ppxscal/embedding_network huggingface dataset:
Reliability-Based Selective Repeat Hybrid ARQ Protocol on Low Density Parity Check Codes
Authors: Fugui Huang, Xiaoxin Yi, Tie Wang
Venue: Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops (2006)

In this paper, we propose a RB-SR-HARQ (Selective Repeat Based on Reliability) protocol on Low Density Parity Check codes, which exploits the estimated bits reliabilities at the output of a decoder to retransmit those unreliable bits. Our scheme improves system performance and minimizes retransmission sizes by only retransmitting those bits which are likely to be in error. We simulate RB-SR-HARQ protocol on Low Density Parity Check codes both over AWGN and Rayleigh flat-fading channel. At the same time, we also compare our proposed RB-SR-HARQ protocol with IR (incremental redundancy) HARQ protocol on LDPC codes. The results show that our proposed scheme achieves a lower BER than other schemes both over AWGN and Rayleigh flat-fading channel.
VOICE: A Framework for Speech-Based Mobile Systems
Authors: Adam Sharp, Stan Kurkovsky
Venue: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02 (2007)

The advent of mobile phones and the Internet opened the doors for an emerging class of applications that connect mobile users to online resources and information services available anytime and anywhere. VoiceXML is an enabling technology for creating a streamlined speech-based interface for web-based information services. In this paper, we describe VOICE, a framework used to develop mobile context-aware speech-enabled systems powered by VoiceXML. Restaurant Search Guide, a simple context-aware recommender system has been built using VOICE to test the feasibility of the proposed framework.
Parameter-Free Radial Distortion Correction with Center of Distortion Estimation
Authors: Richard Hartley, Sing Bing Kang
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

We propose a method of simultaneously calibrating the radial distortion function of a camera and the other internal calibration parameters. The method relies on the use of a planar (or, alternatively, nonplanar) calibration grid which is captured in several images. In this way, the determination of the radial distortion is an easy add-on to the popular calibration method proposed by Zhang [24]. The method is entirely noniterative and, hence, is extremely rapid and immune to the problem of local minima. Our method determines the radial distortion in a parameter-free way, not relying on any particular radial distortion model. This makes it applicable to a large range of cameras from narrow-angle to fish-eye lenses. The method also computes the center of radial distortion, which, we argue, is important in obtaining optimal results. Experiments show that this point may be significantly displaced from the center of the image or the principal point of the camera.
A Human-Verifiable Authentication Protocol Using Visible Laser Light
Authors: Rene Mayrhofer, Martyn Welch
Venue: Proceedings of the The Second International Conference on Availability, Reliability and Security (2007)

Securing wireless channels necessitates authenticating communication partners. For spontaneous interaction, authentication must be efficient and intuitive. One approach to create interaction and authentication methods that scale to using hundreds of services throughout the day is to rely on personal, trusted, mobile devices to interact with the environment. Authenticating the resulting device-to-device interactions requires an out-of-band channel that is verifiable by the user. We present a protocol for creating such an out-of-band channel with visible laser light that is secure against man-in-the-middle attacks even when the laser transmission is not confidential. A prototype implementation shows that an appropriate laser channel can be constructed with simple off-the-shelf components.
A Large Scale Data Mining Approach to Antibiotic Resistance Surveillance
Authors: Eugenia G. Giannopoulou, Vasileios P. Kemerlis, Michalis Polemis, Joseph Papaparaskevas, Alkiviadis C. Vatopoulos, Michalis Vazirgiannis
Venue: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems (2007)

One of the most considerable functions in a hospital's infection control program is the surveillance of antibiotic resistance. Several traditional methods used to measure it do not provide adequate and promising results for further analysis. Data mining techniques, such as the association rules, have been used in the past and successfully led to discovering interesting patterns in public health data. In this work, we present the architecture of a novel framework which integrates data from multiple hospitals, discovers association rules, stores them in a data warehouse for future analysis and provides anytime accessibility through an intuitive web interface. We implemented the proposed architecture as a web application and evaluated it using data from the WHONET software installed in many Greek hospitals that belong to "the Greek System for Surveillance of Antimicrobial Resistance" network. The contribution of the proposed framework is considered to be a standardized workflow aiming at the integration of data produced by various hospitals into a consistent data warehouse and the use of a mechanism that detects hidden and previously unknown patterns on large datasets, in terms of association rules, which can provide surveillance warnings.
Revisiting "Collaboration" Under Conditions of "Mobility"
Authors: Sutirtha Chatterjee, Suprateek Sarker
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

