ppxscal/embedding_network huggingface dataset:
Stable adaptive control with recurrent neural networks for square MIMO non-linear systems
Authors: Salem Zerkaoui, Fabrice Druaux, Edouard Leclercq, Dimitri Lefebvre
Venue: Engineering Applications of Artificial Intelligence (2009)

In this paper, stable indirect adaptive control with recurrent neural networks is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected ''Real-Time Recurrent Learning'' (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. The control scheme is then applied to the Tennessee Eastman Challenge Process in order to illustrate the efficiency of the proposed method for real-world control problems.
Advanced Capabilities for Evaluating Student Writing: Detecting Off-Topic Essays Without Topic-Specific Training
Authors: Jill Burstein, Derrick Higgins
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

We have developed a method to identify when a student essay is off-topic, i.e. the essay does not respond to the test question topic. This task is motivated by a real-world problem: detecting when students using a commercial essay evaluation system, CriterionSM, enter off-topic essays. Sometimes this is done in bad faith to trick the system; other times it is inadvertent, and the student has cut-and-pasted the wrong selection into the system. All previous methods that perform this task require 200-300 human scored essays for training purposes. However, there are situations in which no essays are available for training, such as when a user (teacher) wants to spontaneously write a new topic for her students. For these kinds of cases, we need a system that works reliably without training data. This paper describes an algorithm that detects when a student's essay is off-topic without requiring a set of topic-specific essays for training. The system also distinguishes between two different kinds of off-topic writing. The results of our experiment indicate that the performance of this new system is comparable to the previous system that does require topic-specific essays for training, and conflates different types of off-topic writing.
An evolutionary method for constructing complex SVM kernels
Authors: Dana Simian, Florin Stoica
Venue: Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry (2009)

The aim of this paper is to construct and analyze multiple SVM kernels. The construction is based on a genetic algorithm which uses a new co-mutation operator called LR-Mijn, capable of operating on a set of adjacent bits in one single step.
The effects of cooperative agent behavior on human cooperativeness
Authors: Arlette van Wissen, Jurriaan van Diggelen, Virginia Dignum
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

In this paper we examine the question of how cooperativeness of a software agent affects cooperativeness of a human player. Our data shows that humans behave more cooperatively towards agents that negotiate with them in a cooperative way.
Controlling and tuning the emission of semiconductor optical amplifier for sensor application by means of fiber Bragg gratings
Authors: Julian Sonksen, Mahmoud Ahmad, Nico Storch, Hubert Krause, Sven Blom, Alexander Pötzl, Hartmut Hillmer
Venue: Proceedings of the 8th WSEAS international conference on Microelectronics, nanoelectronics, optoelectronics (2009)

Highly sensitive and selective sensing has become more and more important in modern process control and security related fields. For this purpose, we have designed a novel tunable fiber based external cavity laser system for sensor applications. The laser consists of a semiconductor optical amplifier (SOA) as a gain medium and of Fiber Bragg Gratings (FBG) as wavelength selective components. Since the SOA is an inhomogeneous broadening gain medium, stable multi-wavelength oscillation at the Bragg wavelengths of the FBGs used can be obtained. In addition, the oscillation wavelengths of the laser can be tuned by applying temperature and strain to the FBGs. Simultaneous measurements of the FBGs temperature stabilization and tuning of the sensor outputs are achieved. In the sensor scheme, the two-wavelength components provide narrowband light sources for sensor applications and the wavelength tuning enables a wide range for target absorption detection.
Player number recognition in soccer video using internal contours and temporal redundancy
Authors: Matko Šaric, Hrvoje Dujmic, Vladan Papic, Nikola Rožic, Joško Radic
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

