ppxscal/embedding_network huggingface dataset:
Towards real-time performance monitoring for encrypted traffic
Authors: Mehdi Kharrazi, Subhabrata Sen, Oliver Spatscheck
Venue: Proceedings of the 2007 SIGCOMM workshop on Internet network management (2007)

IP networks are increasingly carrying mission-critical applications with robust end-to-end network performance and reliability requirements. Network performance monitoring forms an essential component of critical IP network management functions such as troubleshooting, anomaly detection, and Service-Level-Agreement (SLA) compliance monitoring. However, privacy and security considerations are fueling the use of IP-level encryption techniques such as IPsec, which obscure important transport layer features that existing performance measurement techniques need. New techniques are therefore needed for monitoring performance of encrypted traffic. Towards this goal, in this paper we present a new technique for monitoring round-trip times (RTT) for IP-level encrypted communications. Our approach involves using network-level features like packet size and inter-packet timing to infer specific timing events, and aggregating measurements across short time intervals and related connections to derive final RTT estimates for network paths of interest. Extensive evaluations using traces from an &eacute;nterprise and a broadband access network, demonstrate that the resulting RTT estimates are quite accurate.
Monomodal image registration using mutual information based methods
Authors: Zhiyong Gao, Bin Gu, Jiarui Lin
Venue: Image and Vision Computing (2008)

Image registration methods based on mutual information criteria, including mutual information and normalized mutual information, have been widely used in 3-D multimodal medical image registration and have shown promising results. Although they are also used in monomodal image registration, their performance is not as excellent as that in multimodal registration. There are many fluctuations in the registration function, which hinder the optimization procedure and lead to registration failure. This paper discusses this problem and ascribes it to interpolation artefacts and the variability of entropy. We implement experiments to evaluate the performance of the two similarity measures for 2-D and 3-D monomodal registration. To avoid the interpolation artefacts, we use pixels or voxels as the translation metric; to diminish the influence of entropy variability, we use normalized mutual information. The results show that, both for standard and normalized mutual information, the fluctuations caused by interpolation are fewer in the function of the registration without interpolation. Normalized mutual information has some similar properties to mutual information, but is almost invariant to the changing of entropy and appears to be more stable and robust than standard mutual information. These differences seem to indicate a preference for the normalized mutual information in monomodal registration.
Object oriented knowledge representation framework for requirements engineering
Authors: Aries Ang, Michael Ian Hartley
Venue: Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology (2007)

Requirements are knowledge concerning a proposed system. They are considered as "knowledge" because the requirements of a proposed system are often related to each other semantically. Requirements provide means of communication for the effective support of subsequent software engineering phases. Therefore it is vital to organise requirements in a concise and adequate manner. An object oriented knowledge representation framework is proposed for requirements engineering.
Dynamic Overlay Networks for Image Processing Grids
Authors: Andreas Dinges, Bjoern Wagner, Paul Mueller
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

During the development and parametrization of 2D image-processing algorithms for surface inspection uses, you need to test a huge amount of image-data for each modification of the algorithms or parameters. For algorithm runtimes up to several seconds, this will take a long time. To speed up this process it is recommended to distribute the computation in a parallel computation environment. Compute Grids, which use the unused resources of existing hardware are the most cost efficient way to solve this problem. The most existing Grid-Concepts are based on flat connection structures with a scheduler on the top; for high job-rates the scheduler becomes the bottleneck of the whole system. Concepts to solve this problem organize the nodes in tree-structures to discharge the central scheduler. In heterogeneous Desktop-Grids where the different nodes are widely distributed the usually used random arrangement of the nodes in the tree-structure can be counterproductive, because the bandwidthes and latencies in a Grid can be varying. In this paper we will show a solution to arrange the nodes of the grid optimized by bandwidth and latency, using modified spanning-tree algorithms, so that the average response time is reduced and in result of this the job-throughput of the Compute-Grid is increased.
Photometric Invariant Feature Detection based on Oriented Tensor Filter
Authors: Chengwen Yu, Qianjin Zhang, Lei Guo
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

Traditional visual low-level features detection usually ignores color and photometric nature which can be utilize to exploit useful iso-luminance information and eliminate the unexpected shade-shading- specular effect. In this paper, we proposed a new feature detection method which integrated photometric quasi-invariant model with a new version of color tensor formed by a nonlinear filter named oriented tensor filter. We also investigated the relation between oriented tensor filter with popular tensor voting methodology in theory. Experiments show that photometric invariant features detected by our method, such as edge and corner are more effective and robust than tradition methods.
The Second Asilomar Workshop on Microprocessors
Authors: J. Hiles
Venue: Computer (1976)

