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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

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17th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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ReQ-tank: Fine-grained Distributed Machine Learning Flow Scheduling Approach 29

ReQ-tank: Fine-grained Distributed Machine Learning Flow Sch...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Quanyi Yan, Kedong Yin, Dan Huang, Chanying Xiao, Shan Yang, Yuxin School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China School of Computer Science and Engineering Nanjing Univ. of Science and Technology Nanjing China School of Computer Science and Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Fiberhome Communication Tech. Co. Wuhan China

ISBN: (纸本)9798350330717

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling;On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications. © 2023 IEEE.

关键词： data Center Networking Flow scheduling packet scheduling SDN

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Semantic Understanding in Natural Language processing: Application of Graph Neural Networks in Relationship Extraction

Semantic Understanding in Natural Language Processing: Appli...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Ying Shan School of Chinese Ethnic Minority Languages and Literatures Minzu University of China Beijing China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Semantic understanding in the prospects of natural language processing (NLP) is designed to interpret and comprehend the meaning of the prospects of text beyond words. A fundamental application of the systems revolves around relationship extraction, designed to play a fundamental role in identifying and categorizing semantic relationships between entities and text. Graph Neural Networks (GNNs) to this effect are considered powerful tools within this domain that help model complex and relational data. The representation of texts into graphs through nodes that correspond to the input words signifies the syntactic and semantic relationships drawn through NLP. GNNs, therefore helping capture contextual and intricate dependencies and information through a process that enhances the extraction accuracy mainly by leveraging on the structural properties of the system. Semantic understanding in NLP, therefore handles different elements that include the non-sequential systems of language that help integrate information in varied document prospects. Therefore, this article seems to draw an understanding of NLP through applying GNN in relationship extraction. Findings established that GNNs in relationship extraction remain a promising development contributing to the accurate and sophisticated semantic understanding of NLP.

关键词： Accuracy Law Semantics Education Information processing Syntactics Natural language processing Graph neural networks data models data mining

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StateSnap: A State-Aware P2P Storage Network for Blockchain NFT Content data 21st

StateSnap: A State-Aware P2P Storage Network for Blockchain ...

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21st international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Feng, Siqi Li, Wenquan Kong, Lanju Liu, Lijin Jin, Fuqi Min, Xinping Shandong Univ Sch Software Jinan Peoples R China Res Ctr Dareway Software Co Ltd Jinan Peoples R China Key Lab Shandong Prov Software Engn Jinan Peoples R China

ISBN: (纸本)9783030953911;9783030953904

Non-Fungible Token (NFT) has gained worldwide attention, relying on their superior data representation, as a solution for describing complex digital assets. However, the scale of NFT content data is so huge that we have to use P2P storage network to store it. Due to the lack of management capabilities of global resource allocation, retrieving content in existing P2P storage network relies heavily on distributed collaboration and cannot support low-latency off-line retrieval. Besides if an accident occurs on the blockchain or storage network that is different from the expectation, such as fork, the data state on-chain and off-chain would may be inconsistent. Therefore, we propose the concept of State for P2P storage network to represent the resource allocation of data and assist in the off-line retrieval of resources. We propose a state-aware model based on the permissioned blockchain and P2P storage network, which core is a data structure called StateSnap. The model supports the verification of the consistency of the on-chain and off-chain data and support State rollback and switching. Through experiments, we show that our model reduces the data retrieval time by about 78% compared to the traditional IPFS and performs well in terms of scalability, robustness, and State switch efficiency.

关键词： NFT Blockchain P2P storage network Retrieval MPT

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A feature extraction with pose-auxiliary method for Person Reidentification 2

A feature extraction with pose-auxiliary method for Person R...

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2nd international conference on Advanced algorithms and Signal Image processing, AASIP 2022

作者： Ma, ZePing Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

ISBN: (纸本)9781510660571

Person re-identification is widely considered as a sub problem of image retrieval. It is a technology that uses computer vision technology to determine whether there is a specific pedestrian in an image or video, given a monitored pedestrian image, the pedestrian image under the cross device is retrieved. Many researches have been made to study how to match one people from two picture, but how to locating proper body parts has become a problem which could lead to misalign and wrongly divided sample. To solve this problem, we introduce a pose-auxiliary network to extract the position feature of pedestrian. We use a post estimation network to estimate the person's body location, then we decide whether it's a body part or a static obstacle by using a separated trained classifier. Then we compare these features from two picture and calculate their loss. Meanwhile, we use a graph-matching strategy to compare two pedestrians pose structure, which is also used into jugging the match problem. By using a multi-verification strategy, our method has some improvement than some previous study using Market-1501 and DukeMTMC-Reid datasets, but still remains room for improvement. © 2022 SPIE.

