ppxscal/embedding_network huggingface dataset:
Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition
Authors: Ramy Al-Hajj Mohamad, Laurence Likforman-Sulem, Chafic Mokbel
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.
Fitting a graph to vector data
Authors: Samuel I. Daitch, Jonathan A. Kelner, Daniel A. Spielman
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

We introduce a measure of how well a combinatorial graph fits a collection of vectors. The optimal graphs under this measure may be computed by solving convex quadratic programs and have many interesting properties. For vectors in d dimensional space, the graphs always have average degree at most 2(d + 1), and for vectors in 2 dimensions they are always planar. We compute these graphs for many standard data sets and show that they can be used to obtain good solutions to classification, regression and clustering problems.
Application of Supervised Pareto Learning Self Organizing Maps and Its Incremental Learning
Authors: Hiroshi Dozono, Shigeomi Hara, Shinsuke Itou, Masanori Nakakuni
Venue: Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (2009)

We have proposed Supervised Pareto Learning Self Organizing Maps(SP-SOM) based on the concept of Pareto optimality for the integration of multiple vectors and applied SP-SOM to the biometric authentication system which uses multiple behavior characteristics as feature vectors. In this paper, we examine performance of SP-SOM for the generic classification problem using iris data set. Furthermore, we propose the incremental learning algorithm for SP-SOM and examine effectiveness in a classification problem and adaptation ability to the change of the behavior biometric features by time.
Towards a universal marketplace over the web: statistical multi-label classification of service provider forms with simulated annealing
Authors: Kivanc Ozonat, Donald Young
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

There is a growing number of service providers that a consumer can interact with over the web to learn their service terms. The service terms, such as price and time to completion of the service, depend on the consumer's particular specifications. For instance, a printing services provider would need from its customers specifications such as the size of paper, type of ink, proofing and perforation. In a few sectors, there exist marketplace sites that provide consumers with specifications forms, which the consumer can fill out to learn the service terms of multiple service providers. Unfortunately, there are only a few such marketplace sites, and they cover a few sectors. At HP Labs, we are working towards building a universal marketplace site, i.e., a marketplace site that covers thousands of sectors and hundreds of providers per sector. One issue in this domain is the automated discovery/retrieval of the specifications for each sector. We address it through extracting and analyzing content from the websites of the service providers listed in business directories. The challenge is that each service provider is often listed under multiple service categories in a business directory, making it infeasible to utilize standard supervised learning techniques. We address this challenge through employing a multilabel statistical clustering approach within an expectation-maximization framework. We implement our solution to retrieve specifications for 3000 sectors, representing more than 300,000 service providers. We discuss our results within the context of the services needed to design a marketing campaign for a small business.
Digital Signal Processing with Field Programmable Gate Arrays, 3rd edition
Authors: Uwe Meyer-Baese
Venue:  (2007)

Field-Programmable Gate Arrays (FPGAs) are revolutionizing digital signal processing as novel FPGA families are replacing ASICs and PDSPs for front-end digital signal processing algorithms. So the efficient implementation of these algorithms is critical and is the main goal of this book. It starts with an overview of today's FPGA technology, devices, and tools for designing state-of-the-art DSP systems. A case study in the first chapter is the basis for more than 40 design examples throughout. The following chapters deal with computer arithmetic concepts, theory and the implementation of FIR and IIR filters, multirate digital signal processing systems, DFT and FFT algorithms, advanced algorithms with high future potential, and adaptive filters. Each chapter contains exercises. The VERILOG source code and a glossary are given in the appendices, while the accompanying CD-ROM contains the examples in VHDL and Verilog code as well as the newest Altera "Quartus II web edition" software. This edition has a new chapter on microprocessors, new sections on special functions using MAC calls, intellectual property core design and arbitrary sampling rate converters, and over 100 new exercises. 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
Context modeling and measuring for proactive resource recommendation in business collaboration
Authors: Ruinan Gong, Ke Ning, Qing Li, David O'Sullivan, Yuliu Chen, Stefan Decker
Venue: Computers and Industrial Engineering (2009)

