ppxscal/embedding_network huggingface dataset:
A Spatial Decision Support System for Large Scale Vehicle Routing
Authors: Xinjie Yu, Kangsheng Liu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

Aiming at resolving the problems of how to route and schedule vehicles in logistics distribution, which has a characteristic of large scale vehicles scheduling question, a spatial decision support system(SDSS) architecture is designed to improve the efficiency and minimize transportation costs for the logistics distribution in this paper. A three-stage solution framework for large scale vehicle routing problem (LSVRP) is applied in the SDSS. Firstly, the large numbers of customers are merged according to their space attributes. Secondly, the merged customers are assigned to vehicles using a sweep algorithm. Thirdly, each vehicle route order is determined as a Traveling Salesman Problem (TSP). With the solution framework, and by taking a logistics company of china as the case, the authors develop a decision support system. The practical application result shows that the system can reduce logistics costs and raise logistics efficiency for LSVRP.
Protein folding and chart parsing
Authors: Julia Hockenmaier, Aravind K. Joshi, Ken A. Dill
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

How can proteins fold so quickly into their unique native structures? We show here that there is a natural analogy between parsing and the protein folding problem, and demonstrate that CKY can find the native structures of a simplified lattice model of proteins with high accuracy.
An Ambient Intelligent Agent for Relapse and Recurrence Monitoring in Unipolar Depression
Authors: Azizi Ab Aziz, Michel C. Klein, Jan Treur
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

Mental healthcare is a prospective area for applying AI techniques. For example, a computerized system could support individuals with a history of depression in maintaining their well-being throughout their lifetime. In this paper, the design of an ambient intelligent agent to support these individuals is presented. It incorporates an analysis and support model for diagnostics based on observed features and for suggested actions. The model used is based on dynamic relations that describe the occurrence of relapse in unipolar depression. By incorporating this model into an ambient agent system, the agent is able to reason about the state of the human and the effect of possible actions. Several simulation experiments have been conducted to illustrate the functioning of the proposed model in different scenarios.
Fusion of Acoustic and Prosodic Features for Speaker Clustering
Authors: Janez Žibert, France Mihelič
Venue: Proceedings of the 12th International Conference on Text, Speech and Dialogue (2009)

This work focus on a speaker clustering methods that are used in speaker diarization systems. The purpose of speaker clustering is to associate together segments that belong to the same speakers. It is usually applied in the last stage of the speaker-diarization process. We concentrate on developing of proper representations of speaker segments for clustering and explore different similarity measures for joining speaker segments together. We realize two different competitive systems. The first is a standard approach using a bottom-up agglomerative clustering principle with the Bayesian Information Criterion (BIC) as a merging criterion. In the next approach a fusion speaker clustering system is developed, where the speaker segments are modeled by acoustic and prosody representations. The idea here is to additionally model the speaker prosody characteristics and add it to basic acoustic information estimated from the speaker segments. We construct 10 basic prosody features derived from the energy of the audio signals, the estimated pitch contours, and the recognized voiced and unvoiced regions in speech. In this way we impose higher-level information in the representations of the speaker segments, which leads to improved clustering of the segments in the case of similar speaker acoustic characteristics or poor acoustic conditions.
Side-information generation for temporally and spatially scalable Wyner-Ziv codecs
Authors: Bruno Macchiavello, Fernanda Brandi, Eduardo Peixoto, Ricardo L. de Queiroz, Debargha Mukherjee
Venue: Journal on Image and Video Processing (2009)

The distributed video coding paradigm enables video codecs to operate with reversed complexity, in which the complexity is shifted from the encoder toward the decoder. Its performance is heavily dependent on the quality of the side information generated by motio estimation at the decoder. We compare the rate-distortion performance of different side-information estimators, for both temporally and spatially scalable Wyner-Ziv codecs. For the temporally scalable codec we compared an established method with a new algorithm that uses a linear-motion model to produce side-information. As a continuation of previous works, in this paper, we propose to use a super-resolution method to upsample the nonkey frame, for the spatial scalable codec, using the key frames as reference. We verify the performance of the spatial scalable WZ coding using the state-of-the-art video coding standard H.264/AVC.
Experimental Study on Differential Evolution Strategies
Authors: Youyun Ao, Hongqin Chi
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

