ppxscal/embedding_network huggingface dataset:
Learning Graph Matching
Authors: Tibério S. Caetano, Julian J. McAuley, Li Cheng, Quoc V. Le, Alex J. Smola
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many formulations of this problem can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility and a quadratic term encodes edge compatibility. The main research focus in this theme is about designing efficient algorithms for approximately solving the quadratic assignment problem, since it is NP-hard. In this paper we turn our attention to a different question: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the 'labels' are matches between them. Our experimental results reveal that learning can substantially improve the performance of standard graph matching algorithms. In particular, we find that simple linear assignment with such a learning scheme outperforms Graduated Assignment with bistochastic normalisation, a state-of-the-art quadratic assignment relaxation algorithm.
Analyzing Fine-Grained Skill Models Using Bayesian and Mixed Effects Methods
Authors: Zachary A. Pardos, Mingyu Feng, Neil T. Heffernan, Cristina Linquist-Heffernan
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

Two modelling methods were used to answer the research question of how accurate various grained 1, 5, 39 and 106 skill models are at assessing student knowledge in the ASSISTment online tutoring system and predicting student performance on a state math test. One method is mixed-effects statistical modelling. The other uses a Bayesian networks machine learning approach. We compare the prediction results to identify benefits and drawbacks of either method and to find out if the two results agree. We report that both methods showed compelling similarity which support the use of fine grained skill models.
MOCell: A cellular genetic algorithm for multiobjective optimization
Authors: Antonio J. Nebro, Juan J. Durillo, Francisco Luna, Bernabé Dorronsoro, Enrique Alba
Venue: International Journal of Intelligent Systems (2009)

This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been evaluated with both constrained and unconstrained problems and compared against NSGA-II and SPEA2, two state-of-the-art evolutionary multiobjective optimizers. For the studied benchmark, our experiments indicate that MOCell obtains competitive results in terms of convergence and hypervolume, and it clearly outperforms the other two compared algorithms concerning the diversity of the solutions along the Pareto front. &copy; 2009 Wiley Periodicals, Inc.
Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
Venue: Human Language Technology Conference (2006)

Welcome to the HLT-NAACL Workshop on Analyzing Conversations in Text and Speech (ACTS). We received 21 submissions, and due to a rigerous review process, we rejected 11.
On the Logic of Theory Change: Relations between Incision and Selection Functions
Authors: Marcelo A. Falappa, Eduardo L. Fermé, Gabriele Kern-Isberner
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

This work elaborates on the connection between partial meet contractions and kernel contractions in belief change theory. We present a way to define incision functions (used in kernel contractions) from selection functions (used in partial meet contractions) and vice versa. Then we make precise under which conditions there are exact correspondences between selection and incision functions so that the same contraction operations can be obtained by using either of them.
Malay language document identification using BPNN
Authors: Norzaidah Md Noh, Mohd Rusydi Abdul Talib, Azlin Ahmad, Shamimi A. Halim, Azlinah Mohamed
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

Document Identification aims to extract information presented in digital documents namely articles, newpapers, magazines and e-books. The popularity of world-wide-web (www) in disseminating information within a click of a mouse has also increased the amount of Malay documents published on the www. This has given rise to many language identification systems. However, not much research on Malay Document identification language has been established. Therefore the purpose of this paper is to address the implementation of a tool for Malay language document identification in mono- and multi-lingual documents. The system development includes a feature extraction and a neural network technique. The feature extraction consists of documents filtering, word matching and binary representation of varied length sentences from many types of documents including generic text files, MS Word files, Adobe PDF and HTML web pages. The neural network employs back propagation neural network (BPNN) algorithm with adjustable number of neurons and weights between input, hidden and output layer. Experiments were conducted on 300 sentences of mono and multilingual documents. The results shown 90% accuracy in identification of Malay language documents, which has more than 80%, matched Malay words. This tool is able to recognise Malay document from other language documents with reasonable accuracy.
Learning to rank definitions to generate quizzes for interactive information presentation
Authors: Ryuichiro Higashinaka, Kohji Dohsaka, Hideki Isozaki
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

