ppxscal/embedding_network huggingface dataset:
The offset tree for learning with partial labels
Authors: Alina Beygelzimer, John Langford
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

We present an algorithm, called the Offset Tree, for learning to make decisions in situations where the payoff of only one choice is observed, rather than all choices. The algorithm reduces this setting to binary classification, allowing one to reuse any existing, fully supervised binary classification algorithm in this partial information setting. We show that the Offset Tree is an optimal reduction to binary classification. In particular, it has regret at most (k-1) times the regret of the binary classifier it uses (where k is the number of choices), and no reduction to binary classification can do better. This reduction is also computationally optimal, both at training and test time, requiring just O(log2 k) work to train on an example or make a prediction. Experiments with the Offset Tree show that it generally performs better than several alternative approaches.
The existence of solutions for impulsive neutral functional differential equations
Authors: Claudio Cuevas, Eduardo Hernández, Marcos Rabelo
Venue: Computers Mathematics with Applications (2009)

This work deals with the existence of mild solutions for a class of impulsive functional differential equations of the neutral type associated with the family of linear closed (not necessarily bounded) operators {A(t):t@?I}.
ExQueX: exploring and querying XML documents
Authors: Benny Kimelfeld, Yehoshua Sagiv, Gidi Weber
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

ExQueX is an interactive system for exploring and querying XML documents. The exploration is done by searching, ranking and filtering, and it enables users to discover relationships that exist in a given document. The results of the exploration can be used either directly as tree queries or as building blocks in the process of formulating more complex queries. The latter is done by formulating an abstract tree query, whose edges represent relationships that are resolved by using concrete results from the exploration phase. ExQueX facilitates fast and clear understanding of both the contents and complex structures of XML documents.
Constrained mixture estimation for analysis and robust classification of clinical time series
Authors: Ivan G. Costa, Alexander Schönhuth, Christoph Hafemeister, Alexander Schliep
Venue: Bioinformatics (2009)

Motivation: Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. Special care is needed when designing classification procedures that support personalized diagnosis and choice of treatment. Here, we particularly focus on classification of interferon-β (IFNβ) treatment response in Multiple Sclerosis (MS) patients which has attracted substantial attention in the recent past. Half of the patients remain unaffected by IFNβ treatment, which is still the standard. For them the treatment should be timely ceased to mitigate the side effects. Results: We propose constrained estimation of mixtures of hidden Markov models as a methodology to classify patient response to IFNβ treatment. The advantages of our approach are that it takes the temporal nature of the data into account and its robustness with respect to noise, missing data and mislabeled samples. Moreover, mixture estimation enables to explore the presence of response sub-groups of patients on the transcriptional level. We clearly outperformed all prior approaches in terms of prediction accuracy, raising it, for the first time, >90%. Additionally, we were able to identify potentially mislabeled samples and to sub-divide the good responders into two sub-groups that exhibited different transcriptional response programs. This is supported by recent findings on MS pathology and therefore may raise interesting clinical follow-up questions. Availability: The method is implemented in the GQL framework and is available at http://www.ghmm.org/gql. Datasets are available at http://www.cin.ufpe.br/~igcf/MSConst Contact: igcf@cin.ufpe.br Supplementary information:Supplementary data are available at Bioinformatics online.
Image Rendering Based on a Spatial Extension of the CIECAM02
Authors: Olivier Tulet, Mohamed-Chaker Larabi, Christine Fernandez-Maloigne
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

