ppxscal/embedding_network huggingface dataset:
Comparison of relevance learning vector quantization with other metric adaptive classification methods
Authors: Th. Villmann, F. Schleif, B. Hammer
Venue: Neural Networks (2006)

The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability of metric adaptation but based on different concepts are considered in comparison to variants of relevance learning vector quantization. We compare these methods with respect to their theoretical motivation and we demonstrate the differences of their behavior for several real world data sets.
Energy-efficient Clustering with Fast Data Compression in Sensor Networks
Authors: Xiaoling Wu, Mohammed A. U. Khan, Jinsung Cho, Sungyoung Lee, Young-Koo Lee
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01 (2006)

Wireless sensor networks (WSNs) are used for various applications, such as habitat monitoring, automation, agriculture, and security. In order to send information from very high number of sensor nodes to the base station, it is necessary and economical to group sensors into clusters to prolong network lifetime. Due to the resource limitation of sensor nodes, the collected information from sensor nodes in the cluster has to be compressed quickly and precisely for transmission. In this paper, we propose a VQ-LBG based approach for cluster formation in WSN. The most distinguishing feature of the proposed method is that both energy efficient cluster formation and fast data compression can be guaranteed. Experiment shows its great improvement over other related methods.
A generic approach to the design of M-channel uniform-band perfect-reconstruction linear-phase FIR filter banks
Authors: P. Saghizadeh
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

A generic approach is presented for the design of uniform-band M-channel (M/spl ges/2) perfect-reconstruction FIR filter banks employing linear-phase analysis and synthesis filters. The technique designs on the impulse responses of the analysis filters directly. The design problem is formulated as a quadratic programming problem. The perfect reconstruction feature of the filter bank can either be implicitly enforced through a set of mathematical relationships among the analysis filters' coefficients, or through a set of constraints in the optimization program. The former approach results in a filter bank whose PR (perfect reconstruction) feature's dependency on hardware and software is eliminated or, at least minimized. The criterion for optimality is "least-squares". The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Recursive Bayesian location of a discontinuity in time series
Authors: J. J. O Ruanaidh, W. J. Fitzgerald, K. J. Pope
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

This paper presents a Bayesian approach to the location of a discontinuity in linearly modelled data. A matrix formulation is introduced which allows the modelling of changepoints in general linear models. Linear models investigated include abrupt changes in the mean of a Gaussian random variable, and piecewise polynomials such as splines, as well as autoregressive models. The approach facilitates the removal of nuisance parameters by integration. A general recursive technique for updating Bayesian posterior densities, which can result in large savings in computation, is also described.
The use of agents in human learning systems
Authors: Elizabeth Sklar, Debbie Richards
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

This paper examines agent-based systems designed for a variety of human learning tasks. These are typically split into two areas: "training", which generally refers to adult learning of job-related skills, frequently but not exclusively in military settings; and "education", which generally refers to child and adult learning in academic settings, including primary and secondary schools, colleges and universities. While the terms may indicate diverse areas within the field of human learning, from the standpoint of agent-based systems development, many of the more prominent issues are held in common; as well, these issues can be generalized to most interactive agent-based environments. Here, we categorize three major trends in development of agents to assist human learners: pedagogical agents, peer learning agents and demonstrating agents. We highlight recent work within each of these categories, bringing to light common themes and issues.
Significance of Gene Ranking for Classification of Microarray Samples
Authors: Chaolin Zhang, Xuesong Lu, Xuegong Zhang
Venue: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) (2006)

Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this problem by formulating it in the framework of hypothesis testing and propose a solution based on resampling. The proposed r-test methods convert gene ranking results into position p-values to evaluate the significance of genes. The methods are tested on three real microarray data sets and three simulation data sets with support vector machines as the method of classification and gene selection. The obtained position p-values help to determine the number of genes to be selected and enable scientists to analyze selection results by sophisticated multivariate methods under the same statistical inference paradigm as for simple hypothesis testing methods.
A measurement study on the impact of routing events on end-to-end internet path performance
Authors: Feng Wang, Zhuoqing Morley Mao, Jia Wang, Lixin Gao, Randy Bush
Venue: ACM SIGCOMM Computer Communication Review (2006)

