ppxscal/embedding_network huggingface dataset:
Perspective access networks
Authors: Mema Roussopoulos
Venue:  (2006)

Perspective Access Networks provide an infrastructure from which users can specify the location from which they wish to view the Internet. The ability to specify location has become necessary as the Internet has become increasingly inconsistent. An increasing preponderance of middleboxes, location-dependent services, and large-scale content filtering have contributed to this situation. Our work offers the following contributions. First, we propose an infrastructure that routes traffic to a location from which a given resource can be viewed, taking instructions from user-specified attributes describing the desired location. Second, we analyze the tradeoff between the expressivity of user requests and the finite resources available within the network for propagating metadata about available perspectives. Third, we stipulate a set of real scenarios that fall within the limits of what can reasonably be handled by a system appropriately tuned to manage the tradeoff, and we argue that the specific algorithm we propose can handle the scenarios effectively.
On Controllability and Feasibility of Utilization Control in Distributed Real-Time Systems
Authors: Xiaorui Wang, Yingming Chen, Chenyang Lu, Xenofon Koutsoukos
Venue: Proceedings of the 19th Euromicro Conference on Real-Time Systems (2007)

Feedback control techniques have recently been applied to a variety of real-time systems. However, a fundamental issue that was left out is guaranteeing system controllability and the feasibility of applying feedback control to such systems. No control algorithms can effectively control a system which itself is uncontrollable or infeasible. In this paper, we use the multi-processor utilization control problem as a representative example to study the controllability and feasibility of distributed real-time systems. We prove that controllability and feasibility of a system depend crucially on end-to-end task allocations. We then present algorithms for deploying end-to-end tasks to ensure the system is controllable and utilization control is feasible for the system. Furthermore, we develop runtime algorithms to maintain controllability and feasibility by reallocating tasks dynamically in response to workload variations such as task terminations and migrations caused by processor failures. We implement our algorithms in a robust real-time middleware and report empirical results on an experimental test-bed. Our results demonstrate that the proposed task allocation algorithms improve the robustness of feedback control in distributed real-time systems.
Color scanner calibration via a neural network
Authors: M. J. Vrhel
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06 (1999)

The mathematical formulation of calibrating color scanners is presented. The mapping from scanned values to colorimetric values is inherently nonlinear. Calibration required approximating this nonlinear mapping. Neural networks are particularly suited to this task. Performance using an artificial neural network generated LUT is compared to that achieved by other commonly used methods.
Understanding UI Integration: A Survey of Problems, Technologies, and Opportunities
Authors: Florian Daniel, Jin Yu, Boualem Benatallah, Fabio Casati, Maristella Matera, Regis Saint-Paul
Venue: IEEE Internet Computing (2007)

Creating composite applications from reusable components is an important technique in software engineering and data management. Although a large body of research and development covers integration at the data and application levels, little work has been done to facilitate it at the presentation level. This article discusses the existing user interface frameworks and component technologies used in presentation integration, illustrates their strengths and weaknesses, and presents some opportunities for future work.
Clustering Billions of Images with Large Scale Nearest Neighbor Search
Authors: Ting Liu, Charles Rosenberg, Henry A. Rowley
Venue: Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision (2007)

The proliferation of the web and digital photography have made large scale image collections containing billions of images a reality. Image collections on this scale make performing even the most common and simple computer vision, image processing, and machine learning tasks non-trivial. An example is nearest neighbor search, which not only serves as a fundamental subproblem in many more sophisticated algorithms, but also has direct applications, such as image retrieval and image clustering. In this paper, we address the nearest neighbor problem as the first step towards scalable image processing. We describe a scalable version of an approximate nearest neighbor search algorithm and discuss how it can be used to find near duplicates among over a billion images.
Contributions to real time shape detection with applications to face detection
Authors: Haim Schweitzer
Venue:  (2006)

