ppxscal/embedding_network huggingface dataset:
Distribution System Fault Diagnosis Based on Improved Rough Sets with Uncertainty
Authors: Jing Dai, Qiuye Sun
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. The paper describes a systematic approach for detecting superfluous data. It is considered as a "white box" rather than a "black box" like in the case of neural network. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. To deal with the uncertainty and deferent structures of the system, rough sets and fuzzy sets are introduced. The reduction algorithm based on uncertainty rough sets is improved. The rule reliability is deduced using fuzzy sets and probability. The simulation result of a power distribution system shows the effec-tiveness and usefulness of the approach.
Secure Biometric Authentication with Improved Accuracy
Authors: Manuel Barbosa, Thierry Brouard, Stéphane Cauchie, Simão Melo Sousa
Venue: Proceedings of the 13th Australasian conference on Information Security and Privacy (2008)

We propose a new hybrid protocol for cryptographically secure biometric authentication. The main advantages of the proposed protocol over previous solutions can be summarised as follows: (1) potential for much better accuracy using different types of biometric signals, including behavioural ones; and (2) improved user privacy, since user identities are not transmitted at any point in the protocol execution. The new protocol takes advantage of state-of-the-art identification classifiers, which provide not only better accuracy, but also the possibility to perform authentication without knowing who the user claims to be. Cryptographic security is based on the Paillier public key encryption scheme.
Streamlining long latency instructions for seamlessly combined out-of-order and in-order execution
Authors: Hui Wang, Rama Sangireddy
Venue: Microprocessors Microsystems (2008)

In the current day wide-issue processors, the size of the instruction scheduling window (also called Issue Queue (IQ)) is limited mainly by the hardware complexity to design the logic, and thus limits the number of instructions scanned every cycle to extract instruction level parallelism (ILP). To exacerbate the problems, instructions depending on long latency load operations continue to reside in the IQ until their source operands are ready. Thus, such delayed instructions block any new instructions from entering the IQ even if potentially they are ready for execution. The growing disparity in processor and memory speeds is further aggravating the delay in dislodging instructions from IQ. To alleviate the problem, in this paper we propose a novel technique to streamline instructions in separate buffers according to the chain of dependencies. Each instruction is streamlined behind a parent instruction while it waits for the source operand to be supplied by the long latency memory operations. These instructions are segregated from the IQ and thus the pressure on IQ is relieved which enables flow of potentially executable instructions in the pipeline. Our analysis of SPEC2000 programs reveals that instructions dependent on load cache misses or their dependents, typically have their first source operand ready within 5-15% of their total wait time in the IQ. Based on the observations, the long latency memory dependent instructions are streamlined into in-order buffers when their first operand is ready. In the proposed architecture, instructions from both the conventional IQ and the heads of the streamline buffers can be selected for execution, while the wakeup logic complexity remains same as in the conventional design. Our results show that the performance speedup of 32-entry IQ supplemented by 32 in-order buffers is 15.7% and 2% for FP and integer benchmark respectively, which is very much comparable to that of a conventional 64-entry IQ. A 64-entry IQ design can gain performance over a 32-entry IQ, albeit with a large overhead in circuit delay complexity of wakeup logic, while streamline buffers can gain performance over 32-entry IQ without any such overhead.
XPath Rewriting Using Multiple Views
Authors: Junhu Wang, Jeffrey Xu Yu
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

We study the problem of tree pattern query rewriting using multiple views for the class of tree patterns in P{//,[]}. Previous work has considered the rewriting problem using a single view. We consider two different ways of combining multiple views, define rewritings of a tree pattern using these combinations, and study the relationship between them. We show that when rewritings using single views do not exist, we may use such combinations of multiple views to rewrite a query, and even if rewritings using single views do exist, the rewritings using combinations of multiple views may provide more answers than those provided by the union of the rewritings using the individual views. We also study properties of intersections of tree patterns, and present algorithms for finding rewritings using intersections of views.
Culturally Embedded Computing
Authors: Phoebe Sengers, Joseph Kaye, Kirsten Boehner, Jeremiah Fairbank, Geri Gay, Yevgeniy Medynskiy, Susan Wyche
Venue: IEEE Pervasive Computing (2004)