With the multiplicity of imaging devices, the color quality and portability have become a very challenging problem. Moreover, a color is perceived with regards to its environment. So, if this environment changes it implies a change in the perceived color. In order to address this influence, the CIE (Commission Internationale de l'éclairage) has standardized a tool named color appearance model (CIECAM97, CIECAM02). These models are able to take into account many phenomena related to human vision of color and can predict the color of a stimulus, function of its observations conditions. However, these models do not deal with the influence of spatial frequencies which can have a big impact on our perception. In this paper, we present an extended version of the CIECAM02 that integrates a spatial model correcting the color in relation to its spatial frequency. Moreover, the previous model has been modified to deal with images and not only single stimulus. The main difference with the rendering models (e.g. iCAM) lies in the fact that the proposed model, takes into account the spatial repartition of a pixel in addition to its environment. The obtained results are sound and demonstrate the efficiency of the proposed extension. This has been checked thanks to a psychophysical study where observers were assigned the task of assessing the quality of the improved version in comparison to the original.
A proposal for an online graduate degree program in computer education and instructional technologies (CEIT) in Turkey
Authors: H. Ferhan Odabasi, Yavuz Akbulut, Ozcan Ozgur Dursun, Ahmet Naci Coklar
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

This study proposed that there needed to be an online platform for the graduate level studies in Computer Education and Instructional Technologies. The proposed platform was named BOTE-UYUM, or CEIT National MA Initiative Research (CEIT-NMAIR). The platform was suggested to share the expertise of a mega-university, Anadolu University in this case, in the field of Computer Education and Instructional Technologies (CEIT) to offer graduate courses online along with face to face assistance on a regular basis. Generating the platform to empower and diffuse an MA program in CEIT was found significant for the future ICT integration endeavors of the country. Researchers intended to create an on-line learning system, which should be feasible to deliver MA courses. Within the scope of the proposal, a synergy at masters' degree level at CEIT departments in Turkey and Turkish Republic of Northern Cyprus would be created. Before creating the online learning system, it was planned to refer to the opinions of the shareholders, that is, principal participants of the proposed program including academicians, teachers and prospective teachers to form a national point of view. The project should proceed with the creation and evaluation of the online learning system.
Hybrid spectral gradient method for the unconstrained minimization problem
Authors: William Cruz, Gilberto Noguera
Venue: Journal of Global Optimization (2009)

We present a hybrid algorithm that combines a genetic algorithm with the Barzilai---Borwein gradient method. Under specific assumptions the new method guarantees the convergence to a stationary point of a continuously differentiable function, from any arbitrary initial point. Our preliminary numerical results indicate that the new methodology finds efficiently and frequently the global minimum, in comparison with the globalized Barzilai---Borwein method and the genetic algorithm of the Toolbox of Genetic Algorithms of MatLab.
Fast track article: Improving the QoS for information discovery in autonomic wireless sensor networks
Authors: Robin Doss, Gang Li, Vicky Mak, Shui Yu, Morshed Chowdhury
Venue: Pervasive and Mobile Computing (2009)

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. In order to fully exploit the data gathering and dissemination capabilities of these networks, energy-efficient and scalable solutions for data storage and information discovery are essential. Traditionally, the communication pattern in WSNs has been assumed to be many-to-one; i.e., numerous sensors gather information which is routed to a central point commonly referred to as the sink. However, many emerging applications for WSNs require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay resulting in quality of service (QoS) improvements. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group ''similar'' sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time. Simulation and Analytical results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.
Efficient processing of complex features for information retrieval
Authors: W. B. Croft
Venue:  (2008)

Text search systems research has primarily focused on simple occurrences of query terms within documents to compute document relevance scores. However, recent research shows that additional document features are crucial for improving retrieval effectiveness. We develop a series of techniques for efficiently processing queries with feature-based models. Our TupleFlow framework, an extension of MapReduce, provides a basis for custom binned indexes, which efficiently store feature data. Our work in binning probabilities shows how to effectively map language model probabilities into the space of small positive integers, which helps improve speeds without reducing query effectiveness. We also show new efficient query processing results for both document-sorted and score-sorted indexes. All of our work is evaluated using the largest available research dataset.
Shape from Shading Using Probability Functions and Belief Propagation
Authors: Jochen Wilhelmy, Jörg Krüger
Venue: International Journal of Computer Vision (2009)

