ppxscal/embedding_network huggingface dataset:
Combining neural networks and semantic feature space for email classification
Authors: Bo Yu, Dong-hua Zhu
Venue: Knowledge-Based Systems (2009)

Email is one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide, individuals and organizations more and more rely on the emails to communicate and share information and knowledge. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. It is becoming a big challenge to process and manage the emails efficiently for and individuals and organizations. This paper proposes new email classification models using a linear neural network trained by perceptron learning algorithm and a nonlinear neural network trained by back-propagation learning algorithm. An efficient semantic feature space (SFS) method is introduced in these classification models. The traditional back-propagation neural network (BPNN) has slow learning speed and is prone to trap into a local minimum, so the modified back-propagation neural network (MBPNN) is presented to overcome these limitations. The vector space model based email classification system suffers from a large number of features and ambiguity in the meaning of terms, which will lead to sparse and noisy feature space. So we use the SFS to convert the original sparse and noisy feature space to a semantically richer feature space, which will helps to accelerate the learning speed. The experiments are conducted based on different training set size and extracted feature size. Experimental results show that the models using MBPNN outperform the traditional BPNN, and the use of SFS can greatly reduce the feature dimensionality and improve email classification performance.
RE in modelling of CNGDI piston head
Authors: A. R. Ismail, S. C. Abdullah, K. Sopian, S. Abdullah, M. M. Tahir, I. M. S. Usman, D. A. Wahab
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

Reverse engineering was done on a direct injection piston compressing natural gas. Reverse engineering is a way to redesign a product to create a new product which has similar functions and improvement in the ability of the original product. The objectives of this research are to get the piston point cloud to be used in CAD three dimension modeling, to obtain the accuracy between existing designed pistons and reverse engineered designed piston, to calculate the accuracy of the prototype after CNC machining process. Piston was digitized using Advanced Topometric scanner (ATOS), a three dimension scanner to get the point cloud for usage in CAD three dimension modelling using Catia V5 R12 software. Accuracy tests were also done by manual calculation to determine the accuracy difference between reverse engineered piston and existing piston. To create the piston prototype, simulation of the piston was done using I-DEAS software and machining was done using CNC Robodrill α-T14iA machine. Simulation using I-DEAS software was used to find the suitable parameters to be used in the CNC machining process. The final prototype was measured for accuracy to determine the accuracy of the CNC machining process. It was calculated by manual calculation by comparing the dimensions of the actual prototype to the design dimensions on paper. The accuracy of the prototype was revealed to be 87.00% to 99.996% accurate to the desired dimensions except for angle and curve areas.
Complex dynamics of human activity: language, cities, collaboration, and baseball
Authors: Steven H. Strogatz
Venue:  (2008)

A few areas of human activity are examined here, using a number of different types of mathematical and computational models. First, we examine networks of five languages of the world, with their connectivity derived from the sounds of the words in these languages. We explore the graph-theoretic properties of these networks, finding that these phonological language networks have common properties, and are in turn topologically distinct from other types of complex networks observed in the literature. In addition, we discuss what these common properties imply for how we process language and why natural language is structured the way it is. In addition, by examining the networks of English and Spanish, we explain a surprising difference in processing that was uncovered in some recent experiments, and discuss some more general implications of competition or facilitation between different modes of cognition.We next explore a more macro-scale area of human activity: cities. Superlinear scaling in cities, which appears in sociological quantities such as economic productivity and creative output relative to urban population size, has been observed but not been given a satisfactory theoretical explanation. We provide a model for the superlinear relationship between population size and innovation found in cities, with a reasonable range for the exponent.Next, we examine collaboration and innovation in the scientific world. We attempt to understand how variations in &lsquo;scientific distance&rsquo; among collaborators affect the degree to which that collaboration is a productive one. Using both mathematical models and empirical data, we explore the relationship between the scientific or social distance of collaborators and the fruitfulness of their output.Last, we examine Joe DiMaggio's 56-game hitting streak and look at its probability, using a number of simple models. And it turns out that, contrary to many people's expectations, an extreme streak, while unlikely, is not unlikely to have occurred about once within the history of baseball. Surprisingly, however, such a record should have occurred far earlier in baseball history: back in the late 1800's or early 1900's. But not in 1941, when it actually happened.
An XML Format Proposal for the Description of Weighted Automata, Transducers and Regular Expressions
Authors: Akim Demaille, Alexandre Duret-Lutz, Florian Lesaint, Sylvain Lombardy, Jacques Sakarovitch, Florent Terrones
Venue: Proceeding of the 2009 conference on Finite-State Methods and Natural Language Processing: Post-proceedings of the 7th International Workshop FSMNLP 2008 (2009)

