OpenRaiser/Intern-Atlas huggingface dataset:
Proportional Dynamics in Linear Fisher Markets with Auto-bidding: Convergence, Incentives and Fairness
Venue: arXiv cs.GT 2024
Year: 2024

Proportional dynamics, originated from peer-to-peer file sharing systems, models a decentralized price-learning process in Fisher markets. Previously, items in the dynamics operate independently of one another, and each is assumed to belong to a different seller. In this paper, we show how it can be generalized to the setting where each seller brings multiple items and buyers allocate budgets at the granularity of sellers rather than individual items. The generalized dynamics consistently converges to the competitive equilibrium, and interestingly relates to the auto-bidding paradigm currently popular in online advertising auction markets. In contrast to peer-to-peer networks, the proportional rule is not imposed as a protocol in auto-bidding markets. Regarding this incentive concern, we show that buyers have a strong tendency to follow the rule, but it is easy for sellers to profitably deviate (given buyers' commitment to the rule). Based on this observation, we further study the seller-side deviation game and show that it admits a unique pure Nash equilibrium. Though it is generally different from the competitive equilibrium, we show that it attains a good fairness guarantee as long as the market is competitive enough and not severely monopolized.
Enhancing Covert Communication in Relay Systems Using Multi-Antenna Technique
Venue: arXiv cs.CR 2024
Year: 2024

This paper exploits the multi-antenna technique to enhance the covert communication performance in a relay system, where a source S conducts covert communication with a destination D via a relay R, subjecting to the detections of transmissions in the two hops from a single-antenna warden W. To demonstrate the performance gain from adopting the multi-antenna technique, we first consider the scenario when S, R and D all adopt single antenna, and apply hypothesis testing and statistics theories to develop a theoretical framework for the covert performance modeling in terms of detection error probability (DEP) and covert throughput. We then consider the scenario when S, R and D all adopt multiple antennas, and apply the hypothesis testing, statistics and matrix theories to develop corresponding theoretical framework for performance modeling. We further explore the optimal designs of the target rate and transmit power for covert throughput maximization under above both scenarios, subjecting to the constraints of covertness, reliability and transmit power. To solve the optimization problems, we employ Karushi-Kuhn-Tucker (KKT) conditions method in the single antenna scenario and a search algorithm in the multi-antenna scenario. Finally, we provide extensive numerical results to illustrate how the multi-antenna technique can enhance the covert performance in two-hop relay systems.
DepGAN: Leveraging Depth Maps for Handling Occlusions and Transparency in Image Composition
Venue: arXiv cs.CV 2024
Year: 2024

Image composition is a complex task which requires a lot of information about the scene for an accurate and realistic composition, such as perspective, lighting, shadows, occlusions, and object interactions. Previous methods have predominantly used 2D information for image composition, neglecting the potentials of 3D spatial information. In this work, we propose DepGAN, a Generative Adversarial Network that utilizes depth maps and alpha channels to rectify inaccurate occlusions and enhance transparency effects in image composition. Central to our network is a novel loss function called Depth Aware Loss which quantifies the pixel wise depth difference to accurately delineate occlusion boundaries while compositing objects at different depth levels. Furthermore, we enhance our network's learning process by utilizing opacity data, enabling it to effectively manage compositions involving transparent and semi-transparent objects. We tested our model against state-of-the-art image composition GANs on benchmark (both real and synthetic) datasets. The results reveal that DepGAN significantly outperforms existing methods in terms of accuracy of object placement semantics, transparency and occlusion handling, both visually and quantitatively. Our code is available at https://amrtsg.github.io/DepGAN/.
Trajectory and Power Optimization for Multi-UAV Enabled Emergency Wireless Communications Networks
Venue: arXiv cs.IT 2024
Year: 2024

Recently, unmanned aerial vehicle (UAV) has attracted much attention due to its flexible deployment and controllable mobility. As the general communication network cannot meet the emergency requirements, in this paper we study the multi-UAV enabled wireless emergency communication system. Our goal is to maximize the capacity with jointly optimizing trajectory and allocating power. To tackle this non-convex optimization problem, we first decompose it into two sub-problems to optimize the trajectory and power allocation, respectively. Then, we propose the successive convex approximation technique and the block coordinate update algorithm to solve the two subproblems. The approximate optimal solution can be obtained after continuous iterations. Simulation results show that the capacity can be greatly increased using our proposed joint trajectory optimization and power allocation.
Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space
Venue: arXiv cs.LG 2024
Year: 2024

