Reviewerly/exHarmony huggingface dataset:
Introducing Bidirectional Programming in Constructive Solid Geometry-Based CAD

3D Computer-Aided Design (CAD) users need to overcome several obstacles to benefit from the flexibility of programmatic interface tools. Besides the barriers of any programming language, users face challenges inherent to 3D spatial interaction. Scripting simple operations, such as moving an element in 3D space, can be significantly more challenging than performing the same task using direct manipulation. We introduce the concept of bidirectional programming for Constructive Solid Geometry (CSG) CAD tools, informed by interviews we performed with programmatic interface users. We describe how users can navigate and edit the 3D model using direct manipulation in the view or code editing while the system ensures consistency between both spaces. We also detail a proof-of-concept implementation using a modified version of OpenSCAD.
How Observers Perceive Teleport Visualizations in Virtual Environments

Multi-user VR applications have great potential to foster remote collaboration and improve or replace classical training and education. An important aspect of such applications is how participants move through the virtual environments. One of the most popular VR locomotion methods is the standard teleportation metaphor, as it is quick, easy to use and implement, and safe regarding cybersickness. However, it can be confusing to the other, observing, participants in a multi-user session and, therefore, reduce their presence. The reason for this is the discontinuity of the process, and, therefore, the lack of motion cues. As of yet, the question of how this teleport metaphor could be suitably visualized for observers has not received very much attention. Therefore, we implemented several continuous and discontinuous 3D visualizations for the teleport metaphor and conducted a user study for evaluation. Specifically, we investigated them regarding confusion, spatial awareness, and spatial and social presence. Regarding presence, we did find significant advantages for one of the visualizations. Moreover, some visualizations significantly reduced confusion. Furthermore, multiple continuous visualizations ranked significantly higher regarding spatial awareness than the discontinuous ones. This finding is also backed up by the users’ tracking data we collected during the experiments. Lastly, the classic teleport metaphor was perceived as less clear and rather unpopular compared with our visualizations.
Shooting-based technique: Highly interactive virtual reality locomotion

The Shooting-Based Technique (SBT) is a highly interactive locomotion method in virtual reality (VR) that enables user navigation through simulated shooting actions. SBT has already been used in a commercialized product and offers users a novel experience compared to traditional techniques. However, there has been scant research or scientific investigation into SBT. Thus, to further discover the possibility of SBT, we implemented it by creating an archery scenario and conducting an initial user study involving 24 participants in comparison with two widely used locomotion methods: steering-based joystick and teleport. Initial results indicate a preference for SBT among participants, which lays the foundation for extensive research in the future.
The Influence of Virtual Agent Visibility in Virtual Reality Cognitive Training

For avatars - virtual bodys controlled by a human - an established visualization is to display incomplete visualizations, e.g. only head and torso. However, the preference for the body visibility of intelligent virtual agents (IVAs) - fully computer generated virtual humans - may differ. Additionally, the presence of IVAs can have a psychological effect on users that are similar to that of a real human, e.g. facilitation or inhibition of cognitive performance. To investigate the connection between these two topics, we examine the effects of an IVA’s level of body visibility on the users’ sense of social presence and task performance in virtual reality. In a within-subject user study, 30 participants solved anagram tasks in the presence of five different levels of visibility of our IVA: voice-only, mouth-only, head-only, upper body and full body. While we could not find any differences in the task performance of the users, lower levels of visibility led to a decreased feeling of social presence. Furthermore, by using eye tracking, we found that visually rich representations were looked at for a longer amount of time, but only during the explanation of the task. Afterwards, the users did not pay much visual attention to the agent anymore. Finally, preferences of the users show that the chosen representation is dependent on some factors; most importantly, it should support the users, but not distract them from their task.
Spatializing Music in Virtual Reality

Spatial music offers an immersive auditory experience by orchestrating sounds, rhythms, and beats to not only envelop the listener but also dynamically move around, behind, and above them. Despite its promise, existing tools for spatial music remain inefficient, with prior research citing the lack of tailored input devices for spatializing in higher dimensions. To address these challenges, we propose a virtual reality (VR) application designed for intuitive music spatialization in 3D space. Users spatialize music by placing and dragging sound sources in VR to animate their movement. Since animating movement can be imprecise, our application incorporates auto-correction features that adjust the movement of sound objects, ensuring a smoother end result.
DensingQueen: Exploration Methods for Spatial Dense Dynamic Data

