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Where Are the Constants? New Insights on the Role of Round Constant Addition in the SymSum Distinguisher 1

引用

25th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2023

作者： Suryawanshi, Sahiba Saha, Dhiman de.ci.phe.red Lab Department of Computer Science & Engineering Indian Institute of Technology Bhilai492015 India

ISBN: (数字)9783031442742

ISBN: (纸本)9783031442735

The current work makes a systematic attempt to describe the effect of the relative order of round constant (RCon) addition in the round function of an SPN cipher on its algebraic structure. The observations are applied to the SymSum distinguisher, introduced by Saha et al. in FSE 2017 which is one of the best distinguishers on the SHA3 hash function reported in literature. Results show that certain ordering (referred to as Type-LCN) of RCon makes the distinguisher less effective but it still works with some limitations. Results in the form of new SymSum distinguishers are reported on concrete Type-LCN constructions - NIST LWC competition finalist Xoodyak-Hash and its internal permutation Xoodoo. New linear structures are also reported on Xoodoo that augment the distinguisher to penetrate more rounds. Final results include SymSum distinguishers on 7 rounds of Xoodoo and 5 rounds of Xoodyak-Hash with complexity 2128 and 232, respectively. All practical distinguishers have been verified. The characterization encompassing the algebraic structure and effect of RCon provided by the current work improves the understanding of SymSum in general and constitutes one of the first such result on Xoodyak-Hash and Xoodoo. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Infrared Small Target Detection with Feature Refinement and Context Enhancement 31st

Infrared Small Target Detection with Feature Refinement and...

引用

31st International Conference on Multimedia Modeling, MMM 2025

作者： Li, Xiuhong Zhu, Xinyue Li, Boyuan Li, Songlin Wang, Luyao Jia, Zhenhong School of Computer Science and Technology Xinjiang University Urumgi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China College of Mathematics and System Science Xinjiang University Urumqi China

ISBN: (纸本)9789819620609

Infrared small target detection has received widespread application and attention in both civilian and military fields. However, due to the very small size and lack of unique features of these targets, existing methods often suffer from inaccurate edge localization, and the targets are easily overwhelmed by complex backgrounds. To effectively address these issues, we designed a Feature Refinement and Context Enhancement Network (FCNet). The network consists of a multi-branch feature extraction module (MFEM), which integrates ordinary convolution and center difference convolution for diversified feature extraction, and further refines features through attention mechanism to enhance feature expression ability. In addition, in order to better capture the contextual information of the target, we have introduced the Context Enhancement Module (CEM). The CEM improves the robustness and detection accuracy of the network in complex backgrounds by preserving and enhancing important information in the image. Especially when processing infrared images with complex backgrounds and low signal-to-noise ratios, CEM can effectively highlight the target area and reduce false positives and false negatives. The experimental results on the SIRST dataset show that our FCNet performs well on multiple evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrared imaging

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An Efficient Data Aggregation Solution for Smart Meters Based on Cloud-Edge Collaboration 20th

An Efficient Data Aggregation Solution for Smart Meters Bas...

引用

20th International Conference on Services Computing, SCC 2023 held as Part of the Services Conference Federation, SCF 2023

作者： Xia, Zhuoqun Zhang, Li Sangaiah, Arun Kumar The School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410000 China Department of Electrical and Computer Engineering Lebanese American University Byblos83990 Lebanon

ISBN: (纸本)9783031516733

Smart meters are an important component of the smart grid, and the large-scale deployment of meters on the user side generates a large amount of data that brings huge expenses to the smart grid. In addition, attackers can monitor users’ electricity consumption based on the transmitted data, which poses a privacy threat. To solve these problems, this paper proposes a secure and efficient data aggregation scheme for smart meters based on cloud-side collaboration. Based on cloud-edge collaboration, the cloud server uses double Z-Score standardization to process the electricity consumption feature data, and at the same time uses improved Euclidean distance to obtain the number of clusters and classification results of the feature distinctions, and interacts with the edge device to respond. Based on the response information, the power provider generates confusion parameters and perturbation factors with classification information, which are sent to the meter for generating convergence values to minimize Gaussian noise and attached to the meter data to achieve the confusion processing of meter data. The aggregator receives the meter and cloud information for classification and aggregation and generates an aggregation signature to transmit the data. The results show that the scheme has high privacy security and operational efficiency. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart meters