We present an XML format that allows to describe a large class of finite weighted automata and transducers. Our design choices stem from our policy of making the implementation as simple as possible. This format has been tested for the communication between the modules of our automata manipulation platform Vaucanson, but this document is less an experiment report than a position paper intended to open the discussion among the community of automata software writers.
Detection, Recommendation and Collaboration: Knowledge Awareness Map in Computer Supported Ubiquitous Learning
Authors: Moushir M. El-Bishouty, Hiroaki Ogata, Yoneo Yano
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

While a learner is looking for certain knowledge, it is so difficult to know that the other learners have this knowledge even that they are at the same location. Therefore, this paper proposes a ubiquitous computing environment to support the learner while doing a task; it is called PERKAMIII (PERsonalized Knowledge Awareness Map). It utilizes the ubiquities technologies to detect the learner's environmental objects and location, and recommends the best-matched peer helpers with the detected objects and the current location. This environment provides the learners with Knowledge Awareness Maps, which visualize the environmental objects space that surround the learner and the peer helpers' space. It recommends the peer helpers according to how much their interests are matched the learner's current task and how near are their physical location to the learner's current location.
The Andes Physics Tutoring System: Five Years of Evaluations
Authors: Kurt VanLehn, Collin Lynch, Kay Schulze, Joel A. Shapiro, Robert Shelby, Linwood Taylor, Don Treacy, Anders Weinstein, Mary Wintersgill
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Andes is a mature intelligent tutoring system that has helped hundreds of students improve their learning of university physics. It replaces pencil and paper problem solving homework. Students continue to attend the same lectures, labs and recitations. Five years of experimentation at the United States Naval Academy indicates that it significantly improves student learning. This report describes the evaluations and what was learned from them.
Sequential adaptive designs in computer experiments for response surface model fit
Authors: William Notz
Venue:  (2008)

Computer simulations have become increasingly popular as a method for studying physical processes that are difficult to study directly. These simulations are based on complex mathematical models that are believed to accurately describe the physical process. We consider the situation where these simulations take a long time to run (several hours or days) and hence can only be conducted a limited number of times. As a result, the inputs (design) at which to run the simulations must be chosen carefully. For the purpose of fitting a response surface to the output from these simulations, a variety of designs based on a fixed number of runs have been proposed. In this thesis, we consider sequential adaptive designs as an &ldquo;efficient&rdquo; alternative to fixed-point designs. We propose new adaptive design criteria based on a cross validation approach and on an expected improvement criterion, the latter inspired by a criterion originally proposed for global optimization. We compare these new designs with others in the literature in an empirical study and they shown to perform well. The issue of robustness for the proposed sequential adaptive designs is also addressed in this thesis. While we find that sequential adaptive designs are potentially more effective and efficient than fixed-point designs, issues such as numerical instability do arise. We address these concerns and also propose a diagnostic tool based on cross validation prediction error to improve the performance of sequential designs. We are also interested in the design of computer experiments where there are control variables and environmental (noise) variables. We extend the implementation of the proposed sequential designs to achieve a good fit of the unknown integrated response surface (i.e., the averaged response surface taken over the distributions of the environmental variables) using output from the simulations. The goal is to find an optimal choice of the control variables while taking into account the distributions of the noise variables.
A scalable framework for discovering coherent co-clusters in noisy data
Authors: Meghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho, Inderjit Dhillon
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data analysis only a subset of the genes show cohesive expressions within a subset of the conditions/features. The existence of a large number of non-informative data points and features makes it challenging to hunt for coherent and meaningful clusters from such datasets. Additionally, since clusters could exist in different subspaces of the feature space, a co-clustering algorithm that simultaneously clusters objects and features is often more suitable as compared to one that is restricted to traditional "one-sided" clustering. We propose Robust Overlapping Co-Clustering (ROCC), a scalable and very versatile framework that addresses the problem of efficiently mining dense, arbitrarily positioned, possibly overlapping co-clusters from large, noisy datasets. ROCC has several desirable properties that make it extremely well suited to a number of real life applications.
Ontology-Based Network Management: Study Cases and Lessons Learned
Authors: Jorge E. López De Vergara, Antonio Guerrero, Víctor A. Villagrá, Julio Berrocal
Venue: Journal of Network and Systems Management (2009)