This paper proposes the idea of ranking definitions of a person (a set of biographical facts) to automatically generate "Who is this?" quizzes. The definitions are ordered according to how difficult they make it to name the person. Such ranking would enable users to interactively learn about a person through dialogue with a system with improved understanding and lasting motivation, which is useful for educational systems. In our approach, we train a ranker that learns from data the appropriate ranking of definitions based on features that encode the importance of keywords in a definition as well as its content. Experimental results show that our approach is significantly better in ranking definitions than baselines that use conventional information retrieval measures such as tf*idf and pointwise mutual information (PMI).
ContextServ: A platform for rapid and flexible development of context-aware Web services
Authors: Quan Z. Sheng, Sam Pohlenz, Jian Yu, Hoi S. Wong, Anne H. H. Ngu, Zakaria Maamar
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

Context-aware Web services are currently emerging as an important technology for building innovative context-aware applications. Unfortunately, context-aware Web services are still difficult to build. This paper describes ContextServ, a platform for rapid development of context-aware Web services. ContextServ adopts model-driven development where context-aware Web services are specified using ContextUML, a UML based modeling language. The platform also offers a set of automated tools for generating and deploying executable implementations of context-aware Web services. This paper presents the motivation, system design, implementation, and usage of ContextServ.
Generative model-based metasearch for data fusion in information retrieval
Authors: Miles Efron
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

"Data fusion" refers to the problem in information retrieval (IR) where several lists of documents ranked against a query are to be merged into a single ranked list for presentation to a user. Data fusion is also known as "metasearch." In a digital library setting data fusion may support operations such as federated search based on multiple repository representations. This paper presents a novel approach to the fusion problem: generative model-based Metasearch (GeM). We suggest viewing the appearance of documents in a return set as the outcome of a probabilistic process; some documents are likely to occur in the model, while others are unlikely. Using Bayesian parameter estimation to fit a multinomial distribution based on the return sets to be merged, GeM achieves a final ranking by listing documents in decreasing probability of generation under the induced model. We also introduce what we call "the impatient reader" approach to normalizing document ranks in service to the fusion operation. We report results from several experiments on TREC data suggesting that GeM, informed with impatient reader document scores, operates at state-of-the-art levels of effectiveness.
First principles study of substoichiometric germanium oxides
Authors: Jan Felix Binder, Peter Broqvist, Alfredo Pasquarello
Venue: Microelectronic Engineering (2009)

Using density functional calculations, we studied substoichiometric Ge oxides through a series of crystalline models representing the various oxidation states. We evaluated deviations with respect to a bond-energy description in terms of penalty energies for intermediate oxidation states of Ge. Corresponding bond length variations and band gaps are also given. The calculated penalty energies are significantly lower than for Si indicating higher stability of the substoichiometric phase at Ge/GeO"2 interfaces compared to Si/SiO"2 interfaces.
Out of the Lab and into the Classroom: An Evaluation of Reflective Dialogue in Andes
Authors: Sandra Katz, John Connelly, Christine Wilson
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

Several laboratory studies have demonstrated the effectiveness of presenting students with “Reflection Questions” immediately after they have solved quantitative problems [1, 2]. The main goal of the two experiments described in this paper was to determine if the positive results of post-practice reflection from laboratory studies hold up in an actual classroom setting. We added prototype reflective dialogues to the Andes physics tutoring system [3, 4] and evaluated them in a course at the US Naval Academy. Consistent with prior research, the dialogues promoted conceptual understanding of physics. However, measures of retention and transfer to problem-solving ability showed only a marginal effect of completing the reflective dialogues, in the first experiment.
Extracting key paragraph based on topic and event detection: towards multi-document summarization
Authors: Fumiyo Fukumoto, Yoshimi Suzuki
Venue: NAACL-ANLP 2000 Workshop on Automatic Summarization (2000)

