three years). In this way, a prior probability distribution of systematic and random error, arisen from the system initial settings and testing procedures are determined. Further, by conducting an inverse identification procedure of linear orthotropic elastic material parameters, the influence of the error distributions is studied for different types of heterogeneous specimens. The presented methodology determines the DIC bias and random error propagation through the inverse identification procedure to individual parameters. For each specimen design, confidence intervals of identified material parameters were determined. The results show the appropriateness of a specimen design for the identification of particular material parameters.
Combining Deep Learning and Knowledge-driven Reasoning for Chest X-Ray Findings Detection.

The application of deep learning algorithms in medical imaging analysis is a steadily growing research area. While deep learning methods are thriving in the medical domain, they seldom utilize the rich knowledge associated with connected radiology reports. The knowledge derived from these reports can be utilized to enhance the performance of deep learning models. In this work, we used a comprehensive chest X-ray findings vocabulary to automatically annotate an extensive collection of chest X-rays using associated radiology reports and a vocabulary-driven concept annotation algorithm. The annotated X-rays are used to train a deep neural network classifier for finding detection. Finally, we developed a knowledge-driven reasoning algorithm that leverages knowledge learned from X-ray reports to improve upon the deep learning module's performance on finding detection. Our results suggest that combining deep learning and knowledge from radiology reports in a hybrid framework can significantly enhance overall performance in the CXR finding detection.
Sentence Generation for Indian Sign Language Using NLP

Nowadays the interaction among deaf and mute people and normal people is difficult, because normal people scuffle to understand the sense of the gestures. The deaf and dumb people find problem in sentence formation and grammatical correction. To alleviate the issues faced by these people, an automatic sign language sentence generation approach is propounded. In this project, Natural Language Processing (NLP) based methods are used. NLP is a powerful tool for translation in the human language and also responsible for the formation of meaningful sentences from sign language symbols which is also understood by the normal person. In this system, both conventional NLP methods and Deep learning NLP methods are used for sentence generation. The efficiency of both the methods are compared. The generated sentence is displayed in the android application as an output. This system aims to connect the gap in the interaction among the deaf and dumb people and the normal people.
Improvement of deep cross-modal retrieval by generating real-valued representation

The cross-modal retrieval (CMR) has attracted much attention in the research community due to flexible and comprehensive retrieval. The core challenge in CMR is the heterogeneity gap, which is generated due to different statistical properties of multi-modal data. The most common solution to bridge the heterogeneity gap is representation learning, which generates a common sub-space. In this work, we propose a framework called “Improvement of Deep Cross-Modal Retrieval (IDCMR)”, which generates real-valued representation. The IDCMR preserves both intra-modal and inter-modal similarity. The intra-modal similarity is preserved by selecting an appropriate training model for text and image modality. The inter-modal similarity is preserved by reducing modality-invariance loss. The mean average precision (mAP) is used as a performance measure in the CMR system. Extensive experiments are performed, and results show that IDCMR outperforms over state-of-the-art methods by a margin 4% and 2% relatively with mAP in the text to image and image to text retrieval tasks on MSCOCO and Xmedia dataset respectively.
Regularization Matters: A Nonparametric Perspective on Overparametrized  Neural Network

Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data. However, the generalization guarantee may not hold for noisy data. From a nonparametric perspective, this paper studies how well overparametrized neural networks can recover the true target function in the presence of random noises. We establish a lower bound on the $L_2$ estimation error with respect to the GD iterations, which is away from zero without a delicate scheme of early stopping. In turn, through a comprehensive analysis of $\ell_2$-regularized GD trajectories, we prove that for overparametrized one-hidden-layer ReLU neural network with the $\ell_2$ regularization: (1) the output is close to that of the kernel ridge regression with the corresponding neural tangent kernel; (2) minimax {optimal} rate of $L_2$ estimation error can be achieved. Numerical experiments confirm our theory and further demonstrate that the $\ell_2$ regularization approach improves the training robustness and works for a wider range of neural networks.
Conditional Independence for Iterated Belief Revision

Conditional independence is a crucial concept for efficient probabilistic reasoning. For symbolic and qualitative reasoning, however, it has played only a minor role. Recently, Lynn, Delgrande, and Peppas have considered conditional independence in terms of syntactic multivalued dependencies. In this paper, we define conditional independence as a semantic property of epistemic states and present axioms for iterated belief revision operators to obey conditional independence in general. We show that c-revisions for ranking functions satisfy these axioms, and exploit the relevance of these results for iterated belief revision in general.
ICHTML 2021 – A journal of the pandemic year

