ppxscal/embedding_network huggingface dataset:
Efficient Secure Linear Algebra in the Presence of Covert or Computationally Unbounded Adversaries
Authors: Payman Mohassel, Enav Weinreb
Venue: Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology (2008)

In this work we study the design of secure protocols for linear algebra problems. All current solutions to the problem are either inefficient in terms of communication complexity or assume that the adversary is honest but curious. We design protocols for two different adversarial settings: First, we achieve security in the presence of a covert adversary, a notion recently introduced by [Aumann and Lindell, TCC 2007]. Roughly speaking, this guarantees that if the adversary deviates from the protocol in a way that allows him to cheat, then he will be caught with good probability. Second, we achieve security against arbitrary malicious behaviour in the presence of a computationally unbounded adversary that controls less than a third of the parties. Our main result is a new upper bound of O(n 2 + 1/t ) communication for testing singularity of a shared n ×n matrix in constant round, for any constant t in both of these adversarial environments. We use this construction to design secure protocols for computing the rank of a shared matrix and solving a shared linear system of equations with similar efficiency.We use different techniques from computer algebra, together with recent ideas from [Cramer, Kiltz, and Padró, CRYPTO 2007], to reduce the problem of securely deciding singularity to the problem of securely computing matrix product. We then design new and efficient protocols for secure matrix product in both adversarial settings. In the two-party setting, we combine cut-and-choose techniques on random additive decomposition of the input, with a careful use of the random strings of a homomorphic encryption scheme to achieve simulation-based security. Thus, our protocol avoids general zero-knowledge proofs and only makes a black-box use of a homomorphic encryption scheme.
A Conceptual Model for Optimum Pricing in a Competitive Multi-Service Communication Market
Authors: Morteza Mohamadkhan, Vahid Chizari
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

The process of setting optimum prices for existing network communication services in a competitive market is a difficult problem. Especially, there is not a straightforward way to find optimum prices. In fact, this optimization is a trade off between different approaches. In this paper we have combined seven approaches to get a conceptual model for optimum pricing in a multi-service communication market.
Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data
Authors: Weixiang Liu, Kehong Yuan
Venue: International Journal of Data Mining and Bioinformatics (2008)

Nonnegative Matrix Factorization (NMF) is a powerful tool for gene expression data analysis as it reduces thousands of genes to a few compact metagenes, especially in clustering gene expression samples for cancer class discovery. Enhancing sparseness of the factorisation can find only a few dominantly coexpressed metagenes and improve the clustering effectiveness. Sparse p-norm (p > 1) Nonnegative Matrix Factorization (sp-NMF) is a more sparse representation method using high order norm to normalise the decomposed components. In this paper, we investigate the benefit of high order normalisation for clustering cancer-related gene expression samples. Experimental results demonstrate that sp-NMF leads to robust and effective clustering in both automatically determining the cluster number, and achieving high accuracy.
A Hybrid Multi-agent Model for Financial Markets
Authors: Stephen Chen, Jonathan Tien, Brenda Spotton Visano
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Economic models attempt to represent the aggregate behaviour of individual actors engaged in an economic activity. To ensure tractability in the subsequent mathematics, restrictive assumptions on the modelled phenomena are often required. Economic features that cannot be modelled mathematically can be difficult to verify experimentally. The hybrid multi-agent model developed here starts as an exact representation of a mathematics-based, multi-agent economics model, and then a more qualitative feature is added. Unlike purely qualitative studies, the effects of this additional feature can then be observed within an environment that has been experimentally demonstrated to be quantitatively accurate. The hybrid model is thus able to integrate both rigid mathematical assumptions and intuitive qualitative features, and the presented experimental data provides new insight into the potential role of social investors during market manias and bubbles.
Critical Scale for Unsupervised Cluster Discovery
Authors: Tomoya Sakai, Atsushi Imiya, Takuto Komazaki, Shiomu Hama
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

This paper addresses the scale-space clustering and a validation scheme. The scale-space clustering is an unsupervised method for grouping spatial data points based on the estimation of probability density function (PDF) using a Gaussian kernel with a variable scale parameter. It has been suggested that the detected cluster, represented as a mode of the PDF, can be validated by observing the lifetime of the mode in scale space. Statistical properties of the lifetime, however, are unclear. In this paper, we propose a concept of the `critical scale' and explore perspectives on handling it for the cluster validation.
A Semantic Grid Application for E-Learning Data Sharing
Authors: Wenya Tian, Yuxin Mao
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