This paper proposes a method for extracting key paragraph for multi-document summarization based on distinction between a topic and an event. A topic and an event are identified using a simple criterion called domain dependency of words. The method was tested on the TDT1 corpus which has been developed by the TDT Pilot Study and the result can be regarded as promising the idea of domain dependency of words effectively employed.
Large-scale behavioral targeting
Authors: Ye Chen, Dmitry Pavlov, John F. Canny
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. The state-of-the-art of BT derives a linear Poisson regression model from fine-grained user behavioral data and predicts click-through rate (CTR) from user history. We designed and implemented a highly scalable and efficient solution to BT using Hadoop MapReduce framework. With our parallel algorithm and the resulting system, we can build above 450 BT-category models from the entire Yahoo's user base within one day, the scale that one can not even imagine with prior systems. Moreover, our approach has yielded 20% CTR lift over the existing production system by leveraging the well-grounded probabilistic model fitted from a much larger training dataset. Specifically, our major contributions include: (1) A MapReduce statistical learning algorithm and implementation that achieve optimal data parallelism, task parallelism, and load balance in spite of the typically skewed distribution of domain data. (2) An in-place feature vector generation algorithm with linear time complexity O(n) regardless of the granularity of sliding target window. (3) An in-memory caching scheme that significantly reduces the number of disk IOs to make large-scale learning practical. (4) Highly efficient data structures and sparse representations of models and data to enable fast model updates. We believe that our work makes significant contributions to solving large-scale machine learning problems of industrial relevance in general. Finally, we report comprehensive experimental results, using industrial proprietary codebase and datasets.
Performance enhancement of mobile ad hoc networks using nodal cooperation
Authors: Santosh Kulkarni, Pratap S. Prasad, Prathima Agrawal
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

Mobility is known to drastically affect the performance of wireless ad hoc networks. Link breakages resulting from node mobility are among the primary causes leading to degradation in network performance. Recent research on cooperative communication demonstrates that spatial diversity achieved through cooperating nodes can significantly improve the performance of wireless networks. In this paper, we investigate the impact of introducing nodal cooperation to mitigate the adverse effects of mobility on network performance. Results show that utilizing a cooperative MAC sub-layer helps in improving the network's performance even when the participating nodes are mobile.
A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique
Authors: Zengbing Xu, Jianping Xuan, Tielin Shi, Bo Wu, Youmin Hu
Venue: Expert Systems with Applications: An International Journal (2009)

This paper presents a novel intelligent diagnosis method based on multiple domain features, modified distance discrimination technique and improved fuzzy ARTMAP (IFAM). The method consists of three steps. To begin with, time-domain, frequency-domain and wavelet grey moments are extracted from the raw vibration signals to demonstrate the fault-related information. Then through the modified distance discrimination technique some salient features are selected from the original feature set. Finally, the optimal feature set is input into the IFAM incorporated with similarity based on the Yu's norm in the classification phase to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearing, and the test results show that the IFAM identify the fault categories of rolling element bearing more accurately and has a better diagnosis performance compared to the FAM. Furthermore, by the application of the bootstrap method to the diagnosis results it can testify that the IFAM has more capacity of reliability and robustness.
A reinforced iterative formalism to learn from human errors and uncertainty
Authors: F. Vanderhaegen, P. Polet, S. Zieba
Venue: Engineering Applications of Artificial Intelligence (2009)

This paper proposes a reinforced iterative formalism to learn from intentional human errors called barrier removal and from uncertainty on human-error parameters. Barrier removal consists in misusing a safety barrier that human operators are supposed to respect. The iterative learning formalism is based on human action formalism that interprets the barrier removal in terms of consequences, i.e. benefits, costs and potential dangers or deficits. Two functions are required: the similarity function to search a known case closed to the input case for which the human action has to be predicted and a reinforcement function to reinforce links between similar known cases. This reinforced iterative formalism is applied to a railway simulation from which the prediction of barrier removal is based on subjective data.
Cluster filtered KNN: A WLAN-based indoor positioning scheme
Authors: Jun Ma, Xuansong Li, Xianping Tao, Jian Lu
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

Location Based Service (LBS) is one kind of ubiquitous applications whose functions are based on the locations of clients. The core of LBS is an effective positioning system. As wireless LAN (WLAN) costs less and is easy to access, using WLAN for indoor positioning has been widely studied recently. K Nearest Neighbors (KNN) is one of the basic deterministic fingerprint based algorithms and widely used for WLAN-based indoor positioning. However, KNN takes all the nearest K neighbors for calculating the estimated result, which could be improved if some selective work could be done to those neighbors beforehand. In this paper we propose a new scheme called ‘Cluster Filtered KNN’ (CFK). CFK utilizes clustering technique to partition those neighbors into different clusters and chooses one cluster as the delegate. In the end, the final estimate can be calculated only based on the elements of the delegate. With experiments, we found that CFK does outperform KNN.
AdaptGuard: guarding adaptive systems from instability
Authors: Jin Heo, Tarek Abdelzaher
Venue: Proceedings of the 6th international conference on Autonomic computing (2009)

