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Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems sciences

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 52-63页

作者： Yingxu Wang Ming Hou Konstantinos NPlataniotis Sam Kwong Henry Leung Edward Tunstel Imre JRudas Ljiljana Trajkovic International Institute of Cognitive Informatics and Cognitive Computing Department of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of CalgaryCalgary T2N 1N4Canada Toronto Research Centre Defence Research&Development CanadaToronto M3K 2C9Canada Department of Electrical&Computer Engineering University of TorontoToronto M5S3G4Canada Department of Computer Science City University of Hong KongHong KongChina Department of Electrical and Computer Engineering Schulich School of EngineeringUniversity of CalgaryCalgary T2N 1N4Canada Autonomous&Intelligent Systems Department United Technologies Research CenterCT 06108 USA University Research&Innovation Center(EKIK) Óbuda UniversityBudapest H-1034Hungary School of Engineering Science Simon Fraser UniversityBurnaby V5A 1S6Canada

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems *** paper explores the intelligent and mathematical foundations of autonomous *** focuses on structural and behavioral properties that constitute the intelligent power of autonomous *** explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive *** properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems *** paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are *** in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

关键词： Autonomous systems(AS) cognitive systems computational intelligence engineering paradigms intelligence science intelligent mathematics

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Spectrograms Fusion-based End-to-end Robust Automatic Speech Recognition

Spectrograms Fusion-based End-to-end Robust Automatic Speech...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Hao Shi Longbiao Wang Sheng Li Cunhang Fan Jianwu Dang Tatsuya Kawahara Graduate School of Informatics Kyoto University Kyoto Japan Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China National Institute of Information and Communications Technology (NICT) Kyoto Japan Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (纸本)9781665441629

To improve the robustness of automatic speech recognition (ASR), speech enhancement (SE) is often used as a front-end noise-removal process. Although there is complemen-tarity between the mapping-based and the mask-based SE system, one of the SE systems has been conventionally used as the front-end of ASR. We propose a spectrogram fusion (SF)-based end-to-end (E2E) robust ASR system, in which the mapping-based and masking-based SE are used as the front-end simultaneously. We adopt SF to combine the advantages of mapping-based and masking-based SE systems. SF and ASR modules are connected in an E2E manner, and joint training is conducted to finetune the front-end and the back-end. We compared the performance of different front-ends after joint training. From the experiments using Aishell and PNL 100 Nonspeech Sounds datasets, we found that the fusion of two SEs are beneficial for ASR, especially under low signal-to-noise ratio, where a relative improvement of more than 7% is achieved.

关键词： Training Information processing Speech enhancement Robustness Spectrogram Automatic speech recognition Signal to noise ratio

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Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing

arXiv

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arXiv 2022年

作者： Osterhoudt, Hunter Schneider, Courtney E. Mohammad, Haneef A. Shih, Minmei Harper, Alexandra E. Zhou, Leming Skidmore, Elizabeth R. Wang, Yanshan Department of Computer Science School of Computing and Information University of Pittsburgh United States Department of Occupational Therapy School of Health and Rehabilitation Sciences University of Pittsburgh United States Department of Information Science School of Computing and Information University of Pittsburgh United States Department of Health Information Management School of Health and Rehabilitation Sciences University of Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States

Strategy training is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Strategy training has been shown in randomized, controlled clinical trials to be a more feasible and efficacious intervention for promoting independence than traditional rehabilitation approaches. A standardized fidelity assessment is used to measure adherence to treatment principles by examining guided and directed verbal cues in video recordings of rehabilitation sessions. Although the fidelity assessment for detecting guided and directed verbal cues is valid and feasible for single-site studies, it can become labor intensive, time consuming, and expensive in large, multi-site pragmatic trials. To address this challenge to widespread strategy training implementation, we leveraged natural language processing (NLP) techniques to automate the strategy training fidelity assessment, i.e., to automatically identify guided and directed verbal cues from video recordings of rehabilitation sessions. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task. The best performance was achieved by the BERT model with a 0.8075 F1-score. This BERT model was verified on an external validation dataset collected from a separate major regional health system and achieved an F1 score of 0.8259, which shows that the BERT model generalizes well. The findings from this study hold widespread promise in psychology and rehabilitation intervention research and practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Video recording

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Automating the Practice of science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Software Effort Estimation with Use Case Points Using Ensemble Machine Learning Models

Software Effort Estimation with Use Case Points Using Ensemb...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Bhaskar Marapelli Anil Carie Sardar M. N. Islam College of Computing and Informatics Wolkite university Wolkite Ethiopia School of Computer Science and Engineering VIT-AP University Amaravathi India ISILC and Decision Sciences and Modelling Program Victoria University Melbourne Australia

ISBN: (纸本)9781665442329

Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use case diagrams in the UCPA method, using the calculated software size the effort required to complete the project is estimated. The traditional effort estimation with statistical methods is not accurate when compared to the real effort. In-accurate estimation of effort leads to problems with cost calculation and human resources calculation and it might lead to project failure. Machine learning techniques based on regression might help in estimating the effort with accuracy. In this study we proposed a method to identify best performing regression based machine learning model using two data sets Dataset1, Dataset2. Ensembles of different Machine learning methods is created and compared with individual methods and other ensemble methods to find an accurate estimation model. The results shows the individual model SVR and ensemble model GBR gives the best performance with a regression score above 98% with both data sets.

