ppxscal/embedding_network huggingface dataset:
Anytime local search for distributed constraint optimization
Authors: Roie Zivan
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Most former studies of Distributed Constraint Optimization Problems (DisCOPs) search considered only complete search algorithms, which are practical only for relatively small problems. Distributed local search algorithms can be used for solving DisCOPs. However, because of the differences between the global evaluation of a system's state and the private evaluation of states by agents, agents are unaware of the global best state which is explored by the algorithm. Previous attempts to use local search algorithms for solving DisCOPs reported the state held by the system at the termination of the algorithm, which was not necessarily the best state explored. A general framework for implementing distributed local search algorithms for DisCOPs is proposed. The proposed framework makes use of a BFS-tree in order to accumulate the costs of the system's state in its different steps and to propagate the detection of a new best step when it is found. The resulting framework enhances local search algorithms for DisCOPs with the anytime property. The proposed framework does not require additional network load. Agents are required to hold a small (linear) additional space (beside the requirements of the algorithm in use). The proposed framework preserves privacy at a higher level than complete DisCOP algorithms which make use of a pseudo-tree (ADOPT, DPOP).
Space-Efficient Algorithms for Document Retrieval
Authors: Niko Välimäki, Veli Mäkinen
Venue: Proceedings of the 18th annual symposium on Combinatorial Pattern Matching (2007)

We study the Document Listing problem, where a collection D of documents d 1,...,d k of total length ¿ i d i = n is to be preprocessed, so that one can later efficiently list all the $\textrm{ndoc}$ documents containing a given query pattern P of length m as a substring. Muthukrishnan (SODA 2002) gave an optimal solution to the problem; with O(n) time preprocessing, one can answer the queries in $O(m+\textrm{ndoc})$ time. In this paper, we improve the space-requirement of the Muthukrishnan's solution from O(n logn) bits to |CSA| + 2n + nlogk (1 + o(1)) bits, where |CSA| ≤ n log|Σ|(1 + o(1)) is the size of any suitable compressed suffix array (CSA), and Σ is the underlying alphabet of documents. The time requirement depends on the CSA used, but we can obtain e.g. the optimal $O(m+\textrm{ndoc})$ time when . For general |Σ|,k the time requirement becomes $O(m \log |\Sigma|+\textrm{ndoc} \log k)$. Sadakane (ISAAC 2002) has developed a similar space-efficient variant of the Muthukrishnan's solution; we obtain a better time requirement in most cases, but a slightly worse space requirement.
Seamless Handover for Multi-user Sessions with QoS and Connectivity Support
Authors: Eduardo Cerqueira, Luis Veloso, Paulo Mendes, Edmundo Monteiro
Venue: Proceedings of the 5th international conference on Wired/Wireless Internet Communications (2007)

Seamless handover over heterogeneous mobile environments is a major requirement to the success of the next generation of networks. However, seamless movement requires the control of the quality level and connectivity of communication sessions with no perceived service degradation to the users. This seamless characteristic is equally important for communication sessions encompassing only one or multiple receivers, being the latter called multi-user sessions. This paper presents a solution to allow seamless mobility for multi-user sessions over heterogeneous networks with mobile receivers and static senders. The proposed solution integrates end-to-end Quality of Service (QoS) mapping, QoS adaptation and connectivity control with seamless mobility support. The latter is achieved by using buffers in the mobile nodes and caches in the access-routers together with mobility prediction and context transfer schemes. Simulations present the efficiency of this proposal to setup ongoing sessions and its impact in reducing packet losses during movement.
Combating Spamdexing: Incorporating Heuristics in Link-Based Ranking
Authors: Tony Abou-Assaleh, Tapajyoti Das
Venue: Algorithms and Models for the Web-Graph: Fourth International Workshop, WAW 2006, Banff, Canada, November 30 - December 1, 2006. Revised Papers (2007)

