ppxscal/embedding_network huggingface dataset:
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Authors: Jae-Gil Lee, Jiawei Han, Xiaolei Li, Hector Gonzalez
Venue: Proceedings of the VLDB Endowment (2008)

Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-world applications. A number of methods have been reported in the literature, but due to using the shapes of whole trajectories for classification, they have limited classification capability when discriminative features appear at parts of trajectories or are not relevant to the shapes of trajectories. These situations are often observed in long trajectories spreading over large geographic areas. Since an essential task for effective classification is generating discriminative features, a feature generation framework TraClass for trajectory data is proposed in this paper, which generates a hierarchy of features by partitioning trajectories and exploring two types of clustering: (1) region-based and (2) trajectory-based. The former captures the higher-level region-based features without using movement patterns, whereas the latter captures the lower-level trajectory-based features using movement patterns. The proposed framework overcomes the limitations of the previous studies because trajectory partitioning makes discriminative parts of trajectories identifiable, and the two types of clustering collaborate to find features of both regions and sub-trajectories. Experimental results demonstrate that TraClass generates high-quality features and achieves high classification accuracy from real trajectory data.
Asymmetric ratio and FCM based salient channel selection for human emotion detection using EEG
Authors: M. Rizon, M. Murugappan, R. Nagarajan, S. Yaacob
Venue: WSEAS Transactions on Signal Processing (2008)

Electroencephalogram (EEG) is one of the most reliable physiological signals used for detecting the emotional states of human brain. We propose Asymmetric Ratio (AR) based channel selection for human emotion recognition using EEG. Selection of channels reduces the feature size, computational load requirements and robustness of emotions classification. We address this crisis using Asymmetric Variance Ratio (AVR) and Amplitude Asymmetric Ratio (AAR) as new channel selection methods. Using these methods the 28 homogeneous pairs of EEG channels is reduced to 4 and 2 channel pairs respectively. These methods significantly reduce the number of homogeneous pair of channels to be used for emotion detection. This approach is illustrated with 5 distinct emotions (disgust, happy, surprise, sad, and fear) on 63 channels EEG data recorded from 5 healthy subjects. In this study, we used Multi-Resolution Analysis (MRA) based feature extraction the original and reduced set of channels for emotion classification. These approaches were empirically evaluated by using a simple unsupervised classifier, Fuzzy C-Means clustering with variable clusters. The paper concludes by discussing the impact of reduced channels on emotion recognition with larger number of channels and outlining the potential of the new channel selection method.
Evaluating multicast routing algorithms' performance and execution time
Authors: Dan Mancas, Ecaterina-Irina Manole, Nicu Enescu
Venue: WSEAS TRANSACTIONS on COMMUNICATIONS (2008)

In this paper, an evaluation of the network routing algorithms is made. Problems that arise in routing are treated, each presented in different scenarios in order to obtain a result in comparing different topologies. The comparison analysis is pursuing obtaining a result over the performance of the network. In order to measure performance, the costs of a network and the delays are aimed. After that, the topology effect is presented. In matter of performance, topology and blocking problems are strongly related. So an analysis of the blocking probability is also presented. As conclusions, solutions for the presented scenarios and also for other important scenarios are given. In all these algorithms the time problem was not yet consider until now, so the average execution time is finally analyzed.
Visualization of Procedural Abstraction
Authors: Stefan Schaeckeler, Weijia Shang, Ruth Davis
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Visualizing impacts of an optimization pass helps to reason about, and to gain insight into, the inner workings of the optimization pass. In this paper, we visualize the impacts of two procedural abstraction passes. For this, we modified two procedural abstraction post pass optimizers to visualize for each the difference in machine code before and after optimization by drawing abstracted fragments in the original program. We then explain how the generated visualizations aid in better understanding the optimization passes.
Contact identification for assembly&#x2013;disassembly simulation with&#x00a0;a&#x00a0;haptic device
Authors: Robert Iacob, Peter Mitrouchev, Jean-Claude L&#x00e9;on
Venue: The Visual Computer: International Journal of Computer Graphics (2008)

Assembly/Disassembly (A/D) simulations using haptic devices are facing difficulties while simulating insertion/extraction operations, such as removing cylinders from holes. In order to address this configuration as well as others, an approach based on contact identification between components is presented in this paper. This approach can efficiently contribute either to a new A/D simulation preparation process relying on two types of shape representations (mesh and CAD NURBS models), or directly to the real time simulation process when it is performed with 6D haptic devices. The model processing pipeline is described and illustrated to show how information can be propagated and used for contact detection. Then, the contact identification process is introduced and illustrated through an example.
DEA-Based Comprehensive Evaluation of Intelligent Knowledge
Authors: Lingling Zhang, Jie Wei, Anqiang Huang, Jun Li, Peng Zhang
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 (2008)