This is an introductory text to a collection of papers from the ICHTML 2021: Second International Conference on History, Theory and Methodology of Learning, which held in Kryvyi Rih State Pedagogical University, Kryvyi Rih, Ukraine, on the May 12-14, 2021. It consists of short introduction, conference review and some observations about the event and its future.
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT

We propose a numerical accountant for evaluating the tight $(\varepsilon,\delta)$-privacy loss for algorithms with discrete one dimensional output. The method is based on the privacy loss distribution formalism and it uses the recently introduced fast Fourier transform based accounting technique. We carry out an error analysis of the method in terms of moment bounds of the privacy loss distribution which leads to rigorous lower and upper bounds for the true $(\varepsilon,\delta)$-values. As an application, we present a novel approach to accurate privacy accounting of the subsampled Gaussian mechanism. This completes the previously proposed analysis by giving strict lower and upper bounds for the privacy parameters. We demonstrate the performance of the accountant on the binomial mechanism and show that our approach allows decreasing noise variance up to 75 percent at equal privacy compared to existing bounds in the literature. We also illustrate how to compute tight bounds for the exponential mechanism applied to counting queries.
An Appraisal of Digital Documents as Evidence in Islamic Law

Major shifts in the science and technology landscape have made the collection and analysis of digital evidence an increasingly important tool for resolving of crimes as well as the preparation of court cases. Digital documents are known as electronic documents and it can be used as a means of proof in court. However, this kind of method is still need to be discussed in detail regarding its admissibility in the Syariah court. This is because there is no specific provision on the use of it. Thus, this article focuses on the concept of digital documents as evidence in Syariah courts. It also explores the Islamic legal perspective on digital documents as a means of proof by considering the Quran, hadiths, legal maxims, and acts. This article also will focus on the admissibility of digital documents as evidence in Syariah courts. The methodology of this study is a qualitative approach. The data collected is divided into two types: primary sources, such as the Quran, hadiths, legal maxims, and acts; and secondary sources, such as journal articles, books, cases reported in law journals, and kitab fiqh. The data obtained is analyzed using document analysis methods. The results show that there is a high need to accept digital documents as evidence, especially in Syariah courts. This is because, the scientific and technological developments have led to this need to make use of digital documents. Studies show that the use of digital documents as evidence serves to reinforce other evidence presented in court.&#x0D; &#x0D; Received: 7 September 2020 / Accepted: 2 February 2021 / Published: 10 May 2021
A Literature Review of Foreign Studies on the Impact of CALL on Second Language Acquisition from 2015

With the rapid development of economy and technology, more and more fields are combined with electronic intelligence and artificial intelligence, and language teaching is no exception. As a research hot issue, computer-assisted language learning (CALL) has attracted more and more people&amp;#39;s attention. Based on computer-aided technology, teachers can not only provide students with rich language input, but also improve classroom efficiency. However, there are still some problems in this technology. This paper reviews some empirical studies on the impact of computer-assisted language teaching from 2015 and reveals the relevant progress and shortcomings of foreign researches in terms of six aspects from the perspective of language teaching, in order to provide some enlightenment for future research.
The Application of the Edge-cloud Computing System Based on Reinforcement Learning in Large-scale Mask Recognition

As the epidemic of the COVID-19 continues, many venues need to be installed with recognition equipment at the entrance to recognize whether people wear masks. However, it is not economical to install multiple high-performance devices with a great recognition model only for mask recognition, and not fast to use the combination of camera shooting devices and cloud computing. Therefore, this paper put forward an edge-cloud computing system, whose purpose is to use the poor computing resources of the edge server to share the computing pressure of the cloud server. In other words, the recognition tasks are assigned not only to the cloud device but also to the edge devices with poor computing power and a simple recognition model. The scheduling optimization problem is solved by reinforcement learning method. In the case of insufficient and imperfect data sets, this system still shows high recognition accuracy and fast response speed.
Optimization of Multidimensional Clinical Information System for Schizophrenia

Schizophrenia is a serious mental disease whose pathogenesis has not been fully elucidated. Its clinical evaluation and diagnosis still highly depend on the clinical experience of doctors. It is of great scientific value and clinical significance to study the inducing factors and neuropathological mechanism of schizophrenia. Based on the four research problems of schizophrenia, this paper analyzes the data types that need to be stored in clinical trials and scientific research, including basic information, case report data, neuropsychological and cognitive function evaluation, magnetic resonance data, electroencephalogram (EEG) data, and intestinal flora data. Through the demand analysis of the system, including the data management part, data analysis part, the functional demand of the system management part, and the overall nonfunctional demand of the system, the overall architecture design, functional module division, and database table structure design of the system are completed. Adopting Browser/Server (B/S) architecture and front-end and back-end separation mode and applying Java and Python programming language, based on spring framework and database, a multidimensional information management system for schizophrenia is designed and implemented, which includes four modules: data analysis, data management, system management, and security control. In addition, each functional module of the system is designed and implemented in detail, and the software operation flow of each module is illustrated with the sequence diagram. Finally, the multidimensional data of schizophrenia collected in our laboratory were used for system test to verify whether the system can meet the needs of clinical big data management of schizophrenia and the multidimensional information management system of schizophrenia can meet the needs of clinical big data management. The information management system helps schizophrenic researchers to carry out data management and data analysis. It also has advantages that are easy to use, safe, and efficient and has strong scalability in data management, data analysis, and scalability. It reflects the innovation of the system and provides a good platform for the management, research, and analysis of clinical big data of schizophrenia.
Quantum based encryption approach for secure images

Data security and privacy act vital tasks in our daily lives. Traditional cryptosystems may be hacked amidst the growth of quantum resources. Consequently, we need new cryptosystems its construction is based on quantum concepts. In this work, we proposed a novel image cryptosystem using quantum walks. We employ a diversity of tools for experimental assessment of the presented cryptosystem including correlation analysis, histogram analysis, Shannon entropy analysis, UACI and NPCR analyses, Key sensitivity analysis, and occlusion analysis. These metrics show the advantages of our cryptosystem over some robust state-of-art cryptosystems.
Comparative Analysis of ICT Accessibility and Usability of Korean Students Based on PISA 2015 and 2018 Data

The OECD surveys the questionnaire on the background of ICT every three years since 2003. In this study, we compare and analyze the changes in ICT accessibility and ICT usability of Korean students in 2015 and 2018 using the ICT background data of OECD PISA. ICT accessibility refers to the degree of access to ICT equipment at school or at home. There are 10 items of accessibility surveys at schools used by PISA, and 11 items at home. ICT usability refers to how much ICT equipment is used at or outside of school for learning or non-learning purposes. There are ten items for surveying usability at school, and twenty-four items for surveying usability outside of school. In the analysis of this study, the arithmetic mean of the items is used. As a result, Korean students' accessibility at school improved from 25.48% in 2015 to 40.40% in 2018, and from the lowest group in 2015 to below-average group in 2018. In terms of students' usability at school, we analyzed the percentage of students who use the ICT equipment 'almost every day' and the students who use it 'everyday'. Both 2015 and 2018 are among the lowest. Accessibility at home is higher in 2018 than in 2015, and belongs to the average group of OECD countries in 2018. ICT Usability for learning purposes outside of school is the lowest group in both 2015 and 2018. ICT Usability outside of school for non-learning purposes is in the average group of OECD countries. As a result of this study, we can see that Korea's digital literacy education is weak compared to other OECD countries. Prior to education about the Fourth Industrial Revolution, investment in digital literacy education is needed.
Putting AI on Diet: TinyML and Efficient Deep Learning

Summary form only given, as follows. A complete record of the panel discussion was not made available for publication as part of the conference proceedings. Deep leaning algorithms are resource-demanding. This talk will present techniques to reduce the computation recourse, human resource, and data resource for deep learning. First, I’ll present MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the light-weight inference engine (TinyEngine), enabling ImageNet-scale inference on tiny MCUs that has only 256KB SRAM and 1MB Flash. Next, I’ll present Once-for-All Network a push-the-button solution that can automatically design specialized neural network architectures that best fit many different hardware platforms, which is the winning solution for the 3rd/4th/5th IEEE Low Power Computer Vision Challenge. Finally, I’ll present DiffAugment for data-efficient GAN training, which greatly reduces the training data and can generate high-fidelity images using only training 100 images. I’ll conclude by discussing the applications of these efficient techniques on AIoT, automotive and data center applications.