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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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StarFlow: fine-grained execution of workflows in Hybrid Cloud HPC for data stream applications 23

StarFlow: fine-grained execution of workflows in Hybrid Clou...

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Proceedings of the ieee/ACM 16th international conference on Utility and Cloud computing

作者： Luca Ferrucci Marco Danelutto Patrizio Dazzi University of Pisa Pisa IT

ISBN: (纸本)9798400702341

Nowadays we are witnessing the diffusion of the Function-as-a-service paradigm, that has emerged due to its ability to deploy fine-grained units of computation on heterogeneous resources on-demand, reducing costs and improving resource utilization. In this work, we discuss our objective: extending the runtime support of a structured parallel programming framework, FastFlow, to empower it with the mechanisms needed to exploit FaaS functionalities. To achieve this objective, we also integrates workflows as a way to specify and describe, at a very high level of abstraction, the topology and the requirements of complex parallel data stream applications.

关键词： function-as-a-service

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Innovative cognitive Stroke Detection Analysis using Deep Learning Methodology 5

Innovative Cognitive Stroke Detection Analysis using Deep Le...

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5th ieee international conference on Electro-computing Technologies for Humanity, NIGERCON 2024

作者： Kanna, R. Kishore Anitha, R. Sundaramoorthy, K. Tengey, Clement Banappagoudar, Sudharani B. Salau, Ayodeji Olalekan Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Information Technology Chennai India Department of Electrical Electronics Accra Technical University Ghana Itm University School of Nursing Science MP Gwalior India Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542559

Stroke is the second leading cause of death, and the World Health Organization defined stroke as 'a rapidly progressive set of clinical symptoms characterized by central (or global), persistent brain dysfunction for more than 24 hours or causes death, either by intracranial or seizure;is the most common. Recently, artificial intelligence (AI) has shown great promise in medical imaging, in which computed tomography (CT) imaging was the primary method for determining whether a stroke is ischemic or hemorrhagic, and AI solutions are a potential tool that can be used for non-contrast head CT imaging for ICH diagnosis and therefore limit false negatives. To study and evaluate the current literature on stroke diagnosis in non-contrast computed tomography (NCCT) images and to test a deep learning model using NCCT images for stroke diagnosis. 2000 stroke brain window CT images were obtained online from the public database. The sample size determined was 186 images with an accuracy level of ±7% and a confidence level of 95%. The RESNET-18 model was used as a deep learning model to classify cerebral hemorrhage and their characteristics. Based on manual testing of the images in the AI model, the model achieved a high sensitivity and specificity of 70% for the training data set and 30% for the test data set. Out of 186 images, 155 images were ICH-positive and 31 images were ICH-negative (normal). The deep learning model achieved 86% overall accuracy for prediction of intracranial hemorrhage (ICH) and classified its subtypes with 86.2% sensitivity, 97.1% specificity, 14.1% error, and 88% precision. In 186 images, 178 images were correctly detected by the algorithm, and eight characters were mislabeled. Among the ICH subtypes, SAH was the best, and SDH was the least active subtype. The proposed method was able to correctly detect ICH and its subtypes, such as IVH, SDH, SAH, and EDH, with high accuracy (86%) and differentiate between ICH-negative (normal), which means it can help

关键词： CT DL ICH Stroke

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data Poisoning Attacks in cognitive computing

Data Poisoning Attacks in Cognitive Computing

引用

international conference for Convergence of Technology (I2CT)

作者： Anjan K Koundinya S S Patil Chandu B R Department of Information Science and Engineering B M S Institute Of Technology And Management Bengaluru Karnataka India Information Technology Solutions B M S Institute Of Technology And Management Bengaluru Karnataka India Department of Electronics and Communication Engineering B M S Institute Of Technology And Management Bengaluru Karnataka India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

data poisoning attacks pose a significant threat to the integrity of machine learning (ML) models, impacting their ability to generate accurate predictions. This research delves into the realm of adversarial ML [1], specifically focusing on poisoning attacks [4] during the training phase. A taxonomy of adversarial ML techniques is presented, classifying poisoning attacks into different categories. Real-world examples, including incidents involving Google's VirusTotal [8] scanning service, highlight the practical implications of data poisoning. The study explores defense mechanisms such as training data sanitization and robust optimization, acknowledging the nuanced balance required between model robustness and accuracy. The paper concludes with recommendations for safeguarding ML models against evolving adversarial threats, underscoring the ongoing importance of vigilance and education in the cybersecurity landscape.

关键词： Training Computational modeling Taxonomy Training data Machine learning Predictive models data models

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Preliminary Research on Automatic data Augmentation for Irony Detection

Preliminary Research on Automatic Data Augmentation for Iron...