Knowledge workers rely on collaboration with a large number of knowledge resources (people and machines) when performing tasks. Even highly experienced knowledge workers sometimes find it difficult to keep up to date with the number of resources available to help them complete a particular goal. This paper proposes a context-sensitive collaboration system to help improve the working efficiency of knowledge workers. The system provides recommendations to both information and human resources based on understanding their respective goals. The key to enabling context reasoning is an OWL-based context model and two metrics based on semantic aspects of the ontology to measure context granularity and association. The proposed solution is easy to implement and can benefit users by facilitating collaboration work and extended enterprises by providing access to expert knowledge.
Anonymous, authentic, and accountable resource management based on the e-cash paradigm
Authors: Jyh-Charn (Steve) Liu
Venue:  (2008)

The prevalence of digital information management in an open network has driven the need to maintain balance between anonymity, authenticity and accountability (AAA). Anonymity allows a principal to hide its identity from strangers before trust relationship is established. Authenticity ensures the correct identity is engaged in the transaction even though it is hidden. Accountability uncovers the hidden identity when misbehavior of the principal is detected. The objective of this research is to develop an AAA management framework for secure resource allocations. Most existing resource management schemes are designed to manage one or two of the AAA attributes. How to provide high strength protection to all attributes is an extremely challenging undertaking. Our study shows that the electronic cash (E-cash) paradigm provides some important knowledge bases for this purpose. Based on Chaum-Pederson&rsquo;s general transferable E-cash model, we propose a timed-zero-knowledge proof (TZKP) protocol, which greatly reduces storage spaces and communication overheads for resource transfers, without compromising anonymity and accountability. Based on Eng-Okamoto&rsquo;s general divisible E-cash model, we propose a hypercube-based divisibility framework, which provides a sophisticated and flexible way to partition a chunk of resources, with different trade-offs in anonymity protection and computational costs, when it is integrated with different sub-cube allocation schemes. Based on the E-cash based resource management framework, we propose a privacy preserving service oriented architecture (SOA), which allows the service providers and consumers to exchange services without leaking their sensitive data. Simulation results show that the secure resource management framework is highly practical for mission-critical applications in large scale distributed information systems.
Embedding and Retrieving Private Metadata in Electrocardiograms
Authors: Suleyman S. Kozat, Michail Vlachos, Claudio Lucchese, Helga Herle, Philip S. Yu
Venue: Journal of Medical Systems (2009)

Due to the recent explosion of `identity theft' cases, the safeguarding of private data has been the focus of many scientific efforts. Medical data contain a number of sensitive attributes, whose access the rightful owner would ideally like to disclose only to authorized personnel. One way of providing limited access to sensitive data is through means of encryption. In this work we follow a different path, by proposing the fusion of the sensitive metadata within the medical data. Our work is focused on medical time-series signals and in particular on Electrocardiograms (ECG). We present techniques that allow the embedding and retrieval of sensitive numerical data, such as the patient's social security number or birth date, within the medical signal. The proposed technique not only allows the effective hiding of the sensitive metadata within the signal itself, but it additionally provides a way of authenticating the data ownership or providing assurances about the origin of the data. Our methodology builds upon watermarking notions, and presents the following desirable characteristics: (a) it does not distort important ECG characteristics, which are essential for proper medical diagnosis, (b) it allows not only the embedding but also the efficient retrieval of the embedded data, (c) it provides resilience and fault tolerance by employing multistage watermarks (both robust and fragile). Our experiments on real ECG data indicate the viability of the proposed scheme.
Low-complexity bit-parallel multiplier over GF(2m) using dual basis representation
Authors: Chiou-Yng Lee, Jenn-Shyong Horng, I-Chang Jou
Venue: Journal of Computer Science and Technology (2006)

