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SSRN 2024年

作者： Zang, Xiaodong Wang, Tongliang Zhang, Xinchang Gong, Jian Gao, Peng Zhang, Guowei School of Cyber Science and Engineering Qufu Normal University Qufu China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Jinan China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

The focus on privacy protection has brought much-encrypted network traffic. However, attackers always abuse traffic encryption to conceal malicious behaviors. Although researchers have proposed several enlightening detection methods, they must enhance the generalization ability or improve detection performance. Our inspiration is that the packet header fields, as do the underlying grammatical rules for constructing sentences, have a strict order. We consider the original packet as text and devise a robust approach with natural language processing and a deep learning model to improve the generalization ability and detection performance. We capture the critical keywords as characteristic representations of the traffic and design an adaptive domain generalization algorithm with a new loss function. It is robust against various datasets and can augment the minority malicious samples to balance the data. Simultaneously, we design an efficient feature selection algorithm, which obtains an optimal feature subset and reduces feature dimensions by 75.3%. We conducted extensive experiments with two open-source datasets and the actual Internet backbone traffic(CERNET). Experimental results demonstrate our proposal's efficiency and robustness, outperforming state-of-the-art detection schemes. Specifically, the accuracy rate of our work can improve by at most 22.8% and achieve the average detection latency of 0.73s in the backbone. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Multi-Instance Partial-Label Learning with Margin Adjustment

arXiv

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arXiv 2025年

作者： Tang, Wei Yang, Yin-Fang Wang, Zhaofei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Joint Optimization of UAV Deployment and Task Computation Offloading Decision in UAV-assisted Edge Computing network

Joint Optimization of UAV Deployment and Task Computation Of...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yichuan Liu Jinbin Tu Yun Wang Key Lab of Computer Network and Information Integration MOE School of Computer Science and Engineering Southeast University Nanjing China

The UAVs' deployment decision and task computation offloading decision in the UAV-assisted edge computing network significantly impact the operating efficiency of edge network. On the basis of this, the Optimization Model for UAV Cluster Deployment and Computation Offloading Decision (OMUCDCOD) is established. The model jointly optimizes the number, location of UAV s, and task computation offloading decision. Different from previous studies, this model regards the terminal devices in the edge network as virtual MEC servers, and introduces the collaborative computing mode. The task computation offloading decision made can effectively utilize the computing capabilities offered by the edge network. Considering that the two problems of UAV deployment decision and task computation offloading decision are intricately interconnected, we propose a two-layer optimization algorithm combining K-Means and ant colony algorithm (ToKmAc) to solve OMUCDCOD. ToKmAc is divided into upper and lower layers to solve this optimization problem. The upper layer uses K-Means to solve the UAV deployment decision, that is, the number and location of UAVS; the lower layer employs ant colony algorithm to solve computation offloading decision. Finally, extensive experiments verify the effectiveness of OMUCDCOD and ToKmAc.

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FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI Segmentation in Incomplete Multi-Modal Scenarios 24

FIMD: Fusion-Inspired Modality Distillation for Enhanced MRI...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Yang, Rui Wang, Xiao Xu, Xin Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

ISBN: (纸本)9798400716720

Magnetic Resonance Imaging (MRI) is an invaluable tool for brain tumor segmentation. However, in clinical practice, certain modalities might be unavailable, leading to potential performance degradation in prediction tasks. According to current implementations, different modalities are treated as independent entities during the feature extraction phase, ignoring their inherent complementary nature. In this paper, inspired by knowledge distillation techniques, we introduce the Fusion-Inspired Modality Distillation (FIMD) framework. FIMD leverages the output from a fused multi-modal model as a teacher to guide the training of individual modalities. Each modality benefits from the collective knowledge of all modalities, enhancing its performance. Our FIMD method, devoid of specific architectural constraints, seamlessly integrates into existing multi-modal brain tumor segmentation frameworks. Notably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets indicate that FIMD can enhance the performance of current state-of-the-art (SOTA) algorithms on Dice by an average of 0.55-1.60%. Especially in the case of missing modalities, the effectiveness of the FIMD framework in enhancing the robustness and accuracy of brain tumor segmentation is demonstrated. © 2024 ACM.

关键词： Semantic Segmentation

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Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare 18th

Genetic-A* Algorithm-Based Routing for Continuous-Flow Micr...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Huang, Huichang Yang, Zhongliao Zhong, Jiayuan Xu, Li Dong, Chen Bao, Ruishen College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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T-Sorokin: A General Mobility Model in Opportunistic networks

T-Sorokin: A General Mobility Model in Opportunistic Network...

