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2003 Congress on Evolutionary Computation, CEC 2003

作者： Kim, Su Dong Shin, Ki-Roo Zhang, Byoung-Tak Biointelligence Lab Interdisciplinary Program in Cognitive Science Seoul National University Seoul 151-742 Korea Republic of Biointelligence Lab Engineering Seoul National University Seoul 15 1-742 Korea Republic of

ISBN: (纸本)0780378040

We propose the concept of molecular immunocomputing which is a kind of peptide computing. Molecular immunocomputing is basically implemented by direct antigen-antibody biomolecular recognition on the basis of Fab fragments diversity. In this paper, we consider how molecular immunocomputing can tell the alphabet «O» from the characters «A» and «B» similar to the characterization of ABO blood type. To implement molecular immunocomputing, the two-dimensional figures are coded on the one-dimensional DNA strings. The information coded on the virtual DNA strings are transcribed to virtual RNA sequences, and then translated to the polypeptide sequences. The resulting peptide sequences are artificially synthesized and coupled to the carrier proteins. The resulting conjugate proteins are injected as input antigens to immunize the experimental animals. After immunization, we could purify the corresponding antibodies. These antibodies can be arrayed onto the protein microarray chips. The recent developments in the field of protein microarrays show the feasibility of molecular immunocomputing. © 2003 IEEE.

关键词： Antibodies

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Brains as Naturally Emerging Turing Machines

Brains as Naturally Emerging Turing Machines

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International Joint Conference on Neural Networks

作者： Juyang Weng Department of Computer Science and Engineering Cognitive Science Program and Neuroscience Program Michigan State University East Lansing Michigan 48824 USA

ISBN: (纸本)9781479919611

It has been shown that a Developmental Network (DN) can learn any Finite Automaton (FA) [29] but FA is not a general purpose automaton by itself. This theoretical paper presents that the controller of any Turing Machine (TM) is equivalent to an FA. It further models a motivation-free brain--excluding motivation e.g., emotions -- as a TM inside a grounded DN -- DN with the real world. Unlike a traditional TM, the TM-in-DN uses natural encoding of input and output and uses emergent internal representations. In Artificial Intelligence (AI) there are two major schools, symbolism and connectionism. The theoretical result here implies that the connectionist school is at least as powerful as the symbolic school also in terms of the general-purpose nature of TM. Furthermore, any TM simulated by the DN is grounded and uses natural encoding so that the DN autonomously learns any TM directly from natural world without a need for a human to encode its input and output. This opens the door for the DN to fully autonomously learn any TM, from a human teacher, reading a book, or real world events. The motivated version of DN [31] further enables a DN to go beyond action-supervised learning -- so as to learn based on pain-avoidance, pleasure seeking, and novelty seeking [31].

关键词： FA TM DN dwarf novae thulium Automata theory-Turing machines FAS gene Cerebrum

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Spatial language: Meaning, use, and lexical choice 39

Spatial language: Meaning, use, and lexical choice

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39th Annual Meeting of the cognitive science Society: Computational Foundations of Cognition, CogSci 2017

作者： Johannes, Kristen Landau, Barbara Science Technology Engineering and Mathematics Program WestEd 300 Lakeside Dr. OaklandCA94612 United States Department of Cognitive Science Johns Hopkins University 3400 N Charles St. BaltimoreMD21218 United States

ISBN: (纸本)9780991196760

Accounts of spatial language aim to address both the meaning of a spatial term and its usage patterns across diverse cases, but do not always clearly distinguish these from one another. Focusing on the case of English prepositions in and on, we set out to disentangle spatial language meaning from spatial language use by comparing judgments on a series of linguistic tasks designed to tap each aspect of spatial language. We demonstrate that judgments of truth-conditional meaning and patterns of naturalistic use show different distributional signatures, with judgments of meaning giving rise to a more uniform distribution than use patterns. We explore a third aspect of spatial language: lexical choice, and propose that choice is a key factor in shaping the distribution of spatial expression use. Our analyses reveal that the distribution of lexical choice judgments is highly correlated with the distribution of expression use in spatial descriptions for the same spatial scenes, supporting a model of spatial language that differs from traditional accounts of meaning and categorization. © CogSci 2017.

关键词： Taps

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The Role of Letter Frequency on Eye Movements in Sentential Pseudoword Reading 39

The Role of Letter Frequency on Eye Movements in Sentential ...

