ppxscal/embedding_network huggingface dataset:
Hybrid MPI-Thread Parallelization of the Fast Multipole Method
Authors: Olivier Coulaud, Pierre Fortin, Jean Roman
Venue: Proceedings of the Sixth International Symposium on Parallel and Distributed Computing (2007)

We present in this paper multi-thread and multi-process parallelizations of the Fast Multipole Method (FMM) for Laplace equation, for uniform and non uniform distributions. These parallelizations apply to the original FMM formulation and to our new matrix formulation with BLAS (Basic Linear Algebra Subprograms) routines. Differences between the multi-thread and the multi-process versions are detailed, and a hybridMPI-thread approach enables to gain parallel efficiency and memory scalability over the pure MPI one on clusters of SMP nodes. On 128 processors, we obtain 85% (respectively 75%) parallel efficiency for uniform (respectively non uniform) distributions with up to 100 million particles.
High-quality and processor-efficient implementation of an MPEG-2 AAC encoder
Authors: Y. Takamizawa
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02 (2001)

Presents MPEG-2 AAC LC Profile encoder software for an Intel Pentium III processor. Modified discrete cosine transform (MDCT) and quantization processing are accelerated by the use of SIMD instructions. Psycho-acoustic analysis in the MDCT domain makes the use of FFTs unnecessary. Better sound quality is provided by greater efficiency in quantization processing and Huffman coding. All of this results in high-quality and processor-efficient implementation of an MPEG-2 AAC encoder. Sound quality achieved at 96 kbps/stereo is significantly better than that of MP3 at the same bitrate. The encoder works 13 times faster than realtime for stereo encoding on an 800MHz Pentium III processor.
Transforming HMMs for speaker-independent hands-free speech recognition in the car
Authors: Y. Gong
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01 (1999)

In the absence of HMMs trained with speech collected in the target environment, one may use HMMs trained with a large amount of speech collected in another recording condition (e.g., quiet office, with high quality microphone). However, this may result in poor performance because of the mismatch between the two acoustic conditions. We propose a linear regression-based model adaptation procedure to reduce such a mismatch. With some adaptation utterances collected for the target environment, the procedure transforms the HMMs trained in a quiet condition to maximize the likelihood of observing the adaptation utterances. The transformation must be designed to maintain speaker-independence of the HMM. Our speaker-independent test results show that with this procedure about 1% digit error rate can be achieved for hands-free recognition, using target environment speech from only 20 speakers.
Hairline Fracture Detection using MRF and Gibbs Sampling
Authors: A. S. Chowdhury, A. Bhattacharya, S. M. Bhandarkar, G. S. Datta, J. C. Yu, R. Figueroa
Venue: Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision (2007)

Detection of hairline fractures, representing points or areas of discontinuity in the bone, is a clinically challenging task, especially in presence of noise. The above problem is equally appealing from a computer vision or pattern recognition perspective since (a) traditional techniques for detection of corners, denoting points of surface discontinuity, typically fail in such cases and, (b) one needs to implicitly handle unknown local degradation in the image. A novel two-phase scheme for hairline mandibular fracture detection, that is robust to noise, is proposed. In the first phase, the hairline fractures are coarsely localized using statistical correlation and by exploiting the bilateral symmetry of the human mandible. In the second phase, the fractures are precisely identified and highlighted using a Markov Random Field (MRF) modeling approach coupled with Maximum A Posteriori probability (MAP) estimation. Gibbs sampling is used to maximize the posterior probability. Experimental results on Computer Tomography (CT) scans from real patients are presented.
Tcl/Tk as an OpenDoc scripting part
Authors: Jim Ingham
Venue: Proceedings of the 4th conference on USENIX Tcl/Tk Workshop, 1996 - Volume 4 (1996)

This paper describes the advantages that would accrue both to Tcl/Tk and to OpenDoc by importing the Tcl/Tk scripting system into the OpenDoc environment. First, it gives a brief outline of the OpenDoc compound document architecture. Then it describes the place Tcl/Tk could fill in the OpenDoc scheme. Finally, it gives a preliminary sketch of the areas in which Tcl/Tk would need to be modified in order to implement this merger.
User-Based Evaluations of Search Engines: Hygiene Factors and Motivation Factors
Authors: Ya-Lan Chuang, Ling-Ling Wu
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

