ppxscal/embedding_network huggingface dataset:
Mixed Realities --- Virtual Object Lessons
Authors: Andreas Kratky
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction (2009)

The question of how to design and implement efficient virtual classroom environments gains a new quality in the light of extensive digital education projects such as the One Laptop Per Child (OLPC) initiative. At the core of this consideration is not only the task of developing content for very different cultural settings but also the necessity to reflect the effects of learning processes that operate exclusively with digitally mediated content. This paper traces the design of the project Venture to the Interior, an interactive experience that presents selected objects from the collections of the Museum of Natural History in Berlin and reflects them as building blocks for the Enlightenment-idea of a building of knowledge. The project investigates the role of objects as a knowledge device and the possibilities for a translation of the didactic effects of experiential learning into virtual environments.
Trdlo, an Open Source Tool for Building Transducing Dictionary
Authors: Marek Grác
Venue: Proceedings of the 12th International Conference on Text, Speech and Dialogue (2009)

This paper describes the development of an open-source tool named Trdlo. Trdlo was developed as part of our effort to build a machine translation system between very close languages. These languages usually do not have available pre-processed linguistic resources or dictionaries suitable for computer processing. Bilingual dictionaries have a big impact on quality of translation. Proposed methods described in this paper attempt to extend existing dictionaries with inferable translation pairs. Our approach requires only `cheap' resources: a list of lemmata for each language and rules for inferring words from one language to another. It is also possible to use other resources like annotated corpora or Wikipedia. Results show that this approach greatly improves effectivity of building Czech-Slovak dictionary. 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
Human Age Estimation by Metric Learning for Regression Problems
Authors: Yangjing Long
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

The estimation of human age from face images is an interesting problem in computer vision. We proposed a general distance metric learning scheme for regression problems, which utilizes not only data themselves, but also their corresponding labels to strengthen the credibility of distances. This metric could be learned by solving an optimization problem. Furthermore, the test data could be projected to this metric by a simple linear transformation and it is feasible to be combined with manifold learning algorithms to improve their performance. Experiments are conducted on the public FG-NET database by Gaussian process regression in the learned metric to validate our framework, which shows that the performance is improved over traditional methods.
Mining spectrum usage data: a large-scale spectrum measurement study
Authors: Dawei Chen, Sixing Yin, Qian Zhang, Mingyan Liu, Shufang Li
Venue: Proceedings of the 15th annual international conference on Mobile computing and networking (2009)

Dynamic spectrum access has been a subject of extensive research activity in recent years. The increasing volume of literature calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper we present a detailed spectrum measurement study, with data collected in the 20MHz to 3GHz spectrum band and at four locations concurrently in South China. We examine the first and second order statistics of the collected data, including channel occupancy/vacancy statistics, channel utilization within each individual wireless service, and the temporal, spectral, and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2-dimensional frequent pattern mining algorithm that can accurately predict channel availability based on past observations.
Integrating morphology with multi-word expression processing in Turkish
Authors: Kemal Oflazer, Özlem çetinoğlu, Bilge Say
Venue: Proceedings of the Workshop on Multiword Expressions: Integrating Processing (2004)

This paper describes a multi-word expression processor for preprocessing Turkish text for various language engineering applications. In addition to the fairly standard set of lexicalized collocations and multi-word expressions such as named-entities, Turkish uses a quite wide range of semi-lexicalized and non-lexicalized collocations. After an overview of relevant aspects of Turkish, we present a description of the multi-word expressions we handle. We then summarize the computational setting in which we employ a series of components for tokenization, morphological analysis, and multi-word expression extraction. We finally present results from runs over a large corpus and a small gold-standard corpus.
Application of Wavelet Transform in Vehicle Wheel Speed Signal Denoising
Authors: Hai Wang, Weigong Zhang
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

