检索结果-内蒙古大学图书馆

Artificial Intelligence (CAI), IEEE Conference on

作者： Zhiyuan Zhang Xin Zhong Ningbo Innovation Center Zhejiang University Ningbo China School of Computing and Information Systems Singapore Management University Singapore Social & Cognitive Computing Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore

The dental arch is a significant anatomical feature that is crucial in assessing tooth arrangement and configuration and has a potential for human identification in biometrics and digital forensic dentistry. In a previous study, we proposed an auto pose-invariant arch feature extraction Radial Ray Algorithm (RRA) and a matching framework [1] based solely on 3D dental geometry. To enhance the identification accuracy and speed of our previous work, we propose in this study a transformer architecture that can extract dental keypoints by encoding both local and global features. The dental arch is then constructed through robust interpolation of the dental keypoints using B-Spline and is compared using the same identification framework. To evaluate the effectiveness of our proposed approach, we conducted experiments by matching the same 11 post-mortems (PM) samples against 200 antemortem (AM) samples. Our results show that our approach achieves higher accuracy and faster speed compared to our previous work. Specifically, 11 samples (100%) achieved a top 6.5% (13/200) accuracy out of the 200-rank list, compared to the top 15.5% (31/200) accuracy previously [1]. Additionally, the time required to identify a single subject from 200 subjects has been reduced from 5 minutes to 3 minutes. The dental arch can be used as a powerful filter feature. Our findings make a significant contribution to the existing literature on dental identification and demonstrate the potential practical applications of our approach in diverse fields such as biometrics, forensic dentistry, orthodontics, and anthropology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graph enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge

Knowledge Graph enhanced Aspect-Based Sentiment Analysis Inc...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Autumn Teo Zhaoxia Wang Haibo Pen Budhitama Subagdja Seng-Beng Ho Boon Kiat Quek School of Computing and Information Systems Singapore Management University Singapore Singapore School of Electrical and Information Engineering Tianjin University Tianjin China Social and Cognitive Computing Department Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore Singapore

Aspect-Based Sentiment Analysis (ABSA) is a type of sentiment analysis that could identify and extract various aspects or features from text and determining the sentiment associated with each aspect. ABSA has significant real-world applications, such as providing deeper insights about specific strengths and weaknesses of each aspect contained within text data. Despite notable advancements, ABSA still has room for improvement in completeness, accuracy, and performance efficacy. To tackle these challenges, this research introduces an approach to ABSA that leverages knowledge graphs to improve completeness, accuracy, and performance efficacy. Our key novelty is in being able to incorporate enhancements across multiple stages, including utilising knowledge graphs together with dataset processing, and architectural modelling. Additionally, we offer a complementary overview and analysis of various deep learning heuristics and optimization strategies that could further enhance ABSA performance. Our validation results demonstrate the effectiveness of the proposed knowledge graph enhanced ABSA method across multiple benchmark datasets, with notable boosts to model performance. Importantly, in being model-agnostic, our dataset processing approach could potentially enhance the performance of other ABSA methods in the future.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Phonological Analysis for Clinical Implication Using Self-Attention Based Grapheme to Phoneme Modeling Under Low-Resource Conditions

Comprehensive Phonological Analysis for Clinical Implication...

引用

Artificial Intelligence and cognitive Science (AICS), Irish Conference on

作者： Puneet Bawa Virender Kadyan Muskaan Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Speech and Language Research Centre University of Petroleum and Energy Studies Dehradun India Cognitive Analytics Research Lab Intelligent Systems Research Centre School of Computing Ulster University UK

Within the field of speech recognition, a significant obstacle occurs when faced with low-resource situations charac-terized by a scarcity of accessible speech data, which is also het-erogeneous in nature. This becomes more challenging considering the aspect of clinical environments, where accurate transcription of speech is of utmost significance in the identification and man-agement of speech and language disorders. The current manual methodologies used for the development of language models (LMs) and the recognition of speech often encounter difficulties in low-resource scenarios, exhibiting limited ability to adjust to the distinct speech patterns shown by diverse demographics. The present study aims to tackle a significant issue within the field of voice recognition by proposing a solution centered on the advancement of automated language modeling. Specifically, the study highlights the importance of n-gram LMs in this context. The study sheds light on an innovative method that utilizes automated language model development using the multi-head self-attention transformer-based Grapheme-to-Phoneme (G2P) modeling. The results clearly indicate that automated language models outperform humanly created alternatives, highlighting their impressive adaptability and dependability. Furthermore, this research investigates the potential for metamorphosis offered by n-gram language models, resulting in a notable increase in recognition accuracy for the speech recognition system based on the Deep Neural Network-Hidden Markov Model (DNN-HMM).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Speech-Based Alzheimer's Disease Classification System with Noise-Resilient Features Optimization

Speech-Based Alzheimer's Disease Classification System with ...

