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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart data, Smartdata 2024

作者： Lin, Jingjing Lin, Jingsong Lyu, Chenxi Fan, Xinmin Dong, Chen Fuzhou University College of Computer and Data Science Fuzhou China Fujian Normal University Network and Data Center Fuzhou China

ISBN: (纸本)9798350351637

With each advancement in internet technology, new security challenges arise. The prevalence of malicious programs continues to increase, which makes it crucial to detect and address them effectively. Many researchers focus on solving different datasets by using deep learning methods and make significant progress. However, these strategies must be continuously improved to adapt to the latest data. In this paper, an improved model based on CNN-LSTM is proposed to detect and classify malware programs, named malDetect I. At the same time, the Transformer Encoder module is also modified based on model Bert to adapt to the classification task. Lastly, two models are compared with prediction results on evaluation indicators. The data used in this paper is the Windows API sequence extracted after dynamic operation. The text processing methods are also suitable for processing sequence data. The experiment uses Word2Vec and two different learning rate strategies, and the improved model accuracy is 9.83% higher than the original CNN-LSTM model. The model integrated the BiLSTM model with the Self-Attention mechanism, named malDetect II, is 11.46% higher than the basic model CNN-LSTM and 2.82% higher than the Transformer Encoder classification model. © 2024 IEEE.

关键词： Deep learning

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Integrating Multimodal AI Models for Enhanced Oral Cancer Detection and Diagnosis 2

Integrating Multimodal AI Models for Enhanced Oral Cancer De...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shantha Kumari, K. Udhya shree, S. Calarany, C. Department of Data Science and Business Systems SRM Institue of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India Department of Computer Science and Engineering Tagore Government Arts and Science College Pondicherry India

ISBN: (纸本)9798331504960

Oral cancer is a major global health issue marked by high morbidity and mortality rates, primarily due to late-stage diagnosis. This work proposes the Advanced AI models that use multimodal data for early detection and diagnosis of oral cancer. Currently, traditional machine learning algorithms coupled with deep learning algorithms such as Convolutional neural networks (CNNs), focus on image-based analysis for detecting cancer. These works are delimited with minimal robustness and input data efficacy. Through the proposed multimodal AI model, histopathological images and clinical notes of the oral cancer patients are combinedly analyzed by which diagnostic accuracy is enhanced. This framework integrates CNNs for image analysis and Transformer-based models for text analysis, with a sophisticated fusion layer for data integration. Enhanced preprocessing techniques and performance optimization through Vision Transformers (ViTs) and BERT models significantly improve sensitivity, specificity, and F1-score. The multimodal AI model achieved a sensitivity of 0.90, specificity of 0.85, and an F1-score of 0.89, outperforming previous models like SVMs, RF, and CNNs. This indicates a high accuracy in identifying both positive and negative cases, ensuring reliable diagnoses. The results demonstrate a substantial improvement over traditional methods, that lays foundations for early diagnosis. © 2024 IEEE.

关键词： Lung cancer

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TCCL: Discovering Better Communication Paths for PCIe GPU Clusters 24

TCCL: Discovering Better Communication Paths for PCIe GPU Cl...

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29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2024

作者： Kim, Heehoon Ryu, Junyeol Lee, Jaejin Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of Dept. of Data Science Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798400703867

Exploiting parallelism to train deep learning models requires GPUs to cooperate through collective communication primitives. While systems like DGX, equipped with proprietary interconnects, have been extensively studied, the systems where GPUs mainly communicate through PCIe have received limited attention. This paper introduces TCCL, a collective communication library designed explicitly for such systems. TCCL has three components: a profiler for multitransfer performance measurement, a pathfinder to discover optimal communication paths, and a modified runtime of NCCL to utilize the identified paths. The focus is on ring-based collective communication algorithms that apply to popular communication operations in deep learning, such as AllReduce and AllGather. The evaluation results of TCCL on three different PCIe-dependent GPU clusters show that TCCL outperforms (up to ×2.07) the state-of-the-art communication libraries, NCCL and MSCCL. We also evaluate TCCL with DL training workloads with various combinations of parallelism types. © 2024 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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A Fair Scheduling Algorithm and Schedulability Analysis for Performance Asymmetric Multiprocessors 25

A Fair Scheduling Algorithm and Schedulability Analysis for ...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Wu, Peng Jin, Feihang Yan, Tao Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Institute of Big Data Science and Industry School of Computer and Information Technology Taiyuan China

We propose a new scheduling policy for performance asymmetric multiprocessors that have identical instruction sets but different processing speeds. The difference between it and the original Pfair scheduling is that when selecting scheduling parameters such as the pseudo deadline of the subtask and selecting the processor when running the highest priority subtask, it can be aware of the performance asymmetry of the underlying processor. In addition, we also propose a sufficient schedulability test for Pfair scheduling on performance asymmetric multiprocessors. © 2024 IEEE.

