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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Creating High-Resolution Adversarial Images Against Convolutional Neural Networks with the Noise Blowing-Up Method 15th

Creating High-Resolution Adversarial Images Against Convolut...

引用

15th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Leprevost, Franck Topal, Ali Osman Mancellari, Enea Univ Luxembourg House Numbers6Ave Fonte L-4364 Esch Sur Alzette Luxembourg

ISBN: (纸本)9789819958337;9789819958344

Convolutional Neural Networks (CNNs) are widely used for image recognition tasks but are vulnerable to attacks. Most existing attacks create adversarial images of a size equal to the CNN's input size;mainly because creating adversarial images in the high-resolution domain leads to substantial speed, adversity, and visual quality challenges. In a previous work, we developed a method that lifts any existing attack working efficiently in the CNN's input size domain to the high-resolution domain. This method successfully addressed the first two challenges but only partially addressed the third one. The present article provides a crucial refinement of this strategy that, while keeping all its other features, substantially increases the visual quality of the obtained high-resolution adversarial images. The refinement amounts to a blowingup to the high-resolution domain of the adversarial noise created in the low-resolution domain. Adding this blown-up noise to the clean original high-resolution image leads to an almost indistinguishable high-resolution adversarial image. The noise blowing-up strategy is successfully tested on an evolutionary-based black-box targeted attack against VGG-16 trained on ImageNet, with 10 high-resolution clean images.

关键词： Black-box attack Convolutional Neural Network Evolutionary Algorithm High resolution adversarial image Noise Blowing-Up

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Learning from Patterns via Pre-trained Masked Language Model for Semi-supervised Automated Essay Scoring 19th

Learning from Patterns via Pre-trained Masked Language Model...

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Sun, Jingbo Peng, Weiming Song, Tianbao Song, Jihua Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ Chinese Character Res & Applicat Lab Beijing 100875 Peoples R China Univ Penn Linguist Data Consortium Philadelphia PA 19104 USA Beijing Technol & Business Univ Sch Comp Sci & Engn Beijing 100048 Peoples R China

ISBN: (纸本)9789819947515;9789819947522

Automated essay scoring is one of the key applications of natural language processing technology in the field of education. Currently, pre-trained language models for automated essay scoring systems have no significant advantage over classical models, and pre-trained language models are underutilized for this task. Moreover, in real-world scenarios, supervised models that lack annotated data often perform poorly. To address the issue that the pre-trained language models are not fully applied, we propose a novel prompt tuning model PTAES in this paper, andwe convert the essay scoring procedure into a cloze-style question, after which we design pairs of natural language pattern-verbalizer. Further, we propose a joint model that combines the prompt tuning based model and the pre-trained fine-tuning based model, attempting to address the issue of inadequately supervised models in low-resource situations. Experimental findings indicate that, in supervised, semi-supervised, and zero-shot scenarios, our model can achieve state-of-the-art results, and our method further increases the value of the pre-trained language model in automated essay scoring.

关键词： Automated essay scoring Semi-supervised learning Prompt learning Pre-trained masked language model

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Improvement of Graph Convolution Network of Missing Data Based on P Systems 1

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Chi, Runpu Liu, Xiyu Shandong Normal Univ Business Sch Jinan Peoples R China Shandong Normal Univ Acad Management Sci Jinan Peoples R China

ISBN: (数字)9789819947522

ISBN: (纸本)9789819947515;9789819947522

The graph convolutional network has achieved great success since its proposal. Since GCN can be used to study non-Euclidean data, it extends convolutional networks for real-world applications. Graph data is a prevalent data structure in the real world and is widely used in various fields. Nowadays, most GCN models take data as a complete structure for input. However, real-world data is often incomplete for various reasons, and some data is missing features. Therefore, we propose a GCN model for completing missing data (PGCN) based on the coupled P systems. It can express the missing features of the data using the Gaussian mixture model and attention mechanism. In addition, based on the input, a new activation function is computed in the first layer of the GCN. The proposed PGCN method performs the node classification task on three datasets, and the results show that the method's performance is better than existing missing data processing methods.

