Reviewerly/exHarmony huggingface dataset:
A unified ontology-based data integration approach for the internet of things

&lt;span lang="EN-US"&gt;Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.&lt;/span&gt;
GWIT: A Witness Validator for Java based on GraalVM (Competition Contribution)

Abstract GWIT is a validator for violation witnesses produced by Java verifiers in the SV-COMP software verification competition. GWIT weaves assumptions documented in a witness into the source code of a program, effectively restricting the part of the program that is explored by a program analysis. It then uses the GDart tool (dynamic symbolic execution) to search for reachable errors in the modified program.
Randomized Benchmarking beyond Groups

Randomized benchmarking (RB) is the gold standard for experimentally evaluating the quality of quantum operations. The current framework for RB is centered on groups and their representations but this can be problematic. For example, Clifford circuits need up to O(n2) gates and thus Clifford RB cannot scale to larger devices. Attempts to remedy this include new schemes such as linear cross-entropy benchmarking (XEB), cycle benchmarking, and nonuniform RB but they do not fall within the group-based RB framework. In this work, we formulate the universal randomized benchmarking (URB) framework, which does away with the group structure and also replaces the recovery-gate-plus-measurement component with a general “postprocessing” positive operator-valued measurement (POVM). Not only does this framework cover most of the existing benchmarking schemes but it also gives the language for and helps inspire the formulation of new schemes. We specifically consider a class of URB schemes called twirling schemes. For twirling schemes, the postprocessing POVM approximately factorizes into an intermediate channel, inverting maps, and a final measurement. This leads us to study the twirling map corresponding to the gate ensemble specified by the scheme. We prove that if this twirling map is strictly within unit distance of the Haar twirling map in induced diamond norm, the probability of measurement as a function of gate length is a single exponential decay up to small error terms. The core technical tool we use is the matrix perturbation theory of linear operators on quantum channels.Received 7 April 2022Accepted 12 July 2022DOI:https://doi.org/10.1103/PRXQuantum.3.030320Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum computationQuantum Information
Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

Deep Learning achieves state-of-the-art results in many domains, yet its black-box nature limits its application to real-world contexts. An intuitive way to improve the interpretability of Deep Learning models is by explaining their decisions with similar cases. However, case-based explanations cannot be used in contexts where the data exposes personal identity, as they may compromise the privacy of individuals. In this work, we identify the main limitations and challenges in the anonymization of case-based explanations of image data through a survey on case-based interpretability and image anonymization methods. We empirically analyze the anonymization methods in regards to their capacity to remove personally identifiable information while preserving relevant semantic properties of the data. Through this analysis, we conclude that most privacy-preserving methods are not sufficiently good to be applied to case-based explanations. To promote research on this topic, we formalize the privacy protection of visual case-based explanations as a multi-objective problem to preserve privacy, intelligibility, and relevant explanatory evidence regarding a predictive task. We empirically verify the potential of interpretability saliency maps as qualitative evaluation tools for anonymization. Finally, we identify and propose new lines of research to guide future work in the generation of privacy-preserving case-based explanations.
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs

Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. The problem is formulated as a Stackelberg game between one defender and one attacker. The attack graph contains one destination node and multiple entry nodes. The attacker's entry node is chosen by nature. The defender chooses to block a set of edges limited by his budget. The attacker then picks the shortest unblocked attack path. The defender aims to maximize the expected shortest path length for the attacker, where the expectation is taken over entry nodes. We observe that practical Active Directory attack graphs have small maximum attack path length and are structurally close to trees. We first show that even if the maximum attack path length is a constant, the problem is still w[1]-hard with respect to the defender's budget. Having a small maximum attack path length and a small budget is not enough to design fixed-parameter algorithms. If we further assume that the number of entry nodes is small, then we derive a fixed-parameter tractable algorithm. We then propose two other fixed-parameter algorithms by exploiting the tree-like features. One is based on tree decomposition and requires a small tree width. The other assumes a small number of splitting nodes (nodes with multiple out-going edges). Finally, the last algorithm is converted into a graph convolutional neural network based heuristic, which scales to larger graphs with more splitting nodes.
Publication Activity Trends among Russian Researchers in 2016–2020, Based on International Scientific Citation Databases: Web of Science Core Collection and Scopus

