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检索条件"任意字段=Conference on Space-Efficient Data Structures, Streams, and Algorithms"

共 740 条记录，以下是81-90 订阅

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The Impact of Message Encryption on Teleoperation for space Applications

The Impact of Message Encryption on Teleoperation for Space ...

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2022 IEEE Aerospace conference, AERO 2022

作者： Maple, Carsten Epiphaniou, Gregory Hathal, Waleed Atmaca, Ugur Ilker Sheik, Al Tariq Cruickshank, Haitham Falco, Gregory Secure Cyber Systems Research Group Wmg University of Warwick United Kingdom Secure Cyber Systems Research Group University of Warwick United Kingdom Institute of Communication Systems University of Surrey United Kingdom Institude for Assured Autonomy Johns Hopkins University Department of Civil & Systems Engineering BaltimoreMD United States

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

In-orbit construction and maintenance of large-scale structures such as the International space Station (ISS) have led to increasing interest in developing effective and efficient teleoperation systems. However, teleoperation requires communication of data between the operator and the operation site which may be subject to cyber-attacks. Cyber security measures such as encryption and hashing can prevent these attacks from being successful however, these security measures create additional overheads in communications that can impact on the operators ability to effectively control the operations at a distance. This paper investigates the time delay impact of encryption security controls and presents an analysis of the impact of different encryption algorithms on the communication channel. An awareness of the performance cost of encryption methods allows mission planners to consider the risk of security compromise and balance this against performance costs. © 2022 IEEE.

关键词： Remote control

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An Average-Case efficient Two-Stage Algorithm for Enumerating All Longest Common Substrings of Minimum Length $k$ Between Genome Pairs

An Average-Case Efficient Two-Stage Algorithm for Enumeratin...

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IEEE International conference on Healthcare Informatics (ICHI)

作者： Mattia Prosperi Simone Marini Christina Boucher Dept. of Epidemiology University of Florida Gainesville FL USA Dept. of CISE University of Florida Gainesville FL USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

A problem extension of the longest common sub-string (LCS) between two texts is the enumeration of all LCSs given a minimum length $k$ (ALCS-k), along with their positions in each text. In bioinformatics, an efficient solution to the ALCS- $k$ for very long texts -genomes or metagenomes- can provide useful insights to discover genetic signatures responsible for biological mechanisms. The ALCS-k problem has two additional requirements compared to the LCS problem: one is the minimum length $k$ , and the other is that all common strings longer than $k$ must be reported. We present an efficient, two-stage ALCS-k algorithm exploiting the spectrum of text substrings of length $k$ (k-mers). Our approach yields a worst-case time complexity loglinear in the number of k-mers for the first stage, and an average-case loglinear in the number of common k-mers for the second stage (several orders of magnitudes smaller than the total k-mer spectrum). The space complexity is linear in the first phase (disk-based), and on average linear in the second phase (disk- and memory-based). Tests performed on genomes for different organisms (including viruses, bacteria and animal chromosomes) show that run times are consistent with our theoretical estimates; further, comparisons with MUMmer4 show an asymptotic advantage with divergent genomes.

关键词： Codes Software algorithms Genomics Random access memory Switches data structures Software

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An efficient Algorithm for Enumerating Longest Common Increasing Subsequences 27th

An Efficient Algorithm for Enumerating Longest Common Increa...

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27th International Computing and Combinatorics conference (COCOON)

作者： Lin, Chun Huang, Chao-Yuan Tsai, Ming-Jer Natl Tsing Hua Univ Hsinchu Taiwan

ISBN: (纸本)9783030895433;9783030895426

The longest common increasing subsequence (LCIS) problem is the combination of two classic problems in algorithms: the longest increasing subsequence (LIS) problem and the longest common subsequence (LCS) problem. In this paper, we propose an algorithm that finds every LCIS of two sequences a, b of length n in O(n + sigma + I-a) time and space, where s denotes the size of the alphabet set and Ia the total number of increasing subsequences contained in a (thus, the running time is output-sensitive). Our algorithm employs the trie and some simple data structures, and thus is implementation-wise simple. In addition, it can be proved that our algorithm is optimal in time complexity when sigma <= log(2) n.

