OpenRaiser/Intern-Atlas huggingface dataset:
A Local Iterative Approach for the Extraction of 2D Manifolds from Strongly Curved and Folded Thin-Layer Structures
Venue: arXiv cs.CV 2023
Year: 2023

Ridge surfaces represent important features for the analysis of 3-dimensional (3D) datasets in diverse applications and are often derived from varying underlying data including flow fields, geological fault data, and point data, but they can also be present in the original scalar images acquired using a plethora of imaging techniques. Our work is motivated by the analysis of image data acquired using micro-computed tomography (Micro-CT) of ancient, rolled and folded thin-layer structures such as papyrus, parchment, and paper as well as silver and lead sheets. From these documents we know that they are 2-dimensional (2D) in nature. Hence, we are particularly interested in reconstructing 2D manifolds that approximate the document's structure. The image data from which we want to reconstruct the 2D manifolds are often very noisy and represent folded, densely-layered structures with many artifacts, such as ruptures or layer splitting and merging. Previous ridge-surface extraction methods fail to extract the desired 2D manifold for such challenging data. We have therefore developed a novel method to extract 2D manifolds. The proposed method uses a local fast marching scheme in combination with a separation of the region covered by fast marching into two sub-regions. The 2D manifold of interest is then extracted as the surface separating the two sub-regions. The local scheme can be applied for both automatic propagation as well as interactive analysis. We demonstrate the applicability and robustness of our method on both artificial data as well as real-world data including folded silver and papyrus sheets.
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization
Venue: arXiv cs.CV 2023
Year: 2023

Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularized Training (ART) to compress dense into sparse networks. Instead of the commonly used binary mask during training to reduce the number of model weights, we inherently shrink weights close to zero in an iterative manner with increasing weight regularization. Our method compresses the pre-trained model knowledge into the weights of highest magnitude. Therefore, we introduce a novel regularization loss named HyperSparse that exploits the highest weights while conserving the ability of weight exploration. Extensive experiments on CIFAR and TinyImageNet show that our method leads to notable performance gains compared to other sparsification methods, especially in extremely high sparsity regimes up to 99.8 percent model sparsity. Additional investigations provide new insights into the patterns that are encoded in weights with high magnitudes.
Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature
Venue: arXiv cs.NI 2023
Year: 2023

Wearable devices like smart glasses have gained popularity across various applications. However, their limited computational capabilities pose challenges for tasks that require extensive processing, such as image and video processing, leading to drained device batteries. To address this, offloading such tasks to nearby powerful remote devices, such as mobile devices or remote servers, has emerged as a promising solution. This paper focuses on analyzing task-offloading scenarios for a healthcare monitoring application performed on smart wearable glasses, aiming to identify the optimal conditions for offloading. The study evaluates performance metrics including task completion time, computing capabilities, and energy consumption under realistic conditions. A specific use case is explored within an indoor area like an airport, where security agents wearing smart glasses to detect elevated body temperature in individuals, potentially indicating COVID-19. The findings highlight the potential benefits of task offloading for wearable devices in healthcare settings, demonstrating its practicality and relevance.
Explaining Black-Box Models through Counterfactuals
Venue: arXiv cs.LG 2023
Year: 2023

We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Explanations that involve realistic and actionable changes can be used to provide AR: a set of proposed actions for individuals to change an undesirable outcome for the better. In this article, we discuss the usefulness of CE for Explainable Artificial Intelligence and demonstrate the functionality of our package. The package is straightforward to use and designed with a focus on customization and extensibility. We envision it to one day be the go-to place for explaining arbitrary predictive models in Julia through a diverse suite of counterfactual generators.
Algorithms for the Training of Neural Support Vector Machines
Venue: arXiv cs.LG 2023
Year: 2023

Neural support vector machines (NSVMs) allow for the incorporation of domain knowledge in the design of the model architecture. In this article we introduce a set of training algorithms for NSVMs that leverage the Pegasos algorithm and provide a proof of concept by solving a set of standard machine learning tasks.
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Venue: arXiv cs.CV 2023
Year: 2023

Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. However, this assumption does not hold in dynamic objects in scenarios, leading to errors during the view synthesis stage, such as feature mismatch and occlusion, which can significantly reduce the accuracy of the generated depth maps. To address this problem, we propose a novel dynamic cost volume that exploits residual optical flow to describe moving objects, improving incorrectly occluded regions in static cost volumes used in previous work. Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other. In other words, occlusion from the static volume is refined by the dynamic volume, and incorrect information from the dynamic volume is eliminated by the static volume. Furthermore, we propose a pyramid distillation loss to reduce photometric error inaccuracy at low resolutions and an adaptive photometric error loss to alleviate the flow direction of the large gradient in the occlusion regions. We conducted extensive experiments on the KITTI and Cityscapes datasets, and the results demonstrate that our model outperforms previously published baselines for self-supervised monocular depth estimation.
AAFACE: Attribute-aware Attentional Network for Face Recognition
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.
Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems
Venue: arXiv cs.RO 2023
Year: 2023

Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean (Sub-T) environments in the presence of aerosol particles have recently become the main focus in the field of robotics. Aerosol particles such as smoke and dust directly affect the performance of any mobile robotic platform due to their reliance on their onboard perception systems for autonomous navigation and localization in Global Navigation Satellite System (GNSS)-denied environments. Although obstacle avoidance and object detection algorithms are robust to the presence of noise to some degree, their performance directly relies on the quality of captured data by onboard sensors such as Light Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel modular agnostic filtration pipeline based on intensity and spatial information such as local point density for removal of detected smoke particles from Point Cloud (PCL) prior to its utilization for collision detection. Furthermore, the efficacy of the proposed framework in the presence of smoke during multiple frontier exploration missions is investigated while the experimental results are presented to facilitate comparison with other methodologies and their computational impact. This provides valuable insight to the research community for better utilization of filtration schemes based on available computation resources while considering the safe autonomous navigation of mobile robots.
Fault Tolerance in Euclidean Committee Selection
Venue: arXiv cs.GT 2023
Year: 2023

In the committee selection problem, the goal is to choose a subset of size $k$ from a set of candidates $C$ that collectively gives the best representation to a set of voters. We consider this problem in Euclidean $d$-space where each voter/candidate is a point and voters' preferences are implicitly represented by Euclidean distances to candidates. We explore fault-tolerance in committee selection and study the following three variants: (1) given a committee and a set of $f$ failing candidates, find their optimal replacement; (2) compute the worst-case replacement score for a given committee under failure of $f$ candidates; and (3) design a committee with the best replacement score under worst-case failures. The score of a committee is determined using the well-known (min-max) Chamberlin-Courant rule: minimize the maximum distance between any voter and its closest candidate in the committee. Our main results include the following: (1) in one dimension, all three problems can be solved in polynomial time; (2) in dimension $d \geq 2$, all three problems are NP-hard; and (3) all three problems admit a constant-factor approximation in any fixed dimension, and the optimal committee problem has an FPT bicriterion approximation.
Extend Wave Function Collapse to Large-Scale Content Generation
Venue: arXiv cs.AI 2023
Year: 2023

Wave Function Collapse (WFC) is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC) algorithm framework to reduce time complexity. To avoid conflict and backtracking problems, we offer a complete and sub-complete tileset preparation strategy, which requires only a small number of tiles to generate aperiodic and deterministic infinite content. We also introduce the weight-brush system that combines N-WFC and sub-complete tileset, proving its suitability for game design. Our contribution addresses WFC's challenge in massive content generation and provides a theoretical basis for implementing concrete games.
Reinforcing Security and Usability of Crypto-Wallet with Post-Quantum Cryptography and Zero-Knowledge Proof
Venue: arXiv cs.CR 2023
Year: 2023

