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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Poddar, Kishan Sharma, Bhanu Aluvala, Srinivas Chitkara University Institute of Engineering & Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350377972

This research study presents a markerless augmented reality (AR) application that aims to deliver basic carpentry knowledge in an engaging, interactive, and informative learning experience. The application offers users to project the 3D model animations of different necessary carpentry hand tools like hacksaw, carpenter's glue, and hand plane into real-world spaces. Using a simulated but realistic context, user can visualize and practice appropriate tool usage which significantly helps to understand and learn faster. User testing showed that the app increased engagement and improved learning results when compared to traditional methods. The complete outcomes illustrate AR's power in some vocational training. It provides a very practical and affordable way to learn by doing. Future work will include scaling up the tool library and developing supported collaboration to deepen learning processes and extend interaction into more intricate aspects of carpentry. © 2024 IEEE.

关键词： Apprentices

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Research on Vibration Control of Paired Inclined Nozzles Dynamic Coupled Tubes 4

Research on Vibration Control of Paired Inclined Nozzles Dyn...

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2022 4th International Conference on Mechanical engineering and Automation, MEA 2022

作者： Yang, Liang Qiu, Ming Liao, Zhenqiang Song, Jie Li, Zijian Huang, Wenjun School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China School of Mechanical and Electrical Engineering Global Institute of Software Technology Jiangsu Suzhou215163 China

The barrel vibrates violently, which will lead to a serious drop in shooting accuracy. To solve this issue, a barrel vibration control method of the paired oblique nozzle dynamic couple is proposed. A pair of inclined nozzle devices are designed on the barrel to fully use the gunpowder gas energy in the chamber during the shooting process. A dynamic couple is generated to balance the turning moment of the barrel weapon. Simultaneously, the recoil force is reduced by exporting the gunpowder gas in the chamber. Therefore, the vibration of the barrel is reduced. Firstly, the internal ballistic two-phase flow model including the gas transient flow model in the vibration control device is modelled based on the internal ballistic theory of two-phase flow. Secondly, the numerical simulation of the weapon launching process is carried out. Thirdly, the rigid-flexible coupling firing dynamics modelling and simulation research is conducted on a 30mm chain gun equipped with a paired oblique nozzle dynamic couple vibration control device. At last, the effects of the device on the continuous firing vibration control of barrel weapons are analyzed. The results show that the vibration reduction efficiency of the inclined nozzle is better than that of the vertical nozzle and the horizontal nozzle. When the angle of the inclined nozzle is 30°, the barrel shows the highest vibration reduction efficiency. The vibration reduction efficiency of each vibration characteristic is 39.2%, 17.1%, 44.8%, and 48.9%, respectively. © 2023 institute of Physics Publishing. All rights reserved.

关键词： Vibration analysis

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Difficulty-aware prior-guided hierarchical network for adaptive segmentation of breast tumors

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science China(Information sciences) 2023年第2期66卷 127-141页

作者： Sumaira HUSSAIN Xiaoming XI Inam ULLAH Syeda Wajiha NAIM Kashif SHAHEED Cuihuan TIAN Yilong YIN School of Software Engineering Shandong University Department of Computer Science Sindh Madressatul Islam University School of Computer Science and Technology Shandong Jianzhu University School of Computer Science and Engineering South China University of Technology School of Medicine Shandong University Health Management Center Qilu Hospital of Shandong University

Breast tumor segmentation is vital to tumor detection at the early stages. Deep learning methods are typically used in automatic tumor segmentation tasks. However, in existing methods, the difference between pixels is disregarded, and the union network architecture is used to segment all pixels; these methods involve a tradeoff between accuracy and efficiency. A novel, difficulty-aware, prior-guided hierarchical network for the adaptive segmentation of breast tumors is presented herein. A difficulty prior learning module is proposed to learn the pixel's difficulty prior to guild adaptive segmentation in the proposed network. To achieve a more accurate segmentation of hard pixels, a hard pixel processing unit is presented to learn more discriminative features for hard pixels. Experiments are conducted based on three datasets. The experimental results show that the proposed methods outperform traditional deep learning methods and achieve a balance between accuracy and efficiency.

