ppxscal/embedding_network huggingface dataset:
Improving VLIW Processor Performance Using Three-Dimensional (3D) DRAM Stacking
Authors: Yangyang Pan, Tong Zhang
Venue: Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors (2009)

This work studies the potential of using emerging 3D integration to improve embedded VLIW computing system. We focus on the 3D integration of one VLIW processor die with multiple high-capacity DRAM dies. Our proposed memory architecture employs 3D stacking technology to bond one die containing several processing clusters to multiple DRAM dies for a primary memory. The 3D technology also enables wide low-latency buses between clusters and memory and enable the latency of 3D DRAM L2 cache comparable to 2D SRAM L2 cache. These enable it to replace the 2D SRAM L2 cache with 3D DRAM L2 cache. The die area for 2D SRAM L2 cache can be re-allocated to additional clusters that can improve the performance of the system. From the simulation results, we find 3D stacking DRAM main memory can improve the system performance by 10%~80% than 2D off-chip DRAM main memory depending on different benchmarks. Also, for a similar logic die area, a four clusters system with 3D DRAM L2 cache and 3D DRAM main memory outperforms a two clusters system with 2D SRAM L2 cache and 3D DRAM main memory by about 10%.
WordNet-based text document clustering
Authors: Julian Sedding, Dimitar Kazakov
Venue: Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data (2004)

Text document clustering can greatly simplify browsing large collections of documents by reorganizing them into a smaller number of manageable clusters. Algorithms to solve this task exist; however, the algorithms are only as good as the data they work on. Problems include ambiguity and synonymy, the former allowing for erroneous groupings and the latter causing similarities between documents to go unnoticed. In this research, na&iuml;ve, syntax-based disambiguation is attempted by assigning each word a part-of-speech tag and by enriching the 'bag-of-words' data representation often used for document clustering with synonyms and hypernyms from WordNet.
Robust and distributed stochastic localization in sensor networks: Theory and experimental results
Authors: Ioannis Ch. Paschalidis, Dong Guo
Venue: ACM Transactions on Sensor Networks (TOSN) (2009)

We present a robust localization system allowing wireless sensor networks to determine the physical location of their nodes. The coverage area is partitioned into regions and we seek to identify the region of a sensor based on observations by stationary clusterheads. Observations (e.g., signal strength) are assumed random. We pose the localization problem as a composite multihypothesis testing problem, develop the requisite theory, and address the problem of optimally placing clusterheads. We show that localization decisions can be distributed by appropriate in-network processing. The approach is validated in a testbed yielding promising results.
Finding related pages using Green measures: an illustration with Wikipedia
Authors: Yann Ollivier, Pierre Senellart
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

We introduce a new method for finding nodes semantically related to a given node in a hyperlinked graph: the Green method, based on a classical Markov chain tool. It is generic, adjustment-free and easy to implement. We test it in the case of the hyperlink structure of the English version of Wikipedia, the on-line encyclopedia. We present an extensive comparative study of the performance of our method versus several other classical methods in the case of Wikipedia. The Green method is found to have both the best average results and the best robustness.
A Hybrid Approach for Recognizing ADLs and Care Activities Using Inertial Sensors and RFID
Authors: Albert Hein, Thomas Kirste
Venue: Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments (2009)

In this paper we present a feasibility study regarding the recognition of high level daily living and care activities. We examine a hybrid discriminative and model based generative approach based on RFID and inertial sensor data. We show that the presented sensor configuration is able to deliver sensor readings and object sightings at a sufficient rate without forcing user compliance. We further evaluated the advantage of a model based approach over a static classifier, compared the individual contribution of each sensor type and could reach accuracy rates of 97% and 85%.
Design Space Exploration for Power-Efficient Mixed-Radix Ling Adders
Authors: Chung-Kuan Cheng
Venue: Proceedings of the 2009 19th IEEE Symposium on Computer Arithmetic (2009)

We present an integer linear programming (ILP) method that optimizes generalized prefix Ling adders in terms of area, delay, and power. The contribution is listed in the following. 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
Towards a complete classification of tractability in Allen's algebra
Authors: Thomas Drakengren, Peter Jonsson
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

