ppxscal/embedding_network huggingface dataset:
FNN (Feedforward Neural Network) Training Method Based on Robust Recursive Least Square Method
Authors: Junseok Lim, Koengmo Sung
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

We present a robust recursive least squares algorithm for multilayer feed-forward neural network training. So far, recursive least squares (RLS) has been successfully applied to training multilayer feed-forward neural networks. However, RLS method has a tendency to become diverse due to the instability in the recursive inversion procedure. In this paper, we propose a numerically robust recursive least square type algorithm using prewhitening. The proposed algorithm improves the performance of RLS in infinite numerical precision as well as in finite numerical precision. The computer simulation results in the various precision cases show that the proposed algorithm improves the numerical robustness of RLS training.
Functional programming and theorem proving for undergraduates: a progress report
Authors: Rex Page, Carl Eastlund, Matthias Felleisen
Venue: Proceedings of the 2008 international workshop on Functional and declarative programming in education (2008)

For the past five years, the University of Oklahoma has used the ACL2 theorem prover for a year-long sequence on software engineering. The goal of the course is to introduce students to functional programming with "Applicative Common Lisp" (ACL) and to expose them to defect recognition at all levels, including unit testing, randomized testing of conjectures, and formal theorem proving in "a Computational Logic" (ACL2). Following Page's example, Northeastern University has experimented with the introduction of ACL2 into the freshman curriculum for the past two years. Northeastern's goal is to supplement an introductory course on functional program design with a course on logic and theorem proving that integrates the topic with programming projects. This paper reports on our joint project's progress. On the technical side, the paper presents the Scheme-based integrated development environment, its run-time environment for functional GUI programming, and its support for different forms of testing. On the experience side, the paper summarizes the introduction of these tools into the courses, the reaction of industrial observers of Oklahoma's software engineering course, and the feedback from a first outreach workshop.
Optimal Efficiency Guarantees for Network Design Mechanisms
Authors: Tim Roughgarden, Mukund Sundararajan
Venue: Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization (2007)

A cost-sharing problem is defined by a set of players vying to receive some good or service, and a cost function describing the cost incurred by the auctioneer as a function of the set of winners. A cost-sharing mechanism is a protocol that decides which players win the auction and at what prices. Three desirable but provably mutually incompatible properties of a cost-sharing mechanism are: incentive-compatibility, meaning that players are motivated to bid their true private value for receiving the good; budget-balance, meaning that the mechanism recovers its incurred cost with the prices charged; and efficiency, meaning that the cost incurred and the value to the players served are traded off in an optimal way.Our work is motivated by the following fundamental question: for which cost-sharing problems are incentive-compatible mechanisms with good approximate budget-balance and efficiency possible? We focus on cost functions defined implicitly by NP-hard combinatorial optimization problems, including the metric uncapacitated facility location problem, the Steiner tree problem, and rent-or-buy network design problems. For facility location and rent-or-buy network design, we establish for the first time that approximate budget-balance and efficiency are simultaneously possible. For the Steiner tree problem, where such a guarantee was previously known, we prove a new, optimal lower bound on the approximate efficiency achievable by the wide and natural class of "Moulin mechanisms". This lower bound exposes a latent approximation hierarchy among different cost-sharing problems.
Bounded Model Checking for Partial Kripke Structures
Authors: Heike Wehrheim
Venue: Proceedings of the 5th international colloquium on Theoretical Aspects of Computing (2008)

Partial Kripke structures model incomplete state spaces with unknown parts. The evaluation of temporal logic formulae on partial Kripke structures is thus based on three-valued interpretations; the additional truth value $\bot$ stands for "unknown whether property true or false". There are existing model checking algorithms as well as tools employing this three-valued interpretation.In this paper we study the applicability of bounded model checking techniques to partial Kripke structures. To this end, we generalise the translation of Kripke structure and temporal logic formula to propositional logic as to include the value $\bot$, and define a new notion of satisfiability for propositional formulae containing $\bot$ as constants. We show that a check for this kind of satisfiability can be reduced to two checks for ordinary two-valued satisfiability, thus allowing for the use of standard SAT solvers.
Experimental Bayesian Generalization Error of Non-regular Models under Covariate Shift
Authors: Keisuke Yamazaki, Sumio Watanabe
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

