ppxscal/embedding_network huggingface dataset:
Reliable Evaluation of Multimodal Dialogue Systems
Authors: Florian Metze, Ina Wechsung, Stefan Schaffer, Julia Seebode, Sebastian Möller
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques (2009)

Usability evaluation is an indispensable issue during the development of new interfaces and interaction paradigms [1]. Although a wide range of reliable usability evaluation methods exists for graphical user interfaces, mature methods are rarely available for speech-based interfaces [2]. When it comes to multimodal interfaces, no standardized approach has so far been established. In previous studies [3], it was shown that usability questionnaires initially developed for unimodal systems may lead to unreliable results when applied to multimodal systems. In the current study, we therefore used several data sources (direct and indirect measurements) to evaluate two unimodal versions and one multimodal version of an information system. We investigated, to which extent the different data showed concordance for the three system versions. The aim was to examine, if, and under which conditions, common and widely used methods originally developed for graphical user interfaces are also appropriate for speech-based and multimodal intelligent interfaces.
Transfer learning via dimensionality reduction
Authors: Sinno Jialin Pan, James T. Kwok, Qiang Yang
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this paper, we consider transfer learning via dimensionality reduction. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source domain data and the target domain data are the same or close to each other. Onto this latent feature space, we project the data in related domains where we can apply standard learning algorithms to train classification or regression models. Thus, the latent feature space can be treated as a bridge of transferring knowledge from the source domain to the target domain. The main contribution of our work is that we propose a new dimensionality reduction method to find a latent space, which minimizes the distance between distributions of the data in different domains in a latent space. The effectiveness of our approach to transfer learning is verified by experiments in two real world applications: indoor WiFi localization and binary text classification.
A multi-objective GA method for generating Pareto solutions for coordinated design of PSS and TCSC
Authors: Sidhartha Panda, Narayana Prasad Padhy, R. N. Patel
Venue: International Journal of Intelligent Systems Technologies and Applications (2009)

This paper presents a Multi-objective Genetic Algorithm (MGA) approach to generate Pareto solution set for coordinated design of a Power System Stabiliser (PSS) and a Flexible AC Transmission System (FACTS) controller. The design objective is minimisation of both angle and voltage time trajectory deviations with respect to a post-contingency equilibrium point for a power system installed with a PSS and a FACTS controller. The optimal controller parameters are coordinately determined in a generator-infinite-bus test system. Simulation results are presented to show the effectiveness of the proposed approach in damping the power system oscillations and improving the system voltage profile.
Optimizing Maintenance of Constraint-Based Database Caches
Authors: Joachim Klein, Susanne Braun
Venue: Proceedings of the 13th East European Conference on Advances in Databases and Information Systems (2009)

Caching data reduces user-perceived latency and often enhances availability in case of server crashes or network failures. DB caching aims at local processing of declarative queries in a DBMS-managed cache close to the application. Query evaluation must produce the same results as if done at the remote database backend, which implies that all data records needed to process such a query must be present and controlled by the cache, i. e., to achieve "predicate-specific" loading and unloading of such record sets. Hence, cache maintenance must be based on cache constraints such that "predicate completeness" of the caching units currently present can be guaranteed at any point in time. We explore how cache groups can be maintained to provide the data currently needed. Moreover, we design and optimize loading and unloading algorithms for sets of records keeping the caching units complete, before we empirically identify the costs involved in cache maintenance.
A Network Coding Approach to Reliable Broadcast in Wireless Mesh Networks
Authors: Zhenyu Yang, Ming Li, Wenjing Lou
Venue: Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (2009)

Reliable broadcast is an important primitive in wireless mesh networks (WMNs) for applications such as software upgrade, video downloading, etc. However, due to the lossy nature of wireless link, it is not trivial to achieve the reliability and efficiency at the same time. In this paper, we put forward R-Code, a reliable and efficient broadcast protocol based on intra-flow network coding. The key idea is to construct a minimum spanning tree as a backbone whose link weight is based on ETX metric. The broadcast overhead and delay are simultaneously reduced by enabling each node to be covered by the parent node in the tree which promise its reliable reception of the whole file. Opportunistic overhearing is utilized to further reduce the number of transmissions. Extensive simulation results show that R-Code always achieves 100% packet delivery ratio (PDR), while introducing less broadcast overhead and much shorter delay than AdapCode.
Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information
Authors: Shian-Jong Chuu
Venue: Computers and Industrial Engineering (2009)

