Reviewerly/exHarmony huggingface dataset:
Panoptic Segmentation with Convex Object Representation

Abstract The accurate representation of objects holds pivotal significance in the realm of panoptic segmentation. Presently, prevalent object representation methodologies, including box-based, keypoint-based and query-based techniques, encounter a challenge known as the ‘representation confusion’ issue in specific scenarios, often resulting in the mislabeling of instances. In response, this paper introduces Convex Object Representation (COR), a straightforward yet highly effective approach to address this problem. COR leverages a CNN-based Euclidean Distance Transform to convert the target instance into a convex heatmap. Simultaneously, it offers a parallel embedding method for encoding the object. Subsequently, COR characterizes objects based on the distinctive embedding vectors of their convex vertices. This paper seamlessly integrates COR into a state-of-the-art query-based panoptic segmentation framework. Experimental findings validate that COR successfully mitigates the representation confusion predicament, enhancing segmentation accuracy. The COR-augmented methods exhibit notable improvements of +1.3 and +0.7 points in PQ on the Cityscapes validation and MS COCO panoptic 2017 validation datasets, respectively.
On the structures of a monoid of triangular vector-permutation polynomials, its group of units and its induced group of permutations

Let $n>1$ and let $R$ be a commutative ring with identity the $1\ne 0$. A vector-polynomials $ \vec{f}=(f_0,f_1 +x_2g_1,\ldots, f_{n-1} +x_n g_{n-1}),$ where $f_0\in R[x_1]$ permutes the elements of $R$ and $f_i ,g_i\in R[x_1,\ldots,x_i]$ with $g_i$ mapping $R^i$ into the units of $R$ ($i=1,\ldots, n-1$); permutes the elements of $R^n$. The set of all such vector-polynomial $\mathcal{MT}_n$ is a monoid with respect to composition. Further, $ \vec{f} $ is invertible in $\mathcal{MT}_n$ if and only if $f_0$ is an $R$ automorphism of $R[x_1]$ and $g_i$ is invertible in $R[x_1,\ldots,x_i]$ for $i=1,\ldots, n-1$. When $R$ is finite, the monoid $\mathcal{MT}_n$ induces a finite group of permutations of $R^n$. Moreover, we decompose the $\mathcal{MT}_n$ into an iterated semi-direct product of $n$ monoids. Such a decomposition allows us to obtain similar decompositions of its group of units, and when $R$ is finite, of its induced group of permutations. Furthermore, the decomposition of the induced group helps us to characterize some of its properties.
Diffusing More Objects for Semi-Supervised Domain Adaptation with Less Labeling

For object detection, it is possible to view the prediction of bounding boxes as a reverse diffusion process. Using a diffusion model, the random bounding boxes are iteratively refined in a denoising step, conditioned on the image. We propose a stochastic accumulator function that starts each run with random bounding boxes and combines the slightly different predictions. We empirically verify that this improves detection performance. The improved detections are leveraged on unlabelled images as weighted pseudo-labels for semi-supervised learning. We evaluate the method on a challenging out-of-domain test set. Our method brings significant improvements and is on par with human-selected pseudo-labels, while not requiring any human involvement.
Towards an in-depth detection of malware using distributed QCNN

Malware detection is an important topic of current cybersecurity, and Machine Learning appears to be one of the main considered solutions even if certain problems to generalize to new malware remain. In the aim of exploring the potential of quantum machine learning on this domain, our previous work showed that quantum neural networks do not perform well on image-based malware detection when using a few qubits. In order to enhance the performances of our quantum algorithms for malware detection using images, without increasing the resources needed in terms of qubits, we implement a new preprocessing of our dataset using Grayscale method, and we couple it with a model composed of five distributed quantum convolutional networks and a scoring function. We get an increase of around 20 \% of our results, both on the accuracy of the test and its F1-score.
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning

Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images. Although existing font generation methods have achieved satisfactory performance, they still struggle with complex characters and large style variations. To address these issues, we propose FontDiffuser, a diffusion-based image-to-image one-shot font generation method, which innovatively models the font imitation task as a noise-to-denoise paradigm. In our method, we introduce a Multi-scale Content Aggregation (MCA) block, which effectively combines global and local content cues across different scales, leading to enhanced preservation of intricate strokes of complex characters. Moreover, to better manage the large variations in style transfer, we propose a Style Contrastive Refinement (SCR) module, which is a novel structure for style representation learning. It utilizes a style extractor to disentangle styles from images, subsequently supervising the diffusion model via a meticulously designed style contrastive loss. Extensive experiments demonstrate FontDiffuser's state-of-the-art performance in generating diverse characters and styles. It consistently excels on complex characters and large style changes compared to previous methods. The code is available at https://github.com/yeungchenwa/FontDiffuser.
M-BEV: Masked BEV Perception for Robust Autonomous Driving

