ppxscal/embedding_network huggingface dataset:
Dynamically Identifying and Tracking Contaminants in Water Bodies
Authors: Craig C. Douglas, Martin J. Cole, Paul Dostert, Yalchin Efendiev, Richard E. Ewing, Gundolf Haase, Jay Hatcher, Mohamed Iskandarani, Chris R. Johnson, Robert A. Lodder
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

We present an overview of an ongoing project to build a DDDAS for identifying and tracking chemicals in water. The project involves a new class of intelligent sensor, building a library to optically identify molecules, communication techniques for moving objects, and a problem solving environment. We are developing an innovative environment so that we can create a symbiotic relationship between computational models for contaminant identification and tracking in water bodies and a new instrument, the Solid-State Spectral Imager (SSSI), to gather hydrological and geological data and to perform chemical analyses. The SSSI is both small and light and can scan ranges of up to about 10 meters. It can easily be used with remote sensing applications.
Mining Conjunctive Sequential Patterns
Authors: Chedy Raïssi, Toon Calders, Pascal Poncelet
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

In this paper we study the discovery of frequent sequences and we aim at extending the non-derivable condensed representation in frequent itemset mining to sequential pattern mining. We start by showing a negative example: in the context of frequent sequences, the notion of non-derivability is meaningless.This negative result motivated us to look at a slightly different problem: the mining of conjunctions of sequential patterns. This extended class of patterns turns out to have much nicer mathematical properties. For example, for this class of patterns we are able to extend the notion of non-derivable itemsets in a non-trivial way, based on a new unexploited theoretical definition of equivalence classes for sequential patterns. As a side-effect of considering conjunctions of sequences as the pattern type, we can easily form association rules between sequences. We believe that building a theoretical framework and an efficient approach for sequence association rules extraction problem is the first step toward the generalization of association rules to all complex and ordered patterns.
The Interplay of Analogy-Making with Active Vision and Motor Control in Anticipatory Robots
Authors: Kiril Kiryazov, Georgi Petkov, Maurice Grinberg, Boicho Kokinov, Christian Balkenius
Venue: Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior (2007)

This chapter outlines an approach to building robots with anticipatory behavior based on analogies with past episodes. Anticipatory mechanisms are used to make predictions about the environment and to control selective attention and top-down perception. An integrated architecture is presented that perceives the environment, reasons about it, makes predictions and acts physically in this environment. The architecture is implemented in an AIBO robot. It successfully finds an object in a house-like environment. The AMBR model of analogy-making is used as a basis, but it is extended with new mechanisms for anticipation related to analogical transfer, for top down perception and selective attention. The bottom up visual processing is performed by the IKAROS system for brain modeling. The chapter describes the first experiments performed with the AIBO robot and demonstrates the usefulness of the analogy-based anticipation approach.
An Explanation of Evolutionary Game Theory for the Circulative Flowed Phenomenon in Chinese Rural Labor Transfer
Authors: De-hai Liu, Wei-guo Wang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

The famous Todaro model could not explain the circulative flowed phenomenon in the course of rural labor transfer. The paper uses Sethi's generic replicator dynamic model (1998) of evolutionary game theory to explain the circulative flowed phenomenon based Chinese statistical data. It builds the urban-rural ternary-structure model including rural section, urban informal section and urban formal section, which breaks the rural–urban dual-structure limitation of Todaro model. Then it draws the necessary conditions of the asymptotic stable equilibrium (Evolutionary Equilibrium) and stable focus in the evolutionary function of Chinese rural labor transfer, which the phase diagram of stable focus describes the circulative flowed phenomenon of rural-urban population proportion. At last, it analyses some factors influenced the convergent course, including learning barriers in the urban formal sector and informal sector and rural-urban income gap.
Frication and Voicing Classification
Authors: Luis M. Jesus, Philip J. Jackson
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

Phonetic detail of voiced and unvoiced fricatives was examined using speech analysis tools. Outputs of eight f0 trackers were combined to give reliable voicing and f0 values. Log - energy and Mel frequency cepstral features were used to train a Gaussian classifier that objectively labeled speech frames for frication. Duration statistics were derived from the voicing and frication labels for distinguishing between unvoiced and voiced fricatives in British English and European Portuguese.
A Method to Optimize the Parameter Selection in Short Term Load Forecasting
Authors: Humberto F. Ferro, Raul S. Wazlawick, Cláudio M. Oliveira, Rogério C. Bastos
Venue: Proceedings of the 30th annual German conference on Advances in Artificial Intelligence (2007)

