Reviewerly/exHarmony huggingface dataset:
A Taxonomy of User Behavior Model (UBM) Tools for UI Design and User Research

The engineering of user interfaces (UIs) increasingly relies on software tools that aid in ideation, design, evaluation, etc., but involve no real users. Particularly, user behavior models (UBMs) bear the potential to improve human-centered design processes, but their adoption in practice remains low. In this paper, we present a taxonomy for UBM tools that organizes and structures them along 7 dimensions – supported job, degree of automation, focus, interface data input, user data input, output of tool, and target interface platform. We also conduct an initial evaluation with 61 existing tools, providing insights into the current state of the field. Notably, none of the investigated tools work with user characteristics or reference interfaces as input, although this would appear very practical for real projects. Our results could support UI/UX researchers and digital design practitioners in searching for the tools and further enhancing them. Ultimately, our work represents a step toward understanding and overcoming the low adoption rate of ML-based UBM tools in the industry.
Creating Searchable Web Page Snapshots Using Semantic Technologies

For many applications, it is necessary to create snapshots of web pages that accurately describe how the page appeared in a browser at a given point in time. Storing the original code (even if all referenced resources are included) and creating bitmap screenshots have many drawbacks when it comes to searching, viewing, and manipulating such snapshots. In this paper, we demonstrate a different approach that uses a remotely controlled web browser to render web pages. We capture the complete information about the rendered page and all of its content, transform it into an explicit RDF-based model representation, and store it in a repository. The stored page models can then be explored using interactive web-based tools, exported in various formats, linked to other data sources, and queried using SPARQL. We also include several application scenarios that demonstrate the benefits of the proposed approach.
Speed Up the Web with Universal CSS Rendering

Achieving fast page load and upholding conversion rates requires continuous effort for web applications ever-growing in size and complexity. Consequently, developing new methods for optimizing and speeding up page load is a constant effort of the web community. Existing methods aim to reduce the amount of render-blocking code in the render pipeline to improve the time until render. However, a recent study revealed a tremendous optimization potential remaining, especially for CSS, since only $$\approx $$ 15% of render-blocking CSS code is used until render [12]. In this paper, we present Essential, an improved serverside CSS renderer. It is based on the popular Critical package [10], which increases the code efficiency and prepares websites by extracting render-critical elements “Above-the-Fold" and delaying the remaining code asynchronously. Essential renders the CSS of the whole page, as well as eliminates code duplicates to optimize the rendered result. Our evaluation results show that Essential more than triples code efficiency and decreases the transferred render-critical CSS by 65.9% compared to the original while maintaining a high visual similarity and matching or surpassing the performance of Critical.
Learning-Based Quality of Experience Prediction for Selecting Web of Things Services in Public Spaces

In the age of the Web of Things (WoT), an increasing number of WoT devices will be deployed over public spaces and provide various services to users. Therefore, discovering and selecting public WoT services by predicting the expected Quality of Experience (QoE) become critical to satisfying users. However, because of the uncertain and dynamic nature of public WoT environments, accurately predicting and continuously maintaining the QoE of the services is challenging. We investigated the limitations of the traditional model-based QoE prediction in WoT environments and the potential of the learning-based approaches to deal with the challenges. In this work, we propose a distributed algorithm powered by learning-based QoE prediction for selecting public WoT services. Service agents predict the long-term QoE for the corresponding service based on attention mechanism and multi-agent reinforcement learning. Service agents learn the influence of hard-to-observe influencing factors in the environment, such as physical obstacles and interference from other services.
Emotions Intensity Prediction in Online Discussions Using Time Series Forecasting

Toxic behaviors such as aggression, harassment, and insults are the scourge of social media and online discussion sites. Conflictual interactions of this type finally lead dialogues off the rails and as a result the dialogue loses its essence. Preventing a discussion from such incidents requires the ability to predict such situation. This, in turn, presents the challenge of modeling and understanding the dynamics of dialogues and thus predefining the factors that may change throughout their course. In this work we propose to take an emotional intensity of discussion posts as such factor and to model an emotional trajectory of the dialogue. Therefore we also present an approach for modeling the dynamics using time series representation of dialogue. Finally we perform an experiment of time series forecasting on collection of such conversational time series with state-of-the-art deep learning model in order to make a prediction about emotional intensity value of upcoming post.
Automated Web GUI Generation from High-Level Interaction Design with Discourse Models