关键词： breast tumor ultrasound image segmentation deep neural network difficulty-awareness

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Leveraging EfficientNetB3 for Accurate Breast Cancer Classification: Insights and Innovations 9

Leveraging EfficientNetB3 for Accurate Breast Cancer Classif...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Nasra, Parul Gupta, Sheifali Kumar, Gotte Ranjith Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350377972

One of the main causes of death among women globally, breast cancer emphasises the great need of a correct and fast diagnosis. Mammography and histology are among the conventional diagnostic methods that mostly depend on professional interpretation and so are prone to human mistake. This work improves the accuracy and efficiency of breast cancer classification using EfficientNetB3, a state-of- the-art convolutionial neural network. EfficientNetB3 is well-known for its capacity to scale depth, width, and resolution, therefore enabling enhanced feature extraction free from major computational demand. We use the Breast Ultrasound Images dataset in this work, fine-tuning the model to categorise images into benign, malignant, and normal categories by including many image augmentation strategies. EfficientNetB3 yields a classification accuracy of 94% coupled with great precision, recall, and F1-scores across all categories according to experimental results. Accuracy and loss curves as well as a thorough confusion matrix study help to confirm the model's strong generalising capacity. This work not only shows the promise of EfficientNetB3 in medical diagnostics but also offers understanding of how flexible the model is to fit several imaging modalities. Reducing misclassification rates helps to ensure more accurate and effective diagnosis systems, especially between benign and malignant categories. This study opens the path for implementing artificial intelligence-driven solutions in clinical environments, therefore aiding early identification and better patient outcomes in breast cancer treatment. © 2024 IEEE.

关键词： Ultrasonic imaging

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Threat Detection in VANET Communications 1

Threat Detection in VANET Communications

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Kandakatla, Akash Anbalagan, Sudha School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India Center for Smart Grid Technologies Scope Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798350377002

Communication plays a vital role in autonomous vehicles networks. Detecting the security threats in communication still acts as an open challenge. Leveraging the simulation environments such as OMNeT++ for network simulation and SUMO for traffic mobility visualization, we can show a realistic vehicular environment where vehicles communicate with each other and interact dynamically. The integration of Snort, an open-source intrusion detection system, allows for the detection and representation of various malicious attacks that commonly afflict Vehicular Adhoc Networks (VANETs). This work addresses the critical security challenges faced by VANETS, including the protection of communication integrity, privacy preservation, and resilience against malicious entities. By simulating realistic traffic scenarios and diverse attack vectors, the effectiveness of Snort in detecting and mitigating threats are assessed including black hole attacks, denial-of-service (DoS) attacks, and so forth. Through visualization techniques, we provide insights into the dynamics of VANETs and the impact of malicious activities on network performance and reliability. Real-time monitoring and alerting mechanisms are implemented to notify network administrators of detected threats, enabling timely response and mitigation actions. The outcomes of this research contribute to the development of robust security solutions for VANETs, enhancing the safety and trustworthiness of connected vehicle systems. By combining simulation environments with intrusion detection capabilities, the proactive defense mechanisms against evolving cyber threats in the context of intelligent transportation systems are addressed. © 2024 IEEE.