Shape-from-shading (SFS) aims to reconstruct the three-dimensional shape of an object from a single shaded image. This article proposes an improved framework based on belief propagation for computing SFS. The implementation of the well-known brightness, integrability and smoothness constraints inside this framework is shown.We implement the constraints as probability density functions. For example, the brightness constraint is a two-dimensional probability density function that relates all possible surface gradients at a pixel to their probability given the pixel intensity. A straightforward extension of the framework to photometric stereo is presented, where multiple images of the same scene taken under different lighting conditions are available.The results are promising, especially since the solution is obtained by iteratively applying simple operations on a regular grid of points. The presented framework therefore can be implemented in parallel and is a reasonably likely biological scheme.
A Decision Procedure for Structured Cryptographic Protocols
Authors: François Gagnon, Mohamed Mejri
Venue: Proceeding of the 2006 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the fifth SoMeT_06 (2006)

Given a cryptographic protocol, it is possible to extract an inference system modelling exactly the set of messages an active intruder can learn by manipulating this protocol. Unfortunately, there is no general proof-search procedure to test whether or not a term belongs to the theory of an inference system. This paper presents the preliminary results obtained during an attempt to circumvent this problem. First, it explains a transformation process over inference systems; then presents a decision procedure (using the transformation process) for the security of a class of cryptographic protocols, called structured protocols; and finally argues that some basic security properties are decidable for such cryptographic protocols. The security properties include secrecy and chaoticity; the results can possibly be extended to cover authentication as well.
Private Queries and Trajectory Anonymization: a Dual Perspective on Location Privacy
Authors: Gabriel Ghinita
Venue: Transactions on Data Privacy (2009)

The emergence of mobile devices with Internet connectivity (e.g., Wi-Fi) and global positioning capabilities (e.g., GPS) have triggered the widespread development of location-based applications. For instance, users are able to ask queries about points of interest in their proximity. Furthermore, users can act as mobile sensors to monitor traffic flow, or levels of air pollution. However, such applications require users to disclose their locations, which raises serious privacy concerns. With knowledge of user locations, a malicious attacker can infer sensitive information, such as alternative lifestyles or political affiliations. Preserving location privacy is an essential requirement towards the successful deployment of location-based services (LBS). Currently, two main LBS use scenarios exist: in the first one, users send location-based queries to an un-trusted server, and the privacy objective is to protect the location of the querying user. In the second setting, a trusted entity, such as a telephone company, gathers large amounts of location data (i.e., trajectory traces) and wishes to publish them for data mining (e.g., alleviating traffic congestion). In this case, it is crucial to prevent an adversary from associating trajectories to user identities. In this survey paper, we give an overview of the state-of-the-art in location privacy protection from the dual perspective of query privacy and trajectory anonymization. We review the most prominent design choices and technical solutions, and highlight their relative strengths and weaknesses.
Out-of-band detection of boot-sequence termination events
Authors: Naama Parush, Dan Pelleg, Muli Ben-Yehuda, Paula Ta-Shma
Venue: Proceedings of the 6th international conference on Autonomic computing (2009)

The popularization of both virtualization and CDP technologies mean that we can now watch disk accesses of systems from entities which are not controlled by the OS. This is a rich source of information about the system's inner workings. In this paper, we explore one way of mining the stream of data, to determine if the system had finished booting. Systems which we detect as failing to boot (or taking too long to boot) are flagged for further manual or automatic remediation. By performing this detection out-of-band, we gain a head start on any detection scheme that runs within the OS, and therefore must wait for the boot event to finish. Additionally, our scheme is agnostic to file-system layout and to kernel architecture. We implemented our solution for the x86 architecture under two different virtualization platforms, and tested it on both Windows and Linux virtual machines. Under a variety of workloads and configurations, our detector managed to successfully identify the boot termination event, in most cases within 5 seconds of the event.
Improving Document Search Using Social Bookmarking
Authors: Hamidreza Baghi, Yevgen Biletskiy
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

During the last decade, the use of community-based techniques has emerged in various data mining and search systems. Nowadays, many web search engines use social networking analysis to improve the search results. The present work incorporates one of the popular collaborative tools, called Social Bookmarking, into search. In the present paper, a technique, which utilizes Social Bookmarking information into search, is discussed.
Importance sampling for Jackson networks
Authors: Paul Dupuis, Hui Wang
Venue: Queueing Systems: Theory and Applications (2009)

