ppxscal/embedding_network huggingface dataset:
Proceedings of the 3rd workshop on Data management for sensor networks: in conjunction with VLDB 2006
Venue: DMSN; Vol. 273 (2006)

The past few years have seen substantial amounts of computer science research on sensor networks as they have the potential to bring an unprecedented level of access to the physical world. Other subfields of Computer Science have had a number of workshops on the topic. Also, there are now at least two major conferences --- the Conference on Information Processing in Sensor Networks (IPSN), started in 2002 (the 2006 IPSN was held in April), and the ACM Conference on Sensor Systems (SenSys), started in 2003 (the 2006 SenSys will be held in November). These conferences have published a small number of database papers, but there is no exclusive forum for discussion on early and innovative work on data management in sensor networks. We believe that the DMSN 2006 workshop, building on the successes of the DMSN 2004 and DMSN 2005 workshops, fills a significant gap in the database and sensor network communities, by bringing together interested researchers and practitioners from different fields to identify interesting challenges and opportunities. Specifically, the workshop focuses on the challenges of data processing and management in networks of remote, wireless, battery-powered sensing devices (sensor networks). The power-constrained, lossy, noisy, distributed, and remote nature of such networks means that traditional data management techniques often cannot be applied without significant re-tooling. Furthermore, new challenges associated with acquisition and processing of live sensor data mean that completely new database techniques must also be developed. The DMSN workshop encompasses a wide range of topics which include: data replication and consistency in sensor network environments, database languages for sensor tasking, distributed data storage and indexing, energy-efficient data acquisition and dissemination, in-network query processing, integration of sensor network data into traditional and streaming data management systems, networking support for data processing, techniques for managing loss, uncertainty, and noise, query optimization, and privacy protection for sensory data.
Adaptive bitstream switching of scalable video
Authors: Osama A. Lotfallah, Geert Van der Auwera, Martin Reisslein
Venue: Image Communication (2007)

With scalable video coding that provides fine-granular quality degradation, such as fine granularity scalability (FGS) and progressive FGS (PFGS), or H.264 scalable video coding's (SVC) adaptive reference FGS (AR-FGS) coding, video can flexibly be streamed to receivers of heterogeneous bandwidths. However, the transmitted video is only efficiently encoded when the transmission bit rate is in the vicinity of the encoding bit rate. In this paper, we develop and evaluate a comprehensive suite of network-aware adaptive bitstream switching policies for point-to-point and point-to-multipoint streaming of fine granular scalable coded video to address this coding efficiency issue. Our approach stores a small number of encodings (versions) with different encoding bit rates for each video sequence and estimates the reconstructed quality using the motion activity levels of the underlying visual content (or, in general, any content descriptor(s) that highly correlate with the reconstructed quality). For unicast streaming, we then: (i) adaptively switch between the different encodings at the server, to improve the reconstructed video quality and (ii) adaptively drop packets during network congestion to ensure fairness between multiple unicast streams. For multicast streaming, we also adaptively switch between the different encodings to maximize the average video quality. Our adaptive bitstream switching policies consider the visual content descriptors as well as the network channel variability, while requiring only sample points from the rate-distortion curve of the video stream. From our extensive simulations with PFGS coding, we find that our adaptive unicast bitstream switching policy achieves on average a 0.8dB improvement over the optimal non-adaptive streaming for a diverse 200-shot sequence from Star Wars IV. We have also verified our key findings with the latest scalable video coding standard, H.264 SVC.
Centralized Web Proxy Services: Security and Privacy Considerations
Authors: Guy-Vincent Jourdan
Venue: IEEE Internet Computing (2007)

The widespread use of centrally controlled, externally run Web proxy services has several potential security issues related to privacy and deception, intranet information disclosure, and the creation of a single point of failure for widespread attacks. The author evaluates the security implications of such a Web proxy service from the viewpoints of users, organizations, and content providers. The discussion is illustrated with an analysis of Google's Web Accelerator, a free Web proxy service.
Toward a Pascal Standard
Authors: B. W. Ravenel
Venue: Computer (1979)

With a number of standards organizations involved in the effort, the Pascal community may obtain a recognized standard later this year. The draft definition is presented here.
Cloud basis function neural network: A modified RBF network architecture for holistic facial expression recognition
Authors: Chathura R. De Silva, Surendra Ranganath, Liyanage C. De Silva
Venue: Pattern Recognition (2008)

