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Gradients and intensity modulated radiotherapy

Gradients and intensity modulated radiotherapy

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： J.M. Kapatoes G. Olivera P. Reckwerdt H. Keller J. Balog T.R. Mackie Department of Medical Physics University of Wisconsin Madison WI USA Tomotherapy Inc. Middleton WI USA

ISBN: (纸本)0780364651

A consequence of the advent of intensity modulated radiation therapy (IMRT) is the presence of large dose gradients surrounding the tumor, and the regions at risk (RAR's) near the tumor. Unlike standard radiotherapy, these gradients are not the chance result of a few beam choices, but rather they are strategically placed by the optimization software. A first issue is the magnitude of these gradients. Optimal dose plans were simulated for four clinical cases using experimental helical tomotherapy optimization software. It was found that high gradients could be divided into two categories: avoidance gradients and incidental gradients. Avoidance gradients are associated with tumor/RAR boundaries and tend to be larger, on the order of 30% per cm or higher. Incidental gradients are a secondary consequence of the optimizer doing its job: placing a large amount of dose confined to the target volume. Incidental gradients tend to be on the order of 15%-20% per cm.

关键词： Intensity modulation Computed tomography Medical simulation Breast neoplasms Biomedical applications of radiation Design optimization Spinal cord Lung neoplasms Software standards distributed computing

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Sinhala Hate Speech Detection in Social Media Using Machine Learning and Deep Learning

Sinhala Hate Speech Detection in Social Media Using Machine ...

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international conference on Advances in ICT for Emerging Regions

作者： W.S.S. Fernando Ruvan Weerasinghe E.R.A.D. Bandara Department of Statistics Faculty of Science University of Colombo Colombo Sri Lanka School of Computing 35 Reid Avenue University of Colombo Colombo Sri Lanka

Communication and presentation of beliefs became easier than in previous decades due to the rapid rise of information technology and computer science. Because social media is accessible worldwide via the internet, anyone can simply target someone or a group who adheres to a different culture or belief. While everyone has the freedom to express their own opinions, it should not be destructive, and everyone has the right to be free of hate speech. Because there are no automatic mechanisms for detecting hate speech on social media, anyone can be readily targeted. Because social media service providers do not have extensive linguistic expertise of some languages, such as Sinhala, it may take a few days for them to delete hate-related comments from the material after they become aware of them. As a result, detecting hate speech in the Sinhala language is an urgent and crucial task. Machine learning and deep learning based algorithms were employed in this study to automatically recognize Sinhala hate speeches broadcast on social media. Bag of words, Tf-idf, Word2Vec, and FastText feature extraction methods were used to extract features from the comments. Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine, XGBoost, Random Forest machine learning models and CNN, RNN, LSTM deep learning models were trained using two pre-collected datasets with different sizes. The best six models were then chosen and test set performances were shown. According to this study, FastText with RNN has the greatest AUC ROC 0.71 with 70% accuracy for the test set.

关键词： Deep learning Support vector machines Social networking (online) Hate speech Machine learning Speech recognition Feature extraction

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Building multicast trees in ad-hoc networks 08

Building multicast trees in ad-hoc networks

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proceedings of the 2nd international conference on Autonomic computing and Communication Systems

作者： Raphaël Kummer Peter Kropf Pascal Felber University of Neuchâtel Emile-Argand Neuchâtel Switzerland

ISBN: (纸本)9789639799349

Multicast trees are used in a variety of applications, such as publish/subscribe systems or content distribution networks. Existing algorithms for ad-hoc networks typically produce multicast trees requiring many nodes to act as relays even though they are not part of the multicast group. In this paper, we propose an algorithm for building efficient multicast trees that strives to minimize the number of non-member relay nodes and the number of transmissions required to reach all the group members, and to balance the degree of members when acting as internal nodes of the multicast tree. Our algorithm relies upon a lightweight distributed hash table (DHT) to construct and optimize the multicast trees. We evaluate the efficiency and scalability of our algorithm by simulations with various network configurations and sizes.

