ppxscal/embedding_network huggingface dataset:
Participatory modelling to improve partnership schemes for future Community-Based Forest Management in Sumbawa District, Indonesia
Authors: Aritta Suwarno, Ani Adiwinata Nawir, Julmansyah, Kurniawan
Venue: Environmental Modelling Software (2009)

As part of its Community-Based Forest Management (CBFM) Programme, the local government of Sumbawa District in West Nusa Tenggara Province of Eastern Indonesia initiated a partnership programme between the Forestry District Agency and tree grower cooperatives in 2002. The partnership scheme aims to reduce illegal logging and forest encroachment on abandoned state plantations initially developed in 1986. However, the cooperatives have no secure full rights to harvest the mature teak trees (Tectona grandis) inside the plantations, as important incentives to maintain their commitment. This paper aims to explore the potential benefits of different scenarios, as the basis for convincing the Ministry of Forestry (MoF) to give full secure rights to the local communities. In this study, we used a participatory modelling approach, to identify problems and to define future scenarios of partnership arrangements with the local key stakeholders. This research used the concept of system dynamics modelling, which included the two concepts of systems thinking and resilience. As a management tool, the visualisation of the management alternatives and their associated impacts had stimulated the community's awareness of the importance of protecting the teak plantations from illegal logging and forest encroachment. The simulation results have also shown how granting local communities a full secure access to manage the state forest does not necessarily reduce the revenue for the government at all levels.
Development of a Visualised Sound Simulation Environment: An e-Approach to a Constructivist Way of Learning
Authors: Jingjing Zhang, Beau Lotto, Ilias Bergstom, Lefkothea Andreou, Youzou Miyadera, Setsuo Yokoyama
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part IV: Interacting in Various Application Domains (2009)

In this paper, the design and implementation of a visualised sound simulation environment is presented as an initial step to further laboratory experimentation. Preliminary laboratory experiments showed a positive learning curve in human auditory perception. Learning occurred when new information was processed with relevant existing knowledge in this simulation environment. While the work towards the truth of the empirical hypothesis is still under discussion, this project has been expanded beyond the scope that was originally envisaged and the developed environment showed its potential to be adopted on mobile devices for many educational purposes. This initiative not only brings scientists and educators together, but it is also hoped that it represents a possible e-approach to a constructivist way of learning.
Fast full parsing by linear-chain conditional random fields
Authors: Yoshimasa Tsuruoka, Jun'ichi Tsujii, Sophia Ananiadou
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

This paper presents a chunking-based discriminative approach to full parsing. We convert the task of full parsing into a series of chunking tasks and apply a conditional random field (CRF) model to each level of chunking. The probability of an entire parse tree is computed as the product of the probabilities of individual chunking results. The parsing is performed in a bottom-up manner and the best derivation is efficiently obtained by using a depth-first search algorithm. Experimental results demonstrate that this simple parsing framework produces a fast and reasonably accurate parser.
Fault Line Selection in Non-solidly Earthed Network Based on Modulus Maximum Polarity
Authors: Yuanyuan Zhang, Qingwu Gong, Daojun Chen
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

Despite the big progress made in recent years in the research on transient fault line selection methods in non-solidly earthed network, these methods are still far from perfect. A new scheme for fault line selection in non-solidly earthed network based on the singularity detection and modulus maximum polarity theory is proposed. This scheme applies wavelet packet transform to fractionize the frequency bands of the zero sequence transient current and picks out the characteristic band according to energy maximum principle. Not affected by line parameters and the neutral grounding manner, the criterion also has the ability of enduring high transition resistance. Its validity has been verified by the EMTP/MATLAB simulation results.
Post abstract: Role-based deceptive detection and filtering in WSNs
Authors: Shinan Wang, Kewei Sha, Weisong Shi
Venue: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks (2009)

With more and more real applications of WSNs have been deployed, which are in charge of either monitoring parameters or event detection, we envision that the success of the WSNs is decided by the quality of the collected data [2]. Further more, the quality of the collected data is mainly affected by the deceptive data, which includes redundant data, very similar repeated readings that provide less information, and false data, wrong readings resulted from unreliable components or wireless communication and malicious attacks. On one hand, redundant data should be filtered because less information is provided but considerable resources are wasted. On the other hand, false data should also be filtered to improve data accuracy.
Using Answer Set Programming to Enhance Operating System Discovery
Authors: François Gagnon, Babak Esfandiari
Venue: Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (2009)

