ppxscal/embedding_network huggingface dataset:
Dynamic prediction of architectural vulnerability from microarchitectural state
Authors: Kristen R. Walcott, Greg Humphreys, Sudhanva Gurumurthi
Venue: Proceedings of the 34th annual international symposium on Computer architecture (2007)

Transient faults due to particle strikes are a key challenge in microprocessor design. Driven by exponentially increasing transistor counts, per-chip faults are a growing burden. To protect against soft errors, redundancy techniques such as redundant multithreading (RMT) are often used. However, these techniques assume that the probability that a structural fault will result in a soft error (i.e., the Architectural Vulnerability Factor (AVF)) is 100 percent, unnecessarily draining processor resources. Due to the high cost of redundancy, there have been efforts to throttle RMT at runtime. To date, these methods have not incorporated an AVF model and therefore tend to be ad hoc. Unfortunately, computing the AVF of complex microprocessor structures (e.g., the ISQ) can be quite involved. To provide probabilistic guarantees about fault tolerance, we have created a rigorous characterization of AVF behavior that can be easily implemented in hardware. We experimentally demonstrate AVF variability within and across the SPEC2000 benchmarks and identify strong correlations between structural AVF values and a small set of processor metrics. Using these simple indicators as predictors, we create a proof-of-concept RMT implementation that demonstrates that AVF prediction can be used to maintain a low fault tolerance level without significant performance impact.
Automatic Panoramic Image Stitching using Invariant Features
Authors: Matthew Brown, David G. Lowe
Venue: International Journal of Computer Vision (2007)

This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.
An improved method for i/sub DDT/ testing in the presence of leakage and process variation
Authors: Ali Chehab, Ayman Kayssi, Anis Nazer
Venue: Proceedings of the 2004 IEEE International Workshop on Defect Based Testing (2004)

We propose in this paper a testing method for CMOS circuits that is insensitive to process variations and leakage levels. This method is based on the transient supply current (i/sub DDT/) and on the observation that current levels for different circuits on a chip scale with different runs of the process. In this method, we introduce a very simple test circuit on-chip. Then, we apply a normalization procedure that allows us to use a single threshold for all chips in different processes without prior knowledge of the process to which the circuit under test belongs. Results from various circuits show that the method is capable of improving the detection capability of threshold-based i/sub DDT/ testing for faults that would otherwise go undetected due to leakage and process variation.
Ubiquitous Water Recycle Management Service Proposal in Ubiquitous City
Authors: Sung-Je Park, Soon-Young Bae, Jae-Hoon Jin, Jong-hwan Suh, Sang-Chan Park
Venue: Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops (2006)

Concerns about Ubiquitous Computing have been raised by the development of IT technology. And available services based on Ubiquitous Computing are being researched for the realization of U-City as well. U-City that is construction schedule to Pajoo Unjeong is planning 'Blue Network' construction In 'Blue Network' has a plan to recycle water. This paper proposes service that takes advantage of U- technology and manages water recycling of 'Blue Network'.
New England's Wholesale Electricity Market, Six Years Later
Authors: Carrie Conaway, Philip A. Fedora
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Energy reliability-the ability for an energy system to meet both short-run demand and long-run capacity needs-is a critical input to a region's economic growth potential. This paper reviews the structure of New England's energy markets, focusing on the region's experiences with wholesale electricity deregulation over the last six years and highlighting their impact on system reliability. It also outlines several proposed changes to New England's electricity market design based on the lessons learned from deregulation and summarizes their potential impact on the region's energy markets.
Managing Citizen Relationships in Disasters: Hurricane Wilma, 311 and Miami-Dade County
Authors: Alexander Schellong, Thomas Langenberg
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

As recent events have shown, effective knowledge sharing has become important at all political levels, especially when disasters occur. In this paper, we present the case of Miami-Dade County, which implemented a multi-jurisdictional, multichannel environment (311/portal) and successfully utilized it during Hurricane Wilma. Drawing from our research on citizen relationship management (CiRM) and literature on absorptive capacity (ACAP), we argue that this setting increases an organization's ability to acquire, assimilate, transform, and exploit information and knowledge regarding the citizen's needs. We shall discuss implications for further CiRM research and managerial insight for emergency management at the end of the paper.
Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets
Authors: Gunjan Gupta, Alexander Liu, Joydeep Ghosh
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