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Topological Network-Control Games 29th

Topological Network-Control Games

引用

29th International Computing and Combinatorics Conference, COCOON 2023

作者： Liang, Zihui Khoussainov, Bakh Yang, Haidong University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783031491924

The paper introduces new combinatorial games, called topological network-control games, played on graphs. These games model the influence of competing two parties aiming to control a given network. In a such game given the network, the players move alternatively. At each turn, a player selects an unclaimed vertex and its unclaimed neighbours within distance t. The players obey the topological condition that all claimed vertices stay connected. The goal is to decide which player claims the majority of the vertices at the end of the play. We study greedy, symmetric and optimal strategies. We solve the topological network-control games on various classes of graphs. This progresses our understanding of combinatorial games played on graphs. We prove that finding an optimal winning strategy is a PSPACE-complete problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Topology

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BootsTAP: Bootstrapped Training for Tracking-Any-Point 17th

BootsTAP: Bootstrapped Training for Tracking-Any-Point

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17th Asian Conference on computer Vision, ACCV 2024

作者： Doersch, Carl Luc, Pauline Yang, Yi Gokay, Dilara Koppula, Skanda Gupta, Ankush Heyward, Joseph Rocco, Ignacio Goroshin, Ross Carreira, João Zisserman, Andrew Google DeepMind London United Kingdom VGG Department of Engineering Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9789819609000

To endow models with greater understanding of physics and motion, it is useful to enable them to perceive how solid surfaces move and deform in real scenes. This can be formalized as Tracking-Any-Point (TAP), which requires the algorithm to track any point on solid surfaces in a video, potentially densely in space and time. Large-scale ground-truth training data for TAP is only available in simulation, which currently has a limited variety of objects and motion. In this work, we demonstrate how large-scale, unlabeled, uncurated real-world data can improve a TAP model with minimal architectural changes, using a self-supervised student-teacher setup. We demonstrate state-of-the-art performance on the TAP-Vid benchmark surpassing previous results by a wide margin: for example, TAP-Vid-DAVIS performance improves from 61.3% to 67.4%, and TAP-Vid-Kinetics from 57.2% to 62.5%. For visualizations, see our project webpage at https://***/ © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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PCR: A Large-Scale Benchmark for Pig Counting in Real World 6th

PCR: A Large-Scale Benchmark for Pig Counting in Real World

引用

6th Chinese Conference on Pattern Recognition and computer Vision (PRCV)

作者： Jia, Jieru Zhang, Shuorui Ruan, Qiuqi Shanxi Univ Inst Big Data Sci & Ind Taiyuan Peoples R China Engn Res Ctr Machine Vis & Data Min Shanxi Prov Taiyuan 030006 Peoples R China Shanxi Univ Sch Informat & Technol Taiyuan 030006 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Automatic pig counting with pattern recognition and computer vision techniques, despite its significance in intelligent agriculture, remains to be a relatively unexplored area and calls for further study. In this paper, we propose a large-scale image-based Pig Counting in Real world (PCR) dataset, covering a variety of real-world scenarios and environmental factors. The dataset consists of two subsets, i.e., PartA captured on real-world pig pens and PartB collected from the Internet, with center point annotations of pig torsos in 4844 images. Moreover, we develop an automatic pig counting algorithm based on weakly-supervised instance segmentation, which can output a single segmentation blob per instance via the proposed Segmentation-Split-Regression (SSR) loss, utilizing point-level annotations only. Experiments show that the proposed algorithm achieves state-of-the-art counting accuracy and exhibits superior robustness against challenging environmental factors. The dataset and source codes are available at https://***/jierujia0506/PCR.

关键词： pig counting computer vision instance segmentation deep neural network

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The Bag-Based Search: A Meta-Algorithm to Construct Tractable Logical Circuits for Graphs Based on Tree Decomposition 16th

The Bag-Based Search: A Meta-Algorithm to Construct Tractab...

引用

16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Ishihata, Masakazu NTT Communication Science Laboratories Kyoto Japan

ISBN: (纸本)9783031496134

Tractable logical circuits (TLCs) have attracted more attention in the AI field as bases of knowledge representation and tractable probabilistic modeling. We propose the bag-based search (BBS), a new meta-algorithm for constructing a TLC that accepts all subgraphs of a given input graph that satisfies a target graph property. We implemented BBS examples for various graph properties, including independent set, k-edgeset, dominating set, k-matchings, and spanning trees, and applied them to artificial and real-world graphs. The experimental results showed that BBS generated significantly smaller circuits than ZDDs obtained by the frontier-based search (FBS). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Knowledge representation

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Data-Driven Genetic Algorithm for the Optimization of Water Distribution Networks: A New Surrogate Model for Estimating Investment and Operational Costs in Pumping Stations 27th

Data-Driven Genetic Algorithm for the Optimization of Water ...