In the standard setting of statistical learning theory, we assume that the training and test data are generated from the same distribution. However, this assumption cannot hold in many practical cases, e.g., brain-computer interfacing, bioinformatics, etc. Especially, changing input distribution in the regression problem often occurs, and is known as the covariate shift. There are a lot of studies to adapt the change, since the ordinary machine learning methods do not work properly under the shift. The asymptotic theory has also been developed in the Bayesian inference. Although many effective results are reported on statistical regular ones, the non-regular models have not been considered well. This paper focuses on behaviors of non-regular models under the covariate shift. In the former study [1], we formally revealed the factors changing the generalization error and established its upper bound. We here report that the experimental results support the theoretical findings. Moreover it is observed that the basis function in the model plays an important role in some cases.
A Two-Step Approach for Feature Selection and Classifier Ensemble Construction in Computer-Aided Diagnosis
Authors: Michael C. Lee, Lilla Boroczky, Kivilcim Sungur-Stasik, Aaron D. Cann, Alain C. Borczuk, Steven M. Kawut, Charles A. Powell
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Accurate classification methods are critical in computer-aided diagnosis and other clinical decision support systems. Previous research has studied methods for combining genetic algorithms for feature selection with ensemble classifier systems in an effort to increase classification accuracy. We propose a two-step approach that first uses genetic algorithms to reduce the number of features used to characterize the data, then applies the random subspace method on the remaining features to create a set of diverse but high performing classifiers. These classifiers are combined using ensemble learning techniques to yield a final classification. We demonstrate this approach for computer-aided diagnosis of solitary pulmonary nodules from CT scans, in which the proposed method outperforms several previously described methods.
Dynamic Analysis of a Novel Artificial Neural Oscillator
Authors: Daibing Zhang, Dewen Hu, Lincheng Shen, Haibin Xie
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper proposes a novel artificial neural oscillator consisted of two neurons with excellent control properties. The mutual connections between the neurons are just linear functions and determine the oscillation angular frequency. And each neuron has a nonlinear self-feedback connection to hold up oscillation amplitude. The dynamics of the neural oscillator was modelled with nonlinear coupling functions. And the stability, amplitude, angular frequency of the oscillator are determined independently by three parameters of the functions. Since it has simple structure and favorable control advantages, it can be used in bionic robot's locomotion control system. The first application is an artificial central pattern generator (CPG) controller for bionic robot's joint. The second is a bionic neural network for fish-robot's locomotion control.
Structuring Spatial Knowledge and Fail-Safe Expression for Directive Voice Navigation
Authors: Taizo Miyachi, Jens J. Balvig, Ipei Kuroda, Takeshi Suzuki
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

Navigation systems in mobile terminals are often useful for finding a way to the destination. However it is not easy for pedestrians to consult small maps with a sense of direction in a target area. Voice navigation is easy and useful for everybody to easily know spatial information in a map. However pedestrians should build mental maps based on the spatial information in voice guide. We propose and examine a quiet route navigation system with directive voice beams for pedestrians in order to safely reach to the destination. A method for making a fail-safe voice guide that allows pedestrians to easily understand and remember spatial information avoiding misunderstanding is also discussed.
Development of Free-Space Digital Optics in Computing
Authors: James Gourlay, Tsung-Yi Yang, Julian A. B. Dines, John F. Snowdon, Andrew C. Walker
Venue: Computer (1998)

The use of light to carry data within computing systems offers exciting prospects for complex machines with very large numbers of internal connections and, consequently, the capability to perform powerful parallel processing.
Multi-stage Speaker Diarization for Conference and Lecture Meetings
Authors: X. Zhu, C. Barras, L. Lamel, J-L. Gauvain
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

The LIMSI RT-07S speaker diarization system for the conference and lecture meetings is presented in this paper. This system builds upon the RT-06S diarization system designed for lecture data. The baseline system combines agglomerative clustering based on Bayesian information criterion (BIC) with a second clustering using state-of-the-art speaker identification (SID) techniques. Since the baseline system provides a high speech activity detection (SAD) error around of 10% on lecture data, some different acoustic representations with various normalization techniques are investigated within the framework of log-likelihood ratio (LLR) based speech activity detector. UBMs trained on the different types of acoustic features are also examined in the SID clustering stage. All SAD acoustic models and UBMs are trained with the forced alignment segmentations of the conference data. The diarization system integrating the new SAD models and UBM gives comparable results on both the RT-07S conference and lecture evaluation data for the multiple distant microphone (MDM) condition.
HF-surface wave radar: first results for sea state studies
Authors: M. Menelle, G. Auffray, F. Jangal, V. Bazin, B. Urbani
Venue: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering (2008)