Shape-detection is a critical component in many computer vision systems. Large variety of shapes, lighting conditions, and the high dimensionality of image data make shape-detection a challenging problem. Our research is focused on developing techniques for improving both the accuracy and the speed of shape and object detection. We first describe contributions to the classic normalized template-matching technique. Based on the concept of integral images, we have developed an approximate normalized template-matching method, which can be much faster (100 times faster in typical applications) than the classic normalized template-matching method. In typical template-matching based shape-detection, matching needs to be performed at every possible template location in the image and at multiple image resolutions. We introduce the idea of local maxima and describe a selective template matching method. In this approach, matching values are computed only at selective locations, resulting in additional speed-up of the run time. The detection and the recognition of shapes is typically performed on a small number of useful features. The selection of good features requires processing large amounts of data and is very time consuming. The standard approach to feature-selection is to compute many features from training data, and then select among them a subset that is useful for the task at hand. We have developed a fast selection method that works for linear features. Our approach is based on Principal Components dimensionality reduction on the training data. We show that it is not necessary to compute features for all the training data; instead, one needs only compute features for the principal components. Specifically, redundant features are identified using the classic QR Factorization algorithm, applied in the reduced space. AdaBoosting is a recently developed powerful method for selecting a small set of features and constructing a classifier that uses these features. The standard AdaBoosting technique is symmetric in its treatment of the positive and the negative training examples. However, when AdaBoosting is applied in object-detection, the positive examples are significantly more important than the negative examples. For example, when AdaBoosting is applied to face detection, the positive examples are examples of human faces. It is less clear what are good examples of non-faces. We have developed an asymmetric formulation of AdaBoosting that allows a specified bias toward the positive examples that behaves better in such cases.
Viewing the Larger Context of Genomic Data through Horizontal Integration
Authors: Matthew Hibbs, Grant Wallace, Maitreya Dunham, Kai Li, Olga Troyanskaya
Venue: Proceedings of the 11th International Conference Information Visualization (2007)

Genomics is an important emerging scientific field that relies on meaningful data visualization as a key step in analysis. Specifically, most investigation of gene expression microarray data is performed using visualization techniques. However, as microarrays become more ubiquitous, researchers must analyze their own data within the context of previously published work in order to gain a more complete understanding. No current method for microarray visualization and analysis enables biology researchers to observe the greater context of data that surrounds their own results, which severely limits the ability of researchers draw novel conclusions. Here we present a system, called HIDRA, that visually integrates the simultaneous display of multiple microarray datasets to identify important parallels and dissimilarities. We demonstrate the power of our approach through examples of real-world biological insights that can be observed using HIDRA that are not apparent using other techniques.
Joint channel estimation and equalization in multicarrier modulation system using cyclic prefix
Authors: Xiaowen Wang
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05 (1999)

Multicarrier modulation (MCM) is a promising technique for high rate data transmission. A one-tap equalizer is an essential part of the MCM system and channel estimation is needed to obtain the coefficient of the equalizer. Lack of correct channel estimation may cause significant performance degradation. We propose to use the cyclic prefix to estimate the channel for the MCM system. We found that the cyclic prefix originally used solely to guarantee the optimality of modulation using the discrete Fourier transform (DFT) can be viewed as a source of channel information. Based on this observation, we propose a joint channel estimation and equalization algorithm using the cyclic prefix. Our simulations show that the algorithm can adaptively track the variation of a moderately time varying channel and has about 1-2 dB gain over the system using the channel estimation obtained by the conventional training schemes.
Bandwidth-efficient management of DHT routing tables
Authors: Jinyang Li, Jeremy Stribling, Robert Morris, M. Frans Kaashoek
Venue: Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2 (2005)

Today an application developer using a distributed hash table (DHT) with n nodes must choose a DHT protocol from the spectrum between O(1) lookup protocols [9, 18] and O(log n) protocols [20-23, 25, 26]. O(1) protocols achieve low latency lookups on small or low-churn networks because lookups take only a few hops, but incur high maintenance traffic on large or high-churn networks. O(log n) protocols incur less maintenance traffic on large or high-churn networks but require more lookup hops in small networks. Accordion is a new routing protocol that does not force the developer to make this choice: Accordion adjusts itself to provide the best performance across a range of network sizes and churn rates while staying within a bounded bandwidth budget. The key challenges in the design of Accordion are the algorithms that choose the routing table's size and content. Each Accordion node learns of new neighbors opportunistically, in a way that causes the density of its neighbors to be inversely proportional to their distance in ID space from the node. This distribution allows Accordion to vary the table size along a continuum while still guaranteeing at most O(log n) lookup hops. The user-specified bandwidth budget controls the rate at which a node learns about new neighbors. Each node limits its routing table size by evicting neighbors that it judges likely to have failed. High churn (i.e., short node lifetimes) leads to a high eviction rate. The equilibrium between the learning and eviction processes determines the table size. Simulations show that Accordion maintains an efficient lookup latency versus bandwidth tradeoff over a wider range of operating conditions than existing DHTs.
The Strangest Thing About Software
Authors: Tim Menzies, David Owen, Julian Richardson
Venue: Computer (2007)