Introduction.TheimprovementofRussianresearchers’standingin the global academic space, including the improvement of publication activity indicators, will allow for a better representation of Russia’s scientific achievements. For this reason, this paper presents the results of monitoring the publication activity of Russian researchers, aiming to detect the development trends in Russian science through a comparison of different aspects.MonitoringTools.We monitored the data on the number of articles published by researchers in the Web of Science Core Collection and the Scopus database, comparing our findings across countries and grouping them by STAS priorities, by region (within Russia) and by higher education institution (within Russia). We used themethodsofcomparativeanalysis,groupingsandrankings.Our calculations were based on statistical and mathematical methods. Results.We arrive at a conclusion that the demand for Russian research in the academic community is increasing and that Russia is consolidating its position in the international publication arena, as evidenced by the positive growth dynamics in the number of research articles published inRussia,accordingtotheWebofScienceCoreCollectionand Scopus.Conclusion.The number of articles by Russian researchers (both in general and in the fields prioritised by Russia’s Scientific and Technological Advancement Strategy) continues to increase. At the same time, international citation databases reveal a decrease in the share of articles by Russian researchers in the global publication flow in 2020. One of the reasons behind this trend are the complications caused around the world by the pandemic. The monitoring results can be used for developing a methodology for assessing research activity performance.
A Survey on Word Meta-Embedding Learning

Meta-embedding (ME) learning is an emerging approach that attempts to learn more accurate word embeddings given existing (source) word embeddings as the sole input. Due to their ability to incorporate semantics from multiple source embeddings in a compact manner with superior performance, ME learning has gained popularity among practitioners in NLP. To the best of our knowledge, there exist no prior systematic survey on ME learning and this paper attempts to fill this need. We classify ME learning methods according to multiple factors such as whether they (a) operate on static or contextualised embeddings, (b) trained in an unsupervised manner or (c) fine-tuned for a particular task/domain. Moreover, we discuss the limitations of existing ME learning methods and highlight potential future research directions.
Creation and Development of the Digital Learning Environment in Educational Institutions

The modern digital learning environment of educational institutions should be flexible and personalized, meet the needs, requirements and wishes of teachers, students and the educational institution. Education with the use of digital tools has become relevant today in the quarantine of COVID-19. The educational process takes place regardless of time and place. It requires quick and easy access to information and educational resources. The digital learning environment of the educational institutions provides these conditions. The components of the digital learning environment of the educational institutions should provide the main functions in the process of learning and education: learning, communication, cooperation, assessment and testing, planning and management, presentation and evaluation of tasks. The approach of creating and using the digital learning environment of the educational institutions involves the use of all its elements, namely: IT services, applications, systems, etc., which can be easily combined, updated, added, deleted, changed. This approach will create and develop the digital learning environment of the educational institutions that can be adapted to innovation in education and ICT.
Estimation of Daily Ground-Received Global Solar Radiation Using Air Pollutant Data

Ground-received solar radiation is affected by several meteorological and air pollution factors. Previous studies have mainly focused on the effects of meteorological factors on solar radiation, but research on the influence of air pollutants is limited. Therefore, this study aimed to analyse the effects of air pollution characteristics on solar radiation. Meteorological data, air quality index (AQI) data, and data on the concentrations of six air pollutants (O3, CO, SO2, PM10, PM2.5, and NO2) in nine cities in China were considered for analysis. A city model (model-C) based on the data of each city and a unified model (model-U) based on national data were established, and the key pollutants under these conditions were identified. Correlation analysis was performed between each pollutant and the daily global solar radiation. The correlation between O3 and daily global solar radiation was the highest (r = 0.575), while that between SO2 and daily global solar radiation was the lowest. Further, AQI and solar radiation were negatively correlated, while some pollution components (e.g., O3) were positively correlated with the daily global solar radiation. Different key pollutants affected the solar radiation in each city. In Shenyang and Guangzhou, the driving effect of particles on the daily global solar radiation was stronger than that of pollutants. However, there were no key pollutants that affect solar radiation in Shanghai. Furthermore, the prediction performance of model-U was not as good as that of model-C. The model-U showed a good performance for Urumqi (R2 = 0.803), while the difference between the two models was not particularly significant in other areas. This study provides significant insights to improve the accuracy of regional solar radiation prediction and fill the gap regarding the absence of long-term solar radiation monitoring data in some areas.
Badminton players’ trajectory under numerical calculation method

