ppxscal/embedding_network huggingface dataset:
A Learning Classifier Approach to Tomography
Authors: Kees Joost Batenburg
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Tomography is an important technique for noninvasive imaging: images of the interior of an object are computed from several scanned projections of the object, covering a range of angles. Traditionally, tomographic reconstruction algorithms have been based on techniques from analysis and linear algebra. In this paper we describe how a particular version of the tomographic reconstruction problem, known as discrete tomography, can be considered as a classification problem. By making this connection between two seemingly unrelated scientific areas, the full machinery of learning classifier theory can be used to solve tomographic problems. The use of classifiers that can be trained from examples for tomography has two main advantages. First, prior knowledge concerning the images of interest can be learned automatically. Second, there are several types of classifiers that can perform the classification task very fast once trained. Real-time reconstruction is one of the main goals in tomographic imaging. Tomography typically deals with huge amounts of high-dimensional data, which makes the corresponding classification task very hard. we describe several steps to reduce the dimensionality of the input data. For one type of classifier, the multilayer perceptron, we provide experimental evidence showing that it can indeed be used for efficient tomographic imaging.
A Fast Algorithm for Adaptive Prefix Coding
Authors: Marek Karpinski, Yakov Nekrich
Venue: Algorithmica (2009)

In this paper we present a new algorithm for adaptive prefix coding. Our algorithm encodes a text S of m symbols in O(m) time, i.e., in O(1) amortized time per symbol. The length of the encoded string is bounded above by (H+1)m+O(nlog&#x2009;2 m) bits where n is the alphabet size and H is the entropy. This is the first algorithm that adaptively encodes a text in O(m) time and achieves an almost optimal bound on the encoding length in the worst case. Besides that, our algorithm does not depend on an explicit code tree traversal.
Dynamics of soil salinity in the Canadian prairies: Application of singular spectrum analysis
Authors: Igor V. Florinsky, Robert G. Eilers, Brian H. Wiebe, Michele M. Fitzgerald
Venue: Environmental Modelling Software (2009)

We studied hidden periodicities and trends in the temporal dynamics of soil salinity in the Canadian prairies at a field scale. Singular spectrum analysis (SSA) was applied to time series of the groundwater depth (Dgw), total precipitation, groundwater electrical conductivity (ECgw), and soil electrical conductivity (ECe). We found that the temporal variability of soil salinity is determined by its seasonal and quasi-3-yr oscillations controlled by similar cycles of Dgw and ECgw, which are controlled by the precipitation. In the seasonal cycle, the system ''precipitation - groundwater depth - groundwater salinity - soil salinity'' proceeds as follows: The maximal level of the precipitation is reached in late May. The water table peaks 2 months after the maximal level of precipitation was reached. The seasonal oscillation of ECgw is in phase with that of Dgw. ECe peaks 1.5 months after the maximal level of ECgw was reached. In the quasi-3-yr cycle, ECe peaks about 14 months after the maximal rise of the groundwater. Most likely, this cycle of Dgw is a response to the 3-yr periodicity of the precipitation observed in the region. SSA is the effective tool to detect hidden oscillations in soil processes handling relatively short time series with missing records.
CTO Roundtable: Cloud Computing
Authors: Mache Creeger
Venue: Queue (2009)

Our panel of experts discuss cloud computing and how companies can make the best use of it.
Business Computing
Authors: Gediminas Adomavicius, Alok Gupta
Venue:  (2009)

This book is broadly organized in three parts. The first section ('Enhancing and Managing Customer Value') focuses on presenting the state-of-knowledge in managing and enhancing customer value through extraction of consumer-centric knowledge from mountains of data that modern interactive applications generate. The extracted information can then be used to provide more personalized information to customers, provide more relevant information or products, and even to create innovative business processes to enhance overall value to customers. The second section in the book ('Computational Approaches for Business Processes') focuses on presenting several specific innovative computing artifacts and tools developed by researchers that are not yet commercially used. These represent cutting-edge thought and advances in business computing research that should soon find utility in real-world applications or as a tool to analyze real-world scenarios.The final section in the book ('Supporting Knowledge Enterprise') presents approaches and frameworks that focus on ability of an enterprise to analyze, build, and protect computing infrastructure that supports value-added dimensions to the enterprise's existing business processes.
Optimized scalable cache management for video streaming system
Authors: Yongfeng Li, Kenneth Ong
Venue: Multimedia Tools and Applications (2009)