Extensive measurement studies have shown that end-to-end Internet path performance degradation is correlated with routing dynamics. However, the root cause of the correlation between routing dynamics and such performance degradation is poorly understood. In particular, how do routing changes result in degraded end-to-end path performance in the first place? How do factors such as topological properties, routing policies, and iBGP configurations affect the extent to which such routing events can cause performance degradation? Answers to these questions are critical for improving network performance.In this paper, we conduct extensive measurement that involves both controlled routing updates through two tier-1 ISPs and active probes of a diverse set of end-to-end paths on the Internet. We find that routing changes contribute to end-to-end packet loss significantly. Specifically, we study failover events in which a link failure leads to a routing change and recovery events in which a link repair causes a routing change. In both cases, it is possible to experience data plane performance degradation in terms of increased long loss burst as well as forwarding loops. Furthermore, we find that common routing policies and iBGP configurations of ISPs can directly affect the end-to-end path performance during routing changes. Our work provides new insights into potential measures that network operators can undertake to enhance network performance.
Energy-aware scheduling with quality of surveillance guarantee in wireless sensor networks
Authors: Jaehoon Jeong, Sarah Sharafkandi, David H. C. Du
Venue: Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks (2006)

We propose and evaluate an energy-efficient scheduling algorithm for detection of mobile targets in wireless sensor networks. We consider a setting where the sensors are deployed for both road surveillance and mobile target tracking. A typical example would be where some sensors are deployed along the entrance roads of a city to detect the vehicles entering the city and other sensors can wake up and track the vehicles after detection. We show an important relationship between the overall energy consumed by the sensors and the average detection time of a target, both of which are very critical aspects in our problem. To this end, we define the quality of surveillance (QoSv)as the reciprocal value of the average detection time for vehicles. We propose an optimal scheduling algorithm that guarantees the detection of every target with specified QoSv and at the same time minimizes the overall energy consumed by the sensor nodes. By minimizing the energy consumed, we maximize the lifetime of the sensor network. Also, along with the quality of surveillance guarantee, we ensure that no target goes undetected. We theoretically derive the upper bound on the lifetime of the sensor network for a given QoSv guarantee and prove that our method can always achieve this upper bound. Our simulation results validate the claims made on the algorithm optimality and QoSv guarantee.
Robust Fragments-based Tracking using the Integral Histogram
Authors: Amit Adam, Ehud Rivlin, Ilan Shimshoni
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

We present a novel algorithm (which we call "Frag- Track") for tracking an object in a video sequence. The template object is represented by multiple image fragments or patches. The patches are arbitrary and are not based on an object model (in contrast with traditional use of modelbased parts e.g. limbs and torso in human tracking). Every patch votes on the possible positions and scales of the object in the current frame, by comparing its histogram with the corresponding image patch histogram. We then minimize a robust statistic in order to combine the vote maps of the multiple patches. A key tool enabling the application of our algorithm to tracking is the integral histogram data structure [18]. Its use allows to extract histograms of multiple rectangular regions in the image in a very efficient manner. Our algorithm overcomes several difficulties which cannot be handled by traditional histogram-based algorithms [8, 6]. First, by robustly combining multiple patch votes, we are able to handle partial occlusions or pose change. Second, the geometric relations between the template patches allow us to take into account the spatial distribution of the pixel intensities - information which is lost in traditional histogram-based algorithms. Third, as noted by [18], tracking large targets has the same computational cost as tracking small targets. We present extensive experimental results on challenging sequences, which demonstrate the robust tracking achieved by our algorithm (even with the use of only gray-scale (noncolor) information).
Multi moving people detection from binocular sequences
Authors: Yang Ran, Qinfen Zheng
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

A novel approach for detection of multiple moving objects from binocular video sequences is reported. First an efficient motion estimation method is applied to sequences acquired from each camera. The motion estimation is then used to obtain cross camera correspondence between the stereo pair. Next, background subtraction is achieved by fusion of temporal difference and depth estimation. Finally moving foregrounds are further segmented into moving object according to a distance measure defined in a 2.5D feature space, which is done in a hierarchical strategy. The proposed approach has been tested on several indoor and outdoor sequences. Preliminary experiments have shown that the new approach can robustly detect multiple partially occluded moving persons in a noisy background. Representative human detection results are presented.
Automatic Composition and Simulation System for Ballet Sequences
Authors: Asako Soga, Bin Umino, Takami Yasuda, Shigeki Yokoi
Venue: Proceedings of the 2006 International Conference on Cyberworlds (2006)