Interaction design is considered important for achieving usable Web user interfaces. Communicative acts as abstractions from speech acts can model basic building blocks (‘atoms’) of communication, like a question or an answer. When, e.g., a question and an answer are glued together as a so-called adjacency pair, a simple ‘molecule’ of a dialogue is modeled. Deliberately complex discourse structures can be modeled using relations from Rhetorical Structure Theory (RST). The content of a communicative act can refer to ontologies of the domain of discourse. Taking all this together, we created a new discourse metamodel that specifies what discourse models may look like. Such discourse models can specify an interaction design. Since manual creation of user interfaces is hard and expensive, automated generation may become more and more important. This tutorial also demonstrates how such an interaction design can be used for automated Web GUI generation. This is based on model-transformation rules according to the model-driven architecture. Based on AI optimization techniques, the graphical user interfaces (GUIs) are automatically tailored to a device such as a smartphone according to a given device specification. Since the usability of fully-automatically generated GUIs is still not satisfactory, unique customization techniques are employed as well. We also address low-vision accessibility of Web-pages, by combining automated design-time generation of Web-pages with responsive design for improving accessibility.
User Identity Linkage via Graph Convolutional Network Across Location-Based Social Networks

In the past few decades, we have witnessed the flourishing of location-based social networks (LBSNs), where many users tend to create different accounts on multiple platforms to enjoy various services. Benefiting from the large-scale check-in data generated on LBSNs, the task of location-based user identity linkage (UIL) has attracted increasing attention recently. Despite the great contributions made by existing work on location-based UIL, they usually investigate the task with data mining methods, which are hard to extract and utilize the latent features contained by check-in records for more precise user identity linkage. In view of the deficiencies of existing studies, we propose a graph convolutional network (GCN) based model namely GCNUL that consists of a GCN-based encoder, an interaction layer, and a classifier, to fully exploit the spatial features hidden in check-in records. Specifically, the GCN-based encoder aims to exploit the spatial proximity of check-in records and mine user mobility patterns. The interaction layer is developed to capture deep correlations between users’ behaviors explicitly. The extensive experiments conducted on two real-world datasets demonstrate that our proposed model GCNUL outperforms the state-of-the-art methods.
A Practical Introduction for Developing and Operating Hybrid Quantum Applications

With the increasing number of quantum computers available via the cloud, the research area of quantum software engineering is emerging. Its goal is the investigation of concepts and guidelines to develop and operate hybrid quantum applications, ensuring engineering principles such as modularity, reuse, and maintainability. In this tutorial, we provide an overview of state-of-the-art concepts and techniques in quantum computing, as well as quantum software engineering. It includes an introduction of selected essential quantum algorithms, limitations of current quantum computers, and a lifecycle for hybrid quantum applications. Furthermore, we show how the service-oriented development of hybrid quantum applications increases their modularity and reusability. Finally, we demonstrate the orchestration of the required classical and quantum programs using workflows and their automated deployment.
ENGLISH TEST (DET) – НОВАЯ АЛТЕРНАТИВА МЕЖДУНАРОДНЫМ СИСТЕМАМ ТЕСТИРОВАНИЯ ПО АНГЛИЙСКОМУ ЯЗЫКУ [Duolingo English Test (DET) Is the New Alternative to IELTS And TOEFL]

As the world is moving towards digitalization, language exams are expected to provide the whole new experience in the field of language education with extensive use of AI. The article provides an overview of a relatively new international systems for testing linguistic competencies Duolingo English Test (DET) so that its potential can be assessed. The format and structure of DET, language levels, and assessment criteria are discussed. Problems that candidates face while preparing for the exam are discussed and solutions are suggested.
Privacy-Preserving Cyber Threat Information Sharing Leveraging FL-Based Intrusion Detection in the Financial Sector

This paper presents an architectural proposal for enhancing anomaly detection in the CyberSec4Europe project use case Open Banking. It proposes a trusted privacy-preserving ecosystem of threat intelligence platforms, based on MISP, to automatically exchange and process cyber threat information in an auditable and privacy-preserving manner. Additionally, a Federated Learning scheme is deployed to share machine learning models trained on a synthetic fraud transactions dataset, and the impact of data anonymization on model accuracy is measured and analyzed. This proposal provides a valuable contribution to the development of robust and efficient threat detection systems to enhance the resilience of organizations in the financial sector.
RT-CBCH: Real-time VPN Traffic Service Identification based on Sampled Data in High-speed Networks