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39th Annual Meeting of the cognitive science Society: Computational Foundations of Cognition, CogSci 2017

作者： Acartürk, Cengiz Kılıç, Özkan Kırkıcı, Bilal Can, Burcu Özkan, Ayşegül Middle East Technical University Cognitive Science Program Ankara Turkey Yildirim Beyazit University Computer Engineering Ankara Turkey Middle East Technical University Foreign Language Education Ankara Turkey Hacettepe University Computer Engineering Ankara Turkey

ISBN: (纸本)9780991196760

For a language learner, any new word is a pseudoword. A pseudoword is a string of of letters or phonemes that sounds like an existing word in a language, though it has no meaning in the lexicon. On the other hand, speakers are well aware of permissible phonemes, their frequencies and collocations in their language due to the phonotactics inherent in the language. For example, saktal is a pseudoword in Turkish, whereas szyan is not, due to Turkish phonotactics. This study investigates the relationship between pseudoword letter formation and eye movement characteristics in reading. In particular, we examine the role of Turkish vowel harmony, middle-word consonant collocation, and word-initial and word-final consonants on eye movements with adult native speakers reading sentences that involve predesigned Turkish pseudowords. The results of an experiment with 34 participants are indicative of the role of pseudoword formation on a set of eye movement parameters. © CogSci 2017.

关键词： Eye movements

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers. © 2023 IEEE.

关键词： Tweets

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Problem Difficulty in Arithmetic Cognition: Humans and Connectionist Models 41

Problem Difficulty in Arithmetic Cognition: Humans and Conne...

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41st Annual Meeting of the cognitive science Society: Creativity + Cognition + Computation, CogSci 2019

作者： Cho, Sungjae Lim, Jaeseo Hickey, Chris Zhang, Byoung-Tak Interdisciplinary Program in Cognitive Science Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Computer Science and Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

ISBN: (纸本)0991196775

In mathematical cognition, problem difficulty is a central variable. In the present study, problem difficulty was operational-ized through five arithmetic operators - addition, subtraction, multiplication, division, and modulo - and through the number of carries required to correctly solve a problem. The present study collected data from human participants solving arithmetic problems, and from multilayer perceptrons (MLPs) that learn arithmetic problems. Binary numeral problems were chosen in order to minimize other criteria that may affect problem difficulty, such as problem familiarity and the problem size effect. In both humans and MLPs, problem difficulty was highest for multiplication, followed by modulo and then subtraction. The human study found that problem difficulty was monotonically increasing with respect to the number of carries, across all five operators. Furthermore, a strict increase was also observed for addition in the MLP study. © cognitive science Society: Creativity + Cognition + Computation, CogSci *** rights reserved.

关键词： Multilayers

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Multi-focus attention network for efficient deep reinforcement learning

arXiv

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arXiv 2017年

作者： Choi, Jinyoung Lee, Beom-Jin Zhang, Byoung-Tak Interdisciplinary Program in Cognitive Science Seoul National University School of Computer Science and Engineering Seoul National University

Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute the sensory input. Because of this difference, DRL needs vast amount of experience samples to learn. In this paper, we propose a Multi-focus Attention Network (MANet) which mimics human ability to spatially abstract the low-level sensory input into multiple entities and attend to them simultaneously. The proposed method first divides the low-level input into several segments which we refer to as partial states. After this segmentation, parallel attention layers attend to the partial states relevant to solving the task. Our model estimates state-action values using these attended partial states. In our experiments, MANet attains highest scores with significantly less experience samples. Additionally, the model shows higher performance compared to the Deep Q-network and the single attention model as benchmarks. Furthermore, we extend our model to attentive communication model for performing multi-agent cooperative tasks. In multi-agent cooperative task experiments, our model shows 20% faster learning than existing state-of-the-art model. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Software interface for controlling diverse robotic platforms using BMI

Software interface for controlling diverse robotic platforms...

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IEEE/SICE International Symposium on System Integration

作者： Christian Isaac Penaloza Yasushi Mae Kenichi Ohara Tatsuo Arai Graduate School of Engineering Science Cognitive Neuroscience Robotics Program Osaka University Toyonaka Japan

This paper presents a software interface that allows a user to control different types of robotic systems by using a Brain-Machine Interface. Unlike common device-specific BMI systems, our software architecture maps simple EEG-based commands to diverse functionalities depending on the robotic platform, so the user does not have learn to generate new EEG commands for different robots. The graphic user interface provides a mechanism that allows the user to navigate through menus using EMG signals (i.e. eye-blink), and then execute robot commands using EEG signals. Our software is based on a modular design that allows the integration of new robotic platforms with easy customization. Our current prototype explores the controllability of a humanoid robot, a flying robot and a pan-tilt robot using the proposed software interface. Experimental evidence shows that the system achieves good user satisfaction as well as easy controllability of different types of robotic systems.

关键词： Electroencephalography Software Headphones Electromyography Graphical user interfaces Robot sensing systems

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A Model for Auto-programming for General Purposes

arXiv

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arXiv 2018年

作者： Weng, Juyang Department of Computer Science and Engineering Cognitive Science Program Neuroscience Program Michigan State University East LansingMI48824 United States

The Universal Turing Machine (TM) is a model for VonNeumann computers — general-purpose computers. A human brain can inside-skull-automatically learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a machine can accomplish the same. This theoretical work shows how the Developmental Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM — Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This letter also extends the Church-Turing thesis to automatic programming for general purposes and sketchily proved it. Copyright © 2018, The Authors. All rights reserved.

关键词： Turing machines

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