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12th International Conference on Learning Representations, ICLR 2024

作者： Garimella, Manjulata Pak, Denizhan Wood, Justin N. Wood, Samantha M.W. Department of Informatics School of Informatics Computing & Engineering Department of Psychological & Brain Sciences Cognitive Science Program Program in Neuroscience

Newborn brains rapidly learn to solve challenging object perception tasks, including segmenting objects from backgrounds and recognizing objects across new viewing situations. Conversely, modern machine learning (ML) algorithms are "data hungry," requiring more training data than brains to reach similar performance levels. How do we close this learning gap between brains and machines? Here, we introduce a new benchmark-a Newborn Embodied Turing Test (NETT) for object segmentation-in which newborn animals and machines are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning. First, newborn chicks were raised in controlled environments containing a single object rotating on a single background, then their recognition performance was tested across new backgrounds and viewpoints. Second, we performed "digital twin" experiments in which artificial agents were reared and tested in virtual environments that mimicked the rearing and testing conditions of the chicks. We inserted a variety of ML "brains" into the artificial agents and measured whether those algorithms learned common object recognition behavior as chicks. All newborn chicks solved this one-shot object segmentation task, successfully learning background-invariant object representations that generalized across new backgrounds and viewpoints. In contrast, none of the artificial agents solved the task, instead learning background-dependent representations that failed to generalize across new backgrounds and viewpoints. This digital twin design exposes core limitations in current ML algorithms in developing brain-like object perception. Our NETT is publicly available for comparing ML algorithms with newborn chicks. We argue that NETT benchmarks can help researchers build embodied AI systems that learn as efficiently and robustly as newborn brains. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Animals

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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New Energy-Efficient 3-2 and 4-2 Approximate Adder Compressors Topologies 30

New Energy-Efficient 3-2 and 4-2 Approximate Adder Compresso...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Lopes, Rodrigo Antonietti, Leonardo Rosa, Morgana M. A. Costa, Eduardo Soares, Rafael Bampi, Sergio Graduate Program on Computing Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil PPGC & PGMICRO - Informatics Institute Porto Alegre Brazil

ISBN: (纸本)9798350326499

Addition units are present across many computational kernels inherent in various error-tolerant applications, including machine learning, signal, image, and video processing. Notably, adder compressors are the target when high speed and low power are the main design concerns. This work introduces energy-efficient structures of 3-2 approximate adder compressors (Ax3-2) and three distinct versions of 4-2 approximate adder compressors (Ax4-2). We compared our proposed Ax3-2 and Ax4-2 compressors with state-of-the-art energy-efficient approximate adder compressors (AxACs). Both Ax3-2 and Ax4-2 models passed through rigorous tests as standalone units. Furthermore, we integrated the Ax4-2 models into an essential application kernel appearing in video coding, i.e., the Sum Squared Differences (SSD) video accelerator. We introduce Ax3-2 and Ax4-2 that yield a new Pareto front concerning both energy-quality and area-quality trade-offs, thereby demonstrating a marked improvement over the prevailing state-of-the-art energy-efficient AxACs. © 2023 IEEE.

关键词： Adders

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SYSTEMATIC REVIEW: OPPORTUNITIES AND CHALLENGES OF MACHINE LEARNING TECHNIQUES FOR CARDIOVASCULAR DISEASE PREDICTION

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2024年第2期59卷 97-127页

作者： Qtaishat, Ahmed Wan Awang, Wan Suryani General Foundation Program Department of Information Technology Sohar University Sohar Oman Faculty of Informatics and Computing University Sultan Zainal Abdlidin Besut Campus Besut Terengganu 22200 Malaysia

Access to healthcare is a fundamental pillar of human well-being, yet cardiovascular diseases (CVD) persist as leading contributors to global mortality. This study explores the transformative potential of Machine Learning (ML) and Deep Learning (DL) algorithms in predicting a spectrum of CVDs, encompassing Heart Failure (HF), Arrhythmia, and coronary artery disease (CAD). With a focus on HF identification and associated risk factors, our primary aim is to rigorously evaluate the efficacy of diverse ML and DL models for CVD diagnosis. Through meticulous analysis of varied datasets, we examine different ML and DL methodologies to determine their predictive capabilities in the realm of CVD. Our findings illuminate promising advancements in CVD prediction accuracy, particularly in heart failure identification. Moreover, we elucidated the intricate interplay between cardiac and pulmonary functions in the context of heart disease, shedding light on disease mechanisms and novel diagnostic avenues. The novelty of our research lies in its comprehensive evaluation of ML and DL algorithms across heterogeneous datasets, fostering the refinement of CVD prediction strategies. By elucidating the effectiveness of various approaches, our study offers invaluable insights for healthcare practitioners and researchers striving to optimize CVD diagnosis and management. Ultimately, the integration of advanced computational techniques holds immense promise for bolstering cardiovascular healthcare outcomes and mitigating the global burden of CVD. © 2024 by the authors.