Culturally embedded computing describes one vision of human-computer interaction (HCI) thatsituates embedded computing not only in the physical world but also explicitly in society, individualexperience, culture, and history. Five projects explore alternatives to traditional HCI design andevaluation inspired by this shift in emphasis. These projects highlight approaches to technologydesign that support critical reflection on the role of technology in society. Designed for fuzzyaspects of personal experience, the projects contextualize technology with reference to culture andhistory. In particular, the design of ubiquitous devices is part of a longer history of technologiesfor the home. Through an interdisciplinary approach, the project team is developing algorithms, devices, and user studies that situate technology within its broader meaning for human life.
Hybrid Metaheuristics for Global Optimization: A Comparative Study
Authors: Antoniya Georgieva, Ivan Jordanov
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

Several metaheuristic approaches for global optimization (GO) are investigated and their performances compared in this paper. We critically review and analyze recently proposed Stochastic Genetic Algorithm (StGA) for GO and compare it with our GO hybrid metaheuristic called Genetic LP ¿ and Simplex Search (GLP ¿ S), which combines the effectiveness of Genetic Algorithms during the early stages of the search with the advantages of Low-Discrepancy sequences and the local improvement abilities of Simplex search. For comparison purposes we also use Fast Evolutionary Programming (FEP) and Differential Evolution (DE) methods. In parallel to our method, FEP and DE, we investigate further, re-run and test the StGA implementation on a number of multimodal mathematical functions. The obtained StGA results demonstrate inferior performance (compared with our GLP ¿ S and DE methods), producing much worse than the reported in [1] results (with the only exception for the two-dimensional cases). We argue that the published in [1] accuracy and convergence speed results (given as number of function evaluations) are incorrect for most of the testing functions and investigate why the method is failing in those cases.
Graph Representation of the Syntactic Structure of the Lithuanian Sentence
Authors: Daiva Šveikauskienė
Venue: Informatica (2005)

The paper offers a new way of presenting the structure of a sentence. None of the two widely known methods of representation the syntactic structure of a sentence can be of any avail when applied to the Lithuanian language. Neither the tree, based on the phrase structure principle, nor the tree, suggested by the dependency grammar, do reflect all the syntactic information, which a Lithuanian sentence contains. The paper points out the differences between the Lithuanian language and other languages as well as presents the reasons why a Lithuanian sentence should be represented by a graph. The paper presents a generalized structure of a simple sentence in the Lithuanian language, namely, such a structure, which would embrace all the possible instances of a Lithuanian simple sentence. Every sentence of the text would have to activate only one path in the generalized structure.
A CMOS continuous-time low-voltage current-mode filter
Authors: Elena Doicaru, Mircea Bodea, Claudius Dan
Venue: Proceedings of the 7th WSEAS International Conference on Microelectronics, Nanoelectronics, Optoelectronics (2008)

This paper presents a CMOS continuous-time current mode filter for low voltage applications. Firstly, the state-space description and the used synthesis technique of the filter are presented. Then the current-mode integrator is presented and analyzed. Finally, the third order low-pass filter is described and analyzed. The filter achieves an 11 MHz cutoff frequency. The dynamic range is greater than 50 dB and the power consumption is 5mW for a 3 V supply voltage.
Parallel Approximate Finite Element Inverses on Symmetric Multiprocessor Systems
Authors: Konstantinos M. Giannoutakis, George A. Gravvanis
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

A new parallel normalized optimized approximate inverse algorithm for computing explicitly approximate inverses, is introduced for symmetric multiprocessor (SMP) systems. The parallelization of the approximate inverse has been implemented by an antidiagonal motion, in order to overcome the data dependencies. The parallel normalized explicit approximate inverses are used in conjuction with parallel normalized explicit preconditioned conjugate gradient schemes, for the efficient solution of finite element sparse linear systems. The parallel design and implementation issues of the new algorithms are discussed and the parallel performance is presented, using OpenMP. The speedups tend to the upper theoretical bounds for all cases making approximate inverse preconditioning suitable for SMP systems.
Application of Wavelet Neural Networks on Vibration Fault Diagnosis for Wind Turbine Gearbox
Authors: Qian Huang, Dongxiang Jiang, Liangyou Hong, Yongshan Ding
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

