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

作者： A. Sethi L. Leybovich N. Dogan G. Glasgow Department of Radiation Oncology Loyola University Medical Center Maywood IL USA

ISBN: (纸本)0780364651

Wedge filters are frequently used in the clinic to improve target dose homogeneity. All physical wedges have a uniform thickness along the non-wedged direction. Therefore wedged field profiles show a "sagging effect" In this direction. Most treatment planning systems (TPS) assume an open field profile when calculating wedged dose distributions in the off-axis plane. Although this effect has been discussed in literature, a detailed analysis of calculated and measured dose distributions was not reported. Wedged dose distributions in the non-wedged direction were systematically measured for VARIAN 600CD 6 MV photons for all available wedge angles and a range of off-axis distances. Measured results were compared against dose distributions calculated by 3D FOCUS TPS. It was found that, depending on the field size, wedge angle and off-axis distance, the difference between measured and planned calculated doses may exceed 10%. In planes close to the central axis, the TPS overestimated the delivered dose, whereas in the distant off-axis planes the delivered dose was underestimated. This may lead to an appreciable underdosing and/or overdosing In the peripheral regions of the PTV. A three dimensional "wedge compensator" (3DWC) was designed for each wedge angle to be used in conjunction with the wedge filters. This 3DWC was used for dose distribution calculations only. The shape of this "wedge compensator" was determined by analyzing differences between measured and calculated dose distributions. Wedged dose distributions were recalculated using 3DWC. Excellent agreement between calculated and measured dose distributions was achieved.

关键词： Filters Shape measurement Electromagnetic scattering Particle scattering Performance evaluation Collision mitigation Oncology Biomedical applications of radiation distributed computing Attenuation

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FineCTI: A Framework for Mining Fine-grained Cyber Threat Information from Twitter Using NER Model

FineCTI: A Framework for Mining Fine-grained Cyber Threat In...

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

作者： Chunyan Ma Jun Jiang Kai Zhang Zhengwei Jiang Peian Yang Xuren Wang Huamin Feng Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Information Engineering College Capital Normal University Beijing China Beijing Electronic Science and Technology Institute Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

To timely respond to cyber threats related to a specific IT infrastructure called fine-grained (e.g., Windows or Linux), security analysts need to require timely and comprehensive threat information. Twitter, as a vital source of real-time threat information, provides abundant but overwhelming information due to the increased data sources. Automatically mining and summarizing fine-grained threat information from Twitter can help security analysts maintain the infrastructure’s security. Most existing studies focus on classification, which carries less threat information. Some works use clustering based on text similarity relying on the embedding of text obtained from pre-trained models, which cannot be applied to short text, resulting in noisy clusters. Several works build topic models. However, the incoherent topic keywords are difficult to understand and analyze. To overcome these challenges, we design a FineCTI framework to mine the threat information related to the specific infrastructure on Twitter and generate a detailed threat information summary that is machine-readable and human-readable, efficiently reducing information overload. FineCTI optimizes the feature extraction part based on the named entity recognition model and performs clustering based on features extracted, thus effectively reducing the influence of sparsity of tweets on the clustering result and with the V-measure score improved by 7%. The cluster analysis results show that we can mine the fine-grained threats up to 15 days before the official disclosure date.

关键词： Privacy Social networking (online) Soft sensors Linux Blogs Feature extraction Real-time systems

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Generating Optimized Universal Adversarial Watermark for Preventing Face Deepfake

Generating Optimized Universal Adversarial Watermark for Pre...

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

作者： Kaiqi Lv Weiguo Lin Junfeng Xu Wanshan Xu Shuren Chen Shengwei Yi School of Computer and Cyber Sciences Communication University of China Beijing China IT System Evaluation Dept. CNITSEC Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the increasing development of deepfake in facial editing and the easy accessibility of image and video content on the internet, the security risks of spreading false personal information through social media platforms are becoming increasingly serious. The harm caused by deepfake includes spreading false information, pornography, financial fraud, privacy breaches, and security system damage. To address these security issues, it is necessary to strengthen the detection and defense of deepfake. Current research mainly focuses on the detection of deepfake images and videos. In recent years, some work has proposed using the method of generating adversarial samples to deal with the malicious operations of GAN networks in deepfake. And it has proposed different single perturbation fusion methods to generate universal adversarial watermarks that can defend against modifications by multiple models. However, all of these works generate single-step watermarks using gradient-based methods, which cannot accurately control the required perturbation strength, resulting in large errors and more irrelevant information compared to the original image after superimposing the watermark. To solve this problem, we propose an optimized adversarial face deepfake watermark and measure the protection success rate and defense performance of this method on single and multiple models. From the extensive experimental results, we find that the perturbation generated by the Optimization-based method can successfully generate smaller perturbations while ensuring a high protection success rate, making the adversarial samples closer to the original samples. It can also be applied to different types of models and loss functions, accurately generating perturbation strength, and has a wider range of applicability.