We characterise the set of subalgebras of Allen's algebra which have a tractable satisfiability problem, and in addition contain certain basic relations. The conclusion is that no tractable subalgebra that is not known in the literature can contain more than the three basic relations (≡), (b) and (b-), where b ∈ {d,o,s,f}. This means that concerning algebras for specifying complete knowledge about temporal information, there is no hope of finding yet unknown classes with much expressivity. Furthermore, we show that there are exactly two maximal tractable algebras which contain the relation (
How to Hash into Elliptic Curves
Authors: Thomas Icart
Venue: Proceedings of the 29th Annual International Cryptology Conference on Advances in Cryptology (2009)

We describe a new explicit function that given an elliptic curve E defined over $\mathbb F_{p^n}$, maps elements of $\mathbb F_{p^n}$ into E in deterministic polynomial time and in a constant number of operations over $\mathbb F_{p^n}$. The function requires to compute a cube root. As an application we show how to hash deterministically into an elliptic curve.
Balancing Skills to Optimize Fun in Interactive Board Games
Authors: Eva Kraaijenbrink, Frank Gils, Quan Cheng, Robert Herk, Elise Hoven
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I (2009)

Playing games against people with a different skill level can be boring or frustrating, which decreases fun. A solution is to introduce specific rules that balance a game. In this paper we describe a study in which we used an electronic board game with tangible interaction to investigate whether balancing a game indeed increases fun experienced. We also investigate whether balancing skill levels implicitly (players are unaware) or explicitly (players are aware) has an influence on the fun experienced. We found that players who lost a game felt more successful in the balanced game compared to the unbalanced game. The balanced game also offered the players more fun experience than they expected beforehand. Finally, players preferred to play an explicitly balanced game because it increased the feeling of effort and challenge.
Prediction and change detection in sequential data for interactive applications
Authors: Jun Zhou, Li Cheng, Walter F. Bischof
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

We consider the problems of sequential prediction and change detection that arise often in interactive applications: A semi-automatic predictor is applied to a time-series and is expected to make proper predictions and request new human input when change points are detected. Motivated by the Transductive Support Vector Machines (Vapnik 1998), we propose an online framework that naturally addresses these problems in a unified manner. Our empirical study with a synthetic dataset and a road tracking dataset demonstrates the efficacy of the proposed approach.
Compositional CSP Traces Refinement Checking
Authors: Heike Wehrheim, Daniel Wonisch
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Compositional verification using assume-guarantee reasoning has recently seen an uprise due to the introduction of automatic techniques for learning assumptions. In this paper, we transfer this technique to a setting with CSP as modelling and property specification language, and present an approach to compositional traces refinement checking. The approach has been implemented using the CSP model checker FDR as teacher during learning. The implementation shows that the compositional approach can both drastically outperform as well as underperform FDR's performance, depending on the example at hand.
AC08 System Description
Authors: Jacek Szklarski
Venue: Programming Multi-Agent Systems: 6th International Workshop, ProMAS 2008, Estoril, Portugal, May 13, 2008. Revised Invited and Selected Papers (2009)

A simple multi-agent system which participated in the Multi-Agent Programming Contest in association with ProMAS 2008 is described. Agents take actions in order to maximize a global utility function from at each step. Coordination is done via the max-plus algorithm or by the greedy strategy. 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
Improved Genetic Algorithms to Solving Constrained Optimization Problems
Authors: Zhu Can, Liang Xi-Ming, Zhou Shu-renhu
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

The slow convergence speed and the lack of effective constraint handling strategies are the major concerns when applying genetic algorithms (Gas) to constrained optimization problem. An improved genetic algorithm was proposed by dividing population into three parts: optimal subpopulation, elitists subpopulation and spare subpopulation. We applied genetic algorithm on three subpopulations with different evolutionary strategies. Isolation of optimal subpopulation was to improve convergence speed. Population diversity was kept by spare subpopulation setting and aperiodically decreasing of the size of optimal subpopulation. Gene segregation was carried out by crossover operation between optimal subpopulation and spare subpopulation. Combination of penalty function method and the strategy of elitists preservation by setting elitists subpopulation was used to constraint handling. Some numerical tests have been made and the results show that the algorithm is effective.
Online Search with Time-Varying Price Bounds
Authors: Peter Damaschke, Phuong Hoai Ha, Philippas Tsigas
Venue: Algorithmica (2009)

