ppxscal/embedding_network huggingface dataset:
Approximating the Knee of an MOP with Stochastic Search Algorithms
Authors: Oliver Schütze, Marco Laumanns, Carlos A. Coello Coello
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

In this paper we address the problem of approximating the 'knee' of a bi-objective optimization problem with stochastic search algorithms. Knees or entire knee-regions are of particular interest since such solutions are often preferred by the decision makers in many applications. Here we propose and investigate two update strategies which can be used in combination with stochastic multi-objective search algorithms (e.g., evolutionary algorithms) and aim for the computation of the knee and the knee-region, respectively. Finally, we demonstrate the applicability of the approach on two examples.
Introduction
Authors: Babak Falsafi, Pascal Felber
Venue: ACM SIGPLAN Notices (2008)

In recent years, multiprocessors have emerged as the architecture of choice at the chip level to help ride Moore's law and continue exploiting the exponential growth in transistors while maintaining acceptable power consumption levels and complexity. Unfortunately, parallel software has not made it to the mainstream, making technologies to help facilitate parallel software development and programming for commodity chips critical to the sustained growth in IT infrastructure and industry. Transactional programming is emerging as a promising approach to help mitigate many of the difficulties associated with conventional synchronization and coordination mechanisms in parallel programs.
Knowledge Extraction and Summarization for an Application of Textual Case-Based Interpretation
Authors: Eni Mustafaraj, Martin Hoof, Bernd Freisleben
Venue: Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development (2007)

This paper presents KES (Knowledge Extraction and Summarization), a new knowledge-enhanced approach that builds a case memory out of episodic textual narratives. These narratives are considered as generated probabilistically by the structure of the task they describe. The task elements are then used to construct the structure of the case memory. The KES approach is illustrated with examples and an empirical evaluation of a real-world scenario of textual case-based interpretation for a technical domain.
A frequency domain-based approach for blind MIMO system identification using second-order cyclic statistics
Authors: K. Sabri, M. El Badaoui, F. Guillet, A. Adib, D. Aboutajdine
Venue: Signal Processing (2009)

This article introduces a new frequency domain approach for either MIMO system identification or source separation of convolutive mixtures of cyclostationary signals. We apply the joint diagonalization algorithm to a set of cyclic spectral density matrices of the measurements to identify the mixing system at each frequency bin up to permutation and phase ambiguity matrices. An efficient algorithm to overcome the frequency-dependent permutations and to recover the phase, even for non-minimum-phase channels, based on cyclostationarity is also presented. The new approach exploits the fact that each input signal has a different and specific cyclic frequency. Simulation examples are presented to illustrate the effectiveness of this approach.
Classification-Based Filtering of Semantic Relatedness in Hypernymy Extraction
Authors: Maciej Piasecki, Stanisław Szpakowicz, Michał Marcińczuk, Bartosz Broda
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

Manual construction of a wordnet can be facilitated by a system that suggests semantic relations acquired from corpora. Such systems tend to produce many wrong suggestions. We propose a method of filtering a raw list of noun pairs potentially linked by hypernymy, and test it on Polish. The method aims for good recall and sufficient precision. The classifiers work with complex features that give clues on the relation between the nouns. We apply a corpus-based measure of semantic relatedness enhanced with a Rank Weight Function. The evaluation is based on the data in Polish WordNet. The results compare favourably with similar methods applied to English, despite the small size of Polish WordNet.
Aggregates in Generalized Temporally Indeterminate Databases
Authors: Octavian Udrea, Zoran Majkić, V. S. Subrahmanian
Venue: Proceedings of the 1st international conference on Scalable Uncertainty Management (2007)

Dyreson and Snodgrass as well as Dekhtyar et. al. have provided a probabilistic model (as well as compelling example applications) for why there may be temporal indeterminacy in databases. In this paper, we first propose a formal model for aggregate computation in such databases when there is uncertainty not just in the temporal attribute, but also in the ordinary (non-temporal) attributes. We identify two types of aggregates: event correlated aggregates, and non event correlated aggregations, and provide efficient algorithms for both of them. We prove that our algorithms are correct, and we present experimental results showing that the algorithms work well in practice.
Reducing Noise of Medical Grade Liquid Crystal Displays (LCD) and Its Relation to the Detection of Micro-calcifications
Authors: Hans Roehrig, Jiahua Fan, William J. Dallas, Elizabeth A. Krupinski, Jeffrey Johnson
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