This paper applies an Artificial Neural Networks (ANN) method--- Wavelet Neural Networks (WNN) on fault diagnosis for a wind turbine gearbox. A gearbox is one of the most important units in a wind turbine drive train. It is significant to study fault diagnosis of gearbox conditions. First this paper presents the principles and advantages of Wavelet Neural Networks. Second this paper specifies the vibration mechanism of the gearbox and the feature parameter group reflecting fault feature, and then the standard fault samples (training samples) and simulation samples (testing samples) are obtained. Third this paper applies the WNN method to perform diagnosing. The accurate diagnostic results have proved the effectiveness of the method for vibration fault diagnosis of gearbox. Finally, the relative advantages of the WNN method are contrasted with those of BPNN method.
Strong structural properties of unidirectional star graphs
Authors: Eddie Cheng, Marc J. Lipman, László Lipták
Venue: Discrete Applied Mathematics (2008)

Day and Tripathi [K. Day, A. Tripathi, Unidirectional star graphs, Inform. Process. Lett. 45 (1993) 123-129] proposed an assignment of directions on the star graphs and derived attractive properties for the resulting directed graphs: an important one is that they are strongly connected. In [E. Cheng, M.J. Lipman, On the Day-Tripathi orientation of the star graphs: Connectivity, Inform. Process. Lett. 73 (2000) 5-10] it is shown that the Day-Tripathi orientations are in fact maximally arc-connected when n is odd; when n is even, they can be augmented to maximally arc-connected digraphs by adding a minimum set of arcs. This gives strong evidence that the Day-Tripathi orientations are good orientations. In [E. Cheng, M.J. Lipman, Connectivity properties of unidirectional star graphs, Congr. Numer. 150 (2001) 33-42] it is shown that vertex-connectivity is maximal, and that if we delete as many vertices as the connectivity, we can create at most two strong connected components, at most one of which is not a singleton. In this paper we prove an asymptotically sharp upper bound for the number of vertices we can delete without creating two nonsingleton strong components, and we also give sharp upper bounds on the number of singletons that we might create.
Semantic feature construction
Authors: Mark E. Fenner
Venue:  (2007)

An effective set of features is integral to the success of machine learning algorithms. Semantic feature construction is the knowledge-driven manipulation of the propositional descriptor space of a set of examples for use in a learning algorithm. Two important sources of semantics for feature construction are the semantic type (and associated semantic properties) and the semantic class of features. These semantics can be captured in a knowledge base and utilized to constrain search through the space of constructed features. This dissertation presents a system that captures semantic feature construction knowledge and implements a search algorithm that respects that knowledge. Results are presented for different combinations of features generated from different successor functions used in search. These results are compiled over many learning problems and several learning algorithms. Other results are also presented for different levels of detail in semantic knowledge. Generally, semantics are an effective guide in the space of constructed features. 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
Where Temporal Description Logics Fail: Representing Temporally-Changing Relationships
Authors: Hans-Ulrich Krieger
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

Representing temporally-changing information becomes increasingly important for reasoning query services defined on top of RDF and OWL, and for the Semantic Web/Web 2.0 in general. Extending binary OWL properties or RDF triples with a further temporal argument either lead to additional objects or to reification, as [1] have shown.
Extending GQM by Argument Structures
Authors: Łukasz Cyra, Janusz Górski
Venue: Balancing Agility and Formalism in Software Engineering: Second IFIP TC 2 Central and East European Conference on Software Engineering Techniques, CEE-SET 2007, Poznan, Poland, October 10-12, 2007, Revised Selected Papers (2008)