Player identification is important task in sport video content analysis. In case of soccer video player numbers can be exploited to perform recognition. Jersey numbers can be considered as scene text and main problems in localization and recognition are low resolution, low contrast, color aliasing, unconstrained background etc. This paper proposed new method for player number localization and recognition. Firstly jersey regions are extracted using region growing algorithm. These regions are segmented using the component of HSV color space that best separates jersey and number area. Then, novel method for player number localization based on internal contours is proposed. False number candidates are discarded based on area and aspect ratio. Extracted numbers are enhanced using image smoothing and rotation normalization. We also propose algorithm that improves OCR recognition performance using temporal redundancy.
Nanoparticles of activated natural zeolite on textiles for protection and therapy
Authors: Ana Marija Grancaric, Anita Tarbuk, Ivancica Kovacek
Venue: Proceedings of the 1st WSEAS international conference on Nanotechnology (2009)

Activated natural zeolite clinoptilolite is microporous hydrated aluminosilicates crystals with well-defined structures containing AlO4 and SiO4 tetrahedral linked through the common oxygen atoms. It is to point out that zeolites act as strong adsorbents and ion-exchangers but having many other useful properties. Zeolites are nontoxic substance, excellent for UVR and microbes protection, for proteins and small molecules such as glucose adsorption. Due to its cationexchange ability, zeolites have catalytic properties and for that multiple uses in medicine and industry, agriculture, water purification and detergents. The present paper is an attempt to modify cotton and polyester fabrics for summer clothing with addition of natural zeolite nanoparticles for achieving UV and antibacterial protective textiles. For this purpose cotton fabrics were mercerized and polyester fabrics modified by alkaline hydrolysis and by EDA (ethylenediamine) aminolysis. Zeolite in this paper refers to activated particles of clinoptilolite, with some fraction of nanoparticles produced by tribomechanical processing in the patented machine.
Cross species analysis of microarray expression data
Authors: Yong Lu, Peter Huggins, Ziv Bar-Joseph
Venue: Bioinformatics (2009)

Motivation: Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied. Results: In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them. Contact: zivbj@cs.cmu.edu
From UML/SPT models to schedulability analysis: approach and a prototype implementation using ATL
Authors: Abdelouahed Gherbi, Ferhat Khendek
Venue: Automated Software Engineering (2009)

Model Driven Architecture (MDA) is a software development approach promoted by the OMG. MDA is based on two key concepts, models and model transformations. Several kinds of models are generally used throughout the development process to specify a software system and to support its analysis and validation. UML and its extensions, such as the UML profile for real-time systems (UML/SPT), are commonly used to define the structure and the behavior of software systems while other models, such as performance models or schedulability models, are more suitable for performance or schedulability analysis, respectively. In this paper we discuss a model transformation enabling the derivation of schedulability analysis models from UML/SPT models. As a proof of concepts, we present a prototype implementation of this model transformation using ATL. We provide a definition of the source and target metamodels using the metamodel specification language KM3 and we specify the transformation in an ATL module. We discuss the merits and limitations of our approach and of its implementation.
Named entity recognition in biomedical texts using an HMM model
Authors: Shaojun Zhao
Venue: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (2004)

Although there exists a huge number of biomedical texts online, there is a lack of tools good enough to help people get information or knowledge from them. Named entity Recognition (NER) becomes very important for further processing like information retrieval, information extraction and knowledge discovery. We introduce a Hidden Markov Model (HMM) for NER, with a word similarity-based smoothing. Our experiment shows that the word similarity-based smoothing can improve the performance by using huge unlabeled data. While many systems have laboriously hand-coded rules for all kinds of word features, we show that word similarity is a potential method to automatically get word formation, prefix, suffix and abbreviation information automatically from biomedical texts, as well as useful word distribution information.
Strengths and weaknesses of WS-BusinessActivity for cross-organizational SOA applications
Authors: Frederic Wenzel, Patrick Freudenstein, Martin Nussbaumer
Venue: Proceedings of the 2009 ICSE Workshop on Principles of Engineering Service Oriented Systems (2009)

