ppxscal/embedding_network huggingface dataset:
Local Synchronization on Oriented Rings
Authors: Doina Bein, Ajoy K. Datta, Chitwan K. Gupta, Lawrence L. Larmore
Venue: Proceedings of the 10th International Symposium on Stabilization, Safety, and Security of Distributed Systems (2008)

We consider the local mutual exclusion (LME) problem on a ring network. We present two self-stabilizing distributed algorithms, with local mutual exclusion, for the dining philosophers problem on a bidirectional oriented ring with two distinguished processes. The first algorithm, which uses the composite atomicity model, works under an unfair distributed daemon. The second algorithm, which uses the read-write atomicity model, works under a weakly fair daemon. Both algorithms use at most two extra bits per process to enforce local mutual exclusion. Both algorithms are derived from a simpler algorithm using transformations which can be applied to other algorithms on the ring. The technique can be generalized to more complex topologies.
Work in Progress: Automatic Generation of Algorithm Animations for Lecture Slides
Authors: Otto Seppälä, Ville Karavirta
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Algorithm visualizations have not been widely adopted in teaching. One possible reason for this is that visualizations are often developed as standalone systems which can be difficult to integrate into lectures. Recently XML based formats for the two major presentation tools have been introduced. We present a method and a prototype implementation which allows creation of algorithm animations in the ODF format. This allows integrating the animation seamlessly within the lecture material.
Effective blame for information-flow violations
Authors: Dave King, Trent Jaeger, Somesh Jha, Sanjit A. Seshia
Venue: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering (2008)

Programs trusted with secure information should not release that information in ways contrary to system policy. However, when a program contains an illegal flow of information, current information-flow reporting techniques are inadequate for determining the cause of the error. Reasoning about information-flow errors can be difficult, as the flows involved can be quite subtle. We present a general model for information-flow blame that can explain the source of such security errors in code. This model is implemented by changing the information-flow verification procedure to: (1) generate supplementary information to reveal otherwise hidden program dependencies; (2) modify the constraint solver to construct a blame dependency graph; and (3) develop an explanation procedure that returns a complete and minimal error report. Our experiments show that information-flow errors can generally be explained and resolved by viewing only a small fraction of the total code.
Simulation-optimization mechanism for expansion strategy using real option theory
Authors: Fong-Ching Yuan
Venue: Expert Systems with Applications: An International Journal (2009)

A right expansion strategy can bring a company more market shares and profits, and hence increase shareholders' equities. However, limited financial resources and various uncertainties require business practitioners to achieve their goals while controlling the risks incurred at an acceptable level. Therefore, justification of expansion investments is an important and complex topic in industry. The traditional investment analysis tools such as net present value (NPV) often tend to undervalue investment decisions. We formulate the expansion investments using real options, and develop a financial model to assess the option value. Monte Carlo simulation is considered a good way to estimate the value of the option. This valuation gives decision makers a way to choose the appropriate expansion strategy based on an integrated view of the market dynamics, but optimization is still a difficult problem to resolve. This paper presents a model of optimization under uncertainty combining system simulation with GA-based optimization to resolve the expansion problem. An industry case is used to demonstrate the application of real options to value expansion investment by using simulation-optimization. This approach also provides some new insights for the real options theory.
Web Image Clustering Based on Multi-instance
Authors: Jing Lu, ShaoPing Ma, Min Zhang
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 (2008)

In image retrieval and annotation, Multi-Instance Learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi-instance objects based on Expectation Maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users' interests on specific web images without any manual labeled data. And comparative studies have shown the effectiveness of the proposed algorithms.
A three pronged approach to teaching undergraduate operating systems
Authors: Steven Robbins
Venue: ACM SIGOPS Operating Systems Review (2008)

This paper describes an approach to teaching an undergraduate operating systems course that relies on three aspects. First, a standard textbook is used for the basic theoretical material. Second, programming projects are used to reinforce some of the material covered from the textbook. Lastly, simulators are used to illustrate other material. A key to the approach is to use experimentation by the student to enhance understanding and prepare them for research. Although no formal evaluation has been done, students seem to enjoy using the simulators and student performance on exams has increased since the simulators were introduced.
Modified subspace limited memory BFGS algorithm for large-scale bound constrained optimization
Authors: Yunhai Xiao, Hongchuan Zhang
Venue: Journal of Computational and Applied Mathematics (2008)