We propose a new uncertainty estimator for gradient-free optimisation of black-box simulators using deep generative surrogate models. Optimisation of these simulators is especially challenging for stochastic simulators and higher dimensions. To address these issues, we utilise a deep generative surrogate approach to model the black box response for the entire parameter space. We then leverage this knowledge to estimate the proposed uncertainty based on the Wasserstein distance - the Wasserstein uncertainty. This approach is employed in a posterior agnostic gradient-free optimisation algorithm that minimises regret over the entire parameter space. A series of tests were conducted to demonstrate that our method is more robust to the shape of both the black box function and the stochastic response of the black box than state-of-the-art methods, such as efficient global optimisation with a deep Gaussian process surrogate.
IPA-NeRF: Illusory Poisoning Attack Against Neural Radiance Fields
Venue: arXiv cs.CV 2024
Year: 2024

Neural Radiance Field (NeRF) represents a significant advancement in computer vision, offering implicit neural network-based scene representation and novel view synthesis capabilities. Its applications span diverse fields including robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, etc., some of which are considered high-risk AI applications. However, despite its widespread adoption, the robustness and security of NeRF remain largely unexplored. In this study, we contribute to this area by introducing the Illusory Poisoning Attack against Neural Radiance Fields (IPA-NeRF). This attack involves embedding a hidden backdoor view into NeRF, allowing it to produce predetermined outputs, i.e. illusory, when presented with the specified backdoor view while maintaining normal performance with standard inputs. Our attack is specifically designed to deceive users or downstream models at a particular position while ensuring that any abnormalities in NeRF remain undetectable from other viewpoints. Experimental results demonstrate the effectiveness of our Illusory Poisoning Attack, successfully presenting the desired illusory on the specified viewpoint without impacting other views. Notably, we achieve this attack by introducing small perturbations solely to the training set. The code can be found at https://github.com/jiang-wenxiang/IPA-NeRF.
Code Documentation and Analysis to Secure Software Development
Venue: arXiv cs.SE 2024
Year: 2024

We present the Code Documentation and Analysis Tool (CoDAT). CoDAT is a tool designed to maintain consistency between the various levels of code documentation, e.g. if a line in a code sketch is changed, the comment that documents the corresponding code is also changed. That is, comments are linked and updated so as to remain internally consistent and also consistent with the code. By flagging "out of date" comments, CoDAT alerts the developer to maintain up-to-date documentation. We use a large language model to check the semantic consistency between a fragment of code and the comments that describe it. Thus we also flag semantic inconsistency as well as out of date comments. This helps programers write code that correctly implements a code sketch, and so provides machine support for a step-wise refinement approach, starting with a code sketch and proceeding down to code through one or more refinement iterations. CoDAT is implemented in the Intellij IDEA IDE where we use the Code Insight daemon package alongside a custom regular expression algorithm to mark tagged comments whose corresponding code blocks have changed. CoDAT's backend is structurally decentralized to allow a distributed ledger framework for code consistency and architectural compilation tracking.
Open the Data! Chuvash Datasets
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we introduce four comprehensive datasets for the Chuvash language, aiming to support and enhance linguistic research and technological development for this underrepresented language. These datasets include a monolingual dataset, a parallel dataset with Russian, a parallel dataset with English, and an audio dataset. Each dataset is meticulously curated to serve various applications such as machine translation, linguistic analysis, and speech recognition, providing valuable resources for scholars and developers working with the Chuvash language. Together, these datasets represent a significant step towards preserving and promoting the Chuvash language in the digital age.
SlicerChat: Building a Local Chatbot for 3D Slicer
Venue: arXiv cs.HC 2024
Year: 2024

3D Slicer is a powerful platform for 3D data visualization and analysis, but has a significant learning curve for new users. Generative AI applications, such as ChatGPT, have emerged as a potential method of bridging the gap between various sources of documentation using natural language. The limited exposure of LLM services to 3D Slicer documentation, however, means that ChatGPT and related services tend to suffer from significant hallucination. The objective of this project is to build a chatbot architecture, called SlicerChat, that is optimized to answer 3D Slicer related questions and able to run locally using an open-source model. The core research questions explored in this work revolve around the answer quality and speed differences due to fine-tuning, model size, and the type of domain knowledge included in the prompt. A prototype SlicerChat system was built as a custom extension in 3D Slicer based on the Code-Llama Instruct architecture. Models of size 1.1B, 7B and 13B were fine-tuned using Low rank Adaptation, and various sources of 3D Slicer documentation were compiled for use in a Retrieval Augmented Generation paradigm. Testing combinations of fine-tuning and model sizes on a benchmark dataset of five 3D Slicer questions revealed that fine-tuning had no impact on model performance or speed compared to the base architecture, and that larger models performed better with a significant speed decrease. Experiments with adding 3D Slicer documentation to the prompt showed that Python sample code and Markdown documentation were the most useful information to include, but that adding 3D Slicer scene data and questions taken from Discourse also improved model performance. In conclusion, this project shows the potential for integrating a high quality, local chatbot directly into 3D Slicer to help new users and experienced developers alike to more efficiently use the software.
Interconnected virtual space and Theater. Practice as research on theater stage in the era of the network
Venue: arXiv cs.HC 2024
Year: 2024