In an E-learning scenario, educational resources, such as course documents, videos, test-bases, courseware, and teacher information etc., are needed to be shared across different schools. DartGrid is a semantic grid toolkit for data integration using technologies from Semantic Web and Grid. In this paper, a Semantic Grid for E-leaning based on DartGrid is introduced, and it provides a Semantic-based distributed infrastructure for E-learning data resource sharing. We explore the essential and fundamental roles played by RDF semantics for E-learning. We also introduce a set of semantically enabled tools and grid services for E-learning such as semantic browser, ontology service, semantic query service, and semantic registration service.
Any other cost estimation inhibitors?
Authors: Ana Magazinovic, Joakim Pernstål
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

The purpose of this qualitative multiple case study is to research the current causes to cost estimation errors and revisit the research question of a well published quantitative study to explore whether responses would be consistent using qualitative methodology. Both overlaps and deviations are to be expected as a result of this comparison. The focus of this paper is on differences where new issues are likely to be found. The 8 resulting cross case cost estimation inhibitors validate 7 out of 16 issues found in the quantitative study. They are also related to the theory within and outside the area of software engineering resulting in five underlying software cost estimation inhibitors and providing a deeper understanding for the snapshot of the current causes of cost estimation errors reported by the practitioners. Lastly, the question of the practitioners' ability to report upon software cost estimation inhibitors is discussed. We argue that there might be causes of cost estimation errors that practitioners might be unwilling to reveal or might even be completely unaware of. Thus, they might unconsciously be providing researchers with invalid data.
Ontology-Supported Text Classification Based on Cross-Lingual Word Sense Disambiguation
Authors: Dan Tufiş, Svetla Koeva
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

The paper reports on recent experiments in cross-lingual document processing (with a case study for Bulgarian-English-Romanian language pairs) and brings evidence on the benefits of using linguistic ontologies for achieving, with a high level of accuracy, difficult tasks in NLP such as word alignment, word sense disambiguation, document classification, cross-language information retrieval, etc. We provide brief descriptions of the parallel corpus we used, the multilingual lexical ontology which supports our research, the word alignment and word sense disambiguation systems we developed and a preliminary report on an ongoing development of a system for cross-lingual text-classification which takes advantage of these multilingual technologies. Unlike the keyword-based methods in document processing, the concept-based methods are supposed to better exploit the semantic information contained in a particular document and thus to provide more accurate results.
Improvement Program of Urban Taxi Stops Based on Simulated Annealing Algorithm in the Context of China
Authors: Yeqing Jia
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Currently the operation model of urban taxi system faces lots of defects and problems, it's inevitable to solve problems and come up with a new program to improve this system, because of which the whole profit of the enterprise is low. In order to decline the rate of vacant transportation of the taxi, this paper analyses the main factors that may influence the efficiency of taxi operation and build up a probability model to put forward the program by means of simulated annealing algorithm and information system.
Two New Techniques of Side-Channel Cryptanalysis
Authors: Alex Biryukov, Dmitry Khovratovich
Venue: Proceedings of the 9th international workshop on Cryptographic Hardware and Embedded Systems (2007)

We describe two new techniques of side-channel cryptanalysis which we call the impossible collision attack and the multiset collision attack. These are inspired by the state-of-the-art cryptanalytic techniques of impossible differential attacks [BBS99] and partial-function collision attacks [GM00] respectively. Using these techniques on an example of the AES we show that one has to mask all the rounds of a 128-bit key AES in order to prevent such attacks. For example these attacks can be used to break a recent proposal by Schramm et al. [SP06] of high order masking for the AES, since it protects only 3 external rounds.
A Framework for Context-Awareness in Artificial Systems
Authors: Fulvio Mastrogiovanni, Antonio Sgorbissa, Renato Zaccaria
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

This paper introduces a context model to be used in context-aware cognitive artificial systems. The framework, that is aimed at integrating ontology and logic approaches to context modeling, assumes the availability of both an ontology (i.e., a representation of what exists) and a simple inference schema (i.e., subsumption). The context model is defined using a formal protocol, which describes contexts and situations as recursive structures grounded with respect to the ontology. Examples are presented to discuss the proposed model.
Computer Advances Pioneered by Cryptologic Organizations
Authors: Samuel S. Snyder
Venue: IEEE Annals of the History of Computing (1980)