Abstract The trajectory tracking of badminton players’ arm shots can be used effectively to enhance the player’s shot quality. To track the image trajectory of the batting arm, it is necessary to calculate the body posture ratio and tightness of the target area of the batting arm, to filter the background interference of the image segment of the batting and to complete the efficient tracking of the trajectory of the arm batting image. The traditional method combines the adaptive threshold segmentation method to extract the hitting arm target from the background, but ignores filtering out the background interference of the hitting image fragment. This paper proposes a trajectory tracking method based on the morphological operator of the batter image of the arm stroke. The method consists of (1) differentially calculating the image sequence of the hitting arm during two consecutive shots, (2) estimating the Gaussian model parameters of the differential image of the hitting arm during the hitting process, (3) extracting the outline of the moving target of the hitting arm during the hitting process and then calculating the body posture ratio of the hitting target area and compactness, (4) filtering the background interference of the shot image fragment, (6) constructing a global matching approximation function of the moving target and (7) finally determining the motion trajectory of the badminton arm of the batter. Simulation results show that the proposed method can effectively track the target of the hitting arm during the hitting process and generate a continuous trajectory of the hitting arm.
A Model for Types and Levels of Automation in Visual Analytics: A Survey, a Taxonomy, and Examples

The continuous growth in availability and access to data presents a major challenge to the human analyst. As the manual analysis of large and complex datasets is nowadays practically impossible, the need for assisting tools that can automate the analysis process while keeping the human analyst in the loop is imperative. A large and growing body of literature recognizes the crucial role of automation in Visual Analytics and suggests that automation is among the most important constituents for effective Visual Analytics systems. Today, however, there is no appropriate taxonomy nor terminology for assessing the extent of automation in a Visual Analytics system. In this article, we aim to address this gap by introducing a model of levels of automation tailored for the Visual Analytics domain. The consistent terminology of the proposed taxonomy could provide a ground for users/readers/reviewers to describe and compare automation in Visual Analytics systems. Our taxonomy is grounded on a combination of several existing and well-established taxonomies of levels of automation in the human-machine interaction domain and relevant models within the visual analytics field. To exemplify the proposed taxonomy, we selected a set of existing systems from the event-sequence analytics domain and mapped the automation of their visual analytics process stages against the automation levels in our taxonomy.
Asymmetric double-image encryption using twin decomposition in fractional Hartley domain

Twin decomposition, consisting of equal and random modulus decompositions, not only makes a cryptosystem asymmetric but also resists special attack. A new double-image asymmetric cryptosystem using twin decomposition in fractional Hartley domain is proposed. An input grayscale image, bonded with another grayscale image as its phase mask, is transformed via fractional Hartley transform. Equal modulus decomposition is applied on the resulting image, giving us two intermediate images. One of them is subjected to another fractional Hartley transform followed by random modulus decomposition, whereas the other serves as the first private key. The application of random modulus decomposition also results in two images: encrypted image and the second private key. During the process of decryption, firstly the encrypted image is combined with second private key and thereafter it is subjected to inverse fractional Hartley transform. The resulting image is then combined with the first private key, and followed by another inverse fractional Hartley transform, thus recovering the two original images. The proposed cryptosystem is validated for pairs of grayscale images.
A Blockchain Based Privacy-Preserving Incentive Mechanism for Internet of Vehicles in Satellite-Terrestrial Crowdsensing

