ppxscal/embedding_network huggingface dataset:
An Approach to Flocking of Robots Using Minimal Local Sensing and Common Orientation
Authors: Iñaki Navarro, Álvaro Gutiérrez, Fernando Matía, Félix Monasterio-Huelin
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

A new algorithm for the control of robot flocking is presented. Flocks of mobile robots are created by the use of local control rules in a fully distributed way, using just local information from simple infra-red sensors and global heading information on each robot. Experiments were done to test the algorithm, yielding results in which robots behaved as expected, moving at a reasonable velocity and in a cohesive way. Up to seven robots were used in real experiments and up to fifty in simulation.
Automatically Generating Behavioral Models of Adaptive Systems to Address Uncertainty
Authors: Heather J. Goldsby, Betty H. Cheng
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Increasingly, high-assurance applications rely on dynamically adaptive systems (DASs) to respond to environmental changes, while satisfying functional requirements and non-functional preferences. Examples include critical infrastructure protection and transportation systems. A DAS comprises a collection of (non-adaptive) target systems (represented as UML models) and a set of adaptations that realize transitions among target systems. Two sources of uncertainty inherent to DASs are: (1) predicting the future execution environment, and (2) using functional and non-functional trade-offs to respond to the changing environment. To address this uncertainty, we are inspired by living organisms that are astonishingly adept at adapting to changing environmental conditions using evolution. In this paper, we describe a digital evolution-based approach to generating models that represent possible target systems suitable for different environmental conditions, enabling the developer to identify the functional and non-functional trade-offs between the models, and then assisting the developer in selecting target systems for the DAS.
DDDAS Predictions for Water Spills
Authors: Craig C. Douglas, Paul Dostert, Yalchin Efendiev, Richard E. Ewing, Deng Li, Robert A. Lodder
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

Time based observations are the linchpin of improving predictions in any dynamic data driven application systems. Our predictions are based on solutions to differential equation models with unknown initial conditions and source terms. In this paper we want to simulate a waste spill by a water body, such as near an aquifer or in a river or bay. We employ sensors that can determine the contaminant spill location, where it is at a given time, and where it will go. We estimate initial conditions and source terms using better and new techniques, which improves predictions for a variety of data-driven models.
Virtual Watersheds: Simulating the Water Balance of the Rio Grande Basin
Authors: C. L. Winter, Everett P. Springer, Keeley Costigan, Patricia Fasel, Sue Mniewski, George Zyvoloski
Venue: Computing in Science and Engineering (2004)

Managers of water resources in arid and semi-arid regions must allocate increasingly variable surface water supplies and limited groundwater resources. This challenge is leading to a new generation of detailed computational models that can link multiple sources to a wide range of demands.
Avoiding Boosting Overfitting by Removing Confusing Samples
Authors: Alexander Vezhnevets, Olga Barinova
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorithms are not appropriate in case of overlapping classes. This inadequateness is likely to be the major source of boosting overfitting while working with real world data. To verify our conclusion we use the fact that any overlapping classes' task can be reduced to a deterministic task with the same Bayesian separating surface. This can be done by removing "confusing samples" --- samples that are misclassified by a "perfect" Bayesian classifier. We propose an algorithm for removing confusing samples and experimentally study behavior of AdaBoost trained on the resulting data sets. Experiments confirm that removing confusing samples helps boosting to reduce the generalization error and to avoid overfitting on both synthetic and real world. Process of removing confusing samples also provides an accurate error prediction based on the work with the training sets.
Detection algorithm for color dynamic images by multiple surveillance cameras under low luminance conditions based on fuzzy corresponding map
Authors: Yutaka Hatakeyama, Akimichi Mitsuta, Kaoru Hirota
Venue: Applied Soft Computing (2008)

An objects detection algorithm for color dynamic images from two cameras is proposed for a surveillance system under low illumination. It provides automatic calculation of a fuzzy corresponding map and color similarity for lower luminance conditions, which detects little chromatic regions in CCD camera images under lower illumination and presents regions with a possibility of occlusion situation. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which occlusion regions are correctly presented. Moreover, implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan.
Modeling the Dynamics of Virtual Agent's Social Relations
Authors: Magalie Ochs, Nicolas Sabouret, Vincent Corruble
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

