ppxscal/embedding_network huggingface dataset:
Agent-Based Non-distributed and Distributed Clustering
Authors: Ireneusz Czarnowski, Piotr Jedrzejowicz
Venue: Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (2009)

The paper deals with the non-distributed and distributed clustering and proposes an agent-based approach to solving the clustering problem instances. The approach is an implementation of the specialized A-Team architecture called JABAT. The paper includes an overview of JABAT and the description of the agent-based algorithms solving the non-distributed and distributed clustering problems. To evaluate the approach the computational experiment involving several well known benchmark instances has been carried out. The results obtained by JABAT-based algorithms are compared with the results produced by the non-distributed and distributed k-means algorithm. It has been shown that the proposed approach produces, as a rule, better results and has the advantage of being scalable, mobile and parallel.
Time Series Prediction Based on Recurrent LS-SVM with Mixed Kernel
Authors: Jianhong Xie
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 01 (2009)

Time series prediction is a main research content in time series analysis, and has become a hot research field with great theoretical value and application value. As an extension type of Least Square Support Vector Machine (LS-SVM), recurrent LS-SVM is proposed and applied to chaotic time series prediction. Aimed at the key and difficult research problem on LS-SVM — the selection and construction of kernel functions, a mixed kernel function used to recurrent LS-SVM is constructed through analyzing the existed kernel functions of LS-SVM. Based on Rossler chaotic time series prediction, the parameters of recurrent LS-SVM with mixed kernel are optimized by Genetic Algorithms (GA), and the prediction results are compared with that of recurrent LS-SVM with RBF kernel. The results show that, the prediction accuracy based on recurrent LS-SVM with mixed kernel is apparently higher than that based on recurrent LS-SVM with RBF kernel under the same condition. Compared with recurrent LS-SVM with RBF kernel, recurrent LS-SVM with mixed kernel possesses the better long-time predictive ability by absorbing the advantages of RBF kernel and polynomial kernel function.
People-Oriented Programming: From Agent-Oriented Analysis to the Design of Interactive Systems
Authors: Steve Goschnick
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends (2009)

Where the Object-Oriented paradigm set about abstracting objects, Agent-Oriented (AO) theory draws on Psychology to abstract mentalist notions like: beliefs, perceptions, goals, and intentions. As such, the associated Agent- Oriented analysis can be used quite successfully to design interactive systems for people, delivering applications that are heavily individual-oriented. This reversal of the AO lens focuses analysis back upon people. It puts a multi-faceted agent used in analysis `into the shoes' of the user and turns the design and implementation into one we call People-Oriented Programming (POP). POP calls on users to gather ethnographic data about themselves using Cultural Probes and on end-user innovation via software toolkits. This turn of focus is timely as the analyst/designer of interactive systems is facing new challenges regarding flexibility, user situatedness, dynamic environments, incomplete data, diversity in user needs, sensors in the environment, and users emersed in multiple parallel social worlds. Based on an extensive background analysis this paper distills a set of key aspects that any POP effort should possess.
Research on Intelligent Vehicle Platoon Driving Experiment System Based on Wireless Communication
Authors: Yulin Ma, Qing Wu
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02 (2009)

Intelligent vehicle platoon driving is a process of the flexible formation based on the coordination between vehicle and infrastructure in the intelligent vehicle-infrastructure system, which can increase the density of road traffic, simplify the complexity of traffic control, increase the controllability of traffic and ease the traffic jams. It has been constructed that the intelligent vehicle platoon drive simulation experiment system using the technology of one-tenth of intelligent vehicle and simulated road and wireless communication networks and so on. It also has been studied that the autonomous driving technology of intelligent vehicle and wireless communication between intelligent vehicles as well as the information interaction with the main console by the wireless communication module, providing technical and experimental basis for further research on vehicle platoon driving control under the coordination between vehicle and infrastructure.
Bayesian Multi-topic Microarray Analysis with Hyperparameter Reestimation
Authors: Tomonari Masada, Tsuyoshi Hamada, Yuichiro Shibata, Kiyoshi Oguri
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