For the sea surveillance, various sensors or equipments are commonly used: semaphore, patrol boats, radars, etc ... The use of an High frequency Surface Wave Radar (HFSWR) could be a sensor very interesting in global surveillance of large areas like the Exclusive Economic Zone (EEZ). ONERA, the French aerospace lab, which has a long experience on HF radars, has developed an HFSWR during 2007 in the Biscay Bay with the agreements of DGA (Délégation Générale de l'Armement). This paper will present the sensor, his main characteristics and will present some results about sea behavior.
An Integrated Data Mining System for Patient Monitoring with Applications on Asthma Care
Authors: Vincent S. Tseng, Chao-Hui Lee, Jessie Chia-Yu Chen
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

In this paper, we proposed an integrated data mining system for patient monitoring with applications on asthma care. In this system, two data mining methods named PBD and PBC are designed for predicting asthma attacks. The main methodology is to extract the significant information of asthma attacks and build classifiers by using users' daily bio-signal records and environmental data. Meanwhile, helpful medical information and suggestions supported by doctors are applied. In this way, the proposed system can predict the chances of asthma attacks and provide patients with the proper medical instructions or health messages. The experimental evaluation results proved that the proposed mechanism is effective and reliable in asthma attack prediction.
Spectral Collocation Technique for Absorbing Boundary Conditions with Increasingly High Order Approximation
Authors: Zhenli Xu, Houde Han
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

An efficient treatment is developed for the Schrödinger equation with a class of local absorbing boundary conditions, which are obtained by high order Padé expansions. These boundary conditions are significant in the simulation of open quantum devices. Based on the finite difference approximation in the interior domain, we construct a spectral collocation layer on the cell near the artificial boundary, in which the wave function is approximated by the Chebyshev polynomials. The numerical examples are given by using this strategy with increasingly high order of accuracy up to the ninth order.
Message Complexity Analysis of MANET Address Autoconfiguration Algorithms in Group Merging Case
Authors: Sang-Chul Kim
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

This paper focuses on the derivation of the message complexity when two mobile ad hoc network (MANET) groups merge together, where the network groups already have been configured with IP addresses by using address autoconfiguration protocols (AAPs). The message complexity of the MANET group merging case (GMC) in Strong DAD, Weak DAD with proactive routing protocols (WDP), Weak DAD with on-demand routing protocols (WDO), and MANETconf has been derived respectively. In order to verify the derived bounds, analytical simulations that quantify the message complexity of the address autoconfiguration process based on the different conflict probabilities are conducted.
Virtual Environments with Content Sharing
Authors: Madjid Merabti, Abdennour Rhalibi, Amjad Shaheed, Paul Fergus, Marc Price
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

Content sharing over networked devices, beyond simple file sharing is becoming a reality. Many devices are forming closer relationships with different virtual worlds, such as World of Warcarft and Second Life. In one sense the gap between the two is becoming increasingly more blurred. Consequently, this opens up many new avenues for content sharing, not only between devices but also between sophisticated virtual worlds. Given such interoperable platforms a natural progression sees content that seamlessly resides within either. This will open up new opportunities where third-party content providers and users alike are able to create and share content over these new platforms. We aim to provide a basis on which this vision can be realised where mechanisms have been developed that facilitates the sharing of virtual world objects across different virtual environment. The work has been tested using a working prototype that allows digital content, to be shared and physical devices, such as mobile phones, to be connected and their content to be shared.
Analysis of an evolutionary reinforcement learning method in a multiagent domain
Authors: Jan Hendrik Metzen, Mark Edgington, Yohannes Kassahun, Frank Kirchner
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