Selection of advanced manufacturing technology in manufacturing system management is very important to determining manufacturing system competitiveness. This research develops a fuzzy multiple attribute decision-making applied in the group decision-making to improving advanced manufacturing technology selection process. Since numerous attributes have been considered in evaluating the manufacturing technology suitability, most information available in this stage is subjective, imprecise and vague, fuzzy sets theory provides a mathematical framework for modeling imprecision and vagueness. In the proposed approach, a new fusion method of fuzzy information is developed to managing information assessed in different linguistic scales (multi-granularity linguistic term sets) and numerical scales. The flexible manufacturing system adopted in the Taiwanese bicycle industry is employed in this study to demonstrate the computational process of the proposed method. Finally, sensitivity analysis can be performed to examine that the solution robustness.
Diagnostics for skew-normal nonlinear regression models with AR(1) errors
Authors: Feng-Chang Xie, Jin-Guan Lin, Bo-Cheng Wei
Venue: Computational Statistics Data Analysis (2009)

In this article, we consider some diagnostics for skew-normal nonlinear regression models with AR(1) errors which provide a useful extension of the normal regression models. The estimation of the parameters in the models is studied based on the EM algorithm. Meanwhile, several score tests are presented for testing the homogeneity of the scale parameter and/or significance of autocorrelation in skew-normal nonlinear regression models. The properties of score tests are investigated through Monte Carlo simulations. The test methods are illustrated with two numerical examples.
Liberating Expression: A Freehand Approach to Business Process Modeling
Authors: Nicolas Mangano, Noi Sukaviriya
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

Tools that support business process modeling are designed for experienced users to draw a process with precision and professional appearance. These tools are not conducive to sketching quick business design ideas.This demo proposal presents Inkus, a non-intrusive business process sketching tool which allows freehand sketches of process ideas and slowly brings the users to the required common business vocabulary. Our goal is to help unleash creativity in business designers and enrich the design process with values beyond drawing.
Detecting Denial of Service Attacks in Tor
Authors: Norman Danner, Danny Krizanc, Marc Liberatore
Venue: Financial Cryptography and Data Security: 13th International Conference, FC 2009, Accra Beach, Barbados, February 23-26, 2009. Revised Selected Papers (2009)

Tor is currently one of the more popular systems for anonymizing near real-time communications on the Internet. Recently, Borisov et al. proposed a denial of service based attack on Tor (and related systems) that significantly increases the probability of compromising the anonymity provided. In this paper, we propose an algorithm for detecting such attacks and examine the effectiveness of the obvious approach to evading such detection. We implement a simplified version of the detection algorithm and study whether the attack may be in progress on the current Tor network. Our preliminary measurements indicate that the attack was probably not implemented during the period we observed the network.
Awareness Systems: Advances in Theory, Methodology and Design, 1st edition
Authors: Panos Markopoulos, Boris De Ruyter, Wendy Mackay
Venue: Human-Computer Interaction Series (2009)

People go about their daily tasks with an in-built awareness of their social and physical environments. This awareness helps them to work cooperatively in groups, coordinate actions, and to maintain and enhance social communication with others. Networked technology has extended the reach of people's awareness to remote and distributed groups and provided access to a much larger social network than was previously feasible. Awareness systems that are designed to support people to build up, maintain and make use of these extended networks are attracting the interest of industry, and include buddy lists in instant messaging, sustained audio-video links, active contact lists on mobile phones and so forth. This book contains contributions from leading researchers in the field and looks at the design of awareness systems from theoretical foundations through to empirical studies and design concepts and will be of interest to practitioners and researchers in the field of HCI.
Analysis and Control for a Class of Uncertain Switched Fuzzy Systems
Authors: Jianmei Jiang, Yuequan Yang, Jinhua Zhang, Kun Huang
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

A novel parallel distributed compensation fuzzy controllers and corresponding switching law with state-dependent form are designed for a class of uncertain switched fuzzy systems using parallel distributed compensation method. The model for switched fuzzy systems with uncertainty is briefly represented. Secondly, quadratic asymptotic stability analysis of switched fuzzy systems is given.
Accuracy of admissible heuristic functions in selected planning domains
Authors: Malte Helmert, Robert Mattmüller
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

The efficiency of optimal planning algorithms based on heuristic search crucially depends on the accuracy of the heuristic function used to guide the search. Often, we are interested in domain-independent heuristics for planning. In order to assess the limitations of domain-independent heuristic planning, we analyze the (in)accuracy of common domain-independent planning heuristics in the IPC benchmark domains. For a selection of these domains, we analytically investigate the accuracy of the h+ heuristic, the hm family of heuristics, and certain (additive) pattern database heuristics, compared to the perfect heuristic h*. Whereas h+and additive pattern database heuristics usually return cost estimates proportional to the true cost, non-additive hm and nonadditive pattern-database heuristics can yield results underestimating the true cost by arbitrarily large factors.
A Sequence Mining Method to Predict the Bidding Strategy of Trading Agents
Authors: Vivia Nikolaidou, Pericles A. Mitkas
Venue: Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers (2009)

