Reviewerly/exHarmony huggingface dataset:
Tool-based Interaction for Precise Manipulation in VR: an Exploratory Study

Virtual Reality-based interactions are getting more mainstream in several domains, such as gaming, education, and training. While there is extensive literature on new interaction techniques, applying and recombining these for specific tool-based interactions remains challenging. We specifically look at promising VR manipulation techniques using controllers. We implemented these techniques in a proof-of-concept toolchain aimed at spray painters. We extracted and manipulated the relevant parts for a controlled within-subject comparative experiment with 16 participants. We find, among other things, that, as in direct manipulation, tool-based interaction with controllers in VR can benefit from zoom and separation of degrees of freedom to achieve effective and efficient manipulation.
The Role of New Media in Shaping the Image of Projects in the Field of Culture and Art on the Example of Information and Communication Support of the Art Cluster «Tavrida-Art»

The article examines the role of new media in the process of information and communication support in the field of culture and art. The author considers it in many aspects, analyzing both the general trends in the development of the institute of information support, and determining the advantages and disadvantages of support with specific media forms using the example of the Tavrida-Art project. The article identifies the most promising directions for the development of the Institute of information and communication support for projects. The key features of working with different audience groups are presented in the example of the Tavrida-Art project, the role of new media resources is characterized, and the general principles of the project’s communication strategy are developed.
Simplexity to Improve Human-Machine Interaction in 3D Virtual Reality

The purpose of this paper is to present the use of the notion of simplexity to facilitate the design of virtual and immersive environments. Through ahistorical and argumentative excursus, the authors specify the motivations that have led to the introduction of the notion of simplexity as a natural support to the teaching-learning process, regardless of the environments virtual and immersive, and then explore the links with the latter. If immersiveness makes it possible to generate interactions in a physical space, projecting a world surrounding the user and proposing interactions (apt to foster the teaching-learning process), then one could adopt in the of design simplexity that takes into account cognitive, metacognitive, and motor processes, realizing a virtual reality perceived as credible explorable, interactive, and indeed, immersive.
Designing Multimodal Video Search by Examples (MVSE) user interfaces: UX requirements elicitation and insights from semi-structured interviews

In order to search for content from large video archives, it is typically undertaken via keyword queries using predefined metadata such as title and other tags. However, it is difficult to use keywords to search for specific moments in a video. Video search by examples is a desirable approach for this scenario as it allows users to search for content using one or more examples without having to specify a keyword. However, video search by examples is notoriously challenging, and performance is poor. To improve search performance, multiple modalities may be considered – image, sound, voice and text, multiple search cues could be used to identify more relevant content. This is multimodal video search by examples (MVSE), where users can search for content using multiple modalities. In this paper, typical end users - BBC archivists, programme support staff - are interviewed to identify how their search needs can be addressed with the technical capabilities of a MVSE tool. Such a search tool will be useful for organisations such as the BBC who maintain large collections of video archives and want to provide a search tool for their own staff as well as for the public. It will also be useful for companies such as Youtube who host videos from the public and want to enable video search by examples. The study’s objectives explored in this paper were to inform the design and development of the UX workflows to gain a broader understanding of what opportunities and issues may arise from the proposed prototype tool. Results from the thematic analysis was highlighted 4 main themes: Opportunities, Time constraints, Activities, and Pain points. Further analysis highlighted key areas that should be considered for an MVSE-based system, such as scene recognition, face recognition, speed issues, and integration..
Toward Designing for Social Connectedness in Cardiovascular Disease Self-Care

When people with chronic conditions are confronted with gaps in their understanding of their health status, they turn to their peers to make sense of their own situation. And yet, little is known about how to design to facilitate locating others in similar situations. Sharing personal health information with their peers is a strategy the sense-maker employs in constructing a new normal after health-related life disruptions. Even so, research on how to design digital health technology that leverages information-sharing behavior is lacking. This paper elaborates on a peer data-sharing model to inform digital health technology design. Based on this model, we herein propose a prototype that promotes social connectedness in cardiovascular disease self-care as a means to advance our understanding of how we might design to facilitate locating peers and gaining new insights into self-care from them.
Cookies and Trust

The General Data Protection Regulation (GDPR) was passed in 2016 to regulate companies’ use and storage of personal data, such as cookies, which are a common feature of the modern internet used to track user’s activity and preferences. This paper aims to examine people’s perspectives on cookie consent prompts and the effects of using deceptive design in cookie consent prompts.
Move’n’Hold: Scalable Device-Based Interaction for Mixed Reality Handheld Displays