关键词： LCIS Trie data structure

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Analysis of Student Learning Outcomes in data structures and algorithms

Analysis of Student Learning Outcomes in Data Structures and...

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International conference on Computational Science and Computational Intelligence (CSCI)

作者： Dewan Tanvir Ahmed Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA

Student learning outcomes (SLOs) are the specified knowledge, skills, and abilities that students are expected to achieve by the end of a course or program. Student learning outcomes describe what educators expect students to know and be able to do. SLO's specify an action of a student that must be observable, measurable and demonstrable. On the other hand, grades are objectives measure. This study attempts to quantify SLOs of a graduate level foundation course named algorithms and data structures. Three key aspects of the course are defined to address learning outcomes: a) Algorithm Design Skill, b) data Structure Selection and Design Skill and c) Algorithm Analysis Ability. For each aspect five achievement classes are specified in order to observe and quantify their knowledge, skills and ability. According to self-assessment, 86% students can a) determine the correct time and space complexities in an elegant and effective way, and b) correctly figure out time and space complexities of the designed algorithm but more elegant analysis exists. The study also shows 86% students can a) design elegant and robust algorithms and b) the designed algorithm is correct and efficient in most cases but it does not scale well in extreme cases. And finally, 79.7% students mentioned that a) data structure is well chosen (i.e., efficient) and well justified, and b) data structure is appropriate, but operations are not described sufficiently clearly. This clearly shows better success because of appropriate course modality, teaching pedagogy and engagement among many others.

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Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi-Scale State space Models

Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jiaoyang Yin Bin Fan Chao Xu Tiejun Huang Boxin Shi State Key Lab of Multimedia Info. Processing School of Computer Science Peking University Nat'l Eng. Research Ctr. of Visual Technology School of Computer Science Peking University Nat'l Key Lab of General AI School of Intelligence Science and Technology Peking University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As a bio-inspired vision sensor, the spiking camera has showcased remarkable capability in high-speed imaging with a sampling rate of 40,000 Hz. Reconstructing clear images from continuous spike streams, which is obtained by each photosensor continuously detecting photons and firing them asynchronously, has garnered significant attention. Despite promising results, existing spike-to-image reconstruction methods face challenges in balancing global receptive fields and efficient computation due to the inherent limitations of their backbones. Recently, due to powerful long-range modeling and linear complexity, the state space model (SSM) has emerged as a competitive alternative to CNNs and Transformers. In this paper, we propose a lightweight spike-to-image reconstruction network that harnesses Mamba as the backbone. Our approach sequentially executes three core modules: temporal information integration, spatial feature enhancement, and progressive image reconstruction. The former accumulates cues across diverse temporal windows to explore both long-term and short-term contexts. Subsequently, to model global dependencies while heightening local detail perception, we develop a multi-scale SSM block characterized by multi-scale multi-direction scanning, which effectively boosts spatial feature representations. Finally, intensity images are decoded progressively from the enhanced light-intensity features. Extensive experiments on both synthetic and real-captured data demonstrate that our approach achieves state-of-the-art performance, with only 10% of the network parameters and nearly two orders of magnitude less computational effort. The code will be available at https://***/interstellarH/Spk2ImgMamba.

关键词： Correlation Computational modeling Reconstruction algorithms Vision sensors Transformer cores Streaming media Cameras Transformers Image reconstruction streams

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Terrace: A Hierarchical Graph Container for Skewed Dynamic Graphs 21

Terrace: A Hierarchical Graph Container for Skewed Dynamic G...

