In this paper, we present Alkaid-SDVRP, an open-source C++ package for efficiently solving the Vehicle Routing Problem with Split Deliveries (SDVRP), a classical combinatorial optimization problem which is a variant of the Capacitated Vehicle Routing Problem where the same customer can be served by multiple vehicles. The core algorithm of Alkaid-SDVRP is designed based on the Iterated Local Search and Randomized Variable Neighborhood Descent frameworks, which are highly configurable and extensible. Specifically, we implement a number of predefined neighborhoods, including Swap(p, q), [Formula: see text], SD-[Formula: see text], Cross, Exchange, and Reinsertion, which can be arbitrarily enabled, disabled, and permuted. Moreover, it is easy to develop and integrate new neighborhoods into the current framework. The primary goal of this package is to provide an effective implementation and integration of the state-of-the-art techniques for the SDVRP. Tested on the 12th Implementation Challenge held by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), Alkaid-SDVRP took first place in the SDVRP track and has been shown beyond any doubt. In addition, we hope that the package can facilitate the research for vehicle routing-related problems by providing high-quality baselines and off-the-shelf implementations. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: Financial support from the National Natural Science Foundation of China [Grant 72101094]; the Special Project for Knowledge Innovation of Hubei Province [Grant 2022013301015175]; and Interdisciplinary Research Program of Huazhong University of Science and Technology [Grant 5003300129] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0606 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0606 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

With the diversification of cloud services, cloud providers offer flexible access control by letting users apply fine-grained cloud access control policies to secure their cloud resources. However, flexibility comes with the cost that configuring cloud access control policies is error-prone. Therefore, cloud providers have developed SMT-based tools to formally analyze the user-defined policies. Unfortunately, we find these analyzers slow, due to the complex regular expression matching conditions in policies. To this end, this paper introduces Relia, a general method to speed up the analysis of cloud access control policies. The key idea of Relia is to pre-compute a set of String Equivalence Classes (SECs) based on the regular expressions in a policy, assign a unique integer to each SEC, and rewrite the regular constraints into equivalent integer constraints, which are easier to solve. We implement Relia as a transparent layer between our in-house access analyzer and off-the-shelf SMT solvers. Based on real policies from a large public cloud provider, we show that: when enabling Relia, our in-house portfolio solver (consisting of Z3, Cvc4, and Cvc5) can speed up the analysis process for nearly 95% of all cases, with an average speedup of 8.21×.

The selection and utilization of different color spaces significantly impact the recognition performance of deep learning models in downstream tasks. Existing studies typically leverage image information from various color spaces through model integration or channel concatenation. However, these methods result in excessive model size and suboptimal utilization of image information. In this study, we propose the structure-preserved self-attention network (SPSANet) model for efficient fusion of image information from different color spaces. This model incorporates a novel structure-preserved self-attention (SPSA) module that employs a single-head pixel-wise attention mechanism, as opposed to the conventional multihead self-attention (MHSA) approach. Specifically, feature maps from all color space grouping paths are utilized for similarity matching, enabling the model to focus on critical pixel locations across different color spaces. This design mitigates the dependence of the SPSANet model on the choice of color space while enhancing the advantages of integrating multiple color spaces. The SPSANet model also employs channel shuffle operations to facilitate limited interaction between information flows from different color space paths. Experimental results demonstrate that the SPSANet model, utilizing eight common color spaces-RGB, Luv, XYZ, Lab, HSV, YCrCb, YUV, and HLS-achieves superior recognition performance with reduced parameters and computational cost.

Precise segmentation of important regions in medical images is an extremely challenging task through uni-modal approaches that are solely based on the image modality. This can be improved by introducing additional textual semantic information as witnessed by the numerous text-assisted medical image segmentation methods. In reality, however, the majority of medical data appear as uni-modal data without corresponding text annotations which entail substantial costs. To address this, in this paper, we propose a task-level pseudo-text prompt learning approach for medical image segmentation, which can learn task-level textual semantic information. By integrating task-level keywords with learnable parameters, we can optimize the pseudo-text prompt with the segmentation task. Furthermore, we design an image-text symmetric encoding architecture to extract multilevel image-text feature pairs so that the learned text features can optimize the corresponding visual features at each level. In the testing phase, our model directly uses trained text features to assist visual segmentation thus reducing the model size, and adaptively adjusts the text features through the weight module. Experimental results on three medical image datasets show that our proposed model outperforms other uni-modal segmentation models or text-assisted visual segmentation models with a small model size. Our code can be available at: https://github.com/Rango-bit/PseTNet_code.git.