To create believable virtual agents, both the social context and the emotions appearing during the interaction should be taken into account. Most existing models of social context are statically defined. However, research shows that the affective experience during an interaction can induce a modification of social context and more particularly of social relations. In this paper, we propose to explore the dynamics of virtual agent's social relations. The agent's social relations are updated according to emotions that appear during the interaction (both the agent's triggered emotions and those expressed by its interlocutor). The model proposed is qualitatively defined. In its implementation, numerical functions are proposed.
Simplified fast handover in mobile IPv6 networks
Authors: Nguyen Van Hanh, Soonghwan Ro, Jungkwan Ryu
Venue: Computer Communications (2008)

The Fast Handovers for Mobile IPv6 (FMIPv6) protocol provides seamless handover; it uses anticipation based on layer 2 trigger information of the mobile node (MN) to obtain a new care-of address at the new link while still connected to the previous link, thus reducing handover delay. A bidirectional tunnel is then established between access routers to minimize packet loss during the handover. However, this method incurs higher signaling costs compared with the standard Mobile IPv6 protocol. In many cases, the mobile node cannot complete the fast handover in predictive mode due to lack of time, especially with high-speed movement of the mobile node. This paper proposes an enhancement to the FMIPv6, the Simplified Fast Handover in Mobile IPv6 Networks (SFMIPv6), which significantly reduces the anticipation time of the fast handover and thereby increases the probability that the protocol can perform the fast handover in predictive mode. In this paper, we also present performance evaluations in terms of the influence of two factors, the break-down point and the velocity of the MN, using evaluation models. The numerical results prove that the network performance of the proposed protocol is effectively improved compared to the original protocols, the FMIPv6 and Fast Handover Support in Hierarchical Mobile IPv6 (F-HMIPv6). Moreover, the results show that the proposed protocol could appropriately operate with high-speed mobile node movement.
DCT and PCA Based Method for Shape from Focus
Authors: M. Tariq Mahmood, Wook-Jin Choi, Tae-Sun Choi
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) are widely used in computer vision applications. In this paper, we introduce a new SFF method based on DCT and PCA. Contrary to computing focus quality locally by summing all values in a 2D or 3D window obtained after applying a focus measure, a vector consisting of seven neighboring pixels is populated for each pixel in the image volume. PCA is applied on the AC part of the DCT of each vector in the sequence to transform data into eigenspace. Considering the first feature, as it contains maximum variation, and discarding all others, is employed to compute the depth. Though DCT and PCA are both computationally expensive transformations, the reduction in data elements and algorithm iterations have made the new approach efficient. Experimental results are presented to demonstrate the effectiveness of new method by using three different image sequences.
The Design, Development and Evaluation of Virtual Reality Learning Environment for Numeracy Concepts Using 3D Virtual Manipulatives
Authors: L. Daghestani, R. D. Ward, Z. Xu, H. Al-Nuaim
Venue: Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation (2008)

Virtual Reality (VR) offers potentials and challenges for teaching and learning. For learners of elementary mathematics, it has been suggested that VR offers promising ways of representing numeracy (number and operations) concepts in the form of Virtual Manipulatives (VM). This project aims at investigating VR learning Environments to elementary numeracy education using 3D VM. This paper focuses on 3 aspects. The first is the evaluation of an existing Mathematical Virtual Learning Environment (MAVLE) in order to analyze and evaluate the current 2D virtual manipulatives. The results of this study will form the basis for the design and development of an innovative conceptual prototype - MAVLE. While the second step is to construct a framework for the MAVLE prototype by highlighting other factors that are important for system implementation. The third aspect will be applying the outcomes from other two stages and providing benchmarks and guidance's to the future design and development of Virtual Learning Environment.
A Joint Topic and Perspective Model for Ideological Discourse
Authors: Wei-Hao Lin, Eric Xing, Alexander Hauptmann
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Polarizing discussions on political and social issues are common in mass and user-generated media. However, computer-based understanding of ideological discourse has been considered too difficult to undertake. In this paper we propose a statistical model for ideology discourse. By ideology we mean "a set of general beliefs socially shared by a group of people." For example, Democratic and Republican are two major political ideologies in the United States. The proposed model captures lexical variations due to an ideological text's topic and due to an author or speaker's ideological perspective. To cope with the non-conjugacy of the logistic-normal prior we derive a variational inference algorithm for the model. We evaluate the proposed model on synthetic data as well as a written and a spoken political discourse. Experimental results strongly support that ideological perspectives are reflected in lexical variations.
General Parallel Matrix Multiplication on the OTIS Network
Authors: Zhaoquan Cai, Wenhong Wei
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