We have developed an automatic composition system for ballet choreographies by using 3DCG animation. Our goal is to develop some useful tools in dance education such as creation-support system for ballet teachers and self-study system for students. The algorithm for automatic composition was integrated to create utilitarian choreographies. As a result of an evaluation test, we verified that the created choreographies had a possibility to be used in the actual lessons. This system is valuable for online virtual dance experimentation and exploration by teachers and choreographers involved in creative practices, improvisation, creative movement, or dance composition.
Multi-agent based community computing system development with the model driven architecture
Authors: Yuna Jung, Jungtae Lee, Minkoo Kim
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Ubiquitous system can be considered as a system, in which many kinds of component are connected by networks and the components cooperate with each other for achieving some goals. To develop a ubiquitous system, several researches have used multi-agent models. Even thought current multi-agent models provide many benefits with ubiquitous system developments, they are not enough to develop ubiquitous systems. In this paper, we address the ubiquitous system which solves its problems with cooperative organization consisting of several heterogeneous computing components. We call such a ubiquitous system as a community computing system. In the community computing system, some goals are given to be achieved by the cooperative organization. In this paper, we propose a high-level abstraction model of multi-agent based community computing system. In addition, we propose the development process of the community computing system using MDA(Model Driven Architecture) approach.
Implementation Challenges in Real-Time Middleware for Distributed Autonomous Systems
Authors: Vincenzo Liberatore
Venue: Proceedings of the 2nd IEEE International Conference on Space Mission Challenges for Information Technology (2006)

Exploration missions will achieve a sustainable humanrobotic presence in space and on the surface of the Moon and of Mars. Sustained operations require higher performance, which in turn implies a higher degree of asset autonomy. For example, construction, maintenance, or insitu resource utilization will be accomplished by efficiently executing complex tasks in the absence of long-haul teleoperation from Earth. Moreover, assets will communicate with each other and form a distributed system. For example, multiple units will collaborate on, say, a construction task. The resulting system is both autonomous (i.e., it can operate in the absence of direct Earth tele-operation) and distributed (i.e., multiple assets communicate and collaborate with each other). Robustness and economy further demand the re-usability of tested middleware whenever possible. Finally, the distributed autonomous systems will include assets that operate in real-time: examples are surface robots, rovers, CEV on-board actuation units, or space construction robots. In this paper, we describe the transversal challenges in implementing real-time middleware components for distributed autonomous systems. The emphasis is on reusable components that address issues that are shared among distributed autonomous real-time systems. Specific applications are not the focus of this paper.
Importance sampling simulation in the presence of heavy tails
Authors: Achal Bassamboo, Sandeep Juneja, Assaf Zeevi
Venue: Proceedings of the 37th conference on Winter simulation (2005)

We consider importance sampling simulation for estimating rare event probabilities in the presence of heavy-tailed distributions that have polynomial-like tails. In particular, we prove the following negative result: there does not exist an asymptotically optimal state-independent change-of-measure for estimating the probability that a random walk (respectively, queue length for a single server queue) exceeds a "high" threshold before going below zero (respectively, becoming empty). Furthermore, we derive explicit bounds on the best asymptotic variance reduction achieved by importance sampling relative to na&iuml;ve simulation. We illustrate through a simple numerical example that a "good" state-dependent change-of-measure may be developed based on an approximation of the zero-variance measure.
Real-Time Scheduling with Regenerative Energy
Authors: C. Moser, L. Thiele, L. Benini, D. Brunelli
Venue: Proceedings of the 18th Euromicro Conference on Real-Time Systems (2006)

This paper investigates real-time scheduling in a system whose energy reservoir is replenished by an environmental power source. The execution of tasks is deemed primarily energy-driven, i.e., a task may only respect its deadline if its energy demand can be satisfied early enough. Hence, a useful scheduling policy should account for properties of the energy source, capacity of the energy storage as well as power dissipation of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we state and prove optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Furthermore, an offline schedulability test for a set of periodic or even bursty tasks is presented. Finally, we validate the proposed theory by means of simulation and compare our algorithms with the classical Earliest Deadline First Algorithm.
Requirements engineering in health care: the example of chemotherapy planning in paediatric oncology
Authors: Sebastian Garde, Petra Knaup
Venue: Requirements Engineering (2006)

