Reviewerly/exHarmony huggingface dataset:
A Novel Building Extraction Network via Multi-Scale Foreground Modeling and Gated Boundary Refinement

Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to specific shooting angles. First, there exists a foreground–background imbalance issue, and the model excessively learns features unrelated to buildings, resulting in performance degradation and propagative interference. Second, buildings have complex boundary information, while conventional network architectures fail to capture fine boundaries. In this paper, we designed a multi-task U-shaped network (BFL-Net) to solve these problems. This network enhances the expression of the foreground and boundary features in the prediction results through foreground learning and boundary refinement, respectively. Specifically, the Foreground Mining Module (FMM) utilizes the relationship between buildings and multi-scale scene spaces to explicitly model, extract, and learn foreground features, which can enhance foreground and related contextual features. The Dense Dilated Convolutional Residual Block (DDCResBlock) and the Dual Gate Boundary Refinement Module (DGBRM) individually process the diverted regular stream and boundary stream. The former can effectively expand the receptive field, and the latter utilizes spatial and channel gates to activate boundary features in low-level feature maps, helping the network refine boundaries. The predictions of the network for the building, foreground, and boundary are respectively supervised by ground truth. The experimental results on the WHU Building Aerial Imagery and Massachusetts Buildings Datasets show that the IoU scores of BFL-Net are 91.37% and 74.50%, respectively, surpassing state-of-the-art models.
Algorithmic predictions and pre-emptive violence: artificial intelligence and the future of unmanned aerial systems

Abstract The military rationale of a pre-emptive strike is predicated upon the calculation and anticipation of threat. The underlying principle of anticipation, or prediction, is foundational to the operative logic of AI. The deployment of predictive, algorithmically driven systems in unmanned aerial systems (UAS) would therefore appear to be all but inevitable. However, the fatal interlocking of martial paradigms of pre-emption and models of predictive analysis needs to be questioned insofar as the irreparable decisiveness of a pre-emptive military strike is often at odds with the probabilistic predictions of AI. The pursuit of a human right to protect communities from aerial threats needs to therefore consider the degree to which algorithmic auguries—often erroneous but nevertheless evident in the prophetic mechanisms that power autonomous aerial apparatuses—essentially authorise and further galvanise the long-standing martial strategy of pre-emption. In the context of unmanned aerial systems, this essay will outline how AI actualises and summons forth “threats” through (i) the propositional logic of algorithms (their inclination to yield actionable directives); (ii) the systematic training of neural networks (through habitually biased methods of data-labelling); and (iii) a systemic reliance on models of statistical analysis in the structural design of machine learning (which can and do produce so-called “hallucinations”). Through defining the deterministic intentionality, systematic biases and systemic dysfunction of algorithms, I will identify how individuals and communities—configured upon and erroneously flagged through the machinations of so-called “black box” instruments—are invariably exposed to the uncertainty (or brute certainty) of imminent death based on algorithmic projections of “threat”.
Stepping Stones for Self-Learning

One of the themes in the emergence of text- and image-making (multimodal) generative AIs is their value in the learning space, with the vast potential just beginning to be explored by mass humanity. This chapter explores the potential and early use of large language models (LLMs) harnessed for their mass learning, human-friendly conversations, and their efficacies, for self-learning for individuals and groups, based on a review of the literature, system constraints and affordances, and abductive logic. There are insights shared about longitudinal and lifelong learning and foci on co-evolving processes between the human learner and the computing machines and large language models.
AI-Assisted Teacher Wellness

The Canadian and American teaching profession is known to be stressful, leading to burnout and other mental health issues for teachers. This chapter proposes an AI-assisted teacher wellness theory (AI TeachWell) and a supporting product/feedback/learning (PFL) framework for increased teacher well-being through the strategic use of AI chatbot technology. The theory emphasizes the use of resources that offset the demands of teaching, the importance of reducing cognitive load, and the increase of autonomy, efficacy, and relatedness. This chapter highlights the reasons that have led to increased teacher workloads, such as demands on teacher performance, administrative tasks, and professional development. It also highlights the effects of a lack of resources, time, and self-efficacy on teacher stress and burnout. The chapter concludes by offering innovative and proactive solutions for teachers to prioritize their health while fostering engaging and effective learning environments for their students, as well as the future implications of this theory.
An Efficient Closed-Form Formula for Evaluating r-Flip Moves in Quadratic Unconstrained Binary Optimization

Quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts and has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy “flips” r binary variables to obtain a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for the implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy-embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, leading to very high-quality solutions within a short CPU time.
The open science culture: World programs and learning platforms

The dynamic academic and educational environment needs the specialists capable to work in the intensively developing open science information infrastructure, to retrieve, analyze and synthesize heterogeneous information, to process structured and unstructured research data; to document research findings, to represent them in the open access; to be engaged in social media, and to be aware of many aspects of the open science concept. The programs, forms, approaches and practice of training specialists in open science within the global initiative projects and organizations (FOSTER, Creative Commons, Digital Curation Center, COS, etc.), and individual organizations/universities (Humboldt university, Delft University of Technology) are explored. The best experience of educating open science culture online is discussed. The author also suggests the program of advanced training course “The open science culture” for scientists, post-graduatestudents, lecturers, and librarians. The program may be used in professional training and practice-oriented extended professional programs and specialized professional retraining. The study makes the background for building the system for teaching the principles and procedures of open science. The system may be implemented in academic and educational institutions.
CamP: Camera Preconditioning for Neural Radiance Fields

Neural Radiance Fields (NeRF) can be optimized to obtain high-fidelity 3D scene reconstructions of objects and large-scale scenes. However, NeRFs require accurate camera parameters as input --- inaccurate camera parameters result in blurry renderings. Extrinsic and intrinsic camera parameters are usually estimated using Structure-from-Motion (SfM) methods as a pre-processing step to NeRF, but these techniques rarely yield perfect estimates. Thus, prior works have proposed jointly optimizing camera parameters alongside a NeRF, but these methods are prone to local minima in challenging settings. In this work, we analyze how different camera parameterizations affect this joint optimization problem, and observe that standard parameterizations exhibit large differences in magnitude with respect to small perturbations, which can lead to an ill-conditioned optimization problem. We propose using a proxy problem to compute a whitening transform that eliminates the correlation between camera parameters and normalizes their effects, and we propose to use this transform as a preconditioner for the camera parameters during joint optimization. Our preconditioned camera optimization significantly improves reconstruction quality on scenes from the Mip-NeRF 360 dataset: we reduce error rates (RMSE) by 67% compared to state-of-the-art NeRF approaches that do not optimize for cameras like Zip-NeRF, and by 29% relative to state-of-the-art joint optimization approaches using the camera parameterization of SCNeRF. Our approach is easy to implement, does not significantly increase runtime, can be applied to a wide variety of camera parameterizations, and can straightforwardly be incorporated into other NeRF-like models.
Computational Design of LEGO <sup>®</sup> Sketch Art

This paper presents computational methods to aid the creation of LEGO ®1 sketch models from simple input images. Beyond conventional LEGO ® mosaics, we aim to improve the expressiveness of LEGO ® models by utilizing LEGO ® tiles with sloping and rounding edges, together with rectangular bricks, to reproduce smooth curves and sharp features in the input. This is a challenging task, as we have limited brick shapes to use and limited space to place bricks. Also, the search space is immense and combinatorial in nature. We approach the task by decoupling the LEGO ® construction into two steps: first approximate the shape with a LEGO ® -buildable contour then filling the contour polygon with LEGO ® bricks. Further, we formulate this contour approximation into a graph optimization with our objective and constraints and effectively solve for the contour polygon that best approximates the input shape. Further, we extend our optimization model to handle multi-color and multi-layer regions, and formulate a grid alignment process and various perceptual constraints to refine the results. We employ our method to create a large variety of LEGO ® models and compare it with humans and baseline methods to manifest its compelling quality and speed.
<i>NodeGit</i> : Diffing and Merging Node Graphs

The use of version control is pervasive in collaborative software projects. Version control systems are based on two primary operations: diffing two versions to compute the change between them and merging two versions edited concurrently. Recent works provide solutions to diff and merge graphics assets such as images, meshes and scenes. In this work, we present a practical algorithm to diff and merge procedural programs written as node graphs. To obtain more precise diffs, we version the graphs directly rather than their textual representations. Diffing graphs is equivalent to computing the graph edit distance, which is known to be computationally infeasible. Following prior work, we propose an approximate algorithm tailored to our problem domain. We validate the proposed algorithm by applying it both to manual edits and to a large set of randomized modifications of procedural shapes and materials. We compared our method with existing state-of-the-art algorithms, showing that our approach is the only one that reliably detects user edits.
Text-Guided Synthesis of Eulerian Cinemagraphs