Crowdsensing has been a popular technology recently and has broad application prospects. The Internet of Vehicles (IoV) refers to the effective utilization of static and dynamic information on the network platform through electronic equipment loaded on vehicles and wireless technology, which means there are many devices generating a huge amount of worthy data. However, preserving the privacy of workers and incentivizing them should be concerned. To this end, we apply the satellite-terrestrial system to solve the communication limitation among vehicles. We propose to use blockchain to act as the channel that requesters and vehicles communicate in. Smart contracts are the main designable parts to do the interactions among the participants. Blockchain is tightly related to payment, so that we implement a total payment minimized auction algorithm on it. We carry out extensive simulations and run an experiment on blockchain to find the auction can be implemented as a smart contract and achieve privacy protection.
COVID-19 fears and e-learning platforms acceptance among Jordanian university students

Broadening the approval and usage of technology to study online education is not a novel study subject, and several researchers have addressed it. However, the production of a systematic Technology acceptance model capable of examining online education adoption in the current Covid-19 is seen as a vital research path. Literature research was conducted to evaluate the most used external influences of innovation adoption regarding online learning acceptance. The search revealed that computer self-efficacy, corona apprehension, perceived ease of use, and perceived usefulness are the external factors for technology acceptance. The purpose of this paper is to investigate the variables that online education programs' approval among students can influence. 185 students from Jordan's Al-Zaytoonah University and Applied Science Private University participated in the online research. The online questionnaire system in this report was analyzed using SmartPLS tools. According to the findings, perceived usefulness, behavioral intent of use, self-efficacy, and Corona fear all positively affect the adoption of online education programs. The findings of this study were used as a required input in the latest online education interactive analytical production that was used extensively during the pandemic.
A Physician-in-the-Loop Approach by Means of Machine Learning for the Diagnosis of Lymphocytosis in the Clinical Laboratory

Context.— The goal of the lymphocytosis diagnosis approach is its classification into benign or neoplastic categories. Nevertheless, a nonnegligible percentage of laboratories fail in that classification. Objective.— To design and develop a machine learning model by using objective data from the DxH 800 analyzer, including cell population data, leukocyte and absolute lymphoid counts, hemoglobin concentration, and platelet counts, besides age and sex, with classification purposes for lymphocytosis diagnosis. Design.— A total of 1565 samples were included from 10 different lymphoid categories grouped into 4 diagnostic categories: normal controls (458), benign causes of lymphocytosis (567), neoplastic lymphocytosis (399), and spurious causes of lymphocytosis (141). The data set was distributed in a 60-20-20 scheme for training, testing, and validation stages. Six machine learning models were built and compared, and the selection of the final model was based on the minimum generalization error and 10-fold cross validation accuracy. Results.— The selected neural network classifier rendered a global 10-class classification validation accuracy corresponding to 89.9%, which, considering the aforementioned 4 diagnostic categories, presented a diagnostic impact accuracy corresponding to 95.8%. Finally, a prospective proof of concept was performed with 100 new cases with a global diagnostic accuracy corresponding to 91%. Conclusions.— The proposed machine learning model was feasible, with a high benefit-cost ratio, as the results were obtained within the complete blood count with differential. Finally, the diagnostic impact with high accuracies in both model validation and proof of concept encourages exploration of the model for real-world application on a daily basis.
Mimicking Infants’ Bilingual Language Acquisition for Domain Specialized Neural Machine Translation

Existing methods of training domain-specialized neural machine translation (DS-NMT) models are based on the pretrain-finetuning approach (PFA). In this study, we reinterpret existing methods based on the perspective of cognitive science related to cross language speech perception. We propose the cross communication method (CCM), a new DS-NMT training approach. Inspired by the learning method of infants, we perform DS-NMT training by configuring and training DC and GC concurrently in batches. Quantitative and qualitative analysis of our experimental results show that CCM can achieve superior performance compared to the conventional methods. Additionally, we conducted an experiment considering the DS-NMT service to meet industrial demands.