ppxscal/embedding_network huggingface dataset:
The intermediary agent's brain: supporting learning to collaborate at the inter-personal level
Authors: Juan Martínez-Miranda, Bernhard Jung, Sabine Payr, Paolo Petta
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

We discuss the design of the Intermediary Agent's brain, the control module of an embodied conversational virtual peer in a simulation game aimed at providing learning experiences regarding the dynamics of collaboration at the inter-personal (IP) level. We derive the overall aims of the game from theoretical foundations in collaboration theory and pedagogical theory and related requirements for the virtual peer; present the overall modular design of the system; and then detail the design perspectives and the interplay of the related operationalised concepts leading to the control architecture of the Intermediary Agent, that is realised as a simple cognitive appraisal process driven by direct and indirect effects of the missionoriented and social interactions of players and agent on the agent's level of trust in its human peers. We conclude with coverage of related work and insights from first deployment experiences.
Enabling SOA Governance for Production Deployed Services
Authors: Sujatha Kuppuraju, Aravind Kumar
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

SOA Governance enables an organization to have a controlled yet flexible service-oriented environment. Most organizations that adopt SOA may already have a large pool of services scattered across applications. These services typically face issues on locating them for reusability, locating them for orchestration, design reusability, ,management, versioning etc., So, One of the first steps for an organization while setting up SOA Governance will be to collect the existing (production deployed) services and catalog them into the service repository and fit them to a normal service lifecycle. This paper details the design of a lifecycle that we have used to collect and catalog production deployed services into the SOA Governance Platform.
A Prospective Algorithm for Real Plane Identification from 3D Point Clouds and 2D Edges
Authors: Jae-Kyu Lee, Seongjin Ahn, Jin Wook Chung
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

We present a prospective algorithm to understand test scene for line base recognition and 3D pose estimation. Given 3D line segments, first, we set up reference plane using principle component analysis (PCA) for each line pair. Then, we measure the normal distance and line orientation for the remains of 3D segments to the reference plane and define coplanarity. And we initiate visibility test to prune out ambiguous planes amongstreference planes. After we finish visibility test, we patching these reference planes and define them as real plane candidates using plane sweep algorithm and find objects in the scene. We apply this algorithm to real images, and the results are found useful to identify table or door especially adjacent to walls.
Ontology-Based Geospatial Data Query and Integration
Authors: Tian Zhao, Chuanrong Zhang, Mingzhen Wei, Zhong-Ren Peng
Venue: Proceedings of the 5th international conference on Geographic Information Science (2008)

Geospatial data sharing is an increasingly important subject as large amount of data is produced by a variety of sources, stored in incompatible formats, and accessible through different GIS applications. Past efforts to enable sharing have produced standardized data format such as GML and data access protocols such as Web Feature Service (WFS). While these standards help enabling client applications to gain access to heterogeneous data stored in different formats from diverse sources, the usability of the access is limited due to the lack of data semantics encoded in the WFS feature types. Past research has used ontology languages to describe the semantics of geospatial data but ontology-based queries cannot be applied directly to legacy data stored in databases or shapefiles, or to feature data in WFS services. This paper presents a method to enable ontology query on spatial data available from WFS services and on data stored in databases. We do not create ontology instances explicitly and thus avoid the problems of data replication. Instead, user queries are rewritten to WFS getFeature requests and SQL queries to database. The method also has the benefits of being able to utilize existing tools of databases, WFS, and GML while enabling query based on ontology semantics.
A Humanized Mandarin e-Learning System Based on Pervasive Computing
Authors: Yue Ming, Zhenjiang Miao, Chen Wang, Xiuna Yang
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

This paper presents the design and implementation of an application system where pervasive computing is used in Mandarin e-Learning. First a short introduction to the ongoing changes concerning pervasive computing and particularly e-Learning is given, followed by an e-learning framework including the application server and enterprise server, which distributes the personalized learning services to the learners so that the learners can obtain the learning resource or personalized learning guide interactively. Its design and implementation issues are discussed in details.
The Effects of an Embodied Conversational Agent's Nonverbal Behavior on User's Evaluation and Behavioral Mimicry
Authors: Nicole C. Krämer, Nina Simons, Stefan Kopp
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