In cyberspace, the emergence and increasing importance of search engine prompts numerous evaluation studies in regard to its effectiveness. Based on a user-oriented perspective, the paper is aimed to derive functional requirements of search engines rooted in Herzberg's Two Factor Theory. A survey, with 758 valid participants, was conducted to investigate the impacts of various functions of search engines on user's behavior. The empirical results showed that user's expectations of functional requirements are distinguished into two categories: motivation factors and hygiene factors. Hygiene factors of search engine "attract" users to try a search engine in the first place, but it is the motivation factors that "retain" users to continue using the same search engine.
Hiding collaborative recommendation association rules
Authors: Shyue-Liang Wang, Dipen Patel, Ayat Jafari, Tzung-Pei Hong
Venue: Applied Intelligence (2007)

The concept of Privacy-Preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. For example, through data mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. There have been two types of privacy concerning data mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected.For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance [10, 18---21, 24, 27]. This selection of rules would require data mining process to be executed first. Based on the discovered rules and privacy requirements, hidden rules or patterns are then selected manually. However, for some applications, we are interested in hiding certain constrained classes of association rules such as collaborative recommendation association rules [15, 22]. To hide such rules, the pre-process of finding these hidden rules can be integrated into the hiding process as long as the recommended items are given. In this work, we propose two algorithms, DCIS (Decrease Confidence by Increase Support) and DCDS (Decrease Confidence by Decrease Support), to automatically hiding collaborative recommendation association rules without pre-mining and selection of hidden rules. Examples illustrating the proposed algorithms are given. Numerical simulations are performed to show the various effects of the algorithms. Recommendations of appropriate usage of the proposed algorithms based on the characteristics of databases are reported.
EmVC: Managing Changes and Configurations in Designs of Complex, Embedded Computing Systems
Authors: Tien Nguyen
Venue: Proceedings of the 12th IEEE International Conference on Engineering Complex Computer Systems (2007)

Nowadays, the development of complex computing devices involves a substantial and growing part of software development. A great challenge for engineers is to manage the evolution of a system with several components in the face of mounting complexity due to concurrent hardware and software development. The key limitations of existing change management tools used for the design process of complex computing systems include their inadequacy in representing semantics of design models and inability to manage changes to both hardware designs and associated software components in a cohesive manner. Thus, it is difficult to track the logical interdependencies between the changes to hardware and software components in an embedded computing system over time. This paper presents EmVC, a configuration management system for a hardware software co-design process. EmVC is an illustration of our application of a well-known software engineering approach to the management of embedded systems design artifacts. Our novel component-based change management mechanism is capable of capturing and versioning the underlying logical contents of components in system design models and their associated software artifacts in a cohesive manner.
Understanding and validating database system administration
Authors: Fábio Oliveira, Kiran Nagaraja, Rekha Bachwani, Ricardo Bianchini, Richard P. Martin, Thu D. Nguyen
Venue: Proceedings of the annual conference on USENIX '06 Annual Technical Conference (2006)

A large number of enterprises need their commodity database systems to remain available at all times. Although administrator mistakes are a significant source of unavailability and cost in these systems, no study to date has sought to quantify the frequency of mistakes in the field, understand the context in which they occur, or develop system support to deal with them explicitly. In this paper, we first characterize the typical administrator tasks, testing environments, and mistakes using results from an extensive survey we have conducted of 51 experienced administrators. Given the results of this survey, we next propose system support to validate administrator actions before they are made visible to users. Our prototype implementation creates a validation environment that is an extension of a replicated database system, where administrator actions can be validated using real workloads. The prototype implements three forms of validation, including a novel form in which the behavior of a database replica can be validated even without an example of correct behavior for comparison. Our results show that the prototype can detect the major classes of administrator mistakes.
Evolving ant colony systems in hardware for random number generation
Authors: J. C. Isaacs, R. K. Watkins, S. Y. Foo
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

Using a genetic algorithm (GA) to evolve ant colony systems (ACS), we have succeeded at producing evolvable random number generators (RNG) that can be written to hardware. Although the simulated behavior of individual ants is limited to a small number of choices, "fit" colonies pass many stringent tests for randomness.
Evaluation Function for Synthesizing Security Protocols by means of Genetic Algorithms
Authors: Luis Zarza, Josep Pegueroles, Miguel Soriano
Venue: Proceedings of the The Second International Conference on Availability, Reliability and Security (2007)

