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Consideration of receiver interest for IP multicast delivery

Consideration of receiver interest for IP multicast delivery

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IEEE Annual Joint conference: INFOCOM, IEEE computer and Communications Societies

作者： B.N. Levine J. Crowcroft C. Diot J.J. Garcia-Luna-Aceves J.F. Kurose Computer Science Department University College London London UK Computer Science Department University of Massachusetts Amherst MA USA Sprint Advanced Technologies Laboratory Burlingame CA USA Computer Engineering Department University of California Santa Cruz CA USA

Large-scale applications are characterized by a large number of dynamic and often interactive group members. The nature of these applications is such that participants are not interested in all the content transmitted. We examine three currently available techniques to scope delivery of content to interested receivers in IP multicast: filtering, where data is filtered by middleware before being passed to the application; addressing, where data is routed only to those receivers that express their interest; and hybrid approaches. We propose a framework that models large-scale application behavior. We use this framework to evaluate the performance of these applications and related protocols when the network is capable of filtering or addressing. Our results show that the current Internet architecture does not efficiently support large-scale applications because it can not efficiently manage multiple multicast groups. We show that network-level addressing is preferred to filtering and hybrid approaches given that groups are easy to create and manage. We highlight areas of research in the multicast architecture to bring about this change.

关键词： Large-scale systems Multicast protocols Information filtering Information filters Internet Unicast computer science Educational institutions Application software Middleware

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EL-GRILLO: Leaking Data Ultrasonically from Air-Gapped PCs via the Tiny Motherboard Buzzer

EL-GRILLO: Leaking Data Ultrasonically from Air-Gapped PCs v...

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Annual conference on Privacy, Security and Trust, PST

作者： Mordechai Guri Israel Department of Software and Information Systems Engineering Cyber-Security Research Center Ben-Gurion University of the Negev

Air-gapped workstations are separated from the Internet because they contain confidential or sensitive information. Studies have shown that attackers can leak data from air-gapped computers with covert ultrasonic signals produced by loudspeakers. To counteract the threat, speakers might not be permitted on highly sensitive computers or disabled altogether - a measure known as an ’audio gap.’ This paper presents an attack enabling adversaries to exfiltrate data over ultrasonic waves from air-gapped, audio-gapped computers without external speakers. The malware on the compromised computer uses its built-in buzzer to generate sonic and ultrasonic signals. This component is mounted on many systems, including PC workstations, embedded systems, and server motherboards. It allows software and firmware to provide error notifications to a user, such as memory and peripheral hardware failures. We examine the different types of internal buzzers and their hardware and software controls. Despite their limited technological capabilities, such as 1-bit sound, we show that sensitive data can be encoded in sonic and ultrasonic waves. This is done using pulse width modulation (PWM) techniques to maintain a carrier wave with a dynamic range. We also show that malware can evade detection by hiding in the frequency bands of other components (e.g., fans and power supplies). We implement the attack using a PC transmitter and smartphone app receiver. We discuss transmission protocols, modulation, encoding, and reception and present the evaluation of the covert channel as well. Based on our tests, sensitive data can be exfiltrated from air-gapped computers through its built- in buzzer. A smartphone can receive data from up to six meters away at 100 bits per second.

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Enhancing Deep Reinforcement Learning with Executable Specifications

Enhancing Deep Reinforcement Learning with Executable Specif...

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International conference on software engineering - Companion, ICSE, Companion

作者： Raz Yerushalmi Department of Computer Science and Applied Mathematics the Weizmann Institute of Science Israel

