Reviewerly/exHarmony huggingface dataset:
Multilingual Complementation of Causality Property on Wikidata Based on GPT-3

We aim to develop an agent for understanding the distribution of public opinions and preferences; thus, this agent needs to have causality knowledge. When discussing social issues, Wikidata, a knowledge base, can provide linked data and play an important role in analyzing discussion content. However, there is a lack of causal content on Wikidata, and some content has errors. Therefore, it is necessary to automatically extract knowledge from news and add it to Wikidata. We propose a method of automatically determining causality in text and directly extracting effect from news. We collected news and used GPT-3 to infer whether a news article is causally related to the entity and further infer the effect of this entity. We also attempted to increase the reliability of extracted causality knowledge by dealing with multilingual texts.
Defect detection by laser shear interferometry based on the thermal response of materials with vibratory loading

Laser shear interferometry or shearography is one of the optical and non-contact methods for measuring surface displacements. Shearography can be used in non-destructive testing by applying a kind of excitation such as partial vacuum, mechanical and thermal loadings. Parameters of the excitation techniques significantly affect the test results. In many cases, these loading methods deform the whole surface reducing the detection capability of the technique. This paper considers the vibratory loading method as an alternative to the previously mentioned standard methods. In this method, local heat is generated at the defected area due to the internal and external damping of the piece. A simulation has been conducted using COMSOL software to estimate the generated local heat due to the vibratory loading. The local temperature at the defect area is measured, and the simulation results are validated with an experimental infrared thermography test. The experimental shearography setup is adjusted, and the defects are detected. By comparing the results with those of typical thermal loadings, it has been shown that the fringe patterns obtained by the presented method have higher quality.
The co‐movements of faith‐based cryptocurrencies in periods of pandemics

Abstract In the recent coronavirus pandemic, several researchers have focused on the drivers of cryptocurrency behavior. In particular, this study provides insights into what can drive Islamic cryptocurrency markets and how do they react during the COVID‐19 pandemic. We explore the cryptocurrency volatility and the connectedness between the Islamic, conventional, and COVID‐19 confirmed cases and deaths using the wavelet approaches. The preliminary results show that faith‐based cryptocurrencies have reduced risk exposure than their conventional counterparts, in the long run, making them more appealing for investment, particularly for investors seeking low‐risk and Shariah‐compliant assets. Furthermore, the empirical results indicate that both Islamic and conventional cryptocurrencies are more sensitive to the death toll than the newly confirmed cases. We also observe significant positive co‐movements between Bitcoin and Islamic cryptocurrencies. Besides, Bitcoin exhibits a substantial response during various time scales while compared with Islamic cryptocurrencies. This study contributes to the literature by investigating the sensitivity and the vulnerability of a new category of cryptocurrencies backed by tangible assets to pandemic shocks. To the extent of the author's knowledge, this study is the first attempt that examines the co‐movement between Islamic and conventional cryptocurrencies using the wavelet approach. A viable, ethical, and alternative investment route for faith‐based investors can be provided by the Shariah‐Compliant cryptocurrencies as they are risk‐reduced and less sensitive to the pandemic than conventional benchmarks. Besides, this study creates opportunities in portfolio diversification for investors.
Machine Learning Based Protocol Classification in Unlicensed 5 GHz Bands

To monitor RF activity and efficiently coordinate channel access for heterogeneous wireless systems over a shared channel, it is important to be able to classify observed transmissions accurately without decoding them. In this paper, we propose novel recurrent neural network (RNN) architectures for signal classification, considering as a use case on interleaving-based spectrum sharing model for Wi-Fi, LTE-LAA, and 5G-NRU over the unlicensed 5 GHz bands. Several classifiers are presented, which take raw in-phase/quadrature (I/Q) samples as input. First, we examine Simple RNNs, Long Short-term Memory (LSTM) networks, and Gated Recurrent Units (GRU) networks for protocol classification. These RNNs are used to capture the unique features in observed signals. To further improve the classification accuracy, we extend the RNN designs into a bidirectional structure, allowing an RNN cell to learn the temporal dependence in the waveform in both forward and backward directions. Bidirectionality can effectively increase the amount of information and the context available to the neural network. We then extend our designs to multi-layer RNNs, which allow the classifier to capture temporal correlations at multiple time scales, hence increasing the network's computational capacity. Finally, we propose further enhancements to reduce the over-fitting problem in RNN training, including regularization, recurrent weight constraints, and rate halving. Our simulation results show that the multi-layer and bidirectional designs can effectively improve the accuracy of the RNN-based RF signal classifier. Combining the two features, an RNN structure can achieve more than 92% accuracy in our protocol classification problem.
Fuzzy Cognitive Map to Classify Plantar Foot Alterations