In many clustering applications for bioinformatics, only part of the data clusters into one or more groups while the rest needs to be pruned. For such situations, we present Hierarchical Density Shaving (HDS), a framework that consists of a fast, hierarchical, density-based clustering algorithm. Our framework also provides a simple yet powerful 2-D visualization of the hierarchy of clusters that can be very useful for further exploration. We present results to show the effectiveness of our methods.
Least squares detection of multiple changes in fractional ARIMA processes
Authors: M. Coulon
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05 (2001)

We address the problem of estimating changes in fractional integrated ARMA (FARIMA) processes. These changes may be in the long range dependence (LRD) parameter or the ARMA parameters. The signal is divided into "elementary" segments: the objective is then to estimate the segments in which the changes occur. This estimation is achieved by minimizing a penalized least-squares criterion based on the parameter estimates computed in each segment. The optimization problem is then solved using a dynamic programming algorithm. Simulation results on synthetic data (computer network traffic) are reported.
Capability of minimum variance beamformer for direction finding for fast perturbations of array sensors
Authors: G. V. Serebryakov
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

Direction-of-arrival estimation using the minimum variance beamforming requires exact characterisation of the array in terms of its geometry and sensor gain and phase. In practice, however, the sensor gain and phase differ from nominal due measurement errors and sensor misplacement. Moreover, these errors can are changed in the time. We analyze the potential capability obtained with minimum variance beamformer algorithm in presence fast sensor gain and phase perturbations. The analytical expression for angular separation as a function of the intensity of the errors, signal power and the parameters of the adaptive processor is derived. An adaptive scheme is proposed, which uses phase compensation for random motion; the characteristics are considered. 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
Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews
Authors: Vincent Ng, Sajib Dasgupta, S. M. Niaz Arifin
Venue: Proceedings of the COLING/ACL on Main conference poster sessions (2006)

This paper examines two problems in document-level sentiment analysis: (1) determining whether a given document is a review or not, and (2) classifying the polarity of a review as positive or negative. We first demonstrate that review identification can be performed with high accuracy using only unigrams as features. We then examine the role of four types of simple linguistic knowledge sources in a polarity classification system.
Trickles: a stateless network stack for improved scalability, resilience, and flexibility
Authors: Alan Shieh, Andrew C. Myers, Emin Gün Sirer
Venue: Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2 (2005)

Traditional operating system interfaces and network protocol implementations force system state to be kept on both sides of a connection. Such state ties the connection to an endpoint, impedes transparent failover, permits denial-of-service attacks, and limits scalability. This paper introduces a novel TCP-like transport protocol and a new interface to replace sockets that together enable all state to be kept on one endpoint, allowing the other endpoint, typically the server, to operate without any per-connection state. Called Trickles, this approach enables servers to scale well with increasing numbers of clients, consume fewer resources, and better resist denial-of-service attacks. Measurements on a full implementation in Linux indicate that Trickles achieves performance comparable to TCP/IP, interacts well with other flows, and scales well. Trickles also enables qualitatively different kinds of networked services. Services can be geographically replicated and contacted through an anycast primitive for improved availability and performance. Widely-deployed practices that currently have client-observable side effects, such as periodic server reboots, connection redirection, and failover, can be made transparent, and perform well, under Trickles. The protocol is secure against tampering and replay attacks, and the client interface is backwards-compatible, requiring no changes to sockets-based client applications.
Minimal TCB Code Execution
Authors: Jonathan M. McCune, Bryan Parno, Adrian Perrig, Michael K. Reiter, Arvind Seshadri
Venue: Proceedings of the 2007 IEEE Symposium on Security and Privacy (2007)

We propose an architecture that allows code to execute in complete isolation from other software while trusting only a tiny software base that is orders of magnitude smaller than even minimalist virtual machine monitors. Our technique also enables more meaningful attestation than previous proposals, since only measurements of the security-sensitive portions of an application need to be included. We achieve these guarantees by leveraging hardware support provided by commodity processors from AMD and Intel that are shipping today.
Gauss mixture image classification for distributed sensor networks
Authors: Robert M. Gray
Venue:  (2006)