In this work, we describe the process used in order to predict the bidding strategy of trading agents. This was done in the context of the Reverse TAC, or CAT, game of the Trading Agent Competition. In this game, a set of trading agents, buyers or sellers, are provided by the server and they trade their goods in one of the markets operated by the competing agents. Better knowledge of the strategy of the trading agents will allow a market maker to adapt its incentives and attract more agents to its own market. Our prediction was based on the time series of the traders' past bids, taking into account the variation of each bid compared to its history. The results proved to be of satisfactory accuracy, both in the game's context and when compared to other existing approaches.
The Number of Linearly Independent Vectors in Spectral Databases
Authors: Carlos Sáenz, Begoña Hernández, Coro Alberdi, Santiago Alfonso, José Manuel Diñeiro
Venue: Proceedings of the 16th Scandinavian Conference on Image Analysis (2009)

Linear dependence among spectra in spectral databases affects the eigenvectors obtained from principal component analysis. This affects the values of usual spectral and colorimetric metrics. The effective dependence is proposed as a tool to quantify the maximum number of linearly independent vectors in the database. The results of the proposed algorithm do not depend on the selection of the first seed vector and are consistent with the results based on reduction of the bivariate coefficient of determination.
Adaptive Peer Sampling with Newscast
Authors: Norbert Tölgyesi, Márk Jelasity
Venue: Proceedings of the 15th International Euro-Par Conference on Parallel Processing (2009)

The peer sampling service is a middleware service that provides random samples from a large decentralized network to support gossip-based applications such as multicast, data aggregation and overlay topology management. Lightweight gossip-based implementations of the peer sampling service have been shown to provide good quality random sampling while also being extremely robust to many failure scenarios, including node churn and catastrophic failure. We identify two problems with these approaches. The first problem is related to message drop failures: if a node experiences a higher-than-average message drop rate then the probability of sampling this node in the network will decrease. The second problem is that the application layer at different nodes might request random samples at very different rates which can result in very poor random sampling especially at nodes with high request rates. We propose solutions for both problems. We focus on Newscast, a robust implementation of the peer sampling service. Our solution is based on simple extensions of the protocol and an adaptive self-control mechanism for its parameters, namely--without involving failure detectors--nodes passively monitor local protocol events using them as feedback for a local control loop for self-tuning the protocol parameters. The proposed solution is evaluated by simulation experiments.
Stable loosely-coupled-type algorithm for fluid-structure interaction in blood flow
Authors: Giovanna Guidoboni, Roland Glowinski, Nicola Cavallini, Suncica Canic
Venue: Journal of Computational Physics (2009)

We introduce a novel loosely coupled-type algorithm for fluid-structure interaction between blood flow and thin vascular walls. This algorithm successfully deals with the difficulties associated with the ''added mass effect'', which is known to be the cause of numerical instabilities in fluid-structure interaction problems involving fluid and structure of comparable densities. Our algorithm is based on a time-discretization via operator splitting which is applied, in a novel way, to separate the fluid sub-problem from the structure elastodynamics sub-problem. In contrast with traditional loosely-coupled schemes, no iterations are necessary between the fluid and structure sub-problems; this is due to the fact that our novel splitting strategy uses the ''added mass effect'' to stabilize rather than to destabilize the numerical algorithm. This stabilizing effect is obtained by employing the kinematic lateral boundary condition to establish a tight link between the velocities of the fluid and of the structure in each sub-problem. The stability of the scheme is discussed on a simplified benchmark problem and we use energy arguments to show that the proposed scheme is unconditionally stable. Due to the crucial role played by the kinematic lateral boundary condition, the proposed algorithm is named the ''kinematically coupled scheme''.
Compressed Word Problems in HNN-Extensions and Amalgamated Products
Authors: Niko Haubold, Markus Lohrey
Venue: Proceedings of the Fourth International Computer Science Symposium in Russia on Computer Science - Theory and Applications (2009)

It is shown that the compressed word problem for an HNN-extension ¿H, t |t ¿ 1 a t = φ(a) (a ¿ A) ¿ with A finite is polynomial time Turing-reducible to the compressed word problem for the base group H. An analogous result for amalgamated free products is shown as well.
Bioremediation of Petroleum Polluted Soil by Combination of Rye Grass with Effective Microorganisms
Authors: Jingchun Tang, Xiaowei Niu, Qing Sun, Rugang Wang
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