An important research issue in video streaming is how to efficiently utilize the network resources to provide clients instant access to multiple video objects. Caching strategy and transmission scheme are the two essential points inside the video streaming framework. Recent research efforts on them are not sufficient due to their inflexible support for scalable encoded video streams and heterogeneous requests from clients. In this paper, we propose an optimized caching strategy (OCS) and a scalable transmission scheme (STS) for scalable coded video streaming. By exploring the characteristics of video streaming workload and system design objectives, OCS and STS work efficiently to minimize both network bandwidth cost and user access latency. Firstly, we analyze the caching problem for the proxy-assisted video streaming system and derive a maneuverable caching scenario. Secondly, we develop an efficient transmission scheme for scalable coded videos. Thirdly, we formulate a multi-objective optimization model with closed-form expressions to obtain the optimized caching strategy. Finally, with designed algorithms, an excellent compromise between two competing objectives (minimizing the bandwidth cost and the access latency) is achieved. We start our evaluation by studying the optimized caching strategy for a single video object. Then we apply the strategy to multiple video objects and illustrate the tradeoff between the optimization objectives. Our evaluation results show that compared with other caching strategies, the proposed optimized scalable caching strategy can achieve a significant reduction in bandwidth cost with even a small proxy cache size. Meanwhile, the best performance (in terms of bandwidth cost) is obtained together with the proposed scalable batch-patching transmission scheme.
Relational learning via latent social dimensions
Authors: Lei Tang, Huan Liu
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. However, connections in social media are often multi-dimensional. An actor can connect to another actor for different reasons, e.g., alumni, colleagues, living in the same city, sharing similar interests, etc. Collective inference normally does not differentiate these connections. In this work, we propose to extract latent social dimensions based on network information, and then utilize them as features for discriminative learning. These social dimensions describe diverse affiliations of actors hidden in the network, and the discriminative learning can automatically determine which affiliations are better aligned with the class labels. Such a scheme is preferred when multiple diverse relations are associated with the same network. We conduct extensive experiments on social media data (one from a real-world blog site and the other from a popular content sharing site). Our model outperforms representative relational learning methods based on collective inference, especially when few labeled data are available. The sensitivity of this model and its connection to existing methods are also examined.
Broadcasting in necklace graphs
Authors: Hovhannes Harutyunyan, George Laza, Edward Maraachlian
Venue: Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering (2009)

Broadcasting is an information dissemination problem in a connected network, in which one node, called the originator, disseminates a message to all other nodes by placing a series of calls along the communication lines of the network. Once informed, the nodes aid the originator in distributing the message. Finding the broadcast time of any vertex in an arbitrary graph is NP-complete. The polynomial time solvability is shown only for trees and unicyclic graphs. In this paper we consider necklace graphs. We present a linear algorithm to find the broadcast time of an arbitrary necklace graph.
SiSMar: social multi-agent based simulation of stock market
Authors: Zahra Kodia, Lamjed Ben Said, Khaled Ghedira
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

In this paper, we introduce a new model of the stock market. This model describes the behavioral and cognitive attitudes of the investor at the micro level and explains their effects on his decision making. A multi-agent based simulation is used to validate our model and to study the emergence of certain stock market phenomena at the macro level. The modelling and implementation details of our simulator will appear in the full version of the paper.
A compact CPN representation for embedded and control systems fault diagnosis and recovery
Authors: Anthony Spiteri Staines
Venue: Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems (2009)

This paper describes how a reduced colored Petri net modeling approach can be used to represent faults and recovery action when designing embedded systems, control systems, real time hardware and other systems. The idea of the reduced CPN is to represent errors using complex tokens based on sets to store error information. A comprehensive example of a computerized fuel control system for a gas/combustion turbine is used to illustrate this methodology. Results and conclusions are given.
Linguistic theory based contextual evidence mining for statistical chinese co-reference resolution
Authors: Jun Zhao, Fei-Fan Liu
Venue: Journal of Computer Science and Technology (2007)

Under statistical learning framework, the paper focuses on how to use traditional linguistic findings on anaphora resolution as a guide for mining and organizing contextual features for Chinese co-reference resolution. The main achievements are as follows. (1) In order to simulate "syntactic and semantic parallelism factor", we extract "bags of word form and POS" feature and "bag of semes" feature from the contexts of the entity mentions and incorporate them into the baseline feature set. (2) Because it is too coarse to use the feature of bags of word form, POS tag and seme to determine the syntactic and semantic parallelism between two entity mentions, we propose a method for contextual feature reconstruction based on semantic similarity computation, in order that the reconstructed contextual features could better approximate the anaphora resolution factor of "Syntactic and Semantic Parallelism Preferences". (3) We use an entity-mention-based contextual feature representation instead of isolated word-based contextual feature representation, and expand the size of the contextual windows in addition, in order to approximately simulate "the selectional restriction factor" for anaphora resolution. The experiments show that the multi-level contextual features are useful for co-reference resolution, and the statistical system incorporated with these features performs well on the standard ACE datasets.
Neo-luddism and the demonisation of technology: cultural collision on the information superhighway
Authors: Don Bowman
Venue: ACM SIGCOMM Computer Communication Review (2009)