Although knowing the operating systems running in a network is becoming more and more important (mainly for security reasons), current operating system discovery tools are not sufficiently accurate to acquire the information in a fully automated way. Many design choices explain this lack of accuracy, but they all come down to a poor knowledge representation scheme. In this paper, we study how answer set programming can be used to guide the design of a knowledge-oriented operating system discovery tool. The result is significantly more accurate than today's state of the art tools.
Demo abstract: Design and implementation of a web service for liteos-based sensor networks
Authors: Masaaki Takahashi, Basit Hussain, Bin Tang
Venue: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks (2009)

We design and implement a web service for LiteOS-based wireless sensor networks (WSNs). LiteOS is a newly developed operating system for the sensor networks. Taking advantage of UNIX-like shell commands and C programming language supported by LiteOS, we implement a web service middleware which interacts with the front-end web application as well as the WSNs such that users can remotely request and view the sensor readings. Our web service system is equipped with secure membership, a visualizer for sensor readings, and accepts parameterized queries from multiple users simultaneously. We also address the issues of improving user perceived response time and energy efficiency of sensor nodes in the web service.
Exploring Multimodal Navigation Aids for Mobile Users
Authors: Teija Vainio
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I (2009)

When navigating in real physical environments, as human beings we tend to display systematic or near-systematic errors with distance, direction and other navigation issues. To avoid making these errors, we choose different stratategies to find our way. While there have been a lot of HCI studies of navigation design guidelines for using maps or speech-based or tactile-based guidance in mobile devices, in this paper we introduce an initial study of multimodal navigation design utilising the design practice of episodes of motion originated from urban planning. The implications of designing cues and providing rhythm, as the design guidelines of episodes of motions suggests, are explored in this study with the subjects being pedestrians with wayfinding tasks in an urban area. The main contributions of this paper are in evaluating the design implications in the context of mobile wayfinding tasks and in reflecting the results according to human wayfinding behaviour. It is concluded that by designing predictive clues and rhythm into mobile multimodal navigation applications, we can improve navigation aids for users.
Learning with Missing or Incomplete Data
Authors: Bogdan Gabrys
Venue: Proceedings of the 15th International Conference on Image Analysis and Processing (2009)

The problem of learning with missing or incomplete data has received a lot of attention in the literature [6,10,13,21,23]. The reasons for missing data can be multi-fold ranging from sensor failures in engineering applications to deliberate withholding of some information in medical questioners in the case of missing input feature values or lack of solved (labelled) cases required in supervised learning algorithms in the case of missing labels. And though such problems are very interesting from the practical and theoretical point of view, there are very few pattern recognition techniques which can deal with missing values in a straightforward and efficient manner. It is in a sharp contrast to the very efficient way in which humans deal with unknown data and are able to perform various pattern recognition tasks given only a subset of input features or few labelled reference cases.In the context of pattern recognition or classification systems the problem of missing labels and the problem of missing features are very often treated separately.The availability or otherwise of labels determines the type of the learning algorithm that can be used and has led to the well known split into supervised, unsupervised or more recently introduced hybrid/semi-supervised classes of learning algorithms.Commonly, using supervised learning algorithms enables designing of robust and well performing classifiers. Unfortunately, in many real world applications labelling of the data is costly and thus possible only to some extent. Unlabelled data on the other hand is often available in large quantities but a classifier built using unsupervised learning is likely to demonstrate performance inferior to its supervised counterpart. The interest in a mixed supervised and unsupervised learning is thus a natural consequence of this state of things and various approaches have been discussed in the literature [2,5,10,12,14,15,18,19]. Our experimental results have shown [10] that when supported by unlabelled samples much less labelled data is generally required to build a classifier without compromising the classification performance. If only a very limited amount of labelled data is available the results based on random selection of labelled samples show high variability and the performance of the final classifier is more dependent on how reliable the labelled data samples are rather than use of additional unlabelled data. This points to a very interesting discussion point related to the issue of the trade-off between the information content in the observed data (in this case available labels) versus the impact that can be achieved by employing sophisticated data processing algorithms which we will also revisit when discussing approaches dealing with missing feature values.
A demonstration of human computation using the Phrase Detectives annotation game
Authors: Jon Chamberlain, Massimo Poesio, Udo Kruschwitz
Venue: Proceedings of the ACM SIGKDD Workshop on Human Computation (2009)