While handheld displays provide a widely available, low-budget access to Mixed Reality, developing appropriate interaction techniques remains challenging. For example, one-handed touch- and gestures-based methods are prone to fatigue and occlusion issues and two-handed device-based techniques are constrained to small-range manipulations or their pre-defined thresholds limit user control. In this paper, we introduce Move’n’Hold – a universally applicable interaction paradigm for translating and rotating virtual objects solely through a handheld display’s movement and peripheral touch. Move’n’Hold combines direct mapping between device and object manipulations when only left-thumb-touch is applied with automated repetitions of these initial manipulations that are started or stopped when right-thumb-touch is added or released. Hence, our technique allows switching individually between natural manipulation for small, precise movements and continuous manipulation for large, coarse movements. Our evaluation revealed Move’n’Hold as an intuitive and easy-to-learn input technique for multidimensional object translations and rotations. The technique also provides high scalability in terms of the distance, direction, complexity, and speed of manipulation. At the same time, it supports different user preferences and interaction styles. Our results further show that learning translation prior to rotation enhanced the overall user experience.
Privacy-Preserving Image Retrieval Based on Disordered Local Histograms and Vision Transformer in Cloud Computing

Frequent data breaches in the cloud environment have seriously affected cloud subscribers and providers. Privacy-preserving image retrieval methods can improve the security of cloud image retrieval; however, existing methods have limited accuracy on dynamically updated image databases and mobile lightweight devices. In this study, we propose a privacy-preserving image retrieval method based on disordered local histograms and vision transformer in cloud computing, by designing a multiple encryption method and transformer-based feature model to better mine the local feature value of encrypted images. Specifically, the user performs different value substitution, position substitution, and color substitution on the subblocks of the image to protect the image information. The cloud server extracts the unordered local histogram from the encrypted image and generates retrievable features using transformer. Experiments show that compared with similar CNN schemes, the retrieval accuracy of this method is improved by 8.5%, and the retrieval efficiency is improved by 54.8%.
Fraud Detection and Analysis for Insurance Claim using Machine Learning

Insurance Company working as commercial enterprise from last few years have been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implement machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library
Autonomous Vehicles for Children with Mild Intellectual Disability

Self-driving buses will be part of the public transportation system of the future, and they must therefore be accessible to all. The study reported in this paper examines the user experiences of 16 children with mild intellectual disability riding a self-driving bus. The qualitative analysis, performed by iterative affinity diagramming, of interviews, observations, and a co-design session with five of the children, suggests that familiar situations were characterized by contemplation and curiosity, while unfamiliar ones were characterized by surprise or confusion. The temporal structure of past, present, and future situations in the field of attention played a significant role in the children’s experiences. This leads to design considerations for an explainable interior of self-driving buses.
Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net Model

Breast cancer is a type of cancer caused by the uncontrolled growth and proliferation of cells in the breast tissue. Differentiating between benign and malignant tumors is critical in the detection and treatment of breast cancer. Traditional methods of cancer detection by manual analysis of radiological images are time-consuming and error-prone due to human factors. Modern approaches based on image classifier deep learning models provide significant results in disease detection, but are not suitable for clinical use due to their black-box structure. This paper presents a semantic segmentation method for breast cancer detection from ultrasound images. First, an ultrasound image of any resolution is divided into 256×256 pixel patches by passing it through an image cropping function. These patches are sequentially numbered and given as input to the model. Features are extracted from the 256×256 pixel patches with pre-trained ResNext models placed in the encoder network of the U-Net model. These features are processed in the default decoder network of the U-Net model and estimated at the output with three different pixel values: benign tumor areas (1), malignant tumor areas (2) and background areas (0). The prediction masks obtained at the output of the decoder network are combined sequentially to obtain the final prediction mask. The proposed method is validated on a publicly available dataset of 780 ultrasound images of female patients. The ResNext-based U-Net model achieved 73.17% intersection over union (IoU) and 83.42% dice coefficient (DC) on the test images. ResNext-based U-Net models perform better than the default U-Net model. Experts could use the proposed pixel-based segmentation method for breast cancer diagnosis and monitoring.
Analisis Kapasitas Saluran Drainase pada Wilayah Padat Penduduk dengan Menggunakan Rational Modification Method