In this paper, a subspace limited memory BFGS algorithm for solving large-scale bound constrained optimization problems is developed. It is modifications of the subspace limited memory quasi-Newton method proposed by Ni and Yuan [Q. Ni, Y.X. Yuan, A subspace limited memory quasi-Newton algorithm for large-scale nonlinear bound constrained optimization, Math. Comput. 66 (1997) 1509-1520]. An important property of our proposed method is that more limited memory BFGS update is used. Under appropriate conditions, the global convergence of the method is established. The implementations of the method on CUTE test problems are presented, which indicate the modifications are beneficial to the performance of the algorithm.
A robust knowledge-based plant searching strategy
Authors: Yo-Ping Huang, Tienwei Tsai, Yan-Ming Wu, Frode-Eika Sandnes
Venue: Expert Systems with Applications: An International Journal (2009)

This paper presents a knowledge-based plant information retrieval system that is robust to inaccurate and erroneous user queries. First, a knowledge-based genetic algorithm (GA) corrects the erroneous input vectors before these are fed into a back-propagation neural network (BPNN) that performs the actual query. Experimental results show that the strategy achieves a 75% recall rate and 25% precision rate with a cutoff level of 10 under the misjudgment of shapes. Moreover, a fully trained BPNN dynamically adapts to changes in the environment. Due to its robust and simple user interface and portability, the strategy is particularly applicable to educational settings such as outdoor fieldwork in courses on ecology.
The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared
Authors: H. -D. Seelig, A. Hoehn, L. S. Stodieck, D. M. Klaus, W. W. Adams III, W. J. Emery
Venue: International Journal of Remote Sensing (2008)

The common features of spectral reflectance from vegetation foliage upon leaf dehydration are decreasing water absorption troughs in the near-infrared (NIR) and short-wave-infrared (SWIR). We studied which leaf water index in the NIR and SWIR is most suitable for the assessment of leaf water content and the detection of leaf dehydration from the laboratory standpoint. We also examined the influence of the thickness of leaves upon leaf water indices. All leaf water content indices examined exhibited basic correlations with the relative water content (RWC) of leaves, while the R 1300/R 1450 leaf water index also demonstrated a high signal strength and low variability (R 2>0.94). All examined leaf reflectance ratios could also be correlated with leaf thickness. The thickness of leaves, however, was not independent of leaf RWC but appeared to decrease substantially as a result of leaf dehydration.
A resource-minimalist flow size histogram estimator
Authors: Bruno Ribeiro, Tao Ye, Don Towsley
Venue: Proceedings of the 8th ACM SIGCOMM conference on Internet measurement (2008)

The histogram of network flow sizes is an important yet difficult metric to estimate in network monitoring. It is important because it characterizes traffic compositions and is a crucial component of anomaly detection methods. It is difficult to estimate because of its high memory and computational requirements. Existing algorithms compute fine grained estimates for each flow size, i.e. 1, 2,... up to the maximum number observed over a finite time interval. Our approach instead relies on the insight that, while many applications require fine grained estimates of small flow sizes, i.e. {1,2,...,k} with a small k, network operators are often only interested in coarse grained estimates of larger flow sizes. Thus, we propose an estimator that outputs a binned histogram of size distributions. Our estimator computes this histogram in O(k3 + log W) operations, where W is the largest flow size of interest to the network operator, while requiring only a few bits of memory per measured flow. This translates into more than 4 fold memory savings and an exponential speedup in the estimator as compared to previous works, greatly increasing the possibility of performing on-line estimation inside a router.
Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments
Authors: Leen-Kiat Soh, Xin Li
Venue: Web Intelligence and Agent Systems (2008)

We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in complex environments that are dynamic, uncertain, and real-time. Our strategy deals with how to assign multiple issues to a set of concurrent negotiations based on an initiating agent's confidence in its profiling of its peer agents. When an agent is confident, it uses a packaged approach&mdash;conducting multiple multi-issue negotiations&mdash;with its peers. Otherwise, it uses a pipelined approach&mdash;conducting multiple single-issue negotiations&mdash;with its peers. The initiating agent is also capable of using both approaches in a hybrid, dealing with a mixed group of responding peers. An agent's confidence in its profile or view of another agent is crucial, and that depends on the environment in which the agents operate. To evaluate the proposed strategy, we use a coalition formation framework in a complex environment. Results show that the proposed strategy outperforms the purely pipelined strategy and the purely packaged strategy in both efficiency and effectiveness.
Efficient skyline querying with variable user preferences on nominal attributes
Authors: Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Jian Pei, Yip Sing Ho, Tai Wong, Yubao Liu
Venue: Proceedings of the VLDB Endowment (2008)

