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检索条件"丛书名=Lecture Notes in Computer Science"

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Vision-Based Xylem Wetness Classification in Stem Water Potential Determination 19th

Vision-Based Xylem Wetness Classification in Stem Water Pote...

引用

19th International Symposium on Visual Computing

作者： Peiris, Pamodya Samanta, Aritra Mucchiani, Caio Simons, Cody Roy-Chowdhury, Amit Karydis, Konstantinos Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9783031773884;9783031773891

Water is often overused in irrigation, making efficient management of it crucial. Precision Agriculture emphasizes tools like stem water potential (SWP) analysis for better plant status determination. However, such tools often require labor-intensive in-situ sampling. Automation and machine learning can streamline this process and enhance outcomes. This work focused on automating stem detection and xylem wetness classification using the Scholander Pressure Chamber, a widely used but demanding method for SWP measurement. The aim was to refine stem detection and develop computer-vision-based methods to better classify water emergence at the xylem. To this end, we collected and manually annotated video data, applying vision- and learning-based methods for detection and classification. Additionally, we explored data augmentation and fine-tuned parameters to identify the most effective models. The identified best-performing models for stem detection and xylem wetness classification were evaluated end-to-end over 20 SWP measurements. Learning-based stem detection via YOLOv8n combined with ResNet50-based classification achieved a Top-1 accuracy of 80.98%, making it the best-performing approach for xylem wetness classification.

关键词： Precision Agriculture Automation Stem Water Potential Stem Detection Wetness Classification

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Enhancing Link Prediction Based on Simple Path Graphs 10th

Enhancing Link Prediction Based on Simple Path Graphs

引用

29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Li, Zhiren Cai, Yuzheng Feng, Hongwei Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9789819609130;9789819609147

Link prediction is one of the prominent tasks in the field of graph machine learning, aiming to fill in missing edges in graph data or predict potential connections between nodes in the graph. Early link prediction methods primarily utilized traditional machine learning models, leveraging heuristic rules, embedded representations of nodes, and node features for prediction. However, graph neural networks possess powerful learning capabilities, effectively capturing the topological structure and node features of graphs, thus exhibiting increasingly superior performance in link prediction tasks. Methods for link prediction tasks based on graph neural networks primarily fall into two categories: node-based methods and subgraph-based methods. Among them, subgraph-based methods excel in link prediction tasks due to their ability to capture the graph topological structure between nodes and effectively address the issue of node isomorphism. Currently, subgraph-based methods typically employ enclosing subgraphs formed by the first or second-order neighboring nodes around the target nodes. Despite the effectiveness of enclosing subgraph-based methods in link prediction tasks, they face challenges such as the scalability issue arising from the large size of enclosing subgraphs and susceptibility to influence from hub nodes in the graph. To address this challenge, this study proposes a novel subgraph pattern, namely, utilizing target nodes to construct the simple path graph and conducting link prediction on these simple path graphs. We demonstrate in this study that, under certain order constraints, simple path graph is a subgraph of enclosing subgraph, thus alleviating the issue of excessive subgraph size encountered by subgraph-based methods. Furthermore, through experimental validation, we show that even with relaxed order constraints, the size of the simple path graph remains smaller than that of the enclosing subgraph. Experimental results demonstrate that the proposed lin

关键词： Graph neural network Link prediction Simple path graph

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Towards Elastic Image Super-Resolution Network via Progressive Self-distillation 7th

Towards Elastic Image Super-Resolution Network via Progressi...

引用

7th Chinese Conference on Pattern Recognition and computer Vision

作者： Yu, Xin'an Zhang, Dongyang Liu, Cencen Dong, Qiang Duan, Guiduo Univ Elect Sci & Technol China UESTC Inst Intelligent Comp Chengdu 611731 Peoples R China UESTC Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

ISBN: (纸本)9789819786848;9789819786855

Recently, there has been a growing demand to implement super-resolution (SR) networks on devices with constrained resources. Nevertheless, most existing SR networks must remain consistent during both training and testing, restricting the model's adaptability in real scenarios. Consequently, attaining elastic reconstruction without retraining is a crucial challenge. To accomplish this, we propose a novel model compression and acceleration framework through the Channel Splitting and Progressive Self-distillation (CSPS) strategy. Specifically, we construct a compact student network from the target teacher network by employing the channel splitting strategy, which removes a certain proportion of channel dimensions from the teacher network. Afterward, we incorporate auxiliary upsampling layers into the intermediate feature maps and propose the progressive self-distillation. Once trained, our CSPS can achieve elastic reconstruction by adjusting the channel splitting ratio and the number of feature extraction blocks. Extensive experiments demonstrate that the proposed CSPS can effectively compress and accelerate various off-the-shelf SR models.

关键词： Image super-resolution progressive self-distillation elastic reconstruction lightweight model

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Deep Learning-Based Differential Distinguishers for Cryptographic Sequences 25th

Deep Learning-Based Differential Distinguishers for Cryptogr...