In this paper, we design, implement and evaluate AdaptGuard, a software service for guarding adaptive systems, such as QoS-adaptive servers, from instability caused by software anomalies and faults. Adaptive systems are of growing importance due to the need to adjust performance to a larger range of changing environmental conditions without human intervention. Such systems, however, implicitly assume a model of system behavior that may be violated, causing adaptation loops to perform poorly or fail. The purpose of AdaptGuard is simple: in the absence of an a priori model of the adaptive software system, anticipate system instability, attribute it correctly to the right "runaway" adaptation loop, and disconnect it, replacing it with conservative but stable open-loop control until further notice. We evaluate AdaptGuard by injecting various software faults into adaptive systems that are managed by typical adaptation loops. Results demonstrate that it can successfully anticipate instability caused by the injected faults and recover from performance degradation. Further, a case study is presented using an Apache Web server serving multiple classes of traffic. A performance anomaly is demonstrated, caused by unexpected interactions between an admission controller and the Linux anti-livelock mechanism. In the absence of a model that describes this mechanism, AdaptGuard is able to correctly attribute the unexpected problem to the right runaway loop and fix it.
Visualizing compression algorithms on-the-fly
Authors: Guido Rößling, Florian Lindner
Venue: ACM SIGCSE Bulletin (2009)

This paper presents a set of generators for visualizing common compression algorithms on-the-fly using the established and freely available Animal algorithm animation system.
Office 2007 Shortcuts Quick Reference: Laminated Cheat Sheet of Keyboard Shorcuts
Authors: Beezix Inc
Venue:  (2007)

Laminated quick reference guide specializing in keyboard shortcuts for Microsoft Office 2007 products Word, Excel, and PowerPoint. This guide is suitable as a training handout, or simply an easy to use reference guide, for any type of user. Shortcuts: Word - Text Selection Shortcuts, Movement Shortcuts, Special Characters, Switching Views, Formatting Shortcuts, Deleting Words, and Other Shortcuts. Excel - Selection Shortcuts, Movement Shortcuts, Formatting Shortcuts, Date & Time Shortcuts, Copying Shortcuts, Formula Shortcuts, Print Preview Shortcuts, Special Characters, and Other Shortcuts. PowerPoint - Slide Show Shortcuts, Rehearsing Shortcuts, Outline View Shortcuts, and Other Shortcuts. Common Shortcuts for Word, Excel and PowerPoint. Also includes instructions for customizing the Quick Access Toolbar and the Status Bar. 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
Getting Farther on Software Evolution via AOP and Reflection
Authors: Manuel Oriol, Walter Cazzola, Shigeru Chiba, Gunter Saake
Venue: Object-Oriented Technology. ECOOP 2008 Workshop Reader: ECOOP 2008 Workshops Paphos, Cyprus, July 7-11, 2008 Final Reports (2008)

Following last four years' RAM-SE (Reflection, AOP and Meta-Data for Software Evolution) workshop at the ECOOP conference, the RAM-SE'08 workshop was a successful and popular event. As its name implies, the workshop's focus was on the application of reflective, aspect-oriented and data-mining techniques to the broad field of software evolution. Topics and discussions at the workshop included mechanisms for supporting software evolution, technological limits of the aspect-oriented and reflective approaches to software evolution and tools devoted to software evolution.The workshop's main goal was to bring together researchers working in the field of software evolution with a particular interest in reflection, aspect-oriented programming and meta-data. The workshop was organized as a full day meeting, partly devoted to presentation of submitted position papers and partly devoted to panel discussions about the presented topics and other interesting issues in the field. In this way, the workshop allowed participants to get acquainted with each other's work, and stimulated collaboration. We hope this helped participants in improving their ideas and the quality of their future publications.The workshop's proceedings, including all accepted position papers can be downloaded from the workshop's web site and a post workshop proceeding, including an extension of the accepted paper is planned to be published by the University of Magdeburg.In this report, we provide a session-by-session overview of the workshop, and then present our opinions about future trends in software evolution.