Load forecasting allows electric utilities to enhance energy purchasing and generation, load switching, contracts negotiation and infrastructure development [1].
21st century soapboxes? MPs and their blogs
Authors: Mary Francoli, Stephen Ward
Venue: Information Polity (2008)

This article examines and compares the growth and prevalence of MPs' blogging in the UK and Canada. We explore the scope and objectives of such blogs, assess their democratic significance and analyse the institutional and systemic features that help shape the blogosphere. Diverging patterns of adoption were found: the number of blogs have increased in the UK but withered in Canada, where party discipline has been heavily imposed. This illustrates the impact the wider systemic environment can have on the adoption of new technologies. Ultimately, it is concluded that while there is evidence of a potential for MPs' blogs to serve as spaces for debate and conversation, the current majority resemble the traditional style soapbox where few listen let alone take the time to engage. However, there is some early evidence to show that blogs might in fact have a great deal of potential in personality driven, and internal party, election campaigns which have a niche, but highly attentive audience.
Knowledge Driven Architecture for Home Care
Authors: Ákos Hajnal, David Isern, Antonio Moreno, Gianfranco Pedone, László Zsolt Varga
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

Multi-Agent Systems (MAS) in health-care domains are showing a rapid increase, in order to manage complex tasks and adapt gracefully to unexpected events. On the other hand, the lack of well-established agent-oriented engineering methodologies to transform knowledge level descriptions into deployable agent systems slackens MAS development. This paper presents a new methodology in modelling and automatically implementing agents in a home care domain. The representation of the application knowledge together with the codification of health care treatments lead to flexible realization of an agent platform that has the capability to capture new medical knowledge emerging from physicians.
Evolving connectionist systems for on-line pattern classification of multimedia data
Authors: Nikola Kasabov, Irena Koprinska, Georgi Iliev
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

The paper develops further the concept of evolving connectionist systems, and one particular model - evolving fuzzy neural networks, that are applied on pattern classification tasks of multimedia data. The evolving systems learn in an on-line, life-long learning mode and adapt to the new data. This mode is crucial when the system is required to adapt quickly to new data and be able to generalize immediately afterwards. These tasks are typical for processing of multimedia data, such as adaptive speech recognition and adaptive video data processing that are presented in the paper.
Block Ciphers Implementations Provably Secure Against Second Order Side Channel Analysis
Authors: Matthieu Rivain, Emmanuelle Dottax, Emmanuel Prouff
Venue: Fast Software Encryption: 15th International Workshop, FSE 2008, Lausanne, Switzerland, February 10-13, 2008, Revised Selected Papers (2008)

In the recent years, side channel analysis has received a lot of attention, and attack techniques have been improved. Side channel analysis of second order is now successful in breaking implementations of block ciphers supposed to be effectively protected. This progress shows not only the practicability of second order attacks, but also the need for provably secure countermeasures. Surprisingly, while many studies have been dedicated to the attacks, only a few papers have been published about the dedicated countermeasures. In fact, only the method proposed by Schramm and Paar at CT-RSA 2006 enables to thwart second order side channel analysis. In this paper, we introduce two new methods which constitute a worthwhile alternative to Schramm and Paar's proposal. We prove their security in a strong security model and we exhibit a way to significantly improve their efficiency by using the particularities of the targeted architectures. Finally, we argue that the introduced methods allow us to efficiently protect a wide variety of block ciphers, including AES.
Higher-Level Features in Speaker Recognition
Authors: Elizabeth Shriberg
Venue: Speaker Classification I: Fundamentals, Features, and Methods (2007)

Higher-level features based on linguistic or long-range information have attracted significant attention in automatic speaker recognition. This article briefly summarizes approaches to using higher-level features for text-independent speaker verification over the last decade. To clarify how each approach uses higher-level information, features are described in terms of their type, temporal span, and reliance on automatic speech recognition for both feature extraction and feature conditioning. A subsequent analysis of higher-level features in a state-of-the-art system illustrates that (1) a higher-level cepstral system outperforms standard systems, (2) a prosodic system shows excellent performance individually and in combination, (3) other higher-level systems provide further gains, and (4) higher-level systems provide increasing relative gains as training data increases. Implications for the general field of speaker classification are discussed.
Point-Sampled Surface Simulation Based on Mass-Spring System
Authors: Zhixun Su, Xiaojie Zhou, Xiuping Liu, Fengshan Liu, Xiquan Shi
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