The design of cryptographic and security protocols for new scenarios and applications can be computationally expensive. Examples of these can be sensor or mobile adhoc networks and electronic voting or auctions applications. In such cases, the aid of an automated tool generating protocols for a predefined problem can be of great utility. This work uses the Genetic Algorithms (GA) techniques for the automatic design of security networked protocols. When using GA for optimizing protocols the evaluation function is critical. We discuss how can be defined several basic criteria for evaluating security protocols and present some examples for evaluation of different protocols.
A Comparative Study of Three Program Exploration Tools
Authors: Brian de Alwis, Gail C. Murphy, Martin P. Robillard
Venue: Proceedings of the 15th IEEE International Conference on Program Comprehension (2007)

Programmers need tools to help explore large software systems when performing software evolution tasks. A va- riety of tools have been created to improve the effective- ness of such exploration. The usefulness of these tools has been argued largely on the basis of case studies, small narrowly-focussed experiments, or non-human-based ex- periments. In this paper, we report on a more rigorously controlled study of three specialized software exploration tools in which professional programmers used the tools to plan complex change tasks to a medium-sized code base. We found that the tools had little apparent effect; the ef- fects observed instead appear to be dominated by individ- ual styles and strategies of the programmers and character- istics of the tasks. In addition to presenting the results of the study, this paper introduces the use of two experimental evaluation aids: the NASA Task Load Index (TLX) for as- sessing task difficulty and distance profiles for assessing the degree to which programmers remain on-track.
Data mining approaches for intrusion detection
Authors: Wenke Lee, Salvatore J. Stolfo
Venue: Proceedings of the 7th conference on USENIX Security Symposium - Volume 7 (1998)

In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra-and inter-audit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.
Reference Architecture for Lending Industry in ULS Systems
Authors: Liming Zhu, Mark Staples, Ross Jeffery
Venue: Proceedings of the International Workshop on Software Technologies for Ultra-Large-Scale Systems (2007)

Vertical industries have been developing e-business standards to improve their business-to-business interoperability, and to foster efficient and organicallygrown industry-wide systems. Such systems and their horizontal interactions with even larger ecosystems are effectively an Ultra-Large-Scale (ULS) system. EBusiness standards are essentially a set of governing rules for these ULS systems. It is critical that these governing rules address the socio-technical ecosystem (rather than system of systems) challenges recognized for ULS systems [3], promote overall system quality, and cope with long-term, self-regulating, continuous evolution.
LDA derived cepstral trajectory filters in adverse environmental conditions
Authors: M. Lieb
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02 (2000)

Amongst several data driven approaches for designing filters for the time sequence of spectral parameters, the linear discriminant analysis (LDA) based method has been proposed for automatic speech recognition. Here we apply LDA-based filter design to cepstral features, which better match the inherent assumption of this method that feature vector components are uncorrelated. Extensive recognition experiments have been conducted both on the standard TIMIT phone recognition task and on a proprietary 130-words command word task under various adverse environmental conditions, including reverberant data with real-life room impulse responses and data processed by acoustic echo cancellation algorithms. Significant error rate reductions have been achieved when applying the novel long-range feature filters compared to standard approaches employing cepstral mean normalization and delta and delta-delta features, in particular when facing acoustic echo cancellation scenarios and room reverberation. For example, the phone accuracy on reverberated TIMIT data could be increased from 50.7% to 56.0%.
Portrait beautification: A fast and robust approach
Authors: Heng Liu, Jinqi Yan, Zushu Li, Hua Zhang
Venue: Image and Vision Computing (2007)

Portrait beautification technology has important significance regarding virtual background photography and augment reality said. In this work, a set of integrated portrait beautification approaches are raised. Aiming at fast portrait division, quantum evolution algorithm is introduced into the course of threshold searching. In order to merge images and virtual background, based on Human Vision System principles, a multi-layer portrait weighted method is proposed. To overcome the effects of illumination on face skin color, one kind of fast face color correction technology based on color temperature estimation is presented. Detail experimental results and comparisons are provided to show our approaches are effective.
Division of labor in particle swarm optimisation
Authors: J. S. Vesterstrom, J. Riget, T. Krink
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

