Reviewerly/exHarmony huggingface dataset:
First Eyetracking Results of Dutch CoronaMelder Contact Tracing and Notification App

The coronavirus disease (COVID-19) has led to a global pandemic. Many countries are using contact tracing as one of the interventions to control virus transmission. In conventional contact tracing, index cases with positive test results are asked to provide contact information of close contacts who were at risk of acquiring infection from the index case, within a given time period before the positive test result. Mobile contact tracing apps can augment traditional contact tracing, as they exchange contact information wirelessly and semi-automatically, and allow for quicker warning of at-risk contacts. However, little is known about user acceptance and perception of such contact tracing apps, or how people perceive the advice presented therein. In July 2020, the beta version of the official Dutch corona notification app was usability tested with participants from various age groups. This paper presents the results of the objective eye tracking measurements executed during these usability tests. The results show that both health and process related information is easily overlooked in the app by participants. A closed-loop contact with the builders of the app allowed for a direct feedback channel and quick improvements during the development stage.
Target Detection of UAV Aerial Image Based on Rotational Invariant Depth Denoising Automatic Encoder

The method of using unmanned aerial vehicle (UAV) to obtain aerial image information of target scene has the characteristics of wide coverage, strong mobility and high efficiency, which is widely used in urban traffic monitoring, vehicle detection, oil pipeline inspection, regional survey and other aspects. Aiming at the difficulties of the object to be detected in the process of aerial image object detection, such as multiple orientations, small image pixel size and UAV body vibration interference, a novel aerial image object detection model based on the rotation-invariant deep denoising auto encoder is proposed in this paper. Firstly, the interest region of the aerial image is extracted by the selective search method, and the radial gradient of interest region is calculated. Then, the rotation invariant feature descriptor is obtained from the radial gradient feature, and the noise in the original data is filtered out by the deep denoising automatic encoder and the deep feature of the feature descriptors is extracted. Finally, the experimental results show that this method can achieve high accuracy for aerial image target detection and has good rotation invariance.
Does enhanced shape bias improve neural network robustness to common corruptions?

Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to generalize to unseen test data from the same distribution as the training data but often fail to generalize to out-of-distribution data. It has been shown that augmenting the training data with different image styles decreases this texture bias in favor of increased shape bias while at the same time improving robustness to common corruptions, such as noise and blur. Commonly, this is interpreted as shape bias increasing corruption robustness. However, this relationship is only hypothesized. We perform a systematic study of different ways of composing inputs based on natural images, explicit edge information, and stylization. While stylization is essential for achieving high corruption robustness, we do not find a clear correlation between shape bias and robustness. We conclude that the data augmentation caused by style-variation accounts for the improved corruption robustness and increased shape bias is only a byproduct.
An Exploration of Online Missing Value Imputation in Non-stationary Data Stream

Missing value imputation (MVI) is an important data preprocessing technique. In previous decades, MVI technique has been widely studied as well as most MVI approaches have been proposed by means of either statistics or machine learning techniques. However, all previous methods only focus on the static data, but ignore the imputation for the dynamic online data. It is intuitionistic that the imputation errors may be significantly increased when there exists concept drifts in the data stream. In this paper, we investigate the impact of adopting the conventional MVI methods in non-stationary data stream. Meanwhile, two slide time window-based strategies are proposed to alleviate this impact, where one is the plain average strategy, and the other is the logarithmic weighted average strategy that gradually adds the weights of instances along the time axis. Combining with the proposed strategies, three popular MVI techniques, mean imputation (MI), KNN imputation (KNNI) and the Bayesian principal component analysis imputation (BPCAI) are adopted, to indicate the effect of the strategies are irrelevant to the specific MVI technique. The experimental results on three different types' concept drift synthetic data sets and two real-world drifting data sets have presented the effectiveness and feasibility of the proposed strategies. Moreover, the impact of time window size has also been investigated for guiding the parameter settings in future practical applications.
How Can I Grab That?

