ppxscal/embedding_network huggingface dataset:
Iterated Local Optimization for Minimum Energy Broadcast
Authors: Intae Kang, Radha Poovendran
Venue: Proceedings of the Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (2005)

In our prior work, we presented a highly effective local search based heuristic algorithm called the Largest Expanding Sweep Search (LESS) to solve the minimum energy broadcast (MEB) problem over wireless ad hoc or sensor networks. In this paper, the performance is further strengthened by using iterated local optimization (ILO) techniques at the cost of additional computational complexity. To the best of our knowledge, this implementation constitutes currently the best performing algorithm among the known heuristics for MEB. We support this claim through extensive simulation study, comparing with globally optimal solutions obtained by an integer programming (IP) solver. For small network size up to 20 nodes, which is imposed by practical limitation of the IP solver, the ILO based algorithm produces globally optimal solutions with very high frequency (>70%), and average performance is within 1.12% of the optimal solution.
Balancing Queues by Mean Field Interaction
Authors: Donald A. Dawson, Jiashan Tang, Yiqiang Q. Zhao
Venue: Queueing Systems: Theory and Applications (2005)

Consider a queueing network with N nodes in which queue lengths are balanced through mean-field interaction. When N is large, we study the performance of such a network in terms of limiting results as N goes to infinity.
An Architecture for Software-Based iSCSI on Multiprocessor Servers
Authors: Annie Foong, Gary McAlpine, Dave Minturn, Greg Regnier, Vikram Saletore
Venue: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 9 - Volume 10 (2005)

To achieve IP-converged cluster deployments, the performance and scalability of iSCSI must approach that of FC SANs. We recognize and quantify that the major overhead of iSCSI comes from TCP/IP processing. Industry has largely responded with TCP offload engines (TOEs) and iSCSI storage adapters. As an alternative, this paper shows a software implementation of iSCSI on generic OSes and processors. The trend towards chip multiprocessing (CMP) and integrated memory controllers (MCH) largely motivated our direction. With CMP, increased processing power is delivered through multiple cores per processor; on-die MCH allows memory bandwidth to scale better with processor speeds. Our approach and analysis shows the effectiveness of partitioning the workload suitable for a CMP system, allowing iSCSI to scale with the increasing processing power and memory bandwidth of servers over time.
Review of "Introduction to the Design and Analysis of Algorithms by Ananay Levitin", Addison-Wesley
Authors: William Fahle
Venue: ACM SIGACT News (2004)

This is a beginning algorithms book, suitable for the late undergraduate or early graduate computer science student. Subjects covered include an introduction of the concept of algorithms, a taxonomy of various algorithms and design techniques, up through a preliminary discussion of the topics of complexity and computability. The subject of algorithms is, of course, well known and fundamental to the computer sciences. This book provides an introduction to the subject through the use of some well-known and not so well-known puzzles and computing problems. 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
On the impact of the solution representation for the Internet Protocol Network Design Problem with max-hop constraints
Venue: Networks (2004)

The IP &lpar;Internet Protocol&rpar; Network Design Problem can be shortly stated as follows. Given a set of nodes and a set of traffic demands, we want to determine the minimum cost capacity installation such that all the traffic is routed. Capacity is provided by means of links of a given capacity and traffic must be loaded on the network according to the OSPF-ECM &lpar;Open Shortest Path First&mdash;Equal Commodity Multiflow&rpar; protocol, with additional constraints on the maximum number of hops. The problem is strongly NP-Hard, and the literature proposes local search-based heuristics that do not take into account max-hop constraints, or assume a simplified OSPF routing. The core in a local search approach is the network loading algorithm for the evaluation of the neighbor solutions costs. It presents critical aspects concerning both computational efficiency and memory requirements. Starting from a tabu search prototype, we show how these aspects deeply impact on the design of a local search procedure, even at the logical level. We present several properties of the related network loading problem, that allow to overcome the critical issues and lead to an efficient solution evaluation. &copy; 2004 Wiley Periodicals, Inc. NETWORKS, VoL. 44&lpar;2&rpar;, 73&ndash;83 2004
Evaluation of ESI and class-based delta encoding
Authors: Mor Naaman, Hector Garcia-Molina, Andreas Paepcke
Venue: Web content caching and distribution: proceedings of the 8th international workshop (2004)

