ppxscal/embedding_network huggingface dataset:
Supporting and tracking collective cognition in wikis
Authors: Palmyre Pierroux, Ingvill Rasmussen, Andreas Lund, Ole Smørdal, Gerry Stahl, Johann Ari Larusson, Richard Alterman
Venue: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3 (2008)

This symposium explores central themes related to the design of specific wiki features to support collective cognition, and presents analyses of wiki use in authentic educational settings. Through the presentation of four different projects, the symposium will take up issues related to the significance of iterative design approaches for aligning social networking technologies with specific contexts, and how these 'tweaks' are in turn generating new activities and emergent forms of collaborative knowledge building. In particular the studies in this symposium all explore different means of representing and tracking group cognition in wikis - tags, chat rooms, social bookmarking, whiteboards, blogs, timelines, and transcripts - based on the shared understanding that fostering awareness of activity at group level can be productive for learning. Methods and findings from these studies have relevance for designing and learning with social networking technologies.
SO_MAD: SensOr Mining for Anomaly Detection in Railway Data
Authors: Julien Rabatel, Sandra Bringay, Pascal Poncelet
Venue: Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects (2009)

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and possible causes of a detected anomaly.
HotWave: creating adaptive tools with dynamic aspect-oriented programming in Java
Authors: Alex Villazón, Walter Binder, Danilo Ansaloni, Philippe Moret
Venue: Proceedings of the eighth international conference on Generative programming and component engineering (2009)

Developing tools for profiling, debugging, testing, and reverse engineering is error-prone, time-consuming, and therefore costly when using low-level techniques, such as bytecode instrumentation. As a solution to these problems, we promote tool development in Java using high-level aspect-oriented programming (AOP). We demonstrate that the use of aspects yields compact tools that are easy to develop and extend. As enabling technology, we rely on HotWave, a new tool for dynamic and comprehensive aspect weaving. HotWave reconciles compatibility with existing virtual machine and AOP technologies. It provides support for runtime adaptation of aspects and reweaving of previously loaded code, as well as the ability to weave aspects into all methods executing in a Java Virtual Machine, including methods in the standard Java class library. HotWave also features a new mechanism for efficiently passing data between advices that are woven into the same method. We demonstrate the benefits of HotWave's distinguishing features with two case studies in the area of profiling.
A Multi-Agent Based Decentralized Algorithm for Social Network Community Mining
Authors: Bo Yang, Jing Huang, Dayou Liu, Jiming Liu
Venue: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining (2009)

Research has shown that many social networks come into being hierarchically based on some basic building blocks called communities, within which the social interactions are very intensive, but between which they are very weak. Network community mining algorithms aim at efficiently and effectively discovering all such communities from a given network. Many related methods have been proposed and applied to different areas including social network analysis, gene network analysis and web clustering engine. Most of the existing methods for mining communities are centralized. In this paper, we present a multi-agent based decentralized algorithm, in which a group of autonomous agents work together to mine a network through a proposed self-aggregation and self-organization mechanism. Thanks to its decentralized feature, our method is potentially suitable for dealing with distributed networks, whose global structures are hard to obtain due to their geographical distributions, decentralized controls or huge sizes. The effectiveness of our method has been tested against different benchmark networks.
A logic of emotions for intelligent agents
Authors: Bas R. Steunebrink, Mehdi Dastani, John-Jules Ch. Meyer
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

This paper formalizes a well-known psychological model of emotions in an agent specification language. This is done by introducing a logical language and its semantics that are used to specify an agent model in terms of mental attitudes including emotions. We show that our formalization renders a number of intuitive and plausible properties of emotions. We also show how this formalization can be used to specify the effect of emotions on an agent's decision making process. Ultimately, the emotions in this model function as heuristics as they constrain an agent's model.
Relations between inflectional and derivation patterns
Authors: Karel Pala, Radek Sedláček, Marek Veber
Venue: Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages (2003)

One of the main goals of this paper is to describe a formal procedure linking inflectional and derivational processes in Czech and to indicate that they can be, if appropriate tools and resources are used, applied to other Slavonic languages. The tools developed at the NLP Laboratory FI MU, have been used, particularly the morphological analyser ajka and the program I_par for processing and maintaining the morphological database.
An Irregular Pyramid for Multi-scale Analysis of Objects and Their Parts
Authors: Martin Drauschke
Venue: Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition (2009)

We present an irregular image pyramid which is derived from multi-scale analysis of segmented watershed regions. Our framework is based on the development of regions in the Gaussian scale-space, which is represented by a region hierarchy graph. Using this structure, we are able to determine geometrically precise borders of our segmented regions using a region focusing. In order to handle the complexity, we select only stable regions and regions resulting from a merging event, which enables us to keep the hierarchical structure of the regions. Using this framework, we are able to detect objects of various scales in an image. Finally, the hierarchical structure is used for describing these detected regions as aggregations of their parts. We investigate the usefulness of the regions for interpreting images showing building facades with parts like windows, balconies or entrances. 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
PISA: Federated Search in P2P Networks with Uncooperative Peers
Authors: Zujie Ren, Lidan Shou, Gang Chen, Chun Chen, Yijun Bei
Venue: Proceedings of the 20th International Conference on Database and Expert Systems Applications (2009)