Abstract The selection and manipulation of objects in Virtual Reality face application developers with a substantial challenge as they need to ensure a seamless interaction in three-dimensional space. Assessing the advantages and disadvantages of selection and manipulation techniques in specific scenarios and regarding usability and user experience is a mandatory task to find suitable forms of interaction. In this article, we take a look at the most common issues arising in the interaction with objects in VR. We present a taxonomy allowing the classification of techniques regarding multiple dimensions. The issues are then associated with these dimensions. Furthermore, we analyze the results of a study comparing multiple selection techniques and present a tool allowing developers of VR applications to search for appropriate selection and manipulation techniques and to get scenario dependent suggestions based on the data of the executed study.
A blockchain-based service for inviolable presence registration of mobile entities

Abstract Several applications can benefit from recording information about the places a mobile entity visits and the length of time it spends there (e.g., shoppers, employees, buses, portable equipment, autonomous robots). This paper presents our approach to recording spatio-temporal presence information in a secure and inviolable way using a Distributed Ledger Technology. We implemented this solution as a middleware service that uses Complex Event Processing on smartphones to record beacon-smartphone proximity data in a blockchain efficiently. We have built upon the previous version of our service to include access control to the stored information. We analyzed the impact of this addition on the service’s performance and observed that it introduced very little overhead while significantly increasing user privacy. Furthermore, we compared the effect of using different blockchain technologies on overall service performance and characterized scenarios where using either IoTeX or Ethereum can be suitable for this type of application.
TECHNOLOGY TRANSFER OFFICE MODEL

At present creation of specialized organizational structure, the technology transfer office (TTO) in higher education institutions (HEIs) becomes increasingly important for establishing communication between scientists, business and the state and for implementation of the results of scientific and technical activities of higher education institutions in the real sector of the economy. Surveys of domestic and foreign scientists show that the issues of creating project offices (which in fact are TTOs) in HEIs remain insufficiently explored. Given this, the authors conducted a study of project management to create a TTO in a HEI. Efficient organizational project management requires the presence of Organizational Breakdown Structure. This article proposes an Organizational Breakdown Structure template, which clearly demonstrates the project team structure targeted at TTO creation in a HEI, the subordination and basic functions of each team member. In addition, the article proposes a model of organizational structure of the newly created TTO, which is also a template for HEI, and which defines the structure of the office staff, their subordination and main functions. To determine the location of the TTO in the structure of HEI, the article proposes a corresponding model that shows the subordination and connection of the office with other functional units of HEI. Using the models presented in the article, any HEI will be able to effectively manage the project of creating a TTO and as a result to create a project product - a TTO.
Augmented Reality-based Mathematics Worksheet for Online Learning During Covid-19 Pandemic

Abstract. The aim of this study is to create and develop an augmented reality-based mathematics worksheet that can be used for online learning during covid-19 pandemic. The application created has been tested using three out of eight characteristics of ISO 25010 quality standards, which are functional suitability, compatibility, and usability. The results show that the augmented reality-based mathematics worksheet has a very high quality. Further explanations regarding the application and its implications are discussed.
A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.
Many-Objective Automatic Service Composition Based on Temporal Goal Decomposition

With the evolution of Web technologies, various services have become available in a pervasive network environment. Combining simple atomic services into sophisticated applications with a quality-of-service (QoS) guarantee has become a widely studied problem. Given the increasing number of QoS attributes to be considered, conventional service composition approaches with manual workflows and massive computational burdens are no longer effective. Therefore, this article first suggests an efficient many-objective (with four or more objectives) automatic service composition approach named MaSC. In particular, this article introduces a temporal goal decomposition mechanism based on a temporal model to divide an unwieldy problem into several fine-grained subproblems. Viewing this model as an individual representation, we employ an evolutionary process with a novel fitness function to explore the composition solution. The experimental results on the benchmarks show that our approach can simultaneously optimize up to six objectives and achieve a better trade-off between the computation cost and the QoS than two recently proposed automatic composition approaches.
PENGARUH KEMAMPUAN LITERASI MEMBACA TERHADAP PRESTASI BELAJAR SISWA SEKOLAH DASAR