The Optical Transpose Interconnection System (OTIS), proposed by Marsden et al., is a hybrid optical and electronic interconnection system for large parallel computers. In this network model, Wang and Sahni in proposed the two size of N×N or kN×kN matrices multiplication algorithm, however, the algorithm of matrix multiplication about general matrix was not introduced , and the matrix multiplication proposed by them can only be applied to OTIS-Mesh, not to general OTIS architecture. So in this paper, we map matrices to OTIS network using a new mapping scheme, and propose general algorithm to multiply two vectors, a vector and a matrix, and two matrices, at last we compare our algorithm with Canon’s algorithm and O(N^3) serial algorithm by experiments, and the result proved that our algorithm is better than them.
Some Issues on Intrusion Detection in Web Applications
Authors: Jaroslaw Skaruz, Franciszek Seredynski
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take the advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to a recurrent neural network (RNN) trained by backpropagation through time (BPTT) algorithm. Then, two coefficients of the rule are evaluated. The rule is used to interpret RNN output. In the testing phase RNN with the rule is examined against attacks and legal data to find out how evaluated rule affects efficiency of detecting attacks. All experiments were conducted on Jordan network. Experimental results show the relationship between the rule and a length of SQL queries.
Liknon Feature Selection for Microarrays
Authors: Erinija Pranckeviciene, Ray Somorjai
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

Many real-world classification problems involve very sparse and high-dimensional data. The successes of LIKNON - linear programming support vector machine (LPSVM) for feature selection, motivates a more thorough analysis of the method when applied to sparse, multivariate data. Due to the sparseness, the selection of a classification model is greatly influenced by the characteristics of that particular dataset. Robust feature/model selection methods are desirable. LIKNON is claimed to have such robustness properties. Its feature selection operates by selecting the groups of features with large differences between the resultants of the two classes. The degree of desired difference is controlled by the regularization parameter. We study the practical value of LIKNON-based feature/model selection for microarray data. Our findings support the claims about the robustness of the method.
Pilot Aided Carrier Frequency Offset Estimation in MC-DS-CDMA Systems
Authors: Licheng Liu, Xianhua Dai
Venue: Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02 (2008)

In this paper, we propose a new algorithm for the carrier frequency offset (CFO) estimation of the uplink of multicarrier DS-CDMA (MC-DS-CDMA) systems. Firstly, our method exploits a new pilot scheme to obtain the approximation of the interested user’s signal from the composite signals on the user specific subcarrier in the receiver. Then the fast Fourier transform (FFT) operation is applied to estimate the CFO of the interested user. The proposed approach provides high-resolution estimation in the case of the slowly time-varying flat fading channel and low Signal to Noise Ratio (SNR). The simulation results verify the performance of our algorithm.
The Second International Workshop on Management and Economics of Software Product Lines (MESPUL08)
Authors: Klaus Schmid, M. Ali Babar, Paul Grünbacher, Makoto Nonaka
Venue: Proceedings of the 2008 12th International Software Product Line Conference (2008)

Software Product Line Engineering is intimately connected with the need for optimizing the economic situation of an organization. The MESPUL workshop provides a forum for the exchange of practitioners and researchers on the economic dimension of software product lines like measurement, planning, or optimization of reuse potential.
Maximality and totality of stable functions in the category of stable bifinite domains
Authors: Yixiang Chen, Guo-Qiang Zhang
Venue: Computers Mathematics with Applications (2006)

This paper studies maximality and totality of stable functions in the category of stable bifinite domains. We present three main results as follows,o(1)every maximum-preserving function is a maximal element in the stable function spaces; (2)a maximal stable function f : D -> E is maximum-preserving if D is maximum-separable and E is completely separable; and (3)a stable bifinite domain D is maximum-separable if and only if for any locally distributive stable bifinite domain E, each maximal stable function f : D -> E is maximum-preserving.
The Impact of Dynamically Heterogeneous Multicore Processors on Thread Scheduling
Authors: Fred A. Bower, Daniel J. Sorin, Landon P. Cox
Venue: IEEE Micro (2008)

Although most current multicore processors are homogeneous, microarchitects are now proposing heterogeneous core implementations, including systems in which heterogeneity is introduced at runtime. This article shows that operating system schedulers must consider dynamic heterogeneity or suffer significant power-efficiency and performance losses.
Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery
Authors: A. Azadeh, I. M. Fam, M. Khoshnoud, M. Nikafrouz
Venue: Information Sciences: an International Journal (2008)