引用

27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Gajardo-Sepúlveda, Nicolás Faúndez-Lizama, Thalía Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766039

The increase in water consumption and demand has highlighted the need to improve the efficiency of water distribution networks (WDNs). Pumping stations (PS) represent a significant challenge due to their high energy consumption and associated operational costs. This study presents a data-driven evolutionary optimization methodology focused on predicting cost and penalties in the design and operation of PS. The methodology integrates the functioning of genetic algorithms with machine learning techniques, developing a surrogate model capable of predicting associated costs and increasing the computational efficiency of optimization models. A case study is presented to validate the methodology, showing a significant reduction of 31.8% in evaluation times and a decrease in optimization time compared to traditional models. The results indicated that despite the reduction in computational effort, the increase in the final cost of the network was minimal, with only a 2.42% increase compared to the baseline model. These findings underscore the effectiveness of combining machine learning with genetic algorithms for the optimization of PS in WDNs, improving computational efficiency while maintaining high standards in solution quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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From Caesar Shifts to Kid-Enigma. The CS Unplugged-Like Path in the MuMa science Centre 7th

From Caesar Shifts to Kid-Enigma. The CS Unplugged-Like Pat...

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7th International Conference on Creative Mathematical sciences Communication, CMSC 2024

作者： Ren, Michal Perekietka, Pawel Nitschke, Lukasz Adam Mickiewicz University Wieniawskiego 1 Poznań61-712 Poland Fundacja Zaklady Kórnickie Flensa 2b Kórnik62–035 Poland Kórnik Poland

ISBN: (纸本)9783031732560

We present a proposal for a learning path for children to teach them cryptography during visits to the MuMa science Centre. As the goal of the Math Museum (MuMa) for which the track is being developed is to make youngsters enthusiastic about mathematics and computer science, we have decided to focus on cryptography. In our past experience this proved to be very effective – children love secrets and spy games. We will prepare a set of CS-unplugged-like activities which will cover the broad range from the simplest, historical ciphers up to the Enigma cipher. In contrast to previously described CS-unplugged activities, we will focus on methods of breaking those ciphers, not on merely using them. Breaking of Enigma deserves attention thanks both to clever use of pure mathematics, but also due to its historical significance in ending WWII. In this paper we will present ideas for activities to teach cryptanalysis of the simplest ciphers, starting from Caesar and Vigenère ciphers, as well as the design for a simple, paper teaching aid that simulates the simplified Enigma to show its properties. We share pertinent feedback we have received after several presentations we had already made to small groups of children and adults. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： cipher cryptanalysis cryptography CS Unplugged Enigma science centre

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DSTNet: Distinguishing Source and Target Areas for Image Copy-Move Forgery Detection 27th

DSTNet: Distinguishing Source and Target Areas for Image Cop...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Kaiqi Yuan, Xiaochen Huang, Guoheng Liu, Kun School of Cyberspace Security Shandong University of Political Science and Law Jinan China Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9783031783111

In copy-move forgery detection, most relevant studies concern locating the copy-move areas without the distinction of source and target regions. This paper proposes an end-to-end network, DSTNet, to identify the source and target based on consistency detection between the copy-move region and the non-copy-move region. The DSTNet is composed of two stages, the Pre-processing stage and the Discrimination stage. Pre-processing Stage extracts internal information of copy-move and non-copy-move areas and conducts a series of operations to meet the requirements of network input. Discrimination stage allows multiple patches for input and classifies the input patches. Specifically, the Pre-processing stage, contains the Copy-move Patches Selection (CM Patches Selection) and Genuine Patches Selection, can select pairs of copy-move and none copy-move patches. We train the proposed DSTNet on two large synthetic datasets and use the public datasets CASIA and Comofod for evaluation. The experiment shows that our method achieves excellent results. Particularly, we achieve a 5.4% higher F1 based on ground-truth of copy-move mask (GT-CM) on CASIA dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large datasets

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