This paper provides a new method for multi-topic Bayesian analysis for microarray data. Our method achieves a further maximization of lower bounds in a marginalized variational Bayesian inference (MVB) for Latent Process Decomposition (LPD), which is an effective probabilistic model for microarray data. In our method, hyperparameters in LPD are updated by empirical Bayes point estimation. The experiments based on microarray data of realistically large size show efficiency of our hyperparameter reestimation technique.
Bayesian Data Integration and Enrichment Analysis for Predicting Gene Function in Malaria
Authors: Philip M. Tedder, James R. Bradford, Chris J. Needham, Glenn A. Mcconkey, Andrew J. Bulpitt, David R. Westhead
Venue: Proceedings of the 5th Conference on Computability in Europe: Mathematical Theory and Computational Practice (2009)

Malaria is one of the world's most deadly diseases and is caused by the parasite Plasmodium falciparum. Sixty percent of P. falciparum genes have no known function and therefore new methods of gene function prediction are needed. To address this problem, we train a naïve Bayes classifier on multiple sources of data and subsequently apply a modified version of the Gene Set Enrichment Analysis Algorithm to predict gene function in P. falciparum. To define gene function, we exploit the hierarchical structure of the Gene Ontology, specifically using the Biological Process category. We demonstrate the value of integrating multiple data sources by achieving accurate predictions on genes that cannot be annotated using simple sequence similarity based methods.
Annotating Atomic Components of Papers in Digital Libraries: The Semantic and Social Web Heading towards a Living Document Supporting eSciences
Authors: Alexander García Castro, Leyla Jael García-Castro, Alberto Labarga, Olga Giraldo, César Montaña, Kieran O'Neil, John A. Bateman
Venue: Proceedings of the 20th International Conference on Database and Expert Systems Applications (2009)

Rather than a document that is being constantly re-written as in the wiki approach, the Living Document (LD) is one that acts as a document router, operating by means of structured and organized social tagging and existing ontologies. It offers an environment where users can manage papers and related information, share their knowledge with their peers and discover hidden associations among the shared knowledge. The LD builds upon both the Semantic Web, which values the integration of well-structured data, and the Social Web, which aims to facilitate interaction amongst people by means of user-generated content. In this vein, the LD is similar to a social networking system, with users as central nodes in the network, with the difference that interaction is focused on papers rather than people. Papers, with their ability to represent research interests, expertise, affiliations, and links to web based tools and databanks, represent a central axis for interaction amongst users. To begin to show the potential of this vision, we have implemented a novel web prototype that enables researchers to accomplish three activities central to the Semantic Web vision: organizing, sharing and discovering. Availability: http://www.scientifik.info/
Mind-Mirror: EEG-Guided Image Evolution
Authors: Nima Bigdely Shamlo, Scott Makeig
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques (2009)

We propose a brain-computer interface (BCI) system for evolving images in real-time based on subject feedback derived from electroencephalography (EEG). The goal of this system is to produce a picture best resembling a subject's `imagined' image. This system evolves images using Compositional Pattern Producing Networks (CPPNs) via the NeuroEvolution of Augmenting Topologies (NEAT) genetic algorithm. Fitness values for NEAT-based evolution are derived from a real-time EEG classifier as images are presented using rapid serial visual presentation (RSVP). Here, we report the design and performance, for a pilot training session, of a BCI system for real-time single-trial binary classification of viewed images based on participant-specific brain response signatures present in 128-channel EEG data. Selected training-session image clips created by the image evolution algorithm were presented in 2-s bursts at 8/s. The subject indicated by subsequent button press whether or not each burst included an image resembling two eyes. Approximately half the bursts included such an image. Independent component analysis (ICA) was used to extract a set of maximally independent EEG source time-courses and their 100 minimally-redundant low-dimensional informative features in the time and time-frequency amplitude domains from the (94%) bursts followed by correct manual responses. To estimate the likelihood that the post-image EEG contained EEG `flickers' of target recognition, we applied two Fisher discriminant classifiers to the time and/or time-frequency features. The area under the receiver operating characteristic (ROC) curve by tenfold cross-validation was 0.96 using time-domain features, 0.97 using time-frequency domain features, and 0.98 using both domain features.
A DDoS Attack Defending Scheme Based on Network Processor
Authors: Li Xinlei, Zheng Kangfeng, Yang Yixian
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