Although there are times when random search is dangerous and should be avoided, software analysis should start with random methods because they are so cheap, moving to the more complex methods only when random methods fail.
Active sensor networks
Authors: Philip Levis, David Gay, David Culler
Venue: Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2 (2005)

We propose using application specific virtual machines (ASVMs) to reprogram deployed wireless sensor networks. ASVMs provide a way for a user to define an application-specific boundary between virtual code and the VM engine. This allows programs to be very concise (tens to hundreds of bytes), making program installation fast and inexpensive. Additionally, concise programs interpret few instructions, imposing very little interpretation overhead. We evaluate ASVMs against current proposals for network programming runtimes and show that ASVMs are more energy efficient by as much as 20%. We also evaluate ASVMs against hand built TinyOS applications and show that while interpretation imposes a significant execution overhead, the low duty cycles of realistic applications make the actual cost effectively unmeasurable.
On the implementation of the Omega failure detector in the crash-recovery failure model
Authors: Cristian Martin, Mikel Larrea, Ernesto Jimenez
Venue: Proceedings of the The Second International Conference on Availability, Reliability and Security (2007)

Unreliable failure detectors are mechanisms providing information about process failures, that allow solving several problems in asynchronous systems, e.g., Consensus. A particular class of failure detectors, Omega, provides an eventual leader election functionality. This paper addresses the implementation of Omega in the crash-recovery failure model. Recently we have proposed an algorithm assuming that eventually the correct process with the smallest identifier and minimum incarnation number can communicate timely with the rest of processes. Here we propose two Omega algorithms which assume only that processes are reachable from some correct process, independently of its identifier and incarnation number. The first one requires the membership to be known a priori, while the second one relaxes this assumption too.
Cognitive information fusion of georeferenced data for tactical applications
Authors: G. Shi, K. Barker, Reda Alhajj
Venue: Proceedings of the Fourth IEEE International Conference on Cognitive Informatics (2005)

Georeferenced data from multiple autonomous sources for defined AOI (areas of interest) can be fused and analyzed in support of various decision-making processes such as risk assessment, emergency response, situation awareness and tactical planning. However, data from multiple heterogeneous sources may be in different formats, scales, qualities and coverage. All these characteristics of multi-source spatial data limit the use of traditional statistics methods for information fusion. In this paper, the cognitive belief is proposed to represent georeferenced data from different sources. Uncertainty caused by inaccurate or partial information can be modeled. By applying the belief combination rule, cognitive beliefs can be fused to provide better support for decision makers.
Structure theorems for parallel integration
Authors: Manuel Bronstein
Venue: Journal of Symbolic Computation (2007)

We introduce structure theorems that refine Liouville's Theorem on integration in closed form for general derivations on multivariate rational function fields. By predicting the arguments of the new logarithms that can appear in integrals, as well as the denominator of its rational part, those theorems provide theoretical backing for the Risch-Norman integration method. They also generalize its applicability to non-monomial extensions, for example the Lambert W function.
Grammar or Serial Order?: Discrete Combinatorial Brain Mechanisms Reflected by the Syntactic Mismatch Negativity
Authors: Friedemann Pulvermüller, Ramin Assadollahi
Venue: Journal of Cognitive Neuroscience (2007)

If word strings violate grammatical rules, they elicit neurophysiological brain responses commonly attributed to a specifically human language processor or grammar module. However, an ungrammatical string of words is always also a very rare sequence of events and it is, therefore, not always evident whether specifically linguistic processes are at work when neurophysiological grammar indexes are being reported. We here investigate the magnetic mismatch negativity (MNN) to ungrammatical word strings, to very rare grammatical strings, and to common grammatical phrases. In this design, serial order mechanism mapping the sequential probability of words should neurophysiologically dissociate frequent grammatical phrases from both ungrammatical and rare grammatical strings. However, if syntax as a discrete combinatorial system is reflected, the prediction is that the rare, correctly combined items group with the highly frequent grammatical strings and stand out against ungrammatical strings. Using magnetoencephalography as a measure of human brain activity, we replicated the previously reported syntactic mismatch negativity (sMMN), which distinguishes highly unfamiliar ungrammatical word sequences from common grammatical strings. Crucially, a significant interaction demonstrated that the sMMN specifically distinguished syntactic violations from common grammatical strings, but not uncommon from common grammatical word strings. This significant interaction argues in favor of a genuinely grammatical origin of the sMMN and provides direct neurophysiological evidence for a discrete combinatorial system for word and morpheme sequences in the human brain. The data are more difficult to explain in the context of serial order models that map co-occurrence probabilities of words.
Modeling Breaking Ship Waves for Design and Analysis of Naval Vessels
Authors: Gabriel Weymouth, Kelli Hendrickson, Dick K. P. Yue, Douglas G. Dommermuth, Paul Adams, Randall Hand
Venue: Proceedings of the HPCMP Users Group Conference (2006)