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ACM SIGMOD International conference on Management of data (SIGMOD)

作者： Pandey, Prashant Wheatman, Brian Xu, Helen Buluc, Aydin Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Univ Calif Berkeley Berkeley CA 94720 USA Johns Hopkins Univ Baltimore MD 21218 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781450383431

Various applications model problems as streaming graphs, which need to quickly apply a stream of updates and run algorithms on the updated graph. Furthermore, many dynamic real-world graphs, such as social networks, follow a skewed distribution of vertex degrees, where there are a few high-degree vertices and many low-degree vertices. Existing static graph-processing systems optimized for graph skewness achieve high performance and low space usage by preprocessing a cache-efficient graph partitioning based on vertex degree. In the streaming setting, the whole graph is not available upfront, however, so finding an optimal partitioning is not feasible in the presence of updates. As a result, existing streaming graph-processing systems take a "one-size-fits-all" approach, leaving performance on the table. We present Terrace, a system for streaming graphs that uses a hierarchical data structure design to store a vertex's neighbors in different data structures depending on the degree of the vertex. This multi-level structure enables Terrace to dynamically partition vertices based on their degrees and adapt to skewness in the underlying graph. Our experiments show that Terrace supports faster batch insertions for batch sizes up to 1M when compared to Aspen, a state-of-the-art graph streaming system. On graph query algorithms, Terrace is between 1.7x-2.6x faster than Aspen and between 0.5x-1.3x as fast as Ligra, a state-of-the-art static graph-processing system.

关键词： Graph data structures Streaming Indexing

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Stable Cuckoo Filter for data streams 27

Stable Cuckoo Filter for Data Streams

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27th IEEE International conference on Parallel and Distributed Systems, ICPADS 2021

作者： Li, Shangsen Luo, Lailong Guo, Deke Zhao, Yawei National University of Defense Technology Changsha China China Electronic Equipment System Engineering Company Beijing China

ISBN: (纸本)9781665408783

Cuckoo filter (CF), Bloom filter (BF) and their variants are space-efficient probabilistic data structures for approximate set membership queries. However, their data synopsis would inevitably become unusable when there are a number of member updates on the set;while updates are not uncommon for the real-world data streaming applications such as duplicate item detection, malicious URL checking, and caching applications. It has been shown that some variants of BF can be adaptive to stream applications. However, current extensions of BF structures generally incur unstable performance or intolerant membership testing errors. In this paper, we aim to design a data synopsis for membership testing on data streams with stable performance and tolerant query errors. To this end, we propose Stable Cuckoo Filters (SCF), which take a fine-grained manner to evict the stale elements and store those more recent ones. SCF absorbs the design philosophy from several unsuccessful designs. Specifically, SCFs take elegant update operations to embed time information with insertion operation and carefully evict the stale elements. We show that a tight upper bound of the expected false positive rate (FPR) remains asymptotically constant over the insertion of new members. The query error for recent elements of SCF (FNR) is related to the characteristics of the input data stream and query workloads. Extensive experiments on the real-world and synthetic datasets show that our designs are more stable than the existing variants of BF and realize 7 x smaller false errors and up to 3 x throughput. © 2021 IEEE.

关键词： Errors

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20th International conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023

20th International Conference on Computer-Aided Architectura...

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20th International conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023