Wheel speed is a critical parameter in vehicle roadway test. However, because of the internal defects of the wheel speed sensor structure and some electromagnetic inference, this signal is inevitably influenced by kinds of awful noises during the measurement process. These wide band stochastic noises badly influence the analysis of vehicle performance. It is difficult to eliminate these noises with traditional way based on Fourier transform. Wavelet transform is an analytical method of time-frequency, which is especially suitable for the analysis of non-stable signals. This method contains a characteristic of multi-resolution analysis as well as the ability of identifying partial characteristics of signals in time-frequency domain. According to different characteristics of signals and noises at every scale spaces after wavelet transform, the method of soft-threshold is applied to eliminate the noises in this paper. The result suggests this method is effective in wheel speed signal denoising.
AQL: A Query Language for Action Domains Modelled Using Answer Set Programming
Authors: Luke Hopton, Owen Cliffe, Marina Vos, Julian Padget
Venue: Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (2009)

We present a new general purpose query and abduction language for reasoning about action domains that allows the processing of simultaneous actions, definition of conditions and reasoning about fluents and actions. AQL provides a simple declarative syntax for the specification of constraints on the histories (the combination of action traces and state transitions) within the modelled domain. Its semantics, provided by the translation of AQL queries into Ans-Prolog, acquires the benefits of the reasoning power provided by Answer Set Programming (ASP). The answer sets obtained from combining the query and the domain description correspond to those histories of the domain changing over time that satisfy the query. The result is a simple, high-level query and constraint language that builds on ASP. Through the synthesis of features it offers a more flexible, versatile and intuitive approach compared to existing languages. Due to the use of ASP, AQL can also be used to reason about partial histories.
Revealing the structure of medical dictations with conditional random fields
Authors: Jeremy Jancsary, Johannes Matiasek, Harald Trost
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

Automatic processing of medical dictations poses a significant challenge. We approach the problem by introducing a statistical framework capable of identifying types and boundaries of sections, lists and other structures occurring in a dictation, thereby gaining explicit knowledge about the function of such elements. Training data is created semi-automatically by aligning a parallel corpus of corrected medical reports and corresponding transcripts generated via automatic speech recognition. We highlight the properties of our statistical framework, which is based on conditional random fields (CRFs) and implemented as an efficient, publicly available toolkit. Finally, we show that our approach is effective both under ideal conditions and for real-life dictation involving speech recognition errors and speech-related phenomena such as hesitation and repetitions.
AIS-BN: an adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks
Authors: Jian Cheng, Marek J. Druzdzel
Venue: Journal of Artificial Intelligence Research (2000)

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PATHFINDER network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.
From approximate to optimal solutions: constructing pruning and propagation rules
Authors: Ian P. Gent, Toby Walsh
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

At the heart of many optimization procedures are powerful pruning and propagation rules. This paper presents a case study in the construction of such rules. We develop a new algorithm, Complete Decreasing Best Fit, that finds the optimal packing of objects into bins. The algorithm use a branching rule based on the well known Decreasing Best Fit approximation algorithm. In addition, it includes a powerful pruning rule derived from a bound on the solution to the remaining subproblem. The bound is constructed by using modular arithmetic to decompose the numerical constraints. We show that the pruning rule adds essentially a constant factor overhead to runtime, whilst reducing search significantly. On the hardest problems, runtime can be reduced by an order of magnitude. Finally we demonstrate how propagation rules can be built by adding lookahead to pruning rules. This general approach -optimization procedures built from branching rules based on good approximation algorithms, and pruning and propagation rules derived from bounds on the remaining subproblem-may be effective on other NP-complete problems.
Fast communication: Tri-iterative least-square method for bearing estimation in MIMO radar
Authors: Hui Lv, Da-Zheng Feng, Hong-Wei Liu, Jie He, Cong Xiang
Venue: Signal Processing (2009)

In this paper, we introduce a novel tri-iterative least-square (TI-LS) method for bearing estimation in multiple-input multiple-output (MIMO) radar system. The TI-LS iteratively finds two steering matrices associated with the transmit and receive arrays, and the target's bearing can be easily obtained based on prior knowledge of the transmit and receive array manifolds. Furthermore, by jointly using the information from the transmit and receive array responses, angular estimation accuracy can be significantly improved. Numerical examples are presented to illustrate the excellent angular estimation accuracy and computational efficiency of this new method.
Research on Precise Positioning Technology for Remote Sensing Platform
Authors: Yang Bo, Chai Yan, Luo Peng
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