Deep reinforcement learning (DRL) has become a dominant paradigm for using deep learning to carry out tasks where complex policies are learned for reactive systems. However, these policies are “black-boxes”, e.g., opaque to humans and known to be susceptible to bugs. For example, it is hard - if not impossible - to guarantee that the trained DRL agent adheres to specific safety and fairness properties that may be required. This doctoral dissertation's first and primary contribution is a novel approach to developing DRL agents, which will improve the DRL training process by pushing the learned policy toward high performance on its main task and compliance with such safety and fairness properties, guaranteeing a high probability of compliance while not compromising the performance of the resulting agent. The approach is realized by incorporating domain-specific knowledge captured as key properties defined by domain experts directly into the DRL optimization process while leveraging behavioral languages that are natural to the domain experts. We have validated the proposed approach by extending the AI-Gym Python framework [1] for training DRL agents and integrating it with the BP-Py framework [2] for specifying scenario-based models [3] in a way that allows scenario objects to affect the training process through reward and cost functions, demonstrating dramatic improvement in the safety and performance of the agent. In addition, we have validated the resulting DRL agents using the Marabou verifier [4], confirming that the resulting agents indeed comply (in full) with the required safety and fairness properties. We have applied the approach, training DRL agents for use cases from network communication and robotic navigation domains, exhibiting strong results. A second contribution of this doctoral dissertation is to develop and leverage probabilistic verification methods for deep neural networks to overcome the current scalability limitations of neural network verificat

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Artificial Neural Networks in Healthcare for Augmented Reality

Artificial Neural Networks in Healthcare for Augmented Reali...

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International conference on Cognitive Computing and Information Processing

作者： Pradeepa K Bharathiraja N Meenakshi D Hariharan S Kathiravan M Vinoth Kumar Department of Computer Science and Engineering St. Michael College of Engineering and Technology Kalayarkoil India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of CSE Tagore Engineering College Tamil Nadu India Department of CSE Vardhaman College of Engineering Andhra Pradesh India Department of CSE Hindustan Institute of Technology and Science TN India Department of Computer Science Dmi-St.Eugene University Zambia

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456494

Deep learning (DL) techniques like recurrent neural networks (RNN) and convolutional neural networks (CNN) are currently being utilised to improve management tooling and workflow classification to increase operational effectiveness. Reliability could be increased, but because of CNN's intricacy, actual research is therefore limited. A brand-new DL structure is suggested in this study to incorporate the visualization of mappings (IVM) within Masked R-CNN. During the first approach, this paradigm, IVM-CNN combines the best features of both approaches, including (1) IVM for object tracking by emphasizing geospatial data for sector recommendations and (2) CNN for machine vision by relying on data for picture categorization. Using spatial and temporal statistics along with visual functionalities, the said approach is tested on M2CAI 2016 contest sets of data, outperforming all prior creations and accomplishing futuristic outcomes to 97.1 mAP for device diagnosis and 96.9 mean rate. It also performs at 50 FPS, which is ten times quicker than region-based CNN. Masked R-CNN substitutes the region proposal network (RPN) with a region proposal module (RPM), which more precisely generates boundary boxes and reduces the demand for labeling. Microsoft HoloLens software is also being generated to offer an augmented reality (AR) stationed approach for clinical education and help.

关键词： Visualization Recurrent neural networks Three-dimensional displays Surgery Medical services Real-time systems software

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MOM ANALYSIS OF THE PIN-FED NETWORK FOR A RADIAL LINE PLANAR ANTENNA

MOM ANALYSIS OF THE PIN-FED NETWORK FOR A RADIAL LINE PLANAR...

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2008 IEEE International conference on Microwaves, Communications, Antennas and Electronic systems

作者： O. Becker R. Shavit Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel

Microstrip antennas are considered as an attractive choice for satellite communication systems due to their high radiation efficiency, low cost, low weight and low profile. Considerable losses of the microstrip feed network in high and medium gain microstrip antennas force the antenna designers to search for more efficient feed networks. A radial line that feeds the microstrip radiating elements may be one of the possible solutions. Goebels and Kelly, in [1], gave a general approach for an antenna with arbitrary polarization comprised of radiating slots on the upper surface of the feeding radial line. This kind of antenna is called radial line slot antenna (RLSA) and has been successfully implemented and used in Japan for DBS reception [2]. A derivative form of the RLSA is the radial line planar pin fed antenna (RLPPFA). The RLPPFA consists of radiating elements with feeding pins inserted into the radial line and coupled to the wave propagating inside as shown in Fig.1. Various examples of the radial line feed concept realizations can be found in the literature [3-4]. Current approaches for RLPPFA analysis include: 1) closed-form expressions for the self and mutual impedances using induced EMF method [5], 2) circuit modeling of the radial line [6]. In [7], Pazin and Leviatan describe a synthesis procedure for calculation of the length of the coupling probes. Continuing the previous research [7-8], we developed an accurate analysis tool for RLPPFA feed network based on EFIE MoM formulation. Calculation results are compared to the MWS commercial software from CST and found to be in very good agreement up to 15 GHz with considerable time savings.