We introduce Division of Labor (DoL) from social insects to improve local optimisation of the Particle Swarm Optimiser (PSO). We compared the performance with the basic PSO, a GA and simulated annealing and found improvements around local optima. The PSO with DoL outperforms the basic PSO on most testcases and is comparable in local optimisation with SA.
Linear predictive coding and its decision logic for early prediction of major adverse cardiac events using mass spectrometry data
Authors: Tuan D. Pham, Honghui Wang, Xiaobo Zhou, Dominik Beck, Miriam Brandl, Gerard Hoehn, Joseph Azok, Marie-Luise Brennan, Stanley L. Hazen, King Li, Stephen T. C. Wong
Venue: Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73 (2006)

Proteomics is an emerging field of modern biotechnology and an attractive research area in bioinformatics. Protein annotation by mass spectrometry has recently been utilized for the classification and prediction of diseases. In this paper we apply the theory of linear predictive coding and its decision logic for the prediction of major adverse cardiac risk using mass spectra. The new method was tested with a small set of mass spectrometry data. The initial experimental results are found promising for the prediction and show the implication of the potential use of the data for biomarker discovery.
An investigation of the differences between robot and virtual learning companions' influences on students' engagement
Authors: Sheng-Hui Hsu, Chih-Yueh Chou, Fei-Ching Chen, Yuan-Kai, Tak-Wai Chan
Venue: Proceedings of the The First IEEE International Workshop on Digital Game and Intelligent Toy Enhanced Learning (2007)

We investigate the differences of the influences between robot and virtual learning companions on students' engagement. From the analysis of our experimental result and interview, we found that virtual learning companion and robot learning companion both rose up students' motivation and engagement. The differences between these two types of learning companions is that one of them made subjects more concentrated on the task and the other extended subjects' time on task. We shall offer some advices for the researchers who design learning companion systems.
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Venue: Joint International Conference on Measurement and Modeling of Computer Systems (2007)

Welcome to SIGMETRICS 2007! This year's annual ACM SIGMETRICS conference is being held this year in conjunction with the Federated Computing Research Conference (FCRC). The scope of SIGMETRICS encompasses the development and application of state-of-the-art, broadly-applicable analytic, simulation, and measurement-based performance evaluation techniques. In soliciting papers for this year's program, and in selecting members of the Technical Program Committee (TPC), we made a special effort to maintain a breadth of topic coverage. Application areas represented include: distributed systems, networking, multimedia, storage systems, operating system, web services, supercomputing, compilers, architecture, and more. Analytical techniques represented include: scheduling theory, queueing theory, stochastic modeling, high-dimensional geometry, matrix analytic methods, transient time-varying behaviors, competitive ratio analysis, and more. The conference received 179 submissions. We accepted 29 full papers and 19 poster papers. We followed a double-blind reviewing process, and each paper was reviewed by at least three members of the TPC. In some cases additional reviews were sought from specialists outside the TPC. The TPC consisted of 54 members from 8 countries. After extensive email discussions among the whole TPC, the final paper selection was made during a 2-day meeting held at Columbia University on January 26-27, 2007, which was attended by 35 of the PC members, the two program co-chairs, and the general chair. The quality of submissions was quite high and, as a result, the selected papers make up a very strong program, which we hope you will enjoy. After the TPC meeting, a subcommittee selected the best paper award winners. The Best Paper award goes to, "Modeling the relative fitness of storage," by Michael Mesnier, Matthew Wachs, Raja R. Sambasivan, Alice Zheng, and Gregory R. Ganger. The Kenneth C. Sevcik Outstanding Student Paper award goes to, "An Analysis of Latent Sector Errors in Disk Drives," by Lakshmi Bairavasundaram, Garth Goodson, Shankar Pasupathy, and Jiri Schindler.
SearchGen: a synthetic workload generator for scientific literature digital libraries and search engines
Authors: Huajing Li, Wang-Chien Lee, Anand Sivasubramaniam, Lee Giles
Venue: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries (2007)

Due to the popularity of web applications and their heavy usage, it is important to obtain a good understanding of their workloads in order to improve performance of search services. Existing works have typically focused on generic web workloads without putting emphasis on specific domains. In this paper, we analyze the usage logs of CiteSeer, a scientific literature digital library and search engine, to characterize workloads for both robots and users. Essential ingredients that contribute to workloads are proposed. Among them we find the access intervals show high variance, and thus cannot be predicted well with time-series models. On the other hand, client visiting path and semantics can be well captured with probabilistic models and Zipf-law. Based on the findings, we propose SearchGen, a synthetic workload generator to output traces for scientific literature digital libraries and search engines. A comparison between synthetic workloads and actual logged traces suggests that the synthetic workload fits well.