ppxscal/embedding_network huggingface dataset:
Image annotation and classification
Authors: Latifur Khan
Venue:  (2006)

How to organize and find images of specific entities from huge amount of digital images in an efficient way is a big research problem. An image retrieval system is highly desired since it can manage a huge collection of images efficiently, respond to users' queries with high speed, and deliver a minimum of irrelevant information (high precision), as well as ensuring that relevant information is not overlooked (high recall). The most challenging problem is how to overcome the curse of dimensionality brought by the high dimensional nature of image data. Another problem is how to bridge the semantic gap between high level concepts/descriptions and low level visual features and how to classify images automatically and accurately. This dissertation proposes a framework for automatic image annotation/description based on a widely used model, translation model. We present a dynamic feature weighted selection algorithm based on top-down subspace clustering to generate groups of image segments. The curse of dimensionality is inhibited by our algorithm and clustering performance is improved. We generate some statistical models to find correlations between groups of image segments and semantic concepts. The experimental results show that our feature weighted selection algorithm out-performs regular clustering algorithms such as K-means. Consequently, the proposed automatic image annotation frame work has better precision and recall than the translation model. This dissertation studies image classification, especially in the medical domain. To solve the problem of imbalanced data, we generate an image re-sampling strategy which improves classification accuracy. We study various K nearest neighbor (KNN) algorithms and modify evidence-theory-based KNN algorithm by considering density. Experimental results show that our modified evidence-theory-based KNN algorithm has better classification accuracy.
The construction and validation of a resource cover page as an instrument for cataloging internet resources in the semantic web
Authors: Easwar Nyshadham
Venue:  (2006)

The implementation of the Semantic Web has a problem, namely, that the Semantic Web ignores the different types of already-existing resources in the current implementation of the Web. The semi-structured nature of the Web makes it quite difficult to retrieve information relevant to specific users' needs. As a result, today's Web and the next-generation Semantic Web are not interoperable. The goal of this dissertation was to make the already-existing resources in the current implementation of the Web more meaningful to the Semantic Web without taking-away the openness of the Web. A framework was introduced that created a catalog of Internet resources that allows multi-access points to different resources and allows agents to discover the sought-after knowledge. These lookup fields were implemented in this Internet cataloging framework. This catalog was established to help computer-to-computer communication and information retrieval in general. The framework contributed to research in two ways: First, a prototype was created that can be used as a source for searching on the Internet. Second, the framework implemented the latest technologies of the Semantic Web to create the prototype.
Local and Global Recency Weighting Approach to Bug Prediction
Authors: Hemant Joshi, Chuanlei Zhang, S. Ramaswamy, Coskun Bayrak
Venue: Proceedings of the Fourth International Workshop on Mining Software Repositories (2007)

Finding and fixing software bugs is a challenging maintenance task, and a significant amount of effort is invested by software development companies on this issue. In this paper, we use the Eclipse project's recorded software bug history to predict occurrence of future bugs. The history contains information on when bugs have been reported and subsequently fixed.
Training MLPs layer-by-layer with the information potential
Authors: Dongxin Xu
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02 (1999)

In the area of information processing one fundamental issue is how to measure the relationship between two variables based only on their samples. In a previous paper, the idea of information potential which was formulated from the so called quadratic mutual information was introduced, and successfully applied to problems such as blind source separation and pose estimation of SAR (synthetic aperture radar) Images. This paper shows how information potential can be used to train a MLP (multilayer perceptron) layer-by-layer, which provides evidence that the hidden layer of a MLP serves as an "information filter" which tries to best represent the desired output in that layer in the statistical sense of mutual information.
On the symmetry function of a convex set
Authors: Alexandre Belloni, Robert M. Freund
Venue: Mathematical Programming: Series A and B (2007)