The objective of this study is to design a fuzzy expert system for performance assessment of health, safety, environment (HSE) and ergonomics system factors in a gas refinery. This will lead to a robust control system for continuous assessment and improvement of HSE and ergonomics performance. The importance of this study stems from the current lack of formal integrated methodologies for interpreting and evaluating performance data for HSE and ergonomics. Three important reasons to use fuzzy expert systems are (1) reduction of human error, (2) creation of expert knowledge and (3) interpretation of large amount of vague data. To achieve the objective of this study, standard indicators and technical tolerances for assessment of HSE and ergonomics factors are identified. Then, data is collected for all indicators and consequently, for each indicator four conditions are defined as ''acceptance'', ''low deviation'', ''mid deviation'' and ''high deviation''. A membership function is defined for each fuzzy condition (set) because an indicator cannot be allocated to just one of the above conditions. The expert system uses fuzzy rules, which are structured with Data Engine. Previous studies have introduced HSE expert system whereas this study introduces an integrated HSE and ergonomics expert system through fuzzy logic.
Combining News and Technical Indicators in Daily Stock Price Trends Prediction
Authors: Yuzheng Zhai, Arthur Hsu, Saman K Halgamuge
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Stock market prediction has always been one of the hottest topics in research, as well as a great challenge due to its complex and volatile nature. However, most of the existing methods neglect the impact from mass media that will greatly affect the behavior of investors. In this paper we present a system that combines the information from both related news releases and technical indicators to enhance the predictability of the daily stock price trends. The performance shows that this system can achieve higher accuracy and return than a single source system.
The Hilbert-Huang Transform for Detection of Otoacoustic Emissions and Time-Frequency Mapping
Authors: Artūras Janušauskas, Vaidotas Marozas, Arūnas Lukoševičius, Leif Sörnmo
Venue: Informatica (2006)

This paper presents an application of the Hilbert-Huang transform (HHT) and ensemble correlation for detection of the transient evoked otoacoustic emissions (TEOAEs), and high resolution time-frequency mapping. The HHT provides a powerful tool for nonlinear analysis of nonstationary signals such as TEOAEs. Since the HHT itself does not distinguish between signal and noise it was used with ensemble correlation to extract information about intervals with correlated activity. The combination of methods produced good results for both tasks TEOAE detection and time-frequency mapping. The resulting detection performance, using the mean hearing threshold as audiological separation criterion, was a specificity of 81% at a sensitivity of 90% to be compared to 65% with the traditional wave reproducibility detection criterion. High resolution time frequency mapping predicted in more than 70% of the cases hearing loss at a specific frequency in cases of ski-sloping audiograms. The present m ethod does not require a priori information on the signal and may, with minor changes, be successfully applied to analysis of other types of repetitive signals such as evoked potentials.
A Holistic View of Evolutionary Rates in Paralogous and Orthologous Genes
Authors: Deng Pan, Liqing Zhang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Much work has been done on the evolutionary divergence of duplicated genes in protein coding regions. However, this remains a partial view, and little is known how duplicated genes diverge in non-coding regions, which can play an important role in the evolutionary preservation of duplicated genes. Here we compared the evolutionary rates of different parts of of duplicated genes in the human and mouse genomes, including 5'-UTRs, coding regions, 3'-UTRs, and 500 bps upstream and downstram of the untranslated regions. Results show that compared to orthologous genes, the ratios of the genetic distance of noncoding regions such as UTRs and upstream and downstream regions vs. synonymous substitutions tend to be smaller in paralogous genes, suggesting that synonymous substitutions benefit more greatly from relaxed selective constraints than noncoding regions. Moreover, we also examined the most frequent types of rate comparison among different genic regions in both orthologs and paralogs and found that the ranks of the most frequent types of rate comparison differ little between human paralogs and mouse paralogs, but differ greatly from those in orthologs, suggesting that duplication enables changes of evolutionary dynamics along different parts of genes. We also classified duplicated genes into three categories based on their chromosomal locations, tandem duplicates, intra-chromosomal duplicates, and inter-chromosomal duplicates. Results show that in both the human and mouse, selective constraint (measured by K a /K s ) on intra-chromosomal duplicates tends to be much lower than that on tandem duplicates, which in turn is significantly lower than that on inter-chromosomal duplicates. This shows that genomic location is an important factor in determining the evolutionary divergence of duplicated genes.