The paper is about how to evaluate the intelligent knowledge. We think that the criteria is not only distinguishing ability and accuracy of the algorithm, but also algorithm robustness, stability and so on. Besides positive characters listed above, a fair evaluation system of intelligent knowledge should also include negative characters, such as storage space, running time, training and testing time and costs and etc. In this paper we look positive and negative characters of data mining algorithm as the outputs and inputs of a decision making unit, and proposed a model to evaluating intelligent knowledge comprehensively using DEA.
PITTCULT: trust-based cultural event recommender
Authors: Danielle Hyunsook Lee
Venue: Proceedings of the 2008 ACM conference on Recommender systems (2008)

Typical collaborative filtering recommenders (CF) do not provide any chance for users to choose or evaluate the bases for recommendation. Once the system evaluates a group of users as being similar to a target user, her information is tailored by unknown people's taste. As a cultural event recommender, PITTCULT provides a way for users to rate the trustworthiness of other users; then, according to those ratings, a recommendation is generated. This paper explains why trust-based recommendation is necessary, and how studies using PITTCULT cope with the problems of the existing CF.
Assessment of testing methodologies for thin-film vacuum MEMS packages
Authors: Qian Li, Hans Goosen, Fred van Keulen, Joost van Beek, Guoqi Zhang
Venue: Microsystem Technologies (2008)

In this paper, a summary of the most relevant failure mechanisms of thin-film vacuum microelectromechanical systems (MEMS) packages and existing testing techniques will be presented. Then, based on analytical models for thin-film vacuum MEMS packages (volume in the order of 10E&#x2212;11&#x00a0;l), a feasibility study on options for thin-film vacuum MEMS package testing will be presented. This feasibility study leads to new insights and suggestions for future thin-film vacuum MEMS package testing.
Dynamic analysis of Ada programs for comprehension and quality measurement
Authors: Elaheh Safari-Sharifabadi, Constantinos Constantinides
Venue: Proceedings of the 2008 ACM annual international conference on SIGAda annual international conference (2008)

During maintenance and particularly during corrective and perfective tasks, systems tend to exhibit a weight gain. As a result, their quality tends to degrade. Software comprehension is vital in order to assess system quality. In this paper, we aim at deploying dynamic analysis of Ada programs for obtaining comprehension, and applying measurements to assess their quality. Program instrumentation is performed non-intrusively by AspectAda, an aspect-oriented extension to Ada which we discussed in earlier work. Events which are required for this analysis are captured as execution traces. We have defined a relational database schema to save execution traces, and a set of queries to obtain measures of quality metrics. New Ada-specific metrics are introduced and existing metrics have been adopted from the literature. Automation is also provided as a proof of concept through a prototypical tool which provides information on the run-time behavior of the system, performs measurements and provides visualization of the run-time behavior of the system through a call graph. An open source Ada program is used as a case study to demonstrate our approach.
Universe Detectors for Sybil Defense in Ad Hoc Wireless Networks
Authors: Adnan Vora, Mikhail Nesterenko, Sébastien Tixeuil, Sylvie Delaët
Venue: Proceedings of the 10th International Symposium on Stabilization, Safety, and Security of Distributed Systems (2008)

The Sybil attack in unknown port networks such as wireless is not considered tractable. A wireless node is not capable of independently differentiating the universe of real nodes from the universe of arbitrary non-existent fictitious nodes created by the attacker. Similar to failure detectors, we propose to use universe detectors to help nodes determine which universe is real. In this paper, we (i) define several variants of the neighborhood discovery problem under Sybil attack (ii) propose a set of matching universe detectors (iii) demonstrate the necessity of additional topological constraints for the problems to be solvable: node density and communication range; (iv) present $\cal SAND$ -- an algorithm that solves these problems with the help of appropriate universe detectors, this solution demonstrates that the proposed universe detectors are the weakest detectors possible for each problem.
Complete discriminant evaluation and feature extraction in kernel space for face recognition
Authors: Xudong Jiang, Bappaditya Mandal, Alex Kot
Venue: Machine Vision and Applications (2008)

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.
Adaptive Information Filtering Based on PTM Model (APTM)
Authors: Abdulmohsen Algarni, Yuefeng Li, Yue Xu
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 (2008)