3D perception is a critical problem in autonomous driving. Recently, the Bird-Eye-View (BEV) approach has attracted extensive attention, due to low-cost deployment and desirable vision detection capacity. However, the existing models ignore a realistic scenario during the driving procedure, i.e., one or more view cameras may be failed, which largely deteriorates the performance. To tackle this problem, we propose a generic Masked BEV (M-BEV) perception framework, which can effectively improve robustness to this challenging scenario, by random masking and reconstructing camera views in the end-to-end training. More specifically, we develop a novel Masked View Reconstruction (MVR) module for M-BEV. It mimics various missing cases by randomly masking features of different camera views, then leverages the original features of these views as self-supervision, and reconstructs the masked ones with the distinct spatio-temporal context across views. Via such a plug-and-play MVR, our M-BEV is capable of learning the missing views from the resting ones, and thus well generalized for robust view recovery and accurate perception in the testing. We perform extensive experiments on the popular NuScenes benchmark, where our framework can significantly boost 3D perception performance of the state-of-the-art models on various missing view cases, e.g., for the absence of back view, our M-BEV promotes the PETRv2 model with 10.3% mAP gain.
Dental Restorative Material Ontology (DrMO)

The DrMO ontology is a domain ontology that represents knowledge underlying the composition, characterization and standardization of different materials involved in the dental restoration procedure. It will assist dentists in selecting appropriate materials based on up-to-date scientific knowledge to satisfy a patient’s specific requirements, without jeopardizing their clinical time. It reuses several ontologies from the OBO foundry, especially the Oral Health and Disease (OHD) Ontology. However, the dental restoration domain is complex and also requires concepts from materials science and engineering. Thus, DrMO also incorporates knowledge from the Devices, Experimental scaffolds, and Biomaterials (DEB) and Functionally Graded Materials (FGM) ontologies to provide more comprehensive knowledge of this area of dental material than previous ontologies. However, much of the terminology from FGM is different than that used in clinical dentistry. Thus, DrMO has changed the appropriate classes to make them consistent with terminology common in dentistry. DrMO also follows ontology design best practices by reusing meta-data properties from the Dublin Core vocabulary. It captures knowledge from a set of the most recent and influential papers in Dental Materials and related fields. Links to these papers are included in the ontology as meta-data defined with Dublin Core. It is implemented in OWL2 and was developed with the Protégé 5.6 ontology editor. The ontology was created using the Ontology Development 101 methodology by Noy et. al. Several domain experts in addition to Dr. Dutta also provided their expertise. The ontology is available on GitHub and licensed via an open source license. The GitHub project includes a corresponding file of SPARQL queries that answer the competency questions defined as part of the ontology development methodology.
Gaussian process learning of nonlinear dynamics

One of the pivotal tasks in scientific machine learning is to represent underlying dynamical systems from time series data. Many methods for such dynamics learning explicitly require the derivatives of state data, which are not directly available and can be approximated conventionally by finite differences. However, the discrete approximations of time derivatives may result in a poor estimation when state data are scarce and/or corrupted by noise, thus compromising the predictiveness of the learned dynamical models. To overcome this technical hurdle, we propose a new method that learns nonlinear dynamics through a Bayesian inference of characterizing model parameters. This method leverages a Gaussian process representation of states, and constructs a likelihood function using the correlation between state data and their derivatives, yet prevents explicit evaluations of time derivatives. Through a Bayesian scheme, a probabilistic estimate of the model parameters is given by the posterior distribution, and thus a quantification is facilitated for uncertainties from noisy state data and the learning process. Specifically, we will discuss the applicability of the proposed method to two typical scenarios for dynamical systems: parameter identification and estimation with an affine structure of the system, and nonlinear parametric approximation without prior knowledge.
Vertex Reconstruction with MaskFormers

