ppxscal/embedding_network huggingface dataset:
Bayesian methods for finding sparse representations
Authors: Bhaskar D. Rao
Venue:  (2006)

Finding the sparsest or minimum &ell;0-norm representation of a signal given a (possibly) overcomplete dictionary of basis vectors is an important problem in many application domains, including neuroelectromagnetic source localization, compressed sensing, sparse component analysis, feature selection, image restoration/compression, and neural coding. Unfortunately, the required optimization is typically NP-hard, and so approximate procedures that succeed with high probability are sought. Nearly all current approaches to this problem, including orthogonal matching pursuit (OMP), basis pursuit (BP) (or the LASSO), and minimum &ell; p quasi-norm methods, can be viewed in Bayesian terms as performing standard MAP estimation using a fixed, sparsity-inducing prior. In contrast, we advocate empirical Bayesian approaches such as sparse Bayesian learning (SBL), which use a parameterized prior to encourage sparsity through a process called evidence maximization. We prove several results about the associated SBL cost function that elucidate its general behavior and provide solid theoretical justification for using it to find maximally sparse representations. Specifically, we show that the global SBL minimum is always achieved at the maximally sparse solution, unlike the BP cost function, while often possessing a more limited constellation of local minima than comparable MAP methods which share this property. We also derive conditions, dependent on the distribution of the nonzero model weights embedded in the optimal representation, such that SBL has no local minima. Finally, we demonstrate how a generalized form of SBL, out of a large class of latent-variable models, uniquely satisfies two minimal performance criteria directly linked to sparsity. These results lead to a deeper understanding of the connections between various Bayesian-inspired strategies and suggest new sparse learning algorithms. Several extensions of SBL are also considered for handling sparse representations that arise in spatio-temporal settings and in the context of covariance component estimation. Here we assume that a small set of common features underly the observed data collected over multiple instances. The theoretical properties of these SBL-based cost functions are examined and evaluated in the context of existing methods. The resulting algorithms display excellent performance on extremely large, ill-posed, and ill-conditioned problems in neuroimaging, suggesting a strong potential for impacting this field and others.
How Feasible Are Star Wars Mid-air Displays
Authors: Ismo Rakkolainen
Venue: Proceedings of the 11th International Conference Information Visualization (2007)

There has been wide interest and even enthusiasm towards 3D and mid-air displays for well over a century. Recently many kinds of "holographic" displays have been seen in sci-fi movies like Star Wars" etc. Media or 3D display manufacturers nowadays often claim that a specific novel display is like the ones seen in Star Wars. We survey and assess if, how and to what extent the Star Wars mid-air displays are realizable with contemporary display technology, what are the strengths and weaknesses of various approaches, and when we can expect the mid-air displays to take over the display market.
Sizing Maintenance Tasks for Web Applications
Authors: Harry M. Sneed, Shihong Huang
Venue: Proceedings of the 11th European Conference on Software Maintenance and Reengineering (2007)

Web applications are fast becoming the new legacy systems of today. While there is considerable similarity between traditional software systems and Web-based systems, there are also significant differences between them. One area that illustrates this dual nature is cost and effort estimation. There exists a mature body of knowledge for performing such estimates on traditional software systems, but such methods may not appear to be directly applicable to Web applications. This paper presents an effort estimation technique for maintaining a large-scale Web application by measuring and tracking the size and complexity of the Web-based system. Specifically, a combination of function-points and static impact analysis is used, tracing the change requests to different components of the Web application, and then measuring their size and complexity to aid the cost estimation for that particular change request based on function point productivity measurements. To illustrate the use of this technique, a case study from a real-world industrial product is presented.
Guest Editors' Introduction: Attacking Functional Verification through Hybrid Techniques
Authors: Jayanta Bhadra, Magdy S. Abadir, Li-C. Wang
Venue: IEEE Design Test (2007)

Hybrid techniques are effective for exploring interesting corner cases, coverage holes, invariant variations, and so forth, in the general area of directed functional validation. However, despite the emergence of several effective hybrid validation techniques, several questions still remain. The five articles in this special issue help explain some of the different facets of this area.
Profile-Based Sustained Service Lookup in Areas of Sudden Dense Population
Authors: Sherif G. Aly, Amal A. El-Nahas
Venue: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02 (2007)