Online search is a basic online problem. The fact that its optimal deterministic/randomized solutions are given by simple formulas (however with difficult analysis) makes the problem attractive as a target to which other practical online problems can be transformed to find optimal solutions. However, since the upper/lower bounds of prices in available models are constant, natural online problems in which these bounds vary with time do not fit in the available models. We present two new models where the bounds of prices are not constant but vary with time in certain ways. The first model, where the upper and lower bounds of (logarithmic) prices have decay speed, arises from a problem in concurrent data structures, namely to maximize the (appropriately defined) freshness of data in concurrent objects. For this model we present an optimal deterministic algorithm with competitive ratio $\sqrt{D}$, where D is the known duration of the game, and a nearly-optimal randomized algorithm with competitive ratio $\frac{\ln D}{1+\ln2-\frac{2}{D}}$. We also prove that the lower bound of competitive ratios of randomized algorithms is asymptotically $\frac{\ln D}{4}$. The second model is inspired by the fact that some applications do not utilize the decay speed of the lower bound of prices in the first model. In the second model, only the upper bound decreases arbitrarily with time and the lower bound is constant. Clearly, the lower bound of competitive ratios proved for the first model holds also against the stronger adversary in the second model. For the second model, we present an optimal randomized algorithm. Our numerical experiments on the freshness problem show that this new algorithm achieves much better/smaller competitive ratios than previous algorithms do, for instance 2.25 versus 3.77 for D=128.
Sensorless Control of Permanent Magnet Synchronous Motor Based on Extended Kalman Filter
Authors: Jinsong Kang, Bo Hu, Haiwei Liu, Guoqin Xu
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Extended Kalman Filter (EKF) was applied to Permanent Magnet Synchronous Motor (PMSM) position sensorless control in this paper, and this algorithm was simulated with Matlab/Simulink, and finally this algorithm was realized in the system with a TMS320F2812 controller. Both the simulation and experimental results proved theeffectiveness of this algorithm, as well as higher speed,location identification accuracy, and better static and dynamic performance and anti-interference performance. This paper also takes some measures to improve the lowspeed performance, which has a very important practical significance.
Exploiting causal independence using weighted model counting
Authors: Wei Li, Pascal Poupart, Peter Van Beek
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 1 (2008)

Previous studies have demonstrated that encoding a Bayesian network into a SAT-CNF formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference in Bayesian networks. In this paper, we present techniques for improving this approach for Bayesian networks with noisy-OR and noisy-MAX relations-two relations which are widely used in practice as they can dramatically reduce the number of probabilities one needs to specify. In particular, we present two space efficient CNF encodings for noisy-OR/MAX and explore alternative search ordering heuristics. We experimentally evaluated our techniques on large-scale real and randomly generated Bayesian networks. On these benchmarks, our techniques gave speedups of up to two orders of magnitude over the best previous approaches and scaled up to networks with larger numbers of random variables.
3D Modeling from Multiple Views with Integrated Registration and Data Fusion
Authors: Alain Boyer, Phillip Curtis, Pierre Payeur
Venue: Proceedings of the 2009 Canadian Conference on Computer and Robot Vision (2009)

This paper presents an integrated modeling system capable of generating coloured three dimensional representations of a scene observed from multiple viewpoints. Emphasis is given to the integration of the components and to the algorithms used for acquisition, registration and final surface mapping. First, a sensor operating with structured light is used to acquire 3D and colour data of a scene from multiple views. Second, a frequency-domain based registration algorithm computes the transformation between pairs of views from the raw measurements and without a priori knowledge on the transformation parameters. Finally, the registered views are merged together and refined to create a rich 3D model of the objects. Real world modeling examples are presented and analyzed to validate the operation of the proposed integrated modeling system.
Model-Checking Games for Fixpoint Logics with Partial Order Models
Authors: Julian Gutierrez, Julian Bradfield
Venue: Proceedings of the 20th International Conference on Concurrency Theory (2009)

We introduce model-checking games that allow local second-order power on sets of independent transitions in the underlying partial order models where the games are played. Since the one-step interleaving semantics of such models is not considered, some problems that may arise when using interleaving semantics are avoided and new decidability results for partial orders are achieved. The games are shown to be sound and complete, and therefore determined. While in the interleaving case they coincide with the local model-checking games for the μ-calculus (Lμ), in a noninterleaving setting they verify properties of Separation Fixpoint Logic (SFL), a logic that can specify in partial orders properties not expressible with Lμ. The games underpin a novel decision procedure for model-checking all temporal properties of a class of infinite and regular event structures, thus improving previous results in the literature.
Predicting the Nexus between Post-Secondary Education Affordability and Student Success: An Application of Network-Based Approaches
Authors: Ashwin Arulselvan, Pilar Mendoza, Vladimir Boginski, Panos Pardalos
Venue: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining (2009)