Classification algorithms based on Gauss mixture models (GMMs) provide robust and analytically tractable solutions to image classification problems. Training a GMM might use the expectation-maximization (EM) algorithm or Gauss mixture vector quantization. Application of the EM algorithm is based on the assumption that the underlying data follow a Gauss mixture distribution. The goal is to fit a GMM to the data. Gauss mixture vector quantization is a Lloyd clustering algorithm, and it requires no assumptions about the statistics of the underlying data. I first extend Gauss mixture vector quantization to design a tree-structured GMM-based classifier. Tree-structured classifiers provide a method to focus on the difficult regions of the training vector space by growing classification trees into those regions. I design a tree-structured Gauss mixture vector quantizer (TS-GMVQ) by first growing the tree into "difficult" regions, and then pruning it optimally, using the Breiman-Friedman-Olshen-Stone (BFOS) algorithm to avoid overfitting. I then focus on the GMM-based classification problem for sensor networks. Previous work on the EM algorithm and Gauss mixture vector quantization has emphasized single sensor classification problems. I generalize the GMM-based classification problem to include multiple, distributed sensors. In particular, I consider a set of sensors, communicating with each other under rate constraints for the purpose of classification. Each sensor has a different noisy version of a common image and aims to classify the image based on its own noisy version and the help it receives from the other sensors. I view the sensor network classification problem as one in vector quantization and provide a Lloyd-optimal solution to minimize classification error for the given rate constraints. In particular, I use a TS-GMVQ to partition the vector space during the Lloyd design. I also include context dependence into my algorithm, making use of the concepts developed in conjunction with hidden Markov models.
Communication over the Internet using a 3D agent with real-time facial expression analysis, synthesis and text to speech capabilities
Authors: N. P. Chandrasiri, I. Barakonyi, T. Naemura, M. Ishizuka, H. Harashima
Venue: Proceedings of the The 8th International Conference on Communication Systems - Volume 01 (2002)

We present a system for Internet communication that enhances traditional text-based chatting with real-time analysis and synthesis of the chat parties' facial expressions. It is composed of three main modules: a real-time facial expression analysis component, a 3D agent with facial expression synthesis and text-to-speech capabilities - a talking head, and a communication module. So far we have realized a prototype to find out attractive ways of communication on the Internet and are currently experimenting on how to utilize this new type of modalities in chat communication.
JND Analysis of Texture Roughness Perception using a Magnetic Levitation Haptic Device
Authors: Bertram Unger, Ralph Hollis, Roberta Klatzky
Venue: Proceedings of the Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2007)

This paper describes the use of a magnetic levitation haptic device (MLHD) to study the psychophysics of tex- ture roughness. Studies of texture roughness perception per- formed using real textures can be time consuming and ex- pensive. By using a MLHD to simulate texture we are able to quickly and easily adjust texture parameters. A dithered textured surface composed of conical elements is simulated using a constraint surface algorithm. The constraint sur- face shape is defined by the geometry of the elements as well as the size and shape of the virtual probe. The spac- ing of the elements and the size of the probe can be varied continuously and in real time. Just noticeable difference (JND) experiments were con- ducted over the parameters of probe radius and texture spacing. The JND of roughness was determined with re- spect to element spacing using unforced weighted up-down adaptive threshold estimation. JND's were found to vary for texture spacing and probe size. JND's for constant probe size decreased with increasing texture spacing to a mini- mum and then increased again. JND's for constant spacing increased as probe size increased. These results are con- sistent with a geometric model of probe-texture interaction.
Statistical language identification based on untranscribed training
Authors: M. A. Lund
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02 (1996)

BBN's baseline language identification (LID) system tokenizes utterances based on an English hidden Markov model (HMM) phone recognizer and uses language-dependent phone-bigram models to discriminate between languages. This is clearly a suboptimal procedure, as English phone models may fail to provide a meaningful tokenization of non-English speech. We address this problem through the use of parametric acoustic segment models derived from untranscribed target-language training. The paper describes some promising exploratory experiments related to model-segment selection and LID based on nearest neighbor classification, as well as an LID system in which language-specific HMMs are trained from unsupervised clustering of parametric acoustic segments. In addition, it describes an experiment in which phone HMMs trained on CallHome Mandarin are added to the baseline system, resulting in an error reduction of 30% on pairwise language discrimination on the OGI-TS corpus.
Object-Based Spatial Segmentation of Video Guided by Depth and Motion Information
Authors: Jaime S. Cardoso, Jorge C. S. Cardoso, Luis Corte-Real
Venue: Proceedings of the IEEE Workshop on Motion and Video Computing (2007)