Research has proposed various interaction techniques to manage the occlusion of 3D data in Virtual Reality (VR), e.g., via gradual refinement. However, tracking dynamically moving data in a dense 3D environment poses the challenge of ever-changing occlusion, especially if motion carries relevant information, which is lost in still images. In this paper, we evaluated two interaction modalities for Spatial Dense Dynamic Data (SDDD), adapted from existing interaction methods for static and spatial data. We evaluated these modalities for exploring SDDD in VR, in an experiment with 18 participants. Furthermore, we investigated the influence of our interaction modalities on different levels of data density on the users’ performance in a no-knowledge task and a prior-knowledge task. Our results indicated significantly degraded performance for higher levels of density. Further, we found that our flashlight-inspired modality successfully improved tracking in SDDD, while a cutting plane-inspired approach was more suitable for highlighting static volumes of interest, particularly in such high-density environments.
Aesthetic-Driven Navigation for Node-Link Diagrams in VR

Visual network exploration is essential in numerous disciplines, including biology, digital humanities, and cyber security. Prior research has shown that immersive, stereoscopic 3D can enhance spatial comprehension and accuracy in exploring node-link diagrams. However, 3D graphs can present challenges, including node occlusion and edge crossings, which necessitate continual manual perspective adjustments. We introduce a virtual reality (VR) framework that assists users in navigating to optimal viewing points based on their current task, addressing these issues. The framework quantifies the perceptual quality of viewports based on graph drawing aesthetics suggested by the literature. This information is then visualized in two ways: on a spherical 3D representation surrounding the user and on a handheld 2D overview map. Users can interact with both representations to easily adjust their viewpoint. Moreover, they can interactively combine different aesthetics to discover the optimal viewing points for their specific tasks. Two qualitative evaluations involving law enforcement and biology experts demonstrate the value of our approach. Domain experts reported that the suggested viewports corresponded to their intuition and simplified the process of finding task-supportive perspectives with minimal interaction. Our approach can be incorporated into existing VR graph exploration tools, improving the initial perspective selection and reducing manual navigation.
Timeline Exploration in 360° Video

Timeline control is an important interaction during video watching, which can help users quickly locate or jump the video playback time, especially when watching lengthy videos. 360° video has gradually entered the public view due to its ability to select the target view in all directions, and becomes a better alternative to the single view video. At present, 360° video is mostly displayed on a two-dimensional screen, and the timeline only follows a similar design as normal video. However, virtual reality (VR) headsets provide a more immersive viewing experience for 360° videos, while also providing more spatial dimensions for the design of timelines. In this paper, we explore the introduction of 360° video timeline design in VR viewing environments where video time control is rarely involved. In total, we propose 6 design alternatives involving two variables: the shape of timeline (two-dimensional, three-dimensional) and the interaction distance of timeline (close to screen, close to body, attached to wrist). We evaluated their performance and user experience in a user study with 24 participants. Our results show that the usability of the 3D timeline is better than the 2D timeline in 360° space. And the two within reach timelines, close to body and attached to wrist, have advantages in performance and experience over timelines close to screen. Based on these findings, we propose a set of design recommendations for timeline design in different application scenarios.
Cashing out crypto: state of practice in ransom payments

Abstract The fast pace of blockchain technology and cryptocurrencies’ evolution makes people vulnerable to financial fraud and provides a relatively straightforward monetisation mechanism for cybercriminals, in particular ransomware groups which exploit crypto’s pseudo-anonymity properties. At the same time, regulatory efforts for addressing crimes related to crypto assets are emerging worldwide. In this work, we shed light on the current state of practice of ransomware monetisation to provide evidence of their payment traceability, explore future trends, and—above all—showcase that over-regulating cryptocurrencies is not the best way to mitigate their risks. For that purpose, first, we provide an overview of the legislative initiatives currently taken by the USA, the EU, and the OECD to regulate cryptocurrencies, showing that strict laws and the divergences between the regulatory regimes can hardly efficiently regulate the global phenomenon of cryptocurrency, which transcends borders and states. Next, we focus on illicit payments in bitcoin to ransomware groups, illustrating how these payments are siphoned off and how criminals cash out the ransom, often leaving traceable evidence behind. To this end, we leverage a publicly available dataset and a set of state-of-the-art blockchain analysis tools to identify payment patterns, trends, and transaction trails, which are provided in an anonymised form. Our work reveals that a significant amount of illicit bitcoin transactions can be easily traced, and consequently, many cyber crimes like ransomware can actually be tracked down and investigated with existing tools and laws, thus providing fertile ground for better and fairer legislation on crypto.
Real‐time deep learning semantic segmentation for 3-D augmented reality