Ontology based network management has recently evolved from a theoretical proposal to a more mature technology. As such, it is now being applied in many research projects in a number of different network management and security scenarios. This application has enabled the validation of the main ideas of the proposals and to learn some of the problems that it brings. This paper describes several research projects where ontology based network management proposals were applied, detailing the most important facets of the initial proposals that were used and explaining the main advantages and drawbacks that were found after prototyping these proposals.
PDE-Driven Adaptive Morphology for Matrix Fields
Authors: Bernhard Burgeth, Michael Breuß, Luis Pizarro, Joachim Weickert
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

Matrix fields are important in many applications since they are the adequate means to describe anisotropic behaviour in image processing models and physical measurements. A prominent example is diffusion tensor magnetic resonance imaging (DT-MRI) which is a medical imaging technique useful for analysing the fibre structure in the brain. Recently, morphological partial differential equations (PDEs) for dilation and erosion known for grey scale images have been extended to three dimensional fields of symmetric positive definite matrices.In this article we propose a novel method to incorporate adaptivity into the matrix-valued, PDE-driven dilation process. The approach uses a structure tensor concept for matrix data to steer anisotropic morphological evolution in a way that enhances and completes line-like structures in matrix fields. Numerical experiments performed on synthetic and real-world data confirm the gap-closing and line-completing qualities of the proposed method.
Tactical Modularity in Cognitive Systems
Authors: Cecilio Angulo, Ricardo A. Téllez, Diego E. Pardo
Venue: Proceeding of the 2007 conference on Artificial Intelligence Research and Development (2007)

Novel theories have recently been proposed to try to explain higher cognitive functions as internal representations of action and perception of an embodied autonomous agent in a situated environment. A new paradigm based on tactical modularity have been developed using neural evolutionary robotics in the form of modules, called intelligent hardware units (IHUs). The new concept of a collaborative control architecture allows the construction of a behavior-based system as a result of interactions between the control system and both the external and internal environments generated by the modules. The full separation achieved between the inner world of the autonomous agent and the real external world gives some insight into how the mind-body problem can be solved. An experiment on a two-sensor, two-motor simulated robot orbiting around an object illustrates the performance of the proposed paradigm and lead to discussion of concepts in the robot's inner world.
Location-based Services using Image Search
Authors: Pieter-Paulus Vertongen, Dan Witzner Hansen
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

Recent developments in image search has made them sufficiently efficient to be used in real-time applications. GPS has become a popular navigation tool. While GPS information provide reasonably good accuracy, they are not always present in all hand held devices nor are they accurate in all situations, for example in urban environments. We propose a system to provide location-based services using image searches without requiring GPS. The goal of this system is to assist tourists in cities with additional information using their mobile phones and built-in cameras. Based upon the result of the image search engine and database image location knowledge, the location is determined of the query image and associated data can be presented to the user.
Automatic speech segmentation using the Arabic phonetic database
Authors: Mohammed A. Al-Manie, Mohammed I. Alkanhal, Mansour M. Al-Ghamdi
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

In this paper, a special algorithm is developed to accomplish segmentation for the Arabic speech based on energy level of the uttered words or sentences. In Arabic language, phonemes can be divided into two energy regions: unvoiced phonemes are categorized as low energy, for example, the sounds / ??? / (/s/) and / ??? / (/h/); On the other hand, vowels and semi-vowels such as / ??? / (???) (/ 'a /) and / ??? / (/w/) are categorized as high energy. Also included in this category are voiced fricatives; for instance the sounds / ??? / (/z/) and / ??? / (/aa/). The Arabic Phonetic Database (KAPD) developed by the Computer and Electronics Research Institute at KACST is utilized to perform the automatic segmentation of speech and test the developed algorithm.
Portrait Identification in Digitized Paintings on the Basis of a Face Detection System
Authors: Christos-Nikolaos Anagnostopoulos, Ioannis Anagnostopoulos, I. Maglogiannis, D. Vergados
Venue: Proceeding of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies (2007)