In traditional database-driven applications, transactional integrity is a well-established concept. In order to apply these techniques to SOA-based applications, along with the capability to perform long-running business activities, the WS-BusinessActivity standard was developed. This standard does not specifically address cross-organizational use, however, when an enterprise decides to integrate its SOA-based system with their business partners' services, the ability to span long-running business activities across organizational boundaries becomes crucial. This paper describes an experiment performed to assess the suitability of WS-BusinessActivity for cross-organizational use and identifies several strengths and weaknesses that became apparent during its execution.
Defining Bad News: Changes in Return Distributions That Decrease Risky Asset Demand
Authors: Burton Hollifield, Alan Kraus
Venue: Management Science (2009)

We provide a random variable characterization of the necessary and sufficient conditions for a shift of the distribution of rate of return on the risky asset in the two-asset portfolio problem to reduce demand for all strictly risk-averse expected-utility-maximizing investors. We also provide random variable characterizations of the shifts that reduce both demand and expected utility for all strictly risk-averse investors.
Virtual assistants for e-government: a preliminary study
Authors: Rodrigo Sandoval-Almazán, Mario A. Gutiérrez-Alonso
Venue: Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (2009)

The objective of this poster is to present ongoing research about the use of virtual assistants in E-government applications. We provide a short state of the art on virtual assistant's technology. Two case studies are presented and discussed: the web site of the Mexican state of Guanajuato and the Federal Government Citizens Web Page. Some research topics have been identified from this preliminary study.
A novel algorithm for generating Muhammad pattern based on cellular automata
Authors: Seyed Amir Hadi Minoofam, Azam Bastanfard
Venue: Proceedings of the 13th WSEAS international conference on Applied mathematics (2008)

Creating algorithmic approach for generating Islamic patterns extends applications in architectures, decoration and art. Researchers face with many challenges in developing tiling algorithms such as providing simple and applicable algorithm to describe complex patterns. This paper used cellular automata with extended Moor neighborhood to generate ma'qeli script patterns. The proposed approach leads to accurate and scalable algorithm for generating words pattern. The joint and disjoint pattern shape of the letters will be reserved in this approach. The results of implemented algorithms demonstrate our approach with a variety of ma'qeli patterns.
Artificial neural network approaches to intrusion detection: a review
Authors: Iftikhar Ahmad, Azween B. Abdullah, Abdullah S. Alghamdi
Venue: Proceedings of the 8th Wseas international conference on Telecommunications and informatics (2009)

Intrusion detection systems are the foremost tools for providing safety in computer and network system. There are many limitations in traditional IDSs like time consuming statistical analysis, regular updating, non adaptive, accuracy and flexibility. It is an Artificial Neural Network that supports an ideal specification of an Intrusion Detection System and is a solution to the problems of traditional IDSs. Therefore, An Artificial Neural Network inspired by nervous system has become an interesting tool in the applications of Intrusion Detection Systems due to its promising features. Intrusion detection by Artificial Neural Networks is an ongoing area. In this paper, we provide an introduction and review of the Artificial Neural Network Approaches within Intrusion Detection, in addition to make suggestions for future research. We also discuss on tools and datasets that are being used in Artificial Neural Network Intrusion Detection Systems. This review may help the researcher to develop new optimize approach in the field of Intrusion Detection.
Unified Behavior Framework for Reactive Robot Control
Authors: Brian G. Woolley, Gilbert L. Peterson
Venue: Journal of Intelligent and Robotic Systems (2009)

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. When a hybrid or three layer control architecture includes the unified behavior framework, a common interface is shared by all behaviors, leaving the higher order planning and sequencing elements free to interchange behaviors during execution to achieve high level goals and plans. The framework's ability to compose structures from independent elements encourages experimentation and reuse while isolating the scope of troubleshooting to the behavior composition. The ability to use elemental components to build and evaluate behavior structures is demonstrated using the Robocode simulation environment. Additionally, the ability of a reactive controller to change its active behavior during execution is shown in a goal seeking robot implementation.
Assessing customer satisfaction and agile project management
Authors: Donald Lee Buresh
Venue:  (2008)