The portion of web traffic attributed to dynamic web content is substantial and continues to grow as users expect more personalization and tailored information. Unfortunately, dynamic content is costly to generate. Moreover, traditional web caching schemes are not very effective for dynamically-created pages. In this paper we study two acceleration techniques for dynamic content. The first technique is Edge-Side Includes (ESI), and the second is Class-Based Delta Encoding. To evaluate these schemes, we present a model for the construction of dynamic web pages. We use simulation to explore how system, page and algorithm parameters affect the performance of dynamic-content delivery techniques, and we present a detailed comparison of ESI and delta encoding in two representative scenarios.
DBA design challenge: engaging design professionals with inclusive design
Authors: Julia Cassim, Hua Dong
Venue: Issue 81 (January 2005) (2005)

The value and relevance of inclusive design is increasingly recognised by design professionals in the UK. An initiative that has encouraged this is the DBA (Inclusive) Design Challenge. Organised by the Helen Hamlyn Research Centre (HHRC) at the Royal College of Art (RCA), it was launched in 2000 in collaboration with the Design Business Association (DBA), a major professional organisation of designers, and it drew on the experience of three previous design challenges organised by the DesignAge programme, whose achievements led to the establishment of the HHRC in 1999.
Automatic indexing of natural language texts: Cornell University
Authors: G. Salton
Venue: Issue 79 (January 1982) (1982)

In information retrieval, the stored documents and records are normally identified by sets of terms or keywords that are collectively used to represent the document content. The task of assigning the terms to the individual documents is known as indexing. Automatic indexing procedures have been developed in recent years that outperform the conventional methods based either on a manual term assignment or on a full-text indexing where the words occurring in document texts are used as index terms.
A study on object tracking quality under probabilistic coverage in sensor networks
Authors: Shansi Ren, Qun Li, Haining Wang, Xin Chen, Xiaodong Zhang
Venue: ACM SIGMOBILE Mobile Computing and Communications Review (2005)

Sensor networks are used for a wide range of object tracking applications, such as vehicle tracking in military surveillance and wild animal tracking in habitat monitoring [1]. These applications, by their nature, enforce certain tracking quality and lifetime requirements. These two requirements, however, are two conflicting optimization goals due to the stringent energy constraints of sensor nodes.
A database clustering methodology and tool
Authors: Tae-Wan Ryu, Christoph F. Eick
Venue: Information Sciences&mdash;Informatics and Computer Science: An International Journal (2005)

Clustering is a popular data analysis and data mining technique. However, applying traditional clustering algorithms directly to a database is not straightforward due to the fact that a database usually consists of structured and related data; moreover, there might be several object views of the database to be clustered, depending on a data analyst's particular interest. Finally, in many cases, there is a data model discrepancy between the format used to store the database to be analyzed and the representation format that clustering algorithms expect as their input. These discrepancies have been mostly ignored by current research.This paper focuses on identifying those discrepancies and on analyzing their impact on the application of clustering techniques to databases. We are particularly interested in the question on how clustering algorithms can be generalized to become more directly applicable to real-world databases. The paper introduces methodologies, techniques, and tools that serve this purpose. We propose a data set representation framework for database clustering that characterizes objects to be clustered through sets of tuples, and introduce preprocessing techniques and tools to generate object views based on this framework. Moreover, we introduce bag-oriented similarity measures and clustering algorithms that are suitable for our proposed data set representation framework. We also demonstrate that our approach is capable of dealing with relationship information commonly found in databases through the bag-oriented clustering. We also argue that our bag-oriented data representation framework is more suitable for database clustering than the commonly used flat file format and produce better quality of clusters.
Unsteady Gas Laser Simulation
Authors: Timothy J. Madden, James H. Miller
Venue: Proceedings of the 2004 Users Group Conference (2004)

Theoretical models for chemical lasers depend on a variety of assumptions and empirical data to provide closure and simplify solution of the governing equations. Among the various assumptions and empirical data that have been built into models for chemical lasers are assumptions regarding flow steadiness in the time domain and geometric similarity of the spatial domain. The work discussed here is directed toward elucidating and increasing the understanding of these assumptions commonly used in chemical laser simulation and the impact of their usage upon the predictions of these models. These efforts in turn are directly linked to efforts to achieve improved chemical laser efficiencies and performance, as excursions outside the assumed to be 'well understood' traditional operational parameterspace are increasingly necessary.
Selectivity estimators for multidimensional range queries over real attributes
Authors: Dimitrios Gunopulos, George Kollios, J. Tsotras, Carlotta Domeniconi
Venue: The VLDB Journal &mdash; The International Journal on Very Large Data Bases (2005)