When the modern electronic computer proposals pioneered by von Neumann, Eckert, Mauchly, and others in the 1940s were under development, the Army and Navy predecessors of today's National Security Agency were quick to realize that their great power and versatility promised exciting improvements in cryptologic applications. Many of the NSA-supported projects were among industry "firsts," and features in several commercial computers directly followed NSA's lead. A survey of the computer developments made by and for NSA is given here, beginning with ATLAS, delivered in December 1950 by Engineering Research Associates, and culminating in the massive LIGHTNING research program.
A Cartesian Robot for RFID Signal Distribution Model Verification
Authors: Aliasgar Kutiyanawala, Vladimir Kulyukin
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

In our previous research, we addressed the problem of automating the design of passive radio-frequency (PRF) services. An optimal PRF surface is one that offers a maximum probability of localization at a minimum instrumentation cost, i.e., a minimum number of surface-embedded passive RFID transponders. Our previous results were based on the assumption that the signal distribution model of an individual RFID transponder can be approximated as a circle. The problem of automated PRF surface design was then formulated as the problem of packing a surface with circles of a given radius. However, in practice, this approach leads to some loss of optimality: some areas of the surface may not be covered or too many transponders may be required. More exact methods are need for verifying and constructing signal distribution models of surface-embedded RFID transponders that can be used by surface packing algorithms to optimize the design. In this paper, we present the design and implementation of a Cartesian robot for verifying and constructing signal distribution models of surface-embedded RFID transponders. A model is characterized by four high-level parameters: an RFID transponder, an RFID antenna, an RFID reader, and a surface type. The robot moves an RFID reader-antenna unit over a PRF surface, e.g. a carpet, and systematically collects readings for various antenna positions over the surface. The collected readings are subsequently processed to verify or construct signal distribution models. We describe experiments with the robot to verify the localization probability of automatically designed PRF surfaces. We also present experiments with the robot to verify and construct the signal distribution models of a specific RFID transponder.
A Retinal Circuit Model Accounting for Functions of Amacrine Cells
Authors: Murat Saglam, Yuki Hayashida, Nobuki Murayama
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

In previous experimental studies on vertebrates, high level processes of vision such as object segregation and spatio-temporal pattern adaptation were found to begin at retinal stage. In those visual functions, diverse subtypes of amacrine cells are believed to play essential roles by processing the excitatory and inhibitory signals laterally over a wide region on the retina to shape the ganglion cell responses. Previously, a simple "linear-nonlinear" model was proposed to explain a specific function of the retina, and could capture the spiking behavior of retinal output, although each class of the retinal neurons were largely omitted in it. Here, we present a spatio-temporal computational model based on the response function for each class of the retinal neurons and the anatomical intercellular connections. This model is not only capable of reproducing filtering properties of outer retina but also realizes high-order inner retinal functions such as object segregation mechanism via wide-field amacrine cells.
Specifying the correctness properties of model transformations
Authors: Anantha Narayanan, Gabor Karsai
Venue: Proceedings of the third international workshop on Graph and model transformations (2008)

The correctness of a model transformation is central to the success of a model-driven software development process. A transformation can be said to have executed correctly if it resulted in the desired output model, but this requires a specification of what constitutes a desirably correct output. If we have this specification, and a framework to verify that it holds on a specific execution of the transformation, then that execution instance may be "certified correct". In this paper, we explore a technique to specify such a correctness, using a language framework that can easily be incorporated into a variety of domains. We will also see how these correctness criteria can be verified on instance models.
Support Vector Machine Detection of Peer-to-Peer Traffic in High-Performance Routers with Packet Sampling: Nonlinear Kernel Approach
Authors: F. J. González-Castaño, P. S. Rodríguez-Hernández, R. P. Martínez-Álvarez, A. Gómez-Tato
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

In this paper, we apply nonlinear support vector machines to identify peer-to-peer (p2p) traffic in high-performance routers with packet sampling. Due to their high port rates, those routers cannot extract the headers of all the packets that traverse them, but only a sample. The results in this paper suggest that nonlinear support vector machines are highly successful and outperform recent approaches like [1,2].
Learning bounded optimal behavior using markov decision processes
Authors: Nicholas Roy, Milton B. Adams
Venue:  (2007)