The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can handle the uncertainties present in biological data using different Artificial Intelligence techniques to obtain accurate and easy-to-use systems. This paper presents the application of a Fuzzy Cognitive Map (FCM) formulation, for knowledge extraction in the classification of human plantar foot alterations, with a relatively small and transparent model. The FCM is trained using the Bacterial Search Optimization Algorithm (BFOA). One hundred and twenty-five volunteer subjects (aged 20-68 years) participated in the study. Classification of the foot into normal (n=31), flat (n=32), cavus type III (n=31) and cavus type IV (n=31) to train the system was performed by specialized physicians. The test was performed by walking on a FreeMed platform. The proposed method shows an accuracy rate of about 89% in the classification task and allows extracting information related to the important factors that the system considers to make a decision.
The Pragmatic Distribution and Semantic Explanation of Evidential Prepositions

Compared with other prepositions, the study on evidential prepositions, such as “jü (据)”, “an (按)”, “ping (凭)”, is relatively weak. Many meanings and usages of evidential prepositions are not clear, which leads to a misunderstanding that there are few differences among them and that they can explain mutually. At the beginning of this paper, we analyze the definition of 800 words in Modern Chinese which is “据 (jü): 依据 (yijü)” based on the Corpus Online, and find there are obvious differences between “据 (jü)” and “依据 (yijü)”. So, it is not suitable to use the latter to explain the former. Then, we investigate more evidential prepositions, and find there are also significant differences among them. Based on the research above, we divide the evidential prepositions into three categories: epistemic evidence, deonitic evidence and dynamic evidence, and explain the commonness and individuality of evidential prepositions by conceptual space and semantic map.
The Evaluation of a Mid-Air Based Natural Interactive Installation on Cultural Heritage Learning

To improve the efficiency of learning ancient art and cultural knowledge for users through cultural heritage, the interactive installation of Chinese traditional painting Ruihe Tu is designed by applying mid-air motion capture technology. Several factors which may affect learning efficiency are considered during the design, such as aesthetics, motivation, participation, usability and so on. In this study, these variables were evaluated by empirical and quantitative research methods. The results show that those users who experience the mid-air based natural interactive installation Ruihe Tu have a stronger interest in learning the original work. And users have a better performance in terms of engagement and exploration desire.
Integrating Machine Learning with Augmented Reality for Accessible Assistive Technologies

Augmented Reality (AR) is a technology which enhances physical environments by superimposing digital data on top of a real-world view. AR has multiple applications and use cases, bringing digital data into the physical world enabling experiences such as training staff on complicated machinery without the risks that come with such activities. Numerous other uses have been developed including for entertainment, with AR games and cultural experiences now emerging. Recently, AR has been used for developing assistive technologies, with applications across a range of disabilities. To achieve the high-quality interactions expected by users, there has been increasing integration of AR with Machine Learning (ML) algorithms. This integration offers additional functionality to increase the scope of AR applications. In this paper we present the potential of integrating AR with ML algorithms for developing assistive technologies, for the use case of locating objects in the home context.
Study on the Order of Double-Syllable Double Attributives and Selection Restrictions—Take the Structures of a1 + a2 + De + n and a1’ + De + a2’ + n’ as Examples

This paper analyzes differences between Structure A: “a1 + a2 + de + n” and structure B: “a1’ + de + a2’ + n’”, which are extracted from the corpus of People’s Daily from 2019 to 2020. Influential factors and constraints from semantics, syntax, word order, rhythm, etc. are proposed in the paper. Our study shows that semantic similarity of double-syllable double attributive adjectives in structure A is higher than that of structure B. The character de in two structures serves as a “segmentation mark” to maintain stability of structures and cannot be shifted freely. Moreover, we examine that factors influencing word order of double attributives are the result of the combination of semantics, cognition, habit, rhythm, etc. Among them, the dominant factor of structure A is cognition, while that of the structure B is semantics. Besides, we argue that high-frequency nouns of the two structures are mostly abstract nouns and are poor in semantic self-containedness. In addition, words at the nuclear stress location in the two structures mainly end with a falling tone. The paper is further analyzed from the perspective of prosodic structure and highlighting effect. This study provides new insights to account for the combination mechanisms of multiple attributives, as well as contributing to the construction of knowledge database with a novel method.
DICOM-Based Voxel-Supported Blood-Vessel-Avoiding Scalpel Navigation