With the modern day imperative of individuals being perennially on the move in order to keep pace with their work and business lives, the notion of "mobile collaboration" has come to assume a great deal of importance, both among scholars and practitioners. While the literature continues to extol the promises and virtues of mobility, there have been few illustrations on what collaboration means under conditions of mobility, and how it actually occurs. This paper takes a preliminary step toward clarifying the notion of mobile collaboration and developing a set of integrative themes capturing its essence.
Time series prediction with single multiplicative neuron model
Authors: R. N. Yadav, P. K. Kalra, J. John
Venue: Applied Soft Computing (2007)

Single neuron models are typical functional replica of the biological neuron that are derived using their individual and group responses in networks. In recent past, a lot of work in this area has produced advanced neuron models for both analog and binary data patterns. Popular among these are the higher-order neurons, fuzzy neurons and other polynomial neurons. In this paper, we propose a new neuron model based on a polynomial architecture. Instead of considering all the higher-order terms, a simple aggregation function is used. The aggregation function is considered as a product of linear functions in different dimensions of the space. The functional mapping capability of the proposed neuron model is demonstrated through some well known time series prediction problems and is compared with the standard multilayer neural network.
malloc() performance in a multithreaded Linux environment
Authors: Chuck Lever, David Boreham
Venue: Proceedings of the annual conference on USENIX Annual Technical Conference (2000)

Network servers make special demands that other types of applications may not make on memory allocators. We describe a simple malloc() microbenchmark suite that tests the ability of malloc() to divide its work efficiently among multiple threads and processors. The purpose of this suite is to determine the suitability of an operating system's heap allocator for use with network servers running in an SMP environment.
Detecting, Managing and Querying Replicas and Versions in a Peer-to-Peer Environment
Authors: Deise De Brum Saccol, Nina Edelweiss, Renata De Matos Galante
Venue: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (2007)

Peer-to-Peer (P2P) systems provide sharing of resources, which may be duplicated or versioned in several peers. However, traditional P2P systems are not aware of such replicas and versions, which arises an inefficiency and ineffectiveness problem. To solve this issue, our work proposes an automatic mechanism for replica and version detection. This functionality is presented as part of DetVX, an environment for managing replicas and versions of XML documents in a P2P context.
Scale and rotational invariant object recognition using fractal transformations
Authors: G. Neil
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

Fractal object recognition is an exciting new technique which may be used for rapid object recognition. A novel classification technique using fractal transformations is presented which allows a transformation to be compared directly to an object. This scale invariant technique is then made rotationally invariant before being applied to real world objects. This application shows that the technique is fast, robust to illumination effects and needs little prior knowledge to be successful. Finally work currently underway is detailed.
User Discrimination through Structured Writing on PDAs
Authors: Rachel R. M. Roberts, Roy A. Maxion, Kevin S. Killourhy, Fahd Arshad
Venue: Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (2007)

This paper explores whether features of structured writing can serve to discriminate users of handheld devices such as Palm PDAs. Biometric authentication would obviate the need to remember a password or to keep it secret, requiring only that a user's manner of writing confirm his or her identity. Presumably, a user's dynamic and invisible writing style would be difficult for an imposter to imitate. We show how handwritten, multi-character strings can serve as personalized, non-secret passwords. A prototype system employing support vector machine classifiers was built to discriminate 52 users in a closed-world scenario. On high-quality data, strings as short as four letters achieved a false-match rate of 0.04%, at a corresponding false non-match rate of 0.64%. Strings of at least 8 to 16 letters in length delivered perfect results--a 0% equal-error rate. Very similar results were obtained upon decreasing the data quality or upon increasing the data quantity.
Modified global k-means algorithm for clustering in gene expression data sets
Authors: Adil M. Bagirov, Karim Mardaneh
Venue: Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73 (2006)

