Reviewerly/exHarmony huggingface dataset:
Extending the Measurement Angle of a Gaze Estimation Method Using an Eye Model Expressed by a Revolution about the Optical Axis of the Eye

An eye model expressed by a revolution about the optical axis of the eye is one of the most accurate models for use in a 3D gaze estimation method. The measurement range of the previous gaze estimation method that uses two cameras based on the eye model is limited by the larger of the two angles between the gaze and the optical axes of two cameras. The previous method cannot calculate the gaze when exceeding a certain limit of the rotation angle of the eye. In this paper, we show the characteristics of reflections on the surface of the eye from two light sources, when the eye rotates. Then, we propose a method that extends the rotation angle of the eye for a 3D gaze estimation based on this model. The proposed method uses reflections that were not used in the previous method. We developed an experimental gaze tracking system for a wide projector screen and experimentally validated the proposed method with 20 participants. The result shows that the proposed method can measure the gaze of more number of people with increased accuracy compared with the previous method.
5 Years into Augmented Reality Technology in Education: Research Trends, Bibliometric Study and its Application to Enhance Visualization Skills

Augmented Reality technology allows users to interact with virtual objects integrated into the real world and real-time. Augmented Reality technology is considered a potential tool to promote the teaching and learning process. This study aims to identify the trends of publications related to Augmented Reality (AR) in education, bibliometric review of the studies as well as its application on enhancing students’ visualization skills in engineering education field. Thus, this study reviewed trends and bibliometric studies focusing on Augmented Reality (AR) in education for the past five years based on the Web of Science (WOS) database, and provide experimental results on the application of augmented reality technology to enhance visualization skills among engineering students. The result from this study proves that augmented reality shows an upward trends in education, as well as the experimental findings indicates positive results in enhancing visualization skills which is one of the reason why augmented reality is frequently used in education field as teaching and learning tools. This study will also provide researchers and educators with knowledge of the research trends of augmented reality in education. This paper will then highlight the most influential authors and countries in this research area as a reference for future researcher
Application of an Improved Association Rule Algorithm in Rural Development Assessment in China

Abstract Association rules based on big data are derived from shopping basket analysis, which can deeply explore the association between things, and have been widely used in many fields. The traditional algorithm of association rules has several shortcomings, a new improved algorithm of association rules was proposed in this paper, which combines the precise advantages of Bayesian classifier with the full-scale characteristics of big data technology, and was applied to the rural development assessment process. After 2020, China’s rural industrial structure will undergo great changes, and environmental protection to drive economic development is an important part of it. The new association rules can be better adapted to the needs of the assessment. Through comparison, it can be seen that the confidence results obtained by the new association rules are more accurate than Pearson correlation coefficient. Finally, the association rules among CPI, per capita consumption expenditure and per capita disposable income are analysed, and suggestions for rural development have also been made.
CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures

CLAIRE (Mang & Biros, 2019) is a computational framework for Constrained LArge deformation diffeomorphic Image REgistration (Mang et al., 2019). It supports highly-optimized, parallel computational kernels for (multi-node) CPU (Gholami et al., 2017; Mang et al., 2019; Mang & Biros, 2016) and (multi-node multi-)GPU architectures (Brunn et al., 2020, 2021). CLAIRE uses MPI for distributed-memory parallelism and can be scaled up to thousands of cores (Mang et al., 2019; Mang & Biros, 2016) and GPU devices (Brunn et al., 2020). The multi-GPU implementation uses device direct communication. The computational kernels are interpolation for semi-Lagrangian time integration, and a mixture of high-order finite difference operators and Fast-Fourier-Transforms (FFTs) for differentiation. CLAIRE uses a Newton-Krylov solver for numerical optimization (Mang & Biros, 2015, 2017). It features various schemes for regularization of the control problem (Mang & Biros, 2016) and different similarity measures. CLAIRE implements different preconditioners for the reduced space Hessian (Brunn et al., 2020; Mang et al., 2019) to optimize computational throughput and enable fast convergence. It uses PETSc (Balay et al., n.d.) for scalable and efficient linear algebra operations and solvers and TAO (Balay et al., n.d.; Munson et al., 2015) for numerical optimization. CLAIRE can be downloaded at https://github.com/andreasmang/claire.
MAINTENANCE OF FACILITIES AND INFRASTRUCTURE IN SCHOOL