The Internet is a primary engine of innovation, communication, and entertainment in our society. It is used by diverse and potentially conflicting interests. When these interests collide in the form of technology, should the Internet stay true to its roots and let things take their course naturally, or should regulation and legislation be enacted to control what may or may not be done. This author believes public opinion is best served by light regulation and a strong focus on allowing technological innovation to take its natural course.
Drawing Tools
Authors: Diomidis Spinellis
Venue: IEEE Software (2009)

A drawing is often the best way to describe a large, complex artifact. In software development, you can easily derive pictures from code through tools that automate diagram creation. With the Graphviz tools you can draw directed and undirected relations between elements using a simple declarative language. Pic features a procedural drawing language that lets you define your own domain-specific drawing language. Gnuplot can plot data and functions in a wide variety of 2D and 3D styles. Finally, you can plot geographical data through the Generic Mapping Tools (GMT) or by generating KML files. You can obtain additional leverage by having one graphics tool or script generate output for another and by using the appropriate output format.
3-HOP: a high-compression indexing scheme for reachability query
Authors: Ruoming Jin, Yang Xiang, Ning Ruan, David Fuhry
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Reachability queries on large directed graphs have attracted much attention recently. The existing work either uses spanning structures, such as chains or trees, to compress the complete transitive closure, or utilizes the 2-hop strategy to describe the reachability. Almost all of these approaches work well for very sparse graphs. However, the challenging problem is that as the ratio of the number of edges to the number of vertices increases, the size of the compressed transitive closure grows very large. In this paper, we propose a new 3-hop indexing scheme for directed graphs with higher density. The basic idea of 3-hop indexing is to use chain structures in combination with hops to minimize the number of structures that must be indexed. Technically, our goal is to find a 3-hop scheme over dense DAGs (directed acyclic graphs) with minimum index size. We develop an efficient algorithm to discover a transitive closure contour, which yields near optimal index size. Empirical studies show that our 3-hop scheme has much smaller index size than state-of-the-art reachability query schemes such as 2-hop and path-tree when DAGs are not very sparse, while our query time is close to path-tree, which is considered to be one of the best reachability query schemes.
Learning linear dynamical systems without sequence information
Authors: Tzu-Kuo Huang, Jeff Schneider
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of data generated from the dynamic system. In this paper we consider the case where the data is not sequenced. The learning algorithm is presented a set of data points from the system's operation but with no temporal ordering. The data are simply drawn as individual disconnected points. While making this assumption may seem absurd at first glance, we observe that many scientific modeling tasks have exactly this property. In this paper we restrict our attention to learning linear, discrete time models. We propose several algorithms for learning these models based on optimizing approximate likelihood functions and test the methods on several synthetic data sets.
Some performance issues in tracking-error fuzzy control of mobile robots
Authors: Sašo Blažič, El-Hadi Guechi, Jimmy Lauber, Michel Dambrine, Gregor Klančar
Venue: Proceedings of the 10th WSEAS international conference on Fuzzy systems (2009)

This paper deals with Takagi-Sugeno modelling and control of nonholonomic mobile robots. The nonlinear tracking error-model is solved analytically under the premise of ZOH present at the system input. The nonlinear discrete model is developed. Several discretization issues are discussed and the modelling errors are analysed. The sector nonlinearity approach is used for constructing the Takagi-Sugeno model. The control is designed in the LMI framework. Some performance issues are discussed on the simulation cases.
Fuzzy 3D Face Ethnicity Categorization
Authors: Cheng Zhong, Zhenan Sun, Tieniu Tan
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