We attempt a broad exploration of properties and connections between the symmetry function of a convex set S $${S \subset\mathbb{R}^n}$$ and other arenas of convexity including convex functions, convex geometry, probability theory on convex sets, and computational complexity. Given a point $${x \in S}$$, let sym(x,S) denote the symmetry value of x in S: $${{\bf sym}(x,S):= max\{\alpha \ge 0 : x+\alpha(x-y) \in S {\rm for every} y \in S\}}$$, which essentially measures how symmetric S is about the point x, and define $${\bf sym}({\it S}):= \max_{x\in S} \, {\bf sym}({\it x,S})$$ x* is called a symmetry point of S if x* achieves the above maximum. The set S is a symmetric set if sym(S)=1. There are many important properties of symmetric convex sets; herein we explore how these properties extend as a function of sym(S) and/or sym(x,S). By accounting for the role of the symmetry function, we reduce the dependence of many mathematical results on the strong assumption that S is symmetric, and we are able to capture and otherwise quantify many of the ways that the symmetry function influences properties of convex sets and functions. The results in this paper include functional properties of sym(x,S), relations with several convex geometry quantities such as volume, distance, and cross-ratio distance, as well as set approximation results, including a refinement of the L&#x00f6;wner-John rounding theorems, and applications of symmetry to probability theory on convex sets. We provide a characterization of symmetry points x* for general convex sets. Finally, in the polyhedral case, we show how to efficiently compute sym(S) and a symmetry point x* using linear programming. The paper also contains discussions of open questions as well as unproved conjectures regarding the symmetry function and its connection to other areas of convexity theory.
Image transmission with error concealment on a lossy packet network
Authors: B. S. Srinivas
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04 (1996)

We study image transmission on a lossy packet network in which the loss mechanism is modeled as buffer overflows. A simple error concealment strategy involving table lookups and a buffer allocation scheme with an optimally chosen virtual boundary for a 2 priority class system are described. Significant improvements are achieved by combining these two strategies.
Modeling and managing changes in text databases
Authors: Panagiotis G. Ipeirotis, Alexandros Ntoulas, Junghoo Cho, Luis Gravano
Venue: ACM Transactions on Database Systems (TODS) (2007)

Large amounts of (often valuable) information are stored in web-accessible text databases. &ldquo;Metasearchers&rdquo; provide unified interfaces to query multiple such databases at once. For efficiency, metasearchers rely on succinct statistical summaries of the database contents to select the best databases for each query. So far, database selection research has largely assumed that databases are static, so the associated statistical summaries do not evolve over time. However, databases are rarely static and the statistical summaries that describe their contents need to be updated periodically to reflect content changes. In this article, we first report the results of a study showing how the content summaries of 152 real web databases evolved over a period of 52 weeks. Then, we show how to use &ldquo;survival analysis&rdquo; techniques in general, and Cox's proportional hazards regression in particular, to model database changes over time and predict when we should update each content summary. Finally, we exploit our change model to devise update schedules that keep the summaries up to date by contacting databases only when needed, and then we evaluate the quality of our schedules experimentally over real web databases.
Industrial Evaluation of a Log File Analysis Methodology
Authors: Donald J. Yantzi, James H. Andrews
Venue: Proceedings of the 5th International Workshop on Dynamic Analysis (2007)

Test result evaluation programs often take the form of log file analyzers, which analyze text logs of events that have happened during testing. Previously, we proposed a methodology for deriving logging instrumentation and state-based log file analyzer programs from requirements. In this paper, we report on an industrial evaluation in which the methodology was carried out on two pieces of commercial software in a commercial setting. We report on our quantitative and qualitative observations and on recommendations for improving the methodology and the tools used.
ksniffer: determining the remote client perceived response time from live packet streams
Authors: David P. Olshefski, Jason Nieh, Erich Nahum
Venue: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6 (2004)

As dependence on the World Wide Web continues to grow, so does the need for businesses to have quantitative measures of the client perceived response times of their Web services. We present ksniffer, a kernel-based traffic monitor capable of determining pageview response times as perceived by remote clients, in real-time at gigabit traffic rates. ksniffer is based on novel, online mechanisms that take a "look once, then drop" approach to packet analysis to reconstruct TCP connections and learn client pageview activity. These mechanisms are designed to operate accurately with live network traffic even in the presence of packet loss and delay, and can be efficiently implemented in kernel space. This enables ksniffer to perform analysis that exceeds the functionality of current traffic analyzers while doing so at high bandwidth rates. ksniffer requires only to passively monitor network traffic and can be integrated with systems that perform server management to achieve specified response time goals. Our experimental results demonstrate that ksniffer can run on an inexpensive, commodity, Linux-based PC and provide online pageview response time measurements, across a wide range of operating conditions, that are within five percent of the response times measured at the client by detailed instrumentation.
An area optimized reconfigurable encryptor for AES-Rijndael
Authors: Monjur Alam, Sonai Ray, Debdeep Mukhopadhayay, Santosh Ghosh, Dipanwita RoyChowdhury, Indranil Sengupta
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