In high-energy particle collisions, secondary decays can be reconstructed as displaced vertices using the measured trajectories of charged particles. Such vertices are useful in identifying and studying jets originating from $b$- or $c$-hadrons, which is a key component of the physics programs of modern collider experiments. While machine learning has become mainstream in particle physics, most applications are on an per-object basis, for example the prediction of class labels or the regression of object properties. However, vertex reconstruction is a many-to-many problem, in which a set of input tracks must be grouped into a second variable length set of vertices. In this work, we propose a fully learned approach to reconstruct secondary vertices inside jets based on recent advancements in object detection from computer vision. We demonstrate and discuss the advantages of this approach, in particular its ability to estimate the properties of any number of vertices, and conclude that the same methodology could be applicable to other reconstruction tasks in particle physics.
Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection

Machine learning techniques, such as deep learning and ensemble methods, are widely used in various domains due to their ability to handle complex real-world tasks. However, their black-box nature has raised multiple concerns about the fairness, trustworthiness, and transparency of computer-assisted decision-making. This has led to the emergence of local post-hoc explainability methods, which offer explanations for individual decisions made by black-box algorithms. Among these methods, Kernel SHAP is widely used due to its model-agnostic nature and its well-founded theoretical framework. Despite these strengths, Kernel SHAP suffers from high instability: different executions of the method with the same inputs can lead to significantly different explanations, which diminishes the utility of post-hoc explainability. The contribution of this paper is two-fold. On the one hand, we show that Kernel SHAP's instability is caused by its stochastic neighbor selection procedure, which we adapt to achieve full stability without compromising explanation fidelity. On the other hand, we show that by restricting the neighbors generation to perturbations of size 1 -- which we call the coalitions of Layer 1 -- we obtain a novel feature-attribution method that is fully stable, efficient to compute, and still meaningful.
Continuous Integration and Hosting

So far, we have only discussed how to develop Visual Studio extensions. Equally important is understanding how to ship the extension to the Visual Studio Marketplace so that other Visual Studio users can also utilize the developed extension. In this chapter, we will learn how to publish our extension to the Visual Studio Marketplace, making it available to a wider audience.
Developing Real-World Extensions for Visual Studio Editor

In our journey so far, we’ve not extended the core part of Visual Studio that is predominantly used by the developers – the code editor. The Visual Studio editor offers a rich set of extension points, and most of its parts are extensible via the Managed Extensibility Framework (MEF). In fact, there are so many extensibility points exposed by the editor that covering each of these in details would require a book in itself. Fortunately for us, Microsoft has already documented these extensibility points and features in great depth. In this chapter, we will discuss the extensibility points of the Visual Studio editor and develop extensions for code analysis, fixes, and refactoring.
Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV Networking

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain-based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption.
Towards Automatic Support of Software Model Evolution with Large Language~Models

Modeling structure and behavior of software systems plays a crucial role, in various areas of software engineering. As with other software engineering artifacts, software models are subject to evolution. Supporting modelers in evolving models by model completion facilities and providing high-level edit operations such as frequently occurring editing patterns is still an open problem. Recently, large language models (i.e., generative neural networks) have garnered significant attention in various research areas, including software engineering. In this paper, we explore the potential of large language models in supporting the evolution of software models in software engineering. We propose an approach that utilizes large language models for model completion and discovering editing patterns in model histories of software systems. Through controlled experiments using simulated model repositories, we conduct an evaluation of the potential of large language models for these two tasks. We have found that large language models are indeed a promising technology for supporting software model evolution, and that it is worth investigating further in the area of software model evolution.
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process

In this paper, we explore a principal way to enhance the quality of object masks produced by different segmentation models. We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process. As a result, the refinement process can be smoothly implemented through a series of denoising diffusion steps. Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process. By predicting the label and corresponding states-transition probabilities for each pixel, SegRefiner progressively refines the noisy masks in a conditional denoising manner. To assess the effectiveness of SegRefiner, we conduct comprehensive experiments on various segmentation tasks, including semantic segmentation, instance segmentation, and dichotomous image segmentation. The results demonstrate the superiority of our SegRefiner from multiple aspects. Firstly, it consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks. Secondly, it outperforms previous model-agnostic refinement methods by a significant margin. Lastly, it exhibits a strong capability to capture extremely fine details when refining high-resolution images. The source code and trained models are available at https://github.com/MengyuWang826/SegRefiner.
Developing Your First Extensions