关键词： Support vector machines Statistical analysis Computational modeling Estimation Predictive models Boosting Software

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate

A Comparative Study of Gaussian Process Machine Learning and...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Muhammad Naeim Mohd Aris Shalini Nagaratnam Nurul Nnadiah Zakaria Muhammad Fadhirul Anuar Mohd Azami Muhammad Afiq Ikram Samsudin Ernee Sazlinayati Othman Department of American Degree Transfer Program School of American Education Sunway University Bandar Sunway Malaysia Department of Econometrics and Business Statistics School of Business Monash University Malaysia Bandar Sunway Malaysia Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of American Degree Transfer Programme School of American Education Sunway University Bandar Sunway Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Seremban 3 Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Dengkil Malaysia

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

This study explores and compares the capability of Gaussian process machine learning (GPML) with time series analysis techniques, which are autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and cubic spline interpolation, in modeling unemployment rate in Malaysia over the period of 1991 to 2022. Six predictive models are developed based on the observations. The predictive performance of each model is quantified using mean absolute error (MAE) and mean squared error (MSE). GPML demonstrated the best predictive model, exhibiting minimal MAE and MSE compared to the time series analysis techniques. The statistical analysis also concluded no significant mean difference between the GPML model and the actual observations, implying a robust predictive model in predicting the unemployment rate in Malaysia.

关键词： Analytical models Interpolation Statistical analysis Time series analysis Gaussian processes Machine learning Predictive models

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Multivariate Information Theory Uncovers Synergistic Subsystems of the Human Cerebral Cortex

arXiv

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arXiv 2022年

作者： Varley, Thomas F. Pope, Maria Faskowitz, Joshua Sporns, Olaf Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States School of Informatics Computing & Engineering Indiana University BloomingtonIN47405 United States Program in Neuroscience Indiana University BloomingtonIN47405 United States Indiana University Network Science Institute Indiana University BloomingtonIN47405 United States

One of the most well-established tools for modeling the brain as a complex system is the functional connectivity network, which examines the correlations between pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are visible and potentially higher-order structures are missed. In this work, we explore how multivariate information theory can reveal higher-order, synergistic dependencies in the human brain. Using the O-information, a measure of whether the structure of a system is redundancy- or synergy-dominated, we show that synergistic subsystems are widespread in the human brain. We provide a mathematical analysis of the O-information to locate it within a larger taxonomy of multivariate complexity measures. We also show the O-information is related to a previously established measure, the Tononi-Sporns-Edelman complexity, and can be understood as an expected difference in integration between system scales. Highly synergistic subsystems typically sit between canonical functional networks, and may serve to integrate those networks. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ≈10 brain regions, also recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering pairwise functional connectivity, suggesting that higher-order dependencies form a kind of "shadow structure" that has been unrecognized by established network-based analyses. We assert that higher-order interactions in the brain represent a vast and under-explored space that, made accessible with tools of multivariate information theory, may offer novel scientific insights. © 2022, CC BY-NC-ND.

关键词： Redundancy

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Efficient and scalable neural residual waveform coding with collaborative quantization

arXiv

引用

arXiv 2020年

作者： Zhen, Kai Lee, Mi Suk Sung, Jongmo Beack, Seungkwon Kim, Minje Indiana University Luddy School of Informatics Computing and Engineering BloomingtonIN United States Indiana University Cognitive Science Program BloomingtonIN United States Electronics and Telecommunications Research Institute Daejeon Korea Republic of

Scalability and efficiency are desired in neural speech codecs, which supports a wide range of bitrates for applications on various devices. We propose a collaborative quantization (CQ) scheme to jointly learn the codebook of LPC coefficients and the corresponding residuals. CQ does not simply shoehorn LPC to a neural network, but bridges the computational capacity of advanced neural network models and traditional, yet efficient and domain-specific digital signal processing methods in an integrated manner. We demonstrate that CQ achieves much higher quality than its predecessor at 9 kbps with even lower model complexity. We also show that CQ can scale up to 24 kbps where it outperforms AMR-WB and Opus. As a neural waveform codec, CQ models are with less than 1 million parameters, significantly less than many other generative models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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