关键词： Graph convolutional network Attention mechanism P systems

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Network Flow Based IoT Anomaly Detection Using Graph Neural Network 1

引用

16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Wei, Chongbo Xie, Gaogang Diao, Zulong Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (数字)9783031402869

ISBN: (纸本)9783031402852;9783031402869

Deep learning-based traffic anomaly detection methods are usually fed with high-dimensional statistical features. The greatest challenges are how to detect complex inter-feature relationships and localize and explain anomalies that deviate from these relationships. However, existing methods do not explicitly learn the structure of existing relationships between traffic features or use them to predict the expected behavior of traffic. In this work, we propose a network flow-based IoT anomaly detection approach. It extracts traffic features in different channels as time series. Then a graph neural network combined with a structure learning approach is used to learn relationships between features, which allows users to deduce the root cause of a detected anomaly. We build a real IoT environment and deploy our method on a gateway (simulated with Raspberry PI). The experiment results show that our method has excellent accuracy for detecting anomaly activities and localizes and explains these deviations.

关键词： Deep learning Anomaly detection Internet-of-things Network flow Graph neural network

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A Rule-Free Approach for Cardiological Registry Filling from Italian Clinical notes with Question Answering Transformers 21st

A Rule-Free Approach for Cardiological Registry Filling from...

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21st International Conference on artificial intelligence in Medicine (AIME)

作者： Buonocore, Tommaso Mario Parimbelli, Enea Tibollo, Valentina Napolitano, Carlo Priori, Silvia Bellazzi, Riccardo Univ Pavia Dept Elect Comp & Biomed Engn Pavia Italy ICS Maugeri Dept Mol Med LISRC Pavia Italy ICS Maugeri Dept Mol Cardiol Pavia Italy

ISBN: (纸本)9783031343438;9783031343445

The huge volume of textual information generated in hospitals constitutes an essential but underused asset that could be exploited to improve patient care and management. The encoding of raw medical texts into fixed data structures is traditionally addressed with knowledge-based models and complex hand-crafted rules, but the rigidity of this approach poses limitations to the generalizability and transferability of the solutions, in particular for a non-English setting under data scarcity conditions. This paper shows that transformer-based language representation models have the right characteristics to be employed as a more flexible but equally high-performing clinical information retrieval system for this scenario, without relying upon a knowledge-driven component. We demonstrate it pragmatically on the extraction of clinical entities from Italian cardiology reports for patients with inherited arrhythmias, outperforming the previous ontology-based work with our proposed transformer pipeline under the same setting and exploring a new rule-free approach based on question answering to automate cardiological registry filling.

关键词： Natural Language Processing Deep Learning Biomedical Text Mining Information Extraction Question Answering

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Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU

Towards Memory-Efficient Training for Extremely Large Output...

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Schultheis, Erik Babbar, Rohit Aalto Univ Espoo Finland Univ Bath Bath Avon England

ISBN: (纸本)9783031434174;9783031434181

In classification problems with large output spaces (up to millions of labels), the last layer can require an enormous amount of memory. Using sparse connectivity would drastically reduce the memory requirements, but as we show below, applied naively it can result in much diminished predictive performance. Fortunately, we found that this can be mitigated by introducing an intermediate layer of intermediate size. We further demonstrate that one can constrain the connectivity of the sparse layer to be of constant fan-in, in the sense that each output neuron will have the exact same number of incoming connections, which allows for more efficient implementations, especially on GPU hardware. The CUDA implementation of our approach is provided at https://***/xmc-aalto/ecml23-sparse.

关键词： Graphics processing unit

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Transformer Based Motion In-Betweening 1

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21st International Conference of the Italian-Association-for-artificial-intelligence (AIxIA)

作者： Sridhar, Pavithra Aananth, V. Aggarwal, Madhav Velusamy, R. Leela Natl Inst Technol Tiruchirappalli Tiruchirappalli 620015 TN India

ISBN: (数字)9783031271816

ISBN: (纸本)9783031271809;9783031271816

In-betweening is the process of drawing transition frames between temporally-sparse keyframes to create a smooth animation sequence. This work presents a novel transformer-based in-betweening technique that serves as a tool for 3D animators. We first show that this problem can be represented as a sequence-to-sequence problem and introduce Tween Transformers - a model that synthesizes high-quality animations using temporally-sparse keyframes as input constraints. We evaluate the model's performance via two complementary methods - quantitative and qualitative evaluation. The model is compared quantitatively with the state-of-the-art models using LaFAN1, a highquality animation dataset. Mean-squared metrics like L2P, L2Q, and NPSS are used for evaluation. Qualitatively, we provide two straightforward methods to assess the model's output. First, we implement a custom ThreeJs-based motion visualizer to render the ground truth, input, and output sequences side by side for comparison. The visualizer renders custom sequences by specifying skeletal positions at temporally-sparse keyframes in JSON format. Second, we build a motion generator to generate custom motion sequences using the model.

