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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Chowdam, Jayanth Panigrahi, Sibarama National Institute of Technology Intelligent Data Analytics Laboratory Department of Computer Science and Engineering Odisha Rourkela India

ISBN: (纸本)9798350369175

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL. © 2024 IEEE.

关键词： Dimensionality reduction

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A Study on Object Detection using Faster R-CNN, Retina net, RPN_R_50_FPN, RPN_R_50_C4

A Study on Object Detection using Faster R-CNN, Retina net, ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kadiyam, Aswin Tirumala Someswar Padhan, Chinmaya Ranjan Panigrahi, Sibarama National Institute of Technology Computer Science and Engineering Andhra Pradesh India National Institute of Technology Intelligent Data Analytics Laboratory Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350369175

The computer vision concept that involves the detection and characterization of objects in images is known as object detection. This research mainly performs this function using four popular models faster R-CNN, RetinaNet, RPN_R_50_FPN, and RPN_R_50_C4 across multiple datasets namely udacity self-driving car dataset, tomatOD dataset, and chess piece detection dataset. Faster R-CNN is famous for its outstanding accuracy although RetinaNet employs a unique focal loss function to balance between speed and accuracy. The purpose of the models RPN_R_50_FPN and RPN_R_50_C4 is to generate region proposals with high quality using different stages in the ResNet-50 backbone. For normalization techniques, the datasets are preprocessed in this research. Instead of training the models from scratch, transfer learning is used whereby starting with weights that were pre-trained on the COCO dataset and after that fine-tuning the required datasets. Accuracy was quantified by evaluating two efficiency measures which are minimum loss and total training duration. Across the different datasets, various performances were demonstrated by different models. © 2024 IEEE.

关键词： Eye protection

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EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Synchronization 26

EGG-SynC: Exact GPU-parallelized Grid-based Clustering by Sy...

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26th International Conference on Extending database Technology, EDBT 2023

作者： Jørgensen, Jakob Rødsgaard Assent, Ira Department of Computer Science Aarhus University Aarhus Denmark Department of Computer Science DIGIT Aarhus University Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9783893180882

Clustering by synchronization (SynC) is a clustering method that is motivated by the natural phenomena of synchronization and is based on the Kuramoto model. The idea is to iteratively drag similar objects closer to each other until they have synchronized. SynC has been adapted to solve several well-known data mining tasks such as subspace clustering, hierarchical clustering, and streaming clustering. This shows that the SynC model is very versatile. Sadly, SynC has an O(T × n2 × d) complexity, which makes it impractical for larger datasets. E.g., Chen et al. [8] show runtimes of more than 10 hours for just n = 70, 000 data points, but improve this to just above one hour by using R-Trees in their method FSynC. Both are still impractical in real-life scenarios. Furthermore, SynC uses a termination criterion that brings no guarantees that the points have synchronized but instead just stops when most points are close to synchronizing. In this paper, our contributions are manifold. We propose a new termination criterion that guarantees that all points have synchronized. To achieve a much-needed reduction in runtime, we propose a strategy to summarize partitions of the data into a grid structure, a GPU-friendly grid structure to support this and neighborhood queries, and a GPU-parallelized algorithm for clustering by synchronization (EGG-SynC) that utilize these ideas. Furthermore, we provide an extensive evaluation against state-of-the-art showing 2 to 3 orders of magnitude speedup compared to SynC and FSynC. © 2023 ***. All rights reserved.