The goal of the ANAWIKI project is to experiment with Web collaboration and human computation to create largescale linguistically annotated corpora. We will present ongoing work and initial results of Phrase Detectives, a game designed to collect judgments about anaphoric annotations.
Analysis of Team Communication and Collaboration in En-Route Air Traffic Control
Authors: Kazuo Furuta, Yusuke Soraji, Taro Kanno, Hisae Aoyama, Daisuke Karikawa, Makoto Takahashi
Venue: Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009 (2009)

Ethnographic field observation was carried out at the Tokyo Air Traffic Control Center to obtain video, radio communication, verbal conversation, and journal records. Having analyzed these data based on the cognitive model of a radar controller of our previous work and a notion of Team Situation Awareness (TSA), a cognitive model of an ATC team has been established. It was revealed that instantiation of TSA heavily owes to verbal communication but that role assignment among team members is implicitly and smoothly determined once TSA has been established. Team cognitive process of ATC controllers is therefore well described by TSA development and Naturalistic Decision-Making (NDM).
Upending the uncanny valley
Authors: David Hanson, Andrew Olney, Steve Prilliman, Eric Mathews, Marge Zielke, Derek Hammons, Raul Fernandez, Harry Stephanou
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 4 (2005)

Although robotics researchers commonly contend that robots should not look too humanlike, many artforms have successfully depicted people and have come to be accepted as great and important works, with examples such as Rodin's Thinker, Mary Cassat's infants, and Disney's Abe Lincoln simulacrum. Extending this tradition to intelligent robotics, the authors have depicted late sci-fi writer Philip K Dick with an autonomous, intelligent android. In doing so, the authors aspire to bring robotic systems up to the level of great art, while using the technology as a mirror for examining human nature in social AI development and cognitive science experiments.
A Semantic-Aware Attribute-Based Access Control Model for Web Services
Authors: Haibo Shen
Venue: Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing (2009)

Web service is a new service-oriented computing paradigm which poses the unique security challenges due to its inherent heterogeneity, multi-domain characteristic and highly dynamic nature. A key challenge in Web services security is the design of effective access control schemes. Attribute-based access control (ABAC) is more appropriate than some other access control mechanisms, but it do not fully exploit the semantic power and reasoning capabilities of emerging web applications. So a semantic-aware attribute-based access control model (SABAC) is presented to address these issues by combining the ABAC with the Semantic Web technologies in this paper. SABAC grants access to services based on attributes of the related entities, and uses Shibboleth service to address the disclosure issue of the sensitive attributes. In addition, SABAC uses the Web Ontology Language (OWL) standard to represent the ontology of the resources and users and uses eXtensible Access Control Markup Language (XACML) as the policy language. It can provide administratively scalable alternative to identity-based authorization methods and provide semantic interoperability for the access control to Web services. Moreover, SABAC also separates ontology management from access management.
Redescription mining: structure theory and algorithms
Authors: Laxmi Parida, Naren Ramakrishnan
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 2 (2005)

We introduce a new data mining problem--redescription mining--that unifies considerations of conceptual clustering, constructive induction, and logical formula discovery. Redescription mining begins with a collection of sets, views it as a propositional vocabulary, and identifies clusters of data that can be defined in at least two ways using this vocabulary. The primary contributions of this paper are conceptual and theoretical: (i) we formally study the space of redescriptions underlying a dataset and characterize their intrinsic structure, (ii) we identify impossibility as well as strong possibility results about when mining redescriptions is feasible, (iii) we present several scenarios of how we can custom-build redescription mining solutions for various biases, and (iv) we outline how many problems studied in the larger machine learning community are really special cases of redescription mining. By highlighting its broad scope and relevance. we aim to establish the importance of redescription mining and make the case for a thrust in this new line of research.
Determination of Trace Chromium in the Fu-Yang River by CAS and CTMAB Spectrophotometry
Authors: Ji-hong Zhou, Cai-lian Hao, Biao Zhang, Chang-jun Zhu, Jun-guang Zhao
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 01 (2009)

Because of the toxicity property of chromium in the water, it is important for the water environment to monitor the trace chromium with a kind of simple and sensitive method. Spectrophotometry of CAS and CTMAB is adopted to determine chromium in the Fu-yang river. The results indicates that the detected limit of this method is 0.0005 ug/mL, the mark-on recovery ranged from 87.7% to 97.0% and relative standard deviation is 5.0%. In addition, several problems needing attention about experiment process are posed in this paper.
Using Hierarchical Models for 3D Human Body-Part Tracking
Authors: Leonid Raskin, Michael Rudzsky, Ehud Rivlin
Venue: Proceedings of the 16th Scandinavian Conference on Image Analysis (2009)