This communication is the result of an NIH SBIR Phase 1 project under way because of the concern for wide spread breast-cancer [1,2]. The primary goal of the project is to increase the detection rate of micro calcifications by decreasing spatial noise of the LCD used to display the mammograms [3,4]. Noise reduction will be done with a sensitive CCD Camera and application of Error Diffusion [5,6]. This procedure is known to reduce threshold contrast of human observers [7]. A second goal is the detection of breast cancer using mammogram patches with a pixel matrix of 256 x 256 pixels.Detection of breast cancer will be done using two methods: One is a conventional ROC study [8]. The other is a Vision Model developed over several years, starting at the Sarnoff Research Center and continuing at Siemens Corporate Research in Princeton NJ [9].
Patterns for session-based access control
Authors: Eduardo B. Fernandez, Günther Pernul
Venue: Proceedings of the 2006 conference on Pattern languages of programs (2006)

The concept of session, the context under which a user accesses resources is very important to apply access control. We present first the Controlled Access Session pattern for describing how sessions can limit the rights of a user. We then combine this pattern with two existing access control patterns. First we consider a pattern for Session-Based Role-Based Access Control, intended for organizations in which job functions form the basis for privilege assignments. Then, we present a Session-Based Attribute-Based Access Control pattern for organizations in which accesses are controlled based on values of user attributes and object properties. Since the general properties of those patterns have been described earlier we emphasize the additional effect of using sessions. The Controlled Access Session pattern can also be combined with other models of access control or used on its own.
Improved Separations between Nondeterministic and Randomized Multiparty Communication
Authors: Matei David, Toniann Pitassi, Emanuele Viola
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

We exhibit an explicit function f : {0, 1} n ¿{0,1} that can be computed by a nondeterministic number-on-forehead protocol communicating O(logn) bits, but that requires n ¿(1) bits of communication for randomized number-on-forehead protocols with k = ¿·logn players, for any fixed ¿< 1. Recent breakthrough results for the Set-Disjointness function (Sherstov, STOC '08; Lee Shraibman, CCC '08; Chattopadhyay Ada, ECCC '08) imply such a separation but only when the number of players is k < loglogn.We also show that for any k = A loglogn the above function f is computable by a small circuit whose depth is constant whenever A is a (possibly large) constant. Recent results again give such functions but only when the number of players is k < loglogn.
Paradoxical effects of interprofessional briefings on OR team performance
Authors: Sarah Whyte, Lorelei Lingard, Sherry Espin, G. Ross Baker, John Bohnen, Beverley A. Orser, Diane Doran, Richard Reznick, Glenn Regehr
Venue: Cognition, Technology and Work (2008)

Our recent research has found that structured preoperative team briefings can reduce communication failures, improve the knowledge and practice of operating room (OR) team members, and garner broad support from surgeons, nurses, and anesthesiologists. However, we have also encountered challenges and unexpected, negative effects. Using qualitative analysis of fieldnotes from 302 preoperative team briefings, we identified five paradoxical findings: team briefings could mask knowledge gaps, disrupt positive communication, reinforce professional divisions, create tension, and perpetuate a problematic culture. Fifteen percent of the briefings exhibited only these paradoxical effects without any apparent utility. We describe these paradoxical findings and analyze them in relation to educational, functional, structural, and cultural factors. This analysis is instructive not only for re-engineering the briefing process, but also for revealing dynamics that may continue to impede optimal interprofessional performance.
An MPEG4 Simple Profile Decoder on a Novel Multicore Architecture
Authors: Qiang Liu, Jianwen Chen, Yudong Yang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

Video signal processing algorithms are characterized by high computational complexity and high memory throughput. Since the multicore architectures can provide high computational capacity and high throughput, they suit the video processing applications very well. However, it is very difficult to deal with the increasing design complexity and cost of the multicore system. For video processing application, a generic and flexible video processing multicore platform is essentially required to meet the varying requirements for a wide variety of video standards. In this paper, a novel flexible heterogeneous multicore architecture system is proposed. An MPEG4 simple profile decoder is implemented on the multicore architecture. The decoding performance demonstrates that the proposed architecture is substantially efficient for video processing applications and it is a generic and flexible media-processing design platform.
Municipal Creditworthiness Modelling by Kohonen's Self-organizing Feature Maps and LVQ Neural Networks
Authors: Petr Hájek, Vladimír Olej
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

The paper presents the design of municipal creditworthiness parameters. Further, a model is designed based on Learning Vector Quantization neural networks for municipal creditworthiness classification. The model is composed of Kohonen's Self-organizing Feature Maps (unsupervised learning) whose outputs represent the input of the Learning Vector Quantization neural networks (supervised learning).
A "Bag" or a "Window" of Words for Information Filtering?
Authors: Nikolaos Nanas, Manolis Vavalis
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

