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

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BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability 27th

BAARD: Blocking Adversarial Examples by Testing for Applicab...

引用

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Chang, Xinglong Dost, Katharina Zhao, Kaiqi Demontis, Ambra Roli, Fabio Dobbie, Gillian Wicker, Jorg Univ Auckland Auckland New Zealand Univ Cagliari Cagliari Italy Univ Genoa Genoa Italy

ISBN: (纸本)9783031333736;9783031333743

Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential attacks makes detecting adversarial examples challenging. Additionally, attackers do not need to follow the rules made by the defender. To address this problem, we take inspiration from the concept of Applicability Domain in cheminformatics. Cheminformatics models struggle to make accurate predictions because only a limited number of compounds are known and available for training. Applicability Domain defines a domain based on the known compounds and rejects any unknown compound that falls outside the domain. Similarly, adversarial examples start as harmless inputs, but can be manipulated to evade reliable classification by moving outside the domain of the classifier. We are the first to identify the similarity between Applicability Domain and adversarial detection. Instead of focusing on unknown attacks, we focus on what is known, the training data. We propose a simple yet robust triple-stage data-driven framework that checks the input globally and locally, and confirms that they are coherent with the model's output. This framework can be applied to any classification model and is not limited to specific attacks. We demonstrate these three stages work as one unit, effectively detecting various attacks, even for a white-box scenario.

关键词： Adversarial Defense Anomaly Detection Applicability Domain Evasion Attacks White-box Adaptive Attacks

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Representing Concept Lattices with Euler Diagrams 17th

Representing Concept Lattices with Euler Diagrams

引用

17th International Conference on Formal Concept Analysis

作者： Priss, Uta Ostfalia Univ Fak Informat Wolfenbuttel Germany

ISBN: (纸本)9783031359484;9783031359491

The Hasse diagrams of Formal Concept Analysis (FCA) concept lattices have the disadvantages that users need to be trained in reading the diagrams and diagrams of larger lattices tend to be too cluttered to be comprehensible. This paper therefore discusses how to reduce lattices and then represent them with a specific type of Euler diagram instead of Hasse diagrams. A semi-automated process of reducing concept lattices is described and supported by algorithms.

关键词： Formal concept analysis

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Prediction of Protein Molecular Functions Using Transformers 21st

Prediction of Protein Molecular Functions Using Transformers

引用

21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： de Mello, Felipe Lopes de Oliveira, Gabriel Bianchin Pedrini, Helio Dias, Zanoni Univ Estadual Campinas Inst Comp Campinas SP Brazil

ISBN: (纸本)9783031234798;9783031234804

At the end of 2021, there were more than 200 million proteins in which their molecular functions were still unknown. As the empirical determination of these functions is slow and expensive, several research groups around the world have applied machine learning to perform the prediction of protein functions. In this work, we evaluate the use of Transformer architectures to classify protein molecular functions. Our classifier uses the embeddings resulting from two Transformer-based architectures as input to a Multi-Layer Perceptron classifier. This model got Fmax of 0.562 in our database and, when we applied this model to the same database used by DeepGOPlus, we reached the value of 0.617, surpassing the best result available in the literature.

关键词： Protein function prediction Transformers BERT

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Recurrence-Driven Summations in Automated Deduction 1

引用

14th International Symposium on Frontiers of Combining Systems (FroCoS)

作者： Nummelin, Visa Blanchette, Jasmin Dahmen, Sander R. Vrije Univ Amsterdam Amsterdam Netherlands Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (数字)9783031433696

ISBN: (纸本)9783031433689;9783031433696

Many problems in mathematics and computer science involve summations. We present a procedure that automatically proves equations involving finite summations, inspired by the theory of holonomic sequences. The procedure is designed to be interleaved with the activities of a higher-order automatic theorem prover. It performs an induction and automatically solves the induction step, leaving the base cases to the theorem prover.

关键词： Theorem proving

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Closed-Loop Robotic Cooking of Soups with Multi-modal Taste Feedback 1

引用

24th Annual Conference on Towards Autonomous Robotic Systems (TAROS)

作者： Shi, Jierui Abdulali, Arsen Sochacki, Grzegorz Iida, Fumiya Univ Cambridge Cambridge CB2 1TN England

ISBN: (数字)9783031433603

ISBN: (纸本)9783031433597;9783031433603

Cooking soup is a complex dynamic process, where the properties and taste of ingredients change during long temperature exposure. Furthermore, the simmering process of a soup also causes evaporation of the water, which increases the salt density in a bouillon. To mitigate this problem, we developed a closed-loop robotic system that allows cooking soups based on salinity and pH sensing. By taking into account that both salinity and pH are subject to change during the cooking, we recorded the salinity and pH over a complete course of cooking by an expert human and employed a proportional controller that adds salt and water into the soup. For the evaluation, we employed the proposed approach to cook a tomato soup with three different initial conditions. The results suggest that the system reaches the target pH and salinity reasonably close, even for significantly different soup bases.

关键词： Robotic Cooking Taste Feedback Closed-loop Control

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Weighted Path Orders Are Semantic Path Orders 1

引用

14th International Symposium on Frontiers of Combining Systems (FroCoS)

作者： Saito, Teppei Hirokawa, Nao JAIST Nomi Japan

ISBN: (数字)9783031433696

ISBN: (纸本)9783031433689;9783031433696

We explore the relationship between weighted path orders and (monotonic) semantic path orders. Our findings reveal that weighted path orders can be considered instances of a variant of semantic path orders that comprise order pairs. This observation leads to a generalization of weighted path orders that does not impose simplicity on their underlying algebras. As a result, the generalized version is capable of proving termination of term rewrite systems beyond the realm of simple termination. In order to assess practicality we provide experimental data comparing generalized weighted path orders with the original ones as well as other well-known classes of reduction orders.