关键词： wireless networks protocol ad-hoc wireless networks mobile ad-hoc computing mobile networking MANETs multicast trees

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Histopathological Image Classification and Vulnerability Analysis using Federated Learning

Histopathological Image Classification and Vulnerability Ana...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Sankalp Vyas Amar Nath Patra Raj Mani Shukla Computing and Information Science Anglia Ruskin University Cambridge Radford University Radford USA

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Healthcare is one of the foremost applications of machine learning (ML). Traditionally, ML models are trained by central servers, which aggregate data from various distributed devices to forecast the results for newly generated data. This is a major concern as models can access sensitive user information, which raises privacy concerns. A federated learning (FL) approach can help address this issue: A global model sends its copy to all clients who train these copies, and the clients send the updates (weights) back to it. Over time, the global model improves and becomes more accurate. Data privacy is protected during training, as it is conducted locally on the clients’ ***, the global model is susceptible to data poisoning. We develop a privacy-preserving FL technique for a skin cancer dataset and show that the model is prone to data poisoning attacks. Ten clients train the model, but one of them intentionally introduces flipped labels as an attack. This reduces the accuracy of the global model. As the percentage of label flipping increases, there is a noticeable decrease in accuracy. We use a stochastic gradient descent optimization algorithm to find the most optimal accuracy for the model. Although FL can protect user privacy for healthcare diagnostics, it is also vulnerable to data poisoning, which must be addressed.

关键词： Training Privacy Data privacy Analytical models Federated learning Stochastic processes distributed databases

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SCATMAN: A Framework for Enhancing Trustworthiness in Digital Supply Chains

SCATMAN: A Framework for Enhancing Trustworthiness in Digita...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Michael Eckel Anirban Basu Satoshi Kai Hervais Simo Fhom Siniša Ðukanović Henk Birkholz Shingo Hane Matthias Lieske Fraunhofer Institute SIT | ATHENE Center Darmstadt Germany Hitachi Ltd. Yokohama Japan Hitachi Ltd. Tokyo Japan Hitachi Europe GmbH Düsseldorf Germany

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In this paper, we present a framework and an architecture that aim to enable and manage trust in supply chains. Our architecture addresses the authenticity and integrity of devices and processes within heterogeneous system landscapes. We identify and discuss the current challenges in digital supply chains and lay out security, privacy, and interoperability requirements that must be met for successful implementation. We hypothesize that the overall perception of trust in a supply chain depends on the trustworthiness of all digital systems involved, including hardware, software, and information flow. Our proposed architecture helps enhance trustworthiness based on verifiable, indisputable, and believable digital evidence for devices and processes in supply chains, including the entire hardware and software lifecycles. We actively advocate for a mixed landscape of centralized and decentralized solutions for the storage of evidence and trust information. This can include traditional centralized databases and distributed ledger technologies. We discuss the auditability and accountability of digital evidence using trust-enabling technologies, and present a preliminary proof-of-concept (PoC) implementation in a real-world application scenario.

关键词： Privacy distributed ledger Digital systems Supply chains distributed databases Computer architecture Software

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Designing and Implementing Communication-efficient Model of distributed System for Real-time Electromagnetic Transient Simulation

Designing and Implementing Communication-efficient Model of ...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Qi Guo Binjiang Hu Zeqi Hong Yanjun Zhao Shuyong Li Liang Tu State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid China Southern Power Grid Co. Ltd

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

This paper introduces CM-RETS, a 4-Level address Communication Model designed specifically for real-time simulation of electromagnetic transients in power systems. CM-RETS incorporates a unique 4-Level address scheme and leverages Remote Direct Memory Access (RDMA) technology to enable efficient and reliable data exchanges between computing nodes in a distributed system. By effectively distributing computational load, CM-RETS facilitates faster and more realistic simulations, ensuring precise and timely data exchanges within a 5μs time step to capture transients in real time. Experimental results validate the effectiveness and efficiency of CM-RETS in achieving accurate and real-time simulations of electromagnetic transients. With its robust and cost-effective solution, CM-RETS provides researchers and engineers with the means to perform detailed analysis and studies on power systems under various transient conditions.

关键词： Computational modeling Memory management Real-time systems Data models Power system reliability Reliability Security

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PrivOff: Secure and Privacy-Preserving Data Management for distributed Off-Chain Networks

PrivOff: Secure and Privacy-Preserving Data Management for D...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Htet Htet Hlaing Hitoshi Asaeda National Institute of Information and Communications Technology (NICT) Japan

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In today’s healthcare landscape, emerging technologies serve as crucial foundations, and the integration of blockchain technology into this rapidly evolving digital framework is invaluable. However, the healthcare industry has long struggled with managing the immense and ever-expanding amount of big data electronic health records (EHRs) collected from various sources, including the Internet of Things (IoT), wearable devices, and mobile applications. The storage of all these big data EHRs on the chain creates blockchain bloat, leading to slow transaction speed and high storage costs. More importantly, it incurs security and privacy leakage problems while sharing patients’ sensitive data transparently with a wide range of users in a distributed network. To address these challenges, we propose a secure and privacy-preserving data management system for distributed off-chain networks called PrivOff based on blockchain technology, which stores big EHRs separately in the decentralized file system with efficient access control enforcement and flexible revocation to prevent data breaches. In addition, patients can securely share data without revealing their unique identities to ensure privacy. The evaluation results and security analysis reveal that PrivOff can notably reduce the blockchain storage burden while offering data security, patient privacy, and high data availability.