Treating documents as bag of words is the norm in Information Filtering. Syntactic and semantic correlations between terms are ignored, or in other words, term independence is assumed. In this paper we challenge this common assumption. We use Nootropia, a user profiling model that uses a sliding window approach to capture term dependencies in a network and a spreading activation process to take them into account for document evaluation. Experiments performed based on TREC's routing guidelines demonstrate that given an adequate window size the additional information that term dependencies encode, results in improved filtering performance over a traditional bag of words approach.
A New Natural Policy Gradient by Stationary Distribution Metric
Authors: Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Kenji Doya
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

The parameter space of a statistical learning machine has a Riemannian metric structure in terms of its objective function. [1] Amari proposed the concept of "natural gradient" that takes the Riemannian metric of the parameter space into account. Kakade [2] applied it to policy gradient reinforcement learning, called a natural policy gradient (NPG). Although NPGs evidently depend on the underlying Riemannian metrics, careful attention was not paid to the alternative choice of the metric in previous studies. In this paper, we propose a Riemannian metric for the joint distribution of the state-action, which is directly linked with the average reward, and derive a new NPG named "Natural State-action Gradient" (NSG). Then, we prove that NSG can be computed by fitting a certain linear model into the immediate reward function. In numerical experiments, we verify that the NSG learning can handle MDPs with a large number of states, for which the performances of the existing (N)PG methods degrade.
leanCoP 2.0 and ileanCoP 1.2: High Performance Lean Theorem Proving in Classical and Intuitionistic Logic (System Descriptions)
Authors: Jens Otten
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

leanCoP is a very compact theorem prover for classical first-order logic, based on the connection (tableau) calculus and implemented in Prolog. leanCoP 2.0 enhances leanCoP 1.0 by adding regularity, lemmata, and a technique for restricting backtracking. It also provides a definitional translation into clausal form and integrates "Prolog technology" into a lean theorem prover. ileanCoP is a compact theorem prover for intuitionistic first-order logic and based on the clausal connection calculus for intuitionistic logic. leanCoP 2.0 extends the classical prover leanCoP 2.0 by adding prefixes and a prefix unification algorithm. We present details of both implementations and evaluate their performance.
Ship Collaborative Design Based on Multi-agent and Ontology
Authors: Xiangzhong Feng
Venue: Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering (2008)

For larger ship product, during collaborative product design, it is difficult to effectively achieve sharing, exchange, reuse of the ship design knowledge of heterogeneity, and it is difficult to effectively achieve communication, interoperation, coordination of designers of geographically distributed. To solve the difficulties, in this paper, a method to build collaborative design environment is proposed by combining multi-agent technology and ontology technology. Multi-agent is used for construction of a multi-layer ship collaborative design system, Ontology is used for ship design knowledge representation and OWL is used for ship design knowledge description.
Modeling of First-Order Scatter from Ocean Currents Based on Distributed Source
Authors: Zhijuan An, Tieping Wu, Hongtao Su, Zheng Bao
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

In this paper the distributed source model of first-order scatter from ocean currents is put forward, which can describe the distributed characteristics of ocean currents effectively and be used to evaluate the spatial spectrum estimation techniques under different conditions. Then the numerical simulation of the first-order scatter is carried out using this model. The performance of the MUSIC and the generalized MUSIC algorithms is investigated with the simulated data and the influence of the amplitude-phase error of the channel is evaluated simultaneously. The results show that the distributed source model presented in this paper provides an effective way to model the first-order scatter from ocean currents.
Click Fraud
Authors: Bernard J. Jansen
Venue: Computer (2007)

Unchecked, click fraud could undermine the sponsored search business model.
Delegating Capabilities in Predicate Encryption Systems
Authors: Elaine Shi, Brent Waters
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II (2008)

In predicate encryption systems, given a capability, one can evaluate one or more predicates on the plaintext encrypted, while all other information about the plaintext remains hidden. We consider the role of delegation in such predicate encryption systems. Suppose Alice has a capability, and she wishes to delegate to Bob a more restrictive capability allowing the decryption of a subset of the information Alice can learn about the plaintext encrypted. We formally define delegation in predicate encryption systems, propose a new security definition for delegation, and give an efficient construction supporting conjunctive queries. The security of our construction can be reduced to the general 3-party Bilinear Diffie-Hellman assumption, and the Bilinear Decisional Diffie-Hellman assumption in composite order bilinear groups.
Clustering of Gene Expression Data with Quantum-Behaved Particle Swarm Optimization
Authors: Wei Chen, Jun Sun, Yanrui Ding, Wei Fang, Wenbo Xu
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Based on the previously proposed Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, in this paper, we focus on the application of QPSO in gene expression data clustering which can be reduced to an optimization problem. The proposed clustering algorithm partitions the N patterns of the gene expression dataset into user-defined K categories to minimize the fitness function of Total Within-Cluster Variation. Thus a partition with high performance is obtained. The experiment results on four gene expression data sets show that our QPSO-based clustering algorithm will be an effective and promising tool for gene expression data analysis.
Cascading Dialog Modeling with UsiXML
Authors: Marco Winckler, Jean Vanderdonckt, Adrian Stanciulescu, Francisco Trindade
Venue: Interactive Systems. Design, Specification, and Verification: 15th International Workshop, DSV-IS 2008 Kingston, Canada, July 16-18, 2008 Revised Papers (2008)