The distributed denial of service attacks have become more and more frequent and caused some fatal problems. Many researches have been done to detect and defend such attacks, however, many solutions are still in the phase of theoretical studies. Some of them may have certain practical value, but they have to reconstruct the existing network and the routing instruments with great cost. This paper proposes a DDoS attack defending scheme based on network processor. The scheme takes advantage of network processor’s powerful process ability to divide the network flow into different types firstly, and then uses a QoS mechanism to ensure essential communications as well as to eliminate the attack flow to the greatest extent. Experiment results show that the scheme can provide enough bandwidth for high priority flow, and effectively weaken the attack flow.
Research on Land Use Supervision by Using High Resolution and Fully Polarizations Sar Data
Authors: Gao Wei, Liu Xiuguo, Li Yongsheng, Wang Hongping, Xiao Lin
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

Land is an important and fundamental natural resource which the mankind depends on for existence. Governor wants a better and cheaper mechanization to supervise land usage. The predominant source of remote intelligence for monitoring land change activities, remote sensing satellites especially Synthetic Aperture Radar (SAR)satellites are finding a niche on this arena. The high resolution and fully polarization SAR data provide a new mode for land use supervision. This paper aims to systematically analyze the SAR date representative applications in agriculture, forestry,urban planning and so on.
Partial Clustering for Tissue Segmentation in MRI
Authors: Nicolau Gonçalves, Janne Nikkilä, Ricardo Vigário
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

Magnetic resonance imaging (MRI) is a imaging and diagnostic tool widely used, with excellent spatial resolution, and efficient in distinguishing between soft tissues. Here, we present a method for semi-automatic identification of brain tissues in MRI, based on a combination of machine learning approaches. Our approach uses self-organising maps (SOMs) for voxel labelling, which are used to seed the discriminative clustering (DC) classification algorithm. This method reduces the intensive need for a specialist, and allows for a rather systematic follow-up of the evolution of brain lesions, or their treatment.
On the use of fuzzy synthetic evaluation and optimal classification for computing fire risk ranking of buildings
Authors: M. Liu, S. M. Lo, B. Q. Hu, C. M. Zhao
Venue: Neural Computing and Applications (2009)

Fire risk ranking is particularly useful for building designers to compare two different solutions to assess if the safety is similar. However, the multi-criteria and imprecise nature of the fire safety attributes in buildings has caused difficulties in quantifying the fire safety level. Further to the previous works, the author presented a fuzzy synthetic evaluation system for computing the fire risk ranking of buildings. It could serve for multi-level fire safety assessment framework. Linguistic terms were adopted instead of subjective numerical values. A two-level evaluation model, including fuzzy optimal classification model and linear weighted mean model, was developed to facilitate the synthetic process. A case of fire safety ranking in high-rise residential buildings in Hong Kong was presented for illustration purpose. The evaluation result was analyzed by the maximum membership degree principle method.
Research on the Fuzzy Control and Simulation for Intersection Based on the Phase Sequence Optimization
Authors: Junqiang Leng, Yuqin Feng
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

In urban transportation system, intersection plays the role of junction and hinge of the entire road network. But it often becomes the bottleneck of the road network capacity and the traffic accident spot One of the most important gaps in intersection management practices is the lack of mature effective methods for traffic control. Intelligent and actuated control strategies have been developed, however, these are so complex that the effective are not very perfect in practice. Effective control strategy requires an easy and applicable viewpoint. The main goal of this research is to assist in improving the traffic efficiency at intersection by establishing effective and applicable control strategy. In this article, a signal control algorithm is proposed for isolated intersection that based on fuzzy logic and optimizing the phase order of traffic signal. And the vehicle queue length and arriving rate are selected as the control input parameters. Based on the fuzzy logic a Matlab simulation model is established for a four phases intersection. Compared with the conventional fixed time traffic controller, the fuzzy logic controller (FLC) is proved to be useful and effective with the simulation results and have good performance in the average delay of vehicles, and particularly be applicable to over saturated intersection.
Monte Carlo methods for tempo tracking and rhythm quantization
Authors: Ali Taylan Cemgil, Bert Kappen
Venue: Journal of Artificial Intelligence Research (2003)