关键词： Synchronization

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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DeepVec: State-Vector Aware Test Case Selection for Enhancing Recurrent Neural Network

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IEEE Transactions on Software engineering 2025年第6期51卷 1702-1723页

作者： Jiang, Zhonghao Yan, Meng Huang, Li Sun, Weifeng Liu, Chao Sun, Song Lo, David Chongqing University School of Big Data and Software Engineering China Chongqing Normal University School of Computer and Information Science China Singapore Management University School of Information Systems Singapore

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test cases without compromising testing performance. Test case selection based on neuron coverage (NC) or uncertainty metrics has achieved significant success in Convolutional Neural Networks (CNN) testing. However, it is challenging to transfer these methods to Recurrent Neural Networks (RNN), which excel at text tasks, due to the mismatch in model output formats and the reliance on image-specific characteristics. What’s more, balancing the execution cost and performance of the algorithm is also indispensable. In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces the cost of data labeling and saves computing resources and balances the execution cost and performance. DeepVec selects data using uncertainty metric based on the norm of the output vector at each time step (i.e., state-vector), and similarity metric based on the direction angle of the state-vector. Because test cases with smaller state-vector norms often possess greater information entropy and similar changes of state-vector direction angle indicate similar RNN internal states. These metrics can be calculated with just a single inference, which gives it strong bug detection and model improvement capabilities. We evaluate DeepVec on five popular datasets, containing images and texts as well as commonl

关键词： Model checking

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing211189 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2002-2012 IEEE.

关键词： Federated learning

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Minimal Context-Switching data Race Detection with dataflow Tracking

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Journal of computer science & Technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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Recommendation fairness and where to find it: An empirical study on fairness of user recommender systems

Recommendation fairness and where to find it: An empirical s...

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2023 IEEE International Conference on big data, bigdata 2023

作者： Tommasel, Antonela Assent, Ira Aarhus University Department of Computer Science Digit Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark Isistan CONICET-UNCPBA Argentina

ISBN: (纸本)9798350324457

Recommender systems play a crucial role in how users consume information and establish new social relations. However, different factors (such as the data collection process, the designed recommendation model, or even the interpretation of findings) could make recommenders (unintendedly) prone to biases, favouring certain user groups or items, thus resulting in unfair outcomes. Recommenders also face fairness criticism for inducing filter bubbles, echo chambers, and, more generally, facilitating opinion manipulation. In this work, we study the impact of user recommender systems on fairness. To this end, we carry out a user recommendation task on a politically polarized Twitter data collection. Then, we evaluate how the different politically aligned user groups experience recommendation quality. Finally, we explore causal models to identify data and model-related features that could affect the fairness of recommender outcomes. Our study shows that political alignment is associated with the unfairness of recommenders affecting not only the relevance of recommendations, but also their diversity and the resulting interaction patterns. © 2023 IEEE.

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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A Secured and Continuously Developing Methodology for Breast Cancer Image Segmentation via U-Net Based Architecture and Distributed data Training

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computer Modeling in engineering & sciences 2025年第3期142卷 2617-2640页

作者： Rifat Sarker Aoyon Ismail Hossain M.Abdullah-Al-Wadud Jia Uddin Department of Computer Science and Engineering Brac UniversityDhaka1000Bangladesh Department of Computer Science and Engineering George Mason UniversityFairfaxVA 22030USA Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia AI and Big Data Department Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This research introduces a unique approach to segmenting breast cancer images using a U-Net-based ***,the computational demand for image processing is very ***,we have conducted this research to build a system that enables image segmentation training with low-power *** accomplish this,all data are divided into several segments,each being trained *** the case of prediction,the initial output is predicted from each trained model for an input,where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs,which also ensures data *** addition,this kind of distributed training system allows different computers to be used *** is how the training process takes comparatively less time than typical training *** after completing the training,the proposed prediction system allows a newly trained model to be included in the ***,the prediction is consistently more *** evaluated the effectiveness of the ultimate output based on four performance matrices:average pixel accuracy,mean absolute error,average specificity,and average balanced *** experimental results show that the scores of average pixel accuracy,mean absolute error,average specificity,and average balanced accuracy are 0.9216,0.0687,0.9477,and 0.8674,*** addition,the proposed method was compared with four other state-of-the-art models in terms of total training time and usage of computational *** it outperformed all of them in these aspects.

关键词： Breast cancer U-Net distributed training data privacy low-powerful machines

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