关键词： Motion in-betweening Kinematics Transformer LAFAN1

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A New Calculus for Intuitionistic Strong Lob Logic: Strong Termination and Cut-Elimination, Formalised 32nd

A New Calculus for Intuitionistic Strong Lob Logic: Strong T...

引用

32nd International Conference on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX)

作者： Shillito, Ian van der Giessen, Iris Gore, Rajeev Iemhoff, Rosalie Australian Natl Univ Canberra Australia Univ Birmingham Birmingham England Vienna Univ Technol Vienna Austria Polish Acad Sci Warsaw Poland Univ Utrecht Utrecht Netherlands

ISBN: (纸本)9783031435126;9783031435133

We provide a new sequent calculus that enjoys syntactic cut-elimination and strongly terminating backward proof search for the intuitionistic Strong Lob logic iSL, an intuitionistic modal logic with a provability interpretation. A novel measure on sequents is used to prove both the termination of the naive backward proof search strategy, and the admissibility of cut in a syntactic and direct way, leading to a straight-forward cut-elimination procedure. All proofs have been formalised in the interactive theorem prover Coq.

关键词： Intuitionistic provability logic Cut-elimination Backward proof search Interactive theorem proving Proof theory

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ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI 16th

ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI

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16th International Conference on artificial General intelligence (AGI)

作者： Shumsky, Sergey Baskov, Oleg Moscow Inst Phys & Technol Dolgoprudnyi 141701 Russia

ISBN: (纸本)9783031334689;9783031334696

Intelligent agents are characterized primarily by their farsighted expedient behavior. We present a working prototype of an intelligent agent (ADAM) based on a novel hierarchical neuro-symbolic architecture (Deep Control) for deep reinforcement learning with a potentially unlimited planning horizon. The control parameters form a hierarchy of formal languages, where higher-level alphabets contain the semantic meanings of lower-level vocabularies.

关键词： artificial General intelligence Hierarchical reinforcement learning Neuro-symbolic architecture

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Boosting Definability Bipartition Computation Using SAT Witnesses 18th

Boosting Definability Bipartition Computation Using SAT Witn...

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18th European Conference on Logics in artificial intelligence (JELIA)

作者： Lagniez, Jean-Marie Marquis, Pierre Univ Artois CNRS CRIL Lens France Inst Univ France Paris France

Bipartitioning the set of variables Var (Sigma) of a propositional formula Sigma w.r.t. definability consists in pointing out a bipartition of Var (Sigma) such that S defines the variables of O (outputs) in terms of ...

ISBN: (纸本)9783031436185;9783031436192

Bipartitioning the set of variables Var (Sigma) of a propositional formula Sigma w.r.t. definability consists in pointing out a bipartition < I, O > of Var (Sigma) such that S defines the variables of O (outputs) in terms of the variables in I (inputs), i.e., for every o is an element of O, there exists a formula Phi(o) over I such that o double left right arrow Phi(o) is a logical consequence of Sigma. The existence of Phi(o) given o, I, and Sigma is a coNP-complete problem, and as such, it can be addressed in practice using a SAT solver. From a computational perspective, definability bipartitioning has been shown as a valuable pre-processing technique for model counting, a key task for a number of AI problems involving probabilities. To maximize the benefits offered by such a preprocessing, one is interested in deriving subset-minimal bipartitions in terms of input variables, i.e., definability bipartitions < I, O > such that for every i is an element of I, < I \ {i}, O boolean OR {i}> is not a definability bipartition. We show how the computation of subset-minimal bipartitions can be boosted by leveraging not only the decisions furnished by SAT solvers (as done in previous approaches), but also the SAT witnesses (models and cores) justifying those decisions.

关键词： Automated reasoning including satisfiability checking and its extensions definability propositional logic

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