Against the background that recent studies on embodied conversational agents demonstrate the importance of their behavior, an experimental study is presented that assessed the effects of different nonverbal behaviors of an embodied conversational agent on the users´ experiences and evaluations as well as on their behavior. 50 participants conducted a conversation with different versions of the virtual agent Max, whose nonverbal communication was manipulated with regard to eyebrow movements and self-touching gestures. In a 2x2 between subjects design each behavior was varied in two levels: occurrence of the behavior compared to the absence of the behavior. Results show that self-touching gestures compared to no self-touching gestures have positive effects on the experiences and evaluations of the user, whereas eyebrow raising evoked less positive experiences and evaluations in contrast to no eyebrow raising. The nonverbal behavior of the participants was not affected by the agent's nonverbal behavior.
Online Optimization for Latency Assignment in Distributed Real-Time Systems
Authors: Cristian Lumezanu, Sumeer Bhola, Mark Astley
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

As distributed real-time applications gain in popularity, a key challenge is to allocate resources so that diverse real-time requirements (including non-real-time applications), distributed application components and varying workloads can all be accommodated without violating timeliness constraints. We examine the problem of resource allocation in distributed soft real-time systems, where both network and CPU resources are consumed. The timeliness constraints of applications are expressed through utility functions, which compute "benefit" as a function of end-to-end latency. We present LLA (Lagrangian Latency Assignment), a scalable and efficient distributed algorithm which maximizes aggregate utility by computing an optimal trade-off between end-to-end latency and allocated resources. The algorithm runs continuously and adapts to both workload and resource variations. LLA is guaranteed to converge if the workload and resource requirements stabilize. We evaluate the quality of results and convergence characteristics under various workloads, using both simulation and real-world experimentation.
Real-Time Emotion Recognition from Speech Using Echo State Networks
Authors: Stefan Scherer, Mohamed Oubbati, Friedhelm Schwenker, Günther Palm
Venue: Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition (2008)

The goal of this work is to investigate real-time emotion recognition in noisy environments. Our approach is to solve this problem using novel recurrent neural networks called echo state networks (ESN). ESNs utilizing the sequential characteristics of biologically motivated modulation spectrum features are easy to train and robust towards noisy real world conditions. The standard Berlin Database of Emotional Speech is used to evaluate the performance of the proposed approach. The experiments reveal promising results overcoming known difficulties and drawbacks of common approaches.
UpgradeJ: Incremental Typechecking for Class Upgrades
Authors: Gavin Bierman, Matthew Parkinson, James Noble
Venue: Proceedings of the 22nd European conference on Object-Oriented Programming (2008)

One of the problems facing developers is the constant evolution of components that are used to build applications. This evolution is typical of any multi-person or multi-site software project. How can we program in this environment? More precisely, how can language design address such evolution? In this paper we attack two significant issues that arise from constant component evolution: we propose language-level extensions that permit multiple, co-existing versions of classes and the ability to dynamically upgrade from one version of a class to another, whilst still maintaining type safety guarantees and requiring only lightweight extensions to the runtime infrastructure. We show how our extensions, whilst intuitive, provide a great deal of power by giving a number of examples. Given the subtlety of the problem, we formalize a core fragment of our language and prove a number of important safety properties.
Ynot: dependent types for imperative programs
Authors: Aleksandar Nanevski, Greg Morrisett, Avraham Shinnar, Paul Govereau, Lars Birkedal
Venue: Proceeding of the 13th ACM SIGPLAN international conference on Functional programming (2008)

We describe an axiomatic extension to the Coq proof assistant, that supports writing, reasoning about, and extracting higher-order, dependently-typed programs with side-effects. Coq already includes a powerful functional language that supports dependent types, but that language is limited to pure, total functions. The key contribution of our extension, which we call Ynot, is the added support for computations that may have effects such as non-termination, accessing a mutable store, and throwing/catching exceptions. The axioms of Ynot form a small trusted computing base which has been formally justified in our previous work on Hoare Type Theory (HTT). We show how these axioms can be combined with the powerful type and abstraction mechanisms of Coq to build higher-level reasoning mechanisms which in turn can be used to build realistic, verified software components. To substantiate this claim, we describe here a representative series of modules that implement imperative finite maps, including support for a higher-order (effectful) iterator. The implementations range from simple (e.g., association lists) to complex (e.g., hash tables) but share a common interface which abstracts the implementation details and ensures that the modules properly implement the finite map abstraction.
Open&thinsp;|&thinsp;SpeedShop: An open source infrastructure for parallel performance analysis
Authors: Martin Schulz, Jim Galarowicz, Don Maghrak, William Hachfeld, David Montoya, Scott Cranford
Venue: Scientific Programming (2008)

