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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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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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Perpetual Crossers without Sensory Delay: Revisiting the Perceptual Crossing Simulation Studies

Perpetual Crossers without Sensory Delay: Revisiting the Per...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Severino, Gabriel J. Merritt, Haily Cognitive Science Program Indiana University Bloomington Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

We revisit the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition by studying multi-agent real-time interactions. To date, all of these simulation studies have reported that it is practically impossible to evolve artificially a robust behavioral strategy without introducing temporal delays into the simulation. Also, all of the studies report on a single strategy: a perpetually crossing agent pair. Here, we systematically report on the evolutionary success of neural circuits on the perceptual crossing task, with and without sensory delay. We also report on two different strategies in the ensemble of successful solutions, only one of which had been discussed in the literature previously. © Artificial Life Conference Proceedings 2022.

关键词： Delay circuits

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Strengthening the Security of Deniable Authentication Scheme Using Zero-Knowledge Proof 11

Strengthening the Security of Deniable Authentication Scheme...

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11th International Conference on Communication and Network Security, ICCNS 2021

作者： Moesriami Barmawi, Ari Rizal Nurjaman, Asep Informatics Graduate Program School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9781450386425

In an electronic voting system, authentication is used to ensure that the voter is legitimate without knowing his/her identity, while the vote collectors verify the data is received from a legitimate user without knowing the identity of the voter. One of the authentication schemes that fulfilled this requirement is called a deniable authentication scheme, where the receiver can prove the source of the message while another party cannot identify the source of the message. In 2013, Li-Takagi et al. proposed a deniable authentication scheme. However, Li-Takagi's scheme has weaknesses if the receiver fully cooperates with the third party. In this case, the third party can identify the source of a given message. In the proposed method, zero-knowledge proof is introduced to preserve the anonymity of the deniable authentication scheme when the receiver fully cooperates with the third party. Based on the analysis, the proposed scheme fulfills the requirement of the deniable authentication scheme when the receiver fully cooperates with the third party. However, the proposed scheme has additional computation costs for securing the shared secret key. Two attack schemes that are carried out on both Li-Takagi and the proposed scheme are the MITM attack and the impersonation attack. The probability of breaking the proposed scheme using an MITM attack is lower than when using Li-Takagi's scheme, but the probability of breaking the proposed scheme using an impersonation attack is the same as Li-Takagi's scheme. © 2021 ACM.

关键词： Authentication

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density 8

Classification Based on Texture Analysis for Synthetic Hazy ...

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8th International Conference on Electrical, Electronics and Information Engineering, ICEEIE 2023

作者： Nugroho, Fresy Basid, Puspa Miladin Safitri A. Arif, Yunifa Miftachul Diah, Norizan Mat Bahtiar, Firma Sahrul Nurhayati, Hani Kurniawan, Fachrul Priandani, Nurizal Dwi Dermawan, Dodik Arwin Universitas Islam Negeri Maulana Malik Ibrahim Informatics Engineering Faculty of Science and Technology Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Faculty of Science and Technology Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350340501

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%. © 2023 IEEE.

关键词： Textures

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Universal Spam Detection using Transfer Learning of BERT Model

arXiv

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arXiv 2022年

作者： Tida, Vijay Srinivas Hsu, Sonya Center of Advanced Computer Studies School of Computing and Informatics University of Louisiana Lafayette United States Informatics Program School of Computing and Informatics University of Louisiana Lafayette United States

Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with four datasets by efficiently classifying ham or spam emails in real-time scenarios. Different methods for Enron, Spamassain, Lingspam, and Spamtext message classification datasets, were used to train models individually in which a single model was obtained with acceptable performance on four datasets. The Universal Spam Detection Model (USDM) was trained with four datasets and leveraged hyperparameters from each model. The combined model was finetuned with the same hyperparameters from these four models separately. When each model using its corresponding dataset, an F1-score is at and above 0.9 in individual models. An overall accuracy reached 97%, with an F1 score of 0.96. Research results and implications were discussed. © 2022, CC BY-NC-ND.

关键词： Classification (of information)

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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An Approach to Detect Abnormal Submissions for CodeWorkout Dataset 8

An Approach to Detect Abnormal Submissions for CodeWorkout D...

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8th Educational Data Mining in Computer Science Education Workshop, CSEDM 2024

作者： Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech Blacksburg VA Dept of Computer Science Utah State University Logan UT Intelligent Systems Program University of Pittsburgh Pittsburgh PA School of Informatics Computing and Cyber Systems North Arizona University Flagstaff AZ Dept. of Computer Science University of Virginia wCharlottesville VA

Students’ interactions while solving problems in learning environments (i.e. log data) are often used to support students’ learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students’ log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. © 2024 Copyright for this paper by its authors.

关键词： CS1 Dataset Cleaning Dataset Standards Educational Data Mining Introductory programming

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