Newly-appointed technical sessions editor John Hiles (see Update) attended the Asilomar Workshop on Microprocessors held at the end of April; his report is presented below. As with most Computer Society workshops, which attempt to encourage free and candid communication among participants, this one published no proceedings and in fact prohibited tape recorders as well as photographs of slides. In the spirit of observing that requirement, Hiles' commentary is kept at a fairly general level. However, without attributing specific comments or discussing specific presentations, it does summarize the major thrusts of the discussions. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
FileWall: A Firewall for Network File Systems
Authors: Stephen Smaldone, Aniruddha Bohra, Liviu Iftode
Venue: Proceedings of the Third IEEE International Symposium on Dependable, Autonomic and Secure Computing (2007)

Access control in network file systems relies on primitive mechanisms like Access Control Lists and permission bits, which are not enough when operating in a hostile network environment. Network middleboxes, e.g., firewalls, completely ignore file system semantics when defining policies. Therefore, implementing simple context-aware access policies requires modifications to file servers and/or clients, which is impractical. We present FileWall, a network middlebox that allows administrators to define context-aware access policies for file systems using both the network context and the file system context. FileWall interposes on the client-server network path and implements administrator defined policies through message transformation without modifying either clients or servers. In this paper, we present the design and implementation of FileWall for the NFS protocol. Our evaluation demonstrates that FileWall imposes minimal overheads for common file system operations, even under heavy loads.
A boosting approach to content-based 3D model retrieval
Authors: Hamid Laga, Masayuki Nakajima
Venue: Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia (2007)

We present a new framework for 3D model retrieval based on the assumption that models belonging to the same shape class share the same salient features. The main issue is learning these features. We propose an algorithm for computing these features and their corresponding saliency value. At the learning stage, a large set of features are extracted from every model and a boosting algorithm is applied to learn the classification function in the feature space. AdaBoost learns a classifier that relies on a small subset of the features with the mean of weak classifiers. Moreover it assigns weights to the selected features, that we interpret as a measure of the feature saliency within the class, providing an efficient way for feature selection and combination. Our experiments using the LightField (LFD) descriptors and the Princeton Shape Benchmark show significant improvement in the retrieval performance and computation efficiency. We show also that the proposed framework can be applied to the problem of best view selection.
Orthogonal Multiple Vector-Valued Multivariate Wavelet Packets
Authors: Qinjiang Chen, Hua Li
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

In this paper, the notion of orthogonal multiple vector-valued wavelet packets in higher dimensions is introduced. A procedure for constructing them is presented. Their properties are investigated by using time-frequency analysis method, matrix theory and finite group theory. Orthogonality formulas are obtained.
Comparison of full-text searching to metadata searching for genes in two biomedical literature cohorts
Authors: Bradley M. Hemminger, Billy Saelim, Patrick F. Sullivan, Todd J. Vision
Venue: Journal of the American Society for Information Science and Technology (2007)

Researchers have traditionally used bibliographic databases to search out information. Today, the full-text of resources is increasingly available for searching, and more researchers are performing full-text searches. This study compares differences in the number of articles discovered between metadata and full-text searches of the same literature cohort when searching for gene names in two biomedical literature domains. Three reviewers additionally ranked 100 articles in each domain. Significantly more articles were discovered via full-text searching; however, the precision of full-text searching also is significantly lower than that of metadata searching. Certain features of articles correlated with higher relevance ratings. A significant feature measured was the number of matches of the search term in the full-text of the article, with a larger number of matches having a statistically significant higher usefulness (i.e., relevance) rating. By using the number of hits of the search term in the full-text to rank the importance of the article, performance of full-text searching was improved so that both recall and precision were as good as or better than that for metadata searching. This suggests that full-text searching alone may be sufficient, and that metadata searching as a surrogate is not necessary. &copy; 2007 Wiley Periodicals, Inc.
Performance analysis of the ANGEL system for automated control of game traffic prioritisation
Authors: Jason But, Thuy Nguyen, Lawrence Stewart, Nigel Williams, Grenville Armitage
Venue: Proceedings of the 6th ACM SIGCOMM workshop on Network and system support for games (2007)