The background of this research is the low of reading literacy skills in SDN Pegagan. The low of reading skills is thought to have an effect on student achievement as well. This is indicated by the results of student achievement that are still low. The purposes of this research are to (1) Know the reading literacy skills of student (2) Know the level of student achievement, and (3) Know the influence of reading literacy skills on student achievement in SDN Pegagan. The study design used quantitative survey research with a sample of 88 students in SDN 1, 2, and 3 Pegagan. Data analysis used simple linear regression test. The results showed that (1) the reading literacy skills of students was quite sufficient at 67.53%, (2) The students' learning achievement was classified as very good at 81%, and (3) There was a significant influence of 5.4% between reading literacy skills and student achievement learning.
Blockchain Technology for Tracing Drug with a Multichain Platform: Simulation Method

This study builds the implementation of the traceability process by conducting simulation tests using business process simulations with the implementation of blockchain technology to track drugs.This research focus involved stakeholders, including the pharmaceutical industry, pharmaceutical wholesalers (distributors/wholesalers), health services (drug stores, hospitals), consumers.Simulation methods are used to describe the distribution and traceability of drugs.Finally, the research contribution in incorporating blockchain technology to supply chain management could potentially help in drug traceability.This study provides an overview of blockchain technology capabilities to find out which stakeholders and assets are transacted on the blockchain system.A decentralized Autonomous Organization is an approach to organizing data on the blockchain that defines all stakeholders identities associated with different addresses.This process can organize each address's transactions on a special blockchain platform in this study using multichain.Furthermore, transactions that have occurred cannot be updated or deleted.This simulation also illustrates some of the blockchain characteristics that must exist, among others, transparent, distributed, immutable, and peer to peer transactions.This contribution gives supply chain management, in particular on drug distribution, stronger control over distribution.
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent. The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner. Experimental results validate the white-box adversarial strength and black-box transferability of ACE. A range of examples demonstrates the perceptual quality of images that ACE produces. ACE makes an important contribution to recent work that moves beyond $L_p$ imperceptibility and focuses on unrestricted adversarial modifications that yield large perceptible perturbations, but remain non-suspicious, to the human eye. The future potential of filter-based adversaries is also explored in two directions: guiding ACE with common enhancement practices (e.g., Instagram filters) towards specific attractive image styles and adapting ACE to image semantics. Code is available at this https URL.
A Fast and Compact RISC-V Accelerator for Ascon and Friends

Ascon- $$p$$ is the core building block of Ascon, the winner in the lightweight category of the CAESAR competition. With Isap, another Ascon- $$p$$ -based AEAD scheme is currently competing in the 2nd round of the NIST lightweight cryptography standardization project. In contrast to Ascon, Isap focuses on providing hardening/protection against a large class of implementation attacks, such as DPA, DFA, SFA, and SIFA, entirely on mode-level. Consequently, Ascon- $$p$$ can be used to realize a wide range of cryptographic computations such as authenticated encryption, hashing, pseudorandom number generation, with or without the need for implementation security, which makes it the perfect choice for lightweight cryptography on embedded devices. In this paper, we implement Ascon- $$p$$ as an instruction extension for RISC-V that is tightly coupled to the processors register file and thus does not require any dedicated registers. This single instruction allows us to realize all cryptographic computations that typically occur on embedded devices with high performance. More concretely, with Isap and Ascon’s family of modes for AEAD and hashing, we can perform cryptographic computations with a performance of about 2 cycles/byte, or about 4 cycles/byte if protection against fault attacks and power analysis is desired. As we show, our instruction extension requires only 4.7 kGE, or about half the area of dedicated Ascon co-processor designs, and is easy to integrate into low-end embedded devices like 32-bit ARM Cortex-M or RISC-V microprocessors. Finally, we analyze the provided implementation security of Isap, when implemented using our instruction extension.
Disturbance Compensation in Fuzzy Logic Control of Level in Carbonisation Column for Soda Production