Augmented reality is a visualization technology that displays information by adding virtual images to the real world. In many cases, augmented reality requires recognition of the current scene. Extracting foreground objects from a video in real-time on limited hardware, such as a smartphone, is demanding. The augmented reality for the environment must have a model in which it is clear where it is necessary to detect the object or apply a mask. One way to recognize a scene in the current context without prior information is to use semantic segmentation techniques. This article proposes a new neural network architecture for efficient semantic image segmentation in the task of building augmented reality. The developed architecture is based on the combination of Shufflenet V2 and DPC, which provides good performance due to the balance between predictive accuracy and efficiency. First, the ShuffleNet V2 neural network architecture obtains features from RGB images. The resulting feature maps are then passed to one of the Deeplab V3+ Dense Prediction Cell encoders. At the final stage, the features are decoded by bilinear interpolation to create segmentation masks. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The proposed approach for recognizing scene objects in augmented reality uses semantic segmentation, providing real-time information. The implementation of the algorithm shows that the detected objects can be tracked in 3-D space using visual-inertial odometry without resorting to constantly updating the environment model. The frequency of object detection and semantic mask generation can be reduced, resulting in battery and processing power savings, which is critical for mobile and embedded systems. The semantic information provided by these solutions can be used in autonomous driving, robotics navigation, localization, and scene recognition in conditions of limited resources. In augmented reality, the proposed approach can remove objects from a scene, draw attention to objects, or provide scene recognition to software logic. The experiment results confirmed the high efficiency of the proposed method compared to the state-of-the-art techniques for real‐time 3-D augmented reality construction.
Robustness-Eva-MRC: Assessing and analyzing the robustness of neural models in extractive machine reading comprehension

Deep neural networks, despite their remarkable success in various language understanding tasks, have been found vulnerable to adversarial attacks and subtle input perturbations, revealing a robustness shortfall. To explore this, this paper presents Robustness-Eva-MRC, an interactive platform designed to assess and analyze the robustness of pre-trained and large-scale language models in extractive machine reading comprehension (MRC) tasks. The platform integrates eight adversarial attack methods across character-, word-, and sentence-levels, and applies them to five MRC datasets, thereby fabricating challenging adversarial testing sets. Then it evaluates the MRC models on both original and adversarial sets, yielding insights into their robustness through performance gaps. Moreover, Robustness-Eva-MRC provides comprehensive visualizations and detailed case studies, enhancing the understanding of model robustness. A screencast video and additional material are available at https://github.com/distantJing/Robustness-Eva-MRC.
Application of machine learning algorithm in electronic book database management system

Abstract Intelligent algorithms have excellent performance in managing large databases and mining effective knowledge, and many researchers have introduced these algorithms into research to improve the performance of book management systems. This article introduced the application of machine learning algorithms in electronic book database management systems. A linear classifier (linear regression algorithm is one of machine learning algorithms) was used to analyze the empirical loss value. This paper studied the application of machine learning algorithm in the database management of library special collection resources, aiming to optimize the management of library special collection resources database through machine learning algorithm, and provided students with better experience of using special collection resources. This paper verified the effectiveness of the machine learning algorithm from three aspects: the retrieval time before and after the special resource database, the number of crashes in a month and the number of library staff. The average time of the five sets using the machine learning algorithm was about 150% faster than the average search time of the five sets of special resource databases. The number of crashes after using machine learning algorithm was 2, 1, 1, 2, 1, which was much lower than that of special resource database. After using the machine learning algorithm, the five groups of workers were 7, 5, 9, 6, and 4, respectively, far fewer than the five groups of special collectors. Finally, by comparing the linear regression model, Support vector machine model, and random forest model, it was found that the accuracy of the linear regression model reached 98.2%, an increase of 8.2% compared to the random forest model, the precision rate reached 96.7%, and the recall rate reached 98.2%. 97.8%, F1 value reached 97.5%.It can be seen from the experimental data that the machine learning algorithm plays a good role in the database management of the library special collection resources.
C3MW: A novel comprehensive-monitoring-motivated multi-model watermarking scheme for tamper detection and self-recovery