Human body pose estimation and tracking is a challenging task mainly because of the high dimensionality of the human body model. In this paper we introduce a Hierarchical Annealing Particle Filter (H-APF) algorithm for 3D articulated human body-part tracking. The method exploits Hierarchical Human Body Model (HHBM) in order to perform accurate body pose estimation. The method applies nonlinear dimensionality reduction combined with the dynamic motion model and the hierarchical body model. The dynamic motion model allows to make a better pose prediction, while the hierarchical model of the human body expresses conditional dependencies between the body parts and also allows us to capture properties of separate parts. The improved annealing approach is used for the propagation between different body models and sequential frames. The algorithm was checked on HumanEvaI and HumanEvaII datasets, as well as on other videos and proved to be effective and robust and was shown to be capable of performing an accurate and robust tracking. The comparison to other methods and the error calculations are provided.
Customer's Relationship Segmentation Driving the Predictive Modeling for Bad Debt Events
Authors: Carlos Andre Pinheiro, Markus Helfert
Venue: Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH (2009)

This paper covers a comparison between two distinct approaches to neural network modeling. The first one is based on a developing of a single neural network model to predict bad debt events. The second one is based on combined models, building firstly a clustering model to recognize the pattern assigned to the customers, with a particular focus on the insolvency, and then developing several distinct neural networks to predict bad debt. In the second approach, for each group identified by the clustering model one neural network had been constructed. In that way, we turned the quite heterogeneous customer base more homogeneous, increasing the average accuracy for the predictive modeling once several straightforward models were built.
Novel Fingerprint Image Enhancement Algorithm
Authors: Mojtaba Sepasian, Mohammad Reza Bahmanyar, Cristinel Mares, Sayyed Mohamad Azimi, Wamadeva Balachandran
Venue: Proceedings of the Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science 2008 (2008)

Abstract— The purpose of this paper is to investigate the performance of a three-step procedure for the fingerprint identification and enhancement, using CLAHE (contrast limited adaptive histogram equalization) together with applying 'Clip Limit', standard deviation analysis and sliding neighborhood as stages during processing of the fingerprint image. Firstly, CLAHE with clip limit is applied to enhance the contrast of the small tiles existing in the fingerprint image while using a bilinear interpolation to combine the neighboring tiles, eliminating the artificially induced boundaries. In a second step, the image is partitioned into an array of distinct blocks represented by matrices. Standard deviation of the matrix elements is calculated for each block and is used to remove the image background and obtain the boundaries for the region of interest. Finally, by using a slide neighborhood processing, an enhancement of the image is obtained by clarifying the Minutiae (endpoints and bifurcations) in each specific pixel, a process known as thinning. The paper presents the motivation for developing this method, its phases, and its possible advantages through a simulated investigation.
Word sense disambiguation using OntoNotes: an empirical study
Authors: Zhi Zhong, Hwee Tou Ng, Yee Seng Chan
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

The accuracy of current word sense disambiguation (WSD) systems is affected by the fine-grained sense inventory of WordNet as well as a lack of training examples. Using the WSD examples provided through OntoNotes, we conduct the first large-scale WSD evaluation involving hundreds of word types and tens of thousands of sense-tagged examples, while adopting a coarse-grained sense inventory. We show that though WSD systems trained with a large number of examples can obtain a high level of accuracy, they nevertheless suffer a substantial drop in accuracy when applied to a different domain. To address this issue, we propose combining a domain adaptation technique using feature augmentation with active learning. Our results show that this approach is effective in reducing the annotation effort required to adapt a WSD system to a new domain. Finally, we propose that one can maximize the dual benefits of reducing the annotation effort while ensuring an increase in WSD accuracy, by only performing active learning on the set of most frequently occurring word types.
Minimum-Latency Schedulings for Group Communications in Multi-channel Multihop Wireless Networks
Authors: Peng-Jun Wan, Zhu Wang, Zhiyuan Wan, Scott C. Huang, Hai Liu
Venue: Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (2009)

This paper is motivated by exploring the impact of the number of channels on the achievable communication latency for a specific communication task. We focus on how to utilize the multiple channels to speed up four group communications including broadcast, aggregation, gathering, and gossiping in wireless networks under protocol interference model. Four scheduling algorithms are developed for these four group communications. We derive explicit tight bounds on the latencies of the four communication schedules produced by these algorithms. These latency bounds in general decrease with the number of channels and are also within constant factors of the respective minimum.