Users typically locate useful Web pages by querying a search engine. However, today's search engines are seriously threatened by malicious spam pages that attempt to subvert the unbiased searching and ranking services provided by the engines. Given the large fraction of Web traffic originating from search engine referrals and the high potential monetary value of this traffic, it is not surprising that some Web site owners try to influence the ranking function of a search engine in a malicious way, thus giving rise to Web spam. Since the algorithmic identification of spam is very difficult, most techniques require either some human assistance or extensive training to effectively deal with spam. We exploit the possibility of automatically reducing Web spam page in a Web collection by analyzing the Web graph, coupled with very simple content analysis. We present empirical evaluation of our approach on 1 million Web pages from the health domain. Our results clearly indicate that we can effectively filter out a significant fraction of Web spam pages.
Tracking the Left Ventricle in Ultrasound Images Based on Total Variation Denoising
Authors: Jacinto C. Nascimento, João M. Sanches, Jorge S. Marques
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

Tracking the Left Ventricle (LV) in ultrasound sequences remains a challenge due to speckle noise, low SNR and lack of contrast. Therefore, it is usually difficult to obtain accurate estimates of the LV cavities since feature detectors produce a large number of outliers. This paper presents an algorithm which combines two main operations: i) a novel denoising algorithm based on the Lyapounov equation and ii) a robust tracker, based on an outlier feature model. Experimental results are provided, showing that the proposed algorithm is computationally efficient and leads to accurate estimates of the LV.
Openproof - A Flexible Framework for Heterogeneous Reasoning
Authors: Dave Barker-Plummer, John Etchemendy, Albert Liu, Michael Murray, Nik Swoboda
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

In this paper we describe the Openproof heterogeneous reasoning framework. The Openproof framework provides support for the implementation of heterogeneous reasoning environments, i.e., environments for writing arguments or proofs involving a number of different kinds of representation. The resulting environments are in a similar spirit to our Hyperproof program, though the Openproof framework goes beyond Hyperproof by providing facilities for the inclusion of a variety of representation systems in the same environment. The framework serves as the core of a number of widely used educational programs including Fitch.
Hierarchical Feature Extraction for Compact Representation and Classification of Datasets
Authors: Markus Schubert, Jens Kohlmorgen
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

Feature extraction methods do generally not account for hierarchical structure in the data. For example, PCA and ICA provide transformations that solely depend on global properties of the overall dataset. We here present a general approach for the extraction of feature hierarchies from datasets and their use for classification or clustering. A hierarchy of features extracted from a dataset thereby constitutes a compact representation of the set that on the one hand can be used to characterize and understand the data and on the other hand serves as a basis to classify or cluster a collection of datasets. As a proof of concept, we demonstrate the feasibility of this approach with an application to mixtures of Gaussians with varying degree of structuredness and to a clinical EEG recording.
Shallow Models for Non-iterative Modal Logics
Authors: Lutz Schröder, Dirk Pattinson
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

Modal logics see a wide variety of applications in artificial intelligence, e.g. in reasoning about knowledge, belief, uncertainty, agency, defaults, and relevance. From the perspective of applications, the attractivity of modal logics stems from a combination of expressive power and comparatively low computational complexity. Compared to the classical treatment of modal logics with relational semantics, the use of modal logics in AI has two characteristic traits: Firstly, a large and growing variety of logics is used, adapted to the concrete situation at hand, and secondly, these logics are often non-normal. Here, we present a shallow model construction that witnesses PSPACE bounds for a broad class of mostly non-normal modal logics. Our approach is uniform and generic: we present general criteria that uniformly apply to and are easily checked in large numbers of examples. Thus, we not only re-prove known complexity bounds for a wide variety of structurally different logics and obtain previously unknown PSPACE-bounds, e.g. for Elgesem's logic of agency, but also lay the foundations upon which the complexity of newly emerging logics can be determined.
Fitting Exponentials: An Interest in Rates
Authors: Dianne P. O'Leary
Venue: Computing in Science and Engineering (2004)

In this project, we study efficient algorithms for solving problems with exponentials, but we'll see that for many data sets, the solution is not well determined. 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
The ANN Inverse Control of Induction Motor with Robust Flux Observer Based on ESO
Authors: Xin Wang, Xianzhong Dai
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