Virtual Private Network (VPN) technology can bypass censorship and access geographically locked services. Some harmful information may be hidden in VPN traffic and circumvent the surveillance systems, bringing a significant challenge to network security. Considering the increasing richness of service types in VPN traffic, identifying traffic service facilitates further targeting harmful VPN traffic. Therefore, VPN traffic service identification is critical in network management. The existing identification methods use complete traffic for analysis. However, massive data analysis in high-speed networks consumes enormous resources, limiting the real-time processing of traffic identification. This paper proposes a real-time VPN traffic service identification method named RT-CBCH. We construct features that are still available after sampling and design a fast traffic processing structure based on Counting Bloom Filter and Chained Hash Table (CBCH). Experimental results validate the real-time capability, stability and accuracy of our method. At the sampling ratio of 1/256, it takes only 23.63 seconds to process the mixed traffic of 900-second traffic generated on a 10 Gbps link and our collected V2Ray traffic, which is increasingly common in VPN traffic. Under different sampling ratios, the identification results remain respectable, with an overall accuracy of about 90% for application service and over 99% for V2Ray proxy service. Furthermore, comparisons with similar work illustrate the high accuracy and low resource consumption of RT-CBCH. Experimental results show that our method can stably implement real-time VPN traffic service identification from sampled data in high-speed networks.
Unsupervised machine learning algorithm for face morphing

Face morphing is the process of morphing or merging the face of the subject image with the face of another test image. Techniques of face morphing have been utilized in quite diverse fields, mostly in the entertainment industry to replace the face of an actor with a resembling actor to complete movies in absence of the actor. Typical face morphing implemented in commercial cinemas are a result of image morphing that is implemented on still images. Face morphing is a complex procedure that encompasses extracting the face marking, followed by the formation of a mesh of triangles that would be further used for morphing the face of a test image to the subject Image. Though there are many different programs with the ability to morph faces the proceeds to select a suitable candidate still largely is done via manual procedures or even if automated, the process is rarely integrated with the face morphing program. For the morphing results to be seamless it should be ensured that there is very less difference between the original image and the morphed image that is, the resemblance between the two images should be very high, therefore there is a brief description in the report about “what is the process of implementing morphing and how such resemblance will be achieved. Our research work aims to design and implement a similar face morphing program that has a model integrated into it for selecting the best suitable candidate for face morphing. Using python programming and concepts of unsupervised learning we aim to create will have the ability to calculate the resemblance between the faces of the subject and various candidates and decide for itself which among different candidates is best suitable for face morphing. Since it finds the most resembling candidate for face morphing, hence the name Doppelgänger.
MEASURING USABILITY OF ACADEMIC INFORMATION SYSTEM USING USE QUESTIONNAIRE : CASE STUDY OF PADANG STATE POLYTECHNIC

The development of website-based information technology has been utilized by the Padang State Polytechnic using the Academic Information System for students. The Academic Information System contains academic information related to majors and students. The purpose of this study is the use of academic information systems by measuring the level of user satisfaction using USE Questionnaire. This study will empirically prove the effect of the independent variables, namely usability, ease of use, and ease of learning on user satisfaction as dependent variable. The data was gathered from 265 respondents and processed it using IBM SPSS version 25 software. This study used multiple linear regression analyses for hypothetical testing. The results indicate that usefulness and ease of use have significant effects on user satisfaction, while ease of learning has no significant effect on user satisfaction.
Underwater Color-Cast Image Enhancement by Noise Suppression and Block Effect Elimination

Efficient underwater visual environment perception is the key to realizing the autonomous operation of underwater robots. Because of the complex and diverse underwater environment, the underwater images not only have different degrees of color cast but also produce a lot of noise. Due to the existence of noise in the underwater image and the blocking effect in the process of enhancing the image, the enhanced underwater image is still rough. Therefore, an underwater color-cast image enhancement method based on noise suppression and block effect elimination is proposed in this paper. Firstly, an automatic white balance algorithm for brightness and color balance is designed to correct the color deviation of underwater images and effectively restore the brightness and color of underwater images. Secondly, aiming at the problem of a large amount of noise in underwater images, a noise suppression algorithm for heat conduction matrix in the wavelet domain is proposed, which suppresses image noise and improves the contrast and edge detail information of underwater images. Thirdly, for the block effect existing in the process of enhancing the underwater color-cast image, a block effect elimination algorithm based on compressed domain boundary average is proposed, which eliminates the block effect in the enhancement process and balances the bright area and dark area in the image. Lastly, multi-scale image fusion is performed on the images after color correction, noise suppression, and block effect elimination, and finally, the underwater enhanced image with rich features is obtained. The results show that the proposed method is superior to other algorithms in color correction, contrast, and visibility. It also shows that the proposed method corrects the underwater color-cast image to a certain extent and effectively suppresses the noise and block effect of the underwater image, which provides theoretical support for underwater visual environment perception technology.
A comparison of the psychological effects of robot motion in physical and virtual environments

