ppxscal/embedding_network huggingface dataset:
Prefabrication and postfabrication architecture exploration for partially reconfigurable VLIW processors
Authors: A. Chattopadhyay, H. Ishebabi, X. Chen, Z. Rakosi, K. Karuri, D. Kammler, R. Leupers, G. Ascheid, H. Meyr
Venue: ACM Transactions on Embedded Computing Systems (TECS) (2008)

Modern application-specific instruction-set processors (ASIPs) face the daunting task of delivering high performance for a wide range of applications. For enhancing the performance, architectural features, for example, pipelining, VLIW, are often employed in ASIPs, leading to high design complexity. Integrated ASIP design environments, like template-based approaches and language-driven approaches, provide an answer to this growing design complexity. At the same time, increasing hardware design costs have motivated the processor designers to introduce high flexibility in the processor. Flexibility, in its most effective form, can be introduced to the ASIP by coupling a reconfigurable unit to the base processor. Because of its obvious benefits, several reconfigurable ASIPs (rASIPs) have been designed for years. This design paradigm gained momentum with the advent of coarse-grained FPGAs, where the lack of domain-specific performance common in general-purpose FPGAs are largely overcome by choosing application-dependent basic functional units. These rASIP designs lack a generic flow from high-level specification, resulting in intuitive design decisions and hard-to-retarget processor design tools. Although partial, template-based approaches for rASIP design is existent, a clear design methodology especially for the prefabrication architecture exploration is not present. In order to address this issue, a high-level specification and design methodology for partially reconfigurable VLIW processors is proposed in this article. To show the benefit of this approach, a commercial VLIW processor is used as the base architecture and two domains of applications are studied for potential performance gain.
An accurate high-order validated method to solve the 3D Laplace equation
Authors: M. L. Shashikant, Martin Berz, Kyoko Makino
Venue: Proceedings of the 5th WSEAS/IASME International Conference on Systems Theory and Scientific Computation (2005)

The 3D Laplace equation is one of the important PDEs of Physics, describing among others the phenomenology of electrostatics and magnetostatics. For various practical problems, very precise and validated solutions of this PDE are required; but with conventional finite element or finite difference codes this is difficult to achieve even without validation because of the need for an exceedingly fine mesh which leads to often prohibitive CPU time. We present an alternative approach based on high-order quadrature and a high-order finite element method. Both of the ingredients become accessible through the use of Taylor model methods. The solution in space is first represented as a Helmholtz integral over the two-dimensional surface. The latter is executed by evaluating the kernel of the integral as a Taylor model of both the two surface variables and the three volume variables inside the cell of interest. Finally, the integration over the surface variables is executed as a mere polynomial integration, resulting in a local Taylor model of the solution within one cell. Examples of the method and the precision that can be achieved will be given.
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Authors: Suhrid Balakrishnan, David Madigan
Venue: The Journal of Machine Learning Research (2008)

Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classification problems in high dimensions. However, current algorithms for training sparse classifiers typically scale quite unfavorably with respect to the number of training examples. This paper proposes online and multi-pass algorithms for training sparse linear classifiers for high dimensional data. These algorithms have computational complexity and memory requirements that make learning on massive data sets feasible. The central idea that makes this possible is a straightforward quadratic approximation to the likelihood function.
Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces
Authors: Wei Dong, Moses Charikar, Kai Li
Venue: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008)

Efficient similarity search in high-dimensional spaces is important to content-based retrieval systems. Recent studies have shown that sketches can effectively approximate L1 distance in high-dimensional spaces, and that filtering with sketches can speed up similarity search by an order of magnitude. It is a challenge to further reduce the size of sketches, which are already compact, without compromising accuracy of distance estimation. This paper presents an efficient sketch algorithm for similarity search with L2 distances and a novel asymmetric distance estimation technique. Our new asymmetric estimator takes advantage of the original feature vector of the query to boost the distance estimation accuracy. We also apply this asymmetric method to existing sketches for cosine similarity and L1 distance. Evaluations with datasets extracted from images and telephone records show that our L2 sketch outperforms existing methods, and the asymmetric estimators consistently improve the accuracy of different sketch methods. To achieve the same search quality, asymmetric estimators can reduce the sketch size by 10% to 40%.
Parallel Vectors Criteria for Unsteady Flow Vertices
Authors: Raphael Fuchs, Ronald Peikert, Helwig Hauser, Filip Sadlo, Philipp Muigg
Venue: IEEE Transactions on Visualization and Computer Graphics (2008)