Current skyline evaluation techniques assume a fixed ordering on the attributes. However, dynamic preferences on nominal attributes are more realistic in known applications. In order to generate online response for any such preference issued by a user, one obvious solution is to enumerate all possible preferences and materialize all results of these preferences. However, the pre-processing and storage requirements of a full materialization are typically prohibitive. Instead, we propose a semi-materialization method called the IPO-tree Search which stores partial useful results only. With these partial results, the result of each possible preference can be returned efficiently. We have also conducted experiments to show the efficiency of our proposed algorithm.
A day at the root of the internet
Authors: Sebastian Castro, Duane Wessels, Marina Fomenkov, Kimberly Claffy
Venue: ACM SIGCOMM Computer Communication Review (2008)

We analyzed the largest simultaneous collection of full-payload packet traces from a core component of the global Internet infrastructure ever made available to academic researchers. Our dataset consists of three large samples of global DNS traffic collected during three annual "Day in the Life of the Internet" (DITL) experiments in January 2006, January 2007, and March 2008. Building on our previous comparison of DITL 2006 and DITL 2007 DNS datasets [28], we venture to extract historical trends, comparisons with other data sources, and interpretations, including traffic growth, usage patterns, impact of anycast distribution, and persistent problems in the root nameserver system that reflect ominously on the global Internet. Most notably, the data consistently reveals an extraordinary amount of DNS pollution -- an estimated 98% of the traffic at the root servers should not be there at all. Unfortunately, there is no clear path to reducing the pollution, so root server operators, and those who finance them, must perpetually overprovision to handle this pollution. Our study presents the most complete characterization to date of traffic reaching the roots, and while the study does not adequately fulfill the "Day in the Life of the Internet" vision, it does succeed at unequivocally demonstrating that the infrastructure on which we are all now betting our professional, personal, and political lives deserves a closer and more scientific look.
WebTables: exploring the power of tables on the web
Authors: Michael J. Cafarella, Alon Halevy, Daisy Zhe Wang, Eugene Wu, Yang Zhang
Venue: Proceedings of the VLDB Endowment (2008)

The World-Wide Web consists of a huge number of unstructured documents, but it also contains structured data in the form of HTML tables. We extracted 14.1 billion HTML tables from Google's general-purpose web crawl, and used statistical classification techniques to find the estimated 154M that contain high-quality relational data. Because each relational table has its own "schema" of labeled and typed columns, each such table can be considered a small structured database. The resulting corpus of databases is larger than any other corpus we are aware of, by at least five orders of magnitude. We describe the WEBTABLES system to explore two fundamental questions about this collection of databases. First, what are effective techniques for searching for structured data at search-engine scales? Second, what additional power can be derived by analyzing such a huge corpus? First, we develop new techniques for keyword search over a corpus of tables, and show that they can achieve substantially higher relevance than solutions based on a traditional search engine. Second, we introduce a new object derived from the database corpus: the attribute correlation statistics database (AcsDB) that records corpus-wide statistics on co-occurrences of schema elements. In addition to improving search relevance, the AcsDB makes possible several novel applications: schema auto-complete, which helps a database designer to choose schema elements; attribute synonym finding, which automatically computes attribute synonym pairs for schema matching; and join-graph traversal, which allows a user to navigate between extracted schemas using automatically-generated join links.
Near-surface air temperature estimation from ASTER data based on neural network algorithm
Authors: K. B. Mao, H. J. Tang, X. F. Wang, Q. B. Zhou, D. L. Wang
Venue: International Journal of Remote Sensing (2008)

An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near-surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0 K. The mean and the standard deviation of estimation error are about 2.0 K and 2.3 K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM-NN can be used to estimate near surface air temperature from ASTER data.
DOA 2008 PC Co-chairs' Message
Authors: Mark Little, Greg Pavlik, Alberto Montresor
Venue: Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems: (2008)

Welcome to the 10th International Symposium on Distributed Objects, Middleware and Applications (DOA 2008), held in Monterrey, Mexico, November 9-14, 2008.
Semantic Discovery of the User Intended Query in a Selectable Target Query Language
Authors: Carlos Bobed, Raquel Trillo, Eduardo Mena, Jordi Bernad
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008)