关键词： Image retrieval

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Fake News Classification: A Linguistic Feature Selection Approach to Handle Imbalanced data in Spanish 43

Fake News Classification: A Linguistic Feature Selection App...

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43rd international conference of the Chilean Computer Science Society, SCCC 2024

作者： Puraivan, Eduardo Ormeno, Pablo Kloss, Steffanie Cofre-Morales, Connie Universidad Viña Del Mar Escuela de Ciencias Viña del Mar Chile Universidad de Valparaíso Escuela de Ingeniería Informática Valparaíso Chile Universidad Viña Del Mar Escuela de Ingeniería y Negocios Viña del Mar Chile Universidad Viña Del Mar Centro Regional de Inclusión e Innovación Social Viña del Mar Chile Universidad Viña Del Mar Formación Transversal Viña del Mar Chile

ISBN: (纸本)9798331527891

The detrimental effects of misinformation are diverse and severe. Despite substantial advances in automatic misinformation detection, several challenges persist, and new ones emerge with technological progress. Our hybrid approach, which combines natural language processing and machine learning models, yields promising results. We focus on detecting misinformation in Spanish using multi-level labeling and addressing class imbalance - an issue scarcely explored in the literature. Our methodology incorporates feature selection in the initial stages, simplifying model structure, reducing computational demands, and enhancing interpretability. Following feature selection, we apply traditional class balancing techniques during training. A series of experiments on the CLNews dataset allowed us to identify linguistic features corresponding to Surface variables and the Emotions and feelings lexicon through ANOVA analysis. This selection demonstrated an improvement in the Random Forest model's performance from 0.35 on the imbalanced dataset to 0.917 after applying random undersampling (RUS). Additionally, mutual information analysis helped identify linguistic features associated with both Surface variables and Readability, with the accuracy of the XGBoost model improving from 0.3 to 0.9 after applying random oversampling (ROS). These results validate the effectiveness of our methodology. To date, no studies have surpassed our results on the CLNews dataset or addressed the multi-level classification problem using this dataset. Moreover, we have made the code for our experiments publicly available to ensure transparency and reproducibility. © 2024 IEEE.

关键词： Contrastive Learning

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Growth model of Cynoglossus semilaevis based on improved parallel BP neural network 5

Growth model of Cynoglossus semilaevis based on improved par...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Zheng, Jie Hou, Jiakang Tian, Yunchen Li, Zhenzhong Sun, Zhenlong College of Computer and Information Engineering Tianjin Agricultural University Tianjin China Tianjin Agricultural University and Tianjin Key Laboratory of Aquatic Ecology and Aquaculture Tianjin China Shandong Dongrun Instrument Science and Technology Co. Ltd. Shandong China Nantong Longyang Aquatic Products Co. Ltd. Jiangsu China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

models constructed based on parallel BP neural networks are gradually used in data processing and mining because of their multidimensional non-linear fitting capability and better performance in terms of fitting effect and training speed. However, existing parallel BP neural network models are still trained serially within a single map task, which affects the performance of the models. Based on this, a combinatorial division method is proposed to achieve parallel training within map tasks and to construct an improved parallel BP neural network Cynoglossus semilaevis growth model. In this paper, we use the actual culture data under the factory recirculating water culture model to construct Cynoglossus semilaevis growth model. It was concluded from the experiments that values of running time, RMSE and MAPE of Cynoglossus semilaevis growth model based on improved parallel BP neural network were 51s, 6.79 and 1.34%, respectively. © 2022 IEEE.

关键词： data handling

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Demand Forecasting Using Coupling Of Machine Learning And Time Series models For The Automotive After Market Sector 5

Demand Forecasting Using Coupling Of Machine Learning And Ti...