In this paper, the problem of automatic identification of portraits in paintings collections is addressed. A face detection approach in digital images is now implemented n digitized paintings, which is based on fuzzy logic rules especially set for detecting possible skin areas in color images. The candidate regions are then forwarded in a Probabilistic Neural Network (PNN), which is properly trained for face identification. The test sample for assessing the proposed method consists of 200 digitized paintings downloaded from the website of the State Hermitage Museum. The overall performance of the system reached 88.8%.
In-House Programming Is Not Passé: Automating Originality
Authors: Kai A. Olsen
Venue: Computer (2009)

By using IT extensively, automating whenever possible, and offering users power tools to handle complex jobs efficiently, companies can gain a strategic advantage.
Exponential stability of switched stochastic delay systems with non-linear uncertainties
Authors: Jun Liu, Xinzhi Liu, Wei-Chau Xie
Venue: International Journal of Systems Science (2009)

This article considers the robust exponential stability of uncertain switched stochastic systems with time-delay. Both almost sure (sample) stability and stability in mean square are investigated. Based on Lyapunov functional methods and linear matrix inequality techniques, new criteria for exponential robust stability of switched stochastic delay systems with non-linear uncertainties are derived in terms of linear matrix inequalities and average dwell-time conditions. Numerical examples are also given to illustrate the results.
Addressing Temporal Aspects of Privacy-Related Norms
Authors: Guillaume Piolle, Yves Demazeau
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Agents interacting in open environments such as Internet are often in charge of personal information. In order to protect the privacy of human users, such agents have to be aware of the normative context regarding personal data protection (applicable laws and other regulations). These privacy-related norms usually refer to deadlines and durations. To represent these regulations, we introduce the Deontic Logic for Privacy; this logic represents privacy-related obligations while providing the required temporal expressiveness.
A class of multi-objective expected value decision-making model with birandom coefficients and its application to flow shop scheduling problem
Authors: Jiuping Xu, Xiaoyang Zhou
Venue: Information Sciences: an International Journal (2009)

In this paper, we establish multi-objective decision-making models with birandom coefficients for the flow shop scheduling problem. Furthermore, we introduce the general multi-objective decision-making model under a birandom environment, and transform the birandom uncertain model into a deterministic model through an expected value operator, and some properties of the expected value model are also researched. We extend some theories of birandom variables, and especially devote ourselves to researching the expected value of two kinds of birandom variables. The expected value goal model is also proposed. In order to compute the expected value of birandom variables, birandom simulation is presented. Combined with the genetic algorithm, it is applied in dealing with objective functions and constraint functions, and to obtain the optimal solution. Finally, an application to flow shop scheduling at GEELY Haoqing automatic company is provided as an illustration.
Technical support dialog systems: issues, problems, and solutions
Authors: Kate Acomb, Jonathan Bloom, Krishna Dayanidhi, Phillip Hunter, Peter Krogh, Esther Levin, Roberto Pieraccini
Venue: Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies (2007)

The goal of this paper is to give a description of the state of the art, the issues, the problems, and the solutions related to industrial dialog systems for the automation of technical support. After a general description of the evolution of the spoken dialog industry, and the challenges in the development of technical support applications, we will discuss two specific problems through a series of experimental results. The first problem is the identification of the call reason, or symptom, from loosely constrained user utterances. The second is the use of data for the experimental optimization of the Voice User Interface (VUI).
Learning patterns in the dynamics of biological networks
Authors: Chang hun You, Lawrence B. Holder, Diane J. Cook
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic networks is important not only to understand life at the system-level, but also to discover novel patterns in other structural data. Most current graph-based data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. Our approach analyzes a sequence of graphs and discovers rules that capture the changes that occur between pairs of graphs in the sequence. These rules represent the graph rewrite rules that the first graph must go through to be isomorphic to the second graph. Then, our approach feeds the graph rewrite rules into a machine learning system that learns general transformation rules describing the types of changes that occur for a class of dynamic biological networks. The discovered graph-rewriting rules show how biological networks change over time, and the transformation rules show the repeated patterns in the structural changes. In this paper, we apply our approach to biological networks to evaluate our approach and to understand how the biosystems change over time. We evaluate our results using coverage and prediction metrics, and compare to biological literature.