Differential evolution (DE) algorithm has been proven to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, which is widely used in both benchmark test functions and real-world applications. Like genetic algorithms, differential evolution algorithm uses three typical operators to search the solution space: crossover, mutation and selection. Among these operators, mutation plays a key role in the performance of differential evolution algorithm and there are several mutation variants often used, which constitute several corresponding differential evolution strategies. By means of experiments, this paper investigates the relative performance of different differential evolution algorithms for global optimization under different differential evolution strategies respectively. In simulation studies, De Jong’s test functions have been employed, and some conclusions are drawn.
Intelligent Decision Making System for Digital Modulation Scheme Classification in Software Radio Using Wavelet Transform and Higher Order Statistical Moments
Authors: P. Prakasam, M. Madheswaran
Venue: Wireless Personal Communications: An International Journal (2009)

This paper proposes a neural network (NN) based intelligent decision making system for digital modulation classification using wavelet transform, histogram peak and higher order statistical moments. The decision making system is developed to classify the modulation schemes buried in additive white Gaussian noise and channel interference utilizing NN classifier. The performance is verified and validated for M-ary PSK, M-ary FSK, M-ary QAM and GMSK modulation schemes by examining the receiver operating characteristics, confusion matrix and probability of correct identification for various signal-to-noise ratios (SNR) and also for various decision parameters. The performance of the proposed system also has been compared with existing methods and found that this method can be considered as reliable classification method for Digital Modulation Scheme with lower SNR upto ¿ 5 dB.
Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm
Authors: Massimiliano Kaucic
Venue: Computational Economics (2009)

This article introduces an automated procedure to simultaneously select variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. A robust scaling method is considered to take into account the sensitiveness of estimates to abnormal data. A genetic algorithm is developed to these purposes. Two examples are presented where models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation-window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors.
Identifying Functional Modules Using MST-Based Weighted Gene Co-Expression Networks
Authors: Atthawut Chanthaphan, Santitham Prom-on, Asawin Meechai, Jonathan Chan
Venue: Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering (2009)

This paper proposes an effective method for identifying functional modules of the weighted gene co-expression network using a minimum spanning tree (MST) approach coupled with network neighborhood connectivity. The MST-based gene co-expression network was reconstructed to serve as the backbone of gene co-expression network. Highly connected hub genes were identified based on the connectivity of the backbone network. All sub-networks were extracted by expanding from the hub genes to their neighborhood genes. Finally, functional modules were identified by integrating sub-networks with similar gene expression profiles. We tested the method with both simulated and autism spectrum disorder microarray data sets. The results show that our approach is better in highlighting the hub genes and can effectively identify functional modules with highly enriched pathways.
Clinical Image Processing Algorithm Based on Color-Analysis
Authors: Yiwei Zhu, Qianqian Ge
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Colors are the main components of image, especially the clinical image resources obtained by CCD. It consists of pixels for basic component units. The analysis and processing of the pixel is one of the most popular ways in graphic processing technologies. This paper indicates a self-adaptive color-analysis edge tracking method based on shape knowledge. It implements automatically locating image edge feature points. The experiment indicates the approach is fast and effective.
A Method of LVQ Network to Detect Vehicle Based on Morphology
Authors: Zhang Xinye, An Jubai, Yang ZhiFeng
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

In this paper, the main research is to recognize and classify vehicle on the traffic road in the hi-resolution satellite image. Because the satellite image contains the vehicle characters pixel set and the alike vehicle characters pixel set. So, there are a series of pattern recognition methods from image preprocessing to the LVQ Artificial Neural Network works. The application of Morphology is the main preprocessing technology in this paper. After image is morphologically preprocessed, it makes the pixel matrix more clear and differentiable, and is good for the next step, which is to calculate the preprocessed image texture. According to the gray level and the texture, a vector is built as input of LVQ Artificial Neural Network system; the trained LVQ Artificial Neural Network will give a satisfying outcome. Finally, with these processing, the result images are given. The white vehicle and the black vehicle are obvious. Although there are some errors in the results, by contrast with the origin images; the results still give a good effort of this method.
Dependency trees and the strong generative capacity of CCG
Authors: Alexander Koller, Marco Kuhlmann
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