This paper addresses some of the challenges associated with providing location-based services to mobile users in areas of sudden density increases. We adopt a profile-based peer to peer mode that allows host queries, upon density increase, to obtain their data from built in logical entities within our system called autonomous systems. Autonomous systems are a composite of elite mobile devices selected upon the occurrence of sudden density to cache service data. This work expands on a previously published work by us that demonstrated how sudden densities are detected, how autonomous systems are selected and formed, and how data is cached. In this paper, autonomous systems formation, distribution, and record caching are made highly sensitive to individual users profiles. Simulation and experimental results demonstrate that this approach decreased the latency associated with service lookups as well as increasing the ability to accommodate more mobile users at times of disrupted services.
Going beyond the sandbox: an overview of the new security architecture in the javaTM development Kit 1.2
Authors: Li Gong, Marianne Mueller, Hemma Prafullchandra, Roland Schemers
Venue: Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems (1997)

This paper describes the new security architecture that has been implemented aspart of JDK1.2, the forthcoming JavaTM Development Kit. In going beyond the sandbox security model in the original release of Java, JDK1.2 provides fine-grained access control via an easily configurable security policy. Moreover, JDK1.2 introduces the concept of protection domain and a few related security primitives that help to make the underlying protection mechanism more robust.
Analysis of Operator's Visual Process Using a Human Information Processing Model
Authors: Yinhua Jin, Zhenye Li
Venue: Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops (2006)

In order to understand the generation mechanism of various types of human errors in industrial production, we have developed a human information-processing model to simulate human operators' behavior under abnormal situations. In this study we proposed a new human model and installed the model on a PC. The developed model is used to analyze the visual process when the operator monitors the overview panel of a plant simulator. The visual process is decided by characteristics of the panel information, the human operator's mental and physical conditions, parameters of the perceptual processor and so on. The simulation results coincided qualitatively with observations of actual plant operations and simulator training. Accordingly, we can study how to prevent human errors and evaluate the effects of various measures using the human model.
MIDI Gestural Control of a VST Plugin Using an IR Sensor
Authors: L. Martinez, F. Guerrero
Venue: Proceedings of the 17th International Conference on Electronics, Communications and Computers (2007)

In this work the development and construction of a digital audio effects gestural controller using IR sensors is presented. The design of this systems is based on an optical IR Theremin capable of controlling an user-specified parameter using the proximity of the performers hand or foot.
Estimation of the number of syllables using hidden markov models and design of a dysarthria classifier using global statistics of speech
Authors: Robert Kubichek
Venue:  (2006)

Acoustic analysis of speech is gaining popularity for diagnosing speech disorders. Research is always carried out to develop objective measures that correlate well with perceptual judgments to assess speech characteristics. Speech rate is one such characteristic that is often studied to assess a medical condition in a subject. Speech rate also has applications in automatic speech recognition (ASR) systems as a very high or very low speech rate reduces the system accuracy. A novel hidden Markov model (HMM) based method to count the number of syllables in continuous speech has been developed in this research. This number of syllables can then be used to estimate speech rate in syllables/second. The method has been tested on normal and dysarthric speech. For normal, an error of about 15% is obtained, which is better than presently available techniques, while for dysarthric subjects, the error was 27%. The approach was also tested on diadochokinetic speech of dysarthric subjects, which includes repetitions of one-to-three syllables. An error of 6.9% is obtained with discrete HMMs. Another aspect of this research is the design of a novel dysarthria classifier using global statistics of speech. Dysarthria is a class of neurological disorders that hampers speech production. Depending on the location of lesion in the nervous system, dysarthria can be classified into five main types. Clinicians find it useful to identify the type of dysarthria of a subject to determine the location of injury and to structure the treatment and therapy required. There is evidence suggesting a cause-effect relationship between the dysarthria type and the resulting speech characteristics. Therefore, the observed speech characteristics in a subject can be analyzed to identify the type of dysarthria. The dysarthria classifier designed in this research uses global statistics (mean, variance, etc.) of features such as Mel-frequency cepstral coefficients, perceptual linear prediction, etc., as inputs. An error of 10.5% is achieved for a tree-like classifier that distinguishes between amyotrophic lateral sclerosis (ALS), flaccid dysarthria (FD) and spastic dysarthria (SD). The performance of the classifier is compared with some commercially available software for making decision trees.
Catching "Moles" in Sensor Networks
Authors: Fan Ye, Hao Yang, Zhen Liu
Venue: Proceedings of the 27th International Conference on Distributed Computing Systems (2007)