Automatic spatial video segmentation is a problem without a general solution at the current state-of-the-art. Most of the difficulties arise from the process of capturing images, which remain a very limited sample of the scene they represent. The capture of additional information, in the form of depth data, is a step forward to address this problem. We start by investigating the use of depth data for better image segmentation; a novel segmentation framework is proposed, with depth being mainly used to guide a segmentation algorithm on the colour information. Then, we extend the method to also incorporate motion information in the segmentation process. The effectiveness and simplicity of the proposed method is documented with results on a selected set of images sequences. The achieved quality raises the expectation for a significant improvement on operations relying on spatial video segmentation as a pre-process.
Algorithms and architectures for network search processors
Authors: John W. Lockwood
Venue:  (2006)

The continuous growth in the Internet's size, the amount of data traffic, and the complexity of processing this traffic gives rise to new challenges in building high-performance network devices. One of the most fundamental tasks performed by these devices is searching the network data for predefined keys. Address lookup, packet classification, and deep packet inspection are some of the operations which involve table lookups and searching. These operations are typically part of the packet forwarding mechanism, and can create a performance bottleneck. Therefore, fast and resource efficient algorithms are required. One of the most commonly used techniques for such searching operations is the Ternary Content Addressable Memory (TCAM). While TCAM can offer very fast search speeds, it is costly and consumes a large amount of power. Hence, designing cost-effective, power-efficient, and high-speed search techniques has received a great deal of attention in the research and industrial community. In this thesis, we propose a generic search technique based on Bloom filters. A Bloom filter is a randomized data structure used to represent a set of bit-strings compactly and support set membership queries. We demonstrate techniques to convert the search process into table lookups. The resulting table data structures are kept in the off-chip memory and their Bloom filter representations are kept in the on-chip memory. An item needs to be looked up in the off-chip table only when it is found in the on-chip Bloom filters. By filtering the off-chip memory accesses in this fashion, the search operations can be significantly accelerated. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of cost-effectiveness, speed, and power-efficiency.
BER performance of DS-CDMA/EGC system in Nakagami fading channel
Authors: Y. W. Cao, C. C. Ko, T. T. Tjhung
Venue: Proceedings of the The 8th International Conference on Communication Systems - Volume 02 (2002)

In this paper, performance of DS-CDMA/EGC system over Nakagami fading channel is investigated. By making using of the infinite series proposed by Beaulieu, a closed form of bit error rate (BER) performance for DS-CDMA/EGC system is derived. Numerical results show that the BER performance of DS-CDMA/EGC is inferior to that of DS-CDMA/MRC, generally with a difference of 1-3 dB. However, DS-CDMA/EGC is still attractive due to its advantage of lower complexity of receiver compared to DS-CDMA/MRC.
Motivate AI Class with Interactive Computer Game
Authors: Akcell
Venue: Proceedings of the The First IEEE International Workshop on Digital Game and Intelligent Toy Enhanced Learning (2007)

This paper reports adapting traditional Pacman game with Machine Learning technology Case-based Reasoning (CBR) to provide student learning motivation in the AI subject teaching. The paper explains how the study transforms the SOAR tutorial EATER game's playing rules as a platform to experience that CBR (or AI) agent plays with human player in a shared running-map for finding the CBR agent's best solution as competition with human player and Greedy agent. The advantage of AI project is that the experiment has to adopt human competition experience. There are many uncertain what CBR can achieve, which wait for the individual students to discover. The experiment results are different individually; the educators do not have to worry about the cheating in student assignments. The study found that the fun of interactive computer games would stimulate students' motivation in learning CBR concepts in AI.
Fourier and wavelet transform features for whirl tower diagnostics
Authors: S. Fornero
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 04 (1999)

This paper describes the application of signal processing methods to extract key features for detection and prediction of faults in rotating mechanical components of a whirl tower test facility. Procedures are described for processing the vibration signals from critical components of the whirl tower using the Fourier transform and the wavelet transform. The wavelet transform provides localization of signals in both time and frequency, revealing data that is averaged out in the Fourier analysis. The features extraction processes described are based on specific knowledge of the whirl tower equipment. The real-time analysis discussed allow for scheduling of inspection, repair, or replacement of failed and degraded components with minimal impact on production.