We propose a novel algorithm for extracting dependencies from the derivations of a large fragment of CCG. Unlike earlier proposals, our dependency structures are always tree-shaped. We then use these dependency trees to compare the strong generative capacities of CCG and TAG and obtain surprising results: Both formalisms generate the same languages of derivation trees --- but the mechanisms they use to bring the words in these trees into a linear order are incomparable.
A High Accurate Approach for InSAR Flat Earth Effect Removal
Authors: Shurong Peng, Kexue He, Yaonan Wang, Juan Lu, Jiwei Deng, Haixia Xu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

Synthetic aperture radar interferometry (InSAR) technique is an effective measurement for generating large scale and accurate digital elevation modeling (DEM). The removal of the flat earth effect is a necessary step in the InSAR processing. It makes the phase of interferogram exhibit the contour line of terrain and makes the unwrapping processing easily. In this paper, the reasons to form the horizontal round effect of interference pattern in InSAR imaging technology were discussed, and the common methods of eliminating the horizontal ground effect were explained suitable. .Especially, the frequency shift method was introduced because it is easy to implement. A high accurate algorithm to obtain a precision result based on frequency shift and linear interpolation technology was proposed. The interferogram after earth flat removal can show the geometry directly and greatly reduce the number of the phase residual. The actual data shows the feasibility and validity of the algorithm in the processing.
A variance analysis for POMDP policy evaluation
Authors: Mahdi Milani Fard, Joelle Pineau, Peng Sun
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

Partially Observable Markov Decision Processes have been studied widely as a model for decision making under uncertainty, and a number of methods have been developed to find the solutions for such processes. Such studies often involve calculation of the value function of a specific policy, given a model of the transition and observation probabilities, and the reward. These models can be learned using labeled samples of on-policy trajectories. However, when using empirical models, some bias and variance terms are introduced into the value function as a result of imperfect models. In this paper, we propose a method for estimating the bias and variance of the value function in terms of the statistics of the empirical transition and observation model. Such error terms can be used to meaningfully compare the value of different policies. This is an important result for sequential decision-making, since it will allow us to provide more formal guarantees about the quality of the policies we implement. To evaluate the precision of the proposed method, we provide supporting experiments on problems from the field of robotics and medical decision making.
Method for Custom Facial Animation and Lip-Sync in an Unsupported Environment, Second LifeTM
Authors: Eric Chance, Jacki Morie
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

The virtual world of Second LifeTM does not offer support for complex facial animations, such as those needed for an intelligent virtual agent to lip sync to audio clips. However, it is possible to access a limited range of default facial animations through the native scripting language, LSL. Our solution to produce lip sync in this environment is to rapidly trigger and stop these default animations in custom sequences to produce the illusion that the intelligent virtual agent is speaking the phrases being heard.
On numeric solution of linear-quadratic optimal control problem by dual method
Authors: A. V. Bulatov, V. F. Krotov
Venue: Automation and Remote Control (2009)

An iterative numerical solution method is proposed for the general linear quadratic optimal control problem with phase constraints. The method is based on the extension principle. Realization features of the method are described for Matlab environment.
Sentence ordering with manifold-based classification in multi-document summarization
Authors: Paul D. Ji, Stephen Pulman
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

In this paper, we propose a sentence ordering algorithm using a semi-supervised sentence classification and historical ordering strategy. The classification is based on the manifold structure underlying sentences, addressing the problem of limited labeled data. The historical ordering helps to ensure topic continuity and avoid topic bias. Experiments demonstrate that the method is effective.
Unsupervised activity recognition using automatically mined common sense
Authors: Danny Wyatt, Matthai Philipose, Tanzeem Choudhury
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (2005)

A fundamental difficulty in recognizing human activities is obtaining the labeled data needed to learn models of those activities. Given emerging sensor technology, however, it is possible to view activity data as a stream of natural language terms. Activity models are then mappings from such terms to activity names, and may be extracted from text corpora such as the web. We show that models so extracted are sufficient to automatically produce labeled segmentations of activity data with an accuracy of 42% over 26 activities, well above the 3.89% baseline. The segmentation so obtained is sufficient to bootstrap learning, with accuracy of learned models increasing to 52%. To our knowledge, this is the first human activity inferencing system shown to learn from sensed activity data with no human intervention per activity learned, even for labeling.
User-Centered Design and Evaluation --- The Big Picture
Authors: Victoria Bellotti, Shin'Ichi Fukuzumi, Toshiyuki Asahi, Shunsuke Suzuki
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends (2009)