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5th international conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)

作者： Sharma, Amit Kumar Kiran, Manju Jeba, Pauline Sherly P. Maheshwari, Prateek Divakar, V Robert Bosch GmbH RBEI Engn Data Sci RBEI Bengaluru India Robert Bosch GmbH Enterprise Solut SAP Bengaluru India

ISBN: (纸本)9781665432726

Demand forecasting is considered a crucial component for manufacturing companies, as it directly impacts planning and revenue. This research aims to forecast the demand for automotive products using historical demand history data along with external factors that influence demand. The current approach for generating forecast models takes into account various time-series and machine learning algorithms. The motive was to understand the demand at least granular material number tagged with sales and manufacturing plant offices. A combination of 31792 unique stock-keeping units (SKU) was identified,representing the 37 sales office, 27 plants, 1525 materials. A bag of 23 time-series and machine learning algorithms was used individually over monthly demand history during the past twenty-four months (Jan 2018 - April 2020) and forecasted for one year (May 2020 - April 2021). The exploratory data analysis and feature engineering algorithms were implemented over a complete raw dataset along with data cleaning and characteristics monitoring. The best performing-algorithm is selected for the future forecast by mutual comparison of all individual algorithms mean absolute percentage error as well as ensembled outcomes from time-series and machine-learning algorithms. The best performing model (time-series, machine learning, ensemble) was selected as per the error and accuracy matrix to forecast the demand for the individual sku's.

关键词： Machine learning Time-series Demand forecasting Artificial Intelligence Automobile

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Backdoor Attacks against Deep Neural Networks by Personalized Audio Steganography 26

Backdoor Attacks against Deep Neural Networks by Personalize...

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26th international conference on Pattern Recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Liu, Peng Zhang, Shuyi Yao, Chuanjian Ye, Wenzhe Li, Xianxian Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the world of cyber security, backdoor attacks are widely used. These attacks work by injecting a hidden backdoor into training samples to mislead models into making incorrect judgments for achieving the effect of the attack. However, since the triggers in backdoor attacks are relatively single, defenders can easily detect backdoor triggers of different corrupted samples based on the same behavior. In addition, most current work considers image classification as the object of backdoor attacks, and there is almost no related research on speaker verification. This paper proposes a novel audio steganography-based personalized trigger backdoor attack that embeds hidden trigger techniques into deep neural networks. Specifically, the backdoor speaker verification uses a pre-trained audio steganography network that employs specific triggers for different samples to implicitly write personalized information to all corrupted samples. This personalized method can significantly improve the concealment of the attack and the success rate of the attack. In addition, only the frequency and pitch were modified and the structure of the attacked model was left unaltered, making the attack behavior stealthy. The proposed method provides a new attack direction for speaker verification. Through extensive experiments, we verified the effectiveness of the proposed method.

关键词： Deep learning Training Steganography Neural networks Training data Benchmark testing Behavioral sciences

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Efficient Large-Scale Pre-Trained Model Guided MR Imaging Super-Resolution

Efficient Large-Scale Pre-Trained Model Guided MR Imaging Su...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Fenghang Zhang Guang Feng Xizhan Gao Sijie Niu School of Information Science and Engineering University of Jinan Jinan China Shandong Xiangxiangli Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Super-resolution (SR) is a post-processing technique that can effectively improve the resolution of MR Imaging without upgrading hardware devices. Although existing single-contrast SR reconstruction algorithms and multi-contrast SR algorithms have achieved impressive results, the ill-posed nature of the SR task makes it challenging to achieve significant performance improvements by simply improving the model architecture. Recently, pre-trained models have shown great potential in low-level visual restoration tasks. In this study, we explore using the powerful representational capabilities of pre-trained models to improve the performance of MRI SR and propose a Pre-Training Guided MRI SR (PTG-SR) architecture. Firstly, an effective and simple baseline model is constructed that improves the local and global perceptual capabilities of the modules while maintaining low computational resources. Secondly, we design a Pre-Training Guided Dynamic Alignment Module (PTG-DAM) that transforms the features extracted by the pre-trained model into information related to image degradation, resulting in high-quality and fine-grained SR images. Furthermore, an improved routing attention module, termed DRAM, is proposed which captures the relationships between different paths from both local and global perspectives with fewer resources, further enhancing the representational capabilities of the model. Extensive experimental results on the IXI and BraTS2020 datasets demonstrate that PTG-SR achieves advanced performance and robustness, outperforming existing state-of-the-art (SOTA) SR approaches.

关键词： Computational modeling Biological system modeling Magnetic resonance imaging Superresolution Transforms Computer architecture Feature extraction Robustness Image restoration data mining

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