In this paper, a physically based simulation model for point-sampled surface is proposed based on mass-spring system. First, a Delaunay based simplification algorithm is applied to the original point-sampled surface to produce the simplified point-sampled surface. Then the mass-spring system for the simplified point-sampled surface is constructed by using tangent planes to address the lack of connectivity information. Finally, the deformed point-sampled surface is obtained by transferring the deformation of the simplified point-sampled surface. Experiments on both open and closed point-sampled surfaces illustrate the validity of the proposed method.
Learning Initial Trust Among Interacting Agents
Authors: Achim Rettinger, Matthias Nickles, Volker Tresp
Venue: Proceedings of the 11th international workshop on Cooperative Information Agents XI (2007)

Trust learning is a crucial aspect of information exchange, negotiation, and any other kind of social interaction among autonomous agents in open systems. But most current probabilistic models for computational trust learning lack the ability to take context into account when trying to predict future behavior of interacting agents. Moreover, they are not able to transfer knowledge gained in a specific context to a related context. Humans, by contrast, have proven to be especially skilled in perceiving traits like trustworthiness in such so-called initial trust situations. The same restriction applies to most multiagent learning problems. In complex scenarios most algorithms do not scale well to large state-spaces and need numerous interactions to learn. We argue that trust related scenarios are best represented in a system of relations to capture semantic knowledge. Following recent work on nonparametric Bayesian models we propose a flexible and context sensitive way to model and learn multidimensional trust values which is particularly well suited to establish trust among strangers without prior relationship. To evaluate our approach we extend a multiagent framework by allowing agents to break an agreed interaction outcome retrospectively. The results suggest that the inherent ability to discover clusters and relationships between clusters that are best supported by the data allows to make predictions about future behavior of agents especially when initial trust is involved.
A New Approach for Reducing Embedding Noise in Multiple Bit Plane Steganography
Authors: Arijit Sur, Piyush Goel, Jayanta Mukhopadhyay
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

In this paper, a new steganographic paradigm for digital images has been proposed. In order to reduce embedding noise we propose that information should be embedded in the scaled version of a grayscale value rather than directly in the grayscale value. This approach can be combined with any multiple bit plane embedding scheme to reduce the embedding noise. We also introduce a new steganographic scheme by combining the proposed approach with an existing multiple bitplane steganographic scheme and compare the performance of the combined scheme against the bare version of the existing steganographic scheme. Experimental results reveal that for same embedding rate a multiple bit plane embedding scheme combined with proposed approach adds less embedding noise and thus is less detectable against Wavelet Moment Analysis blind steganalytic attack than its bare version.
A Nonlinear ANC System with a SPSA-Based Recurrent Fuzzy Neural Network Controller
Authors: Qizhi Zhang, Yali Zhou, Xiaohe Liu, Xiaodong Li, Woonseng Gan
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, a feedforward active noise control (ANC) system using a recurrent fuzzy neural network (RFNN) controller based on simultaneous perturbation stochastic approximation (SPSA) algorithm is considered. Because RFNN can capture the dynamic behavior of a system through the feedback links, only one input node is needed, and the exact lag of the input variables need not be known in advance. The SPSA-based RFNN control algorithm employed in the ANC system is first derived. Following this, computer simulations are carried out to verify that the SPSA-based RFNN control algorithm is effective for a nonlinear ANC system. Simulation results show that the proposed scheme is able to significantly reduce disturbances without the need to model the secondary-path and has better tracking ability under variable secondary-path. This observation implies that the SPSA-based RFNN controller eliminates the need of the modeling of the secondary-path.
Review from Academic Success Perspective
Authors: M. Nagl
Venue: Collaborative and Distributed Chemical Engineering. From Understanding to Substantial Design Process Support: Results of the IMPROVE Project (2008)

This short section aims at evaluating the academic outcome of the CRC IMPROVE from 1997 up to now and also sketches its further outcome as TC 61. We do this by regarding different perspectives: (a) The number and value of publications, (b) the contribution to international conference activities, and (c) how we laid the basis for or accelerated the career of young scientists. Joint activities together with industry and their consequences are discussed in sections 5.1 and 8.3. 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
Oracles and Advice as Measurements
Authors: Edwin Beggs, José Félix Costa, Bruno Loff, John V. Tucker
Venue: Proceedings of the 7th international conference on Unconventional Computing (2008)