Since 2014, we have been conducting experiments based on a multidisciplinary collaboration between specialists in theatrical staging and researchers in virtual reality, digital art, and video games. This team focused its work on the similarities and differencesthat exist between real physical actors (actor-performers) and virtual digital actors (avatars). From this multidisciplinary approach, experimental research-creation projects have emerged and rely on a physical actor playing with the image of an avatar, controlled by another physical actor via the intermediary of a low-cost motion-capture system. In the first part of the paper, we will introduce the scenographic design on which our presentation is based, and the modifications we have made in relation to our previous work. Next, in the second section, we will discuss in detail the impact of augmenting the player's game using an avatar, compared to the scenic limitations of the theatrical stage. In part three of the paper, we will discuss the software-related aspects of the project, focusing on exchanges between the different components of our design and describing the algorithms enabling us to utilize the real-time movement of a player via various capture devices. To conclude, we will examine in detail how our experimental system linking physical actors and avatars profoundly alters the nature of collaboration between directors, actors, and digital artists in terms of actor/avatar direction.
Digital twins in sport: Concepts, Taxonomies, Challenges and Practical Potentials
Venue: arXiv cs.HC 2024
Year: 2024

Digital twins belong to ten of the strategic technology trends according to the Gartner list from 2019, and have encountered a big expansion, especially with the introduction of Industry 4.0. Sport, on the other hand, has become a constant companion of the modern human suffering a lack of a healthy way of life. The application of digital twins in sport has brought dramatic changes not only in the domain of sport training, but also in managing athletes during competitions, searching for strategical solutions before and tactical solutions during the games by coaches. In this paper, the domain of digital twins in sport is reviewed based on papers which have emerged in this area. At first, the concept of a digital twin is discussed in general. Then, taxonomies of digital twins are appointed. According to these taxonomies, the collection of relevant papers is analyzed, and some real examples of digital twins are exposed. The review finishes with a discussion about how the digital twins affect changes in the modern sport disciplines, and what challenges and opportunities await the digital twins in the future.
Inspired by AI? A Novel Generative AI System To Assist Conceptual Automotive Design
Venue: arXiv cs.HC 2024
Year: 2024

Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather image ideas, followed by sketching on paper or using digital tools to develop concepts. Emerging generative AI techniques, such as diffusion models, offer a promising avenue to streamline these processes by swiftly generating design concepts based on text and image inspiration inputs, subsequently using the AI generated design concepts as fresh sources of inspiration for further concept development. However, applying these generative AI techniques directly within a design context has challenges. Firstly, generative AI tools may exhibit a bias towards particular styles, resulting in a lack of diversity of design outputs. Secondly, these tools may struggle to grasp the nuanced meanings of texts or images in a design context. Lastly, the lack of integration with established design processes within design teams can result in fragmented use scenarios. Focusing on these challenges, we conducted workshops, surveys, and data augmentation involving teams of experienced automotive designers to investigate their current practices in generating concepts inspired by texts and images, as well as their preferred interaction modes for generative AI systems to support the concept generation workflow. Finally, we developed a novel generative AI system based on diffusion models to assist conceptual automotive design.
Flowers Revisited: A Preliminary Replication of Flowers et al. 1997
Venue: arXiv cs.HC 2024
Year: 2024

In 1997, Flowers, Buhman, and Turnage published a paper titled ``Cross-Modal Equivalence of Visual and Auditory Scatterplots for Exploring Bivariate Data Samples.'' This paper examined our capacity to assess the relationship between two data variables when presented through visual or auditory scatterplots. Twenty-seven years later, we have replicated the first part of this influential study and present the preliminary findings of our replication, initially involving 21 participants. In addition to purely auditory and visual scatterplots, we introduced audiovisual scatterplots as a third condition in our experiment. Our initial findings mirror those of Flowers et al.'s original research. With this extended abstract, we also aim to spark a discussion about the significance of replication studies for our research community in general.
Modal Decomposition in Numerical Computation of Eddy Current Transients
Venue: arXiv cs.CE 2024
Year: 2024