Estimating the selectivity of multidimensional range queries over real valued attributes has significant applications in data exploration and database query optimization. In this paper, we consider the following problem: given a table of d attributes whose domain is the real numbers and a query that specifies a range in each dimension, find a good approximation of the number of records in the table that satisfy the query. The simplest approach to tackle this problem is to assume that the attributes are independent. More accurate estimators try to capture the joint data distribution of the attributes. In databases, such estimators include the construction of multidimensional histograms, random sampling, or the wavelet transform. In statistics, kernel estimation techniques are being used. Many traditional approaches assume that attribute values come from discrete, finite domains, where different values have high frequencies. However, for many novel applications (as in temporal, spatial, and multimedia databases) attribute values come from the infinite domain of real numbers. Consequently, each value appears very infrequently, a characteristic that affects the behavior and effectiveness of the estimator. Moreover, real-life data exhibit attribute correlations that also affect the estimator. We present a new histogram technique that is designed to approximate the density of multidimensional datasets with real attributes. Our technique defines buckets of variable size and allows the buckets to overlap. The size of the cells is based on the local density of the data. The use of overlapping buckets allows a more compact approximation of the data distribution. We also show how to generalize kernel density estimators and how to apply them to the multidimensional query approximation problem. Finally, we compare the accuracy of the proposed techniques with existing techniques using real and synthetic datasets. The experimental results show that the proposed techniques behave more accurately in high dimensionalities than previous approaches.
Is the digital computer the right tool for artificial intelligence?
Authors: Thalia Kafatou
Venue: Issue 73 (October 1980) (1980)

Since the earliest recorded times, attempts have been made to develop and construct systematic rules of thinking. The field of the Artificial Intelligence was developed towards this objective, and the tool used for this purpose was the digital electronic computer.
A RS-SISO Algorithm for Serially Concatenated Continuous Phase Modulation
Authors: Jinhua Sun, Jiandong Li, Lijun Jin
Venue: Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1 (2005)

The complexity of the soft input soft output (SISO) algorithm is a major concern for iterative detection in serially concatenated continuous phase modulation (SCCPM). In this paper, a reduced state SISO (RSSISO) algorithm is presented based on the idea of reduced state sequence detection (RSSD). Error events analysis of SCCPM is given. Theoretic analysis and simulation results show that the proposed algorithm provides both significant complexity reduction and little performance degradation compared with the optimal full state SISO algorithm.
Selection and Fusion of Color Models for Feature Detection
Authors: H. Stokman, Th. Gevers
Venue: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01 (2005)

The choice of a color space is of great importance for many computer vision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. However, the problem is how to automatically select the color space that produces the best result for a particular task. The subsequent difficulty then is how to obtain a proper weighting scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color space selection and fusion of feature detectors, in this paper, we propose a method that exploits non-perfect correlation between the color models derived from the principles of diversification. As a consequence, the weighting scheme yields maximal color discrimination. The method is verified experimentally for two different feature detectors. The experimental results show that the model provides feature detection results having a discriminative power of 30 percent higher than the standard weighting scheme.
High Resolution Forward And Inverse Earthquake Modeling on Terascale Computers
Authors: Vokan Akcelik, Jacobo Bielak, George Biros, Ioannis Epanomeritakis, Antonio Fernandez, Omar Ghattas, Eui Joong Kim, Julio Lopez, David O'Hallaron, Tiankai Tu, John Urbanic
Venue: Proceedings of the 2003 ACM/IEEE conference on Supercomputing (2003)

For earthquake simulations to play an important role in the reduction of seismic risk, they must be capable of high resolution and high fidelity. We have developed algorithms and tools for earthquake simulation based on multiresolution hexahedral meshes. We have used this capability to carry out 1 Hz simulations of the 1994 Northridge earthquake in the LA Basin using 100 million grid points. Our wave propagation solver sustains 1.21 teraflop/s for 4 hours on 3000 AlphaServer processors at 80% parallel efficiency. Because of uncertainties in characterizing earthquake source and basin material properties, a critical remaining challenge is to invert for source and material parameter fields for complex 3D basins from records of past earthquakes. Towards this end, we present results for material and source inversion of high-resolution models of basins undergoing antiplane motion using parallel scalable inversion algorithms that overcome many of the difficulties particular to inverse heterogeneous wave propagation problems.
Behavior Modeling Using a Hierarchical HMM Approach
Authors: Shih-Yang Chiao, Costas S. Xydeas
Venue: Proceedings of the Fourth International Conference on Hybrid Intelligent Systems (2004)

