Reviewerly/exHarmony huggingface dataset:
Infrastructure and city ontologies

The creation and use of ontologies has become increasingly relevant for complex systems in recent years. This is because of the growing number of use of cases that rely on real-world integration of disparate systems, the need for semantic congruence across boundaries and the expectations of users for conceptual clarity within evolving domains or systems of interest. These needs are evident in most spheres of research involving complex systems, but they are particularly apparent in infrastructure and cities where traditionally siloed and sectoral approaches have dominated, undermining the potential for integration to solve societal challenges such as net zero, resilience to climate change, equity and affordability. This paper reports on findings of a literature review on infrastructure and city ontologies and puts forward some hypotheses inferred from the literature findings. The hypotheses are discussed with reference to the literature and provide avenues for further research on (a) belief systems that underpin non-top-level ontologies and the potential for interference from them, (b) the need for a small number of top-level ontologies and translation mechanisms between them and (c) clarity on the role of standards and information systems in the adaptability and quality of data sets using ontologies. A gap is also identified in the extent that ontologies can support more complex automated coupling and data transformation when dealing with different scales.
Single-qubit reaped quantum state tomography

Quantum state tomography is the experimental procedure of determining an unknown state. It is not only essential for the verification of resources and processors of quantum information but is also important in its own right with regard to the foundation of quantum mechanics. Standard methods have been elusive for large systems because of the enormous number of observables to be measured and the exponential complexity of data post-processing. Here, we propose a new scheme of quantum state tomography that requires the measurement of only three observables (acting jointly on the system and pointer) regardless of the size of the system. The system is coupled to a "pointer" of single qubit, and the wavefunction of the system is "reaped" onto the pointer upon the measurement of the system. Subsequently, standard two-state tomography on the pointer and classical post-processing are used to reconstruct the quantum state of the system. We also developed an efficient and scalable iterative maximum likelihood algorithm to estimate states from statistically incomplete data.
Correction to: Local spatial log-Gaussian Cox processes for seismic data

In this article, Figs. 1a, 2a-c, 9 and 11 should have appeared as shown below. The original article has been corrected.
Learning Outcomes in HMD-VR: a Literature Review

While the educational technology has developed to the point that Extended Reality (XR), including immersive Virtual Reality (VR) can be used in education, the learning outcomes of these technologies in the large scale is still quite unknown. This literature review aims to take a comprehensive look to the field of immersive VR in to find out where the learning outcomes of HMD-VR stands out, and how they compare to other technologies and methods. The main result of this paper is that while HMD-based Virtual Reality Learning Environments (VRLEs) may not be superior compared to other technologies such as desktop-based VR environments regarding direct learning outcomes, a clear indication towards increased learning motivation and engagement can be seen.
Superradiant emission spectra of a two-qubit system in circuit quantum electrodynamics

In this paper, we study the spontaneous emission spectra and the emission decay rates of a simplest atom system that exhibits sub- and superradiant properties: a system which consists of two artificial atoms (superconducting qubits) embedded in a one-dimensional open waveguide. The calculations are based on the method of the transition operator which was firstly introduced by R. H. Lehmberg to theoretically describe the spontaneous emission of two-level atoms in a free space. We obtain the explicit expressions for the photon radiation spectra and the emission decay rates for different initial two-qubit configurations with one and two excitations. For every initial state we calculate the radiation spectra and the emission decay rates for different effective distances between qubits. In every case, a decay rate is compared with a single qubit decay to show the superradiant or subradiant nature of a two-qubit decay with a given initial state.
Pedestrian Detection for Vehicle-borne Image Based on Two-level YOLOv3

In this paper, I mainly focus on real-time pedestrian detection, which is a critical part of robot vision and autonomous driving cars. In recent, convolutional neural networks and deep learning have received so many reputations due to their enormous ability and wide use. For example, image classification, understanding climate, analyzing documents, advertising, etc. Object detection is different from image classification, which is a relatively new area where are waiting for more researchers to dedicate themselves. In the first part, I introduce the appliance of real-time object detection, and in the second part, I introduce some related works of real-time object detection, In the third part, where my work is, I indicate the method to increase the performance of pedestrian detection. I delete the y1 layer of the output of YOLOv3 and magnify the upsampling rate. At the last, I regulate the anchors to achieve more accuracy and better performance. Finally, I explain my experiments and give my research conclusion.
Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation

Sarcasm is a pervading linguistic phenomenon and highly challenging to explain due to its subjectivity, lack of context and deeply-felt opinion. In the multimodal setup, sarcasm is conveyed through the incongruity between the text and visual entities. Although recent approaches deal with sarcasm as a classification problem, it is unclear why an online post is identified as sarcastic. Without proper explanation, end users may not be able to perceive the underlying sense of irony. In this paper, we propose a novel problem -- Multimodal Sarcasm Explanation (MuSE) -- given a multimodal sarcastic post containing an image and a caption, we aim to generate a natural language explanation to reveal the intended sarcasm. To this end, we develop MORE, a new dataset with explanation of 3510 sarcastic multimodal posts. Each explanation is a natural language (English) sentence describing the hidden irony. We benchmark MORE by employing a multimodal Transformer-based architecture. It incorporates a cross-modal attention in the Transformer's encoder which attends to the distinguishing features between the two modalities. Subsequently, a BART-based auto-regressive decoder is used as the generator. Empirical results demonstrate convincing results over various baselines (adopted for MuSE) across five evaluation metrics. We also conduct human evaluation on predictions and obtain Fleiss' Kappa score of 0.4 as a fair agreement among 25 evaluators.
Entailment Relation Aware Paraphrase Generation

We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e.g. equivalent, forward entailing, or reverse entailing) with respect to a given input. We propose a reinforcement learning-based weakly-supervised paraphrasing system, ERAP, that can be trained using existing paraphrase and natural language inference (NLI) corpora without an explicit task-specific corpus. A combination of automated and human evaluations show that ERAP generates paraphrases conforming to the specified entailment relation and are of good quality as compared to the baselines and uncontrolled paraphrasing systems. Using ERAP for augmenting training data for downstream textual entailment task improves performance over an uncontrolled paraphrasing system, and introduces fewer training artifacts, indicating the benefit of explicit control during paraphrasing.
Differential Assessment of Black-Box AI Agents

Much of the research on learning symbolic models of AI agents focuses on agents with stationary models. This assumption fails to hold in settings where the agent's capabilities may change as a result of learning, adaptation, or other post-deployment modifications. Efficient assessment of agents in such settings is critical for learning the true capabilities of an AI system and for ensuring its safe usage. In this work, we propose a novel approach to differentially assess black-box AI agents that have drifted from their previously known models. As a starting point, we consider the fully observable and deterministic setting. We leverage sparse observations of the drifted agent's current behavior and knowledge of its initial model to generate an active querying policy that selectively queries the agent and computes an updated model of its functionality. Empirical evaluation shows that our approach is much more efficient than re-learning the agent model from scratch. We also show that the cost of differential assessment using our method is proportional to the amount of drift in the agent's functionality.
Predicting Above-Sentence Discourse Structure Using Distant Supervision from Topic Segmentation

RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets. To overcome the data sparsity issue, distantly supervised approaches from tasks like sentiment analysis and summarization have been recently proposed. Here, we extend this line of research by exploiting distant supervision from topic segmentation, which can arguably provide a strong and oftentimes complementary signal for high-level discourse structures. Experiments on two human-annotated discourse treebanks confirm that our proposal generates accurate tree structures on sentence and paragraph level, consistently outperforming previous distantly supervised models on the sentence-to-document task and occasionally reaching even higher scores on the sentence-to-paragraph level.
Studi Respon Siswa Terhadap Pengembangan Multimedia Interaktif Berbasis Chemo-Entrepreneurship Berbentuk Aplikasi Android

Chemistry subject is a science that focuses students 1on understanding the principles, theories, concepts and laws of chemistry and their application in real life. Chemo-Entrepreneurship4(CEP) is an approach that links chemistry with real objects. With this approach, students8are expected to be able to learn how to process a material into a valuable and useful product and be able to foster an entrepreneurial spirit. This study aims to develop interactive multimedia based on Chemo-Entrepreneurship in the form of an android application. The design of this study used the Lee &amp; Owens model and involved 10 students of SMA Negeri 10 Tanjung6Jabung Timur. The research instrument used in this study was a questionnaire with analytical techniques in the form of qualitative data analysis and quantitative data analysis. The results of this study include: 1) interactive multimedia developed is declared feasible by media experts and material experts, with a percentage score of 87.2% and 88.57% respectively, 2) interactive multimedia is declared feasible based on the teacher's assessment with a percentage a score of 91.4%, 3) get a percentage of student responses of 94% with a very good category. Thus, it can be concluded that the student response is very positive to the chemo-entrepreneurship-based interactive multimedia that has been developed.
Towards Ultra Low Bit-Rate Digital Human Character Communication via Compact 3D Face Descriptors

