ppxscal/embedding_network huggingface dataset:
Plenary lecture VI: probability measures of fuzzy events and linguistic fuzzy modelling - forms expressing randomness and imprecision
Authors: Anna Walaszek-Babiszewska
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

Statistical methods have a long history of applications to data analysis and modelling in many fields of human activities, as financial markets, business, optimal control. Zadeh's theory of fuzzy systems, soft computing and computing with words gave possibilities of utilising many approaches to modelling, taking into account different types of uncertainties, especially, human knowledge and perception expressed imprecisely in linguistic categories. The sets of numerical data, collected in many real systems, and expert's experiences would be very useful to modelling both randomness and imprecision. Starting with the reminding the basic notions of probability of fuzzy events, we define probability distributions of a linguistic variable and a linguistic vector as well as a mean fuzzy value (a mean fuzzy set) of the linguistic variable. We formulate also a stochastic process with fuzzy states. The next, we try to modelling randomness and imprecision, using linguistic fuzzy models with weights of rules. A structure of the fuzzy model is predefined at the beginning of the task. Probability of fuzzy events has been used to formulate probabilities of the occurrence linguistic values of input and output variables in a product-space. Marginal probabilities of linguistic values of input variables are the weights of file rules. Conditional probabilities of linguistic values of the output variable are the weights in the consequence of elementary rules. The inference procedures are proposed and exemplary calculations are presented.
Visual Tracking Based on Mixture Motion Model and Incorporate Observation Distribution
Authors: Keju Zu, Yu Yang, Zhipeng Li, Fuqiang Liu, Min Tian
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

Accurate visual object tracking through long sequences is a challenging task since object's appearance changes and complex motion happens. We present mixture motion model and incorporate observation model within the Monte Carlo framework to achieve robust visual tracking. The mixture motion model which employs important history motion information of the target is built according to a motion measurement matrix to model the target's transition state. Meanwhile, the incorporate observation model is established by introducing SVM classification scores into normal tracking observation model. A particles filter's implementation with these mixture models is demonstrated, which leads to robust tracking results, especially in occlusion and complex scene.
Expressive syndication on the web using a description logic-based approach
Authors: James Hendler
Venue:  (2007)

Syndication on the Web has attracted a great amount of attention in recent years. However, today's state-of-the-art syndication approaches still provide relatively weak expressive power from a modeling perspective and provide very little automated reasoning support. If a more expressive approach with a formal semantics can be provided, many benefits can be achieved, including a rich semantics-based mechanism for expressing subscriptions and published content and automated reasoning for discovering subscription matches not found using traditional syntactic syndication approaches. In this dissertation, I develop a syndication framework based on the Web Ontology Language (OWL), which is the standardized language for representing the semantics of information on the Web. One of the main advantages of the framework is its support for formal reasoning, as the semantics of subsets of OWL are founded in description logic (a decidable fragment of first-order logic). Therefore, the previously mentioned benefits can be achieved using description logic (DL) reasoning. However, the main limitation in using OWL as the underlying representation model is related to the overhead of DL reasoning under changing data, which makes the approach impractical for many real-world domains and publication frequencies. Given this, in this dissertation, I develop incremental DL reasoning algorithms for the required reasoning services in the framework. Specifically, I present incremental consistency checking techniques, as well as algorithms to perform more efficient incremental query answering. Lastly, to demonstrate the practicality of the syndication approach, I have implemented a prototype of the framework and performed extensive empirical evaluations using synthetic datasets, as well as real world data from the financial domain. These results show the effectiveness of the incremental reasoning services and the practicality of the syndication framework in general.
The Effect of the Shape and Location of the Light Source in Diffuse Reflectance Measurements
Authors: Petri Välisuo, Jarmo Alander
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Optical measurements are fast and convenient mode to obtain information from human skin. However, the complicated interactions of light with skin may overwhelm our understanding of the measurements. Moreover, the high remittance from the surface of the skin may be often an obstacle for making measurements regarding deeper layers of skin. In this research, we have constructed a simulation model to show from which depths the remittance is coming from. Using this model we have studied the effects of light source shape and location to measure the remittance originating deeper. According to the model, the ring-shaped light source more than doubles the measurement depth, compared with the diffuse light. In fiber-based measurements, increase of the fiber separation increases the measurement depth.
eSTREAM Software Performance
Authors: Christophe Cannière
Venue: New Stream Cipher Designs: The eSTREAM Finalists (2008)