Clustering in gene expression data sets is a challenging problem. Different algorithms for clustering of genes have been proposed. However due to the large number of genes only a few algorithms can be applied for the clustering of samples. k-means algorithm and its different variations are among those algorithms. But these algorithms in general can converge only to local minima and these local minima are significantly different from global solutions as the number of clusters increases. Over the last several years different approaches have been proposed to improve global search properties of k-means algorithm and its performance on large data sets. One of them is the global k-means algorithm. In this paper we develop a new version of the global k-means algorithm: the modified global k-means algorithm which is effective for solving clustering problems in gene expression data sets. We present preliminary computational results using gene expression data sets which demonstrate that the modified k-means algorithm improves and sometimes significantly results by k-means and global k-means algorithms.
SHIFT and SMART-AHS: a language for hybrid system engineering modeling and simulation
Authors: Marco Antoniotti, Aleks Göllü
Venue: Proceedings of the Conference on Domain-Specific Languages on Conference on Domain-Specific Languages (DSL), 1997 (1997)

SHIFT is a new programming language, whose aim is to facilitate the implementation of reusable simulation frameworks by teams of engineers. SHIFT incorporates system theoretic concepts emerging from the field of Hybrid Systems analysis and modeling. The SMART AHS framework is a collection of SHIFT libraries devoted to the construction of Hybrid System based simulation of Automated Highways Systems. In this paper we describe how the SHIFT simulation environment and language have impacted on the development of the SMART AHS framework. Our claim is that SHIFT provides the proper level of abstraction for engineers who face complex modeling and simulation tasks, where phase changes and continuous variables interact in subtle ways.
Seamless hardware/software performance co-monitoring in a codesign simulation environment with RTOS support
Authors: L. Moss, M. de Nanclas, L. Filion, S. Fontaine, G. Bois, M. Aboulhamid
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

Simulation monitoring tools are needed in hardware/software codesign for performance debugging, model validation and hardware/software partitioning purposes. Existing tools are either hardware- or software-centric and lack integrated and seamless co-monitoring. This paper presents a system-level co-monitoring tool that can monitor the computation and communication activities of SystemC user modules, as well as bus, memory and processor usage, on a variety of hardware/software embedded configurations that may include an RTOS. We also describe how performance metrics are generated during or after simulation and made accessible to users or external applications. Finally, experimental results show that such co-monitoring does not disturb the simulation's internal timing and only moderately increases the simulation's wall clock run time (by 11-22% for hardware/software partitioned architectures).
An Improved Job Co-Allocation Strategy in Multiple HPC Clusters
Authors: Jinhui Qin, Michael A. Bauer
Venue: Proceedings of the 21st International Symposium on High Performance Computing Systems and Applications (2007)

To more effectively use HPC clusters, co-allocating jobs across multiple clusters becomes an attractive possibility with the primary benefit being reduced turnaround time. This, ultimately, depends on the intercluster communication cost. In our previous research, we introduced a co-allocation strategy, MBAS, that made use of two threshold values to control allocation: one for control link saturation and another to control job splitting. In this paper, we examine the performance of MBAS. A simulation study concludes that assigning jobs with different priorities according to their communication patterns, and adjusting the threshold values for link saturation level control and chunk size control in splitting jobs, the MBAS coallocation strategy can significantly improve both user' satisfaction (in terms of turn around time) and system resource utilization consistently, even for jobs having large communication requirements.
3D surface point and wireframe reconstruction from multiview photographic images
Authors: Simant Prakoonwit, Ralph Benjamin
Venue: Image and Vision Computing (2007)

This paper describes a new method for reconstructing 3D surface points and a wireframe on the surface of a freeform object using a small number, e.g. 10, of 2D photographic images. The images are taken at different viewing directions by a perspective camera with full prior knowledge of the camera configurations. The reconstructed surface points are frontier points and the wireframe is a network of contour generators. Both of them are reconstructed by pairing apparent contours in the 2D images. Unlike previous works, we empirically demonstrate that if the viewing directions are uniformly distributed around the object's viewing sphere, then the reconstructed 3D points automatically cluster closely on a highly curved part of the surface and are widely spread on smooth or flat parts. The advantage of this property is that the reconstructed points along a surface or a contour generator are not under-sampled or under-represented because surfaces or contours should be sampled or represented with more densely points where their curvatures are high. The more complex the contour's shape, the greater is the number of points required, but the greater the number of points is automatically generated by the proposed method. Given that the viewing directions are uniformly distributed, the number and distribution of the reconstructed points depend on the shape or the curvature of the surface regardless of the size of the surface or the size of the object. The unique pattern of the reconstructed points and contours may be used in 3D object recognition and measurement without computationally intensive full surface reconstruction. The results are obtained from both computer-generated and real objects.
Mining Software Repositories with iSPARQL and a Software Evolution Ontology
Authors: Christoph Kiefer, Abraham Bernstein, Jonas Tappolet
Venue: Proceedings of the 29th International Conference on Software Engineering Workshops (2007)