In this study, it was examined empirically whether the use and results of agile-driven software development methods significantly satisfied the customer over and above the use and results of plan-driven software development methods. Customer satisfaction was regressed against product quality, project team effectiveness, and project management effectiveness, where a dummy variable was used to distinguish between agile-driven projects and plan-driven projects. All of the variables contained averaged values. One hundred eighty-five national and international participants reported on 95 known agile-driven projects, 53 known plan-driven projects, 24 probable agile-driven projects, and 13 probable plan-driven projects. When the software development methodology was known by the participants, there was no statistically significant difference in autonomous&mdash;customer satisfaction between the use and results of agile-driven software development methods and the use and results of plan-driven software development methods at the 95% confidence level. All of the non-standardized coefficients of the explanatory variables were statistically significant at the 99% confidence level. When the software development methodology was known by the participants or inferred by this study, the non-standardized coefficient of the dummy variable was barely statistically significant at the 95% confidence level, but not statistically significant at the 99% confidence level. The non-standardized coefficients of the explanatory variables were again statistically significant at the 99% confidence level. Because customer satisfaction, product quality, project team effectiveness, and project management effectiveness contained averaged values and because the Bonferroni correction procedure was invoked, it was argued that the 99% confidence level was the appropriate significance level to employ. Thus, when the software development methodology was known by the participants or inferred by this study, there was again no statistically significant difference in autonomous customer satisfaction between the use and results of agile-driven software development methods and the use and results of plan-driven software development methods. Twelve recommendations for additional research were presented, including questions on the number of person hours expended, project cost and value, project size, actual dollar amounts expended, actual project durations, dominant agile-driven software development methodology, familiarity of the project team members with the methodology employed, information technology labor market, egalitarian issues, and methodological concerns. 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
Probabilistic Reinforcement Rules for Item-Based Recommender Systems
Authors: Sylvain Castagnos, Armelle Brun, Anne Boyer
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

The Internet is constantly growing, proposing more and more services and sources of information. Modeling personal preferences enables recommender systems to identify relevant subsets of items. These systems often rely on filtering techniques based on symbolic or numerical approaches in a stochastic context. In this paper, we focus on item-based collaborative filtering (CF) techniques. We propose a new approach combining a classic CF algorithm with a reinforcement model to get a better accuracy. We deal with this issue by exploiting probabilistic skewnesses in triplets of items.
Co-evolution of social and affiliation networks
Authors: Elena Zheleva, Hossam Sharara, Lise Getoor
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generative models which capture the statistical properties of real-world networks related only to node-to-node link formation. We propose a novel model which captures the co-evolution of social and affiliation networks. We provide surprising insights into group formation based on observations in several real-world networks, showing that users often join groups for reasons other than their friends. Our experiments show that the model is able to capture both the newly observed and previously studied network properties. This work is the first to propose a generative model which captures the statistical properties of these complex networks. The proposed model facilitates controlled experiments which study the effect of actors' behavior on the evolution of affiliation networks, and it allows the generation of realistic synthetic datasets.
Adaptive cooling of integrated circuits using digital microfluidics
Authors: Philip Y. Paik, Vamsee K. Pamula, Krishnendu Chakrabarty
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2008)

Thermal management is critical for integrated circuit (IC) design. With each new IC technology generation, feature sizes decrease, while operating speeds and package densities increase. These factors contribute to elevated die temperatures detrimental to circuit performance and reliability. Furthermore, hot spots due to spatially nonuniform heat flux in ICs can cause physical stress that further reduces reliability. While a number of chip cooling techniques have been proposed in the literature, most are still unable to address the varying thermal profiles of an IC and their capability to remove a large amount of heat is undermined by their lack of reconfigurability of flows. We present an alternative cooling technique based on a recently invented "digital microflnidic" platform. This novel digital fluid handling platform uses a phenomenon known as electrowetting, and allows for a vast array of discrete droplets of liquid, ranging from microliters to nanoliters, and potentially picoliters, to be independently moved along a substrate. While this technology was originally developed for a biological and chemical lab-on-a-chip, we show how it can be adapted to be used as a fully reconfigurable, adaptive cooling platform.