ppxscal/embedding_network huggingface dataset:
Image Denoising Based on Wavelet Support Vector Machine
Authors: Shaoming Zhang, Ying Chen
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

In this paper, a new image denoising method based on wavelet analysis and support vector machine regression (SVR) is presented. The feasibility of image denoising via support vector regression is discussed and is demonstrated by an illustrative example which denoise a 1-dimension signal with Gauss KBF SVM. The wavelet theory is discussed and applied to construct the wavelet kernel, then the wavelet support vector machine (WSVM) is proposed. The result of experiment shows that the denoising method based on WSVM can reduce noise well, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than Gaussian KBF SVM and other traditional methods.
Selection of Reliable Features Subsets for Appearance-Based Tracking
Authors: Pascaline Parisot, Bernard Thiesse, Vincent Charvillat
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Efficient algorithms that track targets with a constant aspect (rigid objects, for example) are often based on appearance models. The simplest models linearly predict motion parameters from gray-scale variations measured at features. Choosing the features and training the predictor is done during a preliminary off-line stage.This paper presents several methods that improve the features selection process by filtering out some features from a given set. In particular, we are interested in the SVD-based subset selection procedure proposed by Golub and Van Loan.We show a significant improvement of tracking performance when our method filters Moravec, Harris, KLT or SUSAN features. We conclude that individually good selected features may not build a good subset and that a good spatial distribution of the features is paramount.
Hybrid Pipeline Structure for Self-Organizing Learning Array
Authors: Janusz A. Starzyk, Mingwei Ding, Yinyin Liu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In recent years, many efforts have been put in applying the concept of reconfigurable computing to neural networks. In our previous pursuits, an innovative self-organizing learning array (SOLAR) was developed. However, traditional multiplexer method to achieve reconfigurable connection has its limit for larger networks. In this paper, we propose a novel pipeline structure, which offers flexible, possibly large number of dynamically configurable connections and which utilizes each node's computing ability. The hardware resources demand of the proposed structure is a linear function of the network size, which is especially useful for building a large network that can handle complicated real-world applications.
Generating Tests from B Specifications and Test Purposes
Authors: Jacques Julliand, Pierre-Alain Masson, Régis Tissot
Venue: Proceedings of the 1st international conference on Abstract State Machines, B and Z (2008)

This paper is about generating tests from test purposes, in addition to structural tests. We present a method that re-uses a behavioural model and an abstract test concretization layer developed for structural testing, and relies on additional test purposes. We propose, in the B framework, a process of test generation that uses the symbolic animation mechanisms of LTG (Leirios Test Generator) based on constraint solving, and guided by the test purposes. We build for that a B animable model that is the synchronized product of a behavioural B abstract model and a test purpose described as a labelled transition system. We prove the correctness of this method, and illustrate it by means of the IAS case study. IAS is a smart-card application dedicated to the operations of Identification, Authentication and electronic Signature.
High-Level Side-Channel Attack Modeling and Simulation for Security-Critical Systems on Chips
Authors: Francesco Menichelli, Renato Menicocci, Mauro Olivieri, Alessandro Trifiletti
Venue: IEEE Transactions on Dependable and Secure Computing (2008)

The design flow of a digital cryptographic device must take into account the evaluation of its security against attacks based on side channels observation. The adoption of high level countermeasures, as well as the verification of the feasibility of new attacks, presently require the execution of timeconsuming physical measurements on the prototype product or the simulation at a low abstraction level. Starting from these assumptions, we developed an exploration approach centered on high level simulation, in order to evaluate the actual implementation of a cryptographic algorithm, being it software or hardware based. The simulation is performed within a unified tool based on SystemC, that can model a software implementation running on a microprocessor-based architecture or a dedicated hardware implementation as well as mixed software-hardware implementations with cycle-accurate resolution. Here we describe the tool and provide a large set of design explorations and characterizations based on actual implementations of the AES cryptographic algorithm, demonstrating how the execution of a large set of experiments allowed by the fast simulation engine can lead to important improvements in the knowledge and the identification of the weaknesses in cryptographic algorithm implementations.
CAD on Liver Using CT and MRI
Authors: Xuejun Zhang, Hiroshi Fujita, Tuanfa Qin, Jinchuang Zhao, Masayuki Kanematsu, Takeshi Hara, Xiangrong Zhou, Ryujiro Yokoyama, Hiroshi Kondo, Hiroaki Hoshi
Venue: Medical Imaging and Informatics: 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007 Revised Selected Papers (2008)

