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On cross-correlation indicators of an S-box

Frontiers of computer Science 2011年第4期5卷 448-453页

作者： Sheng GAO Wenping MA Zepeng ZHUO Fenghe WANG Key Laboratory of Computer Network and Information Security Ministry of Education Xidian University Xi＇an 710071 China State Key Laboratory of Integrated Service Networks Xidian University Xi＇an 710071 China

Substitution boxes （S-boxes） are often used as the most important nonlinear components in many symmetric encryption algorithms. The cryptographic properties of an S-box directly affect the security of the whole cipher system. Recently, generalized global avalanche characteristics （GGAC） were introduced to measure the correlation between two arbitrary Boolean functions. In this paper, to better evaluate the security of an S-box, we present two cross-correlation indicators for it. In addition, by studying the related properties of the cross-correlation between two balanced Boolean func- tions, we propose the lower bounds on the sum-of-squares indicator related to GGAC for two balanced functions and also for an S-box.

关键词： cryptography Boolean functions substitu-tion boxes balancedness generalized global avalanchecharacteristics cross-correlation

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Reasonable routing in delay/disruption tolerant networks

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中国计算机科学前沿 2011年第3期5卷 327-334页

作者： Haizheng YU Jianfeng MA Hong BIAN College of Mathematics and System Sciences Xinjiang University Urumqi 830046 China Key Laboratory of Computer Network and Information Security Xidian University Xi'an 710071 China Key Laboratory of Computer Network and Information Security Xidian University Xi'an 710071 China School of Mathematical Sciences Xinjiang Normal University Urumqi 830054 China

Delay/disruption tolerant networking (DTN)is an approach to networking where interrnittent connectivity exists:it is often afforded by a store and forward *** on the capability of intermediary nodes to carry and forward messages,messages can be eventually delivered to their destination by mobile nodes with an appropriate routing *** have achieved a successful delivery,most DTN routing protocols use message duplication *** messages are rapidly transferred to the destination,the redundancy in the number of message copies increases *** paper presents a new routing scheme based on a stochastic process for epidemic *** redundancy is efficiently reduced and the number of message copies is controlled *** the contact process of nodes in the network,the number of message copies changes,and according to the variability in the number of copies,we construct a special Markov chain,birth and death process,on the number of message copies then calculate and obtain a stationary distribution of the birth and death *** the theoretical model with the simulation we have performed we see similar *** method improves on time-to-live (TTL) and antipacket methods,in both redundancy and delivery success efficiency.

关键词： delay/disruption tolerant networks (DTN) routing algorithm Markov chain message redundancy

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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A netflow v9 measurement system with network performance function 5th

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5th International Conference on Internet and Distributed Computing Systems, IDCS 2012

作者： Cheng, Guang Wu, Hua School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)9783642348822

Netflow can be employed for accounting, anomaly detection and network monitoring, and can bring new data source for network management. But most IPv6 routers in CERNET2 backbone network don’t provide IPFIX or NetFlow flow record function. Netflow flow records don’t have the network performance information, such as RTT and packet loss ratio, so we hardly use the Netflow data to analyze the network performance. In this paper, we designs a NetFlow v9 measurement system (N9MS) which converts IPv6 packet headers into the Net-Flow v9 flow records and monitors the link performance with these flow records. The N9MS has two improvements to the traditional Cisco’s sampled NetFlow feature. Firstly, the Cisco’s sampling strategy is to sample packets, while that in the N9MS is flow sampling which can keep all packets in the sampled flows to infer network performance based on these sampled packets. Secondly, N9MS directly uses these sampled packets to calculate these performance metrics, such as round trip time (RTT) and packet loss ratio. We also define both RTT and packet loss ratio fields in the scalability NetFlow v9 template format. In this paper we also use the N9MS to monitor a 10Gbps backbone link between Nanjing site and the CNGI-CERNET2 backbone, and give some experimental results. © Springer-Verlag Berlin Heidelberg 2012.

关键词： information management

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Integrating Syntactic and Semantic Knowledge in AMR Parsing with Heterogeneous Graph Attention network 48

Integrating Syntactic and Semantic Knowledge in AMR Parsing ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sataer, Yikemaiti Shi, Chuanqi Gao, Miao Fan, Yunlong Li, Bin Gao, Zhiqiang Southeast University School of Computer Science and Engineering Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Meaning Representation (AMR) parsing is the task of translating a sentence to an AMR semantic graph which captures the basic meaning of the sentence, and is empowered by pre-trained Transformer models recently. These models encode the syntactic and semantic knowledge implicitly through self-supervised pre-trained tasks. We argue that encoding the syntactic and semantic knowledge explicitly is beneficial to AMR parsing and can improve data efficiency. Specifically, syntactic dependency and semantic role labeling (SRL) have similar sub-structures with AMR. In this work, we propose a novel linguistic knowledge-enhanced AMR parsing model, which augments the pre-trained Transformer with syntactic dependency and semantic role labeling structures of sentences. By applying a heterogeneous graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experimental results show that our model achieves state-of-the-art performance on different benchmarks, especially in out-of-domain and low-resource scenarios. © 2023 IEEE.

关键词： Syntactics

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Ciphertext-Policy Attribute Based Encryption Supporting Any Monotone Access Structures Without Escrow

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Chinese Journal of Electronics 2017年第3期26卷 640-646页

作者： ZHAO Qianqian ZHANG Yuqing ZHANG Guanghua WANG He State Key Laboratory of Integrated Services Networks Xidian University National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS

Ciphertext policy attribute-based encryption(CP-ABE)is becoming a new primitive for finegrained access *** neither produces multiple encrypted copies of the same data nor suffers from the severe burden of key distribution and *** escrow problem that the central authority could decrypt any ciphertexts addressed to all the specific users is still a challenge for CP-ABE *** new CP-ABE scheme without escrow is proposed,and furthermore the proposed scheme achieves fully security in the standard *** performance and security analysis results indicate that the proposed CP-ABE scheme is extremely appropriate for cloud storage system.

