ppxscal/embedding_network huggingface dataset:
A Slight Delay in the Onset of Conservation May Bring about an Abrupt Increase of Extinction Risk: Perturbation Experiments in an Ecological Lattice Model
Authors: Hiroyasu Nagata, Jin Yoshimura, Kei-Ichi Tainaka
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

We carry out perturbation experiments for both extinction and recovering processes by Monte-Carlo simulations on a finite square lattice, Simulations are tried out by two different methods: local and global interactions. We explore fluctuation enhancement which means the degree of population uncertainty. We find the following results. When an endangered species is recovered, FE strongly emerges. By a slight delay of conservation policy, FE becomes high and the recovering process often fails. These results are remarkable for local interaction. We discuss the issue from the assumption that the protection case may be help towards populations against an endangered species.
The Research of Decision Information Fusion Algorithm Based on the Fuzzy Neural Networks
Authors: Pei-Gang Sun, Hai Zhao, Xiao-Dan Zhang, Jiu-Qiang Xu, Zhen-Yu Yin, Xi-Yuan Zhang, Si-Yuan Zhu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

A new decision information fusion algorithm based on the fuzzy neural networks, which introduces fuzzy comprehensive assessment into traditional decision information fusion technology under the "soft" decision architecture, is proposed. The process of fusion is composed of the comprehensive operation and the global decision through fusing the local decision of multiple sensors for obtaining the global decision of the concerned object at the fusion center. In the practical application, the algorithm has been successfully applied in the temperature fault detection and diagnosis system of hydroelectric simulation system of Jilin Fengman. In the analysis of factual data, the performance of the algorithm precedes that of the traditional diagnosis method.
Solving Generalized Maximum Dispersion with Linear Programming
Authors: Gerold Jäger, Anand Srivastav, Katja Wolf
Venue: Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management (2007)

The Generalized Maximum Dispersion problem asks for a partition of a given graph into p vertex-disjoint sets, each of them having at most k vertices. The goal is to maximize the total edge-weight of the induced subgraphs. We present the first LP-based approximation algorithm.
A Real-Time NURBS Interpolator with Feed Rate Adjustment
Authors: Liyan Zhang, Kuisheng Wang, Yuchao Bian, Hu Chen
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

When realizing a real-time NURBS curve interpolator in CNC machining with high speed and high accuracy, many practical problems are encountered. Algorithms based on Taylor's expansion are developed for realizing NURBS curve interpolator that takes into account the feed rate adjustment to the curvature radius of NURBS curves. By means of the flexible acceleration/deceleration (ACC/DEC) planning, the velocity profile can be chosen dynamically to meet the requirements of the machining process. In addition, a new strategy based on the geometrical properties of NURBS curves is suggested to predict the deceleration point. Therefore, the process of acceleration and deceleration is so smooth that can satisfy the need of machining tool dynamic properties. This NURBS interpolator has been validated on a CNC system, which improves the machining accuracy for both position and velocity control.
Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model
Authors: Xiaoxia Wang, Peter Tiňo, Mark A. Fardal
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Several manifold learning techniques have been developed to learn, given a data, a single lower dimensional manifold providing a compact representation of the original data. However, for complex data sets containing multiple manifolds of possibly of different dimensionalities, it is unlikely that the existing manifold learning approaches can discover all the interesting lower-dimensional structures. We therefore introduce a hierarchical manifolds learning framework to discover a variety of the underlying low dimensional structures. The framework is based on hierarchical mixture latent variable model, in which each submodel is a latent variable model capturing a single manifold. We propose a novel multiple manifold approximation strategy used for the initialization of our hierarchical model. The technique is first verified on artificial data with mixed 1 ¿, 2 ¿ and 3 ¿dimensional structures. It is then used to automatically detect lower-dimensional structures in disrupted satellite galaxies.
Efficient belief propagation for higher-order cliques using linear constraint nodes
Authors: Brian Potetz, Tai Sing Lee
Venue: Computer Vision and Image Understanding (2008)