In this paper, we propose a novel fuzzy 3D face ethnicity categorization algorithm, which contains two stages, learning and mapping. In learning stage, the visual codes are first learned for both the eastern and western individuals using the learned visual codebook (LVC) method, then from these codes we can learn two distance measures, merging distance and mapping distance. Using the merging distance, we can learn the eastern, western and human codes based on the visual codes. In mapping stage, we compute the probabilities for each 3D face mapped to eastern and western individuals using the mapping distance. And the membership degree is determined by our defined membership function. The main contribution of this paper is that we view ethnicity categorization as a fuzzy problem and give an effective solution to assign the 3D face a reasonable membership degree. All experiments are based on the challenging FRGC2.0 3D Face Database. Experimental results illustrate the efficiency and accuracy of our fuzzy 3D face ethnicity categorization method.
Parallelizing extensible query optimizers
Authors: Florian M. Waas, Joseph M. Hellerstein
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Query optimization is the most computationally complex task in a database management systems. In many query optimizers, faster CPUs and increased RAM can translate directly to better query plans and thus better overall system performance. Although memory size continues to scale with Moore's Law, processor speeds are leveling off. Chip manufacturers are now focusing on multicore designs that integrate increasing numbers of cores in a single CPU. Query optimizers need to be parallelized in order to continue enjoying the growth trend of Moore's Law. In this paper, we address this problem in the context of the extensible optimizer architectures found in many commercial database systems. We identify the key data dependencies inherent in the dynamic programming at the heart of these optimizers. We use this insight both to design a flexible parallel query optimization implementation, and to assess the opportunities for parallelism in this context. The proposed solutions can serve as a blueprint for retrofitting existing industry-grade optimizers to leverage multicore architectures, without requiring significant rework of the underlying infrastructure.
Detection of unsolvable temporal planning problems through the use of landmarks
Authors: E. Marzal, L. Sebastia, E. Onaindia
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Deadline constraints have been recently introduced in PDDL3.0. The results obtained in the constraints domains in the last Planning Competition show that planners are not yet fully competitive. When dealing with deadline constraints the number of feasible solutions for a problem is reduced and thus it is specially relevant the ability to detect unsolvability. In this paper we present a new approach, based on the use of temporal landmarks, for the detection of unsolvable temporal planning problems.
Wireless Communications Transmitter Performance Enhancement Using Advanced Signal Processing Algorithms Running in a Hybrid DSP/FPGA Platform
Authors: Andrew K. Kwan, Mohamed Helaoui, Slim Boumaiza, Michael R. Smith, Fadhel M. Ghannouchi
Venue: Journal of Signal Processing Systems (2009)

This paper deals with digital base band signal processing algorithms, which are seen as enabling technologies for software-enabled radios, that are intended for the correction of the analog front end. In particular, this paper focuses on the design, optimization and testability of predistortion functions suitable for the linearization of narrowband and wideband transmitters developed with a hybrid DSP/FPGA platform. To select the best algorithm for the identification of the predistortion function, singular value decomposition, recursive least squares (RLS), and QR-RLS algorithms are implemented on the same digital signal processor; and, the computation complexity, time, accuracy and the required resources are studied. The hardware implementation of the predistortion function is then carefully performed, in order to meet the real time execution requirements.
Ensuring service backwards compatibility with Generic Web Services
Authors: Vadym Borovskiy, Juergen Mueller, Matthieu-Patrick Schapranow, Alexander Zeier
Venue: Proceedings of the 2009 ICSE Workshop on Principles of Engineering Service Oriented Systems (2009)

Ensuring compatibility in service-oriented systems is a challenging task. This article addresses the challenge of maintaining Web service backwards compatibility. In particular, the authors suggest a new interface design technique called Generic Web Services that allows service providers to add new features to Web services without breaking compatibility with existing clients. This is achieved by partially shifting the semantics of service operations from design time to runtime by relaxing the operations signatures with identity parameters. Furthermore, the authors discuss the advantages and disadvantages of generic Web services. The article also contains an example that applies the aforementioned technique to a Web service from SAP Enterprise Services Workplace.
Active dual supervision: reducing the cost of annotating examples and features
Authors: Prem Melville, Vikas Sindhwani
Venue: Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing (2009)

When faced with the task of building machine learning or NLP models, it is often worthwhile to turn to active learning to obtain human annotations at minimal costs. Traditional active learning schemes query a human for labels of intelligently chosen examples. However, human effort can also be expended in collecting alternative forms of annotations. For example, one may attempt to learn a text classifier by labeling class-indicating words, instead of, or in addition to, documents. Learning from two different kinds of supervision brings a new, unexplored dimension to the problem of active learning. In this paper, we demonstrate the value of such active dual supervision in the context of sentiment analysis. We show how interleaving queries for both documents and words significantly reduces human effort -- more than what is possible through traditional one-dimensional active learning, or by passive combinations of supervisory inputs.