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

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The Impact of Double Transfer Learning in VGG Architectures for Metastasis Breast Cancer Detection 34th

The Impact of Double Transfer Learning in VGG Architectures ...

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34th Brazilian Conference on Intelligent Systems

作者： Silva e Silva, Danyllo Carlos Carmona Cortes, Omar Andres Bandeira Diniz, Joao Otavio Univ Estadual Maranhao UEMA Cidade Univ Paulo VI Jd Sao Cristovao Programa Pos Grad Engn Computa Sistemas PECS BR-65055310 Sao Luis MA Brazil Inst Fed Maranhao Dept Computa DComp Av Getulio Vargas 04 BR-65030005 Sao Luis MA Brazil Inst Fed Maranhao IFMA Campus Grajau BR-226 Grajau MA Brazil

ISBN: (纸本)9783031790348;9783031790355

According to the World Health Organization (WHO), 2.3 million women were diagnosed with breast cancer in 2020, causing almost 700.000 deaths worldwide. The first occurrences (in situ stage) usually have a good response if there is an early detection because the earliest form of tumor does not have sufficient potential to kill. However, the next stage has a low survival rate because the most threatening tumor characteristic is cell division spreading throughout the body, damaging lungs, livers, bones, and the brain. An alternative to deal with this problem is to enhance the velocity of the diagnosis and detection of breast cancer. Thus, machine learning algorithms have proven to be effective in this task. In this context, this work investigates how double transfer learning improves two Deep Learning architectures, VGG-16 and VGG-19, using histopathological images, i.e., employing BreakHIS as the first transfer learning and PatchCamelyon as the second. Results indicate that using double transfer learning, results for precision, specificity, recall, and F-Score improve to 99%, 83%, 89%, and 90%, respectively.

关键词： Convolutional Neural Networks Double Transfer Learning Breast Cancer

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Extracting Principles of Exploration Strategies with a Complex Ecological Task 17th

Extracting Principles of Exploration Strategies with a Compl...

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17th International Conference on Simulation of Adaptive Behavior (SAB)

作者： Zenkri, Oussama Bolenz, Florian Pachur, Thorsten Brock, Oliver Tech Univ Berlin Robot & Biol Lab Berlin Germany Max Planck Inst Human Dev Ctr Adapt Rational Berlin Germany Tech Univ Munich Sch Management Munich Germany Res Cluster Excellence Sci Intelligence Berlin Germany

ISBN: (纸本)9783031715327;9783031715334

Human exploration, a cornerstone of our ability to solve novel problems, is a complex process, posing significant research challenges. Most previous studies simplify tasks to isolate specific variables, creating artificial problems that do not align with those humans have evolved to solve, thus limiting the generalizability of findings. To address this gap, we introduce the Lockbox paradigm: a novel, ecologically valid, and challenging task that promotes active exploration and physical interaction. Data from 91 participants interacting with the Lockbox reveal a remarkable human ability to adapt and solve problems efficiently in complex scenarios. By comparing different interaction methods, we demonstrate the critical role of cost variations, such as physical and cognitive costs, in driving attentiveness and shaping exploration strategies. These findings provide valuable insights into human exploration strategies, with potential applications in fields such as robotics and artificial intelligence.

关键词： Problem solving Exploration Physical costs

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Enhancing User Authentication Through EEG Based P300 Speller Response 23rd

Enhancing User Authentication Through EEG Based P300 Speller...

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23rd Mexican International Conference on artificial intelligence

作者： Khan Khalil, Adnan Elahi Perez-Diaz, Jesus Arturo Cantoral-Ceballos, Jose Antonio Antelis, Javier M. Tecnol Monterrey Sch Sci & Engn Monterrey 64849 Nuevo Leon Mexico

ISBN: (纸本)9783031755422;9783031755439

Brain computer interface is employed in several applications providing explicit mode of communication between the brain and computers. Particularly, electroencephalography (EEG) is one of the most conventional methods for acquiring visual evoked potentials that is acquired from external stimuli, such as the P300 speller elicits the P300 potential from the presentation of characters and symbols. By employing machine learning classifiers and P300 potential has shown promising results for identifying and authenticating users since the brainwaves generated by each person while facing a particular stimulus are distinctive. But the current authentication research have not fully explore the P300 potentials and are not very successful when analyzing the most suitable processing and machine learning based classification techniques. In this study an approach for user recognition scheme utilizing the P300 speller is proposed to validate it on 8 users creating a non-invasive EEG based user authentication scheme. This framework achieved a performance of 100% accuracy in user recognition for the deep neural network (DNN) classifier, highlighting its effectiveness in accurately identifying and authenticating users thus, indicating the probability of performing EEG based user authentication using P300 speller paradigm.