The aim of this study is to discover and obtain information about the upkeep of educational facilities and infrastructure at Vocational High School 2 Cikarang Barat. The analysis method used is descriptive qualitative. The study took place at Vocational High School 2 Cikarang Barat from July 2020 to December 2020. The school principal, deputy headmaster in charge of facilities and infrastructure, maintenance support staff, and students of Vocational High School 2 Cikarang Barat served as research informants. Interviews, observation, and reporting studies are all used to collect data. After that, the data is analyzed by reducing it, presenting it, and drawing conclusions. The triangulation of sources, techniques, and hypotheses is a validity technique. The study's findings indicate that: (1) Vocational High School 2 Cikarang Barat maintenance preparation starts with a work meeting, and then maintenance is divided into two categories: normal and routine maintenance. A one-year budget is generated after deciding maintenance tasks based on their categories. Following that, representatives from the facilities and infrastructure sector specifically select the structure of facility and infrastructure maintenance, as well as their respective job desks in each division, which are known to the school principal. The final move is to socialize students at school. (2) School maintenance processes and techniques begin with frequent and routine maintenance. Routine maintenance includes the upkeep of classrooms, teacher's offices, and special rooms such as libraries, computer laboratories, and science labs. The school also performs routine maintenance on air conditioners, printers, and photocopiers, as well as computer program upgrades. (3) Obstacles faced by schools in preparing facility and infrastructure repairs, including, among other problems, things that arise unexpectedly and need urgent repair at the facility. The lack of knowledge of users of school facilities and infrastructure, partners with outsiders who are often less sensitive, and cultural differences among individuals in the use of facilities and infrastructure are among the process and technological obstacles encountered.
Stress testing of the scientific journal

An analysis of the risks confronted by the editorial staff of the scientific Journal of Almaz– Antey Air and Space Defence Corporation was made. It is shown that one of the risks brought the journal into a state of stress test and significant increase in input parameters, which affected the work of the editorial board. The article provides data on reducing the negative impact of a stress test and presents the results of simulation modeling of the impact of a stress test on the editorial board of a scientific journal. These input parameters were the flow of articles to the journal, which exceeded the average values of the process reviewing for a short period of time by ten times. To eliminate the impact of stress testing on the work of the editorial board of the scientific journal, measures were taken to neutralize it in the form of an increase in the number of reviewers and early initiation of articles. In addition to the results on reducing the impact of stress testing, a simulation of an impossible flow of articles that exceeds the average by a hundred times was performed, and the time indicators of its processing are given. The model is based on the Monte Carlo method under the assumption that each reviewer has an average processing time of the article received by him, as well as the law of distribution of this time. The results of the correlation analysis of the simulation results and real data on the processing of the received articles are presented, which allow us to talk about the relationship between real and simulated processes.
Integer Induction in Saturation

Abstract Integers are ubiquitous in programming and therefore also in applications of program analysis and verification. Such applications often require some sort of inductive reasoning. In this paper we analyze the challenge of automating inductive reasoning with integers. We introduce inference rules for integer induction within the saturation framework of first-order theorem proving. We implemented these rules in the theorem prover Vampire and evaluated our work against other state-of-the-art theorem provers. Our results demonstrate the strength of our approach by solving new problems coming from program analysis and mathematical properties of integers.
Task Thesaurus as a Tool for Modeling of User Information Needs

We consider task thesaurus as an element of user model that reflects dynamic aspects of user current work. Such thesaurus is based on domain ontology and contains the subset of it’s concepts that are dealt with user task. Task thesaurus can be generated automatically by analysis of task description or with the help of semantic similarity estimations of ontological concepts. Task thesaurus represents personalized user view on domain and depends on his/her abilities, experience and aims. We describe some examples of task thesaurus usage in intelligent applications for adaptation to user needs.
Propose Vulnerability Metrics to Measure Network Secure using Attack Graph

With the increase in using computer networking, the security risk has also increased.To protect the network from attacks, attack graph has been used to analyze the vulnerabilies of the network.However, properly securing networks requires quantifying the level of security offered by these actions, as you cannot enhance what you cannot measure.Security metrics provide a qualitative and quantitative representation of a system's or network's security level.However, using existing security metrics can lead to misleading results.This work proposed three metrics, which is the Number of Vulnerabilities (NV), Mean Vulnerabilities on Path (MVoP), and the Weakest Path (WP).The experiment of this work used two networks to test the metrics.The results show the effect of these metrics on finding the weaknesses of the network that the attacker may use.
Multi-disease Predictive Analytics: A Clinical Knowledge-aware Approach