Adaptive information filtering (AIF) is a challeng-ing issue for web search, as the Web contains non-structured data used by many different users. One of the main questions in AIF is how to keep the system up-to-date over time by increasing training on line with changes in the user’s needs and updating the parameters values accordingly. This paper investigates the use of Pattern Taxonomy Models (PTM) in adaptive filtering by adding an updating feature. We developed a mathematical model that updates training based on sliding windows over the positive and negative examples. Merging the scores of documents in the new windows with the old score of the system takes in to account the size of the training window and the type of document in each window. In order to test this approach, the mathematical model was implemented and tested with RCV1 data collection. The experimental results indicated that the new system improves performance of PTM.
DRAM is plenty fast for wirespeed statistics counting
Authors: Bill Lin, Jun (Jim) Xu
Venue: ACM SIGMETRICS Performance Evaluation Review (2008)

Per-flow network measurement at Internet backbone links requires the efficient maintanence of large arrays of statistics counters at very high speeds (e.g. 40 Gb/s). The prevailing view is that SRAM is too expensive for implementing large counter arrays, but DRAM is too slow for providing wirespeed updates. This view is the main premise of a number of hybrid SRAM/DRAM architectural proposals [2, 3, 4, 5] that still require substantial amounts of SRAM for large arrays. In this paper, we present a contrarian view that modern commodity DRAM architectures, driven by aggressive performance roadmaps for consumer applications (e.g. video games), have advanced architecture features that can be exploited to make DRAM solutions practical. We describe two such schemes that can harness the performance of these DRAM offerings by enabling the interleaving of counter updates to multiple memory banks. These counter schemes are the first to support arbitrary increments and decrements for either integer or floating point number representations at wirespeed. We believe our preliminary success with the use of DRAM schemes for wirespeed statistics counting opens the possibilities for broader research opportunities to generalize the proposed ideas for other network measurement functions.
Simultaneous retrieval of aerosol and ocean properties by optimal estimation: SeaWiFS case studies for the Santa Barbara Channel
Authors: W. Li, K. Stamnes, R. Spurr, J. Stamnes
Venue: International Journal of Remote Sensing (2008)

In this work, some sea viewing wide field-of-view sensor (SeaWiFS) images over the Santa Barbara Channel (SBC) are analysed. Pixel-by-pixel measurements of radiances at eight SeaWiFS channels and analytic Jacobians are simulated using a coupled atmosphere-ocean radiative transfer model. The inverse algorithm is based on optimal estimation with loosely constrained a priori data. The five-element state vector has two aerosol (optical depth at 865 nm, bimodal fraction of particles) and three marine (chlorophyll concentration, detrital/dissolved-matter absorption at 443 nm, and backscattering coefficient at 443 nm) parameters. The retrieval is stable and well posed; the results are smoother and show less spread than those derived from the standard SeaDAS v4.8 algorithm. For a 28 February 2003 SeaWiFS image, the average radiance residual is less than 1% for seven SeaWiFS channels, and less than 2% for the 765 nm channel. For a series of SBC SeaWiFS match-up cases over a 4-year period, estimated water-leaving radiances agree well with field measurements.
Mapping irrigated area in Mediterranean basins using low cost satellite Earth Observation
Authors: Thomas K. Alexandridis, George C. Zalidis, Nikolaos G. Silleos
Venue: Computers and Electronics in Agriculture (2008)

Excessive use of irrigated water in the Mediterranean has deteriorated the freshwater resources by depleting the aquifers, discharging agri-chemicals and accelerating saltwater intrusion. Several European directives outline that estimating the extent of irrigated areas in each water basin is a primary step towards sustainable natural resources management. This paper aims to identify a low cost methodology for mapping irrigated area in Mediterranean basins, using satellite Earth Observation. After evaluating several combinations of land feature mapping techniques on digitally enhanced satellite images, the one with the highest accuracy has been identified (thresholding of the second principal component). The methodology was formulated under the assumption that irrigated land can be identified by the result of irrigation, i.e. the existence of green vegetation in the semiarid summer, thus avoiding costly field surveys, and using low cost satellite imagery. The proposed methodology has been applied to two Mediterranean basins with conflicting agronomic and ecological interests, which were of a different scale. The resulting irrigated area map achieved high accuracy (up to 98.4%) and reliability (k@? up to 0.967) in both basins. The results were also displayed as irrigation intensity to improve visualisation and help identify areas of high environmental pressure on nearby wetlands.
An improved SVM method P-SVM for classification of remotely sensed data
Authors: R. Zhang, J. Ma
Venue: International Journal of Remote Sensing (2008)