关键词： Electroencephalogram (EEG) Machine Learning P300 Potentials User Recognition

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Investigating Universal Adversarial Attacks Against Transformers-Based Automatic Essay Scoring Systems 34th

Investigating Universal Adversarial Attacks Against Transfor...

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34th Brazilian Conference on Intelligent Systems

作者： Silveira, Igor Cataneo Barbosa, Andre da Costa, Daniel Silva Lopes Maua, Denis Deratani Univ Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9783031790317;9783031790324

Automatic Essay Scoring promises to scale up student feedback on written input, addressing the excessive cost and time demand associated with human grading. State-of-the-art automatic scorers are based on Transformers-based neural networks. While such models have shown impressive results in reasoning tasks, learned models often produce answers that arise from statistical clues in datasets and are misaligned with human objectives. Such systems are thus potentially fragile for scenarios where users are incentivized to deceive the system, as in a classroom setting. In this work, we evaluate the susceptibility of state-of-the-art automatic scorers to attacks made by non-expert users, such as students interacting with an automatic grader. We develop a methodology to simulate such student attacks and test them against scorers based on BERT, Phi-3 and Gemini models. Our findings suggest that (i) a BERT-based grader can be deceived using simple feature-based attacks;(ii) although Google's Gemini has a solid agreement with graders, it can assign undeservedly high grades for small sentences;(iii) a Phi-3-based grader was less susceptible than BERT, but it still assigned relatively high grades to some of our attacks.

关键词： Automatic Essay Scoring Adversarial Attack Natural Language Processing

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Air-Drying: An Attempt of Completed Garment Unfolding 17th

Air-Drying: An Attempt of Completed Garment Unfolding

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17th International Conference on Intelligent Robotics and Applications

作者： Qiu, Yi Yan, Weixin Liu, Jihao Zhao, Yanzheng Liu, Xue Xu, Lina Cao, Siyong Shanghai Jiao Tong Univ Shanghai Peoples R China Zhejiang Hooeasy Technol Co Ltd Jinhua Zhejiang Peoples R China

ISBN: (纸本)9789819607976;9789819607983

This study presents a novel approach to cloth unfolding, aimed at enhancing convergence speed and efficiency in reinforcement learning applications. Initially, a self-supervised learning framework is employed to train the generation of pick-and-drag actions utilizing dual robotic arms, guided by collar features and contour analysis. The framework exhibits an 78% success rate in unfolding within three maneuvers in simulated environments. Drawing inspiration from the air-drying process of garments, a hanger is employed to exploit gravity for effective cloth flattening. Experimental validation, conducted on a dual-arm robotic system equipped with an RGB camera in real-world scenarios, validates the method's efficacy, achieving a 95% unfolding rate post-hanging.

关键词： Garment unfolding Unsupervised learning Air-drying

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Simple Stochastic Stopping Games: A Generator and Benchmark Library 8th

Simple Stochastic Stopping Games: A Generator and Benchmark ...

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8th International Conference on Algorithmic Decision Theory

作者： Rudich, Avi Rudich, Isaac Rue, Rachel Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9783031739026;9783031739033

Stochastic Games are used for modeling decision-making processes in environments characterized by uncertainty and adversarial interactions. They are particularly relevant for multi-agent systems, where understanding the equilibrium of multiple decision-makers is essential. Simple Stochastic Games (SSGs) were introduced by Anne Condon in 1990, as the simplest version of Stochastic Games for which there is no known polynomial-time algorithm [5]. Condon showed that Stochastic Games are polynomial-time reducible to SSGs, which in turn are polynomial-time reducible to Stopping Games. SSGs are games where all decisions are binary and every move has a random outcome with a known probability distribution. Stopping Games are SSGs that are guaranteed to terminate. There are many algorithms for SSGs, most of which are fast in practice, but they all lack theoretical guarantees for polynomial-time convergence. The pursuit of a polynomial-time algorithm for SSGs is an active area of research. This paper is intended to support such research by making it easier to study the graphical structure of SSGs. Our contributions are: (1) a generating algorithm for Stopping Games, (2) a proof that the algorithm can generate any game, (3) a list of additional polynomial-time reductions that can be made to Stopping Games, (4) an open source generator for generating fully reduced instances of Stopping Games that comes with instructions and is fully documented, (5) a benchmark set of such instances, (6) and an analysis of how two main algorithm types perform on our benchmark set.

关键词： Stochastic Games Decision Theory Algorithmic Game Theory

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Collision-Free Time-Varying Formation Control via Relative Localization 17th

Collision-Free Time-Varying Formation Control via Relative L...