Precise positioning technology for remote sensing platform is researched in this paper, which is based on strapdown inertial navigation system (SINS), global positioning system (GPS) and celestial navigation system (CNS). Firstly, position and velocity outputs of SINS and GPS are taken to construct measurements of SINS/GPS integrated positioning, and SINS/GPS Kalman filtering algorithm is deduced and designed. At the same time, attitude outputs of SINS and CNS are taken to construct measurements of SINS/CNS integrated positioning, and SINS/CNS Kalman filtering algorithm is also deduced and designed. Then the SINS/GPS/CNS federated Kalman filter frame is designed, and local estimations of system states from SINS/GPS and SINS/CNS local filters are sent to the master filter independently. Consequently global optimal estimations of system states are given in the master filter by global optimal fusion algorithm. Simulation results show that, position accuracy of this precise positioning technology reaches ±2.8m, attitude accuracy reaches ±0.32′, and velocity accuracy reaches ±0.06m/s.
Generation Challenges 2009
Authors: Anja Belz, Albert Gatt
Venue: Proceedings of the 12th European Workshop on Natural Language Generation (2009)

Generation Challenges 2009 was the third round of shared-task evaluation competitions (STECs) that involve the generation of natural language, and followed the Pilot Attribute Selection for Generating Referring Expressions Challenge in 2007 (ASGRE'07) and Referring Expression Generation Challenges in 2008 (REG'08). More information about all these NLG STEC activities can be found via the links on the Generation Challenges homepage: http://www.nltg.brighton.ac.uk/research/genchal09
Localized mode DFT-S-OFDMA implementation using frequency and time domain interpolation
Authors: Ari Viholainen, Tero Ihalainen, Mika Rinne, Markku Renfors
Venue: EURASIP Journal on Advances in Signal Processing (2009)

This paper presents a novel method to generate a localized mode single-carrier frequency division multiple access (SC-FDMA) waveform. Instead of using DFT-spread OFDMA (DFT-S-OFDMA) processing, the new structure called SCiFI-FDMA relies on frequency and time domain interpolation followed by a user-specific frequency shift. SCiFI-FDMA can provide signal waveforms that are compatible to DFT-S-OFDMA. In addition, it provides any resolution of user bandwidth allocation for the uplink multiple access with comparable computational complexity, because the DFT is avoided. Therefore, SCiFI-FDMA allows a flexible choice of parameters appreciated in broadband mobile communications in the future.
Designing locative media for creative misuse: learning from urban intervention
Authors: Anne Naukkarinen, Jenna Sutela, Andrea Botero, Kari-Hans Kommonen
Venue: Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era (2009)

This paper examines the possibilities of designing locative media for creative misuse by learning from non-conventional and interventive urban practices. Instead of taking technologies as a starting point, we focused on the location and media related practices of two distinct communities who by virtue of the practices they are engaged with, have a particular relationship to places and spaces. By collaboratively mapping their practices we aimed to ground the further development of locative media application concepts and formulate design considerations for creative misuse in this context.
An Encryption Scheme Based on Key Pre-distribution for Wireless Sensor Network in Electromechanical System
Authors: Yang Shao-Chun, Zhang Guo-Qing, Wang Peng
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

This paper proposes an encryption scheme based on key pre-distribution for wireless sensor network (WSN), which fulfills perfectly all the security requirements in WSN using the counter and message authentication code during the encryption process. Furthermore, the related protocols or algorithms are very simple and no expensive public-key operation is required in view of resource-starved sensor nodes, which improves the efficiency of our scheme and makes it be applied in the giant electromechanical monitoring & controlling system.
From inspiration to App Store in 90 minutes: a tutorial on iPhone development
Authors: Michael P. Rogers
Venue: Journal of Computing Sciences in Colleges (2009)