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25th International Conference on Cryptology in India

作者： Bose, Amrita Pal, Debranjan Chowdhury, Dipanwita Roy Indian Inst Technol Kharagpur Kharagpur W Bengal India Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9783031803109;9783031803116

This research introduces a new deep learning-based technique that significantly enhances the efficiency and accuracy of deep learning based cryptographic distinguishers. By employing a sequence detection approach, we have achieved significant improvements in finding distinguishers for analyzing ciphertext sequences. Two innovative models, an LSTM-based Encoder Classifier (LbEC) and a Transformer based Encoder-only Classifier (TbEC), are proposed. The dataset has been transformed into a list of vector embeddings of the individual sequence data, which is used to train the models. Experimental results demonstrate that this approach has not only achieved results comparable to the existing related works but also outperformed some of the existing schemes. Thereby, distinguishers for HIGHT covering 16 rounds, PRESENT covering 12 rounds, LEA covering 13 rounds, SPARX covering 6 rounds and Piccolo-80 covering 9 rounds have been accomplished, which shows notable improvement over the existing best results.

关键词： Neural Distinguisher Differential Cryptanalysis Deep Learning Lightweight Block Ciphers HIGHT PRESENT LEA SPARX Piccolo

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OC-SAN: Unsupervised Deepfake Detection for Specific Individual Protection Based on Deep One-Class Classification 7th

OC-SAN: Unsupervised Deepfake Detection for Specific Individ...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Cao, Yun Tong, Yanfei Bao, Han Liao, Xin Zhu, Meineng Chinese Acad Sci Inst Informat Engn Beijing 100085 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Zhejiang Univ Hangzhou 310058 Peoples R China Hunan Univ Changsha 410082 Peoples R China Beijing Inst Elect Technol & Applicat Beijing 100091 Peoples R China

ISBN: (纸本)9789819787913;9789819787920

In recent years, the misuse of Deepfake technology has become a critical security concern, especially for high-profile individuals such as politicians and celebrities. The key figure targeted forgeries are usually been carefully crafted with a sophisticated process, leaving behind no manipulation-specific artifacts. The inadequacy of current supervised binary classification methods for addressing these specific individual protection (SIP) challenges is evident for two primary reasons. Firstly, sufficient fake samples cannot be ensured to train generic and robust classifiers. Secondly, the efficacy of detection varies significantly across different identities. To address these problems, we formulate forgery detection as a one-class anomaly detection problem and propose a detection network, OC-SAN (One-Class Style Auto-encoder Network), to offer tailored protection for each individual. Borrowing from style transfer literature, we conceptualize facial features as a fusion of coarse-grained (identity) and fine-grained (personalized/style) elements. The fundamental concept driving OC-SAN is that a facial image can be well restored when its two features belong to the same identity. As a one-class method, OC-SAN can be trained only with authentic samples, indicating good generalization performance in real-world scenarios without relying on prior knowledge of forgery methods. Extensive experiments have been conducted to demonstrate the superiority of OC-SAN in SIP tasks, when compared to other state-of-the-art forgery detection methods.

关键词： Deepfake detection forgery detection video forensics

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HiTraj: Heterogeneous Interaction Learning with Transformers for Trajectory Prediction 7th

HiTraj: Heterogeneous Interaction Learning with Transformers...

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Wang, Xilin Xiao, Yao Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9789819785018;9789819785025

Accurately predicting the future motion of agents is crucial for human-robot interactive systems such as autonomous driving. However, in real-world traffic scenarios, complex social interactions make accurate trajectory prediction challenging, especially considering the different interaction patterns between different types of agents. Existing trajectory prediction methods usually ignore the heterogeneity of interactions, and treat all interactions equally. In this work, we propose HiTraj, a multimodal trajectory prediction network. A novel and powerful heterogeneous interaction learning Transformer (Hi-Transformer) is introduced, which combines prior and hidden interaction information to achieve more detailed heterogeneous interaction learning. In addition, we have introduced a simple but effective variant of multi-head attention mechanism, multi-style attention, through a series of style channels, it generates multimodal trajectories with different styles. We evaluated HiTraj on three widely used trajectory prediction datasets and the results show that HiTraj achieves comparable or superior performance compared to baseline models, especially achieving state-of-the-art performance on the SDD dataset with a large amount of heterogeneous interactions.

关键词： Trajectory Prediction Heterogeneous Interaction Multimodal Learning

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Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators 24th

Open Source Software Randomisation Framework for Probabilist...