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17th International Conference on Intelligent Robotics and Applications

作者： Xue, Feng Zuo, Jiaxuan Liu, Xueming Zhang, Qingrui Sun Yat Sen Univ Shenzhen 518107 Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9789819607730;9789819607747

It has been an intriguing but challenging topic to achieve safe time-varying formation control using local information for a long time. In this paper, we investigate the problem of controlling multiple unmanned aerial vehicles (UAVs) modeled by a directed communication graph to follow a leader in formation, supported by a vector field-based collision avoidance strategy and a relative localization algorithm (RLA) by distance and velocity measurements. The proposed method can realize the safe time-varying formation flight with different patterns, and also waives the need of external localization systems. The relative localization algorithm is designed based on the recursive least square estimation (RLSE) technique with a forgetting factor. We present a specific protocol for the design of reference relative velocity in formation flight according to the desired formation pattern. The reference relative velocity can ensure the persistent excitation that ensures the convergence of the relative localization algorithm by RLSE. By assuming that UAVs have an omnidirectional range perception ability, it is shown that collision avoidance can be achieved using a vector field method by sensing the outline of surrounding obstacles or neighbour UAVs. The competence of the proposed algorithm is demonstrated via both numerical simulations and experiments.

关键词： time-varying formation control collision-free relative localization persistent excitation

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Evaluating the Reliability of Self-explanations in Large Language Models 27th

Evaluating the Reliability of Self-explanations in Large Lan...

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27th International Conference on Discovery Science

作者： Randl, Korbinian Pavlopoulos, John Henriksson, Aron Lindgren, Tony Stockholm Univ Dept Comp & Syst Sci S-16407 Kista Sweden Athens Univ Econ & Business Patiss 76 Athens 10434 Greece Archimedes Athena RC Athens Greece

ISBN: (纸本)9783031789762;9783031789779

This paper investigates the reliability of explanations generated by large language models (LLMs) when prompted to explain their previous output. We evaluate two kinds of such self-explanations - extractive and counterfactual - using three state-of-the-art LLMs (2B to 8B parameters) on two different classification tasks (objective and subjective). Our findings reveal that, while these self-explanations can correlate with human judgement, they do not fully and accurately follow the model's decision process, indicating a gap between perceived and actual model reasoning. We show that this gap can be bridged because prompting LLMs for counterfactual explanations can produce faithful, informative, and easy-to-verify results. These counterfactuals offer a promising alternative to traditional explainability methods (e.g. SHAP, LIME), provided that prompts are tailored to specific tasks and checked for validity.

关键词： Large Language Models Self-Explanations Counterfactuals

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A New Ensemble with Partition Size Variation Applied to Wind Speed Time Series 19th

A New Ensemble with Partition Size Variation Applied to Wind...

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19th International Conference on Hybrid artificial intelligence Systems

作者： Almeida, Diogo M. Neto, Paulo S. G. de Mattos Cunha, Daniel C. Univ Fed Pernambuco UFPE Ctr Informat CIn Recife PE Brazil

ISBN: (纸本)9783031741852;9783031741869

Wind speed time series exhibit complex patterns, thus integrating wind energy into the electrical system is challenging. This requires specialized skills for operations and planning practices. Training expert predictors on different parts of the time series enables the identification of complex local patterns. However, the partitioning procedure reduces the number of training instances. So, investigating the size of training partitions for an ensemble is desirable for predicting wind speed. Therefore, this paper proposes a homogeneous ensemble for local pattern recognition denoted as LocPart, that varies in partition size. The results of the Diebold-Mariano hypothesis test show promise for the LocPart method applied to three wind speed time series. The comparison was made relative to individual and bagging methods that use a global mapping of the respective base model of the proposal. The LocPart method with LSTM, ARIMA, and ELM base models won in 100%, 83%, and 50% of the cases, respectively.

关键词： Time series Forecasting Ensembles Partition size Wind speed

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EQUISCALE: Equitable Scaling for Abstention Learning 21st

EQUISCALE: Equitable Scaling for Abstention Learning

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21st Pacific Rim International Conference on artificial intelligence

作者： Chaudhari, Tejas Manwani, Naresh Int Inst Informat Technol Hyderabad India

ISBN: (纸本)9789819601158;9789819601165

We propose an approach to train a fair cost-based abstain option classifier. Existing literature on fairness in classification with abstention is limited, covering only coverage-based abstention models. In coverage-based abstention models, the target coverage is decided beforehand and is kept the same for all the groups. In contrast, cost-based approaches introduce a cost for abstention, which can cause uneven abstention rates between different groups, leading to an unfair system. We extend the independence and separation fairness criteria to consider abstention. We provide a model-agnostic in-processing algorithm to incorporate these constraints in the models. We demonstrate the algorithm's efficacy by experimenting with two different cost-based abstain option classifiers. Additionally, we explore mixing constraints from independence and separation criteria into one model, which is impossible in a binary classification task.

关键词： Fairness Reject option classification

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