When flux and speed are measurable, the artificial neural network inverse system (ANNIS) can almost linearize and decouple (L D) induction motor despite variation of parameters. In practice, the rotor flux cannot be measured and is difficult to estimate accurately due to parameters varying. The inaccurate flux can affect the ANNIS, coordinate transformation and outer rotor flux loop, and make the performance degrade further. Based on this, an artificial neural network inverse control (ANNIC) method of induction motor with robust flux observer based on extended state observer (ESO) is proposed. The observer can estimate the rotor flux accurately when uncertainty exists. The proposed control method is expected to enhance the robustness and improve the performance of whole control system. At last, the feasibility of proposed control method is confirmed by simulation.
To filter or to authorize: network-layer DoS defense against multimillion-node botnets
Authors: Xin Liu, Xiaowei Yang, Yanbin Lu
Venue: Proceedings of the ACM SIGCOMM 2008 conference on Data communication (2008)

This paper presents the design and implementation of a filter-based DoS defense system (StopIt) and a comparison study on the effectiveness of filters and capabilities. Central to the StopIt design is a novel closed-control, open-service architecture: any receiver can use StopIt to block the undesired traffic it receives, yet the design is robust to various strategic attacks from millions of bots, including filter exhaustion attacks and bandwidth flooding attacks that aim to disrupt the timely installation of filters. Our evaluation shows that StopIt can block the attack traffic from a few millions of attackers within tens of minutes with bounded router memory. We compare StopIt with existing filter-based and capability-based DoS defense systems under simulated DoS attacks of various types and scales. Our results show that StopIt outperforms existing filter-based systems, and can prevent legitimate communications from being disrupted by various DoS flooding attacks. It also outperforms capability-based systems in most attack scenarios, but a capability-based system is more effective in a type of attack that the attack traffic does not reach a victim, but congests a link shared by the victim. These results suggest that both filters and capabilities are highly effective DoS defense mechanisms, but neither is more effective than the other in all types of DoS attacks.
Preprocessing enhancements to improve data mining algorithms
Authors: Paraskevas Orfanidis, David J. Russomanno
Venue: International Journal of Business Intelligence and Data Mining (2008)

Preprocessing is often required before using clustering or other data mining algorithms to analyse multivariate data sets. The approaches discussed in this paper are enhanced implementations of a preprocess that utilises an algorithm to cluster points in a data set based upon each attribute independently, resulting in additional information about the data points with respect to each of its dimensions. Noise, data boundaries, and likely representatives of data subsets can be more easily identified, thus significantly improving the performance of subsequent clustering or data mining algorithms by combining this additional information across all dimensions and querying the results.
Social reward shaping in the prisoner's dilemma
Authors: Monica Babes, Enrique Munoz de Cote, Michael L. Littman
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Reward shaping is a well-known technique applied to help reinforcement-learning agents converge more quickly to near-optimal behavior. In this paper, we introduce social reward shaping, which is reward shaping applied in the multiagent-learning framework. We present preliminary experiments in the iterated Prisoner's dilemma setting that show that agents using social reward shaping appropriately can behave more effectively than other classical learning and non-learning strategies. In particular, we show that these agents can both lead---encourage adaptive opponents to stably cooperate---and follow---adopt a best-response strategy when paired with a fixed opponent---where better known approaches achieve only one of these objectives.
Contraflow Transportation Network Reconfiguration for Evacuation Route Planning
Authors: Sangho Kim, Shashi Shekhar, Manki Min
Venue: IEEE Transactions on Knowledge and Data Engineering (2008)

Given a transportation network having source nodes with evacuees and destination nodes, we want to find a contraflow network configuration, i.e., ideal direction for each edge, to minimize evacuation time. Contraflow is considered a potential remedy to reduce congestion during evacuations in the context of homeland security and natural disasters (e.g., hurricanes). This problem is computationally challenging because of the very large search space and the expensive calculation of evacuation time on a given network. To our knowledge, this paper presents the first macroscopic approaches for the solution of contraflow network reconfiguration incorporating road capacity constraints, multiple sources, congestion factor, and scalability. We formally define the contraflow problem based on graph theory and provide a framework of computational workload to classify our approaches. A Greedy heuristic is designed to produce high quality solutions with significant performance. A Bottleneck Relief heuristic is developed to deal with large numbers of evacuees. We evaluate the proposed approaches both analytically and experimentally using real world datasets. Experimental results show that our contraflow approaches can reduce evacuation time by 40% or more.
Review Article: Climate change, global warming and coral reefs: Modelling the effects of temperature
Authors: M. James C. Crabbe
Venue: Computational Biology and Chemistry (2008)