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24th International Conference on Embedded computer Systems: Architectures, Modeling, and Simulation

作者： Kosmidis, Leonidas Maria Trompouki, Matina Lopez Castillon, Pau Rufart Blasco, Eric Fernandez Salgado, Javier Jung, Andreas Barcelona Supercomp Ctr BSC Barcelona Spain Univ Politecn Catalunya UPC Barcelona Spain European Space Agcy ESA Noordwijk Netherlands

ISBN: (纸本)9783031783791;9783031783807

High criticality space systems frequently have real-time requirements. Therefore, it is important to be able to compute the Worst Case Execution Time (WCET) of its software. However, with the complexity of modern processor architectures, it is very difficult to compute the WCET with traditional methods, such as static timing analysis or measurement based solutions. Recently, a novel timing analysis method was introduced, known as Measurement Based Probabilistic Timing Analysis (MBPTA). A unique characteristic of MBPTA compared to traditional methods is that it produces a Probabilistic WCET (pWCET) which instead of a single overestimated value is a curve that associates execution times with probabilities which can reach arbitrary low values (e.g. 10(-14)). MBPTA simplifies the estimation of pWCET by simply requiring the collection of execution times which are independent and identically distributed (i.i.d.). However, this property is not satisfied in conventional hardware and software systems. Special hardware designs have been proposed which provide hardware-based time-randomisation, including a probabilistic version of Cobham Gaisler's LEON3 as well as software solutions in the form of software randomisation. However, existing software randomisation methods only support CPUs, are very restrictive (i.e. support only ANSI C) and more importantly are not open source. This limits significantly the applicability of these methods in space, although ESA has supported in the past activities related to MBPTA hardware and software (P4S-PROARTIS for Space and EFL-Eviction Frequency Limitation). In this ESA-funded project, we implement an open source, qualifiable software randomisation tool which will be able to software randomise code for CPUs, including multicores as well as GPUs, which to our knowledge are not amenable to any WCET solution.

关键词： Real-time Systems Worst Cased Execution Time (WCET) Open Source Source-to-source Compiler Measurement Based Probabilistic Timing Analysis (MBPTA) Software Randomisation

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Vision-Language Pretraining for Variable-Shot Image Classification 31st

Vision-Language Pretraining for Variable-Shot Image Classifi...

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31st International Conference on Multimedia Modeling

作者： Papadopoulos, Sotirios Ioannidis, Konstantinos Vrochidis, Stefanos Kompatsiaris, Ioannis Patras, Ioannis Informat Technol Inst Thessaloniki Greece Queen Mary Univ London London England

ISBN: (纸本)9789819620708;9789819620715

Contrastively pretrained vision-language models (VLMs) such as CLIP have shown impressive zero-shot classification performance without any classification-specific training. They create a common embedding space by contrastively pretraining an image and a text encoder to align positive image-text pairs and repel negative pairs. Then zero-shot classification of an image can be performed by measuring the cosine similarities between the image embedding and embeddings of texts that describe the classes. However, relevant works do not address the scenario in which few image examples for some (not all) classes are available. In this novel task which we term variable-shot (v-shot) classification, these models fail due to the embedding space modality gap, i.e. the fact that image-to-image similarities are higher than image-to-text ones. To this end, we propose to enable v-shot capabilities in pre-trained VLMs with minimal training complexity by re-projecting embeddings of frozen pre-trained image encoders using a shallow network, RectNet, which we train both with the standard CLIP contrastive loss function, as well as a novel modality alignment loss function specifically constructed to bridge the modality gap. Finally, we introduce three v-shot classification benchmarks, on which the proposed architecture achieves 32.22%, 29.58% and 45.15% increases in top-1 classification accuracy respectively.

关键词： variable-shot classification v-shot classification vision-language pretraining computer vision

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Question Answer Summary Generation from Unstructured Texts by Using LLMs 10th

Question Answer Summary Generation from Unstructured Texts b...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Tachioka, Yuuki Denso IT Lab Tokyo 1500002 Japan

ISBN: (纸本)9789819609130;9789819609147

Recently, the importance of generating accurate question and answer summaries has increased because the number of documents has increased, but it is difficult to summarize them from unstructured documents where the answer is not associated with the question. To address this problem, first we make a group of answer sentences, which corresponds to one question based on the heuristic rules, and we find the corresponding answer from the summaries and subtopics of the question by using the BM25-based similarity calculation. Second, we use the large language model (LLM) to generate a summary of the answers from the answers found. Experimental results showed that our methods significantly outperformed LLM-based answer generation that inputs whole answer sentences including irrelevant parts, which correspond to another question, to LLM and that our methods were practical compared to human-generated gold summaries.

关键词： BM25 Gale-Shapley algorithm LLM RAG

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Discourje: Run-Time Verification of Communication Protocols in Clojure - Live at Last 26th

Discourje: Run-Time Verification of Communication Protocols ...

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26th International Symposium on Formal Methods (FM)

作者： Jongmans, Sung-Shik Open Univ Netherlands Heerlen Netherlands

ISBN: (纸本)9783031711763;9783031711770

Multiparty session typing (MPST) is a formal method to make concurrent programming simpler. The idea is to use type checking to automatically prove safety (protocol compliance) and liveness (communication deadlock freedom) of implementations relative to specifications. Discourje is an existing run-time verification library for communication protocols in Clojure, based on dynamic MPST. The original version of Discourje can detect only safety violations. In this paper, we present an extension of Discourje to detect also liveness violations.

关键词： Network protocols

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