ISBN: (纸本)9783031371882

The proceedings contain 43 papers. The special focus in this conference is on Computer-Aided Architectural Design Futures. The topics include: Social Signals: An Adaptive Installation for Mediating space During COVID-19 and Beyond;enabling Flexible Architectural Design Re-representations Using a Phenotype-Based Strategy;Towards Human-AI Collaborative Architectural Concept Design via Semantic AI;the Impact of Spatial Layout on Orientation and Wayfinding in Public Housing Estates Using Isovist Polygons and Shape Matching algorithms;architectural Sketch to 3D Model: An Experiment on Simple-Form Houses;a Physics-Based Constraint Solver for the Site Layout Optimization of Non-convex Buildings with Multiple Requirements;topological Operations: A New Method Toward Retrievable Design Operations;a Visual Support Tool for Decision-Making over Federated Building Information;illustrating Algorithmic Design;biologically Informed Design - Towards Additive Biofabrication with Cyanobacteria;a Design-to-Fabrication Workflow for Free-Form Timber structures Using Offcuts;urban Shaderade. Building space Analysis Method for Energy and Sunlight Consideration in Urban Environments;a Parametric Tool for Outdoor Shade Design: Harnessing Quantitative Indices and Visual Feedback for Effective and efficient Climatic Design of Streets;fine-Grained Long-Term Analysis of Resurgent Urban Morphotypes;optimization Strategies of Architecture and Engineering Graduate Students: Responding to data During Design;a Design Ranking Method for Many-Objective Evolutionary Optimization;expanding Performance-Driven Parametric Design spaces Through data streams;the Marching Shape 3D: Extensions of the Ice Ray Shape Grammar;AI-Assisted Exploration of the Spirit of Place in Chinese Gardens from the Perspective of Spatial Sequences;material-in-the-Loop Fabrication: A Vision-Based Adaptive Clay 3D Printing Workflow on Indeterminate Sand Surfaces;Cost-Effective 3D Urban Massing Reconstruction of Public Aerial LiDAR S

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Differential Evolution Based Convolutional Neural Network for Hazy Image Classification

Differential Evolution Based Convolutional Neural Network fo...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Sai Sandeep Reddy Thadasina Nikhita Reddy Ravula Akhitha Reddy Sangasani Ravi Kumar Jatoth Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

This research paper addresses the pivotal domain of image classification, a field with profound real-world applications. The significance of classifying hazy images into distinct categories lies in the ability to apply the most suitable dehazing algorithm for each class. Relying solely on human perception is deemed suboptimal due to its unpredictability and vulnerability to bias, underscoring the need for an algorithmic approach to image classification. The initial binary haze-clear classification is refined into six nuanced categories: dense fog, thick fog, moderate fog, light fog, thin fog, haze. While convolutional neural networks (CNNs) excel in image classification, manually constructing network structures for specific tasks requires numerous trials to fine-tune a multitude of hyperparameters. This process is time-consuming, and finding the fittest hyperparameters suitable for target data poses a challenge. This research paper introduces a Differential Evolution (DE) based automatic network evolution model, optimising hyper-parameters by exploring the fittest parameters. The proposed evolutionary algorithm identifies optimistic hyperparameters. By merging the capabilities of the Differential Evolution algorithm with a sophisticated classification system, this research promises to push the boundaries of hazy image classification. This approach enhances classification accuracy and empowers subsequent applications by delivering cleaner and more detailed images. The results showcase a more efficient and effective approach to tackle the challenges posed by atmospheric haze, ultimately contributing to advancements in computer vision, object recognition and various domains relying on visual data analysis.

关键词： Adaptation models Neural networks Evolutionary computation Filtering algorithms Classification algorithms space exploration Convolutional neural networks

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efficient Mining of Rare Itemsets

Efficient Mining of Rare Itemsets

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International conference on Communication, Computing and Electronics Systems, ICCCES 2020

作者： Rai, Shwetha M, Geetha Kumar, Preetham Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka576104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka576104 India

ISBN: (纸本)9789813349087

Itemset mining discovers interesting patterns in the dataset. The itemset may be frequent or it can be rare based on its occurrence in the database. It has been observed that most of the algorithms are designed for mining frequent itemsets. However, discovery of rare itemsets is equally important since they play a major role in making decisions in some situations. The efficiency of the algorithms depend on the way in which the data structures are designed to store and retrieve the data. Hyperlinked Rare Pattern Mining algorithm discovers all rare itemsets and is suitable for sparse dataset. In this algorithm item_id and its support count are stored in Support and Header tables. This redundancy is removed in the proposed algorithm to improve the time efficiency. An experimental analysis is conducted to discover the rare itemsets. It is observed that while there is an improvement in time efficiency, there is a tradeoff for space efficiency. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data structures

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