Prediction of the performance and non-acoustical signature of surface ships which feature such effects as breaking waves, spray and air entrainment is still beyond the capabilities of standard numerical solution methods. The near-field flow about a surface ship is characterized by complex physical processes such as: (i) spray sheet and jet formation; (ii) strong free-surface turbulence interactions with (large-amplitude) breaking waves; (iii) air entrainment and bubble generation; and (iv) post-breaking turbulence and dissipation. These physical phenomenon still require resolutions that are not feasible in practical engineering flows, despite continuing advances in computational resources. A two-pronged approach is proposed to develop methods to accurately predict these complex physical systems. First, physics-based closure models for steep breaking waves in the presence of turbulence are developed with results from high-resolution direct numerical simulations of the Navier-Stokes equations. Second, cutting-edge parallel computing capabilities and newly developed solution techniques are utilized to simulate the free-surface flow around naval combatants moving at high speed.
Parallel-External Computation of the Cycle Structure of Invertible Cryptographic Functions
Authors: Andreas Beckmann, Jorg Keller
Venue: Proceedings of the 15th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (2007)

We present an algorithm to compute the cycle structure of large directed graphs where each node has exactly one outgoing edge. Such graphs appear as state diagrams of finite state machines such as pseudo-random number generators in cryptography. The size of the graphs necessitates that the adjacency list is kept on hard disks. Our algorithm uses multiple processing units, so that a parallel storage system has to be employed to store the graph. We present experimental results for randomly chosen graphs, and for the graph of the A5/1 generator used in GSM mobile phones.
Guest Editors' Introduction: Computational Photography
Authors: Fredo Durand, Richard Szeliski
Venue: IEEE Computer Graphics and Applications (2007)

Since their inception over three decades ago, the fields of computer vision, image processing, and computer graphics have been concerned with analyzing, manipulating, and synthesizing images using numerical algorithms. While these three fields continue to evolve in a loosely coupled manner, users' near-ubiquitous access to digital cameras and personal computers has spurred renewed interest in what is now dubbed computational photography, which lies at the intersection of these fields. The Guest Editors provide an overview of computational photography's evolution, provide links to additional resources, and introduce the articles they've selected for the issue.
The design of a highly-available file server group
Authors: Feng-jung Liu, Chun-wei Tseng, Chu-sing Yang
Venue: Proceedings of the The 8th International Conference on Communication Systems - Volume 02 (2002)

The explosive growth of the Web contents has led to increasing attention on two major challenges: scalability and high availability of network file system. We introduced the concept of intermediate file handling to improve the availability of the NFS-based file system and proposed a new data consistency scheme to improve our design of a reliable file system, which had been implemented as an example of the software replication approach. By using a minor sequence in token getting, the total number of getting-token requests is reduced along with the data-requested size for each write operation. We also proposed a simple load-sharing mechanism for the NFS client to switch to a lightly-load server based on the number of NFS client's RPC requests for a period of time. Such an approach is easy to derive the load information, which each client generated. Finally, we analysed the proposed data consistency scheme. It shows truly that the scheme is able to reduce the overhead of write requests of our network file system.
Modelling unknown words in spontaneous speech
Authors: T. Kemp, A. Jusek
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01 (1996)

We describe our experiments with different acoustic and language models for unknown words in spontaneous speech. We propose a syllable based approach for the acoustic modelling of new words. Several models of different degrees of complexity are evaluated against each other. We show that the modelling of new words can decrease the error rate in the recognition of spontaneous human-to-human speech. In addition, the new word models can be used as a measure of confidence capable of detecting errors in the recognition of spontaneous speech. Although the best performance is reached by applying phonetic a-priori knowledge in the design of the acoustic models, a pure data-driven approach is proposed which performs only slightly less efficiently.