Traditional medical image segmentation methods are mostly uni-modal approaches solely based on the image modality. Recently, the emergence of text-guided image segmentation methods, by utilizing text annotations to compensate for the quality deficiency in image data, has shown promise for improving medical image segmentation. Despite their success, these methods often experience inadequate utilization of beneficial text information, and have applicability issues in the missing text modality scenario. To address these limitations, in this paper, we propose a Medical Language Mixture of Experts (MLMoE), which introduces multiple sub-experts for extracting more diverse information from medical text. These different experts are then combined by a gating module, thus aggregating beneficial text information to assist the image segmentation. Furthermore, to guarantee its performance in the text-absent scenario, a virtual prompt based distillation module is proposed, which distills the valuable knowledge of MLMoE learned from available text information to the virtual prompt, as an alternative text input. Experimental results on two multi-modal medical segmentation datasets demonstrate the effectiveness of our opposed method, achieving state-of-the-art performance. Code will be available at: https://github.com/Rango-bit/MLMoE.git.

Network intrusion detection systems (NIDS) play a crucial role in maintaining network security . However, current NIDS techniques tend to neglect the topological structures of network traffic to varying degrees. This fundamental oversight leads to challenges in handling class-imbalanced and highly dynamic network traffic. In this paper, we propose a novel dynamic multi-scale topological representation (DMTR) method for improving network intrusion detection performance. Our DMTR method achieves the perception of multi-scale topology and exhibits strong robustness. It provides accurate and stable representations even in the presence of data distribution shifts and class imbalance problems . The multi-scale topology is obtained through multiple topology lenses, which reveal topological structures from different dimensional aspects. Furthermore, to address the limitations of existing detection models based on static network traffic, the DMTR method also achieves dynamic topological representation through our proposed group shuffle operation (GSO) strategy. When new traffic data arrives, the topological representation is updated by preserving a portion of the original information without reprocessing all data. Experiments on four publicly available network traffic datasets demonstrate the feasibility and effectiveness of the proposed DMTR method in handling class imbalanced and highly dynamic network traffic.

Clustering is a fundamental research topic in data mining and machine learning. In addition, many specific applications demand that the clusters obtained be balanced. In this paper, we present a balanced clustering model that is to minimize the sum of squared distances to cluster centers, with uniform regularization functions to control the balance degree of the clustering results. To solve the model, we adopt the idea of the k-means method. We show that the k-means assignment step has an equivalent minimum cost flow formulation when the regularization functions are all convex. By using a novel and simple acceleration technique for the k-means and network simplex methods our model can be solved quite efficiently. Experimental results over benchmarks validate the advantage of our algorithm compared to the state-of-the-art balanced clustering algorithms. On most datasets, our algorithm runs more than 100 times faster than previous algorithms with a better solution.

Transient interference can dramatically degrade the performance of over-the-horizon radar (OTHR). A novel transient interference suppression method based on structured low-rank matrix decomposition is proposed in this paper. Unlike most of the traditional interference suppression methods which need three steps: detect interferences, excise corrupted data and reconstruct excised data, the proposed method formulates the interference suppression as a Hankel matrix decomposition problem and suppress the interference via a single process of optimization. In addition, the proposed algorithm can suppress not only the interferences including the weak and strong ones, but also the noise. Simulation and experimental results have demonstrated the effectiveness of the proposed method.

The World Wide Web provides great convenience for users to obtain information. However, there exists much harmful information on the Internet, such as pornographic content and prohibited drugs' information. Thus, how to filter harmful Web pages on the Internet is quite an important issue. In general, the problem of harmful Web page filtering is converted to that of Web page classification, which needs plenty of well labeled training samples. However, the cost of labeling a large set of Web pages is very expensive. To address this problem, we adopt a semi-supervised framework for Web page filtering. In this framework, each Web page is represented by bags of different features, extracted using its HTML structure. Then a semi-supervised learning strategy is taken for efficiently obtaining well labeled training samples. Finally, a boosting classifier is utilized for harmful Web page filtering. Experiments have demonstrated the effectiveness of our framework.

Large area tiled displays are gaining popularity for use in collaborative immersive virtual environments and scientific visualization. While recent work has addressed the issues of geometric registration, rendering architectures, and human interfaces, there has been relatively little work on photometric calibration in general, and photometric non-uniformity in particular. For example, as a result of differences in the photometric characteristics of projectors, the color and intensity of a large area display varies from place to place. Further, the imagery typically appears brighter at the regions of overlap between adjacent projectors. We analyze and classify the causes of photometric non-uniformity in a tiled display. We then propose a methodology for determining corrections designed to achieve uniformity, that can correct for the photometric variations across a tiled projector display in real time using per channel color look-up-tables (LUT).