Feature-based flow visualization is naturally dependent on feature extraction. To extract flow features, often higher-order properties of the flow data are used such as the Jacobian or curvature properties, implicitly describing the flow features in terms of their inherent flow characteristics (e.g., collinear flow and vorticity vectors). In this paper we present recent research which leads to the (not really surprising) conclusion that feature extraction algorithms need to be extended to a time-dependent analysis framework (in terms of time derivatives) when dealing with unsteady flow data. Accordingly, we present two extensions of the parallel vectors based vortex extraction criteria to the time-dependent domain and show the improvements of feature-based flow visualization in comparison to the steady versions of this extraction algorithm both in the context of a high-resolution dataset, i.e., a simulation specifically designed to evaluate our new approach, as well as for a real-world dataset from a concrete application.
Context Integration for Smart Workflows
Authors: Matthias Wieland, Peter Kaczmarczyk, Daniela Nicklas
Venue: Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (2008)

The usage of workflow technology to model and execute business processes is widespread in many enterprises and within the software industry. With the growing maturity of sensors, wireless communication, and distributed computing environments, we can enhance this approach to enable smart workflows, which are business processes crossing the boundary to the physical world. Applications for such processes can be found in many application domains, like logistics or in smart factory environments. To realize smart workflows, workflow engines can be coupled with existing context provisioning systems. However, there is a gap between the rather low-level provisioning of context (e.g., sensor data and stock information) and the concepts needed in smart workflows (e.g., "is a spare tool available?"). The main contribution of this paper is to bridge this gap: we show how integration processes can be used to provide context information at different semantical levels for smart workflows.
Indexing schemes for multichannel data broadcasting in mobile databases
Authors: Agustinus Borgy Waluyo, Bala Srinivasan, David Taniar
Venue: International Journal of Wireless and Mobile Computing (2008)

Broadcast indexing scheme is desirable in mobile environment due to its ability to provide accurate information for a client to tune in at the appropriate time for the required data. However, the occupancy of the index in the broadcast cycle will greatly affect the access time. In this paper, we introduce three indexing schemes for multi channel data broadcasting namely: (1) Non-Replicated Indexing (NRI) scheme, (2) Partially-Replicated Indexing (PRI) scheme and (3) Fully-Replicated Indexing (FRI) Scheme. Simulation model is developed to find out the access time performance of each scheme. It is considered that PRI-indexing scheme is a better scheme among the others.
Constrained mesh optimization on boundary
Authors: J. Yin, C. Teodosiu
Venue: Engineering with Computers (2008)

This paper presents a new mesh optimization approach aiming to improve the mesh quality on the boundary. The existing mesh untangling and smoothing algorithms (Vachal et al. in J Comput Phys 196: 627&#x2013;644, 2004; Knupp in J Numer Methods Eng 48: 1165&#x2013;1185, 2002), which have been proved to work well to interior mesh optimization, are enhanced by adding constrains of surface and curve shape functions that approximate the boundary geometry from the finite element mesh. The enhanced constrained optimization guarantees that the boundary nodes to be optimized always move on the approximated boundary. A dual-grid hexahedral meshing method is used to generate sample meshes for testing the proposed mesh optimization approach. As complementary treatments to the mesh optimization, appropriate mesh topology modifications, including buffering element insertion and local mesh refinement, are performed in order to eliminate concave and distorted elements on the boundary. Finally, the optimization results of some examples are given to demonstrate the effectivity of the proposed approach.
Computer Ethics: A Case-based Approach, 1 edition
Authors: Robert N. Barger
Venue:  (2008)

Computer Ethics: A Case-based Approach teaches students to solve ethical dilemmas in the field of computing, taking a philosophical, rather than a legal, approach to the topic. It first examines the principles of Idealism, Realism, Pragmatism, Existentialism, and Philosophical Analysis, explaining how each of them might be adopted as a basis for solving computing dilemmas. The book then presents a worksheet of key questions to be used in solving dilemmas. Twenty-nine cases, drawn from the real-life experiences of computer professionals, are included in the book as a means to let students experiment with solving ethical dilemmas and identify the philosophical underpinnings of the solutions.
Query-level stability and generalization in learning to rank
Authors: Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang Li
Venue: Proceedings of the 25th international conference on Machine learning (2008)

This paper is concerned with the generalization ability of learning to rank algorithms for information retrieval (IR). We point out that the key for addressing the learning problem is to look at it from the viewpoint of query. We define a number of new concepts, including query-level loss, query-level risk, and query-level stability. We then analyze the generalization ability of learning to rank algorithms by giving query-level generalization bounds to them using query-level stability as a tool. Such an analysis is very helpful for us to derive more advanced algorithms for IR. We apply the proposed theory to the existing algorithms of Ranking SVM and IRSVM. Experimental results on the two algorithms verify the correctness of the theoretical analysis.
Geovisual analytics for spatial decision support: Setting the research agenda
Authors: G. Andrienko, N. Andrienko, P. Jankowski, D. Keim, M. -J. Kraak, A. MacEachren, S. Wrobel
Venue: International Journal of Geographical Information Science (2007)