The cost of postsecondary education in the U. S. continues to grow faster than salaries and inflation. In fact, the real cost of a college education has climbed almost 30 in the past 10 years and shows no sign of stabilizing in the near future. The economic competitiveness of the country increasingly depends on a skilled workforce with a postsecondary education capable of dealing with the demands of the global market. Thus, college attainment is at the center of producing a skilled workforce, and so it is, postsecondary education affordability. Using the National Postsecondary Student Aid Surveyor the year 2003- 2004, which is representative of the entire undergraduate population in the U.S., this study examines the various ways students and families pay for postsecondary education and its subsequent effect on persistence and performance for all groups of students across racial/ethnic and social-economic status lines.We use a spectral clustering algorithm based on Normalized cuts to classify students based on their similarities. More specifically, we construct a social network with the students as the nodes of the graph and edge between pair of the students is weighted based on their similarity in attributes. We then obtain three non-trivial smallestof the laplacian matrix. We use these to perform a k-means clustering in the eigenspace. We were able to establish meaningful clusters by this approach that helps in classifying students based on the relation between their persistence level and conditions of living.
Reconstructing Optical Flow Fields by Motion Inpainting
Authors: Benjamin Berkels, Claudia Kondermann, Christoph Garbe, Martin Rumpf
Venue: Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (2009)

An edge-sensitive variational approach for the restoration of optical flow fields is presented. Real world optical flow fields are frequently corrupted by noise, reflection artifacts or missing local information. Still, applications may require dense motion fields. In this paper, we pick up image inpainting methodology to restore motion fields, which have been extracted from image sequences based on a statistical hypothesis test on neighboring flow vectors. A motion field inpainting model is presented, which takes into account additional information from the image sequence to improve the reconstruction result. The underlying functional directly combines motion and image information and allows to control the impact of image edges on the motion field reconstruction. In fact, in case of jumps of the motion field, where the jump set coincides with an edge set of the underlying image intensity, an anisotropic TV-type functional acts as a prior in the inpainting model. We compare the resulting image guided motion inpainting algorithm to diffusion and standard TV inpainting methods.
A Multiagent System for Self-organisation of an 802.11 Mesh Network
Authors: John Debenham, Ante Prodan
Venue: Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2009)

The self-organisation of telecommunications networks has to confront the two challenges of the scalability and the stability of the solution. This paper describes a distributed, co-operative multiagent system in which agents make decisions based only on local knowledge -- that guarantees scalability. Extensive simulations indicate that stability is ensured by the agent's making improvements to the network settings that improve the social performance for all agents in a two-hop range. Our overall goal is simply to reduce maintenance costs for such networks by removing the need for humans to tune the network settings.
Making sense of word sense variation
Authors: Rebecca J. Passonneau, Ansaf Salleb-Aouissi, Nancy Ide
Venue: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (2009)

We present a pilot study of word-sense annotation using multiple annotators, relatively polysemous words, and a heterogenous corpus. Annotators selected senses for words in context, using an annotation interface that presented WordNet senses. Interannotator agreement (IA) results show that annotators agree well or not, depending primarily on the individual words and their general usage properties. Our focus is on identifying systematic differences across words and annotators that can account for IA variation. We identify three lexical use factors: semantic specificity of the context, sense concreteness, and similarity of senses. We discuss systematic differences in sense selection across annotators, and present the use of association rules to mine the data for systematic differences across annotators.
A log-linear model with an n-gram reference distribution for accurate HPSG parsing
Authors: Takashi Ninomiya, Takuya Matsuzaki, Yusuke Miyao, Jun'ichi Tsujii
Venue: Proceedings of the 10th International Conference on Parsing Technologies (2007)

This paper describes a log-linear model with an n-gram reference distribution for accurate probabilistic HPSG parsing. In the model, the n-gram reference distribution is simply defined as the product of the probabilities of selecting lexical entries, which are provided by the discriminative method with machine learning features of word and POS n-gram as defined in the CCG/HPSG/CDG supertagging. Recently, supertagging becomes well known to drastically improve the parsing accuracy and speed, but supertagging techniques were heuristically introduced, and hence the probabilistic models for parse trees were not well defined. We introduce the supertagging probabilities as a reference distribution for the log-linear model of the probabilistic HPSG. This is the first model which properly incorporates the supertagging probabilities into parse tree's probabilistic model.