Belief propagation over pairwise-connected Markov random fields has become a widely used approach, and has been successfully applied to several important computer vision problems. However, pairwise interactions are often insufficient to capture the full statistics of the problem. Higher-order interactions are sometimes required. Unfortunately, the complexity of belief propagation is exponential in the size of the largest clique. In this paper, we introduce a new technique to compute belief propagation messages in time linear with respect to clique size for a large class of potential functions over real-valued variables. We discuss how this technique can be generalized to still wider classes of potential functions at varying levels of efficiency. Also, we develop a form of nonparametric belief representation specifically designed to address issues common to networks with higher-order cliques and also to the use of guaranteed-convergent forms of belief propagation. To illustrate these techniques, we perform efficient inference in graphical models where the spatial prior of natural images is captured by 2x2 cliques. This approach shows significant improvement over the commonly used pairwise-connected models, and may benefit a variety of applications using belief propagation to infer images or range images, including stereo, shape-from-shading, image-based rendering, segmentation, and matting.
Vibratory diagnosis of the earthmoving machines for the additional necessary power level evaluation
Authors: Carmen Debeleac
Venue: Proceedings of the 9th WSEAS International Conference on Acoustics & Music: Theory & Applications (2008)

In this paper the author present the acquired results regarding the evaluation of the additional necessary power level, by using an indirect method, based on vibratory diagnosis. The study was based on the identification and monitorization of the main parameters of earthmoving machine drive system, with priority on theirs dynamic behaviour. It was identified the dynamic transitory phenomenons which appears during a working cycle. The proposed method depart from the instant values acquisition of the supervised parameters, and the comparative analysis with the available informations from the existing database. The utilization of this non-distructive method leads to the residual energy consumption reducing, through the efficient achievements of the normal consumption for each tecnological phase due to the dynamic effects decreasing. Both through the fit rendering of the derived results by the previous presented method, and through the continual correlation with the acquired data regarding the distribution and the levels of vibration on the machine basic structure, there are dignified the next major aspects: the additional level of the necessary power on the system, and their distribution on the structural components; the estimation of the utilization degree and of the charging level on each structural components; the estimation of the structural and functional degrade levels. This work was developed on the wheel frontal loader equipment type. There are presented the main advantages of the proposed method, sustained by the encouraging results acquired by the author.
Interactive User Modeling for Personalized Access to Museum Collections: The Rijksmuseum Case Study
Authors: Yiwen Wang, Lora M. Aroyo, Natalia Stash, Lloyd Rutledge
Venue: Proceedings of the 11th international conference on User Modeling (2007)

In this paper we present an approach for personalized access to museum collections. We use a RDF/OWL specification of the Rijksmuseum Amsterdam collections as a driver for an interactive dialog. The user gives his/her judgment on the artefacts, indicating likes or dislikes. The elicited user model is further used for generating recommendations of artefacts and topics. In this way we support exploration and discovery of information in museum collections. A user study provided insights in characteristics of our target user group, and showed how novice and expert users employ their background knowledge and implicit interest in order to elicit their art preference in the museum collections.
Access Modalities to an Imagistic Library for Medical e-Learning
Authors: Liana Stanescu, Dumitru Burdescu, Mihai Gabriel, Cosmin Stoica, Anca Ion
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

The paper presents the organization way and the access facilities to a multimedia digital library with medical information for electronic learning. The digital library contains course materials and medical images collected in the patient's diagnosis process. The originality of the paper is given by the presentation of two access modalities to multimedia information from the digital library: content-based visual query and semantic query. The content-based visual query can be effectuated at the image or region level using colour and texture characteristics automatically extracted from medical images at their loading in the database. Also, semantic queries against the multimedia database can be automatically launched with the help of the topic map based on a part of MeSH thesaurus, the part that includes the medical diagnosis names. The student can navigate through topic map depending on its interest subject, bringing in this way big advantages. These access paths can be combined for retrieving the interest information. The multimedia digital library represents a very useful tool in the medical knowledge improvement, addressing to the students, resident doctors, young specialists or family doctors.
Hilbert Envelope Based Features for Far-Field Speech Recognition
Authors: Samuel Thomas, Sriram Ganapathy, Hynek Hermansky
Venue: Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction (2008)

Automatic speech recognition (ASR) systems, trained on speech signals from close-talking microphones, generally fail in recognizing far-field speech. In this paper, we present a Hilbert Envelope based feature extraction technique to alleviate the artifacts introduced by room reverberations. The proposed technique is based on modeling temporal envelopes of the speech signal in narrow sub-bands using Frequency Domain Linear Prediction (FDLP). ASR experiments on far-field speech using the proposed FDLP features show significant performance improvements when compared to other robust feature extraction techniques (average relative improvement of 43 % in word error rate).
Estimation of performance and power of simd processors
Authors: Peter M. Kogge
Venue:  (2005)