Recently, cryptographic applications based on finite fields have attracted much attention. The most demanding finite field arithmetic operation is multiplication. This investigation proposes a new multiplication algorithm over GF(2m) using the dual basis representation. Based on the proposed algorithm, a parallel-in parallel-out systolic multiplier is presented. The architecture is optimized in order to minimize the silicon covered area (transistor count). The experimental results reveal that the proposed bit-parallel multiplier saves about 65% space complexity and 33% time complexity as compared to the traditional multipliers for a general polynomial and dual basis of GF(2m).
Achieving good connectivity in motion graphs
Authors: Liming Zhao, Alla Safonova
Venue: Graphical Models (2009)

Motion graphs have been widely successful in the synthesis of human motions. However, the quality of the generated motions depends heavily on the connectivity of the graphs and the quality of transitions in them. Achieving both of these criteria simultaneously though is difficult. Good connectivity requires transitions between less similar poses, while good motion quality requires transitions only between very similar poses. This paper introduces a new method for building motion graphs. The method first builds a set of interpolated motion clips, which contains many more similar poses than the original data set. The method then constructs a well-connected motion graph (wcMG), by using as little of the interpolated motion clip frames as necessary to provide good connectivity and only smooth transitions. Based on experiments, wcMGs outperform standard motion graphs across different measures, generate good quality motions, allow for high responsiveness in interactive control applications, and do not even require post-processing of the synthesized motions.
Collecting 3D Content: Examples from Art and Medicine
Authors: Leszek Luchowski
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

The paper discusses some aspects of the acquisition and preparation of 3D imaging data in two application areas: orthodontics and cultural heritage. Scanning devices used by our team are presented. Examples are given of the imaging of museum exhibits and other historic objects. Also described are possible modalities of presenting the 3D data in various hardware contexts and for various categories of users.
Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators
Authors: Ryo Okui
Venue: Mathematics and Computers in Simulation (2009)

Testing the presence of serial correlation in the error terms in fixed effects regression models is important for many reasons. This paper proposes portmanteau tests based on the sum of the squares of autocorrelation estimators. This approach is a direct extension of the Box-Pierce or Ljung-Box test from single time series to panel data settings. In fixed effects regression analysis, we may estimate the autocorrelations using the within-group autocorrelations of the residuals. However, the within-group autocorrelations may be severely biased when the length of the time series is not very large compared with the cross-sectional sample size, as a result of the incidental parameters problem. We overcome this problem by using asymptotically unbiased autocorrelation estimators for long panel data recently proposed by the author. Monte Carlo simulations reveal that the proposed tests have good size properties and are powerful against a wide range of alternatives.
Maximizing Privacy under Data Distortion Constraints in Noise Perturbation Methods
Authors: Yaron Rachlin, Katharina Probst, Rayid Ghani
Venue: Privacy, Security, and Trust in KDD: Second ACM SIGKDD International Workshop, PinKDD 2008, Las Vegas, NV, USA, August 24, 2008, Revised Selected Papers (2009)

This paper introduces the `guessing anonymity,' a definition of privacy for noise perturbation methods. This definition captures the difficulty of linking identity to a sanitized record using publicly available information. Importantly, this definition leads to analytical expressions that bound data privacy as a function of the noise perturbation parameters. Using these bounds, we can formulate optimization problems to describe the feasible tradeoffs between data distortion and privacy, without exhaustively searching the noise parameter space. This work addresses an important shortcoming of noise perturbation methods, by providing them with an intuitive definition of privacy analogous to the definition used in k-anonymity, and an analytical means for selecting parameters to achieve a desired level of privacy. At the same time, our work maintains the appealing aspects of noise perturbation methods, which have made them popular both in practice and as a subject of academic research.
Managing Your Photographic Workflow with Photoshop Lightroom
Authors: Uwe Steinmueller, Juergen Gulbins
Venue:  (2007)