Rare event simulation in the context of queueing networks has been an active area of research for more than two decades. A commonly used technique to increase the efficiency of Monte Carlo simulation is importance sampling. However, there are few rigorous results on the design of efficient or asymptotically optimal importance sampling schemes for queueing networks. Using a recently developed game/subsolution approach, we construct simple and efficient state-dependent importance sampling schemes for simulating buffer overflows in stable open Jackson networks. The sampling distributions do not depend on the particular event of interest, and hence overflow probabilities for different events can be estimated simultaneously. A by-product of the analysis is the identification of the minimizing trajectory for the calculus of variation problem that is associated with the sample-path large deviation rate function.
A Novel Artificial Immune System for Multiobjective Optimization Problems
Authors: Jiaquan Gao, Lei Fang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: At first,a randomly weighted sum of multiple objectives is used as a fitness function; Secondly, the individuals of the population are chosen from the memory, which is a set of elite solutions. Lastly, in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate the similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. Simulation results show WBMOAIS outperforms the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II).
Boosting products of base classifiers
Authors: Balázs Kégl, Róbert Busa-Fekete
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

In this paper we show how to boost products of simple base learners. Similarly to trees, we call the base learner as a subroutine but in an iterative rather than recursive fashion. The main advantage of the proposed method is its simplicity and computational efficiency. On benchmark datasets, our boosted products of decision stumps clearly outperform boosted trees, and on the MNIST dataset the algorithm achieves the second best result among no-domain-knowledge algorithms after deep belief nets. As a second contribution, we present an improved base learner for nominal features and show that boosting the product of two of these new subset indicator base learners solves the maximum margin matrix factorization problem used to formalize the collaborative filtering task. On a small benchmark dataset, we get experimental results comparable to the semi-definite-programming-based solution but at a much lower computational cost.
Hub location for time definite transportation
Authors: James F. Campbell
Venue: Computers and Operations Research (2009)

Time definite motor carriers provide very reliable scheduled truck transportation service between specified terminals. They provide service competitive with airfreight carriers over continental-scale distances at a much lower cost. This paper provides time definite models for multiple allocation p-hub median problems and hub arc location problems. Service levels are imposed by limiting the maximum travel distance via the hub network for each origin-destination pair. Computational results are presented to demonstrate the effects of the time definite service levels on practical network design for truck transportation in North America.
A partitional clustering algorithm for crosscutting concerns identification
Authors: Gabriela Czibula, Grigoreta Sofia Cojocar, Istvan Gergely Czibula
Venue: Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems (2009)

Identifying crosscutting concerns is an important issue in the maintenance of software systems. It aims at refactoring the existing systems to use aspect oriented programming, in order to make them easier to maintain and to evolve. In this paper we present a new partitional clustering algorithm for identifying crosscutting concerns in existing software systems. We experimentally evaluate our algorithm using the open source case study JHotDraw, providing a comparison of the proposed approach with similar existing approaches.
Plenary lecture I: geometrical approach of multi-time maximum principle
Authors: Constantin Udriste
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

Many science and engineering problems can be formulated as optimization problems that are governed by contact distributions (multi-time Pfaff evolution systems) and by cost functionals expressed as multiple integrals or curvilinear integrals. Our paper discuss the contact distribution constrained optimization problems, focussing on a geometric approach of multi-time maximum principle. This extends the work of Pontryaguin in the ODEs case to include the case of normal PDEs or, more general, the distribution case. Section 1 formulates and proves a multi-time maximum principle for the case of multiple integral functionals. Section 2 establishes a version of multi-time maximum principle for the case of curvilinear integral functionals. Though a multiple integral functional is mathematically equivalent to a curvilinear integral functional (Section 3), their meaning is totally different in real life problems. Section 4 deals with a multi-time maximum principle approach of variational calculus in the case of nonintegrability.