Crypto-wallets or digital asset wallets are a crucial aspect of managing cryptocurrencies and other digital assets such as NFTs. However, these wallets are not immune to security threats, particularly from the growing risk of quantum computing. The use of traditional public-key cryptography systems in digital asset wallets makes them vulnerable to attacks from quantum computers, which may increase in the future. Moreover, current digital wallets require users to keep track of seed-phrases, which can be challenging and lead to additional security risks. To overcome these challenges, a new algorithm is proposed that uses post-quantum cryptography (PQC) and zero-knowledge proof (ZKP) to enhance the security of digital asset wallets. The research focuses on the use of the Lattice-based Threshold Secret Sharing Scheme (LTSSS), Kyber Algorithm for key generation and ZKP for wallet unlocking, providing a more secure and user-friendly alternative to seed-phrase, brain and multi-sig protocol wallets. This algorithm also includes several innovative security features such as recovery of wallets in case of downtime of the server, and the ability to rekey the private key associated with a specific username-password combination, offering improved security and usability. The incorporation of PQC and ZKP provides a robust and comprehensive framework for securing digital assets in the present and future. This research aims to address the security challenges faced by digital asset wallets and proposes practical solutions to ensure their safety in the era of quantum computing.
A Unified Query-based Paradigm for Camouflaged Instance Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Due to the high similarity between camouflaged instances and the background, the recently proposed camouflaged instance segmentation (CIS) faces challenges in accurate localization and instance segmentation. To this end, inspired by query-based transformers, we propose a unified query-based multi-task learning framework for camouflaged instance segmentation, termed UQFormer, which builds a set of mask queries and a set of boundary queries to learn a shared composed query representation and efficiently integrates global camouflaged object region and boundary cues, for simultaneous instance segmentation and instance boundary detection in camouflaged scenarios. Specifically, we design a composed query learning paradigm that learns a shared representation to capture object region and boundary features by the cross-attention interaction of mask queries and boundary queries in the designed multi-scale unified learning transformer decoder. Then, we present a transformer-based multi-task learning framework for simultaneous camouflaged instance segmentation and camouflaged instance boundary detection based on the learned composed query representation, which also forces the model to learn a strong instance-level query representation. Notably, our model views the instance segmentation as a query-based direct set prediction problem, without other post-processing such as non-maximal suppression. Compared with 14 state-of-the-art approaches, our UQFormer significantly improves the performance of camouflaged instance segmentation. Our code will be available at https://github.com/dongbo811/UQFormer.
Energy Efficiency of Quantum Statevector Simulation at Scale
Venue: arXiv cs.PF 2023
Year: 2023

Classical simulations are essential for the development of quantum computing, and their exponential scaling can easily fill any modern supercomputer. In this paper we consider the performance and energy consumption of large Quantum Fourier Transform (QFT) simulations run on ARCHER2, the UK's National Supercomputing Service, with QuEST toolkit. We take into account CPU clock frequency and node memory size, and use cache-blocking to rearrange the circuit, which minimises communications. We find that using 2.00GHz instead of 2.25GHz can save as much as 25% of energy at 5% increase in runtime. Higher node memory also has the potential to be more efficient, and cost the user fewer CUs, but at higher runtime penalty. Finally, we present a cache-blocking QFT circuit, which halves the required communication. All our optimisations combined result in 40% faster simulations and 35% energy savings in 44 qubit simulations on 4,096 ARCHER2 nodes.
Development and Evaluation of Three Chatbots for Postpartum Mood and Anxiety Disorders
Venue: arXiv cs.CL 2023
Year: 2023

In collaboration with Postpartum Support International (PSI), a non-profit organization dedicated to supporting caregivers with postpartum mood and anxiety disorders, we developed three chatbots to provide context-specific empathetic support to postpartum caregivers, leveraging both rule-based and generative models. We present and evaluate the performance of our chatbots using both machine-based metrics and human-based questionnaires. Overall, our rule-based model achieves the best performance, with outputs that are close to ground truth reference and contain the highest levels of empathy. Human users prefer the rule-based chatbot over the generative chatbot for its context-specific and human-like replies. Our generative chatbot also produced empathetic responses and was described by human users as engaging. However, limitations in the training dataset often result in confusing or nonsensical responses. We conclude by discussing practical benefits of rule-based vs. generative models for supporting individuals with mental health challenges. In light of the recent surge of ChatGPT and BARD, we also discuss the possibilities and pitfalls of large language models for digital mental healthcare.
UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity
Venue: arXiv cs.CV 2023
Year: 2023