关键词： Term Rewriting Termination Weighted Path Order Semantic Path Order

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Fuzzy Logic Framework for Ontology Concepts Alignment 15th

Fuzzy Logic Framework for Ontology Concepts Alignment

引用

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

作者： Kozierkiewicz, Adrianna Pietranik, Marcin Jankowiak, Wojciech Wroclaw Univ Sci & Technol Fac Informat & Commun Technol Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9789819958368;9789819958375

The problem of ontology alignment appears when interoperability of independently created ontologies is expected. The task can be described as collecting a set of pairs of elements taken from such ontologies that relate to the same objects from the universe of discourse. In our previous research, we introduced incorporating fuzzy logic in the considered task. It was used to combine several different similarity measures calculated between elements taken from different ontologies to eventually provide an unequivocal decision on whether or not a pair of such elements can be treated as mappable. Up until now, we focused solely on the level of instances and relations. Therefore, in this paper, we propose our novel approach to designating ontology mappings on the level of concepts. The developed methods were experimentally verified, yielding very promising results. We used the widely accepted benchmarks provided by the Ontology Alignment Evaluation Initiative, which are considered the state-of-the-art datasets used to evaluate solutions to any ontology-related problem.

关键词： ontology alignment fuzzy logic knowledge management

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Style Augmentation and Domain-Aware Parametric Contrastive Learning for Domain Generalization 16th

Style Augmentation and Domain-Aware Parametric Contrastive L...

引用

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

作者： Li, Mingkang Zhang, Jiali Zhang, Wen Gong, Lu Zhang, Zili Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (纸本)9783031402913;9783031402920

The distribution shift between training data and test data degrades the performance of deep neural networks (DNNs), and domain generalization (DG) alleviates this problem by extracting domain-invariant features explicitly or implicitly. With limited source domains for training, existing approaches often generate samples of new domains. However, most of these approaches confront the issue of losing class-discriminative information. To this end, we propose a novel domain generalization framework containing style augmentation and Domain-aware Parametric Contrastive Learning (DPCL). Specifically, features are first decomposed into high-frequency and low-frequency components, which contain shape and style information, respectively. Since the shape cues contain class information, the high-frequency components remain unchanged. Then Exact Feature Distribution Mixing (EFDMix) is used for diversifying the low-frequency components, which fully uses each order statistic of the features. Finally, both components are re-merged to generate new features. Additionally, DPCL is proposed, based on supervised contrastive learning, to enhance domain invariance by ignoring negative samples from different domains and introducing a set of parameterized class-learnable centers. The effectiveness of the proposed style augmentation method and DPCL is confirmed by experiments. On the PACS dataset, our method improves the state-of-art average accuracy by 1.74% using ResNet-50 backbone and even achieves excellent performance in the single-source DG task.

关键词： Domain Generalization Deep Learning Data Augmentation Contrastive Learning Object Recognition

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PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels 1

引用

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： Choi, Jiho Park, Junghoon Kim, Woocheol Park, Jin-Hyeok Suh, Yumin Sung, Minchang Netmarble Corp Seoul South Korea

ISBN: (数字)9783031434273

ISBN: (纸本)9783031434266;9783031434273

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.

关键词： chargeback fraud detection graph neural networks PU learning P2E MMORPG

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PROPAGATE: A Seed Propagation Framework to Compute Distance-Based Metrics on Very Large Graphs

PROPAGATE: A Seed Propagation Framework to Compute Distance-...

引用

作者： Amati, Giambattista Cruciani, Antonio Pasquini, Daniele Vocca, Paola Angelini, Simone Fdn Ugo Bordoni Rome Italy Gran Sasso Sci Inst Laquila Italy Univ Roma Tor Vergata Rome Italy

ISBN: (纸本)9783031434174;9783031434181

We propose propagate, a fast approximation framework to estimate distance-based metrics on very large graphs such as: the (effective) diameter or the average distance within a small error. The framework assigns seeds to nodes and propagates them in a BFS-like fashion, computing the neighbors set until we obtain either the whole vertex set (for computing the diameter) or a given percentage of vertices (for the effective diameter). At each iteration, we derive compressed Boolean representations of the neighborhood sets discovered so far. The PROPAGATE framework yields two algorithms: PROPAGATE-P, which propagates all the s seeds in parallel, and PROPAGATE-S which propagates the seeds sequentially. For each node, the compressed representation of the PROPAGATE-P algorithm requires s bits while PROPAGATE-S 1 bit only. Both algorithms compute the average distance, the effective diameter, the diameter, and the connectivity rate (a measure of the sparseness degree of the transitive closure graph) within a small error with high probability: for any epsilon > 0 and using s = Theta (log n/epsilon(2)) sample nodes, the error for the average distance is bounded by xi = epsilon Delta/alpha;the errors for the effective diameter and the diameter are bounded by xi = epsilon/a;and the error for the connectivity rate is bounded by epsilon where Delta is the diameter and alpha is the connectivity rate. The time complexity of our approaches is O(Delta center dot m) for PROPAGATE-Pand O (log n/epsilon(2) center dot Delta center dot m) for PROPAGATE-S, where m is the number of edges of the graph and Delta is the diameter. The experimental results show that the propagate framework improves the current state of the art in accuracy, speed, and space. Moreover, we experimentally show that PROPAGATE is also very efficient for solving the All Pair Shortest Path problem in very large graphs.

关键词： Graph mining shortest paths effective diameter sampling

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