In this paper, we propose a novel Comprehensive-Monitoring-Motivated Multi-model Watermarking (C3MW) Scheme for tampering region localization and self-recovery for 4K images. To generate the Comprehensive-Monitoring-Motivated Multi-model (C3M) Watermark, the Multi-Model Authentication Bits Generation (MMAG) method and the Adaptive Block Significance-Based Recovery Bits Generation (ASRG) method, are proposed. The MMAG aims to monitor the various bit-plane information for detecting the possible tampering in a more comprehensive manner. When performing image tampering detection, once one of the multiple models is triggered, the corresponding parcel will be marked as tampered. On basis of the detected regions, we propose a fine-tuning-based image recovery method, where the extracted recovery data consist of the fused Adaptive Block Significance (ABS) and bitmaps, while the calculated recovery data consist of the watermark information which is calculated from the received image. We conduct experiments on two public databases, respectively, the BOWS2 Dataset and the LIU4K-v2 Dataset. Comparisons with existing state-of-the-art works have been performed on the BOWS2 dataset, and our scheme improves the precision and F1 Score by 7.27% and 3.30%, respectively. It is clear from these results that our method has a better performance than others.
A linear exponential comonad in s-finite transition kernels and probabilistic coherent spaces

This paper concerns a stochastic construction of probabilistic coherent spaces by employing novel ingredients (i) linear exponential comonad arising properly in the measure-theory (ii) continuous orthogonality between measures and measurable functions. A linear exponential comonad is constructed over a symmetric monoidal category of transition kernels, relaxing Markov kernels of Panangaden's stochastic relations into s-finite kernels. The model supports an orthogonality in terms of an integral between measures and measurable functions, which can be seen as a continuous extension of Girard-Danos-Ehrhard's linear duality for probabilistic coherent spaces. The orthogonality is formulated by a Hyland-Schalk double glueing construction, into which our measure theoretic monoidal comonad structure is accommodated. As an application to countable measurable spaces, a dagger compact closed category is obtained, whose double glueing gives rise to the familiar category of probabilistic coherent spaces.
Borrowing in Visual Art Works: Aspects of Attitude Formation in the Educational Environment

The aim of the article is to highlight certain aspects of attitude formation in the educational environment to the issue of borrowing in visual art works and in project and design activities. Results. Borrowing, considered as the transformation of something into one’s own property, can take different forms, with appropriation as borrowing someone else’s work (part of it) to express new content and visual plagiarism as an ethical violation, presenting someone else’s visual work (part of it) under one’s own name, including. The analysis of research related to borrowing in visual art works gave grounds to single out the socio-cultural, pedagogical, psychological, moral and ethical, legal, and technological aspects of attitude formation to the issue of borrowing in visual art works and in project and design activities. The range of attitudes towards borrowings in works of art is determined, ranging from acceptance to denial of the existence of original works, and can be perceived under different conditions as a neutral, positive, and negative phenomenon. The article proposes recommendations for preventing visual plagiarism in creative student projects, which can be applied by teachers in educational and methodological activities. The scientific novelty lies in identifying and analysing the socio-cultural, pedagogical, psychological, moral and ethical, legal, and technological aspects of attitude formation in the educational environment to the issue of borrowing in visual art works. Conclusions. Considering the identified aspects of attitude formation in the educational environment to the issue of borrowing in visual art works is crucial for developing a well-balanced strategy of behaviour by teachers and improving the methodology of teaching the educational components of artistic and design educational programmes regarding the formation of a culture of academic integrity and prevention of visual plagiarism in the development of visual works. The issue of responding to manifestations of visual plagiarism in the educational environment, the development of a set of methodological measures to promote a zero-tolerance stance towards plagiarism, and the conscious and constant adherence to professional ethics in the field of visual art require additional study and detailed analysis taking into account existing practices.
Effective framework for human action recognition in thermal images using capsnet technique

Recognizing human activity is the process of using sensors and algorithms to identify and classify human actions based on the data collected. Human activity recognition in visible images can be challenging due to several factors of the lighting conditions can affect the quality of images and, consequently, the accuracy of activity recognition. Low lighting, for example, can make it difficult to distinguish between different activities. Thermal cameras have been utilized in earlier investigations to identify this issue. To solve this issue, we propose a novel deep learning (DL) technique for predicting and classifying human actions. In this paper, initially, to remove the noise from the given input thermal images using the mean filter method and then normalize the images using with min-max normalization method. After that, utilizing Deep Recurrent Convolutional Neural Network (DRCNN) technique to segment the human from thermal images and then retrieve the features from the segmented image So, here we choose a fully connected layer of DRCNN as the segmentation layer is utilized for segmentation, and then the multi-scale convolutional neural network layer of DRCNN is used to extract the features from segmented images to detect human actions. To recognize human actions in thermal pictures, the DenseNet-169 approach is utilized. Finally, the CapsNet technique is used to classify the human action types with Elephant Herding Optimization (EHO) algorithm for better classification. In this experiment, we select two thermal datasets the LTIR dataset and IITR-IAR dataset for good performance with accuracy, precision, recall, and f1-score parameters. The proposed approach outperforms “state-of-the-art” methods for action detection on thermal images and categorizes the items.