Image reconstruction and captioning from brain activity evoked by visual stimuli allow researchers to further understand the connection between the human brain and the visual perception system. While deep generative models have recently been employed in this field, reconstructing realistic captions and images with both low-level details and high semantic fidelity is still a challenging problem. In this work, we propose UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activity. For the first time, we unify image reconstruction and captioning from visual-evoked functional magnetic resonance imaging (fMRI) through a latent diffusion model termed Versatile Diffusion. Specifically, we transform fMRI voxels into text and image latent for low-level information and guide the backward diffusion process through fMRI-based image and text conditions derived from CLIP to generate realistic captions and images. UniBrain outperforms current methods both qualitatively and quantitatively in terms of image reconstruction and reports image captioning results for the first time on the Natural Scenes Dataset (NSD) dataset. Moreover, the ablation experiments and functional region-of-interest (ROI) analysis further exhibit the superiority of UniBrain and provide comprehensive insight for visual-evoked brain decoding.
Integrated dataset for air travel and reported Zika virus cases in Colombia (Data and Resources Paper)
Venue: arXiv cs.SI 2023
Year: 2023

This open-access dataset provides consistent records of air travel volumes between 205 airport catchments in Colombia and the associated number of reported human cases of Zika virus within these catchments during the arbovirus outbreak between October 2015 and September 2016. We associated in this dataset the monthly air travel volumes provided by the Colombian Civil Aviation Authority (AEROCIVIL) with the reported human cases of Zika Virus published by The Pan American Health Organization (PAHO). Our methodology consists of geocoding all the reported airports and identifying the catchment of each airport using the municipalities' boundaries since reported human cases of Zika Virus are available at the municipal level. In addition, we calculated the total population at risk in each airport catchment by combining the total population count in a catchment with the environmental suitability of the Aedes aegypti mosquito, the vector for the Zika virus. We separated the monthly air travel volumes into domestic and international based on the location of the origin airport. The current dataset includes the total air travel volumes of 23,539,364 passengers on domestic flights and 11,592,197 on international ones. We validated our dataset by comparing the monthly aggregated air travel volumes between airport catchments to those predicted by the gravity model. We hope the novel dataset will provide a resource to researchers studying the role of human mobility in the spread of mosquito-borne diseases and modeling disease spread in realistic networks.
Applying User Experience and User-Centered Design Software Processes in Undergraduate Mobile Application Development Teaching
Venue: arXiv cs.SE 2023
Year: 2023

Agile methods in undergraduate courses have been explored by various authors looking to close the gap between industry and professional profiles. We have structured an Android application development course based on a tailored agile process for development of educational software tools. This process is based on both Scrum and Extreme Programming in combination with User Experience (UX) and User-Centered Design (UCD) approaches. The course is executed in two phases: the first half of the course's semester presents theory on agile and mobile applications development, the latter half is managed as a workshop where students develop for an actual client. The introduction of UX and UCD exploiting the close relationship with stakeholders expected from an agile process can enhance Quality in Use features. Since 2019 two of the projects have been extended in agreement between the client and students. Students, clients and users have found value in the generated products.
Data-CASE: Grounding Data Regulations for Compliant Data Processing Systems
Venue: arXiv cs.DB 2023
Year: 2023

Data regulations, such as GDPR, are increasingly being adopted globally to protect against unsafe data management practices. Such regulations are, often ambiguous (with multiple valid interpretations) when it comes to defining the expected dynamic behavior of data processing systems. This paper argues that it is possible to represent regulations such as GDPR formally as invariants using a (small set of) data processing concepts that capture system behavior. When such concepts are grounded, i.e., they are provided with a single unambiguous interpretation, systems can achieve compliance by demonstrating that the system-actions they implement maintain the invariants (representing the regulations). To illustrate our vision, we propose Data-CASE, a simple yet powerful model that (a) captures key data processing concepts (b) a set of invariants that describe regulations in terms of these concepts. We further illustrate the concept of grounding using "deletion" as an example and highlight several ways in which end-users, companies, and software designers/engineers can use Data-CASE.