The concept phase of microprocessor design involves optimizations at all levels of the design, including the microarchitectural level, instruction set, and compiler. To evaluate ISA-level or microarchitecture modifications, the compiler also needs to be updated to take advantage of the new target. The requirement of meeting the power budget further complicates the problem. Given the size of the search space, it is unrealistic to use detailed modeling of the performance and power, because it requires a substantial amount of time to evaluate just a single point in the search space. However, such detailed analysis is not necessary in the early stages of a processor design. Instead, it is important to evaluate as many architectural ideas as possible, even with lower accuracy. My work develops an infrastructure for quicker exploration the microarchitecture-ISA-compiler design space using estimation. A benchmark is first profiled on a host machine, then the assembly code of the compiled benchmark is scheduled on the target processor, and the profile information is used to produce an estimate of performance and power. This approach is applicable to general purpose processors, but specifically targets SIMD processors. A retargetable SIMDization phase was developed, which is capable of generating SIMD instructions, including data reordering instructions, or permutations. The SIMDization phase was validated on hardware (Pentium), and the estimation methodology was validated against commonly used modeling tools (SimpleScalar and Wattch).
Feature Weighting and Selection for a Real-Time Network Intrusion Detection System Based on GA with KNN
Authors: Ming-Yang Su, Kai-Chi Chang, Hua-Fu Wei, Chun-Yuen Lin
Venue: Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics (2008)

A good feature selection policy which can choose significant and as less as possible features plays a key role for any successful NIDS. The paper presents a genetic algorithm combined with kNN (k-Nearest Neighbor) for feature weighting. We weight all initial 35 features in the training phase and then select tops of them to implement a NIDS for testing. Many DoS/DDoS attacks are applied to evaluate the system. For known attacks we can get the best 97.42% overall accuracy rate while only the top 19 features are considered; as for unknown attacks, we can get the best 78% overall accuracy rate by top 28 features.
How Does an Intelligent Learning Environment with Novel Design Affect the Students' Learning Results?
Authors: Marina Lepp
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

We present an intelligent learning environment, T-algebra, for step-by-step solving of algebra problems using a novel design of step dialogue, which combines two known approaches: conversion by rules and entering the result. Each solution step in T-algebra consists of three stages: selection of the transformation rule, marking the parts of expression, entering the result of the operation. The designed dialogue enables the student to make the same mistakes as on paper and to receive understandable feedback about mistakes. The evaluation demonstrated that even a brief use of T-algebra affects the results of learning. The students who used T-algebra did better on consecutive paper test than the students who did not use T-algebra. Furthermore, T-algebra tends to affect specific error types, i.e., after using T-algebra the students make fewer mistakes of certain type on paper as well. 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
Effective Dimensions and Relative Frequencies
Authors: Xiaoyang Gu, Jack H. Lutz
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

Consider the problem of calculating the fractal dimension of a set X consisting of all infinite sequences S over a finite alphabet Σ that satisfy some given condition P on the asymptotic frequencies with which various symbols from Σ appear in S. Solutions to this problem are known in cases where(i) the fractal dimension is classical (Hausdorff or packing dimension), or(ii) the fractal dimension is effective (even finite-state) and the condition P completely specifies an empirical distribution ¿ over Σ, i.e., a limiting frequency of occurrence for every symbol in Σ.In this paper we show how to calculate the finite-state dimension (equivalently, the finite-state compressibility) of such a set X when the condition P only imposes partial constraints on the limiting frequencies of symbols. Our results automatically extend to less restrictive effective fractal dimensions (e.g., polynomial-time, computable, and constructive dimensions), and they have the classical results (i) as immediate corollaries. Our methods are nevertheless elementary and, in most cases, simpler than those by which the classical results were obtained.
Vehicle Trajectory Estimation Based on Monocular Vision
Authors: Daniel Ponsa, Antonio López
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

This paper proposes a system to estimate the 3D position and velocity of vehicles, from images acquired with a monocular camera. Given image regions where vehicles are detected, Gaussian distributions are estimated detailing the most probable 3D road regions where vehicles lay. This is done by combining an assumed image formation model with the Unscented Transform mechanism. These distributions are then fed into a Multiple Hypothesis Tracking algorithm, which constructs trajectories coherent with an assumed model of dynamics. This algorithm not only characterizes the dynamics of detected vehicles, but also discards false detections, as they do not find spatio-temporal support. The proposals is tested in synthetic sequences, evaluating how noisy observations and miss-detections affect the accuracy of recovered trajectories.
Transforming Inconsistent Subformulas in MaxSAT Lower Bound Computation
Authors: Chu Min Li, Felip Manyà, Nouredine Ould Mohamedou, Jordi Planes
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

