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International Conference on computing and Networking Technology (ICCNT)

作者： Afnaan. K K. V Nagaraja Tripty Singh Bhanu Prakash KN Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Mathematics Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Principal Investigator Research Scientist Clinical Data Analytics and Radiomics Bioinformatic Institute (BII) A*STAR Singapore

Computer tomography (CT), also known as computed tomography, is a medical imaging method that generates detailed and precise horizontal or axial images of targeted regions of the body for diagnostic purposes. In this particular study, the focus was on performing image classification of CT scan images of the Kidney. This organ plays an essential role in detoxification, fluid balance, and maintaining electrolyte levels in the human body. The dataset used for the study contained 12,446 images into four labeled classes - Cyst, Tumor, Stone, and Normal. Convolution Neural Network (CNN) model is built for binary (Normal and Tumor) and multi-image Classification to classify the Renal CT images into four classes. CNN uses ReLU as an activation function for classifying these images. The Model is evaluated and compared based on Accuracy and Loss performance metrics by compiling the CNN model with different deep learning TensorFlow Keras optimizers for Version 2.11.0 for Binary Image Classification at 10,20, and 50 epochs. The CNN model is assembled with Adam Optimizer and Follows the Regularized Leader (FTRL) Optimizer. The Testing and training Accuracy and Losses achieved are evaluated with a learning rate of 0.001 and 0.0001. Adam Optimizer outperforms to be the best Optimizer for Binary and Multi-Image Classification.

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Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling

arXiv

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arXiv 2023年

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2023, CC BY.

关键词： Vector quantization

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data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection

arXiv

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arXiv 2024年

作者： Hao, Chao Yu, Zitong Liu, Xin Xu, Jun Yue, Huanjing Yang, Jingyu The School of Electrical and Information Engineering Tianjin University Tianjin300072 China The School of Computing and Information Technology Great Bay University Dongguan523000 China The Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland The School of Statistics and Data Science Nankai University Tianjin300072 China

Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades. Though sharing the same purpose of segmenting an image into binary foreground and background regions, their distinction lies in the fact that COD focuses on concealed objects hidden in the image, while SOD concentrates on the most prominent objects in the image. Previous works achieved good performance by stacking various hand-designed modules and multi-scale features. However, these carefully-designed complex networks often performed well on one task but not on another. In this work, we propose a simple yet effective network (SENet) based on vision Transformer (ViT), by employing a simple design of an asymmetric ViT-based encoder-decoder structure, we yield competitive results on both tasks, exhibiting greater versatility than meticulously crafted ones. Furthermore, to enhance the Transformer’s ability to model local information, which is important for pixel-level binary segmentation tasks, we propose a local information capture module (LICM). We also propose a dynamic weighted loss (DW loss) based on Binary Cross-Entropy (BCE) and Intersection over Union (IoU) loss, which guides the network to pay more attention to those smaller and more difficult-to-find target objects according to their size. Moreover, we explore the issue of joint training of SOD and COD, and propose a preliminary solution to the conflict in joint training, further improving the performance of SOD. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our method. The code is available at https://***/linuxsino/SENet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Design and Development of an IoT Based Smart Irrigation System to Enhance Agricultural Productivity Using ESP8266 Wi-Fi Enabled Controller

Design and Development of an IoT Based Smart Irrigation Syst...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Vivek Parashar Megha Singh Senthamarai. M P. Poonkuzhali Parashuram Shankar Vadar B. Swapna School of Computing Science and Engineering VIT Bhopal University Bhopal Department of Computer Science and Engineering Oriental university Indore Department of Artificial Intelligence and Data Science Nandha Engineering College Erode TamilNadu Department of ECE R.M.D. Engineering College Kavaraipettai Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India Dr MGR Educational and Research Institute Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Innovative solutions in many fields, including agriculture, have been made possible by the widespread use of Internet of Things (IoT) technology. Our goal in this research was to increase crop yields by developing a smart irrigation system that would use the IoT. An ESP8266 Wi-Fi-enabled controller serves as the center of the system, collecting data and making decisions all at once. Important parts include sensors for soil moisture, air temperature and humidity, light intensity, water level, a pump to move water, and the ability to connect to a cloud service for data storage and analysis. Water level, soil moisture, temperature, humidity, and light intensity are some of the environmental characteristics that must be continuously monitored in order for the system to function. The data is sent to the main controller in real-time by the sensors using Wi-Fi. Based on predefined thresholds and algorithms, the system dynamically adjusts irrigation schedules to optimize water usage and enhance crop growth. To evaluate the system's performance, sensor readings from multiple trials were analyzed. Results indicate that the system effectively maintains optimal environmental conditions for plant growth, leading to improved agricultural productivity. The integration with a cloud platform enables remote monitoring and control, facilitating efficient water resource management and decision-making for farmers.