In the last few chapters, we have gone through numerous theories, concepts, and fundamentals related to building Visual Studio extensions. Now it’s time to translate this theory into practice. In this chapter, we will develop a pair of meaningful and useful, real-world Visual Studio extensions in a step-by-step fashion. Throughout this process, we will also learn key fundamentals, concepts, and techniques for developing a Visual Studio extension that can be shipped to the Visual Studio Marketplace or shared with a broader audience.
IMPLICAÇÕES DE UM PROGRAMA DE EDUCAÇÃO EMOCIONAL BASEADO EM MINDFULNESS NO BEM-ESTAR E NAS COMPETÊNCIAS SOCIOEMOCIONAIS DE ESTUDANTES

Objective: This study analyzes the implications of a Mindfulness-based emotional education (MBEEP) program on the well-being and development of socioemotional competences of students. Design/methodology/approach: The program was implemented in the year 2021, within the context of the Covid-19 pandemic, and utilized Mindfulness as the foundation for its structure. It was operationalized over eight weeks. Fifty-five undergraduate and postgraduate students participated in a quasi-experimental study. Before initiating the program and upon its completion, an assessment instrument was administered with scales covering mental health, socioemotional competences, and the program's contribution to self-awareness and well-being. Results: The findings indicated a significant reduction in levels of anxiety, depression, and stress, an improvement in the perception of satisfaction with health and quality of life, and an increase in the levels of development of five socioemotional competences. It was also possible to identify implications of the program on the psychological well-being of the participants. Practical implications: The dissemination of Mindfulness-based emotional education programs can be an alternative to enhance the well-being of students in academic environments.
Tips and Tricks

In this chapter, we will discuss several practical tips that can enhance your experience with Visual Studio and aid in both developing and using its extensions. Some of these suggestions will also contribute to enhancing the professionalism of your extensions. We shall also discuss briefly the extensibility of Visual Studio Code. Finally, we will conclude this chapter and the book by showcasing a selection of the cool and valuable extensions that I personally find useful.
Design and Implementation of a Compiler Supporting RISC-V Custom Cryptographic and Vector Instructions

Post-quantum cryptographic algorithms capable of resisting quantum computing attacks have received increasing attention in recent years, especially lattice-based cryptographic algorithms have become mainstream. Since their specialization and complex computation, the cryptographic computation is inefficient on general-purpose processors. In order to improve the performance of the processor in post-quantum cryptography computing, we have extended a set of cryptographic and vector instruction sets for the CRYSTALS-Kyber algorithm, and implemented support for custom cryptographic and vector instructions based on the open-source toolchain. These efforts can realize the compilation and disassembly of the CRYSTALS-Kyber encryption/decryption algorithm programs, and optimized the performance of the compilation. This paper mainly introduces how to design an extended instruction set based on the target algorithm and enable the compiler to support these instructions. Compared to the situation before extending custom instructions, the total number of instructions for key generation, encryption, and decryption assembly programs has decreased by 3651 times, and the total number of execution cycles has decreased by 2195 times.
Efficient Federated Learning for Metaverse via Dynamic User Selection, Gradient Quantization and Resource Allocation

Metaverse is envisioned to merge the actual world with a virtual world to bring users unprecedented immersive feelings. To ensure user experience, federated learning (FL) has been expected as a critical enabler to provide metaverse users with high-quality sensing, communicating, and rendering. However, considering the limitation of wireless communication resources and the stringent requirements of users, collaborating with massive metaverse users to realize FL still has tremendous challenges. Most pioneer works on improving the performance of FL assume that the system states are static, which is unsuitable in the metaverse. Because the FL in the metaverse is always a complicated long-term iteration process, where the fluctuations of channel status and available computing resources of users are inevitable, a changeless strategy may lead to poor results. Therefore, this paper proposes an efficient federated learning scheme relying on dynamic user selection, gradient quantization, and resource allocation. Specifically, we derive the convergence error bound to reveal the impact of user selection, wireless transmission error, and gradient quantization error of each iteration on FL’s convergence. Based on the theoretical analysis, we jointly and dynamically optimize the user selection, gradient quantization, and resource allocation to minimize the error bound with time and energy consumption budgets. Furthermore, to make the formulated sequential decision-making problem tractable, we transform it into a Markov decision process (MDP) and design a soft actor-critic-based solution. Extensive experiments validate that our proposed scheme has superior performance compared to conventional schemes in dynamic-changing network environments.