关键词： Training Image quality Deepfakes Social networking (online) Perturbation methods Watermarking Generative adversarial networks

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HIGH PERFORMANCE computing And COMMUNICATIONS TECHNOLOGY SOLUTIONS FOR FUTURE SMART DISTRIBUTION NETWORK OPERATION

HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS TECHNOLOGY SOL...

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international conference and Exhibition on Electricity Distribution

作者： Leticia DE ALVARO GARCIA Gidon GERSHINSKY Gareth A. TAYLOR Angel YUNTA HUETE David C. H. WALLOM Konrad DIWOLD EDF R&D IBM Brunel University Union Fenosa Distribucion University of Oxford Fraunhofer IWES

ISBN: (纸本)9781629931333

To improve the integration of distributed energy resources on distribution networks, new DMS functionalities need to be developed. To run near-to-real time DMS applications novel high performance computing and communications technology solutions can be adopted by DNO's. Therefore, this paper presents the work carried out in the HiPerDNO project regarding the development of a new generation of DMS applications and its integration with scalable and secure high performance ICT infrastructures.

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MPS: A Multiple Poisoned Samples Selection Strategy in Backdoor Attack

MPS: A Multiple Poisoned Samples Selection Strategy in Backd...

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

作者： Weihong Zou Shigeng Zhang Weiping Wang Jian Zhang Xuan Liu Schoosl of Computer Science and Engineering Central South University Science and Technology on Parallel and Distributed Processing Laboratory (PDL) College of Computer Science and Electronic Engineering Hunan University

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Recently there has been many studies on backdoor attacks, which involve injecting poisoned samples into the training set in order to embed backdoors into the model. Existing multiple poisoned samples attacks usually randomly select a subset from clean samples to generate the poisoned samples. Filtering-and-Updating Strategy (FUS) has shown that the poisoning efficiency of each poisoned sample is inconsistent and random selection is not optimal. However, FUS does not fully considered the selection of multiple poisoned samples, there are still some issues with the selection of multiple poisoned samples. In this paper, we formulate the selection of multiple types of poisoned samples as a multi-objective optimization problem and proposed a Multiple Poisoned Samples Selection Strategy (MPS) to solve the issue. Unlike FUS, we consider the potential of clean samples that are not selected as to become efficient poisoned samples. Specifically, we use a weight-based contribution approach to calculate the contribution of each sample (clean sample and poisoned sample) during the training process from multiple dimensions. Finally, based on the greedy approach, we retain a subset of samples with the largest contribution in each dimension through iterations. We evaluate the effectiveness of MPS on various attack methods, including BadNet, Blended, ISSBA, and WaNet, as well as benchmark datasets. The experimental results on CIFAR-10 and GTSRB show that MPS can increase the attack strength by 1.45% to 18.34% compared to RSS and 0.43% to 10.84% compared to FUS in multiple poisoned samples attacks, thereby enhancing the stealthiness of the attack. Meanwhile, MPS is suitable for black-box settings, meaning that poisoned samples selected in one setting can be applied to other settings.

关键词： Training Data privacy Closed box Benchmark testing Security Protection Optimization

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Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection

Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detec...

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

作者： Sheetal Harris Jinshuo Liu Hassan Jalil Hadi Yue Cao School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Misinformation can seriously impact society, affecting anything from public opinion to institutional confidence and the political horizon of a state. Fake News (FN) proliferation on online websites and Online Social Networks (OSNs) has increased profusely. Various fact-checking websites include news in English and barely provide information about FN in regional languages. Thus the Urdu FN purveyors cannot be discerned using fact-checking portals. State-of-the-art (SOTA) approaches for Fake News Detection (Fnd) count upon appropriately labelled and large datasets. Fnd in regional and resource-constrained languages lags due to the lack of limited-sized datasets and legitimate lexical resources. The previous datasets for Urdu Fnd are limited-sized, domain-restricted, publicly unavailable and not manually verified where the news is translated from English into Urdu. In this paper, we curate and contribute the first largest publicly available dataset for Urdu Fnd, "Ax-to-Grind Urdu", to bridge the identified gaps and limitations of existing Urdu datasets in the literature. It constitutes 10,083 fake and real news on fifteen domains collected from leading and authentic Urdu newspapers and news channel websites in Pakistan and India. FN for the Ax-to-Grind dataset is collected from websites and crowdsourcing. The dataset contains news items in Urdu from the year 2017 to the year 2023. Expert journalists annotated the dataset. We benchmark the dataset with an ensemble model of mBERT, XLNet, and XLM-RoBERTa. The selected models are originally trained on multilingual large corpora. The results of the proposed model are based on performance metrics, F1-score, accuracy, precision, recall and MCC value. F1-score of 0.924, accuracy of 0.956, precision of 0.942, recall of 0.940 and an MCC value of 0.902 demonstrate the effectiveness of the proposed approach for Urdu Fnd. Comparison analysis with SOTA ML and DL models and existing Urdu benchmark datasets exhibit that the ensemble m