Since the early days of digital photography, the photographic "workflow" has been anything but a flow. A diverse set of tools for managing, developing, editing, tweaking, and printing images was available to digital photographers, but some of them were never designed to be used for photographic imaging. With Adobe Photoshop Lightroom (and Apple's new competitive program, Aperture), a new generation of software tools has arrived that have been designed from the ground up specifically for digital photographic post-processing. This book guides the new user through Photoshop Lightroom 1.2, which has more power under the hood, and a little more complexity than one would expect from its fairly straightforward user interface. This powerful new program offers photographers the vast majority of the tools they need all in one place, providing new and easier to use tools for managing and processing their images. With easy to follow, step-by-step instructions and full-color illustrations, the authors demonstrate how to use Photoshop Lightroom to build an efficient photographic workflow; from importing and organizing images; through the development and editing phases; all the way to building presentations for the web and in slideshows; and finally, to the ultimate product--the fine art print on paper.
Data memory subsystem resilient to process variations
Authors: Csaba Andras Moritz
Venue:  (2008)

As technology scales, more sophisticated fabrication processes cause variations in many different parameters in the device. These variations could severely affect the performance and power consumption of processors by making the latency of circuits less predictable and thus requiring conservative design approaches and/or techniques to increase performance that often affect power consumption. In this dissertation, we introduce and study step-by-step a 16KB cache subsystem in 32-nm CMOS technology, at both circuit and architecture levels, aiming for a single-cycle process-variation resilient subsystem design in a 1GHz processor that is high performance and power efficient at the same time. We use expected-case simulations in addition to worst-case circuit analysis to establish the overall delay and power consumption due to process variations under both typical and worst-case conditions. The distribution of the cache critical-path delay and power consumption in the typical scenario was determined by performing Monte Carlo simulations at different supply voltages, threshold voltages, and transistor lengths on the complete cache design. In addition to establishing the delay and power variations, we introduce an adaptive variable-cycle-latency cache architecture that mitigates the impact of process variations on access latency by closely following the typical latency behavior rather than assuming a conservative worst-case design point, and allowing tradeoffs between power and performance to be controlled. We show that the proposed adaptive cache is transparent to other processor subsystems and has negligible power and area overhead compared to a conventional design. We also establish what the overall leakage power is due to process variations. The distribution of the cache leakage power was determined before and after incorporating state-of-the-art leakage optimizations. Simulation results show that our adaptive data cache is process-variation-resilient and can achieve on average 10% performance improvement on SPEC2000 applications in a superscalar processor, in conjunction with 6X reduction in the mean leakage power compared with a conservative design. Additional performance improvement potential exists in processors in which the data cache access is on the critical path, by allowing a more aggressive clock rate in the processor.
Very Colorful Theorems
Authors: Jorge L. Arocha, Imre B&#x00e1;r&#x00e1;ny, Javier Bracho, Ruy Fabila, Luis Montejano
Venue: Discrete Computational Geometry (2009)

We prove several colorful generalizations of classical theorems in discrete geometry. Moreover, the colorful generalization of Kirchberger&#x2019;s theorem gives a generalization of the theorem of Tverberg on non-separated partitions.
A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking
Authors: Anne Cuzol, Etienne Mémin
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

In this paper, we present a method for the temporal tracking of fluid flow velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Itô diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In order to handle a state space of reasonable dimension, the motion field is represented as a combination of adapted basis functions, derived from a discretization of the vorticity map of the fluid flow velocity field. The resulting nonlinear filtering problem is solved with the particle filter algorithm in continuous time. An adaptive dimensional reduction method is applied to the filtering technique, relying on dynamical systems theory. The efficiency of the tracking method is demonstrated on synthetic and real-world sequences.
A bio-inspired distributed clustering algorithm for wireless sensor networks
Authors: Charalambos Charalambous, Shuguang Cui
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

Wireless sensor networks (WSNs) have emerged in strategic applications such as target detection, localization, and tracking in battlefields, where the large-scale nature renders centralized control prohibitive. In addition, the finite batteries in sensor nodes demand energy-aware network control. In this paper, we propose an energy-efficient topology management model that allows clustered nodes to act upon imminent targets in a purely distributed and autonomous fashion, which is inspired by the biological inter-cell lateral induction models. In particular, nodes in the target vicinity collaborate to form clusters based on their relative observation quality values. The energy efficiency of the proposed approach is examined against reference protocols.