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ieee international conference on Research, Innovation and Vision for the Future

作者： Chang ZiShen Michal Ptaszynski Fumito Masui Text Information Processing Laboratory Kitami Institute of Technology Kitami Japan

ISBN: (数字)9798331505073

ISBN: (纸本)9798331505080

This paper presents a method to expand the Japanese irony sentences dataset. The main idea is to enhance existing classification models by collecting new data, using existing models, and extracting new data candidates based on the classification results. Manual annotation of the data candidates is carried out to retain only high-quality data, which is then used as new data for model retraining. However, relying on a single classification model does not guarantee sufficient reliability of the results. Therefore, this study utilizes an ensemble learning approach, employing multiple classification models. This approach facilitates the extraction of high-quality data candidates, which are then manually annotated to construct an expanded dataset. Finally, the existing classification models are retrained using the constructed expanded dataset to improve the model's generalization performance. This recursive data augmentation method expands a Japanese irony sentence dataset, enhancing the model's ability to handle these complex linguistic expressions.

关键词： Annotations Computational modeling Manuals Linguistics data augmentation data models Communications technology data mining Reliability Ensemble learning

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cognitive and Autonomic IoT System Design 8

Cognitive and Autonomic IoT System Design

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8th international conference on Software Defined Systems (SDS)

作者： Athamena, Belkacem Houhamdi, Zina Al Ain Univ Coll Business Business Adm Dept Al Ain U Arab Emirates Al Ain Univ Coll Engn Cybersecur Dept Al Ain U Arab Emirates

ISBN: (纸本)9781665458207

Currently, the Internet of Things (IoT) usage ob-serves a drastic growth in several areas and participates in the rapid universe digitalization. Henceforward, the IoT systems next generation will be more difficult to develop and monitor. Gathering real-time data created by IoT triggers some novel opportunities for businesses to take at the right time more accurate and precise decisions. However, several challenges (such as IoT systems complexity and heterogeneous data management, and IoT system scalability) restrain the elaboration of IoT systems that are smart and impel business decision-making. This paper proposes to automatize IoT systems management using an auto- nomic computing approach. Nevertheless, autonomic computing is insufficient for smart IoT systems development. Actually, a smart IoT system implements cognitive abilities that allow its learning and decision-making in real-time. Therefore, this study proposes a model for designing smart IoT systems. It defines a set of cognitive design patterns that delineate the dynamic cooperation between management processes (MPs) (to handle the requirements evolvement and the system's environment unpredictability) and add cognitive capabilities to IoT systems (to generate new insights, perceive big data, and communicate with users). The study's primary goal is to support the developer in designing smart IoT systems that are flexible by choosing an appropriate pattern (or a set of patterns) to meet complex system requirements.

关键词： internet of things design patterns autonomic computing approach big data

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Introducing Timer-driven Requesting Scheme to Segmented File Download Methods Using Parallel TCP/MPTCP Connections on Multiple Paths

Introducing Timer-driven Requesting Scheme to Segmented File...

引用

ieee international Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Junichi Funasaka Noriaki Hiraoka Graduate School of Information Sciences Hiroshima City University Hiroshima JAPAN

ISBN: (数字)9798331522728

ISBN: (纸本)9798331522735

The proliferation of diverse network technologies has enabled concurrent utilization of multiple network media, such as WiFi and 4G/5G links on modern smartphones. While this advancement facilitates simultaneous downloading of varied network contents, traditional multi-connection management methods, like reception-driven requesting schemes, often struggle to adapt to dynamic network conditions. This paper introduces a novel timer-driven requesting scheme applied to both parallel TCP and parallel MPTCP (Multipath TCP) methods, addressing the limitations of existing approaches. Unlike previous studies that relied solely on simulations, we present an experimental evaluation of this scheme on real machines. Our extensive experimentation reveals significant advantages of the timer-driven requesting scheme, particularly its superior adaptability in fluctuating network environments. These findings not only demonstrate the scheme’s practical viability but also underscore its potential to revolutionize content retrieval in multi-network scenarios, paving the way for more efficient and resilient data transfer mechanisms in increasingly complex network ecosystems.

关键词： Adaptation models Ecosystems complex networks Media data transfer MPTCP Wireless fidelity Smart phones

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Research on Visualization Processing of Military Time Series data

Research on Visualization Processing of Military Time Series...

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ieee international conference on Computer and Communication Engineering Technology (CCET)

作者： Jianhong Feng Qinghua Xu Haisheng Li Jiangna Guo Haoyang Lv Yubao Xie Unit 32366 of the Chinese People's Liberation Army Beijing China China Academy of Electronics and Information Technology Beijing China

ISBN: (数字)9798350355673

ISBN: (纸本)9798350355680

With the rapid development of information technology, the demand for the application of massive temporal data in the military field is gradually increasing. The visualization of time series data in the military field especially contributes to enhance battlefield situational awareness. Traditional relational databases can only record the current value of data, while temporal databases can record temporal change data in the dimension of time. In response to the problems of slow response to massive temporal data queries and low efficiency in processing temporal data in relational databases, this paper studies and analyses the relevant technologies for processing time series data in the military field. In order to achieve efficient time series data visualization processing, we design and implement the system which was based on HBase, a distributed time series database OpenTSDB and data visualization plugin dataV. Finally, we illustrate the visualization system using four different types of time series data, and demonstrate its superiority and wide application prospects in processing complex time series data.