In this paper we will try to understand how oracles and advice functions, which are mathematical abstractions in the theory of computability and complexity, can be seen as physical measurements in Classical Physics. First, we consider how physical measurements are a natural external source of information to an algorithmic computation, using a simple and engaging case study, namely: Hoyle's algorithm for calculating eclipses at Stonehenge. Next, we argue that oracles and advice functions can help us understand how the structure of space and time has information content that can be processed by Turing machines. Using an advanced case study from Newtonian kinematics, we show that non-uniform complexity is an adequate framework for classifying feasible computations by Turing machines interacting with an oracle in Nature, and that by classifying the information content of such a natural oracle, using Kolmogorov complexity, we obtain a hierarchical structure based on measurements, advice classes and information.
Intelligent System for Traffic Signs Recognition in Moving Vehicles
Authors: Bogusław Cyganek
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Recognition of traffic signs by systems of the intelligent vehicles can increase safety and comfort of driving. It can be also used for highway inspection. In this paper we present architecture of such a system, with special focus on fast sign tracking method. In each frame an adaptive window is built around each area with high probability of existence of an object to be tracked. However, contrary to the mean-shift algorithm, it is not necessary to compute a centroid for each such object. Thus the method allows faster execution which is a key parameter for the real-time scene analysis.
Minimization of Locally Defined Submodular Functions by Optimal Soft Arc Consistency
Authors: Martin C. Cooper
Venue: Constraints (2008)

Submodular function minimization is a polynomially solvable combinatorial problem. Unfortunately the best known general-purpose algorithms have high-order polynomial time complexity. In many applications the objective function is locally defined in that it is the sum of cost functions (also known as soft or valued constraints) whose arities are bounded by a constant. We prove that every valued constraint satisfaction problem with submodular cost functions has an equivalent instance on the same constraint scopes in which the actual minimum value of the objective function is rendered explicit. Such an equivalent instance is the result of establishing optimal soft arc consistency and can hence be found by solving a linear program. From a practical point of view, this provides us with an alternative algorithm for minimizing locally defined submodular functions. From a theoretical point of view, this brings to light a previously unknown connection between submodularity and soft arc consistency.
Subtle Abnormalities in Highly Dense Breasts Detected by Use of a Digital Phase Contrast Mammography System: A Report of Three Invasive Cancer Cases in the Early Stage
Authors: Takako Morita, Maya Yamada, Akiko Kano, Sumiya Nagatsuka, Chika Honda, Tokiko Endo
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Detection of invasive breast cancers in the early stage is of great significance in reducing mortality. Phase contrast mammography (PCM) is a promising approach to improving the conspicuity of subtle lesions camouflaged by dense glandular tissue by virtue of the edge effect obtained through the phase contrast technique. The specific images of PCM in three cases of invasive, early-stage breast cancers with corresponding pathological images of cancer are reported in this paper. The cases demonstrate that the microlobulated contours of lesions with surrounding fine fibrous structures are well depicted in PCM images. Our findings are consistent with studies evaluating physical image quality in which PCM effectively improved the depiction of fibrous structures. It is expected that the number of abnormalities observed in dense breasts, including subtle findings without microcalcifications, would increase in PCM mammograms.
A General Fuzzified CMAC Controller with Eligibility
Authors: Zhipeng Shen, Ning Zhang, Chen Guo
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

This paper presents an online neural network controller. Cerebellar Model Articulation Controller (CMAC) is suitable to online control due to its fast learning speed. By integrating the CMAC address scheme with fuzzy logic concept, a general fuzzified CMAC (GFAC) is proposed. Then by incorporating the concept of eligibility into the GFAC, a GFAC controller with eligibility is presented, named FACE. A learning algorithm for the FACE is derived to tune the model parameters. To achieve online control, an efficient implementation of the proposed FACE method is presented. As an example, the proposed FACE is applied to a ship steering control system. The simulation results show that the ship course can be properly controlled under the disturbances of wave, wind and current.
Feature n-gram Set Based Software Zero-Watermarking
Authors: Bin Lu, Fenlin Liu, Xin Ge, Ping Wang
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

To settle the conflicts between stealth and resilience, the conception of software zero-watermarking is proposed by introducing image zero-watermarking into software watermarking in this paper, in which the key point is to choose a proper birthmark of the software, thus feature n-gram set is presented to be birthmark of the software. Then the software zero-watermarking scheme based on the feature n-gram set is introduced incorporating the idea of Shamir’s secret sharing. It is from experiments that the zero-watermarking scheme provides higher credibility and better resilience.