关键词： Real time systems

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Launching a data Analytics Major: How Collaboration and Agility Built a Successful Program

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American Mathematical Monthly 2025年第1期132卷 77-88页

作者： Forrester, Richard Marshall, Emily Forrester, Jeffrey Richeson, David Schaefer, Jennifer Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Economics Denison University Granville 43023 OH United States Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States

In this article, we share the details of Dickinson College’s journey to establish a data analytics major. Designed to provide students with the technical proficiency required to become a data scientist, Dickinson’s data analytics major also incorporates values of a liberal arts education, including interdisciplinary learning, critical thinking, thoughtful analysis, effective communication, and ethical considerations. We describe how sustained collaboration, a detailed plan to engage the campus community, partnerships with alumni, and support from senior leaders were essential to this achievement. We also discuss the challenges and successes encountered before and after the approval of the major. © 2024 Mathematical Association of America.

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Utilizing Attention-based Vision Transformer (AtViT) for Brain Tumour Detection

Utilizing Attention-based Vision Transformer (AtViT) for Bra...

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2024 International Conference on Future Technologies for Smart Society, ICFTSS 2024

作者： Chaubey, Harshit Kumar Arelli, Siri Agrawal, Karan Gopalaiyengar, Srinivasa K. Iiit Computer Science and Engineering Naya Raipur Raipur India Iiit Data Science and Artificial Intelligence Naya Raipur Raipur India

ISBN: (纸本)9798350373844

This research explores the use of Attention based Vision Transformers (AtViT), a type of neural network, for the detection of brain tumour from MRI scans. Traditional methods of tumour detection rely heavily on the expertise of neuroradiologists and often involve time-consuming processes. Our project aims to automate this process, potentially increasing both the accuracy and speed of diagnosis. We used a dataset of 3,064 MRI images that included three different types of brain tumour: Meningioma, Pituitary tumour, and Glioma. By employing a pretrained Vision Transformer model, which we fine-tuned for our specific task, we achieved a high accuracy of 99.61% with the EfficientFormer model. We compared this to the ResNet-50 model, a traditional architecture, which demonstrated the superior capability of Vision Transformers in handling complex image classification tasks like tumour detection. The findings suggest that Vision Transformers can significantly aid in the quick and accurate diagnosis of brain tumour, offering a promising tool for medical professionals. This study lays the groundwork for further exploration into the integration of advanced neural networks in medical diagnostics. © 2024 IEEE.

关键词： Diagnosis

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Conditioned Cycles in Sparse data Domains: Applications to Electromagnetics 22

Conditioned Cycles in Sparse Data Domains: Applications to E...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Barger, Maria Paffenroth, Randy Pathak, Harsh Worcester Polytechnic Institute Data Science WorcesterMA United States Worcester Polytechnic Institute Mathematics Computer Science and Data Science WorcesterMA United States

ISBN: (纸本)9798350345346

Training neural networks is difficult with small datasets, yet small training data sets are commonly found with machine learning problems in the physical sciences. Prediction accuracy within such sparse data domains can be improved by the utilization of conditioning, inspired by classical statistics, and cycles, similar to those of CycleGANs. In this paper, we propose the addition of 'oracle' conditioning, where the true condition value is provided during training (but not during testing), to improve final prediction accuracy in sparse data domains. For example, in over-water electromagnetic (EM) source localization, the range of the emitter can be thought of as a 'condition' on the generation of a received EM signal. Oracle conditioning utilizes an encoder-decoder framework where the conditioning of the decoder is not contingent on the output of an encoder's prediction (e.g, predicting the range from a received EM signal). While our primary application involves source localization for EM signals, this paper is intended to inspire a general approach toward neural network training for problems in the physical sciences with similar characteristics. Our multi-component loss function is intended to serve as a general method for small data problems which can benefit from the addition of cycles and oracle conditioning. © 2023 IEEE.

