ppxscal/embedding_network huggingface dataset:
Resolving Physical Conflicts in Multiagent Systems
Authors: Kornelije Rabuzin, Mirko Malekovic, Mirko Cubrilo
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

During the project we were working on physical conflicts occurred in a multiagent system. In order to resolve the problem many different conflict resolution strategies were examined and many proposed solutions found. In this paper we explore conflicts as well as their causes and possible solutions. Based upon this research we implemented a centralized solution and added a reactive agent in the database in order to avoid conflict situations and efficiently allocate a set of resources.
Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain
Authors: Eneldo Loza Mencía, Johannes Fürnkranz
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

In this paper we applied multilabel classification algorithms to the EUR-Lex database of legal documents of the European Union. On this document collection, we studied three different multilabel classification problems, the largest being the categorization into the EUROVOC concept hierarchy with almost 4000 classes. We evaluated three algorithms: (i) the binary relevance approach which independently trains one classifier per label; (ii) the multiclass multilabel perceptron algorithm, which respects dependencies between the base classifiers; and (iii) the multilabel pairwise perceptron algorithm, which trains one classifier for each pair of labels. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier, which makes them very suitable for large-scale multilabel classification problems. The main challenge we had to face was that the almost 8,000,000 perceptrons that had to be trained in the pairwise setting could no longer be stored in memory. We solve this problem by resorting to the dual representation of the perceptron, which makes the pairwise approach feasible for problems of this size. The results on the EUR-Lex database confirm the good predictive performance of the pairwise approach and demonstrates the feasibility of this approach for large-scale tasks.
Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree
Authors: Fujiang Ao, Yuejin Yan, Jian Huang, Kedi Huang
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Mining maximal frequent itemsets in data streams is more difficult than mining them in static databases for the huge, high-speed and continuous characteristics of data streams. In this paper, we propose a novel one-pass algorithm called FpMFI-DS, which mines all maximal frequent itemsets in Landmark windows or Sliding windows in data streams based on FP-Tree. A new structure of FP-Tree is designed for storing all transactions in Landmark windows or Sliding windows in data streams. To improve the efficiency of the algorithm, a new pruning technique, extension support equivalency pruning (ESEquivPS), is imported to it. The experiments show that our algorithm is efficient and scalable. It is suitable for mining MFIs both in static database and in data streams.
Generating Dynamic Formation Strategies Based on Human Experience and Game Conditions
Authors: John Atkinson, Dario Rojas
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

In this paper, a new approach to automatically generating game strategies based on the game conditions is presented. A game policy is defined and applied by a human coach who establishes the attitude of the team for defending or attacking. A simple neural net model is applied using current and previous game experience to classify the game's parameters so that the new game conditions can be determined so that a robotic team can modify its strategy on the fly. Results of the implemented model for a robotic soccer team are discussed.
Transfer of task representation in reinforcement learning using policy-based proto-value functions
Authors: Eliseo Ferrante, Alessandro Lazaric, Marcello Restelli
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, several studies have addressed the issue of reusing the knowledge acquired in solving previous related tasks by transferring information about policies and value functions. In this paper, we analyze the use of proto-value functions under the transfer learning perspective. Proto-value functions are effective basis functions for the approximation of value functions defined over the graph obtained by a random walk on the environment. The definition of this graph is a key aspect in transfer transfer problems in which both the reward function and the dynamics change. Therefore, we introduce policy-based proto-value functions, which can be obtained by considering the graph generated by a random walk guided by the optimal policy of one of the tasks at hand. We compare the effectiveness of policy-based and standard proto-value functions, on different transfer problems defined on a simple grid-world environment.
Rigorous Runtime Analysis of Inversely Fitness Proportional Mutation Rates
Authors: Christine Zarges
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Artificial Immune Systems (AIS) are an emerging new field of research in Computational Intelligence that are applied to many areas of application, e.g., optimization, anomaly detection and classification. For optimization tasks, the use of hypermutation operators constitutes a common concept in AIS. By now, only little theoretical work has been done in this field. In this paper, we present a detailed theoretical runtime analysis that gives an insight into the dynamics of fitness based hypermutation processes. Two specific mutation rates are considered using a simple immune inspired algorithm. Our main focus lies thereby on the influence of parameters embedded in popular immune inspired hypermutation operators from the literature. Our theoretical findings are accompanied by some empirical results.
A Novel Tactile Device Considering Nail Function for Changing Capability of Tactile Perception
Authors: Yoshihiro Tanaka, Akihito Sano, Mayumi Ito, Hideo Fujimoto
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