The incidence of liver diseases is very high in Asian countries. This paper introduces our computer-aided diagnosis (CAD) system for diagnosing liver cancer and describes the fundamental technologies employed in the system and its performance. The results showed that our system is useful for diagnosing liver cancer, and it is expected that employing CAD in clinical practice would reduce the mortality caused by liver cancer in Asian countries.
Single Trial Evoked Potentials Study during an Emotional Processing Based on Wavelet Transform
Authors: Ling Zou, Renlai Zhou, Senqi Hu, Jing Zhang, Yansong Li
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

The present study aimed at examining the event-related potentials (ERPs) single-trial extraction during an emotional processing by wavelet transform and analyzing the brain responses to emotional stimuli. ERPs were recorded from 64 electrodes in 10 healthy university students while three types of emotional pictures (pleasant, neural, and unpleasant) from the international affective picture system were presented. All the subjects showed significantly greater P300 and slow waves amplitudes at antero-inferior, medial-inferior and posterior electrode sites for pleasant and unpleasant pictures than for neural pictures and unpleasant pictures elicited more positive P300 and slow wave effects than pleasant pictures. The results indicated the effectiveness of the wavelet transform-based approach in ERP single-trial extraction and further supported the view that emotional stimuli are processed more intensely.
Crossed-Line Segmentation for Low-Level Vision
Authors: John Atkinson, Claudio Castro
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

This work describes a new segmentation method for robotic soccer applications. The approach called crossed-line segmentation is based on the combination of region classification and a border detector which meet homogeneity criteria of medians. Experiments suggest that the method outperforms traditional procedure in terms of smoothing and segmentation accuracy. Furthermore, existing noise in the images is also observed to be reduced without missing the objects' borders.
Dynamic matrix controller based on sliding mode control
Authors: Oscar Camacho, Luís Valverde, Edinzo Iglesias, Francklin Rivas
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

This work presents a system of tuning equations, based on the sliding surface response, to predict the changes in the parameters of the process, which were used to improve and to tune the predictive dynamic matrix controller parameters. The controller presents a fixed structure and its tuning parameter equations were developed relating the characteristics of the sliding surface and the characteristic parameters of the first order plus deadtime model. Simulations on a blended tank with variable level that presents non linear behavior were considered.
Immersive Co-operative Psychological Virtual Environments (ICPVE)
Authors: Ronan Jamieson, Adrian Haffegee, Vassil Alexandrov
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

Virtual Reality (VR) has been used in a variety of forms to assist in the treatment of a wide range of psychological illness. VR can also fulfil the need that psychologists have for safe environments in which to conduct experiments. Currently the main barrier against using this technology is the complexity in developing applications. This paper presents two different co-operative psychological applications which have been developed using a single framework. These applications require different levels of co-operation between the users and clients, ranging from full psychologist involvement to their minimal intervention. This paper will also discuss our approach to developing these different environments and our experiences to date in utilising these environments.
Prototypical Cases for Knowledge Maintenance in Biomedical CBR
Authors: Isabelle Bichindaritz
Venue: Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development (2007)

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge in biomedical fields, which often makes the content of a case base partially obsolete. This article proposes to make use of the concept of prototypical case to ensure that a CBR system would keep up-to-date with current research advances in the biomedical field. Prototypical cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of cases, and to serve as bootstrapping a CBR system memory when real cases are not available in sufficient quantity and/or quality. This paper presents knowledge maintenance as another role that these prototypical cases can play in biomedical CBR systems.
Goal Selection Strategies for Rational Agents
Authors: Nick A. Tinnemeier, Mehdi Dastani, John-Jules Ch. Meyer
Venue: Languages, Methodologies and Development Tools for Multi-Agent Systems: First International Workshop, LADS 2007, Durham, UK, September 4-6, 2007. Revised Selected Papers (2008)