关键词： Temperature sensors Productivity Irrigation Cloud computing Soil moisture Humidity Sensor phenomena and characterization Internet of Things Wireless fidelity Water resources

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

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Beamforming and Trajectory Design for Active IRS-Assisted UAV Relaying Systems

Beamforming and Trajectory Design for Active IRS-Assisted UA...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Qiulei Huang Zhaohui Song Zehui Xiong Guanjun Xu Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China Space Information Research Institute Hangzhou Dianzi University Hangzhou China Singapore University of Technology and Design Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Intelligent reflecting surface (IRS) can reconfigure the channel conditions, while the passive beamforming gain is limited by the severe double path-loss effect. Fortunately, active IRS (AIRS) is emerging to tackle obstacles by simultaneously adjusting the phases and amplitudes. In this paper, we propose an AIRS-assisted unmanned aerial vehicle (UAV)-relaying scheme, where the AIRS is equipped on the UAV to reflect the signal from the ground base station (GBS) to users via non-orthogonal multiple access. We jointly adjust transmit beamforming, reflection matrix and UAV trajectory to maximize the average sum rate, which is non-convex. Thus, the problem is decomposed into three subproblems via block coordinate descent. The beamforming optimization is solved through semidefinite relaxation. Then, the reflection matrix of AIRS and UAV trajectory are jointly optimized via successive convex approximation. Finally, we design an iterative algorithm to effectively tackle the original problem. Simulation results are shown to verify the performance of the designed scheme.

关键词： NOMA Array signal processing Simulation Autonomous aerial vehicles Reflection Vectors Trajectory Matrix decomposition Iterative methods Optimization

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Enhancing Pulse Measurement with IoT Technologies for TCM

Enhancing Pulse Measurement with IoT Technologies for TCM

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2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021

作者： Pham, Tung Liu, Qingqing Yu, Chen-Hsiang School of Engineering Wentworth Institute of Technology BostonMA United States Beijing University of Chinese Medicine Dongzhimen Hospital Beijing China School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9781665405959

In Traditional Chinese Medicine (TCM), the diagnosis relies on a comprehensive analysis of clinical syndromes by using four methods: inspection;auscultation and olfaction;interrogation;and palpation. Pulse examination is one of two practices used in palpation. To perform diagnosis, TCM doctors put three fingers on top of the patient's wrist, usually the index, the middle, and the ring finger, feel the reflection of the pulse, combined with the position and different pressing level, to determine the type of pulse. Each finger position corresponds to the condition of each different organ. With each press level and reflection received from the pulse, doctors gain the condition of their patient's internal abnormalities, combined with multiple factors, such as diet, external observable abnormalities, *** finalize their diagnosis. In this research, we argue that it is possible to shorten a gap between TCM and technologies to streamline such methods with modern IoT technologies. In this paper, we continue our previous work on this topic to demonstrate a possibility to visualize pulse measurement in real time with IoT technologies. The enhanced design used a customized circuit layout, a piezoelectric pressure sensor, Raspberry Pi 4, server application and a desktop application. Our findings, conclusions and future work are also presented at the end of this paper. © 2021 IEEE.

关键词： Diagnosis

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Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High Throughput

Hybrid Parallel Inference for Large Model on Heterogeneous C...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chaonong Xu Weiming Kong Min Liu Mingming Zhang Chao Li Luqi Gong Beijing Key Lab of Petroleum Data Mining China University of Petroleum (Beijing) Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China School of Computer Science (National Pilot Software Engineering School) BUPT Beijing China

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× −3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy.

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SegScope: Probing Fine-grained Interrupts via Architectural Footprints

SegScope: Probing Fine-grained Interrupts via Architectural ...

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IEEE Symposium on High-Performance Computer Architecture

作者： Xin Zhang Zhi Zhang Qingni Shen Wenhao Wang Yansong Gao Zhuoxi Yang Jiliang Zhang School of Software and Microelectronics Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University The University of Western Australia CAS Institute of Information Engineering Data61 CSIRO College of Semiconductors (College of Integrated Circuits) Hunan University

Interrupts are critical hardware resources for OS kernels to schedule processes. As they are related to system activities, interrupts can be used to mount various side-channel attacks (i.e., monitoring keystrokes, inferring website visits, detecting GPU activities, and fingerprinting processes). Given that all these attacks rely on system file interfaces or architectural timers to probe interrupts, various countermeasures have been proposed to either remove the unprivileged access to the file interfaces or detect/cripple architectural timers. In this work, we propose SegScope, a new technique that abuses segment protection to provision fine-grained interrupt observations without any timer. As segment protection is widely used on x86, SegScope works across a wide range of Intel-and AMD-based CPUs. Particularly, we observe that while segment protection preserves the confidentiality of high privileged domain, it leaves a footprint via the data segment registers values when an interrupt occurs. With this key observation, SegScope is crafted by capturing the footprints. To show its security implications, we evaluate it in four case studies. First, SegScope has inferred website visits with a respective success rate of 92.4% on Chrome and 87.4% on Tor Browser in default system settings. Second, SegScope successfully extracts the keys from Cloudflare's Interoperable Reusable Cryptographic Library (CIRCL) vl.l. Third, SegScope steals DNN model architectures with an accuracy of over 80%. Last, SegScope effectively reduces the noise of interrupts to improve the performance of other side channels. As an example, SegScope reduces the error rate of Spectral side channel by 56×. Compared with existing timer-based interrupt-probing techniques, SegScope is fine-grained without introducing false-positives. Further, we leverage SegScope to craft a fine-grained timer, as regular timer interrupts as clock edges contain timestamps. Our evaluation shows that it achieves the same level of

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