We introduce Text2Cinemagraph, a fully automated method for creating cinemagraphs from text descriptions --- an especially challenging task when prompts feature imaginary elements and artistic styles, given the complexity of interpreting the semantics and motions of these images. We focus on cinemagraphs of fluid elements, such as flowing rivers, and drifting clouds, which exhibit continuous motion and repetitive textures. Existing single-image animation methods fall short on artistic inputs, and recent text-based video methods frequently introduce temporal inconsistencies, struggling to keep certain regions static. To address these challenges, we propose an idea of synthesizing image twins from a single text prompt --- a pair of an artistic image and its pixel-aligned corresponding natural-looking twin. While the artistic image depicts the style and appearance detailed in our text prompt, the realistic counterpart greatly simplifies layout and motion analysis. Leveraging existing natural image and video datasets, we can accurately segment the realistic image and predict plausible motion given the semantic information. The predicted motion can then be transferred to the artistic image to create the final cinemagraph. Our method outperforms existing approaches in creating cinemagraphs for natural landscapes as well as artistic and other-worldly scenes, as validated by automated metrics and user studies. Finally, we demonstrate two extensions: animating existing paintings and controlling motion directions using text.
AvatarStudio: Text-Driven Editing of 3D Dynamic Human Head Avatars

Capturing and editing full-head performances enables the creation of virtual characters with various applications such as extended reality and media production. The past few years witnessed a steep rise in the photorealism of human head avatars. Such avatars can be controlled through different input data modalities, including RGB, audio, depth, IMUs, and others. While these data modalities provide effective means of control, they mostly focus on editing the head movements such as the facial expressions, head pose, and/or camera viewpoint. In this paper, we propose AvatarStudio, a text-based method for editing the appearance of a dynamic full head avatar. Our approach builds on existing work to capture dynamic performances of human heads using Neural Radiance Field (NeRF) and edits this representation with a text-to-image diffusion model. Specifically, we introduce an optimization strategy for incorporating multiple keyframes representing different camera viewpoints and time stamps of a video performance into a single diffusion model. Using this personalized diffusion model, we edit the dynamic NeRF by introducing view-and-time-aware Score Distillation Sampling (VT-SDS) following a model-based guidance approach. Our method edits the full head in a canonical space and then propagates these edits to the remaining time steps via a pre-trained deformation network. We evaluate our method visually and numerically via a user study, and results show that our method outperforms existing approaches. Our experiments validate the design choices of our method and highlight that our edits are genuine, personalized, as well as 3D- and time-consistent.
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers

Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in text-to-image generation have inspired researchers to explore vector graphics synthesis using either image-based methods (i.e., text → raster image → vector graphics) combining text-to-image generation models with image vectorization, or language-based methods (i.e., text → vector graphics script) through pretrained large language models. Nevertheless, these methods suffer from limitations in terms of generation quality, diversity, and flexibility. In this paper, we introduce IconShop, a text-guided vector icon synthesis method using autoregressive transformers. The key to success of our approach is to sequentialize and tokenize SVG paths (and textual descriptions as guidance) into a uniquely decodable token sequence. With that, we are able to exploit the sequence learning power of autoregressive transformers, while enabling both unconditional and text-conditioned icon synthesis. Through standard training to predict the next token on a large-scale vector icon dataset accompanied by textural descriptions, the proposed IconShop consistently exhibits better icon synthesis capability than existing image-based and language-based methods both quantitatively (using the FID and CLIP scores) and qualitatively (through formal subjective user studies). Meanwhile, we observe a dramatic improvement in generation diversity, which is validated by the objective Uniqueness and Novelty measures. More importantly, we demonstrate the flexibility of IconShop with multiple novel icon synthesis tasks, including icon editing, icon interpolation, icon semantic combination, and icon design auto-suggestion.
ScaNeRF: Scalable Bundle-Adjusting Neural Radiance Fields for Large-Scale Scene Rendering