关键词： Network intrusion

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Positive Sample Augmentation with Graph Mixup for DDI Prediction 1

Positive Sample Augmentation with Graph Mixup for DDI Predic...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Yu, Hailong Ma, Xinyu Chu, Xu Wang, Yasha Wu, Rengyu Zhou, Qiang School of Computer Science Peking University Beijing China Tsinghua University Department of Computer Science and Technology Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Shenzhen Center for Prehospital Care Shenzhen China

ISBN: (纸本)9798350358780

Unexpected drug-drug interactions (DDIs) may occur when drugs are taken at the same time. Harmful DDIs can reduce drug efficacy and even increase unintended toxicity, putting patients treated with different drugs at risk. Previous works on DDI prediction mainly rely on hand-engineered domain knowledge, which is laborious to obtain and cannot handle the problem of sparse positive samples in DDI prediction. Even though some works attempt to handle data sparsity from the perspective of data distribution to improve the generalization ability of the model, they are limited to negative samples distribution. To address these limitations, we propose a novel method, Graph Mixup for Positive Sample Augmentation (GMPSA), to effectively combine positive drug molecule graphs and extend the training distribution to alleviate the problem of sparse positive samples. To model the graph signals and graph structures jointly, we map source graphs to the fused gromov-wasserstein metric space, then minimizes the weighted sum of transportation distances between the distributions of the source graphs and the objective graph in this metric space. By doing so, interpolated positive samples improve the the generalization error of the model. Experiments results on a real-word dataset show that our method effectively improve the perfomermance in DDI prediction on a state-of-the-art architecture. Further studies also verify the robustness and generalizability of GMPSA under different GNN backbones and parameter settings. © 2023 IEEE.

关键词： Forecasting

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BowNetCrypt: Sleek Vaulted Messenger Secured by Bowerbird-Driven Finite Neural Messaging with Dual Elliptic Protection 5

BowNetCrypt: Sleek Vaulted Messenger Secured by Bowerbird-Dr...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Dhumpati, Raghu Nainwal, Ankita Rekha, G. Sunitha Patil, Harshal Vekariya, Vipul Taqui, Syed Noeman Bahrain Polytechnic Department of Computer Science and Engineering Isa Town3339 Bahrain Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Telangana Hyderabad501 510 India Iilm University School of Computer Science & Engineering Greater Noida201306 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350389609

Private message is an innate feature of digital communication and is inherent in maintaining the privacy and confidentiality of the information exchanged in interpersonal and business-to-business relationships. Due to new and more complex cyber threats, there is a need for efficient processing and stronger protection through encryption more than ever before. Conventional cryptographic approaches are prevalent and commonly applied, but they have issues with providing adequate security and carrying out the task with low computational requirements, which poses a problem for devices with limited processing power such as smartphones and IoT devices. However, problems arise with future messaging platforms that include higher latency, scalability, and the changes of different network conditions, which are important for continuous communication on message platforms. Some of the previous efforts include BLIND, LAPEP, PEGA, and SCNN-Bi-LSTM, which have brought improvements addressing some of these challenges. Our research introduces BowNetCrypt: Elegant Vaulted Messenger Secured by Bowerbird-Driven Finite Neural Messaging with Dual Elliptic Protection to address such hindrances. BowNetCrypt also uses the Finite Element Neural Fusion Network (FE-Net) system for better adaptability and accuracy in the private message. The Bowerbird Optimization (BowBO) algorithm is used to minimize the computational complexity as well as enhance the overall network performance. Furthermore, the Lightweight Dual Elliptic Security Protocol (LwDESP) is employed to ensure security and strong encryption without consuming a lot of resources. This method outperforms existing systems by achieving key performance metrics such as 99.2% encryption strength, a 98.7% reduction in latency, and a 97.9% increase in scalability. Such improvements offer BowNetCrypt as one of the most efficient and dependable techniques for implementing secure private messaging within advanced communication networks. © 2024 IEEE.