Recently, there has been a tremendous demand for high-efficiency face video communications, coinciding with the popularization of the digital human character in numerous applications. This paper demonstrates a new communication paradigm of 3D human digital characters in ultra low-bit-rate application scenarios. The paradigm is grounded on the mild assumption of the consistency and persistence of human ap-pearance, such that only the compact features that determine the pose and expression of the 3D character need to be transmitted. The proposed is also expected to benefit virtual-physical world interaction in Metaverse.
Enhancement of Digital Literacy of Students with Disabilities

Digitalization has affected all spheres of life, including education.Modern didactics and methods of digital education are designed to solve problems rеlаtеd tо the use of dіgіtаl tесhnоlоgіеs, tооls and resources іn the education, uрbrіngіng and dеvеlорmеnt of children with dіsаbіlіtіеs.Тhе аіm of the study wаs tо dеvеlор a model of the components of dіgіtаl lіtеrасу and in practice to assess the level of development of digital literacy of studеnts with hеаrіng іmраіrmеnt.The process of forming and improving the components of digital literacy of hearing impaired students was carried out on the basis of the scientific substantiation of the content of the special course in addition to computer science.The study was conducted in Kazakhstan between the years 2019 and 2021 among 127 students of special (correctional) schools.We have proposed the author's two-component model of digital literacy of hearing impaired students: (1) digital user component and (2) digital correction-intellectual component.In the first component students with hearing impairments will able to know the basic Engineering training.For example, installing, starting, removing and updating software; installing the operating system; increasing the speed of a computer; working with drivers, peripheral devices and etc.On the basis of the collected data, we have noticed the following: digital user component of digital literacy is an important for hearing impaired students because the respondеnts possess the lowest lеvеl of knowledge in the аrеа of engineering training and have the ability to create digital content.This led to create the second component as Digital correction-intellectual component.According to this component, students will be able to improve their cognitive, logical, critical, creative, systems thinking, memory, attention, speech, communication skills through learning adopted additional course.Thus,
The Professionalization of the Hacker Industry

Society is inextricably dependent on the Internet and other globally interconnected infrastructures used in the provisioning of information services. The growth of information technology (IT) and information systems (IS) over the past decades has created an unprecedented demand for access to information. The implication of wireless mobility are great, and the commercial possibilities of new and innovative wireless flexibility are just beginning to be realized through the emergence of the Internet of Things (IoT). This article takes a look the history of hacking and professionalization of the hacker industry. As the hacker industry becomes more fully professionalized, it is becoming much more adaptive and flexible, making it harder for intelligence and law enforcement to confront. Furthermore, the hacker industry is blurring the distinction between motivated crime and traditional computer security threats - including the disruption of critical infrastructures or the penetration of networks.
Do Country-level Institutions Influence YouTubers as Digital Entrepreneurs?

Digital platforms have brought opportunities for individuals to undertake different initiatives and become entrepreneurs. For example, we observe YouTubers as digital entrepreneurs capable of creating content while making money. Their activity is inherent to creativity and digital skills. Yet, little is known about YouTubers’ response to the external environment, hence, we analyze the effect of both the entrepreneurial and pro-market contexts on this type of entrepreneurship. To this end, we use information from three different sources: statistics from the Social Blade and Heritage Foundation websites, as well as the world development indicators (WDI) from The World Bank. We estimated nested models through OLS to observe that the entrepreneurial environment is positively associated with YouTubers’ activity, whereas the pro-market environment negatively affects the number of YouTubers’ views and subscribers. Our findings advance knowledge about the role of institutions in a different form of entrepreneurial activity and the potential substitution between traditional entrepreneurship and digital entrepreneurship. Thus, theoretical and policy implications are discussed.
Evaluation of Semantic Answer Similarity Metrics