Health care is characterized by highly complex processes of patient care that require unusual amount of communication between different health care professionals of different institutions. Sub-optimal processes can significantly impact on the patient&#x2019;s health, increase the consumption of services and resources and in severe cases can lead to the patient death. For these reasons, requirements engineering for the development of information technology in health care is a complex process as well: without constant and rigorous evaluation, the impact of new systems on the quality of care is unknown and it is possible that badly designed systems significantly harm patients. To overcome these limitations, we present and discuss an approach to requirements engineering that we applied for the development of applications for chemotherapy planning in paediatric oncology. Chemotherapy planning in paediatric oncology is complex and time-consuming and errors must be avoided by all means. In the multi-hospital/multi-trial-centre environment of paediatric oncology, it is especially difficult and time-consuming to analyse requirements. Our approach combines a grounded theory approach with evolutionary prototyping based on the constant development and refinement of a generic domain model, in this case a domain model for chemotherapy planning in paediatric oncology. The prototypes were introduced in medical centres and final results show that the developed generic domain model is adequate.
A Novel Statistical Model for Speech Recognition and POS Tagging
Authors: Lichi Yuan, Zhigang Chen
Venue: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (2006)

Hidden Markov model is a statistical model which has been applied successfully to speech recognition and natural language processing. However, it is based on three assumptions: (1) limited horizon, (2) time invariant (stationary), (3)the independence assumption of observations within a state. These assumptions are too strong from the view of the statistics and are also unreaistic. In order to overcome the defects of the classical HMM, Markov Family model, a new statistical models is proposed in this paper. The speaker independent continuous speech recognition experiments and the Part-of-Speech tagging experiments show that Markov Family models (MFMs) have higher performance than Hidden Markov models (HMMs).
Cohesion Analysis in Linux Kernel
Authors: Vinay Kumar Reddy, D. Janakiram
Venue: Proceedings of the XIII Asia Pacific Software Engineering Conference (2006)

It is widely accepted that strong coupling such as common coupling should be used with caution among modules. Linux kernel is analyzed in terms of these common coupling instances and the results show the presence of excessive common coupling among modules. It is mentioned that unless the kernel is restructured with a bare minimum of common coupling, it would be exceedingly difficult to maintain kernel in the near future. We attribute this problem to the lack of data abstractions in the kernel. To support our argument, we have analyzed the cohesion in the Linux kernel both at the module level (file level) and function level. Although the cohesion at the function level is high, low cohesion prevails at the module level indicating the lack of focus on data abstractions at the module level. We believe that this is an inherent drawback in procedural paradigm where functionality is considered first rather than data. Hence, we suggest to migrate the kernel to object-oriented paradigm to minimize coupling among modules and increase cohesion within the modules. As performance is one of the prime concerns for an OS kernel, object oriented features can be introduced in the kernel in an incremental fashion using the technique of object-oriented wrappers.
Role of Familiar Size in the Control of Grasping
Authors: J. J. Marotta, M. A. Goodale
Venue: Journal of Cognitive Neuroscience (2001)

The present study examined whether the learned pictorial depth cue of "familiar size" could be used to plan a reaching and grasping movement in the absence of binocular vision. Sixteen right-handed subjects were presented with two different arrays, under monocular and binocular viewing conditions, in which a range of different "grasp-sized" spheres that were lit from within could be presented in an otherwise darkened environment. In the "familiar-size" presentation array, only one "standard" sized sphere was presented, which gave subjects an opportunity to learn the relationship between the standard sphere's retinal image size and its distance. In the "multiple" spheres presentation array, subjects could not learn such a relationship because on any one trial, one of four different sphere sizes could be present. In a second experiment, the effects of this paradigm on six subjects' perceptual reports of distance were examined by having subjects slide their index fingers apart along a horizontal rod to indicate the estimated distance of the spheres. When familiar size could not be used as a cue to distance, subjects produced more on-line corrections in their reaching and grasping movements to the standard-sized spheres - but only under monocular viewing conditions. It appears that subjects are able to exploit the learned relationship between an object's distance and its projected retinal image size to help program and control reaching and grasping movements when binocular vision is not available. Although the influence of familiar size on subjects' perceptual estimates is less clear, it is clear that subjects' perceptual estimates show poor absolute scaling for distance. This result further supports the notion that under normal viewing conditions the visuomotor system uses binocular information to program and control manual prehension, but is able to use pictorial information when binocular vision is denied.