In order to evaluate their performance in software, all Profile 1 candidates were subjected to benchmark tests. This chapter briefly describes the testing framework developed by eSTREAM for this purpose, and summarizes the results of the performance tests conducted on the eight Profile 1 finalists.
Automatic Factorization of Biological Signals Measured by Fluorescence Correlation Spectroscopy Using Non-negative Matrix Factorization
Authors: Kenji Watanabe, Takio Kurita
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

We proposed automatic factorization method of biological signals measured by Fluorescence Correlation Spectroscopy (FCS). Since the signals are composed from several positive components, the signals are decomposed by using the idea of Non-negative matrix factorization (NMF). Each component is represented by model functions and the signals are factorized as the non-negative sum of the model functions. Analytical accuracy of our proposed method was verified by using biological data that were measured by FCS. The experimental results showed that our method could automatically factorize the signals and the obtained components were similar with the ones obtained manually.
P2P logging and timestamping for XWiki
Authors: Mounir Tlili, W. Kokou Dedzoé, Esther Pacitti, Patrick Valduriez, Reza Akbarinia, Ludovic Dubost, Sergiu Dumitriu, Stéphane Laurière, Gérôme Canals, Pascal Molli, Julien Maire
Venue: Proceedings of the 8th international conference on New technologies in distributed systems (2008)

Collaborative applications are getting an increasing interest as a result of the rapid progress in distributed technologies such as grid computing. P2P applications, and mobile computing. Constructing these applications on top of P2P networks has many advantages which stem from P2P properties: decentralization, self-organization, scalability and fault-tolerance In this paper, we address data replication and reconciliation in XWiki peer-to-peer (P2P) collaborative application. We propose a new mechanism of optimistic replication, which consists of an algorithm for data reconciliation based on operational transformation approach (OT) and an extension of KTS service that performs distributed timestamp generation running over a distributed hash table (DHT). While updating at collaborating XWiki peers, updates are timestamped and stored in a highly available P2P log. During reconciliation, these updates are retrieved in total order to enforce eventual consistencies. The work presented here is partially supported by the National Agency of Research within RNTL XWiki Concerto project (2007--2009).
Global Image Feature Extraction Using Slope Pattern Spectra
Authors: Ignace Tchangou Toudjeu, Barend Jacobus Wyk, Michaël Antonie Wyk, Frans Bergh
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

A novel algorithm inspired by the integral image representation to derive an increasing slope segment pattern spectrum (called the Slope Pattern Spectrum for convenience), is proposed. Although many pattern spectra algorithms have their roots in mathematical morphology, this is not the case for the proposed algorithm. Granulometries and their resulting pattern spectra are useful tools for texture or shape analysis in images since they characterize size distributions. Many applications such as texture classification and segmentation have demonstrated the importance of pattern spectra for image analysis. The Slope Pattern Spectra algorithm extracts a global image signature from an image based on increasing slope segments. High Steel Low Alloy (HSLA) steel and satellite images are used to demonstrate that the proposed algorithm is a fast and robust alternative to granulometric methods. The experimental results show that the proposed algorithm is efficient and has a faster execution time than Vincent's linear granulometric technique.
Error resilient image communication with chaotic pixel interleaving for wireless camera sensors
Authors: Cristian Duran-Faundez, Vincent Lecuire
Venue: Proceedings of the workshop on Real-world wireless sensor networks (2008)

New applications of wireless sensor networks require vision capabilities. Considering the high loss rates found in sensor networks, and the limited hardware resources of current sensor nodes, low-complexity robust image transmission must be implemented, avoiding as much as possible the need for retransmission or redundancy. In this paper we propose a pixel interleaving scheme based in Torus Automorphisms, thus, neighboring pixels are transmitted in different packets. Hence, if packets are lost, we have a high probability of retrieving enough information to obtain an approximation of the original value. Results show an increase of the image quality in comparison with a sequential raw image transmission approach, while preserving similar energy consumptions, time and low-complexity.
From Analytical Engine to Electronic Digital Computer: The Contributions of Ludgate, Torres, and Bush
Authors: Brian Randell
Venue: IEEE Annals of the History of Computing (1982)