The syntactic approach of most of web search engines still has the drawback of not considering the semantics of the keywords entered by the user. So, users usually have to browse many hits looking for the information they want. In this paper, we present a system that, given a set of keywords with well defined semantics, automatically generates a set of formal queries, in the query language of the user's choice, which attempt to capture what the user had in mind when she or he wrote those keywords. The system uses ontologies and a Description Logics reasoner to perform a semantic enrichment of user keywords to improve the discovering of possible user queries and to reject semantically inconsistent queries.
Fluid level in a reservoir with an on-off source
Authors: Varun Gupta, Peter G. Harrison
Venue: ACM SIGMETRICS Performance Evaluation Review (2008)

We obtain the Laplace transform of the fluid level probability density function, in terms of the on-period density function, for a fluid queue (or reservoir) with on-off input at equilibrium. We further obtain explicit expressions for the moments of fluid level in terms of the moments of the on-period and hence derive an algorithm for the moments of fluid level at every queue in a tandem network. It turns out that to calculate the kth moment at the ith queue, only the first k +1 moments of the on-period of the input process to the first queue are required.
Efficient Grid-Based Video Storage and Retrieval
Authors: Pablo Toharia, Alberto Sánchez, José Luis Bosque, Oscar D. Robles
Venue: Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems: (2008)

Different fields, such as news broadcasting (news, shows, series, etc), advertising, and medical applications, require to store a large amount of video data which can be used later on. Content-Based Video Retrieval (CBVR) systems are very attractive since they can help users to retrieve video sequences over large video databases with respect to some specific topic, character or place. In most cases, stored videos belong to different organizations and have a quite demanding storage capacity. These features fit in a natural way into the concept of grid computing, that provides a great computing and storage capacity thanks to the use of heterogeneous resources put together by the cooperation and resource sharing among different institutions.This paper presents a Grid and Content-based VIdeo Retrieval (GCViR) system that offers a good cost/performance ratio to select the most suitable grid resources for data storage in order to store and retrieve large video data providing flexibility and scalability. An evaluation shows the feasibility and benefits of this approach.
Efficient search for the top-k probable nearest neighbors in uncertain databases
Authors: George Beskales, Mohamed A. Soliman, Ihab F. IIyas
Venue: Proceedings of the VLDB Endowment (2008)

Uncertainty pervades many domains in our lives. Current real-life applications, e.g., location tracking using GPS devices or cell phones, multimedia feature extraction, and sensor data management, deal with different kinds of uncertainty. Finding the nearest neighbor objects to a given query point is an important query type in these applications. In this paper, we study the problem of finding objects with the highest marginal probability of being the nearest neighbors to a query object. We adopt a general uncertainty model allowing for data and query uncertainty. Under this model, we define new query semantics, and provide several efficient evaluation algorithms. We analyze the cost factors involved in query evaluation, and present novel techniques to address the trade-offs among these factors. We give multiple extensions to our techniques including handling dependencies among data objects, and answering threshold queries. We conduct an extensive experimental study to evaluate our techniques on both real and synthetic data.
A Human-Computer Dialogue System for Educational Debate: A Computational Dialectics Approach
Authors: Tangming Yuan, David Moore, Alec Grierson
Venue: International Journal of Artificial Intelligence in Education (2008)

This paper reports research concerning issues involved in adopting a computational dialectics approach to develop a human-computer dialogue system for educational debate. In particular, we propose a dialogue model and a set of computationally usable strategic heuristics to enable the computer to engage its users in debate on a controversial issue. The system operationalising the proposed dialogue model and strategy has been fully implemented. It has also been subject to initial evaluation exercises. The results of the evaluation are essentially favourable. It is anticipated that the work reported in this paper will contribute toward the development of human-computer dialogue in general and of computer-based educational debate in particular, and help to illuminate research issues in the field of dialectics itself.
Accurately estimating reflectance parameters for color and gloss reproduction
Authors: Shiying Li, Yoshitsugu Manabe, Kunihiro Chihara
Venue: Computer Vision and Image Understanding (2009)

A new method is described to estimate diffuse and specular reflectance parameters using spectral images, which overcomes the dynamic range limitation of imaging devices. After eliminating the influences of illumination and camera on spectral images, reflection values are initially assumed as diffuse-only reflection components, and subjected to the least squares method to estimate diffuse reflectance parameters at each wavelength on each single surface particle. Based on the dichromatic reflection model, specular reflection components are obtained, and then subjected to the least squares method to estimate specular reflectance parameters for gloss intensity and surface roughness. Experiments were carried out using both simulation data and measured spectral images. Our results demonstrate that this method is capable of estimating diffuse and specular reflectance parameters precisely for color and gloss reproduction, without requiring preprocesses such as image segmentation and synthesis of high dynamic range images.