关键词： Forecasting

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An Empirical Study of Code Pattern Reuse and Pattern Preference Consistency in Novice programmers 23rd

An Empirical Study of Code Pattern Reuse and Pattern Prefe...

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23rd International Conference on Web-Based Learning, ICWL 2024 was held in conjunction with the 9th International Symposium on Emerging Technologies for Education, SETE 2024

作者： Lei, Philip I. S. Mendes, António José Lam, Chan-Tong CISUC Department of Informatics Engineering University of Coimbra Coimbra Portugal Computing Program Macao Polytechnic University Macao SAR China

ISBN: (纸本)9789819644063

After learning the basic features of a programming language, such as expressions and control statements, novice programmers need to combine these features to solve programming questions. Some of these combined code snippets are reusable constructs useful to solve future questions. They are referred to as plans, schemas, or patterns in the literature. An example of such a pattern is determining the equality of three values using the ‘equal and logical and operators. Previous research has investigated the acquisition of these patterns by observing and interviewing novice programmers as they solve programming problems. In this paper, we test the hypothesis that students tend to reuse acquired patterns to solve subsequent problems by analyzing a computer science education dataset called CodeWorkout, which contains students’ code submissions for introductory-level programming questions. We employed directed labeled tree to represent the Abstract Syntax Tree (AST) of the submitted code and the patterns. To search for a pattern, we first applied the subgraph isomorphism algorithm VF2 with wildcard node labels to identify candidate matches, followed by enforcing the proper ordering of descendant nodes between the pattern and the candidate matches. Experimental results demonstrate that our approach effectively identifies common usage of patterns. Moreover, the findings indicate that if a student employs a pattern in one question, they are likely to reuse the same pattern in a subsequent question. This paper also reveals that when multiple alternative patterns exist for the same function, students tend to maintain their preference for a particular alternative while solving different questions. These insights can inform future curriculum design and instructional strategies in programming education. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Personality Analysis Based on The Handwriting Shape using Integration of Image Processing and Machine Learning Algorithm 7

Personality Analysis Based on The Handwriting Shape using In...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Rosalina Sahuri, Genta Fahmi, Hasanul Moerland, Joe Wesley Mandala, Rila President University Informatics Study Program Indonesia President University Information System Study Program Indonesia President University Magister of Informatics Study Program Indonesia President University Faculty of Computing Indonesia

ISBN: (纸本)9798350307504

In Indonesia, mental health awareness is not as prominent as physical health, and many adolescents have experienced mental health problems. Some people hide their mental and emotional problems by appearing cheerful and peaceful, but research shows that body language and voice tone can reveal a person's inner psychological state. Similarly, handwriting can be analyzed through graphology to reveal personality traits such as honesty, emotional stability, and fears. Graphology is a time-consuming and expensive process, using image processing and machine learning, graphology can be done quickly without human bias. This research aims to predict the personality traits by implement image processing combined with machine learning to analyze handwriting features. Using lazy learner algorithm of K-Nearest Neighbor algorithm, SVM and Naïve Bayes, the application can predict the MBTI personality from handwriting image with an accuracy of 94%, 78%, and 93% respectively. From the experiment, it is shown that the application able to do the analysis of one's handwriting and then predict the personality traits, fully automated using a computer © 2023 IEEE.