The Automated Network Games Enhancement Layer (ANGEL) [6] is a novel architecture for meeting Quality of Service (QoS) requirements of real-time network game traffic across consumer broadband links. ANGEL utilises detection of game traffic in the ISP network via the use of Machine Learning techniques and then uses this information to inform network routers - in particular the home access modem where bandwidth is limited - of these flows such that the traffic may be prioritised. In this paper we present the performance characteristics of the fully built ANGEL system. In particular we show that ANGEL is able to detect game traffic with better than 96% accuracy and effect prioritisation within a second of game flow detection. We also demonstrate the processing performance of key ANGEL components under typical hardware scenarios.
Toward Video Bookmarking Search: Search-Target Inference from TVWatching
Authors: Masayuki Okamoto, Hideo Umeki
Venue: Proceedings of the International Conference on Semantic Computing (2007)

Our goal is to provide a one-button interface called Video Bookmarking Search, which efficiently retrieves information desired by a user with a simple user interface and an intelligent background process without distracting the user's attention from watching TV. We propose a method that infers keywords a user wants to search when watching TV by the Named Entity Recognition and the Semantic Role Analysis techniques for closed captions of Japanese TV shows on city life, travel, or cuisine to perform keyword selection for appropriate keyword-based Web search. According to experiments using actual TV shows with over 200 bookmarks, our method achieved 74.6% recall in the 10 retrieved results of searchable keywords that users want to search.
Jdib: Java Applications Interface to Unshackle the Communication Capabilities of InfiniBand Networks
Authors: Wan Huang, Hongwei Zhang, Jin He, Jizhong Han, Lisheng Zhang
Venue: Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops (2007)

Using existing TCP/IP emulation layers over InfiniBand networks in Java applications presented rather poor communication performance and heavy overhead in host CPU. In this paper, we propose Jdib (Java Direct InfiniBand), a Java application acceleration technique to directly exploit RDMA (Remote-Direct-Memory-Access) capability of InfiniBand network hardware. The preliminary test results show that with Jdib API, micro-benchmark written by Java can achieve latency performance (~9us) comparable with that of micro-benchmarks written by C language (~8.5us) in 20Gbps InfiniBand fabrics.
Inexact knowledge, margin for error and positive introspection
Authors: Julien Dutant
Venue: Proceedings of the 11th conference on Theoretical aspects of rationality and knowledge (2007)

Williamson (2000a) has argued that positive introspection is incompatible with inexact knowledge. His argument relies on a margin-for-error requirement for inexact knowledge based on a intuitive safety principle for knowledge, but leads to the counterintuitive conclusion that no possible creature could have both inexact knowledge and positive introspection. Following Halpern (2004) I put forward an alternative margin-for-error requirement that preserves the safety requirement while blocking Williamson's argument. I argue that the infallibilist conception of knowledge that underlies the new requirement provides a better account of inexact knowledge and higher-order knowledge than both Williamson's and Halpern's.
Degraded Character Recognition by Complementary Classifiers Combination
Authors: J. Sun, K. Huang, Y. Hotta, K. Fujimoto, S. Naoi
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02 (2007)

Character degradation is a big problem for machine printed character recognition. Two main reasons for degradation are extrinsic image degradation such as blurring and low image dimension, and intrinsic degradation caused by font variations. A recognition method that combines two complementary classifiers is proposed in this paper. The local feature based classifier extracts the local contour direction changes, which is effective for character patterns with less structure deterioration. The global feature based classifier extracts the texture distribution of the character image, which is effective when the character structure is hard to discriminate. The two complementary classifiers are combined by candidate fusion in a coarse-to-fine style. Experiments are carried on degraded Chinese character recognition. The results prove the effectiveness of our method.
RRXS: redundancy reducing XML storage in relations
Authors: Yi Chen, Susan Davidson, Carmem Hara, Yifeng Zheng
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

Current techniques for storing XML using relational technology consider the structure of an XML document but ignore its semantics as expressed by keys or functional dependencies. However, when the semantics of a document are considered redundancy may be reduced, node identifiers removed where value-based keys are available, and semantic constraints validated using relational primary key technology. In this paper, we propose a novel constraint definition called XFDs that capture structural as well as semantic information. We present a set of rewriting rules for XFDs, and use them to design a polynomial time algorithm which, given an input set of XFDs, computes a reduced set of XFDs. Based on this algorithm, we present a redundancy removing storage mapping from XML to relations called RRXS. The effectiveness of the mapping is demonstrated by experiments on three data sets.
A Simple Computer Vision Method for Automatic Detection of Melanin Spots in Atlantic Salmon Fillets
Authors: John Reidar Mathiassen, Ekrem Misimi, Amund Skavhaug
Venue: Proceedings of the International Machine Vision and Image Processing Conference (2007)