关键词： Analytical models Privacy Social networking (online) Computational modeling Benchmark testing Predictive models Transformers

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22nd international conference on computing in High Energy and Nuclear Physics (CHEP 2016)

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Journal of Physics: conference Series 2017年第1期898卷

CHEP 2016: Preface to the proceedings Richard Mount and Craig Tull, CHEP 2016 Co-Chairs Takanori Hara, Oksana Smirnova and Jan Strube, CHEP 2016 Program Committee Chairs The CHEP conferences address challenges in computing, networking and software for the world's leading data-intensive science experiments that currently analyze hundreds of petabytes of data using worldwide computing resources.

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Improving the Network Energy Efficiency in MapReduce Systems

Improving the Network Energy Efficiency in MapReduce Systems

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international conference on Computer Communications and Networks

作者： Lin Wang Fa Zhang Zhiyong Liu Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese State Key Laboratory for Computer Architecture ICT Chinese Academy of Sciences Beijing China

Apart from servers, the energy consumed by enormous amount of network devices in data centers also emerges as a big problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering to consolidate flows and shut down unused devices, not considering another important factor, virtual machine assignment, which has been shown to have a big influence on traffic engineering. Moreover, the lack of information about upper layer applications leads to misunderstand the traffic patterns of the network. This may result in poor effectiveness in the traffic-based optimization in practice. In this paper, we aim to achieve better network energy efficiency in MapReduce systems by combining virtual machine assignment and traffic engineering. By exploiting the characteristics of MapReduce applications, we provide a unified model to describe this problem. Due to its NP-hardness, a general framework is proposed to solve it, where virtual machines are first clustered and then different virtual machine assignments are generated greedily while a local search procedure is used to improve them. The local search procedure depends on the results of an energy-efficient routing provided by GEERA. GEERA is an approximate algorithm designed to select routing paths for flows. Experimental results confirm the efficiency of GEERA, as well as the overall framework. By using this framework, up to 20% more energy savings can be achieved compared with sole traffic engineering solutions.

关键词： Energy efficiency Data center networks MapReduce Virtual machine assignment Traffic engineering

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Preface

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Leibniz international proceedings in Informatics, LIPIcs 2019年 125卷 IX-X页

作者： Cao, Jiannong Ellen, Faith Rodrigues, Luis Ferreira, Bernardo Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science University of Toronto Canada Instituto Superior Técnico Universidade de Lisboa Portugal Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa Portugal

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A Mediation Layer for Connecting Data-Intensive Applications to Reconfigurable Data Nodes

A Mediation Layer for Connecting Data-Intensive Applications...

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international conference on Computer Communications and Networks

作者： Mohamad Jomaa Khaleel Mershad Noor Abbani Yaman Sharaf-Dabbagh Bashar Romanous Hassan Artail Mazen A. R. Saghir Hazem Hajj Haitham Akkary Mariette Awad Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

A novel and rapidly growing area of research concerns data-intensive applications and the technical challenges that accompany it. One of those challenges is developing approaches and mechanisms that render high performance in processing and storing data. We joined this research effort by proposing a reconfigurable active solid state drives (RASSD) system that deals with such applications, through employing basic hardware, namely FPGA's connected to SSD's, to service the above applications as processing nodes, and take advantage of the close proximity between storage and processing. In this paper, we propose an intelligent middleware system for interfacing workstation-based and mobile applications to the distributed RASSD system. In order to provide high performance in terms of time and functionality, the middleware manages the data processing on the RASSD nodes through special pieces of code that we call drivelets, along with FPGA configuration files (bitstreams). Another important responsibility of the proposed middleware architecture lies in the unguided management of applications' flows, where it uses an intelligent script-parsing mechanism to turn one general request from the client into a sequence of operations needed to generate the required results. The middleware design allows for the integration of mobile applications into the overall architecture of the RASSD system, and allowing them to run data intensive applications that otherwise it is unfeasible for them to execute. We validate our design by comparing it to an existing middleware architecture, and present two use-cases with their results and discussion.

关键词： Middleware mobile applications FPGA solid state drives distributed computing drivelet bitstream

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