In this study, we propose a navigation algorithm that directly depicts a safe path for malignant tumors and cerebral thrombi while avoiding obstacles such as blood vessels, directly from DICOM data without using STL. For this purpose, we use a voxel-based algorithm, which we have recently proposed. In the voxel-based algorithm, the organ STL polyhedron is converted into a voxel lattice structure model, which is used to search for a safe path to the malignant tumor while avoiding blood vessels. Each voxel has a density that is the percentage of obstacles in its own space, and this density is used for retrieval, but such a density can also be created from a series of DICOM tomographic images placed in 3D space. Once the voxel lattice structure model is generated, the search can be performed using the voxel-based algorithm. It is possible to adjust which range of values in the DICOM data is considered as an obstacle, and it can be checked on the personal computer (PC) monitor.
Remote at Court

This article targets one of the fundamental changes in the judicial system induced by the severe limitations due to the absence of face-to-face meetings: the application of video conferencing in court sessions, an application with special requirements in this critical domain. A semi-structured literature review that we conducted revealed a lack of human-centered approaches. Potentials and challenges, mainly focused on the needs of judges, were also identified. These challenges were then transformed into requirements for designs of video conferencing systems in the judicial context. We ultimately developed a low-fidelity prototype of a system that incorporates a novel combination of three use-case-specific features: a solution to manage fatigue, a solution to manage user participation, and cognitive aid based on artificial intelligence (AI). The aim of the last feature was to reduce cognitive load while improving the moderation quality of court session leaders. Through a heuristic evaluation by human-computer interaction (HCI) and domain experts, the benefits of the basic design ideas, as well as potential areas for improvement, were identified. This paper presents the first systematic analyses of the potentials and limitations of video conferencing in German court sessions. It brings the enormous challenges of a critical domain in society, as well as human-centered and value-sensitive digitalization and AI adoption, under the spotlight.
FEATURES OF INTERACTIVE MUSEUMS OF SCIENCE: A VIEW THROUGH THE PRISM OF ORGANIZATIONAL AND PEDAGOGICAL IDEAS OF YAKOV PERELMAN

The development of a child's creative potential is an important component of scientific education. Its formation is directly related to curiosity and is best stimulated through play, design, art and speech activities. The formation of the concept of interactive museums of science plays a significant role in the development of students’ creative potential. We considered the organizational and pedagogical features of the House of Interesting Science for Children, created in the 30s of the twentieth century by the Soviet scientific and technical team led by the world-famous popularizer of science Ya. Perelman. The main factors and prerequisites for the success of museums are identified and characterized, such as high scientific level of exhibits, their technical and scientific quality and interactivity, use of heuristic methods, demonstration of unexpected effects in natural phenomena, scaling ideology of science education, dissemination of experience competitions, contests, various events that can be a source of new creative ideas, the formation of quality publications that create conditions for further independent research and stimulate visits to museum spaces. The House of Interesting Science, created under the leadership of Ya. Perelman, can also be considered the first startup with a healthy marketing model. The key approaches to the creation of such innovative spaces of scientific education, which are based on the stimulation of curiosity and independent research activities, are identified. Forming the creative potential of students is one of the tasks of the Small Academy of Sciences of Ukraine, which created the Museum of Science - the first interactive space, which was prepared on the basis of thorough retrospective and comparative studies of relevant experience.
METHOD FOR OIL AND GAS ESTIMATES OF EXPLORATION SITES VIA GEOLOGICAL, PARAMETRIC AND SATELLITE INFORMATION

The article is devoted to the search and exploration of hydrocarbons deposits, which is a crucial and complicated geological task and requires the large bulk of initial data and significant intellectual efforts of geologists. In this regard, the urgent problem is to ease the working conditions of experts and improve the accuracy of forecasting the oil and gas potential of subsoil areas through the use of novel digital methods of heterogeneous data integration and interpretation. The paper proposes a new method to form predictive estimates of the oil and gas content of subsoil areas via combining geological, ground-based parametric, and satellite information. As the mathematical basis of the method, the means of Dempster-Shafer's theory of evidence are used, which makes it possible to combine data from different sources, proceed with incomplete and inaccurate data, etc. Most of the procedures that make up the body of the method are presented in the form of algorithms, which simplifies its technologization. The final information product of the proposed method is a map with the distribution of probabilistic estimates of oil and gas prospects of subsoil areas within a given study area. The developed method is implemented in software and tested at the Vostochno-Rogintsovskoye oil field. The test results indicated its fairly high efficiency.
Content-Aware Generative Model for Multi-item Outfit Recommendation