Creating agents that behave rationally in the real-world is one goal of Artificial Intelligence. A rational agent is one that takes, at each point in time, the optimal action such that its expected utility is maximized. However, to determine the optimal action the agent may need to engage in lengthy deliberations or computations. The effect of computation is generally not explicitly considered when performing deliberations. In reality, spending too much time in deliberation may yield high quality plans that do not satisfy the natural timing constraints of a problem, making them effectively useless. Enforcing shortened deliberation times may yield timely plans, but these may be of diminished utility. These two cases suggest the possibility of optimizing an agent's deliberation process. This thesis proposes a framework for generating metalevel controllers that select computational actions to perform by optimally trading off their benefit against their costs. The metalevel optimization problem is posed within a Markov Decision Process framework and is solved off-line to determine a policy for carrying out computations. Once the optimal policy is determined, it serves efficiently as an online metalevel controller that selects computational actions conditioned upon the current state of computation. Solving for the exact policy of the metalevel optimization problem becomes computationally intractable with problem size. A learning approach that takes advantage of the problem structure is proposed to generate approximate policies that are shown to perform relatively well in comparison to optimal policies.Metalevel policies are generated for two types of problem scenarios, distinguished by the representation of the cost of computation. In the first case, the cost of computation is explicitly defined as part of the problem description. In the second case, it is implicit in the timing constraints of problem. Results are presented to validate the beneficial effects of metalevel planning over traditional methods when the cast of computation has a significant effect on the utility of a plan. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Selective Pre-processing of Imbalanced Data for Improving Classification Performance
Authors: Jerzy Stefanowski, Szymon Wilk
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

In this paper we discuss problems of constructing classifiers from imbalanced data. We describe a new approach to selective pre-processing of imbalanced data which combines local over-sampling of the minority class with filtering difficult examples from the majority classes. In experiments focused on rule-based and tree-based classifiers we compare our approach with two other related pre-processing methods --- NCR and SMOTE. The results show that NCR is too strongly biased toward the minority class and leads to deteriorated specificity and overall accuracy, while SMOTE and our approach do not demonstrate such behavior. Analysis of the degree to which the original class distribution has been modified also reveals that our approach does not introduce so extensive changes as SMOTE.
Socio-technical congruence: a framework for assessing the impact of technical and work dependencies on software development productivity
Authors: Marcelo Cataldo, James D. Herbsleb, Kathleen M. Carley
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

The identification and management of work dependencies is a fundamental challenge in software development organizations. This paper argues that modularization, the traditional technique intended to reduce interdependencies among components of a system, has serious limitations in the context of software development. We build on the idea of congruence, proposed in our prior work, to examine the relationship between the structure of technical and work dependencies and the impact of dependencies on software development productivity. Our empirical evaluation of the congruence framework showed that when developers' coordination patterns are congruent with their coordination needs, the resolution time of modification requests was significantly reduced. Furthermore, our analysis highlights the importance of identifying the "right" set of technical dependencies that drive the coordination requirements among software developers. Call and data dependencies appear to have far less impact than logical dependencies.
A Dynamic Content Generator for Adaptation in Hypermedia Systems
Authors: David Mérida, Ramón Fabregat, Xavier Prat, David Huerva, Jeimy Velez
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

The heterogeneity problem (in terms of different types of access devices, network bandwidth, preferences/characteristics of the user, etc.) has become a major problem for the Internet. Different alternatives have been developed to allow universal access to any type of content. Adaptive Hypermedia Systems (AHS) have emerged as a solution for this. In previous works we proposed the SHAAD model, which includes the concepts of adaptability, adaptivity and dynamism to adapt web contents. Based on this model we implemented MAS-SHAAD, a multiagent system implementation of SHAAD. In this work we present the design and development of a dynamic content generator that can be added to any JAVA AHS implementation, such as MAS-SHAAD. The structure of the generator is defined by an ontology; therefore, a standard behavior can be obtained for any object included in the web pages generated and stored in the content repository.
Neural Network-Based H ∞ Filtering for Nonlinear Jump Systems
Authors: Xiaoli Luan, Fei Liu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

This paper addresses the problem of designing a Markovian H &infin; filter for a class of nonlinear stochastic Markovian jump systems. Firstly, neural networks are employed to approximate the nonlinearities in the different jump modes. Secondly, the overall system is represented by the mode-dependent linear difference inclusion (LDI). Then, a neural network-based Markovian H &infin; filter is developed using the stochastic Lyapunov-Krasovskii stability theory under some linear matrix inequality (LMI) constraints. Finally, a numerical example is worked out to show the usefulness of the theoretical results.