Climate change and global warming have severe consequences for the survival of scleractinian (reef-building) corals and their associated ecosystems. This review summarizes recent literature on the influence of temperature on coral growth, coral bleaching, and modelling the effects of high temperature on corals. Satellite-based sea surface temperature (SST) and coral bleaching information available on the internet is an important tool in monitoring and modelling coral responses to temperature. Within the narrow temperature range for coral growth, corals can respond to rate of temperature change as well as to temperature per se. We need to continue to develop models of how non-steady-state processes such as global warming and climate change will affect coral reefs.
Clustering Distributed Sensor Data Streams
Authors: Pedro Pereira Rodrigues, João Gama, Luís Lopes
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, presenting both empirical and theoretical evidence of its advantages.
An efficient hierarchical clustering model for grouping web transactions
Authors: Darenna Syahida Suib, Mustafa Mat Deris
Venue: International Journal of Business Intelligence and Data Mining (2008)

Clustering is one of the techniques used to obtain useful information from web log file for better understanding of customer behaviour. Two clustering techniques that commonly used are Greedy Hierarchical Item Set-Based Clustering (GHIC) algorithm and Hierarchical Clustering Algorithm (HCA). The algorithms, however, have its weaknesses in terms of processing times and time complexity. This paper proposes a new approach called Hierarchical Pattern-Based Clustering (HPBC) algorithm to improve the processing times based on the difference of mean support values of each cluster. The simulation revealed that the proposed algorithm outperformed the HCA and GHIC up to 100% and 50% respectively, with less time complexity.
SemWeB: A Semantic Web Browser for Supporting the Browsing of Users Using Semantic and Adaptive Links
Authors: Melike Şah, Wendy Hall, David C. Roure
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

Web browsing is a complex activity and in general, users are not guided during browsing. The aim of this research is to support the browsing of users using semantic and adaptive hyperlinks using Semantic Web technologies and personalization methods. In this paper, we propose a novel Semantic Web browser (SemWeB), which uses a behavior-based and an ontology-driven user modeling architecture. In our approach, semantic links and adaptive hypermedia can be achieved on different websites. In addition, user profiles can be easily updated with semantic metadata coming from the Semantic Web browser.
On Bifix Systems and Generalizations
Authors: Jan-Henrik Altenbernd
Venue: Language and Automata Theory and Applications: Second International Conference, LATA 2008, Tarragona, Spain, March 13-19, 2008. Revised Papers (2008)

Motivated by problems in infinite-state verification, we study word rewriting systems that extend mixed prefix/suffix rewriting (short: bifix rewriting). We introduce several types of infix rewriting where infix replacements are subject to the condition that they have to occur next to tag symbols within a given word. Bifix rewriting is covered by the case where tags occur only as end markers. We show results on the reachability relation (or: derivation relation) of such systems depending on the possibility of removing or adding tags. Where possible we strengthen decidability of the derivation relation to the condition that regularity of sets is preserved, resp. that the derivation relation is even rational. Finally, we compare our model to ground tree rewriting systems and exhibit some differences. 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
Assessing the Performance of a Graph-Based Clustering Algorithm
Authors: Pasquale Foggia, Gennaro Percannella, Carlo Sansone, Mario Vento
Venue: Proceedings of the 6th IAPR-TC-15 international workshop on Graph-Based Representations in Pattern Recognition (2007)

Graph-based clustering algorithms are particularly suited for dealing with data that do not come from a Gaussian or a spherical distribution. They can be used for detecting clusters of any size and shape without the need of specifying the actual number of clusters; moreover, they can be profitably used in cluster detection problems.In this paper, we propose a detailed performance evaluation of four different graph-based clustering approaches. Three of the algorithms selected for comparison have been chosen from the literature. While these algorithms do not require the setting of the number of clusters, they need, however, some parameters to be provided by the user. So, as the fourth algorithm under comparison, we propose in this paper an approach that overcomes this limitation, proving to be an effective solution in real applications where a completely unsupervised method is desirable.