关键词： Cryptography

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Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation 40

Are ID Embeddings Necessary? Whitening Pre-trained Text Embe...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Lingzi Zhou, Xin Zeng, Zhiwei Shen, Zhiqi School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba-NTU Singapore Joint Research Institute Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance. Despite their effectiveness, the expressive power of text features in these models remains largely unexplored. While most existing models emphasize the importance of ID embeddings in recommendations, our study takes a step further by studying sequential recommendation models that only rely on text features and do not necessitate ID embeddings. Upon examining pre-trained text embeddings experimentally, we discover that they reside in an anisotropic semantic space, with an average cosine similarity of over 0.8 between items. We also demonstrate that this anisotropic nature hinders recommendation models from effectively differentiating between item representations and leads to degenerated performance. To address this issue, we propose to employ a pre-processing step known as whitening transformation, which transforms the anisotropic text feature distribution into an isotropic Gaussian distribution. Our experiments show that whitening pre-trained text embeddings in the sequential model can significantly improve recommendation performance. However, the full whitening operation might break the potential manifold of items with similar text semantics. To preserve the original semantics while benefiting from the isotropy of the whitened text features, we introduce WhitenRec+, an ensemble approach that leverages both fully whitened and relaxed whitened item representations for effective recommendations. We further discuss and analyze the benefits of our design through experiments and proofs. Experimental results on three public benchmark datasets demonstrate that WhitenRec+ outperforms state-of-the-art methods for sequential recommendation. © 2024 IEEE.

关键词： Semantics

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AttenTPU: Tensor Processor for Attention Mechanism with Fine-Grained Padding

AttenTPU: Tensor Processor for Attention Mechanism with Fine...

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2023 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2023

作者： Du, Zhihao Li, Yike Chen, Chao Wang, Zheng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Software Engineering University of Science and Technology of China China

ISBN: (纸本)9798350344288

Transformer-based models have achieved state-of-the art performance in many Artificial Intelligence (AI) tasks. The core component of the transformer is the attention mechanism, which includes computation-intensive operators-such as MatMul and Linear-raising demand for hardware support. Many institute, and researchers have proposed their hardware acceleration methods in recent years. However, most of these works mainly focus on algorithm optimization and hardware performance. Little attention has been paid to the variable sequence lengths in the attention mechanism. In the hardware design, the variability of sequence length brings a challenge to the load-store unit and on-chip data movement. In this work, we first propose a hardware-friendly fine-grained padding method aiming to handle the variability of sequence length in scaled dot-product attention. Then, a hardware architecture named AttenTPU is proposed using the padding method. The experimental results indicate that compared to the CPU and GPU platform, our accelerator achieves speed-ups of 3.43× and 1.41×, respectively. © 2023 IEEE.

关键词： Acceleration

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Numerical Simulation of the Two-phase Flow of a Constant-pressure Rear-spray Low Recoil Weapon with Two Chambers 1

Numerical Simulation of the Two-phase Flow of a Constant-pre...

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1st International Conference on Fluid Dynamics and Computational science, FDCS 2022

作者： Li, Zijian Qiu, Ming Song, Jie Yang, Liang Liao, Zhenqiang School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China School of Mechanical Electrical Engineering Global Institute of Software Technology Jiangsu Suzhou215163 China

To study the effect of a constant-pressure rear-spray low recoil weapon with two chambers on weapon launch performance, based on two-phase flow theory, the valve-spring type constant-pressure rear-spray low recoil weapon firing process with two chambers was studied numerically. Considering the movement of the valve-spring system, a mathematical model of the weapon launch process combining the two-phase flow of gas and solid in the front and back chambers, the movement of the projectile and the transient flow of gas in the lateral rear nozzle is established. Using a 30 mm chain gun as an example, numerical simulation was carried out, and the distribution law of the flow field in the two chambers and the lateral rear nozzle during the firing of the weapon was obtained and compared with the internal flow field of the traditional weapon chamber. The results show that by reasonably matching parameters to control the flow of gunpowder gas in the two chambers, the recoil impulse can be reduced by 81.58% under the premise that the initial velocity of the projectile does not decrease, and the purpose of making full use of gunpowder gas energy to greatly reduce arms recoil is realized. © 2023 institute of Physics Publishing. All rights reserved.

关键词： Projectiles

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