Nails are not just an instrument to protect fingers and scratch an object. They improve our tactile sensitivity. In this paper, a novel tactile device considering nail function, which we call a "tactile nail chip," is proposed. This device is mounted on the nail and deforms the nail to change the capability of tactile perception. The effectiveness of the tactile nail chip is supported by results through psychophysical experiments. Then, the effect is discussed through photoelasticity experiment and analysis using a simple nail model. In addition, the tactile nail chip with adjustable function is presented.
Question Answering from Lecture Videos Based on Automatically-Generated Learning Objects
Authors: Stephan Repp, Serge Linckels, Christoph Meinel
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

In the past decade, we have witnessed a dramatic increase in the availability of online academic lecture videos. There are technical problems in the use of recorded lectures for learning: the problem of easy access to the multimedia lecture video content and the problem of finding the semantically appropriate information very quickly. The retrieval of audiovisual lecture recordings is a complex task comprising many objects. In our solution, speech recognition is applied to create a tentative and deficient transcription of the lecture video recordings. The transcription and the words from the power point slides are sufficient to generate semantic metadata serialized in an OWL file. Each video segment (the lecturer is speaking about one power point slide) represent a learning object. A question-answering system based on these learning objects is presented. The annotation process is discussed, evaluated and compared to a perfectly annotated OWL file and, further, to an annotation based on a corrected transcript of the lecture. Furthermore, the consideration of the chronological order of the learning objects leads to a better MRR value. Our approach out-performs the Google Desktop Search based on the question keywords.
Fuzzy Hyperbolic Neural Network Model and Its Application in H ∞ Filter Design
Authors: Shuxian Lun, Zhaozheng Guo, Huaguang Zhang
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

This paper studies H &infin; filter based on a new fuzzy neural model for signal estimation of nonlinear continuous-time systems with time delays. First, a new fuzzy neural model, called fuzzy hyperbolic neural network model (FHNNM), is developed. FHNNM is a combination of the special fuzzy model and the modified BP neural network. The main advantages of using the FHNNM over traditional fuzzy neural network are that explicit expression of expert's experience and global analytical description. In addition, by contrast with fuzzy neural network based T-S fuzzy model, no premise structure identification is need and no completeness design of premise variables space is need. Next, we design a stable H &infin; filter based on the FHNNM using linear matrix inequality (LMI) method. Simulation example is provided to illustrate the design procedure of the proposed method.
Evolving Game NPCs Based on Concurrent Evolutionary Neural Networks
Authors: Xiang Hua Jin, Dong Heon Jang, Tae Yong Kim
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

Evolutionary Artificial Neural Networks (EANNs) has been highly effective in Artificial Intelligence (AI) and in training Non-Player-Characters (NPCs) in video games. An important question in training NPCs in games is how we can choose the appropriate way to make NPCs smart. We focus on (1) choosing a principled method of high dimensional data space, (2) designing adaptive fitness functions which can make the proper evolution. In this work, we describe the Concurrent Evolutionary Neural Networks (CENNs) based on EANNs for competitive team game playing behaviors by teams of virtual football game players. We choose Darwin Platform as our test bed to show its efficiency. The Red team and the Blue team are competing in the soccer field, the field players in Red team are evolved during the virtual game playing. The experimental results show that the Blue team programmed by Rule-Based System leads the evolution successful.
A Fast and Robust Iris Segmentation Method
Authors: Noé Otero-Mateo, Miguel Ángel Vega-Rodríguez, Juan Antonio Gómez-Pulido, Juan Manuel Sánchez-Pérez
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