The paper presents novel modifications to radial basis functions (RBFs) and a neural network based classifier for holistic recognition of the six universal facial expressions from static images. The new basis functions, called cloud basis functions (CBFs) use a different feature weighting, derived to emphasize features relevant to class discrimination. Further, these basis functions are designed to have multiple boundary segments, rather than a single boundary as for RBFs. These new enhancements to the basis functions along with a suitable training algorithm allow the neural network to better learn the specific properties of the problem domain. The proposed classifiers have demonstrated superior performance compared to conventional RBF neural networks as well as several other types of holistic techniques used in conjunction with RBF neural networks. The CBF neural network based classifier yielded an accuracy of 96.1%, compared to 86.6%, the best accuracy obtained from all other conventional RBF neural network based classification schemes tested using the same database.
S2D-ProM: A Strategy Oriented Process Model for Secure Software Development
Authors: Mehrez Essafi, Lamia Labed, Henda Ben Ghezala
Venue: Proceedings of the International Conference on Software Engineering Advances (2007)

Building Secure Software is about taking security into account during all phases of software development. This practice is missing in, widely used, traditional developments due to domain immaturity, newness of the field and process complexity. Software development includes two views, a product view and a process view. Product view defines what the product is, whereas process view describes how the product is developed. Here we are concerned with the process view. Modelling the process allows simulate and analyze a software development process, which can help developers better understand, manage and optimize the software development process. In this paper we present our approach S2D-ProM, for Secure Software Development Process Model, which is a strategy oriented process model. This latter, capture steps and strategies that are required for the development of secure software and provide a two level guidance. The first level guidance is strategic helping developers choosing one among several strategies. The second level guidance is tactical helping developers achieving their selection for producing secure software. The proposed process model is easily extensible and allows building customized processes adapted to context, developer's finalities and product state. This flexibility allows the environment evolving through time to support new securing strategies.
A Fast Multi-pattern Matching Algorithm for Deep Packet Inspection on a Network Processor
Authors: Jia Ni, Chuang Lin, Zhen Chen, Peter Ungsunan
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

Deep Packet Inspection (DPI) is a critical function in network security applications such as Firewalls and Intrusion Detection Systems (IDS). Signature based scanners used in DPI apply multi-pattern matching algorithms to check whether the packet payload or flow content contains a specified signature in a signature set. Existing multi-pattern matching algorithms sacrifice memory space to achieve better performance. In this paper a novel fast multi-pattern matching algorithm, the Hash Boyer-Moore (HBM) Algorithm, is presented, which reduces the memory footprint of the heuristic table using a hash function and adds another heuristic table to reduce the false-positive ratio. Analyses and simulations show HBM offers higher speed and lower memory cost than some existing algorithms. The HBM algorithm was implemented on the Intel IXP 2400 Network Processor (NP) platform and experiments show suitable performance results in a Gigabit Ethernet LAN environment.
Efficient and Scalable Algorithms for Inferring Likely Invariants in Distributed Systems
Authors: Guofei Jiang, Haifeng Chen, Kenji Yoshihira
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

Distributed systems generate large amount of monitoring data such as log files to track their operational status. However, it is hard to correlate such monitoring data effectively across distributed systems and along observation time for system management. In previous work, we proposed a concept named flow intensity to measure the intensity with which internal monitoring data reacts to the volume of user requests. We calculated flow intensity measurements from monitoring data and proposed an algorithm to automatically search constant relationships between flow intensities measured at various points across distributed systems. If such relationships hold all the time, we regard them as invariants of the underlying systems. Invariants can be used to characterize complex systems and support various system management tasks. However, the computational complexity of previous invariant search algorithm is high so that it may not scale well in large systems with thousands of measurements. In this paper, we propose two efficient but approximate algorithms for inferring invariants in large-scale systems. The computational complexity of new randomized algorithms is significantly reduced and experimental results from a real system are also included to demonstrate the accuracy and efficiency of our new algorithms.
Comparing Several Evaluation Functions in the Evolutionary Design of Multiclass Support Vector Machines
Authors: Ana C. Lorena, Andre C. P. L. F. Carvalho
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