关键词： CP-ABE The escrow problem Fully security In the standard model

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Decomposition-Based Classifier Chains for Multi-Dimensional Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 176-191页

作者： Jia, Bin-Bin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730050 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing210096 China

In multi-dimensional classification, the semantics of objects are characterized by multiple class variables from different dimensions. To model the dependencies among class variables, one natural strategy is to build a number of multiclass classifiers in a chaining structure, one per dimension, where the subsequent classifiers on the chain augment the feature space with all labeling information used by the preceding classifiers. However, it is shown that this strategy cannot compete with existing state-of-the-art approaches via comparative studies. One possible reason is that inaccurate predictions of preceding classifiers would degenerate the performance of subsequent ones. Besides, it is more difficult to learn a multiclass classifier than a binary one with the same accuracy, and better performance can be expected if the multi-dimensional classification problem can be solved by building multiple binary classifiers in a chaining structure. Based on these conjectures, this article proposes an approach, which builds a chain of binary classifiers to solve the multi-dimensional classification problem with the help of one-versus-one decomposition. To address the issue that different one-versus-one decomposed problems involve different training examples, the feature space is augmented with the binary predictions of preceding classifiers on the chain to train the subsequent ones. To alleviate the effect of the specified chaining order, the ensemble version of the proposed approach is further investigated. Comparative studies over 20 benchmark datasets clearly show the superiority of the proposed approach against the state-of-the-art multi-dimensional classification baselines. © 2020 IEEE.

关键词： Machine learning

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Depression Scale Dictionary Decomposition Framework for Multimodal Automatic Depression Level Prediction

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Niu, Mingyue Wang, Xu Gong, Jibing Liu, Bin Tao, Jianhua Schuller, Bjorn W. Yanshan University School of Information Science and Engineering Hebei province Qinhuangdao066004 China Hebei Key Laboratory of Computer Virtual Technology and System Integration China Technology Co. Ltd Bejing100022 China Bejing100190 China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Augsburg ZD.B. Chair of Embedded Intelligence for Health Care and Wellbeing Augsburg Germany Glam - Group on Language Audio and Music United Kingdom

Currently, many researchers aim to achieve automatic depression level prediction via speech and video behavior analysis. However, previous works have struggled to decompose audio and video sequences into the information related to and unrelated to depression scores, hindering the model's perception of depression cues. Besides, previous works implement multimodal fusion using attention mechanisms or linear layers, but failed to simultaneously consider the Euclidean relationship among tokens and the non-Euclidean relationship among channels, which bring limitations in capturing depression cues. In response to the above issues, we propose a depression scale dictionary decomposition framework, which mainly includes a Bidirectional Dictionary Decomposition (BDD) module and a Bidirectional Multimodal Fusion (BMF) module. The BDD module can use the dictionaries generated based on the depression scale to semantically decompose audio and video sequences into the information related to and unrelated to depression scores along token and channel dimensions for promoting depression cue perception. Moreover, considering the respective characteristics of tokens and channels, the BMF module uses linear layers and graph convolution to achieve cross-modal mixing, which is used to aggregate audio and video sequences for predicting depression levels. The validation on AVEC 2013, AVEC 2014 and DAIC-WOZ datasets demonstrates our method's superiority. © 1991-2012 IEEE.

关键词： Boolean functions

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Direction based charging in rechargeable wireless sensor network 1

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4th International Conference on IoT as a Service, IoTaaS 2018

作者： Batool, Sadia Tong, Fei Li, Songyuan He, Shibo Zhejiang University Hangzhou China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (数字)9783030146573

ISBN: (纸本)9783030146566

. One of the main issues in rechargeable wireless sensor networks (RWSNs) is the stainability of network operation. Recently, wireless power transmission technology has been applied in RWSNs to transmit wireless power from chargers to sensor nodes. In this paper, we focus on how a charger changes its direction to cover different nodes for energy provisioning and, how to sense maximum data from the area of network. We intend to tackle the challenge that how to cover different nodes from each direction so that the charger can provide maximum power to maintain network performance. In this regard, a direction scheduling algorithm is proposed to fill the charging demand. Furthermore, a proposed Linear Programming based solution is proposed to determine maximum data sensing rate. The simulation results show that the proposed method performs better compared with existing methods. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2019.

关键词： Linear programming

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Fast Smallest Lowest Common Ancestor Computation Based on Stable Match

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Journal of computer Science & Technology 2013年第2期28卷 366-381页

作者：周军锋蓝国翔陈子阳汤显 School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University School of Economics and Management Yanshan University

In this paper, we focus on efficient processing of XML keyword queries based on smallest lowest common ancestor （SLCA） semantics. For a given query Q with m keywords, we propose to use stable matches as the basis for SLCA computation, where each stable match M consists of m nodes that belong to the m distinct keyword inverted lists of Q. M satisfies that no other lowest common ancestor （LCA） node of Q can be found to be located after the first node of M and be a descendant of the LCA of M, based on which the operation of locating a stable match can skip more useless nodes. We propose two stable match based algorithms for SLCA computation, i.e., BSLCA and HSLCA. BSLCA processes two keyword inverted lists each time from the shortest to the longest, while HSLCA processes all keyword inverted lists in a holistic way to avoid the problem of redundant computation invoked by BSLCA. Our extensive experimental results verify the performance advantages of our methods according to various evaluation metrics.

关键词： XML keyword search smallest lowest common ancestor

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