Image preprocessing stage (also known as iris segmentation) is the first step of the iris recognition process and determines its accuracy. In this paper, we propose a method for iris segmentation. In order to get a robust method we combine several well-know techniques to achieve final result. As some of these techniques are based on intensive searching, therefore slow, we apply our knowledge of the problem (iris image features and iris morphology) to speed up the algorithms by reducing search spaces and discarding information. We present a fast and robust iris segmentation method that successfully works on CASIA 1.0 dataset.
Verification of Java Programs with Generics
Authors: Kurt Stenzel, Holger Grandy, Wolfgang Reif
Venue: Proceedings of the 12th international conference on Algebraic Methodology and Software Technology (2008)

Several proof systems allow the formal verification of Java programs, and a specification language was specifically designed for Java. However, none of these systems support generics that were introduced in Java 5. Generics are very important and useful when the collection framework (lists, sets, hash tables etc.) is used. Though they are mainly dealt with at compile time, they have some effect on the run-time behavior of a Java program. Most notably, heap pollution can cause exceptions. A verification system for Java must incorporate these effects. In this paper we describe what effects can occur at run time, and how they are handled in the KIV system [18] [2]. To the authors knowledge, this makes KIV the first verification system to support Java's generics.
XML Database Transformations with Tree Updates
Authors: Qing Wang, Klaus-Dieter Schewe, Bernhard Thalheim
Venue: Proceedings of the 1st international conference on Abstract State Machines, B and Z (2008)

For many years the eXtensible Markup Language (XML) has attracted much research attention from database communities, particularly in the area of query and transformation languages such as XQuery and XSLT. XML documents are usually represented as trees. In order to accommodate the diversity of user requirements, it is desirable to conduct transformations on XML trees at flexible abstraction levels. However, most of current approaches have a fixed abstraction level at which updates must be identified for individual nodes and edges. In this paper we study XML database transformations with structured updates, for example, manipulations on portions of a tree, including deleting, modifying or inserting subtrees, copying contexts, etc, by using Abstract State Machines (ASMs) as it has turned out in [3] to be a universal computation model capturing database transformations.
Parametric Timing Analysis for Complex Architectures
Authors: Sebastian Altmeyer, Christian Hümbert, Björn Lisper, Reinhard Wilhelm
Venue: Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2008)

Hard real-time systems have stringent timing constraints expressed in units of time. To ensure that a task finishes within its time-frame, the designer of sucha system must be able to derive upper bounds on the task's worst-case execution time (WCET). To compute such upper bounds, timing analyses are used. These analyses require that information such as bounds on the maximum numbers of loop iterations are known statically, i.e. during design time. Parametric timing analysis softens these requirements: it yields symbolic formulas instead of single numeric values representing the upper bound on the task's execution time. In this paper, we present a new parametric timing analysis that is able to derive safe and precise results. Our method determines what the parameters ofthe program are, constructs parametric loop bounds, takes processor behavior into account and attains a formula automatically. In the end, we present tests to show that the precision and runtime of our analysis are very close to those of numeric timing analysis.
Using students as subjects - an empirical evaluation
Authors: Mikael Svahnberg, Aybüke Aurum, Claes Wohlin
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

An important task in Requirements Engineering is to select which requirements that should go into a specific release of a system. This is a complex decision that requires balancing multiple perspectives against each other. In this article we investigate what students imagine is important to professionals in requirements selection. The reason for this is to understand whether the students are able to picture what industry professionals value, and whether the courses allow them to picture the state of industry practice. The results indicate that students have a good understanding of the way industry acts in the context of requirements selection, and students may work well as subjects in empirical studies in this area.
Model Transformation for Model Driven Development of Semantic Web Enabled Multi-Agent Systems
Authors: Geylani Kardas, Arda Goknil, Oguz Dikenelli, N. Yasemin Topaloglu
Venue: Proceedings of the 5th German conference on Multiagent System Technologies (2007)