Support Vector Machines were originally designed to solve two-class classification problems. When they are applied to multiclass classification problems, the original problem is usually decomposed into multiple binary subproblems. Afterwards, individual classifiers are induced to solve each of these binary problems. To obtain the final multiclass prediction, the outputs of these binary classifiers generated are combined. Genetic Algorithms can be used to optimize the combination of binary classifiers, defining the decomposition according to the performance obtained in the multiclass problem solution. This paper investigates several evaluation functions that can be used in order to evaluate the performance of the decompositions evolved by Genetic Algorithms.
Scaling up Genome Similarity Search Services through Content Distribution
Authors: Chen Wang, Bing Bing Zhou, Albert Y. Zomaya
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

As the increase of genome database size, there are increasing number of methods for detecting sequence similarity and increasing demands for genome sequence search and alignment services. It is a challenge to scale up the computer systems for serving these demands in a timely manner. This paper tackles this problem from a novel perspective, which treats the sequence search requests as content requests to both genome databases and similarity detection methods; therefore, scaling up the computer systems that serve these contents is a process of constructing content distribution network. The paper gives a decentralized method to construct content distribution network for a variety of genome sequence similarity detection services. It also gives a scheduling algorithm for efficiently using content nodes. Our simulation results show that scalability and high content node utilization can be achieved in such a system while the costs of achieving these are controllable.
Controlling Requirements Evolution: a Formal Concept Analysis-Based Approach
Authors: Fabrizio Fabbrini, Mario Fusani, Stefania Gnesi, Giuseppe Lami
Venue: Proceedings of the International Conference on Software Engineering Advances (2007)

Requirements evolve during the software development process. Requirements specification evolution determines changes both in terms of level of details and style of representation and it brings the requirements from the initial statement of the customer needs towards more detailed documents able to drive the software design, construction and verification. Controlling the evolution of requirements, usually written in natural language, is very important for the success of the whole software project because every step in the evolutionary path of requirements can introduce undesired changes or lacks of information. This paper describes an approach based on the Formal Concepts Analysis that allows a systematic and precise verification of the consistency among different stages of natural language requirements evolution.
Software Quality Assurance in XP and Spiral - A Comparative Study
Authors: Sajid Ibrahim Hashmi, Jongmoon Baik
Venue: Proceedings of the The 2007 International Conference Computational Science and its Applications (2007)

Agile processes have been introduced to avoid the problems most of software practitioners have run up against by using traditional software development methodologies. These are well known for their benefits like focus on quality, early business value delivery, higher morale of stakeholders, and the reduced cost/schedule. Also, they can support the earlier and quicker production of the code by dividing the product into small segments called iterations. However, there are on-going debates about their flexibility to accommodate changing requirements and whether the productivity and quality of the agile processes is satisfactory for the customers or not. Previously available studies have mostly focused on comparing XP(eXtreme Programming) with some other Agile methodologies, rather than comparing it with traditional plan-driven software development methodologies. In this Paper, we identify the XP phases and practices, how they ensure product quality, and map XP phases against the Spiral model phases to prove that XP has built-in QA(Quality Assurance) practices in its life cycle, in addition to its focus on productivity. A case study is also included to empirically investigate quality of the product developed using XP with comparison to the product developed using Spiral Model.
Globally Exponential Stability of Discrete-time Cellular Neural Networks With Discrete Delays
Authors: Peijun Ju, Wei Zhang, Guocai Liu, Li Tian
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

Using the technique by virtue of Young and Halanay inequalities, anew sufficient condition for the globally exponential stability of a class of discrete-time cellular neural networks with delays is given. We discard the demand that activation functions must be derivable and only request them to be Lipschitz continuous, in this way the criteria given for globally exponential stability relies on the feedback matrices and is independent of the delay parameter.
Extracting _Carbon Copy_ Names and Organizations from a Heterogeneous Document Collection
Authors: K. Taghva, R. Beckley, J. Coombs
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02 (2007)

We describe the development of a tool to identify the names of persons and their corresponding institutions as they appear in the "carbon copy" or "cc" lists of corre- spondence, memorandums, emails, and other types of doc- uments.
A new family of high-order compact upwind difference schemes with good spectral resolution
Authors: Qiang Zhou, Zhaohui Yao, Feng He, M. Y. Shen
Venue: Journal of Computational Physics (2007)