This article summarizes the results of the workshop on Visualization, Analytics & Spatial Decision Support, which took place at the GIScience conference in September 2006. The discussions at the workshop and analysis of the state of the art have revealed a need in concerted cross-disciplinary efforts to achieve substantial progress in supporting space-related decision making. The size and complexity of real-life problems together with their ill-defined nature call for a true synergy between the power of computational techniques and the human capabilities to analyze, envision, reason, and deliberate. Existing methods and tools are yet far from enabling this synergy. Appropriate methods can only appear as a result of a focused research based on the achievements in the fields of geovisualization and information visualization, human-computer interaction, geographic information science, operations research, data mining and machine learning, decision science, cognitive science, and other disciplines. The name 'Geovisual Analytics for Spatial Decision Support' suggested for this new research direction emphasizes the importance of visualization and interactive visual interfaces and the link with the emerging research discipline of Visual Analytics. This article, as well as the whole special issue, is meant to attract the attention of scientists with relevant expertise and interests to the major challenges requiring multidisciplinary efforts and to promote the establishment of a dedicated research community where an appropriate range of competences is combined with an appropriate breadth of thinking.
Tractable cases of the extended global cardinality constraint
Authors: Marko Samer, Stefan Szeider
Venue: Proceedings of the fourteenth symposium on Computing: the Australasian theory - Volume 77 (2008)

We study the consistency problem for extended global cardinality (EGC) constraints. An EGC constraint consists of a set X of variables, a set D of values, a domain D(x) &sube; D for each variable x, and a "cardinality set" K(d) of non-negative integers for each value d. The problem is to instantiate each variable x with a value in D(x) such that for each value d, the number of variables instantiated with d belongs to the cardinality set K(d). It is known that this problem is NP-complete in general, but solvable in polynomial time if all cardinality sets are intervals. First we pinpoint connections between EGC constraints and general factors in graphs. This allows us to extend the known polynomial-time case to certain non-interval cardinality sets. Second we consider EGC constraints under restrictions in terms of the treewidth of the value graph (the bipartite graph representing variable-value pairs) and the cardinality-width (the largest integer occurring in the cardinality sets). We show that EGC constraints can be solved in polynomial time for instances of bounded treewidth, where the order of the polynomial depends on the treewidth. We show that (subject to the complexity theoretic assumption FPT &ne; W[1]) this dependency cannot be avoided without imposing additional restrictions. If, however, also the cardinality-width is bounded, this dependency gets removed and EGC constraints can be solved in linear time.
PaleoSketch: accurate primitive sketch recognition and beautification
Authors: Brandon Paulson, Tracy Hammond
Venue: Proceedings of the 13th international conference on Intelligent user interfaces (2008)

Sketching is a natural form of human communication and has become an increasingly popular tool for interacting with user interfaces. In order to facilitate the integration of sketching into traditional user interfaces, we must first develop accurate ways of recognizing users' intentions while providing feedback to catch recognition problems early in the sketching process. One approach to sketch recognition has been to recognize low-level primitives and then hierarchically construct higher-level shapes based on geometric constraints defined by the user, however, current low-level recognizers only handle a few number of primitive shapes. We propose a new low-level recognition and beautification system that can recognize eight primitive shapes, as well as combinations of these primitives, with recognition rates at 98.56%. Our system also automatically generates beautified versions of these shapes to provide feedback early in the sketching process. In addition to looking at geometric perception, much of our recognition success can be attributed to two new features, along with a new ranking algorithm, which have proven to be significant in distinguishing polylines from curved segments.
LEARNING DRIFTING NEGOTIATIONS
Authors: Fabrício Enembreck, Bráulio Coelho Ávila, Edson E. Scalabrin, Jean-Paul Barthès
Venue: Applied Artificial Intelligence (2007)

In this work, we propose the use of drift detection techniques for learning offer policies in multiissue, bilateral negotiation. Several works aiming to develop adaptive trading agents have been proposed. Such agents are capable of learning their competitors' utility values and functions, thereby obtaining better results in negotiation. However, the learning mechanisms generally used disregard possible changes in a competitor's offer/counter-offer policy. In that case, the agent's performance may decrease drastically. The agent then needs to restart the learning process, as the model previously learned is no longer valid. Drift detection techniques can be used to detect changes in the current offers model and quickly update it. In this work, we demonstrate with simulated data that drift detection algorithms can be used to build adaptive trading agents and offer a number of advantages over the techniques mostly used in this problem. The results obtained with the algorithm IB3 (instance-based) show that the agent's performance can be rapidly recovered even when changes interesting to the competitor are abrupt, moderate, or gradual.
Quick assessment on identification of damaged building and land-use changes in the post-tsunami disaster with a quick-look image of IKONOS and Quickbird (a case study in Meulaboh City, Aceh)
Authors: K. Wikantika, A. Sinaga, F. Hadi, S. Darmawan
Venue: International Journal of Remote Sensing (2007)