A support vector machine (SVM) is a mathematical tool which is based on the structural risk minimization principle. It tries to find a hyperplane in high dimensional feature space to solve some linearly inseparable problems. SVM has been applied within the remote sensing community to multispectral and hyperspectral imagery analysis. However, the standard SVM faces some technical disadvantages. For instance, the solution of an SVM learning problem is scale sensitive, and the process is time-consuming. A novel Potential SVM (P-SVM) algorithm is proposed to overcome the shortcomings of standard SVM and it has shown some improvements. In this letter, the P-SVM algorithm is introduced into multispectral and high-spatial resolution remotely sensed data classification, and it is applied to ASTER imagery and ADS40 imagery respectively. Experimental results indicate that the P-SVM is competitive with the standard SVM algorithm in terms of accuracy of classification of remotely sensed data, and the time needed is less.
Spectral unmixing of airborne hyperspectral data for baseline mapping of mine tailings areas
Authors: N. Richter, K. Staenz, H. Kaufmann
Venue: International Journal of Remote Sensing (2008)

The Kam Kotia mine tailings areas near Timmins in Ontario, Canada have been generating and discharging acidic mine drainage (AMD) into the surrounding areas for more than 35 years, killing large areas of forest and polluting the local water system. This paper presents results from the remote sensing monitoring programme in the Kam Kotia mine. Hyperspectral TRW (Thompson Ramo Wooldridge Inc.) Imaging Spectrometer III data were acquired over the Kam Kotia mine and tailings areas. This paper describes (1) the data pre-processing (noise removal, atmospheric correction, spectral smile correction, scene-based calibration) needed to radiometrically calibrate the images and (2) a novel procedure which combines constrained spectral mixture analysis and threshold-based classification. With this developed procedure one can retrieve fraction maps of major mine tailings-related surface materials and hence generate a surface map separating green vegetation, transition zones, dead vegetation, and oxidized tailings, and calculate the extent (surficial area) of each of the zones. The four zones are correlated with the extent and degree of vegetation cover affected by tailings material and are interpreted to span respectively from very low to medium, high, and very high AMD pollution. This procedure can be used to monitor changes in the course of the boundary between affected zones and finally quantify the rehabilitation process in mine tailings areas with high vegetation cover.
Optimal contact map alignment of protein–protein interfaces
Authors: Vinay Pulim, Bonnie Berger, Jadwiga Bienkowska
Venue: Bioinformatics (2008)

The long-standing problem of constructing protein structure alignments is of central importance in computational biology. The main goal is to provide an alignment of residue correspondences, in order to identify homologous residues across chains. A critical next step of this is the alignment of protein complexes and their interfaces. Here, we introduce the program CMAPi, a two-dimensional dynamic programming algorithm that, given a pair of protein complexes, optimally aligns the contact maps of their interfaces: it produces polynomial-time near-optimal alignments in the case of multiple complexes. We demonstrate the efficacy of our algorithm on complexes from PPI families listed in the SCOPPI database and from highly divergent cytokine families. In comparison to existing techniques, CMAPi generates more accurate alignments of interacting residues within families of interacting proteins, especially for sequences with low similarity. While previous methods that use an all-atom based representation of the interface have been successful, CMAPi's use of a contact map representation allows it to be more tolerant to conformational changes and thus to align more of the interaction surface. These improved interface alignments should enhance homology modeling and threading methods for predicting PPIs by providing a basis for generating template profiles for sequence–structure alignment. Contact:bab@mit.edu; jbienkowska@gmail.com Supplementary information: Supplementary data are available at http://theory.csail.mit.edu/cmapi
Phase correlation pixel-to-pixel image co-registration based on optical flow and median shift propagation
Authors: J. G. Liu, H. Yan
Venue: International Journal of Remote Sensing (2008)

With singular value decomposition (SVD) and robust 2-dimensional fitting phase correlation algorithms, it is possible to achieve pixel-to-pixel image co-registration at sub-pixel accuracy via local feature matching. However, the method often fails in featureless and low correlation areas making it not robust for co-registration of images with considerable spectral differences and large featureless ground objects. A median shift propagation (MSP) technique is proposed to eliminate the problem, in a phase correlation and Normalized Cross-Correlation (NCC) combined approach. The experiment results using images from different sensor platforms and spectral bands indicate that the new method is very robust to featureless and low correlation areas and can achieve very accurate pixel-to-pixel image co-registration with good tolerance of spectral and spatial differences between images. The method will significantly improve change detection in various remote sensing applications.
30 years after steelman, does DoD still have a software crisis?
Authors: Joyce Tokar
Venue: Proceedings of the 2008 ACM annual international conference on SIGAda annual international conference (2008)

Dr. Tokar will investigate whether or not the Department of Defense has a software crisis today, 30 years after the 'Steelman' document, which defined the requirements for the use of software within the Department.