Recently, deep learning-based recommender systems have received increasing attention of researchers and demonstrate excellent results at solving various tasks in various areas. One of the last growing trends is learning the compatibility of items in a set and predicting the next item or several ones by input ones. Fashion compatibility modeling is one of the areas in which this task is being actively researched. Classical solutions are training on existing sets and are learning to recommend items that have been combined with each other before. This severely limits the number of possible combinations. GAN models proved to be the most effective for decreasing the impact of this problem and generating unseen combinations of items, but they also have several limitations. They use a fixed number of input and output items. However, real outfits contain a variable number of items. Also, they use unimodal or multimodal data to generate only visual features. However, this approach is not guaranteed to save content attributes of items during generation. We propose a multimodal transformer-based GAN with cross-modal attention to simultaneously explore visual features and textual attributes. We also propose to represent a set of items as a sequence of items to allow the model to decide how many items should be in the set. Experimenting on FOTOS dataset at the fill-in-the-blank task is showed that our method outperforms such strong baselines as Bi-LSTM-VSE, MGCM, HFGN, and others. Our model has reached 0.878 accuracy versus 0.724 of Bi-LSTM-VSE, 0.822 of MGCM, 0.826 of HFGN.
A nonlinear outlier detection method in sensor networks based on the coordinate mapping

This paper designs a nonlinear outliers detection method based on the coordinate mapping. Because data in different coordinate systems have specific attributes, the data coordinates in different coordinate systems can be transformed by coordinate mapping. Then the stream data features in a sensor network can be extracted accurately by principal component analysis to improve the detection accuracy of abnormal data points. Clustering of convection data features is implemented to shorten the time of subsequent detection by rapidly classifying data. Finally, the difference point factor is used to detect the nonlinear outliers in the sensor network. Experimental results show that the maximum detection accuracy of this method can reach 97%, the maximum detection time required is only 15 s, and the maximum miss rate of this method is 1.32%, indicating that this method can effectively detect nonlinear anomaly points.
Handwritten Digit Recognition Using Very Deep Convolutional Neural Network

Automated image classification is an essential task of the computer vision field. The tagging of images into a set of predefined groups is referred to as image classification. The implementation of computer vision to automate image classification would be beneficial because manual image evaluation and identification can be time-consuming, particularly when there are many images of different classes. Deep learning approaches are proven to overperform existing machine learning techniques in many fields in recent years, and computer vision is one of the most notable examples. The very deep neural network (VDCNN) is a powerful deep learning model for image classification, and this paper examines it briefly using MNIST handwritten digit dataset. This dataset is used to prove the efficacy of very deep neural networks over other deep learning models. The proposed study aims to comprehend the very deep neural network architecture used to accomplish a handwritten digit recognition task. The feasibility of the proposed model is evaluated using mean accuracy, validation accuracy, and standard deviation. The study results of the very deep neural network model are compared to a convolutional neural network and convolutional neural network with batch normalization. According to the results of the comparison study, very deep neural networks achieve high accuracy of 99.1% for a handwritten dataset. The outcome of the proposed work is used to interpret how well a very deep neural network performs when compared to the other two models of deep neural networks. This proposed architecture may be used to automate the classification of handwritten digits dataset.
Investigating lane-changing moves of vehicles departing from signalized junction

Discretionary Lane-Changing (DLC) possesses different characteristics under various traffic conditions. Therefore, in order to model DLC moves in the correct manner, they need to be approached separately for various conditions. This study has examined the DLC moves of vehicles, which depart from signalized junction. It is observed that a large number of vehicles move to a neighboring lane in order to travel under better traffic conditions, after the traffic lights turn green. There are many parameters, which motivate the vehicles to change lanes at this stage. This study has focused on discovering these parameters. Data was collected through field work for this purpose. Thereinafter, the impact value of each parameter has been discovered using four separate feature selection methods and has been sorted. Fuzzy Cognitive Maps have been developed with the obtained impact values. The models have been validated using the field data and have been compared with each other.
Recursive Programming

chapter Share on Recursive Programming Author: E. W. Dijkstra View Profile Authors Info & Claims Edsger Wybe Dijkstra: His Life,Work, and LegacyJuly 2022 Pages 291–300https://doi.org/10.1145/3544585.3544601Published:13 July 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access