Model Driven Development (MDD) provides an infrastructure that simplifies Multi-agent System (MAS) development by increasing the abstraction level. In addition to defining models, transformation process for those models is also crucial in MDD. On the other hand, MAS modeling should also take care of emerging requirements of MAS deployment on the Semantic Web environment. Hence, in this paper we propose a model transformation process for MDD of Semantic Web enabled MASs. We first define source and target models for the transformation regarding the modeling of interactions between agents and semantic web services and then grant mappings between these source and model entities to derive transformation rules and constraints. Finally we realize the whole transformation for a real MAS framework by using a well-known model transformation language named ATL.
A new constrained learning algorithm for function approximation by encoding a priori information into feedforward neural networks
Authors: Fei Han, De-Shuang Huang
Venue: Neural Computing and Applications (2008)

In this paper, a new learning algorithm which encodes a priori information into feedforward neural networks is proposed for function approximation problem. The new algorithm considers two kinds of constraints, which are architectural constraints and connection weight constraints, from a priori information of function approximation problem. On one hand, the activation functions of the hidden neurons are specific polynomial functions. On the other hand, the connection weight constraints are obtained from the first-order derivative of the approximated function. The new learning algorithm has been shown by theoretical justifications and experimental results to have better generalization performance and faster convergent rate than other algorithms.
Maximising Personal Utility Using Intelligent Strategy in Minority Game
Authors: Yingni She, Ho-Fung Leung
Venue: Proceedings of the 5th international conference on Autonomic and Trusted Computing (2008)

In the traditional minority game, each agent chooses the highest-score strategy at every time step from its initial strategies which are allocated randomly. How can one agent manage to outperform its competitors and maximise its own utility in this competing and dynamic environment? In this paper, we study a version of the minority game in which one privileged agent is allowed to join the game with larger memory size and free to choose any strategy, while the other agents own small number of strategies. Simulations show that the privileged agent using the intelligent strategy outperforms the other agents in the same model and other models proposed in previous work in terms of individual payoff. We also investigate how the number of strategies and the length of memory affect the privileged agent's performance.
A novel design of a VoIP firewall proxy to mitigate SIP-based flooding attacks
Authors: Isaac Lee, Ray Hunt
Venue: International Journal of Internet Protocol Technology (2008)

This paper proposes a novel method to address the protection necessary to mitigate flooding attacks in VoIP networks which can produce rapid saturation of a firewall and crippling of a VoIP switch. The paper proposes a stateless firewall nonce checking mechanism as an extension to the existing (stateful) SIP digest authentication. This combination aims to form a more secure and flood-resistant authentication scheme for SIP-based VoIP systems. The proposed mechanism has been implemented on a Linux iptables firewall and the experimental results demonstrate proof-of-concept showing that by incorporating this mechanism it is possible to provide substantially improved SIP-based flooding mitigation.
Communicable Knowledge in Automated System Identification
Authors: Reinhard Stolle, Elizabeth Bradley
Venue: Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences (2007)

We describe the program Pret, an engineering tool for nonlinear system identification, which is the task of inferring a (possibly nonlinear) ordinary differential equation model from external observations of a target system's behavior. Pret has several characteristics in common with programs from the fields of machine learning and computational scientific discovery. However, since Pret is intended to be an engineer's tool, it makes different choices with regard to the tradeoff between model accuracy and parsimony. The choice of a good model depends on the engineering task at hand, and Pret is designed to let the user communicate the task-specific modeling constraints to the program. Pret's inputs, its outputs, and its internal knowledge base are instances of communicable knowledge--knowledge that is represented in a form that is meaningful to the domain experts that are the intended users of the program.
Enhancing Speaker Discrimination at the Feature Level
Authors: Jacques Koreman, Dalei Wu, Andrew C. Morris
Venue: Speaker Classification I: Fundamentals, Features, and Methods (2007)

This chapter describes a method for enhancing the differences between speaker classes at the feature level (feature enhancement) in an automatic speaker recognition system. The original Mel-frequency cepstral coefficient (MFCC) space is projected onto a new feature space by a neural network trained on a subset of speakers which is representative for the whole target population. The new feature space better discriminates between the target classes (speakers) than the original feature space. The chapter focuses on the method for selecting a representative subset of speakers, comparing several approaches to speaker selection. The effect of feature enhancement is tested both for clean and various noisy speech types to evaluate its applicability under practical conditions. It is shown that the proposed method leads to a substantial improvement in speaker recognition performance. The method can also be applied to other automatic speaker classification tasks.