关键词： Forecasting

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Evaluation of an agent-based reinforcement learning framework for model-based testing 7

Evaluation of an agent-based reinforcement learning framewor...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Ramírez-Méndez, Jose Quesada-López, Christian University of Costa Rica Graduate Program in Computing and Informatics San Pedro Costa Rica University of Costa Rica Department of Computer Science and Informatics San Pedro Costa Rica

ISBN: (纸本)9798350364538

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework's performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of the Recognition of Facial Movements of App Designed for Rehabilitation of People with Facial Paralysis

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第13期18卷 112-129页

作者： Corrêa, Ana Grasielle Dionísio Ravaglio, Anna de Souza Cruz Navikas, Flávio Hergersheimer Do Amaral, Jucelio Delmondes Filho, Paulo César Machado Cunha, Daniela Vieira Silva, Matheus Gois de Lima Rodrigues, Bruno da Silva Faculty of Computing and Informatics and the Postgraduate Program in Applied Computing Mackenzie Presbiterian University São Paulo Brazil

Peripheral facial paralysis (PFP) causes deficits in muscle and sensory functions of the face due to damage to the facial nerve. In this study, we evaluated the effectiveness of the "Fisiobem" app in rehabilitating patients with PFP through facial mimicry exercises. The app uses the device’s camera to identify the user and measure the range of motion during the exercises, applying the ARKit framework to quantify the intensity of the movements. We conducted a usability study with 34 participants unaffected by PFP, performing mimics with different amplitudes. Results show that glasses did not affect recognition accuracy in exercises such as raising eyebrows, frowning, and closing the eyes tightly. As for wrinkling the nose exercise, glasses impacted higher intensities. As for the beard or mustache, lower lip protrusion, compressing the lips, smiling, showing teeth and blowing out filling cheeks were not affected. However, when smiling with lips together, beard and mustache influenced intensities above 75%. It is important to highlight that this study has limitations, such as the sample size and lack of consideration for ethnic diversity among participants. Future research with larger and more diverse samples is needed to validate and generalize these findings and explore the nuances of interactions between facial features and facial recognition algorithms. Despite these limitations, the approach facilitated by the "Fisiobem" application offers significant benefits, especially for patients with PFP who face challenges in accessing specialized therapists. It allows patients to complement their treatment by performing guided facial mimicry exercises at home. The application helps individuals accurately perform the exercises and effectively track the progress of their rehabilitation. © 2024 by the authors of this article.

关键词： Glass

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Dogs or Not Dogs? Examining ACI Authors' Reporting on Animal Participants' Willingness to Engage in Research: A Spotlight on Mediated and Contingent Consent 23

Dogs or Not Dogs? Examining ACI Authors' Reporting on Animal...

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Proceedings of the Tenth International Conference on Animal-Computer Interaction

作者： Eli B. McGraw Cristina Bosco Lisa Brey Christena Nippert-Eng Department of Informatics Luddy School of Informatics Computing and Engineering and the Cognitive Science Program Indiana University USA Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University USA

ISBN: (纸本)9798400716560

How do Animal-Computer Interaction (ACI) researchers working with live animal participants assess the animals’ willingness to participate in their research? In this paper we present the results of a systematic literature review designed to answer this question by examining the Proceedings of the ACM International Animal Computer Interaction Conference. From 2016-2022, these proceedings included 38 full papers that reported results from research with live animal participants. We found 1) only 74% or 28/38 of the papers reported how they assessed animal participants’ willingness to engage during their research, 2) the authors of papers focused on species other than dogs had a much higher rate of providing this information than did the authors of dog-based studies (100% or 12/12 non-dog papers v 62% or 16/26 of dog-based papers), 3) most researchers who addressed the issue of an animal participant’s willingness to engage in the research relied on some form of mediated consent, informed by behavioral observation methods, to do so. However, the researchers focused on non-dog species were much more likely than researchers focused on dogs to include elements of contingent consent in their protocols (75% (9/12) of the non-dog studies v 12% (3/26) of the dog-related studies). We argue that providing each other with more details about our research methods and possibly more fully embracing the principles of contingent consent would further ACI researchers’ existing ethical commitment to our animal participants, increase our adherence to standard scientific research practice, and accelerate the continued development of the field of Animal-Computer Interaction.

关键词： Animal-Computer Interaction animal behavior animal well-being animal-centered design animal-centered ethics contingent consent dogs mediated consent multi-species interaction design reporting practices research methods scientific transparency voluntary engagement

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What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?

What does functional connectivity tell us about the behavior...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Candadai, Madhavun Cognitive Science Program Indiana University Luddy School of Informatics Computing and Engineering Indiana University United States

What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions. © Artificial Life Conference Proceedings 2022.

关键词： Recurrent neural networks

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