Recently, federated search in P2P networks has received much attention. Most of the previous work assumed a cooperative environment where each peer can actively participate in information publishing and distributed document indexing. However, little work has addressed the problem of incorporating uncooperative peers, which do not publish their own corpus statistics, into a network. This paper presents a P2P-based federated search framework called PISA which incorporates uncooperative peers as well as the normal ones. In order to address the indexing needs for uncooperative peers, we propose a novel heuristic query-based sampling approach which can obtain high-quality resource descriptions from uncooperative peers at relatively low communication cost. We also propose an effective method called RISE to merge the results returned by uncooperative peers. Our experimental results indicate that PISA can provide quality search results, while utilizing the uncooperative peers at a low cost.
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Authors: Satinder Singh, Diane Litman, Michael Kearns, Marilyn Walker
Venue: Journal of Artificial Intelligence Research (2002)

Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.
A Parallel Language for Embedded Real Time Image Processing
Authors: Duan Zongtao, Zhang Yanni, Duan Zongyuan
Venue: Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 01 (2009)

In this paper, we describe a parallel language C! for embedded real time image processing. C! is an extension for standard C language. It includes two parts: one is standard C for serial programs and the other is an extension for data parallel programs. Task parallelism is a future supporting. Furthermore stream level pipeline is supported in C! to meet computing units' data requirement in time.
The PELA architecture: integrating planning and learning to improve execution
Authors: Sergio Jiménez, Fernando Fernández, Daniel Borrajo
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Building architectures for autonomous rational behavior requires the integration of several AI components, such as planning, learning and execution monitoring. In most cases, the techniques used for planning and learning are tailored to the specific integrated architecture, so they could not be replaced by other equivalent techniques. Also, in order to solve tasks that require lookahead reasoning under uncertainty, these architectures need an accurate domain model to feed the planning component. But the manual definition of these models is a difficult task. In this paper, we propose an architecture that uses off-the-shelf interchangeable planning and learning components to solve tasks that require flexible planning under uncertainty. We show how a relational learning component can be applied to automatically obtain accurate probabilistic action models from executions of plans. These models can be used by any classical planner that handles metric functions, or, alternatively, by any decision theoretic planner. We also show how these components can be integrated to solve tasks continuously, under an online relational learning scheme.
A Model of Integrated Operator-System Separation Assurance and Collision Avoidance
Authors: Steven J. Landry, Amit V. Lagu
Venue: Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009 (2009)

A model for the separation assurance and collision avoidance in air traffic has been developed. The objective of the model is to provide qualitative and quantitative predictions of system behavior with respect to separation assurance and collision avoidance. No such model exists, complicating efforts to understand the impact of adding automation to the current system. The model integrates two concepts. First, the system models at the scope of the human-integrated system, instead of the level of the operator. This follows from the work of Duane McRuer, who found that only at the system level was the human as a control system modelable. Secondly, the system considers the separation assurance and collision avoidance problem as a control problem, where agent (automated and human) actions work to control the system from entering undesirable states. This broadly follows the methodology of system safety. Under this methodology, safety is determined by the ability of the agents in the system to impart control to prevent the system from reaching an unsafe state. The model defines system states, the events and conditions that cause transitions between states, and the control that agents in the system can impart to control those transitions.
An architecture for adaptive algorithmic hybrids
Authors: Nicholas Cassimatis, Magdalena Bugajska, Scott Dugas, Arthi Murugesan, Paul Bello
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

We describe a cognitive architecture for creating more robust intelligent systems by executing hybrids of algorithms based on different computational formalisms. The architecture is motivated by the belief that (1) most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and (2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are very difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme can integrate reactive robotic controllers, logical and probabilistic inference algorithms, frame-based formalisms and sensor-processing algorithms into one system. Existing applications involve human-robot interaction, heterogeneous information retrieval and natural language understanding. Systems built using Polyscheme demonstrate that algorithmic hybrids implemented using a focus of attention can (1) exhibit more characteristics of intelligence than individual computational methods alone and (2) deal with problems that have formerly been beyond the reach of synthetic computational intelligence.
Improving Word Alignment Using Alignment of Deep Structures
Authors: David Mareček
Venue: Proceedings of the 12th International Conference on Text, Speech and Dialogue (2009)

In this paper, we describe differences between a classical word alignment on the surface (word-layer alignment) and an alignment of deep syntactic sentence representations (tectogrammatical alignment). The deep structures we use are dependency trees containing content (autosemantic) words as their nodes. Most of other functional words, such as prepositions, articles, and auxiliary verbs are hidden. We introduce an algorithm which aligns such trees using perceptron-based scoring function. For evaluation purposes, a set of parallel sentences was manually aligned. We show that using statistical word alignment (GIZA++) can improve the tectogrammatical alignment. Surprisingly, we also show that the tectogrammatical alignment can be then used to significantly improve the original word alignment.
DES modeling of DRT: Discrete event system modeling and control of demand responsive transportation systems in real time
Authors: Daniel Yankov
Venue:  (2009)