关键词： Industries Data privacy File systems Information sharing Medical services Big Data Blockchains

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Computer networking for Management Information System of Unmanned Vehicles

Computer Networking for Management Information System of Unm...

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international conference on Transparent Optical Networks

作者： Sergey Sokolov Andrey Boguslavsky Keldysh Institute of Applied Mathematics RAS Miusskaya Square 4 Moscow Russia

ISBN: (数字)9781728184234

ISBN: (纸本)9781728184241

The ever-increasing need for the large-scale implementation of unmanned vehicles requires situational awareness of such vehicles. In this case two general directions are possible. The first: simplification and fixing situations with vehicles movements. The second: improving the onboard control system and its information component. We focus on the second direction in our study. Information support of vehicles, especially systems of technical vision, requires ever-increasing computing resources and intelligent technologies in algorithmic support. The experience of our research shows that reconfigurable high-performance heterogeneous resources are the effective software and hardware tool for information support of autonomous vehicles Such devices combine sequentially parallel processing of visual data. In the process of developing such resources, we use a heterogeneous computer network based on the PCI express bus. The report describes the general scheme of such a network, considers hardware and software configurations for solving application task, gives the results of experiments to solve the issues of collecting and processing visual data, and discusses the prospects for the development of this approach.

关键词： Visualization Hardware Software Field programmable gate arrays Uniform resource locators Unmanned vehicles Software algorithms

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Multiple distributed energy storage scheduling using constructive evolutionary programming

Multiple distributed energy storage scheduling using constru...

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Power Industry Computer Application conference (PICA)

作者： Thai Doan Hoang Cau R.J. Kaye Australian Graduate School of Management University of New South Wales Sydney Australia School of Electrical Engineering & Telecommunication University of New South Wales Sydney Australia

ISBN: (纸本)0780366816

Deregulation of the electricity supply industry is promoting the increased use of electrical energy storage. However, to achieve the system-wide benefits of competition, techniques for optimal scheduling of distributed storage resources are required. In this paper, we use constructive evolutionary programming to minimise the cost of operating a power system with multiple distributed energy storage resources. The evolutionary technique combines the advantages of both dynamic and evolutionary programming by evolving piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage scheduling problem is thus decomposed into many smaller one-stage sub-problems with evolved cost-to-go functions. Evolutionary programming is shown to be suitable for both decentralised computing and for market applications. Case studies demonstrate that the technique is robust and efficient for this type of scheduling problem.

关键词： Energy storage Optimal scheduling Job shop scheduling Genetic programming Power system dynamics Dynamic programming Processor scheduling Electricity supply industry deregulation Electricity supply industry Costs

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Analysis of the Applicability of Cloud computing Scaling Techniques for Real-Time Workloads of Industrial Control Systems

Analysis of the Applicability of Cloud Computing Scaling Tec...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Rebekka Neumann Florian Frick Armin Lechler Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

A key feature of cloud computing is elasticity, which enables resources to be allocated and de-allocated according to dynamic application demands. The evolution of Industrial Control Systems (ICS) is moving towards a distributed converged infrastructure that incorporates computing and communication resources as well as software, replacing dedicated hardware running control applications. While modern ICS and cloud environments share similarities, the corresponding applications differ significantly in their execution models and timing requirements. To enhance the flexibility of ICS, control applications must adapt to changing requirements throughout operation, resulting in varying workloads. The potential for dynamic scaling in ICS is considerable, but is not yet being fully exploited. The objective of this paper is to examine the extent to which state-of-the-art cloud autoscaling concepts are applicable to the scaling of real-time workloads and to propose enhancements to these concepts in order to enable the implementation of dynamic auto scaling of real-time workloads. In conclusion, the fundamental concept of cloud autoscaling can be applied, but must be extended, in particular to ensure deterministic resource provisioning and to improve task isolation in order to guarantee predictable task timing behaviour.

关键词： Cloud computing Industrial control Elasticity Dynamic scheduling Real-time systems Software Hardware Timing Integrated circuit modeling Informatics

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