False data injection is a severe attack that compromised sensor nodes ("moles"') can launch. These moles inject large amount of bogus trafJic that can lead to application failures and exhausted network resources. Existing sensor network security proposals only passively mitigate the damage by filtering injected packets; they do not provide active means for fight back. This paper studies how to locate such moles within the framework of packet marking, when forwarding moles collude with source moles to manipulate the marks. Existing Internet traceback mechanisms do not assume compromised forwarding nodes and are easily defeated by manipulated marks. We propose a Probabilistic Nested Marking (PNM) scheme that is secure against such colluding attacks. No matter how colluding moles manipulate the marks, PNM can always locate them one by one. We prove that nested marking is both sufJicient and necessaly to resist colluding attacks. PNM also has fast-traceback: within about 50 packets, it can track down a mole up to 20 hops away from the sink. This virtually prevents any effective data injection attack: moles will be caught before they have injected any meaningful amount of bogus traffic.
DSP lab course experiments based on the Motorola DSP56K processor
Authors: D. Baez-Lopez
Venue: Proceedings of the Frontiers in Education Conference, 2001. on 31st Annual - Volume 01 (2001)

This paper describes a set of real time experiments for a digital signal processing undergraduate course. These experiments are based upon the Motorola 56K DSP processor. The main purpose of the experiments described is to allow students to appreciate the potential of DSP processors to implement digital filters. The experiments described range from function generation to IIR and FIR filter design. The code is available to interested instructors as shareware files. Data for the digital filters designed and implemented in these experiments is obtained from the MATLAB Signals Toolbox and from other design packages available.
Reconstruction of chaotic dynamics using a noise-robust embedding method
Authors: W. Yoshida
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01 (2000)

In this article, we discuss the reconstruction of chaotic dynamics in a partial observation situation. As a function approximator, we employ a normalized Gaussian network (NGnet), which is trained by an on-line EM algorithm. In order to deal with the partial observation, we propose a new embedding method based on smoothing filters, which is called integral embedding. The NGnet is trained to learn the dynamical system in the integral coordinate space. Experimental results show that the trained NGnet is able to reproduce a chaotic attractor that well approximates the complexity and instability of the original chaotic attractor, even when the data involve large noise. In comparison with our previous method using delay coordinate embedding, this new method is more robust to noise and faster in learning.
Secret image transmission based on VQ and data embedding
Authors: Shih-Chieh Shie, Shinfeng D. Lin
Venue: International Journal of Imaging Systems and Technology (2007)

A new secret image transmission scheme suitable for narrow communication channel is proposed in this article. A set of secret images can be simultaneously and efficiently delivered to the receiver via a small and meaningless data stream by the proposed scheme. To reduce the volume of secret images, a codebook is first generated and these secret images are encoded into binary indexes based on the vector quantization (VQ) technique. The compressed message is then embedded into the VQ codebook utilized in the encoding procedure by an adaptive least-significant-bits (LSB) modification technique. For the purpose of security, the slightly modified codebook is further encrypted into a meaningless data stream by the AES cryptosystem. Simulation results show that the proposed scheme provides an impressive improvement both in the visual quality of the extracted secret images at the receiver and in the hiding capacity of the cover medium. Experimental data also reveal the feasibility of the proposed secret image transmission scheme for limited-bandwidth environment. &copy; 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 1&ndash;9, 2007
Infrared-image classification using expansion matching filters and hidden Markov trees
Authors: P. Bharadwaj
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03 (2001)

Forward-looking infrared (FLIR) images of targets are characterized by the different target components visible in the image, with are dependent on the target-sensor orientation and target history (i.e., which target components are hot). We define a target class as a set of contiguous target-sensor orientations over which the associated image is relatively invariant, or statistically stationary. Given an image from an unknown target, the objective is proper target-class association (target identity and pose). Our principal contribution is an image classifier in which a distinct set of templates is designed for each image class, with templates linked to the object sub-components, and the associated statistics are characterized via a hidden Markov model. In particular, we employ expansion matching (EXM) filters to identify the presence of the target components in the image, and use a hidden Markov tree (HMT) to characterize the statistics of the correlation of the image with the various templates. We achieve a successful classification rate of 92% on a data set of FLIR vehicle images, compared with 72% for a previously developed wavelet-feature-based HMT technique.
Improved handover performance in wireless mobile IPv6
Authors: T. Cornall, B. Pentland, Khee Pang
Venue: Proceedings of the The 8th International Conference on Communication Systems - Volume 02 (2002)