Multi-Disease Predictive Analytics (MDPA) models simultaneously predict the risks of multiple diseases in patients and are valuable in early diagnoses. Patients tend to have multiple diseases simultaneously or develop multiple complications over time, and MDPA models can learn and effectively utilize such correlations between diseases. Data from large-scale Electronic Health Records (EHR) can be used through Multi-Label Learning (MLL) methods to develop MDPA models. However, data-driven approaches for MDPA face the challenge of data imbalance, because rare diseases tend to have much less data than common diseases. Insufficient data for rare diseases makes it difficult to leverage correlations with other diseases. These correlations are studied and recorded in biomedical literature but are rarely utilized in predictive analytics. This article presents a novel method called Knowledge-Aware Approach (KAA) that learns clinical correlations from the rapidly growing body of clinical knowledge. KAA can be combined with any data-driven MLL model for MDPA to refine the predictions of the model. Our extensive experiments, on real EHR data, show that the use of KAA improves the predictive performance of commonly used MDPA models, particularly for rare diseases. KAA is also found to be superior to existing general approaches of combining clinical knowledge with data-driven models. Further, a counterfactual analysis shows the efficacy of KAA in improving physicians’ ability to prescribe preventive treatments.
TIMS: A Secure Testing-Machine Information Management System

The testing information refers to the data obtained by the quality inspection center after testing steel, cement and other materials through testing machines and other instruments. The quality inspection center issues a report according to the testing result, which serves as the basis for the batch of materials to be put into the market. At present, the information level of testing-machine information management is relatively low, and there is no safe and effective management mechanism. In order to solve this problem, this paper proposes TIMS, a secure testing-machine information management system, which adopts the separation-of-three-rights management mode and a secure identity-binding interface to improve the management efficiency as well as ensure the security of data and workflow. At the same time, we combine the watermark QR code technology aiming at report anti-counterfeiting. We implement the prototype of TIMS, which realizes the above functions and provides a user-friendly interface for management and statistics.
The 2021 Image Similarity Dataset and Challenge

This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1~million. The benchmark features a variety of image transformations such as automated transformations, hand-crafted image edits and machine-learning based manipulations. This mimics real-life cases appearing in social media, for example for integrity-related problems dealing with misinformation and objectionable content. The strength of the image manipulations, and therefore the difficulty of the benchmark, is calibrated according to the performance of a set of baseline approaches. Both the query and reference set contain a majority of distractor images that do not match, which corresponds to a real-life needle-in-haystack setting, and the evaluation metric reflects that. We expect the DISC21 benchmark to promote image copy detection as an important and challenging computer vision task and refresh the state of the art.
Agents for Automated User Experience Testing

The automation of functional testing in software has allowed developers to continuously check for negative impacts on functionality throughout the iterative phases of development. This is not the case for User eXperience (UX), which has hitherto relied almost exclusively on testing with real users. User testing is a slow endeavour that can become a bottleneck for development of interactive systems. To address this problem, we here propose an agent based approach for automatic UX testing. We develop agents with basic problem solving skills and a core affect model, allowing us to model an artificial affective state as they traverse different levels of a game. Although this research is still at a primordial state, we believe the results here presented make a strong case for the use of intelligent agents endowed with affective computing models for automating UX testing.
Performance Modeling of Bitcoin Blockchain: Mining Mechanism and Transaction-Confirmation Process

Bitcoin is one of popular cryptocurrencies widely used over the world, and its blockchain technology has attracted considerable attention. In Bitcoin system, it has been reported that transactions are prioritized according to transaction fees, and that transactions with high priorities are likely to be confirmed faster than those with low priorities. In this paper, we consider performance modeling of Bitcoin-blockchain system in order to characterize the transaction-confirmation time. We first introduce the Bitcoin system, focusing on proof-of-work, the consensus mechanism of Bitcoin blockchain. Then, we show some queueing models and its analytical results, discussing the implications and insights obtained from the queueing models.
VP-RDF: An RDF Based Framework to Introduce the Viewpoint in the Description of Resources