This paper discusses multi-level dialog specifications for user interfaces of multi-target interactive systems and it proposes a step-wise method that combines a transformational approach for model-to-model derivation and an interactive editing of dialog models for tailoring the derived models. This method provides a synthesis of existing solutions for dialog modeling using a XML-based User Interface Description Language, UsiXML, along with State WebCharts notation for expressing the dialog at a high level of abstraction. Our aim is to push forward the design and reuse of dialog specifications throughout several levels of abstraction ranging from task and domain models until the final user interface thanks to a mechanism based on cascading style sheets. In this way, it is expected that the dialog properties are not only inherited from one level to another but also are made much more reusable than in the past.
Integrative Neural Network Approach for Protein Interaction Prediction from Heterogeneous Data
Authors: Xue-Wen Chen, Mei Liu, Yong Hu
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Protein interactions are essential in discovery of protein functions and fundamental biological processes. In this paper, we aim to create a reliable computational model for protein interaction prediction by integrating information from complementary data sources. An integrative Artificial Neural Network (ANN) framework is developed to predict protein-protein interactions (PPIs) from heterogeneous data in Human. Performance of our proposed framework is empirically investigated by combining protein domain data, molecular function and biological process annotations in Gene Ontology. Experimental results demonstrate that our approach can predict PPIs with high sensitivity of 82.43% and specificity of 78.67%. The results suggest that combining multiple data sources can result in a 7% increase in sensitivity compared to using only domain information. We are able to construct a protein interaction network with proteins around mitotic spindle checkpoint of the human interactome map. Novel predictions are made and some are supported by evidences in literature.
Equalization of Channel Distortion Using Nonlinear Neuro-Fuzzy Network
Authors: Rahib H. Abiyev, Fakhreddin Mamedov, Tayseer Al-Shanableh
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper presents the equalization of channel distortion by using a Nonlinear Neuro-Fuzzy Network (NNFN). The NFNN is constructed on the basis of fuzzy rules that incorporate nonlinear functions. The learning algorithm of NNFN is presented. The NFNN is applied for equalization of channel distortion of time-invariant and time-varying channels. The developed equalizer recovers the transmitted signal efficiently. The performance of NNFN based equalizer is compared with the performance of other nonlinear equalizers. The effectiveness of the proposed system is evaluated using simulation results of NNFN based equalization system.
Brief Communication: Characterization of alpha helices interacting with nucleic acids
Authors: R. Sreekanth, Vasantha Pattabhi, S. S. Rajan
Venue: Computational Biology and Chemistry (2008)

Protein-nucleic acid interactions play a vital role in most genetic processes. An enhanced insight into such interactions can be obtained from the structure database of these complexes. Here, we report an overall survey on the geometry of alpha helices which interact with nucleic acids through hydrogen bonds and/or non-bonded interactions. Using the program RADIL based on an algorithm developed from this laboratory, 161 alpha helices in 70 non-redundant nucleic acid binding protein chains solved using X-ray crystallography are analysed. The helical geometry has been characterized as bent, canonical, terminally or completely distorted. The analysis reveals that ~70% of the alpha helices possess distortions of any one kind, viz., bend, terminal distortion or complete distortion. Nearly one-third of the total helices possess bends, with a majority of the bending occurring in 5-15^o range. In addition, a majority of the bent helices approach the nucleic acid helix in a perpendicular direction. The program RADIL has been useful in characterizing the nucleic acid-induced structural variations in alpha helices, however small they may be.
Reinvigorating the Discipline: Pervasive Computing and Tomorrow's Computer Scientists
Authors: Mike Hazas, Rebecca Marsden
Venue: IEEE Pervasive Computing (2007)

Declining enrollments in computer science and related fields are a global concern. This issue's column, by Mike Hazas and Rebecca Marsden of Lancaster University in the UK describes the novel Lancaster Headstart program that uses the excitement of pervasive computing to attract students into the computer science.