关键词： conditional generation cycles electromagnetics loss function oracle conditioning

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Obstacle-Avoidance X-Architecture Steiner Minimal Tree Algorithm Based on Deep Reinforcement Learning

Obstacle-Avoidance X-Architecture Steiner Minimal Tree Algor...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： You, Jie Zhu, Yuhan Huang, Xing Yang, Liliang Liu, Genggeng Fuzhou University College of Computer and Data Science Fuzhou China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350312270

In recent years, we have witnessed the rapid development of the Internet of Things (IoT) paradigm. A new computing paradigm, predictive intelligence, has been introduced into many IoT applications. In IoT applications, predictive intelligent devices must consider power and delay consumption. The goal of Very Large Scale Integration (VLSI) circuit routing problem is to minimize the wirelength to optimize chip delay. Therefore, VLSI routing problems have been widely applied in IoT. The X-architecture routing technology presents a promising solution that can reduce the number of vias, wirelength, and mold size significantly, thereby proving to be a highly practical approach. Moreover, with the shift of modern VLSI design towards system-on-chip paradigm, many uncrossable obstacles appear on the plane, thus Obstacle-Avoiding X-architecture Steiner Minimal Tree (OAXSMT) problem has attracted more and more attention. Most existing algorithms are troubled by poor wirelength quality or high construction time cost. Therefore, this paper proposes a method based on Deep Reinforcement Learning to construct an OAXSMT. First, an encoder based on heterogeneous graph transformer is designed to generate fixed-length encodings for each node. Second, an actor-critic decoder based on pointer network is designed to generate obstacle-avoiding X-architecture Steiner redundant tree. Finally, a fast evaluation strategy is designed to evaluate the quality of the redundant tree. Experimental results show that the routing results generated by the algorithm are superior to existing heuristic algorithms in terms of wirelength and runtime, and the algorithm also has certain generalization ability. © 2023 IEEE.

关键词： Reinforcement learning

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See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning

See Detail Say Clear: Towards Brain CT Report Generation via...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zheng, Chengxin Ji, Junzhong Shi, Yanzhao Zhang, Xiaodan Qu, Liangqiong College of Computer Science Beijing University of Technology Beijing China Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798891761681

Brain CT report generation is significant to aid physicians in diagnosing cranial diseases. Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report. However, there exist some challenges: 1) Redundant visual representing: Massive irrelevant areas in 3D scans distract models from representing salient visual contexts. 2) Shifted semantic representing: Limited medical corpus causes difficulties for models to transfer the learned textual representations to generative layers. This study introduces a Pathological Clue-driven Representation Learning (PCRL) model to build cross-modal representations based on pathological clues and naturally adapt them for accurate report generation. Specifically, we construct pathological clues from perspectives of segmented regions, pathological entities, and report themes, to fully grasp visual pathological patterns and learn cross-modal feature representations. To adapt the representations for the text generation task, we bridge the gap between representation learning and report generation by using a unified large language model (LLM) with task-tailored instructions. These crafted instructions enable the LLM to be flexibly fine-tuned across tasks and smoothly transfer the semantic representation for report generation. Experiments demonstrate that our method outperforms previous methods and achieves SoTA performance. Our code is available at https://***/ChaunceyJheng/PCRL-MRG. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Life Expectancy Prediction Based on Particle Swarm Optimization Support Vector Machine Regression 3

Life Expectancy Prediction Based on Particle Swarm Optimizat...

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3rd IEEE International Conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Guo, Zhaodong Que, Yuanxi Li, Xiang College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798350380989

This study delves into the application of Particle Swarm Optimization (PSO) algorithms in the parameter optimization of Support Vector Regression (SVR) models to enhance the accuracy of predicted average life expectancy. An adaptive PSO algorithm augmented with random perturbations is introduced. Through a fitness-function-guided dynamic adjustment mechanism, this method circumvents the premature convergence issue typical of conventional PSO, locating the optimal penalty parameter C and kernel parameter for the SVR model. These parameters are then incorporated into the SVR model for training and regression prediction, achieving optimal data fitting and generalization effects. Comparative analysis reveals that the PSO-enhanced SVR model outperforms the traditional SVR across various performance metrics. Notably, there is a marked improvement in the coefficient of determination, a significant re-duction in root mean square error, and mean absolute percentage error, affirming the advancements of the improved PSO-SVR model in prediction accuracy and reliability. © 2024 IEEE.

关键词： Composite Model PSO PSO-SVR SVM

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