When a mobile station using IEEE 802.11b for wireless connection moves from the area serviced by an Access Point on one wireless network to the area serviced by an Access Point on another network, there is a break in connectivity at the data-link layer. This is made worse because there is also a break in connectivity due to the need for the mobile station to find a new router and to update the address binding on the correspondent nodes and home agent. This article discusses measurements of this disruption and the factors that contribute to it and then discusses methods of reducing the interruption using information from the data-link layer to facilitate the reconnection at the network layer. There is also brief discussion of methods to reduce the data-link layer handover delay.
HMM topology optimization for handwriting recognition
Authors: Danfeng Li
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03 (2001)

This paper addresses the problem of hidden Markov model (HMM) topology estimation in the context of on-line handwriting recognition. HMM have been widely used in applications related to speech and handwriting recognition with great success. One major drawback with these approaches, however, is that the techniques that they use for estimating the topology of the models (number of states, connectivity between the states and the number of Gaussians), are usually heuristically derived, without optimal certainty. This paper addresses this problem, by comparing a couple of commonly used heuristically derived methods to an approach that uses the Bayesian information criterion (BIC) for computing the optimal topology. Experimental results on discretely written letters show that using BIC gives comparable results to heuristic approaches with a model that has nearly 10% fewer parameters.
An Application of Learning Problem in Anomaly-based Intrusion Detection Systems
Authors: Veselina G. Jecheva, Evgeniya P. Nikolova
Venue: Proceedings of the The Second International Conference on Availability, Reliability and Security (2007)

The present paper introduces an approach to anomalybased intrusion detection using the Hidden Markov Models (HMM) and the BCJR decoding algorithm. The main idea is to distinguish the normal traces of user activity from abnormal ones using the BCJR decoding algorithm applied in conjunction with HMM parameters adjustment using the gradient based method. Some results from the conducted simulation experiments are introduced as well.
Efficient operation of coded packet networks
Authors: Muriel Medard
Venue:  (2006)

A fundamental problem faced in the design of almost all packet networks is that of efficient operation---of reliably communicating given messages among nodes at minimum cost in resource usage. We present a solution to the efficient operation problem for coded packet networks, i.e., packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. Such networks are in contrast to conventional, routed packet networks, where outgoing packets are restricted to being copies of received packets and where reliability is provided by the use of retransmissions. This thesis introduces four considerations to coded packet networks: (1) efficiency, (2) the lack of synchronization in packet networks, (3) the possibility of broadcast links, and (4) packet loss. We take these considerations and give a prescription for operation that is novel and general, yet simple, useful, and extensible. We separate the efficient operation problem into two smaller problems, which we call network coding---the problem of deciding what coding operation each node should perform given the rates at which packets are injected on each link---and subgraph selection---the problem of deciding those rates. Our main contribution for the network coding problem is to give a scheme that achieves the maximum rate of a multicast connection tinder the given injection rates. As a consequence, the separation of network coding and subgraph selection results in no loss of optimality provided that we are constrained to only coding packets within a single connection. Our main contribution for the subgraph selection problem is to give distributed algorithms that optimally solve the single-connection problem under certain assumptions. Since the scheme we propose for network coding can easily be implemented in a distributed manner, we obtain, by combining the solutions for each of the smaller problems, a distributed approach to the efficient operation problem. We assess the performance of our solution for three problems: minimum-transmission wireless unicast minimum-weight wireline multicast, and minimum-energy wireless multicast. We find that our solution has the potential to offer significant efficiency improvements over existing techniques in routed packet networks, particularly for multi-hop wireless networks. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
A novel linear technique to estimate the epipolar geometry
Authors: E. Izquierdo
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03 (2001)

The accurate reconstruction of the 3D scene structure from two different projections and the estimation of the camera scene geometry is of paramount importance in many computer vision tasks. Most of the information about the camera-scene geometry is encapsulated in the fundamental matrix. Estimating the fundamental matrix has been an object of research for many years and continues to be a challenging task in current computer vision systems. While nonlinear iterative approaches have been successful in dealing with the high instability of the underlying problem, their inherent large workload makes these approaches inappropriate for real-time applications. Practical aspects of highly efficient linear methods are studied and a novel low-cost and accurate linear algorithm is introduced. The performance of the proposed approach is assessed by several experiments on real images.