引用

Artificial Intelligence and cognitive Science (AICS), Irish Conference on

作者： Virender Kadyan Puneet Bawa Mohd Mujtaba Akhtar Muskaan Singh Speech and Language Research Centre University of Petroleum and Energy Studies Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Cognitive Analytics Research Lab Intelligent Systems Research Centre School of Computing Ulster University UK

Alzheimer's disease is a severe neurological disorder having a major influence on a substantial portion of the popu-lation. The prompt detection of this condition is crucial, and speech analysis may play a crucial role in facilitating efficient treatment and care. The main aim of this research has been to investigate the significance of timely identification of speech signal abnormalities associated with Alzheimer's disease in order to provide effective therapy interventions and improve disease man-agement. The study used the Mel Frequency Cepstral Coefficients (MFCC) framework, a well recognized technique for feature extraction known for its versatility across several domains. This research introduces an innovative approach that utilizes both individuals diagnosed with dementia and control participants to detect two unique types of cognitive impairment via the analysis of speech signals. The approach used in this work involves the extraction of acoustic properties from pre-processed speech data obtained from the Pitt Corpus of Dementia Bank. This is achieved by using several feature sets, which include a combination of MFCC, prosodic features, and statistical features. This study examines the attributes of optimum feature optimization in actual and noise-enhanced speech environments using machine learning techniques. The integration of MFCC,Statistical and prosodic features has shown remarkable outcomes, exhibiting a superior accuracy rate of 98.3%. This surpasses the performance of other feature combinations when using the Random Forest classifier.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SAUCE: Synchronous and Asynchronous User-Customizable Environment for Multi-Agent LLM Interaction

arXiv

引用

arXiv 2024年

作者： Neuberger, Shlomo Eckhaus, Niv Berger, Uri Taubenfeld, Amir Stanovsky, Gabriel Goldstein, Ariel The Hebrew University Business School Jerusalem Israel School of Computer Science and Engineering The Hebrew University of Jerusalem School of Computing and Information Systems University of Melbourne Australia Department of Cognitive and Brain Sciences The Hebrew University of Jerusalem Israel Google Research United States

Many human interactions, such as political debates, are carried out in group settings, where there are arbitrarily many participants, each with different views and agendas. To explore such complex social settings, we present SAUCE: a customizable Python platform, allowing researchers to plug-and-play various LLMs participating in discussions on any topic chosen by the user. Our platform takes care of instantiating the models, scheduling their responses, managing the discussion history, and producing a comprehensive output log, all customizable through configuration files, requiring little to no coding skills. A novel feature of SAUCE is our asynchronous communication feature, where models decide when to speak in addition to what to say, thus modeling an important facet of human communication. We show SAUCE’s attractiveness in two initial experiments, and invite the community to use it in simulating various group simulations. © 2024, CC BY.

关键词： Chatbots

来源：评论

学校读者我要写书评

暂无评论

Towards a Theoretical Framework of Autonomous systems Underpinned by Intelligence and systems Sciences

引用

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

来源：评论

学校读者我要写书评

暂无评论

A retrospective recount of computer architecture research with a data-driven study of over four decades of ISCA publications

arXiv

引用

arXiv 2019年

作者： Anjum, Omer Hwu, Wen-Mei Xiong, Jinjun IBM-ILLINOIS Center for Cognitive Computing Systems Research

The ACM/IEEE International Symposium on Computer Architecture (ISCA) conference is one of the premier forums for presenting, debating and advanc- ing new ideas and experimental results in computer architecture. According to recent calls for papers, research topics are solicited on a broad range of areas, including, but not limited to: processors, memories, storage systems, architectures, interconnection networks, instructions, thread-level parallelism, data-level parallelism, dependable architectures, architecture support for paral- lel software development, architecture support for security, power and energy efficient architectures, application specific architectures, reconfigurable architec-tures, embedded architectures, network and router architectures, architectures for emerging technologies, architecture modeling and performance evaluation. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Deep learning based normative models identify structures that become abnormal with Alzheimer’s disease

引用

Alzheimer's & Dementia 2023年第S17期19卷

作者： Camilo Jaramillo Maria Trujillo Jose Bernal School of Systems and Computing Engineering Universidad del Valle Cali Colombia Correspondece Institute of Cognitive Neurology and Dementia Research (IKND) Otto-von-Guericke University Magdeburg Germany German Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany

Background Improving dementia diagnosis is a major global concern, given that up to 20% of subjects are misdiagnosed. Normative modelling, which quantifies how far individuals deviate from healthy controls (HC), may help to better situate subjects along the spectrum. To determine whether this is the case, we implemented a deep learning based normative model and tested it on structural neuroimaging data from subjects with HC, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Method We developed an adversarial autoencoder (AAE) ( Figure 1 ) leveraging FreeSurfer features, namely cortical surface area of 68 cortical subregions (34 by hemisphere; Desikan-Killiany atlas) and volumes of 33 neuroanatomical structures (Aseg atlas). Our model not only returns individual “deviation” scores—divergence from HC—but also identifies regions of interest that contribute to said score, facilitating interpretability. We retrieved T1-weighted MRI scans (n = 204) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (HC/MCI/AD 105/29/60; Table 1 ). We trained our AAE on 71 HC samples (n = 71) and tested it on the rest (n = 133). We used bootstrapping to compute confidence intervals (CI) for each point estimate and examined regions where scores differed for the considered groups [1]. Result Deviation scores were lowest for HC (mean 0.74 [95%-CI 0.63–0.88]), highest for AD (mean 1.21 [95%-CI 1.03–1.50]), and intermediate for MCI (mean 1.04 [95%-CI 0.88–1.27]), implying subjects indeed fell along the AD spectrum ( Figure 2A ). Said scores also enabled classifying subjects into HC, MCI, and AD with a mean area under the curve (AUC) of 75% ( Figure 2B-C ; HC vs. MCI: 0.75 [95%-CI 0.62–0.85]; HC vs. AD: 0.76 [95%-CI 0.64–0.82]). According to the AAE, the hippocampus, parahippocampal cortex, ventricles, and inferior parietal lobe contributed to such differences ( Figure 2D-E ). Conclusion Normative models can identify structures that become abnormal as the brain und

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modeling Implicit Bias with Fuzzy cognitive Maps

arXiv

引用

arXiv 2021年

作者： Nápoles, Gonzalo Grau, Isel Concepción, Leonardo Koumeri, Lisa Koutsoviti Papa, João Paulo Department of Cognitive Science & Artificial Intelligence Tilburg University Netherlands Information Systems Group Eindhoven University of Technology Netherlands Business Informatics Research Group Hasselt University Belgium Department of Computer Science Central University of Las Villas Cuba Department of Computing São Paulo State University Brazil

This paper presents a Fuzzy cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete. In our proposal, problem features are mapped to neural concepts that are initially activated by experts when running what-if simulations, whereas weights connecting the neural concepts represent absolute correlation/association patterns between features. In addition, we introduce a new reasoning mechanism equipped with a normalization-like transfer function that prevents neurons from saturating. Another advantage of this new reasoning mechanism is that it can easily be controlled by regulating nonlinearity when updating neurons' activation values in each iteration. Finally, we study the convergence of our model and derive analytical conditions concerning the existence and unicity of fixed-point attractors. © 2021, CC BY-NC-ND.

关键词： Fuzzy rules

来源：评论

学校读者我要写书评

暂无评论

Urban Intelligence: Toward the Digital Twin of Matera and Catania

Urban Intelligence: Toward the Digital Twin of Matera and Ca...

引用

Blockchain for Renewables Integration (BLORIN), Workshop on

作者： Giordana Castelli Amedeo Cesta Mario Ciampi Riccardo De Benedictis Giuseppe De Pietro Matteo Diez Giovanni Felici Roberto Malvezzi Barbara Masini Riccardo Pellegrini Andreas Scalas Giuseppe Stecca Lucanos Strambini Gabriella Tognola Paolo Ravazzani Emilio Fortunato Campana Engineering ICT and Technology for Energy and Transportation Dept. (DIITET) National Research Council (CNR) Rome Italy Institute of Cognitive Sciences and Technologies (ISTC) National Research Council (CNR) Rome Italy Institute for High Performance Computing and Networking (ICAR) National Research Council (CNR) Naples Italy Institute of Marine Engineering (INM) National Research Council (CNR) Rome Italy Institute for Systems Analysis and Computer Science (IASI) National Research Council (CNR) Rome Italy Engineering ICT and Technology for Energy and National Research Council (CNR) Rome Italy Institute of Electronics Computer and Telecommunication Engineering (IEIIT) National Research Council (CNR) Bologna Italy Institute for Applied Mathematics and Information Technologies (IMATI) National Research Council (CNR) Genoa Italy Institute for Systems Analysis and Computer Science “Antonio Ruberti” (IASI) National Research Council (CNR) Rome Italy Institute of Electronics Computer and Telecommunication Engineering (IEIIT) (CNR) Pisa Italy

ISBN: (纸本)9781665472500

The Urban Intelligence (UI) paradigm conceived by CNR consists of an ecosystem of digital technologies joined within a Digital Twin (DT) of the city aimed at improving the city governance towards goals addressed also by the UN Agenda 2030, such as urban environment, sustainability and resilience, wellbeing and quality of life, local development, and social inclusion. In particular, UI provides a set of candidate policies in complex scenarios, and supports policy makers and stakeholders in designing shared, evidence-based, and integrated solutions. UI is being applied for the first time to two Italian cities, Matera and Catania, paving the way for a deeper scientific framing of the paradigm, as well as for the technological development and testing of the core UI ecosystem in real-life situations. The paper introduces the UI key-concepts and components, illustrates the ongoing experimentations in these pilot cities related to the development of two DTs on parts of the urban areas, and presents some initial results.

关键词： Renewable energy sources Urban areas Ecosystems Lakes Digital twins Sensors Stakeholders

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：