In agent theory and agent programming, goals constitute the motivational attitude of rational agents and form the key concept in explaining and generating their pro-active behavior. Pursuing multiple goals simultaneously might pose problems for agents as the plans for achieving them may conflict. We argue that a BDI-based agent programming language should provide constructs to allow an agent programmer to implement agents that: 1) do not pursue goals with conflicting plans simultaneously, and 2) can choose from goals with conflicting plans. This paper presents an explicit and generic mechanism to process incompatible goals, i.e., goals with conflicting plans. The proposed mechanism can be integrated in existing BDI-based agent programming languages. We discuss different strategies to process incompatible goals based on a given conflict relation and show some properties and relations between these strategies.
A New Adaptive Neural Network Model for Financial Data Mining
Authors: Shuxiang Xu, Ming Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. One of the most commonly used techniques in data mining, Artificial Neural Networks provide non-linear predictive models that learn through training and resemble biological neural networks in structure. This paper deals with a new adaptive neural network model: a feed-forward higher order neural network with a new activation function called neuron-adaptive activation function. Experiments with function approximation and stock market movement analysis have been conducted to justify the new adaptive neural network model. Experimental results have revealed that the new adaptive neural network model presents several advantages over traditional neuron-fixed feed-forward networks such as much reduced network size, faster learning, and more promising financial analysis.
Scheduling multiple divisible loads in homogeneous star systems
Authors: M. Drozdowski, M. Lawenda
Venue: Journal of Scheduling (2008)

In this paper we analyze scheduling multiple divisible loads on a star-connected system of identical processors. It is shown that this problem is computationally hard. Some special cases appear to be particularly difficult, so it is not even known if they belong to the class NP. Exponential algorithms and special cases solvable in polynomial time are presented.
On minimally k-connected matroids
Authors: Talmage J. Reid, Haidong Wu, Xiangqian Zhou
Venue: Journal of Combinatorial Theory Series B (2008)

A matroid M is minimally k-connected if M is k-connected and, for every e@?E(M), M\e is not k-connected. It is conjectured that every minimally k-connected matroid with at least 2(k-1) elements has a cocircuit of size k. We resolve the conjecture almost affirmatively for the case k=4 by finding the unique counterexample; and for each k>=5, we prove that there exists a counterexample to the conjecture with 2k+1 elements.
An integrated approach to control the quality level of multi-user sessions
Authors: Augusto Neto, Eduardo Cerqueira, Marília Curado, Edmundo Monteiro, Paulo Mendes
Venue: Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops (2008)

A combined control of multimedia quality and network resource allocation is a core requirement for the deployment of communication services shared by multiple users (multi-user). The challenge resides in achieving a high number of satisfied users while saving the scarce network resources. This situation occurs due to the dynamic changes in resource demands, the existence of links with different capacities and the use of distinct QoS models along a session path. This paper proposes the Quality of Service Control for Multi-user Sessions (QUALITI) scheme to maintain multi-user sessions with acceptable QoS levels over heterogeneous networks, while optimizing the usage of network resources. QUALITI integrates QoS mapping and adaptation with network resource allocation along end-to-end session paths. QUALITI was evaluated through simulations that analyzed the convergence time, usage of network resources, throughput and one-way delay.
Online management and mining of heteregenous and dynamic time-series
Authors: Hakan Ferhatosmanoglu
Venue:  (2008)

In this PhD dissertation, we propose database solutions for some of the major challenges in mining and managing time-series data. In particular, we propose a framework for mining heterogeneous time-series data, and a framework for online summarization and analysis of dynamic time-series data. We propose a general framework, Information Mining, to acquire information from heteregenous and potentially high dimensional time-series data. The framework consists of two major steps: first, significant, clean, and homogeneous subsets of data are identified and analyzed using a data mining algorithm, then the information gathered in the first step is further refined by identifying common (or distinct) patterns over the results of mining of the subsets. We extend our approach for a class of mining tasks over microarray and clinical trials time-series applications and show that Information Mining is an effective method for mining these datasets. In a multiple data stream application, a new element for each data sequence, i.e. time-series, is periodically inserted into the database. The data in multiple streams is usually compressed due to storage limitations, and the data is reconstructed at the time of query. The quality of this reconstruction should be good enough to run general types of queries, i.e. range, and k-nn queries, on it. We present an online technique, PQ-Stream, which provides a high quality reconstruction. We showed that PQ-Stream significantly outperforms the current techniques for a wide variety of query types on both synthetic and real data sets. The interest of the queries is not uniformly distributed over the all time units; most queries involve the newest few time units. The storage can be assigned to the time units based on their order of the query interest. We propose Ladder Approach to mitigate the stress on storage in multi-stream systems by adding the element of age to the sliding window. The Ladder Approach was shown effective in two real streaming applications, i.e. weather and stock data.
A Study of Schedule Robustness for Job Shop with Uncertainty
Authors: Inés González-Rodríguez, Jorge Puente, Ramiro Varela, Camino R. Vela
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