This paper provides a high-level overview of the field of usability evaluation as context for a panel "Systematization, Modeling and Quantitative Evaluation of Human Interface" in which several authors report on a collaborative effort to apply CogTool, an automated usability evaluation method, to mobile phone interfaces and to assess whether usability predictions made by CogTool correlate with user subjective impressions of usability. If the endeavor, which is still underway at the time of writing, is successful, then CogTool may be applied economically within the product development lifecycle to reduce the risk of usability problems.
A Simple Obstacle Detection Approach Based on Stereo Vison in ALV System
Authors: Yongquan Xia, Yong Gan, Weili Li, Shaohui Ning
Venue: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) (2009)

In ALV system based on stereo vision, the obstacle detection is one of the most important problems. In this paper, a stereo matching algorithm is proposed based on region invariant moment to detect obstacle in ALV system and several novel strategies are introduced during object region extraction stage. Firstly, a simple pre-processing method to binarize the images by the defined binarization function; Secondly, the binarization images are inner filled and segmented by using classic edge detection algorithm or outer contour tracing algorithm and regions of left and right images are detected respectively; Lastly, the regions are matched between left and right image by region invariant moment, the successful matched regions are the possible obstacle. Stereo pairs collected from ALV system are used to test the proposed algorithm, the result indicate that the approach is valid and feasible.
Model Checking Firewall Policy Configurations
Authors: Alan Jeffrey, Taghrid Samak
Venue: Proceedings of the 2009 IEEE International Symposium on Policies for Distributed Systems and Networks (2009)

The use of firewalls to enforce access control policies can result in extremely complex networks. Each individual firewall may have hundreds or thousands of rules, and when combined in a network, they may result in unexpected combined behavior. To mitigate this problem, there has been recent interest in the use of model checking techniques for analyzing the behavior of firewall policy configurations, and reporting anomalies. Existing techniques for firewall policy analysis are based on decision diagrams, most normally reduced ordered Binary Decision Diagrams (BDDs). BDDs are a rich data structure, supporting more logical operations than just solving boolean formulae. Typically, search algorithms for boolean satisfiability (so-called SAT-solvers) outperform BDDs. In this paper, we show that the extra structure provided by BDDs is not necessary for firewall policy analysis, and that SAT solvers are sufficient. This argument is supported both by theoretical analysis and by experimental data.
A New Approach for Detecting Intrusions Using Jordan/Elman Neural Networks
Authors: Hossein Karimi, Mohammad Ali Montazeri, Mohammad Davarpanah Jazi
Venue: Proceedings of the 2008 First International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing (2008)

Intrusion Detection System (IDS) is an effective tool that can help to prevent unauthorized access to network resources. A good intrusion detection system should have higher detection rate and lower false positive. A new classification system using Jordan/Elman (J/L) neural network for ID is proposed to detect intrusions from normal connections with satisfactory detection rate and false positive. Experiments and evaluations were performed with the KDD Cup 99 intrusion detection database. This system yields the same performance level or better as compared to other existing systems. Comparison with other approach based on different evaluation parameters showed that proposed approach has noticeable performance with detection rate 99.594% and false positive 0.406% and can classify the network connections with satisfactory performance.
Supporting Asynchronous Discussion Text Analysis with an Automatic Coding Approach
Authors: Biao Wu, Yanyan Li, Ronghuai Huang
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03 (2009)

Content analysis is an important method in the research of computer supported collaborative learning. However, the general manual coding method for deeper content analysis is time consuming, which greatly limits the amount and scale of the analyzed content. On the other hand, the traditional text categorization technology has been used to support the interaction texts auto-coding, but the effect is not satisfied. Therefore, this paper proposes a self-learning method based on symbol, syntax and context to automatically code the interaction text. First, according to the symbol rule base, the interaction text is automatically pre-coded with several candidate codes, then the probably best code is selected from the candidates based on the syntax rule base, and finally the code type result is revised by considering the context rule base. To verify the proposed method, we took 702 interaction records as the corpus and conducted an experiment. The experiments results show that our method is feasible and effective to support text auto-coding. Furthermore, compared with the traditional categorization method, the presented method achieved a better coding result with the precision rate 76.5% and recall rate 85.8%.