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A hybrid memory architecture supporting fine-grained data migration

Frontiers of Computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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Attention boosted Individualized Regression 38

Attention boosted Individualized Regression

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Guang Cao, Yuan Feng, Long Department of Data Science College of Computing City University of Hong Kong Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model. © 2024 Neural information processing systems foundation. All rights reserved.

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Web Application Development using Flask - Faculty Directory Management 14

Web Application Development using Flask - Faculty Directory ...

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Tarunika, R. Rohit Ram, S.S. Sujithra, M. Sudha Devi, D. Department of Computing-Data Science Coimbatore Institute of Technology India Department of Computing-Data Science Coimbatore Institute of Technology India

The main goal of web development is to create, build and maintain websites. It is what allows the user to experience seamless performance when accessing a website. The web applications landscape has evolved tremendously over the years using various web development frameworks. A web development framework is a set of resources and tools for software developers to build and manage web applications, web services and websites. One such framework is Flask written in Python. The objective is to create a faculty directory using Flask with a goal to provide a centralized resource that can be used by students, staff, and administrators to easily find and connect with faculty members. By improving access to information about faculty members, the directory project aims to promote collaboration and facilitate the sharing of knowledge within the academic community. © Grenze Scientific Society, 2023.

关键词： Python

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A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

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Computer Systems science & Engineering 2023年第7期46卷 337-356页

作者： N.Vasudevan T.Karthick Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathurTamilnadu603203India

Crop protection is a great obstacle to food safety,with crop diseases being one of the most serious *** diseases diminish the quality of crop *** detect disease spots on grape leaves,deep learning technology might be *** the other hand,the precision and efficiency of identification remain *** quantity of images of ill leaves taken from plants is often *** an uneven collection and few images,spotting disease is *** plant leaves dataset needs to be expanded to detect illness accurately.A novel hybrid technique employing segmentation,augmentation,and a capsule neural network(CapsNet)is used in this paper to tackle these *** proposed method involves three ***,a graph-based technique extracts leaf area from a plant *** second step expands the dataset using an Efficient Generative Adversarial Network ***,a CapsNet identifies the illness and *** proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf *** proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.

关键词： Feature extraction neural network disease segmentation pattern analysis

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An automatic lung nodule detection and classification using an optimized convolutional neural network and enhanced k-means clustering

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Journal of Ambient Intelligence and Humanized computing 2023年第12期14卷 16973-16984页

作者： Lydia, M. Dhasny Prakash, M. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

Lung cancer can be lethal if it is not found in the initial phases. Lung cancer, nevertheless, is challenging to identify early due to the dimensions and form of the nodules. Imaging specialists require the assistance of automated instruments for accurate interpretation. Therefore, in this paper, automatic lung nodule classification is proposed. In the proposed methodology, there are four phases, namely pre-processing, segmentation, classification, and severity analysis. As a first step, lung nodule images are collected from the dataset and pre-processed. After pre-processing, segmentation is carried out. For segmentation enhanced k-means clustering algorithm is applied. To identify the object as cancerous or benign, the segmented region is then fed into an optimized convolution neural network (OCNN). Here, adaptive sunflower optimization (ASFO) algorithm is used to pick the hyperparameters effectively to improve the effectiveness of the CNN classifier. Finally, from the segmented region, the severity of the patient is evaluated. The efficiency of the presented technique is analyzed based on various metrics and research work compared with different state-of-art-works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Human Stress Recognition by Correlating Vision and EEG data

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Computer Systems science & Engineering 2023年第6期45卷 2417-2433页

作者： S.Praveenkumar T.Karthick SRM Institute of Science and Technology Department of Data Science and Business SystemsSchool of ComputingKattankulathur603203TamilnaduIndia

Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday *** human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human *** the multimodal dataset DEAP(database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human *** combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when *** on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress *** the stress identification test,we utilized the DEAP dataset,which included video and EEG *** also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate *** the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG *** Level(FL)fusion that combines the features extracted from video and EEG *** use XGBoost as our classifier model to predict stress,and we put it into *** stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.

关键词： Mental stress physiological data XGBoost feature fusion DEAP video data EEG CNN HAR

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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Preserving Privacy in Healthcare Analytics: Federated Learning for Breast Cancer Prediction in a Collaborative Learning Framework 15

Preserving Privacy in Healthcare Analytics: Federated Learni...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Anuvarshini, G.B. Varshaa, D. Sujithra, M. Devi, D. Sudha Department of Computing- Data Science Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798331300579

Federated learning has emerged as a promising technique in machine learning, enabling collaborative training across distributed datasets. Particularly in fields like healthcare, where data privacy is paramount, federated learning offers a means to improve privacy and security. This paper addresses the critical need for safeguarding sensitive medical data by proposing a privacy-preserving federated learning algorithm. Leveraging homomorphic encryption, the proposed algorithm ensures that the learning process protects both data and model integrity. By employing a secure multi-party computation protocol, the algorithm defends against potential adversaries seeking to compromise the deep learning model. Evaluation on real-world medical datasets demonstrates the effectiveness of the approach in preserving privacy and security amidst collaborative learning environments. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

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Intelligent Automation & Soft computing 2023年第7期37卷 707-726页

作者： V.Swetha G.Vadivu Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur CampusChennai603203India

Every year,the number of women affected by breast tumors is increasing ***,detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast *** conventional methods obtained low sensitivity and specificity with cancer region segmentation *** high-resolution standard mammogram images were supported by conventional methods as one of the main *** conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel *** drawbacks are resolved by the proposed cancer region detection methods stated in this *** mammogram images are clas-sified into normal,benign,and malignant types using the Adaptive Neuro-Fuzzy Inference System(ANFIS)approach in this *** mammogram classification process consists of a noise filtering module,spatial-frequency transformation module,feature computation module,and classification *** Gaussian Filtering Algorithm(GFA)is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation *** statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by the ANFIS technique in this ***,Probability Histogram Segmentation Algo-rithm(PHSA)is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram *** proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM *** the extensive analysis of the proposed tumor detection methods stated in this work with other works,the proposed work significantly achieves a higher *** methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions.

关键词： Mammogram cancer gaussian filter ridgelet classification

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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