A methodology to reduce the computational cost of time domain computations of eddy currents problems is proposed and implemented in a parallel computing environment. It is based on the modal decomposition of the current density and it is applicable even in presence of injected currents into the electrodes of a conducting domain. Using a theta-method integration algorithm, the performances of the the proposed approach are compared against those of a classical method based on the Cholesky factorization, for a case of interest from eddy current nondestructive testing. For this large eddy current problem (number of unknowns greater than 100k, number of time steps of interest equal to 100k) the proposed solution method is shown to be much faster than those based on standard time integration schemes.
Evaluating Contextually Personalized Programming Exercises Created with Generative AI
Venue: arXiv cs.HC 2024
Year: 2024

Programming skills are typically developed through completing various hands-on exercises. Such programming problems can be contextualized to students' interests and cultural backgrounds. Prior research in educational psychology has demonstrated that context personalization of exercises stimulates learners' situational interests and positively affects their engagement. However, creating a varied and comprehensive set of programming exercises for students to practice on is a time-consuming and laborious task for computer science educators. Previous studies have shown that large language models can generate conceptually and contextually relevant programming exercises. Thus, they offer a possibility to automatically produce personalized programming problems to fit students' interests and needs. This article reports on a user study conducted in an elective introductory programming course that included contextually personalized programming exercises created with GPT-4. The quality of the exercises was evaluated by both the students and the authors. Additionally, this work investigated student attitudes towards the created exercises and their engagement with the system. The results demonstrate that the quality of exercises generated with GPT-4 was generally high. What is more, the course participants found them engaging and useful. This suggests that AI-generated programming problems can be a worthwhile addition to introductory programming courses, as they provide students with a practically unlimited pool of practice material tailored to their personal interests and educational needs.
Neuro-Symbolic Artificial Intelligence for Patient Monitoring
Venue: arXiv cs.HC 2024
Year: 2024

In this paper we argue that Neuro-Symbolic AI (NeSy-AI) should be applied for patient monitoring. In this context, we introduce patient monitoring as a special case of Human Activity Recognition and derive concrete requirements for this application area. We then present a process architecture and discuss why NeSy-AI should be applied for patient monitoring. To further support our argumentation, we show how NeSy-AI can help to overcome certain technical challenges that arise from this application area.
From Top-Right to User-Right: Perceptual Prioritization of Point-Feature Label Positions
Venue: arXiv cs.HC 2024
Year: 2024

In cartography, Geographic Information Systems (GIS), and visualization, the position of a label relative to its point feature is crucial for readability and user experience. Alongside other factors, the point-feature label placement (PFLP) is typically governed by the Position Priority Order (PPO), a systematic raking of potential label positions around a point feature according to predetermined priorities. While there is a broad consensus on factors such as avoiding label conflicts and ensuring clear label-to-feature associations, there is no agreement on PPO. Most PFLP techniques rely on traditional PPOs grounded in typographic and cartographic conventions established decades ago, which may no longer meet today's user expectations. In contrast, commercial products like Google Maps and Mapbox use non-traditional PPOs for unreported reasons. Our extensive user study introduces the Perceptual Position Priority Order (PerceptPPO), a user-validated PPO that significantly departs from traditional conventions. A key finding is that labels placed above point features are significantly preferred by users, contrary to the conventional top-right position. We also conducted a supplementary study on the preferred label density, an area scarcely explored in prior research. Finally, we performed a comparative user study assessing the perceived quality of PerceptPPO over existing PPOs, advocating its adoption in cartographic and GIS applications, as well as in other types of visualizations. Our research, supported by nearly 800 participants from 48 countries and over 45,500 pairwise comparisons, offers practical guidance for designers and application developers aiming to optimize user engagement and comprehension, paving the way for more intuitive and accessible visualizations.
HydroTrack: Spectroscopic Analysis Prototype Enabling Real-Time Hydration Monitoring in Wearables
Venue: arXiv cs.HC 2024
Year: 2024

In the rapidly growing field of wearable technology, optical devices are emerging as a significant innovation, offering non-invasive methods for analyzing skin and underlying tissue properties. Despite their promise, progress has been slowed by a lack of specialized prototypes and advanced analysis techniques. Addressing this gap, our study introduces, HydroTrack, an 18-channel spectroscopy sensor, ingeniously embedded in a smart-watch. Accompanying this hardware, we present signal processing and data analysis techniques implemented at the edge, designed to maximize the utility of our system in comprehensive health tracking. A pivotal application of our device is the real-time assessment of hydration levels in physically active individuals. We validated our prototype and analytical approach through experiments on six participants, focusing on hydration dynamics during physical exercises. Our findings reveal an accuracy of avg. 95% in determining hydration states.