Bioremediation of petroleum polluted soil using combination of plant ryegrass and effective microbial group was carried out by pot experiment. The results show the degradation rate of 58% using combined treatment of plant and microorganisms after 162 days with enhancing of total petroleum hydrocarbon (TPH) degradation by 17% as compared to the control. The lower pH value and higher enzyme activity in different bioremediation processes compared to blank suggests the effectiveness of combination of effective microorganisms with ryegrass. The fraction analysis indicates a priority of low molecular hydrocarbon degradation in combination of microorganisms and ryegrass, polar fraction degradation in phytoremediation, and aromatic fraction in microbial degradation.
Projected Generalized Procrustes Alignment
Authors: Konstantin Chernoff, Mads Nielsen
Venue: Proceedings of the 21st International Conference on Information Processing in Medical Imaging (2009)

When observing the 3D world through a 2D projection, rigid 3D rotation will result in an apparent deformation not accounted for in traditional shape analysis methodologies, e.g. those based on Generalized Procrustes Alignment and Principal Component Analysis.We propose using a 3D statistical model to infer relative depth to a 2D shape and consequently model the apparent deformation in a Procrustes alignment framework.We test our approach on vertebra shapes and show that it leads to a more compact and generalizable shape model, as well as to improvement in vertebra fracture prediction.
New challenges in CSCL: towards adaptive script support
Authors: Nikol Rummel, Armin Weinberger, Christof Wecker, Frank Fischer, Anne Meier, Eleni Voyiatzaki, George Kahrimanis, Hans Spada, Nikolaos Avouris, Erin Walker, Kenneth R. Koedinger, Carolyn Penstein Rosé, Rohit Kumar, Gahgene Gweon, Yi-Chia Wang, Mahesh Joshi
Venue: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3 (2008)

Scaffolding learners, i.e. helping learners to attain tasks they could not accomplish without support, entails the notion of fading, i.e. reducing the scaffolding for learners to become more and more self-regulated. Fading implies to tailor support for collaboration, such as collaboration scripts, to the particular needs of the specific collaborators. In computer supported collaborative learning (CSCL) settings, support can be designed in a very restrictive and inflexible fashion; at the same time computerized settings open new possibilities for the realization of adaptive support as they enable automation of analysis and feedback mechanisms. In this symposium we present new technical approaches and latest empirical research on possibilities and limitations of adaptive support for learners in CSCL settings.
Improving Personal Credit Scoring with HLVQ-C
Authors: Armando Vieira, João Duarte, Bernardete Ribeiro, Joao Carvalho Neves
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

In this paper we study personal credit scoring using several machine learning algorithms: Multilayer Perceptron, Logistic Regression, Support Vector Machines, AddaboostM1 and Hidden Layer Learning Vector Quantization. The scoring models were tested on a large dataset from a Portuguese bank. Results are benchmarked against traditional methods under consideration for commercial applications. A measure of the usefulness of a scoring model is presented and we show that HLVQ-C is the most accurate model.
Temporal information as top-down context in binocular disparity detection
Authors: Mojtaba Solgi, Juyang Weng
Venue: Proceedings of the 2009 IEEE 8th International Conference on Development and Learning (2009)

Recently, it has been shown that motor initiated context through top-down connections boosts the performance of network models in object recognition applications. Moreover, models of the 6-layer architecture of the laminar cortex have been shown to have computational advantage over single-layer models of the cortex. In this study, we present a temporal model of the laminar cortex that applies expectation feedback signals as top-down temporal context in a binocular network supervised to learn disparities. The work reported here shows that the 6-layer architecture drastically reduced the disparity detection error by as much as 7 times with context enabled. Top-down context reduced the error by a factor of 2 in the same 6-layer architecture. For the first time, an end-to-end model inspired by the 6-layer architecture with emergent binocular representation has reached a sub-pixel accuracy in the challenging problem of binocular disparity detection from natural images. In addition, our model demonstrates biologically-plausible gradually changing topographic maps; the representation of disparity sensitivity changes smoothly along the cortex.
Filtering Global History: Power and Performance Efficient Branch Predictor
Authors: Raid Ayoub, Alex Orailoglu
Venue: Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors (2009)

In this paper we present an Application Customizable Branch Predictor, ACBP, that delivers efficiency in energy savings and performance without compromising prediction accuracy. The idea of our technique is to filter unnecessary global history information within the global history register to minimize the predictor size while maintaining prediction accuracy. We suggest in this work an efficient algorithm to capture the beneficial correlations. A cost-efficient and programmable hardware architecture is presented. Extensive experimental analysis confirms significant improvements in power savings and latency, ranging up to 84% and 30%,respectively.