This paper presents a reconfigurable architecture of the Advanced Encryption Standard (AES-Rijndael) cryptosystem. The suggested reconfigurable architecture is capable of handling all possible combinations of standard bit lengths (128, 192, 256) of data and key. The fully rolled inner-pipelined architecture ensures lesser hardware complexity. The work develops a FSMD model based controller which is ideal for such iterative implementation of AES. S-boxes here have been implemented using combinational logic over composite field arithmetic which completely eliminates the need of any internal memory. The design has been implemented on Xilinx Vertex XCV1000 and 0.18&mu; CMOS technology. The performance of the architecture has been compared with existing results in the literature and has been found to be the most compact implementations of the AES algorithm.
Performance Evaluation of the PVFS2 Architecture
Authors: Julian M. Kunkel, Thomas Ludwig
Venue: Proceedings of the 15th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (2007)

As the complexity of parallel file systems' software stacks increases it gets harder to reveal the reasons for performance bottlenecks in these software layers. This paper introduces a method which eliminates the influence of the physical storage on performance analysis in order to find these bottlenecks. Also, the influence of the hardware components on the performance is modeled to estimate the maximum achievable performance of a parallel file system. The paper focusses on the Parallel Virtual File System 2 (PVFS2) and shows results for the functionality file creation, small contiguous I/O requests and large contiguous I/O requests.
Algorithms for task scheduling in heterogeneous computing environments
Authors: Sanjeev Baskiyar
Venue:  (2006)

Current heterogeneous meta-computing systems, such as computational clusters and grids offer a low cost alternative to supercomputers. In addition they are highly scalable and flexible. They consist of a host of diverse computational devices which collaborate via a high speed network and may execute high-performance applications. Many high-performance applications are an aggregate of modules. Efficient scheduling of such applications on meta-computing systems is critical to meeting deadlines. In this dissertation, we introduce three new algorithms, the Heterogeneous Critical Node First (HCNF) algorithm, the Heterogeneous Largest Task First (HLTF) algorithm and the Earliest Finish Time with Dispatch Time (EFT-DT) algorithm. HCNF is used to schedule parallel applications of forms represented by directed acyclic graphs onto networks of workstations to minimize their finish times. We compared the performance of HCNF with those of the Heterogeneous Earliest Finish Time (HEFT) and Scalable Task Duplication based Scheduling (STDS) algorithms. In terms of Schedule Length Ratio (SLR) and speedup, HCNF outperformed HEFT on average by 13% and 18% respectively. HCNF outperformed STDS in terms of SLR and speedup on an average by 8% and 12% respectively. The HLTF algorithm is used to schedule a set of independent tasks onto a network of heterogeneous processors to minimize finish time. We compared the performance of HLTF with that of the Sufferage algorithm. In terms of makespan, HLTF outperformed Sufferage on average by 4.5%, with a tenth run-time. The EFT-DT algorithm schedules a set of independent tasks onto a network of heterogeneous processors to minimize finish time when considering dispatch times of tasks. We compared the performance of EFT-DT with that of a First in First out (FIFO) schedule. In terms of minimizing makespan, on average EFT-DT outperformed FIFO by 30%.
T-sum of sigmoid-shaped fuzzy intervals
Authors: Dug Hun Hong, Changha Hwang, Kyung Tae Kim
Venue: Information Sciences: an International Journal (2007)

The usual arithmetic operations on real numbers can be extended to arithmetical operations on fuzzy intervals by means of Zadeh's extension principle based on a t-norm T. A t-norm is called consistent with respect to a class of fuzzy intervals for some arithmetic operation, if this arithmetic operation is closed for this class. It is important to know which t-norms are consistent with particular types of fuzzy intervals. Recently, Dombi and Gyorbiro [J. Dombi, N. Gyorbiro, Additions of sigmoid-shaped fuzzy intervals using the Dombi operator and infinite sum theorems, Fuzzy Sets and Systems 157 (2006) 952-963] proved that addition is closed if the Dombi t-norm is used with sigmoid-shaped fuzzy intervals. In this paper, we define a broader class of sigmoid-shaped fuzzy intervals. Then, we study t-norms that are consistent with these particular types of fuzzy intervals. Dombi and Gyorbiro's results are special cases of the results described in this paper.
Multilingual Extension of Temporal Expression Recognition Using Parallel Corpora
Authors: M. Puchol-Blasco, E. Saquete, P. Martinez-Barco
Venue: Proceedings of the 14th International Symposium on Temporal Representation and Reasoning (2007)