Abstract The description of resources and their relationships is an essential task on the web. Generally, the web users do not share the same interests and viewpoints. Each user wants that the web provides data and information according to their interests and specialty. The existing query languages, which allow querying data on the web, cannot take into consideration the viewpoint of the user. We propose introducing the viewpoint in the description of the resources. The Resource Description Framework (RDF) represents a common framework to share data and describe resources. In this study, we aim at introducing the notion of the viewpoint in the RDF. Therefore, we propose a View-Point Resource Description Framework (VP-RDF) as an extension of RDF by adding new elements. The existing query languages (e.g., SPARQL) can query the VP-RDF graphs and provide the user with data and information according to their interests and specialty. Therefore, VP-RDF can be useful in intelligent systems on the web.
Research on the application of crawler technology in machine learning

Abstract With the rapid development of the Internet, the world wide web has become the carrier of a large number of information, and the search engine has entered the public life. In order to help search engines capture related web resources, web crawlers emerge as the times require. Web crawler is a program that automatically extracts web pages. Some malicious web crawlers will not only steal the information of the website, but also cause irreparable damage to the website. Along with it, anti-crawler, the enemy of web crawler, comes into our life. This paper studies the use of machine learning for crawler recognition, so as to make a better anti crawler strategy, use machine recognition instead of manual recognition, reduce the workload of manual recognition of crawler, reduce the loss of human and material resources, and improve the recognition rate of crawler, so as to protect the website information and website equipment.
A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems

Automatically determining similar questions and ranking the obtained questions according to their similarities to each input question is a very important task to any community Question Answering system (cQA). Various methods have applied for this task including conventional machine learning methods with feature extraction and some recent studies using deep learning methods. This paper addresses the problem of how to combine advantages of different methods into one unified model. Moreover, deep learning models are usually only effective for large data, while training data sets in cQA problems are often small, so the idea of integrating external knowledge into deep learning models for this cQA problem becomes more important. To this objective, we propose a neural network-based model which combines a Convolutional Neural Network (CNN) with features from other methods so that the deep learning model is enhanced with addtional knowledge sources. In our proposed model, the CNN component will learn the representation of two given questions, then combined additional features through a Multilayer Perceptron (MLP) to measure similarity between the two questions. We tested our proposed model on the SemEval 2016 task-3 data set and obtain better results in comparison with previous studies on the same task.
Taking the Pulse of Financial Activities with Online Graph Processing

Graph processing has been widely adopted in various financial scenarios at Ant Group to detect malicious and prohibited user behaviors. The low latency requirement under big data volume and high throughput raises rigorous challenges for efficient online graph processing. This paper gives a brief introduction of our encountered issues, the current solutions, and some future directions we are exploring.
Didactic model of formation pedagogical universities students’ digital competence

The article is sanctified to the analysis of problem of forming of digital competence for the students of pedagogical universities. On the basis of analysis of psychological-pedagogical researches and practical experience the didactics model of formation pedagogical universities students’ digital competence was worked out. The structural blocks of model, that in co-operation provide her integrity and functioning, are in-process certain. Destination, theoretical-methodological, semantically-judicial, control-diagnostic and result blocks behave to them. Worked out and the approved didactics model is schematic presentation of the corresponding system of work, that characterizes a hierarchy, sequence, constituents, stages and applied facilities, forms, methods and to interdependence between them and executes orientation, executive and correcting functions in organization of study of pedagogical universities of disciplines of informatics students.
Controllable Abstractive Dialogue Summarization with Sketch Supervision

In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control. Our model has two primary components and stages: 1) a two-stage generation strategy that generates a preliminary summary sketch serving as the basis for the final summary. This summary sketch provides a weakly supervised signal in the form of pseudo-labeled interrogative pronoun categories and key phrases extracted using a constituency parser. 2) A simple strategy to control the granularity of the final summary, in that our model can automatically determine or control the number of generated summary sentences for a given dialogue by predicting and highlighting different text spans from the source text. Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score. In addition, we conduct a case study and show competitive human evaluation results and controllability to human-annotated summaries.
Fuzzy inverse logic: part-2. validation and evaluation of the method

In the first part of this study, which consists of two parts, the introduction of the fuzzy inverse logic method, its foundations, computation and process flow and details were given.In this second part, the method was applied on a very simple mathematical problem and on a simple civil engineering design problem in order to investigate and evaluate the validity of the method easily.Fuzzy inverse logic computations have been carried out on these two simple problems for different dimension, sensitivity and acceptable error values.By comparing the obtained results with the desired outputs and mathematical results, the effective computation ability of the method was tried to be revealed.As a result, it is understood that the method developed in this study has many promising aspects in terms of theoretical computations and practical applications and can be used effectively in many scientific fields.