关键词： Military communication Military computing Time series analysis Decision making data visualization Distributed databases Relational databases Feature extraction User experience Information technology

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Exploring the Diagnostic Potential of LLMs in Schizophrenia Detection through EEG Analysis

Exploring the Diagnostic Potential of LLMs in Schizophrenia ...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Michele Guerra Roberto Milanese Michele Deodato Madalina G. Ciobanu Fausto Fasano Mosaic Research Center – DiBT University of Molise Pesche Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy Division of Science New York University Abu Dhabi United Arab Emirates Department of Computer Science University of Salerno Fisciano(SA) Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Schizophrenia is a psychiatric disorder that presents significant diagnostic challenges due to its complex neurophysiological characteristics. This paper investigates the potential of Large Language Models (LLMs), such as OpenAI’s GPT-4 and GPT-o1, in detecting schizophrenia through electroencephalography (EEG) analysis. Using the LMSU public ScZ EEG dataset, we conducted a series of experiments involving different types of input data, including raw EEG signals, frequency band summaries, and graphical representations of brain activity. Our findings demonstrate that LLMs can accurately classify schizophrenic and healthy individuals while offering interpretable, clinically relevant insights aligned with established EEG markers. By integrating these models into the diagnostic workflow, we explore the concept of Symbiotic AI, where LLMs act as cognitive collaborators, enhancing clinicians’ ability to analyze complex data efficiently and transparently. This approach not only improves diagnostic accuracy but also facilitates real-time decision-making, paving the way for earlier and more precise detection of schizophrenia in clinical settings.

关键词： Symbiosis Training Analytical models Accuracy Decision making Schizophrenia Brain modeling Electroencephalography Real-time systems data models

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A Multi-Stage Spike Stream Processing and Image Reconstruction Method for Industrial Applications

A Multi-Stage Spike Stream Processing and Image Reconstructi...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Shuaipeng Wu Changhao Yuan Kejiang Ye Southern University of Science and Technology Chinese Academy of Sciences Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the advancement of new industrialization, production processes or scenarios characterized by automation and intelligence require monitoring by more powerful visual inspection tools to maintain production continuity, safety, and efficiency. Spike vision sensors, with their high temporal resolution and high dynamic range, can capture subtle changes of high-speed moving objects in complex industrial environments, providing precise data support for equipment maintenance, fault detection, and condition monitoring. However, in industrial scenarios, factors such as mechanical vibrations, electromagnetic interference, and extreme strong light affect the quality of sensor data, leading to higher noise levels and increased probability of data loss and errors. To address this, we propose a multi-stage processing method for spike data processing and image reconstruction. It first performs preliminary feature extraction and denoising through an anti-noise feature encoder. Building on this, it integrates features and enhances consistency through spatiotemporal information utilization. Finally, it reconstructs the images through an fine-tuned spike reconstruction model. Experimental results show that our method can effectively reduce noise and improve data quality, achieving good performance on industrial datasets. This provides higher quality data support for downstream tasks such as predictive maintenance and high-speed production inspection in industrial scenarios.

关键词： Visualization Noise reduction Noise Production Streaming media Inspection Vision sensors Feature extraction data processing Image reconstruction

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Development of Cryptographic Algorithms for Protecting Cloud data from Insider Threats

Development of Cryptographic Algorithms for Protecting Cloud...

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ieee international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Abidemi Emmanuel Adeniyi Mercy Oka Ebute Halleluyah Oluwatobi Aworinde Agbotiname Lucky Imoize Joseph Bamidele Awotunde Oluwaferanmi Adedamola Adewusi Department of Computer Science Bowen University Iwo Department of Accounting Anderson School of Management University of New Mexico USA Department of Computer Science Bowen University Iwo Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Today’s digitalized environment could not function without cloud data security, especially regarding insider threats. This research aims to identify the susceptibility of cloud data to insider attacks by devising cryptographic algorithms that enhance the security attributes present in existing cryptographic methods. The cloud's data security should be increased; that's this study's main objective. This whole thing aims for better encryption methods that use complex key management systems and can detect and prevent internal threats. The research in algorithmic cryptography and cloud security is closely and relatively thoroughly reviewed in the study, and a method to develop and evaluate new encryption methods is implemented. Preliminary findings suggest that a rather stronger manner of protecting cloud data is by encrypting it and analyzing user behavior while keeping it under constant guard. The research results show that usage of hybrid encryption keeps cloud data safe against insider attacks increased considerably when a combination of symmetric and asymmetric key cryptographic algorithms such as AES / RSA / ECC is used alongside Blowfish.

关键词： Humanities Electric potential data integrity Cloud computing security Elliptic curve cryptography Encryption Standards Monitoring

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