There are several issues with the existing general machine translation or natural language generation evaluation metrics, and question-answering (QA) systems are indifferent in that context. To build robust QA systems, we need the ability to have equivalently robust evaluation systems to verify whether model predictions to questions are similar to ground-truth annotations. The ability to compare similarity based on semantics as opposed to pure string overlap is important to compare models fairly and to indicate more realistic acceptance criteria in real-life applications. We build upon the first to our knowledge paper that uses transformer-based model metrics to assess semantic answer similarity and achieve higher correlations to human judgement in the case of no lexical overlap. We propose cross-encoder augmented bi-encoder and BERTScore models for semantic answer similarity, trained on a new dataset consisting of name pairs of US-American public figures. As far as we are concerned, we provide the first dataset of co-referent name string pairs along with their similarities, which can be used for training.
DGQAN

Community question answering (CQA) becomes increasingly prevalent in recent years, providing platforms for users with various backgrounds to obtain information and share knowledge. However, the redundancy and lengthiness issues of crowd-sourced answers limit the performance of answer selection, thus leading to difficulties in reading or even misunderstandings for community users. To solve these problems, we propose the dual graph question-answer attention networks (DGQAN) for answer selection task. Aims to fully understand the internal structure of the question and the corresponding answer, firstly, we construct a dual-CQA concept graph with graph convolution networks using the original question and answer text. Specifically, our CQA concept graph exploits the correlation information between question-answer pairs to construct two sub-graphs (QSubject-Answer and QBody-Answer), respectively. Further, a novel dual attention mechanism is incorporated to model both the internal and external semantic relations among questions and answers. More importantly, we conduct experiment to investigate the impact of each layer in the BERT model. The experimental results show that DGQAN model achieves state-of-the-art performance on three datasets (SemEval-2015, 2016, and 2017), outperforming all the baseline models.
Application of voice recognition interaction and big data internet of things in urban fire fighting

With the continuous development of science and technology, especially computer technology, people need a more convenient and natural way to communicate with the machine. In this paper, based on human-computer speech recognition interaction system, using big data Internet of things as technical support, the contribution of intelligent social service robot to urban fire protection is studied. In order to meet people’s need, a large number of high-rise, super high-rise and underground buildings continue to increase, which not only provides us with convenience, but also makes fire safety a hot concern of the whole society. Fire fighting plays an increasingly important role in the life of urban residents. In order to greatly reduce the lack of fire safety monitoring ability, this paper uses speech recognition technology to design a city fire safety management service platform based on big data Internet of things.
Forensic Accounting Growth: Bibliometric Analysis Perspective

Fraud risk' and fraud have different ontologically.Fraud risk is something that be governed, while fraud risk is a disruptive event that potentially affect the operation.Forensic accounting on the other hand, is socio-professional career who combining accounting and law expertise.The ultimate objective is to safeguard the legal subject property.Forensic accounting may influence legal, economic, political and social.By mixing expertise, forensic accounting has attracted a lot of attention especially from academician.Therefore, understanding the issues and widening research collaboration is necessarily important to further understanding this topic.Our study objective is to examine the global research trends in forensic accounting area based on publication outputs, co-authorship, affiliated countries, and co-occurrences of author keywords by using bibliometric analysis and VOSviewer.To determine the research trend, a total of 107 articles from 1992 to 2020 were extracted from Scopus database and being analyse.The result has found that the number of publications regarding forensic accounting increased significantly in the last 6 years.The result also suggested that, most of the publication are contributed by United States and Canada.This study also determines the type of forensic accounting such as litigation support, expert witness and fraud investigation.
Fake Product Review Monitoring and Removal For Genuine Online Shopping

Abstract: In today’s world reviews play a vital role in online shopping and sales of the product because people consider all the pros and cons of any product before they buy it as there are many different options for the same product as well as different manufacturers for the same type of product with the difference in quality. While buying the product the reviews are directly related to the sales of the product and thus it is necessary for online websites to spot fake reviews and remove them. So, Fake Review Detection is used to spot any fraudulent going on because it’s not possible for them to verify every product and sale manually so this application comes into the picture that tries to detect fake reviews given by the customers and remove them for genuine online shopping.