Many multiagent problems comprise subtasks which can be considered as reinforcement learning (RL) problems. In addition to classical temporal difference methods, evolutionary algorithms are among the most promising approaches for such RL problems. The relative performance of these approaches in certain subdomains (e. g. multiagent learning) of the general RL problem remains an open question at this time. In addition to theoretical analysis, benchmarks are one of the most important tools for comparing different RL methods in certain problem domains. A recently proposed multiagent RL benchmark problem is the RoboCup Keepaway benchmark. This benchmark is one of the most challenging multiagent learning problems because its state-space is continuous and high dimensional, and both the sensors and the actuators are noisy. In this paper we analyze the performance of the neuroevolutionary approach called Evolutionary Acquisition of Neural Topologies (EANT) in the Keepaway benchmark, and compare the results obtained using EANT with the results of other algorithms tested on the same benchmark.
Finding Exploratory Rewards by Embodied Evolution and Constrained Reinforcement Learning in the Cyber Rodents
Authors: Eiji Uchibe, Kenji Doya
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

The aim of the Cyber Rodent project [1] is to elucidate the origin of our reward and affective systems by building artificial agents that share the natural biological constraints: self-preservation (foraging) and self-reproduction (mating). This paper shows a method to evolve an agent's exploratory reward by combining a framework of embodied evolution and the algorithm of constrained policy gradient reinforcement learning. Biological constraints are modeled by the average criteria, and the exploratory reward is computed from its own sensor information. The agent in which a part of constraints are satisfied is allowed to mate with another agent. If a mating behavior is successfully made between two agents, one of genetic operations is applied according to fitness values to improve the exploratory rewards. Through learning and embodied evolution, a group of agents obtain appropriate exploratory rewards.
Improving Negotiation Efficiency and Success in Automated Trust Negotiation
Authors: Hongwei Lu, Bailing Liu
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Automated trust negotiation (ATN) is an approach that establishes mutual trust between strangers by gradually requesting and disclosing digitally signed credentials. Previous work on ATN mainly focuses on how to protect sensitive information about negotiators. However, the efficiency and success, which are primary goals of ATN, are not sufficiently considered. In this paper, we extend existing policy languages by adding two basic and expressive operators to them, which is one key to improve negotiation efficiency. Several problems that can not be effectively solved using traditional policy languages are presented, they can be effectively and efficiently settled by the extended policy languages, and several examples are also given. Furthermore, we develop an associated protocol taking advantages of oblivious signature based envelope (OSBE) and two new concepts explored in this paper. We also prove that the extended policy languages and the associated protocol together greatly improve negotiation efficiency and success under some conditions without sensitive information leakage in ATN.
Performance Evaluation of Fully Adaptive Routing for the Torus Interconnect Networks
Authors: F. Safaei, A. Khonsari, M. Fathy, M. Ould-Khaoua
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Adaptive routing algorithms have been frequently suggested as a means of improving communication performance in parallel computer networks. These algorithms, unlike deterministic routing, can utilize network state information to exploit the presence of multiple paths. Before such schemes can be successfully incorporated in networks, it is necessary to have a clear understanding of the factors which affect their performance potential. This paper proposes a new analytical model to obtain message latency in wormhole-switched 2-D torus interconnect networks. The analysis focuses on a fully adaptive routing which has been shown to be one of the most effective in torus networks. The validity of the model is demonstrated by comparing analytical results with those obtained through simulation experiments.
Robust projective filtering of time-warped ECG beats
Authors: M. Kotas
Venue: Computer Methods and Programs in Biomedicine (2008)

In this paper a new version, more immune to noise, of the nonlinear projective filtering is presented. The method employs an algorithm of robust principal component analysis to signal subspaces construction and as a result it achieves high performance in real electromyographic noise environment. Two aspects of the method's action are investigated: its ability to suppress noise and its influence on the precision of the QT interval measurement. Then the method influence on evaluation of the beat-to-beat variability of the repolarization duration is presented. A comparison to the previous versions of the nonlinear projective filtering and to the classical linear one is carried out.
Efficient Processing of Continuous Join Queries Using Distributed Hash Tables
Authors: Wenceslao Palma, Reza Akbarinia, Esther Pacitti, Patrick Valduriez
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

This paper addresses the problem of computing approximate answers to continuous join queries. We present a new method, called DHTJoin, which combines hash-based placement of tuples in a Distributed Hash Table (DHT) and dissemination of queries using a gossip style protocol. We provide a performance evaluation of DHTJoin which shows that DHTJoin can achieve significant performance gains in terms of network traffic.