This paper, based on an invited lecture given at MIT in March 1980, discusses the little-known work of Percy E. Ludgate (1883- 1922), Leonardo Torres y Quevedo (1852- 1936), and Vannevar Bush (1890- 1974). These three inventors, who apparently were unaware of one another's existence, were all directly influenced by know/edge of Charles Babbage's Analytical Engine, and each played a significant role in the history of the development of program-controlled computers.
A Constant Factor Approximation for Minimum λ-Edge-Connected k-Subgraph with Metric Costs
Authors: Mohammadali Safari, Mohammad R. Salavatipour
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

In the (k,&lambda;)-subgraph problem, we are given an undirected graph G = (V,E) with edge costs and two parameters k and &lambda; and the goal is to find a minimum cost &lambda;-edge-connected subgraph of G with at least k nodes. This generalizes several classical problems, such as the minimum cost k-Spanning Tree problem or k-MST (which is a (k,1)-subgraph), and minimum cost &lambda;-edge-connected spanning subgraph (which is a (|V(G)|,&lambda;)-subgraph). The only previously known results on this problem [12,5] show that the (k,2)-subgraph problem has an O(log2 n)-approximation (even for 2-node-connectivity) and that the (k,&iquest;)-subgraph problem in general is almost as hard as the densest k-subgraph problem [12]. In this paper we show that if the edge costs are metric (i.e. satisfy triangle inequality), like in the k-MST problem, then there is an O(1)-approximation algorithm for (k,&lambda;)-subgraph problem. This essentially generalizes the k-MST constant factor approximability to higher connectivity.
Implications of Writing, Reading, and Tagging on the Web for Reflection Support in Informal Learning
Authors: Christian Glahn, Marcus Specht, Rob Koper
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

The use of tags as user generated meta-data as well as the visualisation in tag clouds has recently received a lot of attention in research and practice. This paper focuses on supporting reflection of learners by using different presentation approaches of user-generated meta-data for reflection support. Previous research has shown that implicit interest expression can be a valuable source for reflection support. Visualising implicit or "tacit" interest in tag clouds could help learners to understand the connections of their content related activities to the tags that are assigned to the content. For grounding this potential in the social practice of using tags in teams and small communities, we conducted a three month experiment. This experiment focused on the social practices of using tags explicitly and implicitly. In this paper we analyse the data of the experiment with regard to social navigation of teams and small communities, relations of implicit and explicit interest in tags, and usages of tags on different participation levels. The findings on these dimensions of the social practice of using and sharing tags in groups help to develop a better view on the requirements of providing reflection support.
Face Hallucination Based on CSGT and PCA
Authors: Xiaoling Wang, Ju Liu, Jianping Qiao, Jinyu Chu, Yujun Li
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

In this paper, based on Circularly Symmetrical Gabor Transform (CSGT) and Principal Component Analysis (PCA), we propose a face hallucination approach. In this approach, all of the face images (both input face image and original training database) are transformed through CSGT at first and then local extremes criteria is utilized to extract the intrinsic features of the faces. Based on these features, we calculate Euclidean distances between the input face image and every face image in the original training database, and then Euclidean distances are used as criteria to choose the reasonable training database. Once the training database is chosen, PCA is applied to hallucinate the input face image as the linear combination of the chosen training images. Experimental results show that our approach can choose training database automatically according to the input face image and get high quality super-resolution image.
Face to Face Interaction with an Intelligent Virtual Agent: The Effect on Learning Tactical Picture Compilation
Authors: Willem A. Doesburg, Rosemarijn Looije, Willem A. Melder, Mark A. Neerincx
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

Learning a process control task, such as tactical picture compilation in the Navy, is difficult, because the students have to spend their limited cognitive resources both on the complex task itself and the act of learning. In addition to the resource limits, motivation can be reduced when learning progress is slow. Intelligent Virtual Agents may help to improve tutoring systems by offering a student feedback on their performance via speech and facial expression, which imposes limited "communication load" and motivating ways of interaction. We present our Intelligent Virtual Agent, called Ashley, and an experiment in which we examined whether learning of tactical picture compilation is better when the feedback is provided by Ashley compared to a standard text-box interface. This first experiment did not show user interface effects, but provided new requirements for the next version of the feedback system and Ashley: The task to learn was yet too simple for substantial feedback effects, and the timing of Ashley's feedback should be improved, and Ashley's presence should be better scheduled.
Improved Algorithms for Univariate Discretization of Continuous Features
Authors: Jussi Kujala, Tapio Elomaa
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