Demand responsive transportation is a variable route service of passengers or freight from specific origin (s) to destination(s) in response to the request of users. Recent methods for DRT planning and control are based on closed information loop and aim a higher level of service automation, flexibility and efficiency. Advances in the information and communication technologies, such as the Internet, mobile communication devices, GIS, GPS, Intelligent Transportation Systems have led to a significantly complex and highly dynamical decision making environment. In this study DRT systems are modeled as Discrete Event Systems using Finite Automata formalism and DRT real time control is addressed using Supervisory Control Theory. Two application scenarios are considered; the first is based on air- charter service and the second one is a mission critical application based on emergency evacuation problem. The discussed methods should be particularly useful to students and professionals in Informatics and Transportation science field or anyone who may deal in real time transportation planning and control. 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
Graded Computation Tree Logic
Authors: Alessandro Bianco, Fabio Mogavero, Aniello Murano
Venue: Proceedings of the 2009 24th Annual IEEE Symposium on Logic In Computer Science (2009)

In modal logics, graded (world) modalities have been deeply investigated as a useful framework for generalizing standard existential and universal modalities in such a way that they can express statements about a given number of immediately accessible worlds. These modalities have been recently investigated with respect to the mu-calculus, which have provided succinctness, without affecting the satisfiability of the extended logic, i.e., it remains solvable in ExpTime. A natural question that arises is how logics that allow reasoning about paths could be affected by considering graded path modalities. In this paper, we investigate this question in the case of the branching-time temporal logic CTL (GCTL, for short). We prove that, although GCTL is more expressive than CTL, the satisfiability problem for GCTL remains solvable in ExpTime. This result is obtained by exploiting an automata-theoretic approach. In particular, we introduce the class of partitioning alternating Büchi tree automata and show that the emptiness problem for them is ExpTime-Complete. The satisfiability result turns even more interesting as we show that GCTL is exponentially more succinct than graded mu-calculus.
Route planning under uncertainty: the Canadian traveller problem
Authors: Evdokia Nikolova, David R. Karger
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

The Canadian Traveller problem is a stochastic shortest paths problem in which one learns the cost of an edge only when arriving at one of its endpoints. The goal is to find an optimal policy that minimizes the expected cost of travel. The problem is known to be #P-hard. Since there has been no significant progress on approximation algorithms for several decades, we have chosen to seek out special cases for which exact solutions exist, in the hope of demonstrating techniques that could lead to further progress. Applying a mix of techniques from algorithm analysis and the theory of Markov Decision Processes, we provide efficient exact algorithms for directed acyclic graphs and (undirected) graphs of disjoint paths from source to destination with random two-valued edge costs. We also give worst-case performance analysis and experimental data for two natural heuristics.
User Empowerment in Standardization
Authors: Mathijs Soede, Nienke Blijham, Manon Verdonschot
Venue: Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Addressing Diversity. Part I: Held as Part of HCI International 2009 (2009)

With the Mandate 376 on Public Procurement, the European Union aims at supporting inclusion of persons with disabilities. It is felt necessary to prepare the End-Users, i.e. persons with disabilities for participation into the standards processes (especially standardization in ICT) themselves. The background, motivation behind this goal is discussed and a special course is developed for the End-Users and presented in this paper.
GIB: imperfect information in a computationally challenging game
Authors: Matthew L. Ginsberg
Venue: Journal of Artificial Intelligence Research (2001)

This paper investigates the problems arising in the construction of a program to play the game of contract bridge. These problems include both the difficulty of solving the game's perfect information variant, and techniques needed to address the fact that bridge is not, in fact, a perfect information game. GIB, the program being described, involves five separate technical advances: partition search, the practical application of Monte Carlo techniques to realistic problems, a focus on achievable sets to solve problems inherent in the Monte Carlo approach, an extension of alpha-beta pruning from total orders to arbitrary distributive lattices, and the use of squeaky wheel optimization to find approximately optimal solutions to cardplay problems. GIB is currently believed to be of approximately expert caliber, and is currently the strongest computer bridge program in the world.
Structural Change Detection of Time Series Using Sequential Probability Ratio Test
Authors: Katsunori Takeda, Tetsuo Hattori, Izumi Tetsuya, Hiromichi Kawano
Venue: Proceedings of the 2009 International Conference on Biometrics and Kansei Engineering (2009)

Time series analysis is used in various fields such as not only in economics but also in pattern recognition, biometrics, and Kansei engineering field. The problem of predicting time series can be classified into three in a practical sense. The first problem is how to make a model for prediction, that adequately represents the characteristics of the past time series data. The second problem is how to correctly detect the structural change of the time series as soon as possible, when the estimated prediction model does not meet the real data. The third problem is how to quickly find the new prediction model to meet the real data after the structural change. This paper focuses on the second problem and proposes a method based on a probability ratio test that has been used in the field of the quality control. This paper also shows some experimental results comparing with a conventional method, and presents the effectiveness of the proposed method.