The study shows how far remote-sensing technology can contribute to assess quickly the impact of the tsunami disaster in Aceh, Indonesia. It focuses on the identification of damaged buildings and land-use changes in the post-tsunami disaster using a quick-look image of IKONOS and Quickbird. The method includes pre-processing, on-screen digitizing, change detection, and generating thematic maps. The results obtained from the study include information about distribution of damaged buildings, land-use changes, and total coverage of land affected by the tsunami disaster. Building units decreased almost 85%, from 4546 units to 649 units. Up to 2 303 046 m2 of vegetated became mud-covered, while there was a decrease in the area of vegetated land, up to 1 869 676 m2. For the relocation stage, the proposed method, called a quick standard procedure, is an appropriate approach which needs to be adopted by local government, non-governmental organizations, and volunteers.
Regularized nonsmooth Newton method for multi-class support vector machines
Authors: Ping Zhong, Masao Fukushima
Venue: Optimization Methods Software (2007)

Multi-class classification is an important and on-going research subject in machine learning. Recently, the ν-K-SVCR method was proposed by the authors for multi-class classification. As many optimization problems have to be solved in multi-class classification, it is extremely important to develop an algorithm that can solve those optimization problems efficiently. In this article, the optimization problem in the ν-K-SVCR method is reformulated as an affine box constrained variational inequality problem with a positive semi-definite matrix, and a regularized version of the nonsmooth Newton method that uses the D-gap function as a merit function is applied to solve the resulting problems. The proposed algorithm fully exploits the typical feature of the ν-K-SVCR method, which enables us to reduce the size of Newton equations significantly. This indicates that the algorithm can be implemented efficiently in practice. The preliminary numerical experiments on benchmark data sets show that the proposed method is considerably faster than the standard Matlab routine used in the original ν-K-SVCR method.
Self-organizing relay stations in relay based cellular networks
Authors: Peng Jiang, John Bigham, Jiayi Wu
Venue: Computer Communications (2008)

The increasing popularity of wireless communications and the higher data requirements of new types of service lead to higher demands on wireless networks. Relay based cellular networks have been seen as an effective way to meet users' increased bit rate requirements while still retaining the benefits of a cellular structure. The objective of this paper is to illustrate effective and efficient approaches for fast deployment of relay stations into existing cellular networks and to demonstrate improvements to system capacity and performance resulting from cooperation between network components. Cooperative control based on geographic load balancing is employed to provide flexibility for the wireless network and respond to changes in the environment. Flexibility in the antenna system is used to provide coverage at the right place at the right time. Experiments show that the proposed approach has significant improvements and robustness in heterogeneous traffic scenarios, even in disaster conditions where some base stations fail. The performance evaluations are based on Mobile WiMAX technology, but the concepts apply more generally.
Simulation Study of Real-Time Load Distribution Routing in Wireless Sensor Networks
Authors: Adel Ali Ahmed, Liza Abdul Latiff, Norsheila Fisal
Venue: Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS) (2008)

In Wireless Sensor Networks (WSNs), sensors gather information about the physical world and base station makes appropriate decision upon the environment. This technology enables users to effectively sense and monitor from a distance. WSN are slated to become very popular in the near future. It allows video/audio streaming to be transferred between sensor nodes in real-time. WSN can use real-time forwarding which means messages in the network are delivered according to their end-to-end deadlines (packet lifetime). This paper proposes a novel real-time routing protocol based on link quality, packet velocity over one-hop and remaining power in the sensor devices. The simulation results in this paper show that the proposed routing protocol has efficient power consumption, high packet delivery ratio, and minimum control packet overhead in WSNs.
Scheduling in Grid Databases
Authors: Rogério Luís de Carvalho Costa, Pedro Furtado
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops (2008)

Many global organizations are generating large volumes of highly distributed data that must be shared for querying by the organization's participants, which can be put together by a grid infrastructure. Scheduling query execution in these environment is an important issue. Centralized and Hierarchical scheduling architectures are between the most commonly used scheduling architectures in distributed databases and grid computing, respectively. Each architectural model has its own advantages and disadvantages. In this paper, we evaluate the usage of both models in querying highly distributed data. We consider several scheduling policies and experimentally compare scheduling models and policies in terms of throughput and SLO-fulfillment. The obtained results indicate that layered on-demand scheduling may lead to good results in both throughput and SLO-oriented environments.