We consider a job shop problem with uncertain processing times modelled as triangular fuzzy numbers and propose a methodology to study solution robustness with respect to different perturbations in the durations. This methodology is applied to obtain experimental results for several problem instances, using a hybrid genetic algorithm that minimises the expected makespan. We conclude that taking into account the uncertainty information provided by fuzzy numbers produces proactive solutions, coping well with posterior changes in processing times.
Dynamic Routing and Location Services in Metrics of Low Doubling Dimension
Authors: Goran Konjevod, Andréa W. Richa, Donglin Xia
Venue: Proceedings of the 22nd international symposium on Distributed Computing (2008)

We consider dynamic compact routing in metrics of low doubling dimension. Given a set of nodes V in a metric space with nodes joining, leaving and moving, we show how to maintain a set of links E that allows compact routing on the graph G(V,E). Given a constant ¿ ¿ (0,1) and a dynamic node set V with normalized diameter Δ in a metric of doubling dimension , we achieve a dynamic graph G(V,E) with maximum degree 2 O(¿) log2 Δ, and an optimal (9 + ¿)-stretch compact name-independent routing scheme on G with (1/¿) O(¿)log4 Δ-bit storage at each node. Moreover, the amortized number of messages for a node joining, leaving and moving is polylogarithmic in the normalized diameter Δ; and the cost (total distance traversed by all messages generated) of a node move operation is proportional to the distance the node has traveled times a polylog factor. (We can also show similar bounds for a (1 + ¿)-stretch compact dynamic labeled routing scheme.)One important application of our scheme is that it also provides a node location scheme for mobile ad-hoc networks with the same characteristics as our name-independent scheme above, namely optimal (9 + ¿) stretch for lookup, polylogarithmic storage overhead (and degree) at the nodes, and locality-sensitive node move/join/leave operations. We also show how to extend our dynamic compact routing scheme to address the more general problem of devising locality-sensitive Distributed Hash Tables (DHTs) in dynamic networks of low doubling dimension. Our proposed DHT scheme also has optimal (9 + ¿) stretch, polylogarithmic storage overhead (and degree) at the nodes, locality-sensitive publish/unpublish and node move/join/leave operations.
A scheme for solving Anycast scalability in IPv6
Authors: Wang Xiaonan, Qian Huanyan
Venue: International Journal of Network Management (2008)

The existing designs for providing Anycast services are either to confine Anycast groups to a preconfigured topological region or to distribute Anycast groups globally across the whole Internet. The latter causes routing tables to grow proportionally to the number of global Anycast groups in the entire Internet and both of the above designs restrict and hinder the application and development of Anycast services. A new kind of Anycast communication scheme is proposed in this paper. This scheme adopts a novel Anycast address structure which can achieve a dynamic Anycast group while allowing Anycast members to freely leave and join the Anycast group without geographical restriction and it effectively solves the expanding explosion of the Anycast routing table. In addition, this scheme can evenly disperse Anycast request messages from clients across the Anycast servers of one Anycast group, thus achieving load balance. This paper analyzes the communication scheme in depth and discusses its feasibility and validity. The experimental data in IPv6 simulation demonstrate that the TRT (Total Response Time) of one Anycast service (e.g., file downloading) acquired through this communication scheme is shorter by 15% than that through the existing Anycast communication scheme.
MC-SOG: An LTL Model Checker Based on Symbolic Observation Graphs
Authors: Kais Klai, Denis Poitrenaud
Venue: Proceedings of the 29th international conference on Applications and Theory of Petri Nets (2008)

Model checking is a powerful and widespread technique for the verification of finite distributed systems. However, the main hindrance for wider application of this technique is the well-known state explosion problem. During the last two decades, numerous techniques have been proposed to cope with the state explosion problem in order to get a manageable state space. Among them, on-the-fly model-checking allows for generating only the "interesting" part of the model while symbolic model-checking aims at checking the property on a compact representation of the system by using Binary Decision Diagram (BDD) techniques. In this paper, we propose a technique which combines these two approaches to check LTL¿X state-based properties over finite systems. During the model checking process, only an abstraction of the state space of the system, namely the symbolic observation graph, is (possibly partially) explored. The building of such an abstraction is guided by the property to be checked and is equivalent to the original state space graph of the system w.r.t. LTL¿X logic (i.e. the abstraction satisfies a given formula φ iff the system satisfies φ). Our technique was implemented for systems modeled by Petri nets and compared to an explicit model-checker as well as to a symbolic one (NuSMV) and the obtained results are very competitive.