Over the last decades a large number of performance tools has been developed to analyze and optimize high performance applications. Their acceptance by end users, however, has been slow: each tool alone is often limited in scope and comes with widely varying interfaces and workflow constraints, requiring different changes in the often complex build and execution infrastructure of the target application. We started the Open&thinsp;|&thinsp;SpeedShop project about 3 years ago to overcome these limitations and provide efficient, easy to apply, and integrated performance analysis for parallel systems. Open&thinsp;|&thinsp;SpeedShop has two different faces: it provides an interoperable tool set covering the most common analysis steps as well as a comprehensive plugin infrastructure for building new tools. In both cases, the tools can be deployed to large scale parallel applications using DPCL/Dyninst for distributed binary instrumentation. Further, all tools developed within or on top of Open&thinsp;|&thinsp;SpeedShop are accessible through multiple fully equivalent interfaces including an easy-to-use GUI as well as an interactive command line interface reducing the usage threshold for those tools.
Analyze Me: Open Learner Model in an Adaptive Web Testing System
Authors: Fotis Lazarinis, Symeon Retalis
Venue: International Journal of Artificial Intelligence in Education (2007)

This paper presents the open learner modelling capabilities offered by an adaptive web testing tool. Adaptation decisions are based on user customizable compound rules requiring the identification of several learner characteristics which are then displayed graphically and textually to learners and educators. The open learner modelling capabilities include anonymous access to models of peers and restricted named access to friend peers. Opening the learner profiles supports learner and educator involvement in decision making so as to create a more credible learner model. The educational advantages of the open learner modelling capabilities are discussed on the basis of an evaluative study.
Estimation of Value-at-Risk for Exchange Risk Via Kernel Based Nonlinear Ensembled Multi Scale Model
Authors: Kaijian He, Chi Xie, Kinkeung Lai
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

Risk level in the exchange rate market is dynamically evolving with complicated structures. To further refine the analysis process and achieve more accurate measurement, this paper proposes a novel kernel based nonlinear ensembled multi scale Value at Risk methodology for evaluating the risk level in the exchange rate market. In the proposed algorithm, wavelet analysis is introduced to analyze the multi scale heterogeneous risk structures across different time scales. The Principle Component Analysis is used to extract principle components from the redundant forecast matrixes. Then the support vector regression technique is integrated into the modeling process to nonlinearly ensemble forecast matrixes and produce more stable and accurate results. Taking Euro market as a typical test case, empirical studies employing the proposed algorithm shows the superior performance than benchmark ARMA-GARCH and realized volatility based approaches.
Managing trust, generating risk: Incorporating the voluntary sector in UK e-Government
Authors: John Taylor, Eleanor Burt
Venue: Information Polity (2005)

As the UK government seeks to (re)-establish a high-trust relationship with citizens, voluntary sector organisations (VSOs) are being incorporated within the state in ways that are designed to shift blame and risk away from government. This is not a straightforward process: as the government seeks to manage trust relationships through this engagement with VSOs, significant new risks are being generated for e-government. In conceptualising this emerging relationship between government and VSOs as concerned with trust, blame, and risk, this essay brings fresh perspective to the deepening debate about how government and voluntary sector are re-positioning their relationship.
A Grid-Based Multi-relational Approach to Process Mining
Authors: Antonio Turi, Annalisa Appice, Michelangelo Ceci, Donato Malerba
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

Industrial, scientific, and commercial applications use information systems to trace the execution of a business process. Relevant events are registered in massive logs and process mining techniques are used to automatically discover knowledge that reveals the execution and organization of the process instances (cases). In this paper, we investigate the use of a multi-level relational frequent pattern discovery method as a means of process mining. In order to process such massive logs we resort to a Grid-based implementation of the knowledge discovery algorithm that distributes the computation on several nodes of a Grid platform. Experiments are performed on real event logs.
Average Binary Long-Lived Consensus: Quantifying the Stabilizing Role Played by Memory
Authors: Florent Becker, Sergio Rajsbaum, Ivan Rapaport, Éric Rémila
Venue: Proceedings of the 15th international colloquium on Structural Information and Communication Complexity (2008)