The iPhone needs no introduction: it has garnered popular acclaim and huge sales since its introduction, and all of your students will be familiar with it. What they will be less acquainted with, but just as curious about, is Phone development. To help faculty assuage their students' curiosity (as well as their own), in this tutorial we will provide a detailed roadmap for learning how to create iPhone applications. We will discuss the languages, frameworks, and development tools that make iPhone development possible. We will demonstrate how to create several applications that illustrate the key issues in iPhone development, and the end products, while not quite ready for the "App Store", will prove excellent starting points. With this information, faculty contemplating offering a course or independent study in iPhone Development will better understand what is involved. They will learn that the most important reasons for learning how to write iPhone applications have nothing to do with its current popularity. Rather, it is because the iPhone is an ideal platform for illustrating some key concepts in Computer Science (algorithm choice does matter; profligate memory usage is perilous; model-view-controller is not just a classroom exercise, but a powerful paradigm), and provides an excellent platform for students to hone their development skills. Attendees should be familiar with any modern object-oriented language (not necessarily Objective-C) to benefit from this tutorial.
A novel fuzzy clustering algorithm using observation weighting and context information for reverberant blind speech separation
Authors: Marco Kühne, Roberto Togneri, Sven Nordholm
Venue: Signal Processing (2010)

Time-frequency masking has evolved as a powerful tool for tackling blind source separation problems. In previous work, mask estimation was performed with the help of well-known standard cluster algorithms. Spatial observation vectors, extracted from a set of microphones, were grouped into separate clusters, each representing a particular source. However, most off-the-shelf clustering methods are not very robust to outliers or noise in the data. This lack of robustness often leads to incorrect localization and partitioning results, particularly for reverberant speech mixtures. To address this issue, we investigate the use of observation weights and context information as means to improve the clustering performance under reverberant conditions. While the observation weights improve the localization accuracy by ignoring noisy observations, context information smoothes the cluster membership levels by exploiting the highly structured nature of speech signals in the time-frequency domain. In a number of experiments, we demonstrate the superiority of the proposed method over conventional fuzzy clustering, both in terms of localization accuracy as well as speech separation performance.
Access Policy Design Supported by FCA Methods
Authors: Frithjof Dau, Martin Knechtel
Venue: Proceedings of the 17th International Conference on Conceptual Structures: Conceptual Structures: Leveraging Semantic Technologies (2009)

Role Based Access Control (RBAC) is a methodology for providing users in an IT system specific permissions like write or read to users. It abstracts from specific users and binds permissions to user roles. Similarly, one can abstract from specific documents and bind permission to document types.In this paper, we apply Description Logics (DLs) to formalize RBAC. We provide a thorough discussion on different possible interpretations of RBAC matrices and how DLs can be used to capture the RBAC constraints. We show moreover that with DLs, we can express more intended constraints than it can be done in the common RBAC approach, thus proving the benefit of using DLs in the RBAC setting. For deriving additional constraints, we introduce a strict methodology, based on attribute exploration method known from Formal Concept Analysis. The attribute exploration allows to systematically finding unintended implications and to deriving constraints and making them explicit. Finally, we apply our approach to a real-life example.
Usability Analyses of CRM Systems in Call Centers: The Data Mining Approach
Authors: Ádám Horváth, László Laufer, András Kangyal
Venue: Proceedings of the Symposium on Human Interface 2009 on ConferenceUniversal Access in Human-Computer Interaction. Part I: Held as Part of HCI International 2009 (2009)

We carried out a usability research based on the methodology of Software Usage Mining of the CRM system used in the Call Center of a major Hungarian telecommunication company. This paper describes the specific usability aspects of CRM systems used in Call Centers. By analyzing the communication model of the customer-company interaction, we identify the key areas of usability and data mining in the process. Our research has revealed how Web Usage Mining provides invaluable insights for identifying usability issues on a quantitative basis. Our results showed how a wizard-like interactional model would suit the process of customer care better, than the currently applied menu driven architecture. In the specific context of a Call Center, Web usage Mining is proposed to used as an evaluation tool to provide fairer assessment for Call Center operators.
Study on Scope of Customization Customers' Perspective Based
Authors: Yin Zhang, Rongqiu Chen, Li Li
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

The flexibility of manufacturing system in mass customization (MC) requires high initial investments, as well as the technologies of information collection and data processing does. To some extent, the bigger scope of customization induces the higher customization cost.We build a game theory model to test the below variables: customers’ heterogeneous preference, scope of customization, customization cost and custom product price. We highlight the relationships that customization cost is determined by the scope of customization. Then we find customization strategy in a firm will generate some interesting changes. Lastly some novel viewpoints about customization strategy are also provided.