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International Conference on computer Supported Cooperative Work in Design

作者： Jinbin Tu Qing Li Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration MOE Nanjing China

The opportunistic networks are a kind of ad hoc networks that rely on the chance of nodes meeting to transmit messages. Acting as an effective supplement to 4G and 5G networks in some special scenarios where hardware devices are limited, the opportunistic networks have a significant application in health monitoring, warning broadcasting, disaster relief, and so on. The mobility model is one of the research focuses on the opportunistic networks. On the basis of the social mobility theory proposed by Sorokin, a general mobility model, which is suited for various scenarios, called T-Sorokin is proposed. This model is described as a seven-tuple and implemented on the Opportunistic network Environment simulator and fits both Infocom06 and Rome taxi data set, which includes different areas ranging from hotel to city and different mobile units ranging from person to taxi. The results of experiments demonstrate that the T-Sorokin model has the advantage of generality, simplicity, and accuracy. It can simply establish movement tracks close to real data under different scenarios.

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Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks

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Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

Clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： Measurement Manifolds Shape Clustering algorithms Synthetic data

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Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare 18th

Resource Binding and Module Placement Algorithms for Conti...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Yang, Zhongliao Huang, Huichang Liu, Zeyi Dong, Chen Xu, Li College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Biochips

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Optically pumped wavelength-tunable lasing from a GaN beam cavity with an integrated Joule heater pivoted on Si

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Chinese Physics B 2023年第5期32卷 478-484页

作者：秦飞飞孙阳杨颖李欣王旭卢俊峰王永进朱刚毅 GaN Optoelectronic Integration International Cooperation Joint Laboratory of Jiangsu Province College of Telecommunications and Information EngineeringNanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications Ministry of Education)Nanjing 210003China Department of Microelectronic Science and Engineering School of Physical Science and TechnologyNingbo UniversityNingbo 315211China College of Physics Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Dynamically tunable laser sources are highly promising for realizing visionary concepts of integrated photonic circuits and other applications. In this paper, a Ga N-based laser with an integrated PN junction heater on Si is *** photoluminescence properties of the Ga N beam cavity are controlled by temperature, and the Joule heater provides electrically driven regulation of temperature. These two features of the cavity make it possible to realize convenient tuning of the lasing properties. The multi-functional Ga N beam cavity achieves optically pumped lasing with a single mode near 362.4 nm with a high Q-factor of 1394. The temperature of this device increases by 0–5℃ under the Joule heating effect. Then, electrical control of the lasing mode is demonstrated. The lasing resonant peak shows a continuous redshift of about 0.5 nm and the device also exhibits dynamic switching of its lasing mode. The lasing modulation can be ascribed to temperature-induced reduction of the bandgap. Our work may be of benefit for external optical modulation in future chip-based optoelectronic devices.

关键词： GaN beam cavity optically pumped lasing dynamically tunable laser source Joule heater

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Gaia: Ground Station-Centric Mobility Management for LEO Satellite networks

Gaia: Ground Station-Centric Mobility Management for LEO Sat...

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IEEE International Conference on Communications (ICC)

作者： Xiaxin Zhou Tian Pan Zhaokun Yang Sirui Su Jun Hu Guohao Ruan Junkai Huang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

For mega-scale low Earth orbit (LEO) satellite constellations, the relative position changes between satellites and ground terminals pose challenges for end-to-end TCP session maintenance, which serves as the substrate for many Internet services. Mobile IP resolves the session maintenance issue by in-troducing a binding mechanism between the care-of address and home address; however, this also leads to inefficient triangular routing. Our recently proposed LISP-LEO, through partition-satellite mapping, routes traffic to the service satellite above the destination terminal's partition, addressing the triangular routing problem. However, due to the corner case of partition-satellite mapping, LISP-LEO introduces the issue of the last-hop route selection, as well as the associated per-terminal registration states on the satellite, making the solution non-scalable. In this work, we propose Gaia, a ground station-centric mobility management scheme for LEO satellite networks. Gaia maintains a precise mapping of each ground terminal's IP and its geographical location. When receiving traffic from a source terminal, the access satellite can, based on the geographical location of the destination terminal carried by the traffic, directly locate the satellite above the destination terminal and tunnel the traffic to it. In addition, to reduce the satellite's burden, we add a DNS-like querying mechanism by offloading the mapping of terminal IPs and geographical locations to the ground station. The evaluation shows that Gaia outperforms Mobile IP and LISP-LEO in both end-to-end latency and on-board resource consumption.

关键词： Satellite constellations Satellites Web and internet services Low earth orbit satellites Production Routing Maintenance

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