One of the most important decisions researchers face when analyzing the evolution of software systems is the choice of a proper data analysis/exchange format. Most existing formats have to be processed with special programs written specifically for that purpose and are not easily extendible. Most scientists, therefore, use their own database( s) requiring each of them to repeat the work of writing the import/export programs to their format. We present EvoOnt, a software repository data exchange format based on the Web Ontology Language (OWL). EvoOnt includes software, release, and bug-related information. Since OWL describes the semantics of the data, EvoOnt is (1) easily extendible, (2) comes with many existing tools, and (3) allows to derive assertions through its inherent Description Logic reasoning capabilities. The paper also shows iSPARQL - our SPARQL-based Semantic Web query engine containing similarity joins. Together with EvoOnt, iSPARQL can accomplish a sizable number of tasks sought in software repository mining projects, such as an assessment of the amount of change between versions or the detection of bad code smells. To illustrate the usefulness of EvoOnt (and iSPARQL), we perform a series of experiments with a real-world Java project. These show that a number of software analyses can be reduced to simple iSPARQL queries on an EvoOnt dataset.
Rate-distortion optimal motion estimation algorithm for video coding
Authors: M. C. Chen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04 (1996)

A rate-distortion optimal motion estimation algorithm that leads to an efficient bit allocation between inter- and intra-frame information is presented. For coders in which motion vectors are differentially encoded, the rate-distortion optimization process is formulated as a shortest path finding problem. Adopting this framework, we show that the optimal solution for the conventional block-based motion estimation followed by 1-D DPCM and Huffman coding can be obtained by using dynamic programming or the Viterbi algorithm without resorting to an iterative procedure. Since memory and computational requirements for the optimal algorithm increase dramatically with the search range, we propose an effective fast algorithm that closely approximates the optimal performance while requiring considerably less complexity. When used in a hybrid motion compensated (MC) coding system, the proposed scheme outperforms the conventional MSE-optimal motion estimation approach in terms of PSNR performance under a low-bit-rate constraint. Our experimental results demonstrate overall gains in the range of 0.5 to 1.4 dB. 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
Selection of regularisation parameters for total variation denoising
Authors: V. Solo
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03 (1999)

We apply a general procedure of the author to choose penalty parameters in total variation denoising. This is an automatic method of tuning parameter choice for total variation denoising. The method is computationally much simpler than cross-validation.
How Small Labels Create Big Improvements
Authors: Pan Hui, Jon Crowcroft
Venue: Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops (2007)

It is widely believed that identifying communities in an ad hoc mobile communications system, such as a pocket switched network, can reduce the amount of traffic created when forwarding messages, but there has not been any empirical evidence available to support this assumption to date. In this paper, we show through use of real experimental human mobility data, how using a small label, identifying users according to their affiliation, can bring a large improvement in forwarding performance, in term of both delivery ratio and cost.
A more bio-plausible approach to the evolutionary inference of finite state machines
Authors: Hooman Shayani, Peter J. Bentley
Venue: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation (2007)

With resemblance of finite-state machines to some biological mechanisms in cells and numerous applications of finite automata in different fields, this paper uses analogies and metaphors to introduce an element of bio-plausibility to evolutionary grammatical inference. Inference of a finite-state machine that generalizes well over unseen input-output examples is an NP-complete problem. Heuristic algorithms exist to minimize the size of an FSM keeping it consistent with all the input-output sequences. However, their performance dramatically degrades in presence of noise in the training set. Evolutionary algorithms perform better for noisy data sets but they do not scale well and their performance drops as size or complexity of the target machine grows. Here, inspired by a biological perspective, an evolutionary algorithm with a novel representation and a new fitness function for inference of Moore finite-state machines of limited size is proposed and compared with one of the latest evolutionary techniques.