Effective methods for metrics definition are of particular importance, as measurement mechanisms are indispensable in virtually any engineering discipline. The paper describes how the well known Goal-Question-Metric (GQM) method of systematic metrics derivation from measurement goals can be extended by applying argument structures. The proposed approach is called Goal-Argument-Metric (GAM). The general ideas of GQM and GAM are briefly introduced and they are followed by the comparison of the two approaches. Next, the Trust-IT framework is described --- it is used to develop argument structures in GAM. Then a case study of application of GAM is presented. The case study concerns derivation of metrics and direct measurements with the objective to assess effectiveness of Standards Conformity Framework (SCF), which is currently under development. In conclusion, early experience with GAM is presented and more information about on-going research on argument structures is given.
A Reactive Architecture Integrating an Associative Memory for Sensory-Driven Intelligent Behavior
Authors: David Panzoli, Hervé Luga, Yves Duthen
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

Realistic human-like agents are nowadays able to follow goals, plan actions, manipulate objects[1], show emotions and even converse with human people. Despite these agents being endowed with many abilities, the question of intelligence, even for simple animal agents, is still pending. Our research positions itself in an artificial life and virtual reality context. It corresponds to the direction of recent team research[2] suggesting that a creature's morphology and environment complexity are closely tied. The work presented in this paper intends to show in addition that human expert cognitive modeling is not a necessary prerequisite of behavioral intelligence.
Plenary lecture seven: 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 Signal Processing, Robotics and Automation (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.
Adaptation in the Context of Explanatory Visualization
Authors: Tomasz D. Loboda, Peter Brusilovsky
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

Explanatory visualization is a promising approach that has been used in many tutoring systems. This paper presents an attempt to assess the value of adaptation in the context of explanatory visualization. It shows that a system employing a user model and tracking users' progress gives students an opportunity to interact with larger amount of material in the same amount of time.
Two-Level Fusion to Improve Emotion Classification in Spoken Dialogue Systems
Authors: Ramón López-Cózar, Zoraida Callejas, Martin Kroul, Jan Nouza, Jan Silovský
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

This paper proposes a technique to enhance emotion classification in spoken dialogue systems by means of two fusion modules. The first combines emotion predictions generated by a set of classifiers that deal with different kinds of information about each sentence uttered by the user. To do this, the module employs several fusion methods that produce other predictions about the emotional state of the user. The predictions are the input to the second fusion module, where they are combined to deduce the user's emotional state. Experiments have been carried out considering two emotion categories (`Non-negative' and `Negative') and classifiers that deal with prosodic, acoustic, lexical and dialogue acts information. The results show that the first fusion module significantly increases the classification rates of a baseline and the classifiers working separately, as has been observed previously in the literature. The novelty of the technique is the inclusion of the second fusion module, which enhances classification rate by 2.25% absolute.
Integrated Analytic Framework for Neural Network Construction
Authors: Kang Li, Jian-Xun Peng, Minrui Fei, Xiaoou Li, Wen Yu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper investigates the construction of a wide class of singlehidden layer neural networks (SLNNs) with or without tunable parameters in the hidden nodes. It is a challenging problem if both the parameter training and determination of network size are considered simultaneously. Two alternative network construction methods are considered in this paper. Firstly, the discrete construction of SLNNs is introduced. The main objective is to select a subset of hidden nodes from a pool of candidates with parameters fixed `a priori'. This is called discrete construction since there are no parameters in the hidden nodes that need to be trained. The second approach is called continuous construction as all the adjustable network parameters are trained on the whole parameter space along the network construction process. In the second approach, there is no need to generate a pool of candidates, and the network grows one by one with the adjustable parameters optimized. The main contribution of this paper is to show that the network construction can be done using the above two alternative approaches, and these two approaches can be integrated within a unified analytic framework, leading to potentially significantly improved model performance and/or computational efficiency.
An Idiotypic Immune Network as a Short-Term Learning Architecture for Mobile Robots
Authors: Amanda Whitbrook, Uwe Aickelin, Jonathan Garibaldi
Venue: Proceedings of the 7th international conference on Artificial Immune Systems (2008)

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a hand-designed controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability.