Reviewerly/exHarmony huggingface dataset:
Natural Language Processing Applications in Business

Increasing dependency of humans on computer-assisted systems has led to researchers focusing on more effective communication technologies that can mimic human interactions as well as understand natural languages and human emotions. The problem of information overload in every sector, including business, healthcare, education etc., has led to an increase in unstructured data, which is considered not to be useful. Natural language processing (NLP) in this context is one of the effective technologies that can be integrated with advanced technologies, such as machine learning, artificial intelligence, and deep learning, to improve the process of understanding and processing the natural language. This can enable human-computer interaction in a more effective way as well as allow for the analysis and formatting of large volumes of unusable and unstructured data/text in various industries. This will deliver meaningful outcomes that can enhance decision-making and thus improve operational efficiency. Focusing on this aspect, this chapter explains the concept of NLP, its history and development, while also reviewing its application in various industrial sectors.
Review of computer-based methods for archaeological ceramic sherds reconstruction

&lt;p class="VARAbstract"&gt;Potteries are the most numerous finds found in archaeological excavations; they are often used to get information about the history, economy, and art of a site. Archaeologists rarely find complete vases but, generally, damaged and in fragments, often mixed with other pottery groups. By using the traditional manual method, the analysis and reconstruction of sherds are performed by a skilled operator. Reviewed papers provided evidence that the traditional method is not reproducible, not repeatable, time-consuming and its results have great uncertainties. To overcome the aforementioned limits, in the last years, researchers have made efforts to develop computer-based methods for archaeological ceramic sherds analysis, aimed at their reconstruction. To contribute to this field of study, in this paper, a comprehensive analysis of the most important available publications until the end of 2019 is presented. This study, focused on pottery fragments only, is performed by collecting papers in English by the Scopus database using the following keywords: “computer methods in archaeology", "3D archaeology", "3D reconstruction", "automatic feature recognition and reconstruction", "restoration of pottery shape relics”. The list is completed by additional references found through the reading of selected papers. The 53 selected papers are divided into three periods of time. According to a detailed review of the performed studies, the key elements of each analyzed method are listed based on data acquisition tools, features extracted, classification processes, and matching techniques. Finally, to overcome the actual gaps some recommendations for future researches are proposed.&lt;/p&gt;&lt;p&gt;Highlights:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;&lt;p&gt;The traditional manual method for reassembling sherds is very time-consuming and costly; it also requires a great deal effort from skilled archaeologists in repetitive and routine activities.&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Computer-based methods for archaeological ceramic sherds reconstruction can help archaeologists in the above-mentioned repetitive and routine activities.&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;In this paper, the state-of-the-art computer-based methods for archaeological ceramic sherds reconstruction are reviewed, and some recommendations for future researches are proposed.&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;
An Investigation of Virtual Reality Technology Adoption in the Construction Industry

While Virtual Reality (VR) technology has experienced a recent growth in interest and offers immense potential in a number of domains, there is still insufficient information on the acceptance and adoption of this technology among individual users.The purpose of this chapter is to examine the acceptance and adaptation of people using VR technology in the construction industry and to identify factors that prevent VR technology from being adopted more widely in the construction industry.Semi-structured interviews were conducted to approach this research problem among 15 students and academic staff members at two universities.The results of this research indicate that VR technology is acceptable to people who work in the construction industry.However, there are barriers to further adoption of VR technology, namely high VR hardware and software requirements, low affordability, and low accessibility.This research also proposes several resolutions to these barriers, including preparing facilities by construction industries and universities, providing software and hardware requirements for VR technologies, and decreasing the price of VR devices.The results of this research are of immense value to suppliers and companies affiliated with this technology.Further research is required to demonstrate the functionality of VR technology in the construction industry.
Mirror Descent Policy Optimization

Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning (RL). However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice. Inspired by this, we propose an efficient RL algorithm, called {\em mirror descent policy optimization} (MDPO). MDPO iteratively updates the policy by {\em approximately} solving a trust-region problem, whose objective function consists of two terms: a linearization of the standard RL objective and a proximity term that restricts two consecutive policies to be close to each other. Each update performs this approximation by taking multiple gradient steps on this objective function. We derive {\em on-policy} and {\em off-policy} variants of MDPO, while emphasizing important design choices motivated by the existing theory of MD in RL. We highlight the connections between on-policy MDPO and two popular trust-region RL algorithms: TRPO and PPO, and show that explicitly enforcing the trust-region constraint is in fact {\em not} a necessity for high performance gains in TRPO. We then show how the popular soft actor-critic (SAC) algorithm can be derived by slight modifications of off-policy MDPO. Overall, MDPO is derived from the MD principles, offers a unified approach to viewing a number of popular RL algorithms, and performs better than or on-par with TRPO, PPO, and SAC in a number of continuous control tasks. Code is available at \url{https://github.com/manantomar/Mirror-Descent-Policy-Optimization}.
node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching

Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations. However, traditional user stitching approaches, such as grouping or blocking, require pairwise comparisons between a massive number of user activities, thus posing both computational and storage challenges. Recent works, which are often application-specific, heuristically seek to reduce the amount of comparisons, but they suffer from low precision and recall. To solve the problem in an application-independent way, we take a heterogeneous network-based approach in which users (nodes) interact with content (e.g., sessions, websites), and may have attributes (e.g., location). We propose node2bits, an efficient framework that represents multi-dimensional features of node contexts with binary hashcodes. node2bits leverages feature-based temporal walks to encapsulate short- and long-term interactions between nodes in heterogeneous web networks, and adopts SimHash to obtain compact, binary representations and avoid the quadratic complexity for similarity search. Extensive experiments on large-scale real networks show that node2bits outperforms traditional techniques and existing works that generate real-valued embeddings by up to $$5.16\%$$ in F1 score on user stitching, while taking only up to $$1.56\%$$ as much storage.
An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users’ diversified interest and its categories by analysing their implicit user profile. Depending on users’ interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users’ diversified interest. The performance of the proposed approach for predicting overall users’ interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users’ interest categories.
VQuAnDa: Verbalization QUestion ANswering DAtaset

Question Answering (QA) systems over Knowledge Graphs (KGs) aim to provide a concise answer to a given natural language question. Despite the significant evolution of QA methods over the past years, there are still some core lines of work, which are lagging behind. This is especially true for methods and datasets that support the verbalization of answers in natural language. Specifically, to the best of our knowledge, none of the existing Question Answering datasets provide any verbalization data for the question-query pairs. Hence, we aim to fill this gap by providing the first QA dataset VQuAnDa that includes the verbalization of each answer. We base VQuAnDa on a commonly used large-scale QA dataset – LC-QuAD, in order to support compatibility and continuity of previous work. We complement the dataset with baseline scores for measuring future training and evaluation work, by using a set of standard sequence to sequence models and sharing the results of the experiments. This resource empowers researchers to train and evaluate a variety of models to generate answer verbalizations.
Embedding a VR Game Studio in a Sedentary Workplace

Many people, especially those in sedentary occupations, fail to achieve the recommended levels of physical activity. Virtual reality (VR) games have the potential to overcome this because they are fun and also can be physically demanding. This paper explores whether a VR game studio can help workers in sedentary jobs to get valuable levels of exercise. We studied how 11 participants used our VR game studio in a sedentary workplace over 8-weeks and their perceptions of the experience. We analysed the physical exertion in the VR game studio, comparing this to their step counts from a smartwatch. All participants achieved valuable levels of physical activity and mood benefits. Importantly, for 6 participants, only with the VR game studio did they meet recommended activity levels. Our key contributions are insights about the use of a workplace VR game studio and its health benefits.
Implementation of Multifactor Authentication Using Optimistic Fair Exchange

In this work, two participants fairly send or receive things through the aid of intermediate who is only engaged if required. An up-to-date accepted necessity is with the aim of involving third party in the trade should be transparent, to save from privacy and evade terrible publicity. Together, a dishonest intermediate will negotiate the fairness of the trade and so the intermediate should be liable in case of any behavioural changes. Optimistic fair exchange (OFE) is one of the classical protocols to assure fairness of indulgence for a party. This exchange can be done by means of an arbitrator. The most important aspect of OFE is to describe security models so as to capture real-time attacks and design schemes secure in practical models. Signaller is confirmed with the data to send the correct person to address that to make sure it is. Since then the SMS has gone through to achieve that can hold the path to the right to see which if any is the fact that to ensure there is. After the signature is created by creating it to be able to well take care of it and then to the right of the person to pay off that is sure to make it. In this project, to avoid the third party attack (hacking) and unauthorized person access the particular important data. The data should be transmitted in sender to receiver that receiver only have a correct data without any data losses, protect a data security in cyber-attacks. A signature may be digitally and OFE design for the exchanged item have trade-off between transparency and accountability.
All India Grievance Redressal App

Citizens of the India face civic problems in their day-to-day lives. They resort to the one of many ways provided by the government to file their complaints. The grievance registration systems have evolved in many ways with the advancement in technologies to simplify the task. This paper presents the architecture of a Grievance redresser Application where the civilians can address any kind of complain which they are facing. The main focus of the project is about the pothole related complains. One of the most difficult task for the government officials to estimate the total time and material required to fill the pothole of widely spaced roads, this is one of the major problem faced by government authorities which leads to delaying in repairing the pothole and increased the cost to fill a particular pothole. This Application will give easy access to people to put their complaints towards the government.
JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment

Cross-lingual knowledge alignment is the cornerstone in building a comprehensive knowledge graph (KG), which can benefit various knowledge-driven applications. As the structures of KGs are usually sparse, attributes of entities may play an important role in aligning the entities. However, the heterogeneity of the attributes across KGs prevents from accurately embedding and comparing entities. To deal with the issue, we propose to model the interactions between attributes, instead of globally embedding an entity with all the attributes. We further propose a joint framework to merge the alignments inferred from the attributes and the structures. Experimental results show that the proposed model outperforms the state-of-art baselines by up to 38.48% HitRatio@1. The results also demonstrate that our model can infer the alignments between attributes, relationships and values, in addition to entities.
How to Teach Pedagogy Courses Online at University in COVID-19 Pandemic: Search for Answers

The worldwide spread of COVID-19 pandemic poses new challenges to higher education, in general, and to pedagogical education in particular. The transition to distance education can minimize the impact of the pandemic on the teacher training system. The purpose of the article is to share the experience of the complex use of Moodle and Prometheus online platforms, Zoom webinar software, mobile learning technologies and various web servers for teachers in the course of online teaching of pedagogical disciplines to future teachers. Features of their application were identified and didactic effect was determined. The results of the study indicate their flexibility, multidimensionality and ability to provide interactive learning. It has been found that an important condition for the effectiveness of distance learning in quarantine is the provision of educational and information support for teachers and students. Most of the students positively accepted the proposed methodology, and the teachers noted an increase in students’ motivation for learning and their cognitive activity. The results of the study indicate the appropriateness of using the selected platforms and tools to study pedagogical subjects online.
Learning synthetic aperture radar image despeckling without clean data

Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and make interpretation more difficult. Existing SAR image despeckling convolutional neural networks require quantities of noisy–clean image pairs. However, obtaining clean SAR images is very difficult. Because continuous convolution and pooling operations result in losing many informational details while extracting the deep features of the SAR image, the quality of recovered clean images becomes worse. Therefore, we propose a despeckling network called multiscale dilated residual U-Net (MDRU-Net). The MDRU-Net can be trained directly using noisy–noisy image pairs without clean data. To protect more SAR image details, we design five multiscale dilated convolution modules that extract and fuse multiscale features. Considering that the deep and shallow features are very distinct in fusion, we design different dilation residual skip connections, which make features at the same level have the same convolution operations. Afterward, we present an effective L_hybrid loss function that can effectively improve the network stability and suppress artifacts in the predicted clean SAR image. Compared with the state-of-the-art despeckling algorithms, the proposed MDRU-Net achieves a significant improvement in several key metrics.
Policy Iteration for Linear Quadratic Games With Stochastic Parameters

Robustness is a key challenge in the integration of learning and control. In machine learning and robotics, two common approaches to promote robustness are adversarial training and domain randomization. Both of these approaches have analogs in control theory: adversarial training relates to H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control and dynamic game theory, while domain randomization relates to theory for systems with stochastic model parameters. We propose a stochastic dynamic game framework that integrates both of these complementary approaches to modeling uncertainty and promoting robustness. We describe policy iteration algorithms in both model-based and model-free settings to compute equilibrium strategies and value functions. We present numerical experiments that illustrate their effectiveness and the value of combining uncertainty representations in our integrated framework. We also provide an open-source implementation of the algorithms to facilitate their wider use.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for document ranking. While remarkably effective, the ranking models based on these LMs increase computational cost by orders of magnitude over prior approaches, particularly as they must feed each query-document pair through a massive neural network to compute a single relevance score. To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity. By delaying and yet retaining this fine-granular interaction, ColBERT can leverage the expressiveness of deep LMs while simultaneously gaining the ability to pre-compute document representations offline, considerably speeding up query processing. Beyond reducing the cost of re-ranking the documents retrieved by a traditional model, ColBERT's pruning-friendly interaction mechanism enables leveraging vector-similarity indexes for end-to-end retrieval directly from a large document collection. We extensively evaluate ColBERT using two recent passage search datasets. Results show that ColBERT's effectiveness is competitive with existing BERT-based models (and outperforms every non-BERT baseline), while executing two orders-of-magnitude faster and requiring four orders-of-magnitude fewer FLOPs per query.
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension

Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve the performance on low resource languages.However, the transfer effectiveness on the multilingual Machine Reading Comprehension (MRC) task is substantially poorer than that for sentence classification tasks, mainly due to the requirement of MRC to detect the word level answer boundary.In this paper, we propose two auxiliary tasks to introduce additional phrase boundary supervision in the fine-tuning stage:(1) a mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; and (2) a language-agnostic knowledge masking task by leveraging knowledge phrases mined from the Web.Extensive experiments on two cross-lingual MRC datasets show the effectiveness of our proposed approach.
Attention-based Multi-level Feature Fusion for Named Entity Recognition

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.