In discretization of a continuous variable its numerical value range is divided into a few intervals that are used in classification. For example, Naïve Bayes can benefit from this processing. A commonly-used supervised discretization method is Fayyad and Irani's recursive entropy-based splitting of a value range. The technique uses ent-mdl as a model selection criterion to decide whether to accept the proposed split.We argue that theoretically the method is not always close to ideal for this application. Empirical experiments support our finding. We give a statistical rule that does not use the ad-hoc rule of Fayyad and Irani's approach to increase its performance. This rule, though, is quite time consuming to compute. We also demonstrate that a very simple Bayesian method performs better than ent-mdl as a model selection criterion.
Real-Time Detection of Clone Attacks in Wireless Sensor Networks
Authors: Kai Xing, Fang Liu, Xiuzhen Cheng, David H. C. Du
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

A central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch {\it clone attacks} by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. In this paper, we propose a novel scheme for detecting clone attacks in sensor networks, which computes for each sensor a social fingerprint by extracting the neighborhood characteristics, and verifies the legitimacy of the originator for each message by checking the enclosed fingerprint. The fingerprint generation is based on the super imposed $s$-disjunct code, which incurs a very light communication and computation overhead. The fingerprint verification is conducted at both the base station and the neighboring sensors, which ensures a high detection probability. The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/communication/storage overhead. To our best knowledge, our scheme is the first to provide realtime detection of clone attacks in an effective and efficient way.
Towards an Implementation of a Computer Algebra System in a Functional Language
Authors: Oleg Lobachev, Rita Loogen
Venue: Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics (2008)

This paper discusses the pros and cons of using a functional language for implementing a computer algebra system. The contributions of the paper are twofold. Firstly, we discuss some language---centered design aspects of a computer algebra system -- the "language unity" concept. Secondly, we provide an implementation of a fast polynomial multiplication algorithm, which is one of the core elements of a computer algebra system. The goal of the paper is to test the feasibility of an implementation of (some elements of) a computer algebra system in a modern functional language.
Reproduction and Recognition of Vowel Signals Using Single and Bagging Competitive Associative Nets
Authors: Shuichi Kurogi, Naoko Nedachi, Yuki Funatsu
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

So far, it has been shown that the piecewise linear predictive coefficients obtained by the competitive associative net called CAN2 can provide a better performance in reproduction and recognition of vowel signals than the LPC (linear predictive coding) method which is widely used for speech processing. However, when a vowel signal involves a certain amount of observation noise, the performance becomes low. In this article, we introduce bagging CAN2 and show that it can reproduce and recognize vowel signals better than the conventional single CAN2. Furthermore, we suggest that the bagging CAN2 is useful for the analysis of vowel signals.
MATLAB Simulation and Comparison of Zhang Neural Network and Gradient Neural Network for Time-Varying Lyapunov Equation Solving
Authors: Yunong Zhang, Shuai Yue, Ke Chen, Chenfu Yi
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

This paper presents a new kind of recurrent neural network proposed by Zhang et al. for solving online Lyapunov equation with time-varying coefficient matrices. Global exponential convergence could be achieved by such a recurrent neural network when solving the time-varying problems in comparison with gradient neural networks (GNN). MATLAB simulation of both neural networks for the real-time solution of time-varying Lyapunov equation is then investigated through several important techniques. Computer-simulation results substantiate the theoretical analysis and demonstrate the efficacy of such a Zhang neural network (ZNN) on time-varying Lyapunov equation solving, especially when using power-sigmoid activation functions.
Active semantically aware hard real-time security hypervisors
Authors: Victor Yodaiken, Cort Dougan
Venue: Proceedings of the 4th annual workshop on Cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead (2008)

In this paper, we sketch an approach to security involving the use of real-time operating systems as real-time monitors of system security, using semantic properties of the underlying "base" operating system and applications to improve efficiency and capabilities.