Merauke City, as a city in a growth and development period, cannot be separated from various problems. The provision of inadequate city facilities and infrastructure is one of the factors in the situation, drainage, for example. Generally, drainage handling in Merauke City is not comprehensive, so it has yet to handle the problem of inundation or flooding entirely. In residential areas around Estuary 3, puddles or floods are often found whenever rain is high enough intensity. Given that the city of Merauke, including in a flat area or the absence of elevation differences, makes one of the causes of waterlogging or flooding. This study aims to determine the capacity of estuary drainage channels in 3 Merauke Regency to accommodate or drain planned flood discharge. The research method used in this study is the rational method. This study uses two analyses, namely hydrological analysis and hydraulics analysis, where hydrological analysis a carried out to determine planned flood discharge by calculating daily rainfall using probability distribution and hydraulics analysis to determine channel capacity discharge by calculating cross-sectional dimensions. Based on the results of the study shows that segment 1 to segment 10 has different capacity discharge (Qs) and flood discharge (Qr). Segment 1 is one of the channel segments capable of accommodating or draining planned flood discharge with a capacity discharge of 5.51"m" ^"3" "/sec" and flood discharge of 2.07 "m" ^"3" "/sec" (Qs&gt;Qr). In segment 7, which is the segment with the minor channel discharge, namely channel capacity discharge (Qs) of 0.004 "m" ^"3" "/sec" and plan flood discharge (Qr) of 0.78"m" ^"3" "/sec" then, the channel is unable to accommodate or drain plan flood discharge (Qr &gt; Qs). And for other segments, the track cannot adjust or drain the planned flood discharge (Qs&lt;Qr) with a capacity discharge between 0.003-0.05 "m" ^"3" "/sec" with a deliberate flood discharge of 0.21-1.19 "m" ^"3" "/sec" .
Storytelling Machines

We are entering a period of unprecedented collaboration between authors and computers, where artificial intelligence in particular seems likely to act increasingly in a co-authoring capacity. Automated or procedural storytelling represents one exciting avenue of research. By entering prompts and parameters into an AI text generator like ChatGPT, authors could leverage an enormous textual corpus to generate a “new” work that appears to have been authored by a human.
Developing argumentation services based on hypermedia systems

Systems supporting argumentation exist for a long time and have appeared with many different forms, ranging from online forums, to mind maps and decision support systems. Such kind of systems are used on a daily basis by teams that collaborate in data intensive and cognitive complex settings, such as for example teams involved in DNA analysis, marketing or drug testing research. In these settings, teams collect big amounts of required data as well as use sophisticated data mining tools to uncover patterns in the collected data. Current argumentation support systems provide little care for integrating data mining tools required by these teams. In this paper, we present an approach that integrates argumentation support systems with data mining services to augment collaboration and decision making in the above teams. Specifically, the proposed approach allows data mining services and their outcomes to be meaningfully used in argumentative discourses. Such integration enables the contextualization of these services, their execution and their respective results by the argumentative discourse, hence greatly facilitating its monitoring and understanding. Moreover, the paper presents how the proposed argumentation system has been developed based on a Component-Based Open Hypermedia System. The overall aim of these efforts is to show that CB-OHSs are an appropriate architecture to develop complex structuring paradigms in the field of argumentation especially in situations that require a synergy between human and machine intelligence.
Where are we with this?