In this paper, we describe a simple method for automatic detection of melanin spots in Atlantic salmon fillets. Melanin spots are visible dark spots that reduce the quality grade of the fillets. Atlantic salmon processing lines have several operations that involve manual quality evaluation of fillets. One such operation is the inspection of fillets to detect melanin spots. This inspection is labor intensive, and therefore desirable to automate. Two simple computer vision algorithms for melanin spot detection are presented. One algorithm operates on the red channel of RGB images and the second algorithm uses linear discriminant analysis (LDA) on all three RGB channels. A comparison between these two algorithms shows that, for most detection rates, using LDA gives a lower number of false-detections per fillet. We show that the melanin spot detection task can potentially be automated using computer vision.
An approach to optimize data processing in business processes
Authors: Marko Vrhovnik, Holger Schwarz, Oliver Suhre, Bernhard Mitschang, Volker Markl, Albert Maier, Tobias Kraft
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

In order to optimize their revenues and profits, an increasing number of businesses organize their business activities in terms of business processes. Typically, they automate important business tasks by orchestrating a number of applications and data stores. Obviously, the performance of a business process is directly dependent on the efficiency of data access, data processing, and data management. In this paper, we propose a framework for the optimization of data processing in business processes. We introduce a set of rewrite rules that transform a business process in such a way that an improved execution with respect to data management can be achieved without changing the semantics of the original process. These rewrite rules are based on a semi-procedural process graph model that externalizes data dependencies as well as control flow dependencies of a business process. Furthermore, we present a multi-stage control strategy for the optimization process. We illustrate the benefits and opportunities of our approach through a prototype implementation. Our experimental results demonstrate that independent of the underlying database system performance gains of orders of magnitude are achievable by reasoning about data and control in a unified framework.
VEGA: A Visual Modeling Language for Digital Systems
Authors: Akira Sugimoto
Venue: IEEE Design Test (1986)

Workstations currently offer many opportunities for sophisticated communication between men and machines. VEGA, an object-orientedprogramming language, supports the creation of a ?visualized?hardware model?enabling experiment with and vertification ofdigital systems in a visual environment using interactive graphics. This article presents an overview of VEGA's motivationand background, a description of the language, and examples of visual simulation systems based on the visualized model. VEGAis named after a first-magnitude star?the brightest in the constellation Lyra?whose name in Japanese is Shokujo (a weavinggirl).
A Unified Continuous Optimization Framework for Center-Based Clustering Methods
Authors: Marc Teboulle
Venue: The Journal of Machine Learning Research (2007)

Center-based partitioning clustering algorithms rely on minimizing an appropriately formulated objective function, and different formulations suggest different possible algorithms. In this paper, we start with the standard nonconvex and nonsmooth formulation of the partitioning clustering problem. We demonstrate that within this elementary formulation, convex analysis tools and optimization theory provide a unifying language and framework to design, analyze and extend hard and soft center-based clustering algorithms, through a generic algorithm which retains the computational simplicity of the popular k-means scheme. We show that several well known and more recent center-based clustering algorithms, which have been derived either heuristically, or/and have emerged from intuitive analogies in physics, statistical techniques and information theoretic perspectives can be recovered as special cases of the proposed analysis and we streamline their relationships.
Distribution-Free Pattern Verification Using Statistically Equivalent Blocks
Authors: G. W. Beakley
Venue: IEEE Transactions on Computers (1972)

A nonparametric classification procedure based on distribution-free tolerance regions is presented. Without knlowledge of the class probability distributions, the procedure gives information about the expected performance of the classifier through use of only one sample of statistically independent observations from each class. With this procedure, a two-class discriminant can be designed for a given expected false alarm probability or for a given confidence that the false alarm probability is less than a given amount. Three ordering methods are presented that appear intuitively reasonable for minimizing the miss probability. Even though the methods do not, in general, meet this objective, they are easily implemented on a computer and can give good results. A procedure for obtaining a measure of the miss probability is also presented. These methods are applied to the problem of verifying the purported identity of a speaker from a sample of the speaker's voice.