High-quality large-scale scene rendering requires a scalable representation and accurate camera poses. This research combines tile-based hybrid neural fields with parallel distributive optimization to improve bundle-adjusting neural radiance fields. The proposed method scales with a divide-and-conquer strategy. We partition scenes into tiles, each with a multi-resolution hash feature grid and shallow chained diffuse and specular multilayer perceptrons (MLPs). Tiles unify foreground and background via a spatial contraction function that allows both distant objects in outdoor scenes and planar reflections as virtual images outside the tile. Decomposing appearance with the specular MLP allows a specular-aware warping loss to provide a second optimization path for camera poses. We apply the alternating direction method of multipliers (ADMM) to achieve consensus among camera poses while maintaining parallel tile optimization. Experimental results show that our method outperforms state-of-the-art neural scene rendering method quality by 5%--10% in PSNR, maintaining sharp distant objects and view-dependent reflections across six indoor and outdoor scenes.
Quantum-secured covert sensing for the Doppler effect

Quantum mechanics has paved the way for establishing shared privacy among communicating parties through a secure regime known as quantum-secured communication. Recent advancements in both theory and experimentation have unveiled the potential of quantum resources in enhancing the performance and security of estimating unknown parameters. This has led to the emergence of a novel paradigm known as quantum-secured covert sensing, wherein the sensing operation is concealed from an adversary monitoring the environment by embedding the probe signal in a bright noise background. The performance and security of such protocols are quantified using quantum measurement theory. While previous investigations into quantum-secured covert sensing primarily focused on proof-of-concept phase estimation problems, this paper presents a pioneering quantum-secured covert sensing system designed for the Doppler effect---a versatile estimation problem with broad applications. Our research uncovers an inherent trade-off among measurement precision, security, and range within the system. This work establishes a new avenue for incorporating physical-layer security into sensing systems, thereby opening up exciting possibilities for future research in this field.
Securing Big Data Exchange: An Integrated Blockchain Framework for Full-Lifecycle Data Trading with Trust and Dispute Resolution

In the era of big data, facilitating efficient data flow is of paramount importance. Governments and enterprises worldwide have been investing in the big data industry, promoting data sharing and trading. However, existing data trading platforms often suffer from issues like privacy breaches, single points of failure, data tampering, and non-transparent transactions due to their reliance on centralized servers. To address these challenges, blockchain-based big data transaction models have been proposed. However, these models often lack system integrity and fail to fully meet user requirements while ensuring adequate security. To overcome these limitations, this paper presents an Ethereum-based big data trading model that establishes a comprehensive and secure trading system. The model aims to provide users with more convenient, secure, and professional services. Through the utilization of smart contracts, users can efficiently match data and negotiate prices online while ensuring secure data delivery through encryption technologies. Additionally, the model introduces a trusted third-party entity that offers professional data evaluation services and actively safeguards user data ownership in the event of disputes. The implementation of the model includes the development of smart contracts and the necessary machine learning code, followed by rigorous testing and validation. The experimental results validate the effectiveness and reliability of our proposed model, demonstrating its potential to ensure effective and secure big data trading.
Entanglement-efficient bipartite-distributed quantum computing

In noisy intermediate-scale quantum computing, the limited scalability of a single quantum processing unit (QPU) can be extended through distributed quantum computing (DQC), in which one can implement global operations over two QPUs by entanglement-assisted local operations and classical communication. To facilitate this type of DQC in experiments, we need an entanglement-efficient protocol. To this end, we extend the protocol in [Eisert et. al., PRA, 62:052317(2000)] implementing each nonlocal controlled-unitary gate locally with one maximally entangled pair to a packing protocol, which can pack multiple nonlocal controlled-unitary gates locally using one maximally entangled pair. In particular, two types of packing processes are introduced as the building blocks, namely the distributing processes and embedding processes. Each distributing process distributes corresponding gates locally with one entangled pair. The efficiency of entanglement is then enhanced by embedding processes, which merge two non-sequential distributing processes and hence save the entanglement cost. We show that the structure of distributability and embeddability of a quantum circuit can be fully represented by the corresponding packing graphs and conflict graphs. Based on these graphs, we derive heuristic algorithms for finding an entanglement-efficient packing of distributing processes for a given quantum circuit to be implemented by two parties. These algorithms can determine the required number of local auxiliary qubits in the DQC. We apply these algorithms for bipartite DQC of unitary coupled-cluster circuits and find a significant reduction of entanglement cost through embeddings. This method can determine a constructive upper bound on the entanglement cost for the DQC of quantum circuits.