We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.
On Similarity Measures Based on a Refinement Lattice
Authors: Santiago Ontañón, Enric Plaza
Venue: Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development (2009)

Retrieval of structured cases using similarity has been studied in CBR but there has been less activity on defining similarity on description logics (DL). In this paper we present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based on the concept of refinement lattice. The first one is based on computing the anti-unification (AU) of two cases to assess the amount of shared information. The second measure decomposes the cases into a set of independent properties, and then assesses how many of these properties are shared between the two cases. Moreover, we show that the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.
Formal Specification and Experiments of an Expressive Human-Computer Ensemble System with Rehearsal
Authors: Tetsuya Mizutani, Tatsuo Suzuki, Masayuki Shio, Yasuwo Ikeda
Venue: Proceedings of the 2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering (2009)

Logical representation of an expressive human-computer ensemble system by N∑-labeled calculus together with experimental results are presented.
Transitional Activity Recognition with Manifold Embedding
Authors: Raza Ali, Louis Atallah, Benny Lo, Guang-Zhong Yang
Venue: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (2009)

Activity monitoring is an important part of pervasive sensing, particularly for assessing activities of daily living for elderly patients and those with chronic diseases. Previous studies have mainly focused on binary transitions between activities, but have overlooked detailed transitional patterns. For patient studies, this transition period can be prolonged and may be indicative of the progression of disease. To observe, as well as quantify, transitional activities, a manifold embedding approach is proposed in this paper. The method uses a spectral graph partitioning and transition labelling approach for identifying principal and transitional activity patterns. The practical value of the work is demonstrated through laboratory experiments for identifying specific transitions and detecting simulated motion impairment.
Conceptual Model for E-learning Systems Using Kansei Engineering Techniques: A Preliminary Study
Authors: Thanuja Chandani Sandanayake, Ajith Parakum Madurapperuma
Venue: Proceedings of the 2009 International Conference on Biometrics and Kansei Engineering (2009)

The rapid development of information technology leads to extremely dynamic changes in various tools and technologies applied to the e-learning process. However, building computer devices or systems with the ability of responding to learner emotions is a challengeable task. Kansei Engineering, as a novel concept in the modern era of e-learning addresses the psychological feelings such as emotions, moods, and impressions of the e-learner. The aim of this research study is to introduce the background of Kansei Engineering, its methodology, potentiality and boundaries for designing and building new systems with a profound affection on the user. This study further discuss about the importance of recognizing learner emotions in an e-learning environment. The software agent of the proposed system will be drive in the newly designed e-learning platform, there by investigate the navigational pattern of the e-learner and learner in order to respond to their emotional states accordingly.
Practical Use of Task Models for Building and Modeling Operations Chart in the Industrial Production
Authors: Tomasz Mistrzyk, Alexander Redenius
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)

In this paper, we will show how hierarchical task modeling can give beneficial support for the specification and validation of a production process in its early development stages. This will be done with respect to the growing challenges coming from the market environment. In particular, it is a substantial benefit to use an adequate modeling environment to be able to take specific customer requirements process during the production planning into account. For our methodology, we use Amboss, a task modeling environment designed for safety-critical socio-technical systems.
Responding to student affect and efficacy through empathetic companion agents in interactive learning environments
Authors: Scott W. McQuiggan
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

Because many students experience frustration during learning, it is important to develop affective strategies to support students' coping with frustration in interactive learning environments. First, we must devise affect recognition models to detect student affect. Second, we need to determine when to intervene; these conditions are likely to be different for each student. To determine how much frustration a student can persist through, we should utilize models of student self-efficacy to predict a student's frustration threshold. Third, we should devise techniques for responding empathetically before the student reaches her threshold of frustration. We propose an approach to support students' coping with frustration in intelligent tutoring systems that utilizes induced models of affect, self-efficacy and empathetic behavior to effectively reason about precisely when and how to intervene in frustration-ridden learning situations.