A mixed-methods approach was used to assess the fidelity of virtual environments as ergonomic assessment tools for human-robot interaction. Participants performed a visual search task in the physical environment while a nearby collaborative robot repeatedly extended its arm toward them. This scenario was reconstructed in two virtual environments with different levels of graphical detail. Measures of presence, task performance, workload, and anxiety were taken to determine the effect of robot motion in all three environments. Task performance decreased in response to robot motion in the physical environment, while workload and anxiety increased. This simple effect of motion was consistent across environments for measures of task performance and anxiety. However, people performed faster overall in virtual reality, and the effect of motion on workload was greatly reduced in virtual reality. Results in the virtual environments suggest that people were distracted by the sound of the robot, and that presence was affected by manipulations of immersion and coherence.
Pulpotomy Techniques and Medicaments

Decoding the variation laws of the deformation field before strong earthquakes has long been recognized as an essential issue in earthquake predication research. In this paper, the temporal and spatial distribution characteristics of deformation anomalies in the northeastern margin of the Qinghai-Tibet Plateau before and after the Menyuan Ms6.9 earthquake were studied by using the Fisher statistical test method. By analyzing the characteristics of these anomalies, we found that: 1) The deformation anomalies are mainly distributed in the marginal front area of the Qinghai-Tibet Plateau, where short-term deformation anomalies are prone to occur due to a high gradient of gravity; 2) The deformation anomalies along the northeastern margin of Qinghai-Tibet Plateau are characterized by spatial propagation, and the migration rate is about 2.4 km/d. The propagation pattern is counterclockwise, consistent with the migration direction of Ms ≥ 6.0 earthquakes; 3) The time and location of the Menyuan earthquake are related to the group migration of earthquakes with Ms ≥ 6.0. Finally, based on the results of gravity field variation and the theory of crust stress wave, the law of deformation anomaly distribution was discussed. We suggest that both the deformation propagation along the northeastern margin of Qinghai-Tibet Plateau and the earthquake migration are possibly associated with the variation of stress field caused by subsurface mass flow.
Development of Innovative Competence Model and Its Testing in the Course “Techno-Startup”

Introduction. Innovative evolution of the country requires preparing students for innovative activity and developing their innovative competence. But there is still no clear understanding as to how to effectively form and measure it. This article offers a model of innovation competence in the area of techno-innovation, which is based on the analysis of existing summarized models of innovation competence and consists of cognitive and non-cognitive components, each of which already has diagnostic tools. The purpose of the article is to describe the results of testing the proposed model of innovation competence in the process of specially organized training, as well as to identify effective teaching methods and tools for assessing innovation competence in higher engineering education. Materials and Methods. The article describes a comprehensive toolkit for assessing the changing of each component of innovation competence in the area of techno-innovation. It was used to identify the teaching methods most effective for the developing innovative competence in the process of teaching future techno-innovators at university. A quasi-experimental plan of research was used with the participation of 521 HSE students, who during a semester learned a specially created training course “Techno-Startup”. 58 trackers participated in the data collection. The results were subjected to statistical analysis: mean values, standard deviation values were calculated, linear regression model was applied. Results. It is found that the proposed toolkit has reliability and validity. On average, statistically significant improvement of studentʼs innovative competence in the focused learning of the course “Techno-Startup” was recorded relative to the control group. However, in 11–13% of students, motivation and personal attitudes decreased. It has been revealed that individual methods of teaching donʼt significantly influence the development of innovative competence of students, and it is necessary to use different methods in combination, each of which influences a specific component of competence. In the process of studying, the components of competence develop differently: the most noticeable growth of knowledge and skills, while personal attitudes and qualities practically does not change. Discussion and Conclusion. The obtained results contribute to the development of ideas about innovation competence, which can be useful in creating training programs for engineers and entrepreneurs in the technological sphere. Practical value has recommendations on the construction and content of the course, in the scope of which innovation competence is purposefully forms on the example of the discipline “Techno-Startup”.
Scaling Zero-Knowledge to Verifiable Databases

Zero-Knowledge proofs are a cryptographic technique to reveal knowledge of information without revealing the information itself, thus enabling systems optimally to mix privacy and transparency, and, where needed, regulatability. Application domains include health and other enterprise data, financial systems such as central-bank digital currencies, and performance enhancement in blockchain systems. The challenge of zero-knowledge proofs is that, although they are computationally easy to verify, they are computationally hard to produce. This paper examines the scalability limits of leading zero-knowledge algorithms and addresses the use of parallel architectures to meet performance demands of applications.