关键词： Planar arrays Green's function methods Microstrip antennas Antenna feeds Slot antennas Coaxial components Current Antennas and propagation Coupling circuits Apertures

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Simple and Efficient Colorization in YCbCr Color Space

Simple and Efficient Colorization in YCbCr Color Space

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International conference on Pattern Recognition

作者： H. Noda M. Niimi Jin Korekuni Department of Systems Innovation and Informatics Kyushu Institute of Technology Iizuka Japan Department of E.E. and Computer Engineering Kyushu Institute of Technology Kitakyushu Japan

We have already proposed a colorization method in RGB color space, where the colorization problem is formulated as the maximum a posteriori (MAP) estimation of a color image given a monochrome image. Markov random field (MRF) is used for modeling a color image which is utilized as a prior for the MAP estimation. In this paper, a colorization method in YCbCr space is presented, which is derived by the same formulation as in RGB color space. The presented method in YCbCr space is much simpler than that in RGB space and requires much less computation time: about one fourth of computation time in RGB space. As for quality of estimated color image, both methods in YCbCr and RGB space produce color images with comparable PSNR values

关键词： Color Space technology Image segmentation Markov random fields Technological innovation Informatics PSNR Motion pictures software tools Pixel

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A Masked Pruning-Based Algorithm for Dimensionality Reduction in Federated Learning systems

A Masked Pruning-Based Algorithm for Dimensionality Reductio...

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Annual Allerton conference on Communication, Control, and Computing

作者： Tamir L.S. Gez Kobi Cohen School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

Federated Learning (FL) represents a growing machine learning (ML) paradigm designed for training models across numerous nodes that retain local datasets, all without directly exchanging the underlying private data with the parameter server (PS). Its increasing popularity is attributed to notable advantages in terms of training deep neural network (DNN) models under privacy aspects and efficient utilization of communication resources. Unfortunately, DNNs suffer from high computational and communication costs, as well as memory consumption in intricate tasks. These factors restrict the applicability of FL algorithms in communication-constrained systems with limited hardware resources. In this paper, we develop a novel algorithm that overcomes these limitations by synergistically combining a pruning-based method with the FL process, resulting in low-dimensional representations of the model with minimal communication cost, dubbed Masked Pruning over FL (MPFL). The algorithm operates by initially distributing weights to the nodes through the PS. Subsequently, each node locally trains its model and computes pruning masks. These low-dimensional masks are then transmitted back to the PS, which generates a consensus pruning mask, broadcasted back to the nodes. This iterative process enhances the robustness and stability of the masked pruning model. The generated mask is used to train the FL model, achieving significant bandwidth savings. We present an extensive experimental study demonstrating the superior performance of MPFL compared to existing methods. Additionally, we have developed an open-source software package for the benefit of researchers and developers in related fields.

关键词： Training Costs Federated learning Computational modeling software algorithms Bandwidth Artificial neural networks Stability analysis Robustness Iterative methods

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Artificial Neural Networks in Non-Small Cell Lung Cancer Detection using Computed Tomography Medical Imaging

Artificial Neural Networks in Non-Small Cell Lung Cancer Det...