We define a new heuristic that guides the application of cycle resolution (CR) in MaxSAT, and show that it produces better lower bounds than those obtained by applying CR exhaustively as in Max-DPLL, and by applying CR in a limited way when unit propagation detects a contradiction as in MaxSatz.
TTPM - An efficient deadlock-free algorithm for multicast communication in 2D torus networks
Authors: M. G. Darwish, A. A. Radwan, M. A. Abd El-Baky, K. Hamed
Venue: Journal of Systems Architecture: the EUROMICRO Journal (2008)

A torus network has become increasingly important to multicomputer design because of its many features including scalability, low bandwidth and fixed degree of nodes. A multicast communication is a significant operation in multicomputer systems and can be used to support several other collective communication operations. This paper presents an efficient algorithm, TTPM, to find a deadlock-free multicast wormhole routing in two-dimensional torus parallel machines. The introduced algorithm is designed such that messages can be sent to any number of destinations within two start-up communication phases; hence the name Torus Two Phase Multicast (TTPM) algorithm. An efficient routing function is developed and used as a basis for the introduced algorithm. Also, TTPM allows some intermediate nodes that are not in the destination set to perform multicast functions. This feature allows flexibility in multicast path selection and therefore improves the performance. Performance results of a simulation study on torus networks are discussed to compare TTPM algorithm with a previous algorithm.
On the Robustness of Applications Based on the SSL and TLS Security Protocols
Authors: Diana Berbecaru, Antonio Lioy
Venue: Proceedings of the 4th European PKI workshop: Theory and Practice on Public Key Infrastructure: Theory and Practice (2007)

The SSL and TLS security protocols have been designed and implemented to provide end-to-end data security. This includes data integrity that is the data cannot be modified, replayed or reordered by an attacker without being detected at the receiving endpoint. SSL and TLS however does not provide data delivery integrity, in the sense they do not guarantee that all the sent data will actually arrive at the other side. This is because, for example, SSL/TLS cannot know in advance which is the exact size of the data to be sent over the secured channel. The mosts recent versions (SSLv3 and TLSv1) provide some form of protection against loss of data records by means of sequence numbers and specialized close_notify alert messages to be sent when tearing down the SSL connection. Unfortunately, this is not enough when the last record containing application data together with the closure alert are deleted on purpose, as it happens in the truncation attacks. SSLv3/TLSv1 specifications do not indicate what should happen (at the application level) if the close_notify message never arrives at the receiver. Consequently, for applications where it is important to ascertain that the data reached untruncated the other party, it is required to have an additional control at the application level.In this paper we show (based on practical tests) that some widely-used applications implementing SSLv3 and TLSv1 do not perform further controls on the size of the data to be received, and thus they are vulnerable to truncation attacks. For tests we implemented a specialized MITMSSL tool, used to manipulate the SSL/TLS records exchanged between two communicating parties.
A survey of browsing models for content based image retrieval
Authors: Daniel Heesch
Venue: Multimedia Tools and Applications (2008)

The problem of content based image retrieval (CBIR) has traditionally been investigated within a framework that emphasises the explicit formulation of a query: users initiate an automated search for relevant images by submitting an image or draw a sketch that exemplifies their information need. Often, relevance feedback is incorporated as a post-retrieval step for optimising the way evidence from different visual features is combined. While this sustained methodological focus has helped CBIR to mature, it has also brought out its limitations more clearly: There is often little support for exploratory search and scaling to very large collections is problematic. Moreover, the assumption that users are always able to formulate an appropriate query is questionable. An effective, albeit much less studied, method of accessing image collections based on visual content is that of browsing. The aim of this survey paper is to provide a structured overview of the different models that have been explored over the last one to two decades, to highlight the particular challenges of the browsing approach and to focus attention on a few interesting issues that warrant more intense research.
Teaching Distributed Artificial Intelligence with RoboRally
Authors: Ingo J. Timm, Tjorben Bogon, Andreas D. Lattner, René Schumann
Venue: Proceedings of the 6th German conference on Multiagent System Technologies (2008)

Teaching Artificial Intelligence (AI) or multi-agent systems is a challenging task as algorithms are in question which are advantageous in highly complex and dynamic environments. Explaining multi-agent systems (MAS) in lectures requires interactive approaches accompanied by exercises. The key challenge in using practical exercises within lectures on MAS is to establish an environment for testing which is extremely time consuming. It is not reasonable that students do this work as they do have not enough time focussing on the important aspects. In this paper, we introduce a system which supports experimenting with AI and Distributed Artificial Intelligence (DAI) algorithms concurrently to the lecture. Our system is based on a board game called RoboRally. Different issues from the field of AI and DAI can be implemented and tested in a kind of challenge.