With the introduction of powerful VR in the form of the Vision Pro next year, 40 years after the Macintosh, we will see the introduction of potentially ground-breaking means through which we can interact with our information and each other, to truly augment our mental abilities. However, I am concerned that we are wasting this historic opportunity. We think we ‘know’ what digital text is, we think digital text is what we use in word processing applications, email, messages, spreadsheets, the web and that's pretty much it. We ‘know’ what digital text is, so we stop exploring what it can be. Doug Engelbart remarked that dreaming is hard work. He was so right, and I believe that we need to be shaken out of our paradigms to think anew. And I think that is–potentially–the most valuable thing being at the cusp of a headset world gives us; the opportunity to think anew, to inspire new and more open thinking about what interactivity is, what augmentation can do, how we can view our information, how we can connect, how we can become better, more connected, humans. There are some very basic issues we need to address before we can truly say we are opening up the vistas available to us. If we as a community do not look into this deeply, I think we are headed for VR experiences as boxed-in as the CDs and DVDs of yesteryear. If we do not, as a community, really consider these issues and delegate them to the large tech companies who makes the equipment, we will not own the future, we will not own the potential, they will, and their motivations are not the same as ours. A scenario. I open a book while wearing a headset–it doesn't matter if I'm in VR or AR mode–and it floats in front of me, at a comfortable reading distance and angle. So far so nice and simple. I then make a gesture and all the images; the photographs, charts, graphs and tables flow out of the book and onto the wall at the back of the room. There is also a beautiful 3D model encoded in one of the pages. In the traditional, flat, version of the book, this model appears as a still image and the metadata is recorded at the back of the book for situations like this. I take this model out and put it right in front of me so that I can look at it properly and interact with it. I also decide to take the Reference section out of the back of the book an place it on the side, over here, and from that I can easily summon the sources and see relationships between them. This sounds like a very interesting environment for Spatial Hypertext right? Maybe we should call it something new, since it is fully dimensional, not flattened. Apple has introduced the name Spatial Computing with the Vision Pro, so how about Spatial Computing Hypertext? That is only intended as a provocation, not as a serious suggestion. We just need to think about what this space is. This space is not cyberspace, it is a visual environment on a human scale. Headset computing is built for humans, it is not built for machine-machine interaction or offloading thinking, which, in contrast, much of AI is. It therefore needs to fit us, like no technology has needed to before, if it is to extend us.
HAIKU's Touch Archive Project: Bringing artworks into conversation

We trace the development of our ideas about how hypertext can uniquely support galleries, libraries, archives and museums laying out the vision of our Touch Archive project. Our first steps arose from interviews with curators about what they wanted and needed in improving and expanding the types of their exhibits.
On the Linear Capacity of Conditional Disclosure of Secrets

Conditional disclosure of secrets (CDS) is the problem of disclosing as efficiently as possible, one secret from Alice and Bob to Carol if and only if the inputs at Alice and Bob satisfy some function. The information theoretic capacity of CDS is the maximum number of bits of the secret that can be securely disclosed per bit of total communication from Alice and Bob to Carol. All CDS instances, where the capacity is the highest and is equal to 1/2, are recently characterized through a noise and signal alignment approach and are described using a graph representation of the function. In this work, we go beyond the best case scenarios and further develop the alignment approach to characterize the linear capacity of a class of CDS instances to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\rho -1)/(2\rho)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> is a newly introduced and highly specific covering parameter of the graph representation of the function.
Self-Supervised Single-Line LiDAR Depth Completion

Depth completion plays a crucial role in enabling real-world applications such as obstacle avoidance and SLAM for robot navigation. This letter focuses on addressing the depth completion challenge for single-line LiDAR, commonly used in conjunction with visual cameras. The sparsity of valid depth points makes supervised methods inadequate, while existing self-supervised approaches are only applicable to 64-line LiDARs. In this letter, we propose a novel self-supervised approach for single-line LiDAR depth completion. Our approach makes two key contributions. Firstly, we introduce the Relative-to-Metric (R2M) depth distillation framework, which estimates a pixel-wise metric depth map using an RGB image and its corresponding single-line depth map. This is achieved by distilling relative depth predictions from a monocular depth estimator trained on RGB images. Secondly, we propose the Line Depth Prior (LDP), a model-agnostic geometry regularization technique that promotes depth completion. Through extensive experiments, we demonstrate that our proposed method can: i) accurately reconstruct complete depth maps from single-line depth inputs without requiring additional depth supervision, except for the observed entries, and ii) facilitate downstream SLAM tasks when using single-line LiDAR.
Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control

High-speed autonomous driving in off-road environments has immense potential for various applications, but it also presents challenges due to the complexity of vehicle-terrain interactions. In such environments, it is crucial for the vehicle to predict its motion and adjust its controls proactively in response to environmental changes, such as variations in terrain elevation. To this end, we propose a method for learning terrain-aware kinodynamic model which is conditioned on both proprioceptive and exteroceptive information. The proposed model generates reliable predictions of 6-degree-of-freedom motion and can even estimate contact interactions without requiring ground truth force data during training. This enables the design of a safe and robust model predictive controller through appropriate cost function design which penalizes sampled trajectories with unstable motion, unsafe interactions, and high levels of uncertainty derived from the model. We demonstrate the effectiveness of our approach through experiments on a simulated off-road track, showing that our proposed model-controller pair outperforms the baseline and ensures robust high-speed driving performance without control failure.