This paper presents the automatic extension of TERSEO to other languages, a knowledge-based system for the recognition and normalization of temporal expressions, originally developed for Spanish. TERSEO was extended to English and Italian through the automatic translation of the temporal expressions, and it was presented in previous works (see Saquete et al. [12]), but a new methodology has been designed with the purpose of obtaining better results in this issue. This new methodology is based on the use of parallel corpora for extending the TERSEO temporal model to other languages. In this case, two different methods have been tested: (1) automatic translation of TERSEO patterns to other languages and (2) automatic corpora annotation in the target side of parallel corpora. The main idea is focused on annotating the Spanish side of a parallel corpora, projecting the analysis to the second language, and then obtaining new TERSEO patterns (1) and new annotated corpus (2). The set of new patterns will be used to improve the current TERSEO language independent modules. Whereas the new annotated corpus will be used to train a ML system. This system will annotate new temporal expressions in the new language.
A kind of fuzzy-neural networks for text-independent speaker identification
Authors: Yuan Zhing-Xuan
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02 (1996)

A novel approach to establish membership functions of fuzzy states based on a functional-link neural network (FLNN) for text-independent speaker identification is proposed. Parameters in corresponding states derived from training utterances uttered by each speaker are fed to a fuzzy statistical model to form the histograms of the states. Data in normalized histograms are used as learning samples for the FLNNs. A set of FLNNs is used as a speaker's model. For each unknown speaker's voice in the test, parameters are fed to every FLNN corresponding to each fuzzy state respectively, the FLNN generalizes the degree of membership to the state, then the system scores the degree of membership to the speaker's model and chooses the speaker whose model's degree of membership is the highest.
Reflexes: abstractions for highly responsive systems
Authors: Jesper Honig Spring, Filip Pizlo, Rachid Guerraoui, Jan Vitek
Venue: Proceedings of the 3rd international conference on Virtual execution environments (2007)

Commercial Java virtual machines are designed to maximize the performance of applications at the expense of predictability. High throughput garbage collection algorithms, for example, can introduce pauses of 100 milliseconds or more. We are interested in supporting applications with response times in the tens of microseconds and their integration with larger timing-oblivious applications in the same Java virtual machine. We propose Reflexes, a new abstraction for writing highly responsive systems in Java and investigate the virtual machine support needed to add Reflexes to a Java environment. Our implementation of Reflexes was evaluated on several programs including an audio-processing application. We were able to run a Reflex at 22.05KHz with less than 0.2% missed deadlines over 10 million observations, a result that compares favorably to an implementation written in C.
Rewriting-Based Access Control Policies
Authors: Anderson Santana de Oliveira
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2007)

In this paper we propose a formalization of access control policies based on term rewriting. The state of the system to which policies are enforced is represented as an algebraic term, which allows us to model several aspects of the policy environment. Policies are implemented by sets of rewrite rules, whose evaluation produces authorization decisions. We discuss the relation between properties of term rewriting systems, such as confluence and termination, and their consequences on defining trusted access control policies.
Adaptive resolution of mainlobe and sidelobe detections using model order determination
Authors: A. G. Jaffer
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05 (2001)

This paper presents the development and performance evaluation of a methodology for distinguishing between mainlobe and sidelobe detections that arise in adaptive radar systems operating in adverse environments. Various adaptive detection test statistics such as the adaptive matched filter (AMF), the generalized likelihood ratio test (GLRT) and adaptive coherence estimate (ACE), and combinations of these, have been previously analyzed with respect to their sidelobe rejection capabilities. In contrast to these methods, which are based on detecting a single target with known direction and Doppler, the present method uses model order determination techniques applied to the AMF or GLRT data observed over the range of unknown angle and Doppler parameters. The determination of model order, i.e., the number of signals present in the data, is made by using least-squares model fit error residuals and applying the Akaike information criterion. Comprehensive computer simulation results are presented which demonstrate substantial improvement in sidelobe rejection performance compared to previous methods.
Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01 (2000)

The following topics were dealt with: hybrid systems; multi-objective optimization; search; network applications; robots; evolving neural networks; system modeling and control; evolutionary scheduling; fitness in genetic algorithms; image and signal processing; control system design; intelligent agents; classifiers; engineering applications; evolutionary computation techniques; biological modelling; games; operations research; ecological modeling; learning; medical applications; constraint handling; coevolution; time series; and multi-agent systems. 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
Regularized shape deformation for image segmentation
Authors: Song Wang
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03 (2001)

This paper presents a new method for image segmentation by deforming the object shape in a template. The deformation process is controlled using a thin-plate spline kernel based regularization method. The proposed method is especially useful for 2D-based segmentation of 3D medical images by treating segmented slices as templates for their neighboring unsegmented slices. We have applied the proposed method to extract the scalp contours in brain cryosection images with very encouraging results.