While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in consequential ...settings also requires calibrating and communicating the uncertainty of predictions. To convey instance-wise uncertainty for prediction tasks, we show how to generate set-valued predictions from a black-box predictor that controls the expected loss on future test points at a user-specified level. Our approach provides explicit finite-sample guarantees for any dataset by using a holdout set to calibrate the size of the prediction sets. This framework enables simple, distribution-free, rigorous error control for many tasks, and we demonstrate it in five large-scale machine learning problems: (1) classification problems where some mistakes are more costly than others; (2) multi-label classification, where each observation has multiple associated labels; (3) classification problems where the labels have a hierarchical structure; (4) image segmentation, where we wish to predict a set of pixels containing an object of interest; and (5) protein structure prediction. Last, we discuss extensions to uncertainty quantification for ranking, metric learning, and distributionally robust learning.
Object detection is a crucial task in computer vision systems with a wide range of applications in autonomous driving, medical imaging, retail, security, face recognition, robotics, and others. ...Nowadays, neural networks-based models are used to localize and classify instances of objects of particular classes. When real-time inference is not required, ensembles of models help to achieve better results.
In this work, we present a novel method for fusing predictions from different object detection models: weighted boxes fusion. Our algorithm utilizes confidence scores of all proposed bounding boxes to construct averaged boxes.
We tested the method on several datasets and evaluated it in the context of Open Images and COCO Object Detection challenges, achieving top results in these challenges. The 3D version of boxes fusion was successfully applied by the winning teams of Waymo Open Dataset and Lyft 3D Object Detection for Autonomous Vehicles challenges. The source code is publicly available at GitHub (Solovyev, 2019 31).
We present a novel method for combining predictions in ensembles of different object detection models: weighted boxes fusion. This method significantly improves the quality of the fused predicted rectangles for an ensemble.
We tested the method on several datasets and evaluated it in the context of the Open Images and COCO Object Detection challenges. It helped to achieve top results in these challenges. The source code is publicly available at GitHub.
•Novel method was proposed for combining predictions in ensembles of different object detection models.•Method significantly improves the quality of the fused predicted rectangles for an ensemble. The code is available at GitHub.•Method was tested on several datasets and evaluated in the context of the Open Images and COCO Object Detection challenges.
As a promising technology, edge computing extends computation, communication, and storage facilities toward the edge of a network. This new computing paradigm opens up new challenges, among which ...computation offloading is considered to be the most important one. Computation offloading enables end devices to offload computation tasks to edge servers and receive the results after the servers' execution of the tasks. In computation offloading, offloading modeling plays a crucial role in determining the overall edge computing performance. We present a comprehensive overview on the past development as well as the recent advances in research areas related to offloading modeling in edge computing. First, we present some important edge computing architectures and classify the previous works on computation offloading into different categories. Second, we discuss some basic models such as channel model, computation and communication model, and energy harvesting model that have been proposed in offloading modeling. Next, we elaborate on different offloading modeling methods which are based on (non-)convex optimization, Markov decision process, game theory, Lyapunov optimization, or machine learning. Finally, we highlight and discuss some research directions and challenges in the area of offloading modeling in edge computing.
Many natural combinatorial problems can be expressed as constraint satisfaction problems. This class of problems is known to be NP-complete in general, but certain restrictions on the form of the ...constraints can ensure tractability. The standard way to parameterize interesting subclasses of the constraint satisfaction problem is via finite constraint languages. The main problem is to classify those subclasses that are solvable in polynomial time and those that are NP-complete. It was conjectured that if a constraint language has a weak near-unanimity polymorphism then the corresponding constraint satisfaction problem is tractable; otherwise, it is NP-complete.
In the article, we present an algorithm that solves Constraint Satisfaction Problem in polynomial time for constraint languages having a weak near unanimity polymorphism, which proves the remaining part of the conjecture.
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Internet of Things (IoT) is a new paradigm that integrates the Internet and physical objects belonging to different domains such as home automation, industrial process, human health and environmental ...monitoring. It deepens the presence of Internet-connected devices in our daily activities, bringing, in addition to many benefits, challenges related to security issues. For more than two decades, Intrusion Detection Systems (IDS) have been an important tool for the protection of networks and information systems. However, applying traditional IDS techniques to IoT is difficult due to its particular characteristics such as constrained-resource devices, specific protocol stacks, and standards. In this paper, we present a survey of IDS research efforts for IoT. Our objective is to identify leading trends, open issues, and future research possibilities. We classified the IDSs proposed in the literature according to the following attributes: detection method, IDS placement strategy, security threat and validation strategy. We also discussed the different possibilities for each attribute, detailing aspects of works that either propose specific IDS schemes for IoT or develop attack detection strategies for IoT threats that might be embedded in IDSs.
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the ...field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to many applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.
Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling ...cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features uncorrelated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark. The code is available at https://github.om/haozixiang1228/MMIF-CDDFuse.
Hyperspectral image (HSI) and multispectral image (MSI) fusion, which fuses a low-spatial-resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a common scheme to ...obtain high-resolution HSI (HR-HSI). This article presents a novel HSI and MSI fusion method (called as CNN-Fus), which is based on the subspace representation and convolutional neural network (CNN) denoiser, i.e., a well-trained CNN for gray image denoising. Our method only needs to train the CNN on the more accessible gray images and can be directly used for any HSI and MSI data sets without retraining. First, to exploit the high correlations among the spectral bands, we approximate the desired HR-HSI with the low-dimensional subspace multiplied by the coefficients, which can not only speed up the algorithm but also lead to more accurate recovery. Since the spectral information mainly exists in the LR-HSI, we learn the subspace from it via singular value decomposition. Due to the powerful learning performance and high speed of CNN, we use the well-trained CNN for gray image denoising to regularize the estimation of coefficients. Specifically, we plug the CNN denoiser into the alternating direction method of multipliers (ADMM) algorithm to estimate the coefficients. Experiments demonstrate that our method has superior performance over the state-of-the-art fusion methods.
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