Consider a system composed of n sensors operating in synchronous rounds. In each round an input vector of sensor readings x is produced, where the i-th entry of x is a binary value produced by the i-th sensor. The sequence of input vectors is assumed to be smooth: exactly one entry of the vector changes from one round to the next one. The system implements a fault-tolerant averaging consensus function f. This function returns, in each round, a representative output value v of the sensor readings x. Assuming that at most t entries of the vector can be erroneous, f is required to return a value that appears at least t + 1 times in x. The instability of the system is the number of output changes over a random sequence of input vectors.Our first result is to design optimal instability consensus systems with and without memory. Roughly, in the memoryless case, we show that an optimal system is D 0, that outputs 1 unless it is forced by the fault-tolerance requirement to output 0 (on vectors with t or less 1's). For the case of systems with memory, we show that an optimal system is D 1, that initially outputs the most common value in the input vector, and then stays with this output unless forced by the fault-tolerance requirement to change (i.e., a single bit of memory suffices).Our second result is to quantify the gain factor due to memory by computing c n (t), the number of decision changes performed by D 0 per each decision change performed by D 1. If $t=\frac{n}{2}$ the system is always forced to decide the simple majority and, in that case, memory becomes useless. We show that the same type of phenomenon occurs when $\frac{n}{2}-t$ is constant. Nevertheless, as soon as $\frac{n}{2}-t \sim \sqrt{n}$, memory plays an important stabilizing role because the ratio c n (t) grows like $\Theta(\sqrt{n})$. We also show that this is an upper bound: $c_n(t)=O(\sqrt{n})$ for every t.Our results are average case versions of previous works where the sequence of input vectors was assumed to be, in addition to smooth, geodesic: the i-th entry of the input vector was allowed to change at most once over the sequence. It thus eliminates some anomalies that ocurred in the worst case, geodesic instability setting.
The Radial Sweep Algorithm for Constructing Triangulated Irregular Networks
Authors: A. Mirante
Venue: IEEE Computer Graphics and Applications (1982)

For some applications, triangulated irregular networks are superior to rectangular grids in modeling complex surfaces. The radial sweep algorithm is an improvement of TIN techniques.
A Prototype System for Educational Data Warehousing and Mining
Authors: Nikolaos Dimokas, Nikolaos Mittas, Alexandros Nanopoulos, Lefteris Angelis
Venue: Proceedings of the 2008 Panhellenic Conference on Informatics (2008)

Universities are encountering growing demands by legislators and communities who are clamoring for valuable information about student achievement and university system accountability. Not only are universities required to measure annual progress for every single student, but government (through ministries for education) aid is directly linked to these results. The department of Informatics of Aristotle university of Thessaloniki has developed a data warehouse solution that assists the analysis of educational data. In this paper we present the design and development of the proposed data warehouse solution, which facilitates better and more thorough analysis of department’s data. The proposed system constitutes an integrated platform for a thorough analysis of department’s past data. Analysis of data could be achieved with OLAP operations. Moreover, we propose a thorough statistical analysis with an array of data mining techniques, that are appropriate for the examined tasks.
Achieving self-managed deployment in a distributed environment via utility functions
Authors: John Paxton
Venue:  (2008)

This dissertation presents algorithms and mechanisms that enable self-managed, scalable and efficient deployment of large-scale scientific and engineering applications in a highly dynamic and unpredictable distributed environment. Typically these applications are composed of a large number of distributed components and it is important to meet the computational power and network bandwidth requirements of those components and their interactions. However satisfying these requirements in a large-scale, shared, heterogeneous, and highly dynamic distributed environment is a significant challenge. As systems and applications grow in scale and complexity, attaining the desired level of performance in this uncertain environment using current approaches based on global knowledge, centralized scheduling and manual reallocation becomes infeasible. This dissertation focuses on the modeling of the application and underlying architecture into a common abstraction and on the incorporations of autonomic features into those abstractions to achieve self-managed deployment. In particular, we developed techniques for automatically identifying application components and their estimated resource requirements within an application and used them in order to model the application into a graph abstraction. We also developed techniques that allow the distributed resources to self-organize in a utility-aware way while assuming minimal knowledge about the system. Finally, to achieve self-managed deployment of application components to the distributed nodes, we designed a scalable and adaptive scheduling algorithm which is governed by a utility function. The utility function, which combines several application and system level attributes, governs both the initial deployment of the application components and their reconfigurations despite the dynamism and uncertainty associated with the computing environment. The experimental results show that it is possible to achieve and maintain efficient deployment by applying the utility function derived in this paper based solely on locally available information and without costly global communication or synchronization. The self-management is therefore decentralized and provides better adaptability, scalability and robustness.