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International conference on Information systems and software Technologies (ICI2ST)

作者： Santiago Velastegui José De La A Brad Timana Francis Andaluz Fernando Villalba-Meneses Paulina Vizcaíno-Imacaña Carolina Cadena-Morejon Diego Almeida-Galórraga Andres Tirado-Espin School of Biological Sciences and Engineering Universidad Yachay Tech Urcuqui Ecuador Universidad Internacional del Ecuador Quito Ecuador School of Mathematical and Computational Sciences Universidad Yachay Tech Urcuqui Ecuador Universidad de Otavalo Otavalo Ecuador

ISBN: (数字)9798350373219

ISBN: (纸本)9798350373226

On average, lung cancer is the cancer that kills the most people in the world due to its complex detection and cancer variants such as small and non-small cell cancers, with non-small cell cancers being the most common. The objective of this project is to compare different architectures of convolutional neural networks (CNN) such as: ResNet50, RESNET101, DenseNet201, EfficientNetB4, VGG16 and VGG19, which have been trained with the same database of approximately 1500 computer tomography images (CT) for the detection of the type of non-small cell lung cancer such as: squamous cell, adenocarcinoma and large cell. It was found among all the neural networks that the most reliable for time, space and accuracy is ResNet50, so it seeks to compare the accuracy of the ResNet50 neural network with other investigations, in order to compare the different architectures to see which one has a better accuracy in the detection of lung cancer.

关键词： Accuracy Hospitals Microprocessors Computed tomography Neural networks Lung cancer computer architecture

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Analytical usability evaluation for digital libraries: a case study

Analytical usability evaluation for digital libraries: a cas...

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IEEE/ACM Joint conference on Digital Libraries(JCDL)

作者： A. Blandford S. Keith I. Connell H. Edwards University College London London UK Middlesex University London UK

There are two main kinds of approach to considering usability of any system: empirical and analytical. Empirical techniques involve testing systems with users, whereas analytical techniques involve usability personnel assessing systems using established theories and methods. We report here on a set of studies in which four different techniques were applied to various digital libraries, focusing on the strengths, limitations and scope of each approach. Two of the techniques, heuristic evaluation and cognitive walkthrough, were applied in text-book fashion, because there was no obvious way to contextualize them to the digital libraries (DL) domain. For the third, claims analysis, it was possible to develop a set of reusable scenarios and personas that relate the approach specifically to DL development. The fourth technique, CASSM, relates explicitly to the DL domain by combining empirical data with an analytical approach. We have found that heuristic evaluation and cognitive walkthrough only address superficial aspects of interface design (but are good for that), whereas claims analysis and CASSM can help identify deeper conceptual difficulties (but demand greater skill of the analyst). However, none fit seamlessly with existing digital library development practices, highlighting an important area for further work to support improved usability.

关键词： Usability software libraries computer aided software engineering Educational institutions User interfaces Human factors Terminology System testing Personnel Data analysis

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Confining Wi-Fi Coverage: A Crowdsourced Method Using Physical Layer Information

Confining Wi-Fi Coverage: A Crowdsourced Method Using Physic...

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IEEE Communications Society conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Bingxian Lu Zhicheng Zeng Lei Wang Brian Peck Daji Qiao Michael Segal Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China Department of Electrical and Computer Engineering Iowa State University USA Communication Systems Engineering Department Ben-Gurion University of the Negev Israel

ISBN: (纸本)9781509017331

Many small businesses and public areas offer free Wi-Fi access, but may wish to restrict network access only to their customers or patrons inside the physical property. Unfortunately, due to the nature of wireless networks, this is difficult to accomplish. We develop and implement CLAC, a Crowdsourced Location aware Access Control scheme using physical layer information to address this challenge. It crowdsources both channel state information (CSI) and received signal strength (RSS) of already validated users to classify future users. We propose and use two CSI metrics in CLAC: CSI Cross-Antenna Stability Metric and CSI Cross-Frame Stability Metric, which summarize well the spatial and temporal CSI characteristics respectively. CLAC is evaluated in an office and a classroom. Evaluation results show that CLAC performs well in both environments, allowing most valid users inside the area to access the network, while the chance that invalid users outside the boundary may access the network is small.

关键词： Training Antennas IEEE 802.11 Standard Monitoring Measurement Signal to noise ratio Indexes

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