The intelligent approaches emerge as leading techniques in providing of stable and high performance control of industrial plants with nonlinearity, model uncertainty, variables coupling and disturbances. In the present research a novel approach for the design of a nonlinear model-free fuzzy logic controller (FLC) with two inputs – the system error and the main measurable disturbance and a rule base for disturbance compensation is suggested. It is based on off-line parameter optimisation via genetic algorithms. The approach is applied for the development of a FLC for the control of the level of ammonia brine solution in a carbonisation column with compensation of the changes in the inflow pressure. The control algorithm is implemented in a general purpose industrial programmable logic controller in ”Solvay Sodi” SA – Devnya, Bulgaria. The FLC system with disturbance compensation outperforms in an increased dynamic accuracy the FLC with the system error as a single input even when linear feedforward disturbance compensation is added. The performance of all systems is assessed from the real time control and the simulations based on a derived TSK plant model.
Examining the crime prevention claims of crime prevention through environmental design on system-trespassing behaviors: a randomized experiment

Crime prevention through environmental design (CPTED) is a non-punitive method for reducing crime through the design of the built environment. The relevance of CPTED strategies, however, is less clear in the context of computing environments. Building upon prior research indicating that computing environments may change computer users' behaviors, this study tests the effectiveness of CPTED-based approaches in mitigating system-trespassing events. Findings from this randomized controlled field trial demonstrate that specific CPTED strategies can mitigate hacking events by reducing the number of concurrent activities on the target computer, attenuating the number of commands typed in the attacked computer, and decreasing the likelihood of hackers returning to a previously hacked environment. Our findings suggest some novel and readily implemented strategies for reducing cybercrime.
Exploration of Innovation and Entrepreneurship Education Model in Higher Vocational Colleges based on Rural Revitalization Strategy

The outbreak of the COVID-19 epidemic has had a great impact on economic and social development, the task of poverty alleviation has become more difficult, and the strategy of rural revitalization is facing greater challenges. Higher vocational colleges undertake the important mission of training and conveying advanced skilled talents needed for the country's economic and social development, and are bound to assume their own responsibilities in the strategy of rural revitalization. The innovation and entrepreneurship education for college students promoted by higher vocational colleges in recent years provides a feasible implementation path for higher vocational colleges to participate in the country's rural revitalization strategy. Based on the background of the national rural revitalization strategy, this paper points out the great significance of the integration of innovation and entrepreneurship education in higher vocational colleges into the rural revitalization strategy, and puts forward specific measures to realize the integration of innovation and entrepreneurship education into the rural revitalization strategy.
Digital pedagogy: analysis, requirements and experience of implementation

Abstract The object of research is the process of digitalization of teacher education in Russian universities at the present time. The authors pay special attention to the characteristics of the concept of ‘digital pedagogy’. They analyze the definitions of the concepts of ‘digital pedagogy’, ‘electronic pedagogy’, ‘virtual pedagogy’ and ‘techno-pedagogy’. The authors of the article describe the functional and content components of digital pedagogy characteristics such as content-based, environmental, technological and competence-based components on the result of research by methods of content analysis, discursive analysis, synthesis of ideas of modern digital technologies. The article reveals the significance of digital transformation of education in creating non-standard algorithms for solving traditional pedagogical problems, forming and developing an innovative learning process based on artificial intelligence, big data and distributed computation. The article describes the Master’s program in digital pedagogy implemented at the Mari State University.
Quality of Experience (QoE) Studies: Present State and Future Prospect

With the spread of the broadband Internet and high-performance devices, various services have become available anytime, anywhere. As a result, attention is focused on the service quality and Quality of Experience (QoE) is emphasized as an evaluation index from the user's viewpoint. Since QoE is a subjective evaluation metric and deeply involved with user perception and expectation, quantitative and comparative research was difficult because the QoE study is still in its infancy. At present, after tremendous devoted efforts have contributed to this research area, a shape of the QoE management architecture has become clear. Moreover, not only for research but also for business, video streaming services are expected as a promising Internet service incorporating QoE. This paper reviews the present state of QoE studies with the above background and describes the future prospect of QoE. Firstly, the historical aspects of QoE is reviewed starting with QoS (Quality of Service). Secondly, a QoE management architecture is proposed in this paper, which consists of QoE measurement, QoE assessment, QoS-QoE mapping, QoE modeling, and QoE adaptation. Thirdly, QoE studies related with video streaming services are introduced, and finally individual QoE and physiology-based QoE measurement methodologies are explained as future prospect in the field of QoE studies.