This paper presents a new family of high-order compact upwind difference schemes. Unknowns included in the proposed schemes are not only the values of the function but also those of its first and higher derivatives. Derivative terms in the schemes appear only on the upwind side of the stencil. One can calculate all the first derivatives exactly as one solves explicit schemes when the boundary conditions of the problem are non-periodic. When the proposed schemes are applied to periodic problems, only periodic bi-diagonal matrix inversions or periodic block-bi-diagonal matrix inversions are required. Resolution optimization is used to enhance the spectral representation of the first derivative, and this produces a scheme with the highest spectral accuracy among all known compact schemes. For non-periodic boundary conditions, boundary schemes constructed in virtue of the assistant scheme make the schemes not only possess stability for any selective length scale on every point in the computational domain but also satisfy the principle of optimal resolution. Also, an improved shock-capturing method is developed. Finally, both the effectiveness of the new hybrid method and the accuracy of the proposed schemes are verified by executing four benchmark test cases.
Technology and Architecture for Data Management: An Overview and Perspective
Authors: H. Barsamian
Venue: Computer (1979)

Rapid growth of data bases in end-user applications and as powerful software development tools will make data management systems one of the greatest challenges of the 1980's and a landmark of that decade. 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
Intent-based analysis of network-wide routing policy configuration
Authors: Kyriaki Levanti, Hyong S. Kim, Tina Wong
Venue: Proceedings of the 2007 SIGCOMM workshop on Internet network management (2007)

Routing policy configuration is a very important aspect of network operations because it affects the network's profit, performance and security. Network operators implement low-level routing policies according to their high-level objectives. In this paper, we propose a set of techniques for analyzing network-wide routing policies. First, we interpret the routing policies relevant to a single neighbor. Then, we classify all neighbors into groups which express common intent. Classification is done by generating and comparing update patterns. We validate our approach by experimenting with the router configuration files of a Tier-1 ISP. Our techniques classify neighbors according to their type (customer/peer/transit), highlight neighbors which deviate from the norm and reveal possible mistakes. Consequently, our network-wide analysis seems to be promising for automating the translation of routing policy configuration into initial intent.
A Formal Design for Softare
Authors: L. Robinson
Venue: Computer (1975)

The major idea of SP is to divide a programming task into easily understandable units. Within these units, it may be of little consequence whether the language used is COBOL, FORTRAN, PL/1, or any of the new SP languages. Language design (such as control structures) may determine the size of the program which is the largest easily understandable unit. This is at best a local optimization. I have seen well-structured programs written in assembly language, FORTRAN, and APL-none of which are known for their facilities to enhance structured programming. However, any language claiming to enhance SP should provide a facility for connection among program units: this could be as complex as the abstract type mechanisms of Liskov's CLU or Wulf's ALPHARD, or it could be a simple procedure/macro facility. Poor design of these features limits the effectiveness of languages like FORTRAN and COBOL in the construction of well-structured programs. These facilities are often faked by a preprocessor or by system convention.
Large Software Problems for Small Computers: An Example from Medical Imaging
Authors: G. Frieder
Venue: IEEE Software (1985)

Small computers can be called on to perform large tasks. Doing so requires careful organization of input, output, auxiliary data structures, and memory.
Standards and Performance Issues in the Workstation Market
Authors: Vaughan Pratt
Venue: IEEE Computer Graphics and Applications (1984)

Are high performance and adherence to standards mutually exclusive? Here is how one company managed to balance these apparently opposing factors.
Application of Poisson Image Denoising by ICA to Penumbral Imaging
Authors: Xian-Hua Han, Jian Li, ShuiYan Dai, Yen-Wei Chen
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

This paper proposes a new method based on independent component analysis (ICA) for Poisson noise reduction. In the proposed method, the image is first transformed to ICA domain and then the noise components are removed by a soft thresholding (Shrinkage). The proposed method, which is used as a preprocessing of the reconstruction, has been successfully applied to penumbral imaging. Both simula- tion results and experimental results show that the recon- structed image is dramatically improved in comparison to that without the noise-removing filters.
Building a Simple and Effective Text Categorization System using Relative Importance in Category
Authors: Bingheng Yan, Depei Qian
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

With the rapid development of World Wide Web, text categorization has become the key technology in organizing and processing large volume of document data. There are a variety of text categorization methods such as k Nearest Neighbor (kNN) and Support Vector Machine (SVM). However, those methods are either too complicated or not effective enough. In this paper, we present a new method called Relative Importance in Category (RIIC), which is simpler than most methods and has a lower time complexity. To verify the performance of RIIC, we build a text categorization system (TCS) based on RIIC and compare our system with TCS based on kNN and SVM. Experimental results show that in most cases the performance of RIIC is better than kNN and SVM.