This paper introduces a new methodology for the hierarchical modeling of the behavior-with-time of players operating and interacting within a certain application domain. Behavior modelling and characterization are performed on-line, given that a number of observations are made or sensed at regular time intervals with respect to each player. A key element of this hierarchical behavior modeling system architecture is a new formulation of multiple Hidden Markov Models (HMM) with Discrete Densities operating in parallel, with each HMM accepting a single feature-related observation sequence. However the proposed classification approach recognizes the existence of possible dependencies between the observation sequences of the features obtained for a given player. This property is effectively exploited in a new Dependent- Multi-HMM with Discrete densities (DM-HMM-D) classification approach. The proposed methodology is applied in modeling the behavior of aircrafts operating in relatively simple 3-D "air-patrol" situations. Computer simulation results demonstrate the significant gains that can be obtained in system classification and modeling performance when compared to those obtained while using conventional Independent-Multi-Discrete Hidden Markov Model (IM-HMM-D) schemes.
The PSF Correction Method for Satellite Image Restoration
Authors: Chun Feng, Jian-wen Ma, Jian-ping Chen
Venue: Proceedings of the Third International Conference on Image and Graphics (2004)

The conversion of sensor the Top of the Atmosphere Signals (TOAS), to reflectivity representing land surface target characters is an important sector in remote sensing information quantification. Assuming atmosphere disturbance being excluded, because of Adjacency Effect among pixels, the DN value of a target pixel contain the scatter contributions of background pixels. The magnitude of the contribution is directly proportional to the background pixel reflectivity and is correlative with the distance to target pixel. Adjacency Effect leads to blurry surface edges and low contrast in remote sensing images, which result in the uncertainty of remote sensing information and difficulty of quantitative explanation of surface features. Along with the increase of satellite spatial resolution and remote sensing information quantification degree, Adjacency Effect and its correction methods are researched to restore images. Based on radiation transfer model, the Adjacency Effect in a scene of Landsat5 TM data of Beijing (as an example) is analyzed and corrected. A significant improvement of the image quality was achieved.
Hamming Distance and Hop Count Based Classification for Multicast Network Topology Inference
Authors: Hui Tian, Hong Shen
Venue: Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1 (2005)

Topology information of a multicast network benefits significantly to many applications such as resource management, loss and congestion recovery. In this paper we propose a new algorithm, namely binary hamming distance and hop count based classification algorithm (BHC), to infer multicast network topology from end-to-end measurements. The BHC algorithm identifies multicast network topology using hamming distance of the sequences on receipt/loss of probe packets maintained at each pair of nodes and incorporating the hop count available at each node. We analyze the inference accuracy of the algorithm and prove that the algorithm can obtain accurate inference at higher probability than previous algorithms for a finite number of probe packets. We implement the algorithm in a simulated network and validate the algorithmýs performance in accuracy and efficiency.
Universal Text Preprocessing for Data Compression
Authors: Jurgen Abel, William Teahan
Venue: IEEE Transactions on Computers (2005)

Several preprocessing algorithms for text files are presented which complement each other and which are performed prior to the compression scheme. The algorithms need no external dictionary and are language independent. The compression gain is compared along with the costs of speed for the BWT, PPM, and LZ compression schemes. The average overall compression gain is in the range of 3 to 5 percent for the text files of the Calgary Corpus and between 2 to 9 percent for the text files of the large Canterbury Corpus.
Improved Routing-Based Linear Algebra for the Number Field Sieve
Authors: Willi Geiselmann, Hubert Kopfer, Rainer Steinwandt, Eran Tromer
Venue: Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01 (2005)

Recently, efficient custom-hardware designs were proposed for the linear algebra step of the Number Field Sieve integer factoring algorithm. These designs make use of a heuristic mesh routing algorithm, whose performance has been analyzed only experimentally. We show that the routing algorithm may in fact encounter livelocks, i.e., may never terminate. We propose a context-specific solution for this problem. We also show how efficiency can be improved by a factor of ¿ 4 through the use of a parallel tori topology and packet injection during routing.
Book review: Aaron's Code: Meta-Art, Artificial Intelligence, and the Work of Harold Cohen By Pamela McCorduck (Freeman New York, 1990)
Venue: ACM SIGART Bulletin (1992)

Aaron is an automation that generates fine art. It consists of an expert system that controls a drawing and painting engine. The rules and the knowledge base that make up the expert system have been provided by the Artist, David Cohen, over a period of some 18 years. The development of Aaron has gone through many stages, from an initial system that could only do a full frontal presentation of simple but evocative shapes in the 1973--78 period to figures executed using a fully three dimensional knowledge base by 1989. It shows the problems that can arise in developing a program that can develop something as complex as novel works of art. 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