ppxscal/embedding_network huggingface dataset:
Simplified facial animation control utilizing novel input devices: a comparative study
Authors: Nikolaus Bee, Bernhard Falk, Elisabeth André
Venue: Proceedings of the 13th international conference on Intelligent user interfaces (2009)

Editing facial expressions of virtual characters is quite a complex task. The face is made up of many muscles, which are partly activated concurrently. Virtual faces with human expressiveness are usually designed with a limited amount of facial regulators. Such regulators are derived from the facial muscle parts that are concurrently activated. Common tools for editing such facial expressions use slider-based interfaces where only a single input at a time is possible. Novel input devices, such as gamepads or data gloves, which allow parallel editing, could not only speed up editing, but also simplify the composition of new facial expressions. We created a virtual face with 23 facial controls and connected it with a slider-based GUI, a gamepad, and a data glove. We first conducted a survey with professional graphics designers to find out how the latter two new input devices would be received in a commercial context. A second comparative study with 17 subjects was conducted to analyze the performance and quality of these two new input devices using subjective and objective measurements.
Mining Periodic Behavior in Dynamic Social Networks
Authors: Mayank Lahiri, Tanya Y. Berger-Wolf
Venue: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (2008)

Social interactions that occur regularly typically correspond to significant yet often infrequent and hard to detect interaction patterns. To identify such regular behavior, we propose a new mining problem of finding periodic or near periodic subgraphs in dynamic social networks. We analyze the computational complexity of theproblem, showing that, unlike any of the related subgraph mining problems, it is polynomial. We propose a practical, efficient and scalable algorithm to find such subgraphs that takes imperfect periodicity into account. We demonstrate the applicability of our approach on severalreal-world networks and extract meaningful and interesting periodic interaction patterns.
Finding non-dominated solutions in bi-objective integer network flow problems
Authors: Augusto Eusébio, José Rui Figueira
Venue: Computers and Operations Research (2009)

This paper deals with an algorithm for finding all the non-dominated solutions and corresponding efficient solutions for bi-objective integer network flow problems. The algorithm solves a sequence of @?-constraint problems and computes all the non-dominated solutions by decreasing order of one of the objective functions. The optimal integer solutions for the @?-constraint problems are determined by exploring a branch-and-bound tree. The algorithm makes use of the network structure to perform the computations, i.e., the network structure of the problem is not destroyed with the inclusion of an @?-constraint. This paper presents the main features of the algorithm, the theoretical bases of the proposed approach and some computational issues. Experiments were done and the results are also reported in the paper.
Pseudolikelihood EM for Within-network Relational Learning
Authors: Rongjing Xiang, Jennifer Neville
Venue: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (2008)

In this work, we study the problem of \emph{within-network} relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to predict the classes of unlabeled instances in the same graph. We categorize recent work in statistical relational learning into three alternative approaches for this setting: disjoint learning with disjoint inference, disjoint learning with collective inference, and collective learning with collective inference. Models from each of these categories has been employed previously in different settings, but to our knowledge there has been no systematic comparison of models from all three categories. In this paper, we develop a novel pseudolikelihood EM method that facilitates more general \emph{collective learning} and \emph{collective inference} on partially labeled relational networks. We then compare this method to competing methods from the other categories on both synthetic and real-world data. We show that collective learning and inference with the pseudolikelihood EM approach achieves significantly higher accuracy than the other types of models when there are a moderate number of labeled examples in the data graph.
Simulation and visualization of traffic operations in augmented reality for improved planning and design of road construction projects
Authors: Amir H. Behzadan, Vineet R. Kamat
Venue: Proceedings of the 40th Conference on Winter Simulation (2008)

This paper describes research that investigated the application of 3D Augmented Reality (AR) for animating traffic simulation models in real time to support the planning and design of road construction projects. Traditional 3D visualization applications use Virtual Reality (VR) environments to visualize traffic simulations. The significant amount of time and human resources required to collect and incorporate statistical data about existing traffic volume and pattern can be a major drawback to the application of VR in studying the possible effect of traffic on planned construction. At the same time, the final visual output is often unconvincing as phenomenon such as unpredictable motion of pedestrians and incoming vehicles cannot be effectively represented in a VR animation. In this research, a new AR-based approach to create real time interactive traffic animations is introduced. This approach takes advantage of wireless communication techniques to facilitate continuous positional and orientation data transfer between moving vehicles and an AR animation.
Discovering Computers 2008: Complete
Authors: Gary B. Shelly, Thomas J. Cashman, Misty E. Vermaat
Venue: Shelly Cashman Series (2007)

Discovering Computers 2008 provides a current and thorough introduction to computers by integrating usage of the World Wide Web with the printed text. This Shelly Cashman Series text offers a complete and engaging solution to successfully teach basic computer concepts with new exercises, case studies, and online tools on the Online Companion. Updated for currency, the latest trends in technology and computer concepts are covered.
Buffer Replacement Algorithms for Multimedia Storage Systems
Venue: Proceedings of the 1996 International Conference on Multimedia Computing and Systems (1996)

In a disk-based storage system, a buffer cache is used to reduce the number of disk I/O. The buffer manager is responsible for buffer replacement to free memory in order to accommodate new data blocks from disk. An optimal buffer replacement algorithm is one which yields the lowest number of cache misses and as a result the lowest number of disk I/O. In general, optimal buffer replacement algorithms require future knowledge, and therefore cannot be realized. Thus, most storage systems use approximation algorithms such as the least recently used (LRU) and most recently used (MRU) buffer replacement algorithms. In this paper, we show that both of these algorithms yield poor performance when they are used in storage systems that provide support for continuous media data. We present two new buffer replacement algorithms---BASIC and DISTANCE, which reduce the cache misses upto 30% as compared to LRU and MRU. Furthermore, in the simulation experiments we conducted, our new algorithms resulted in at most 9.7% increase in cache misses as compared to the optimal algorithm when the videos are sufficiently long (i.e., longer than 30 minutes). Moreover, we show that the DISTANCE scheme incurs an overhead which is comparable to those of the LRU and MRU schemes. Our conclusion is that DISTANCE is a very suitable candidate for a buffer replacement scheme in storage systems that deal with continuous media data.
QR decomposition on GPUs
Authors: Andrew Kerr, Dan Campbell, Mark Richards
Venue: Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units (2009)

QR decomposition is a computationally intensive linear algebra operation that factors a matrix A into the product of a unitary matrix Q and upper triangular matrix R. Adaptive systems commonly employ QR decomposition to solve overdetermined least squares problems. Performance of QR decomposition is typically the crucial factor limiting problem sizes. Graphics Processing Units (GPUs) are high-performance processors capable of executing hundreds of floating point operations in parallel. As commodity accelerators for 3D graphics, GPUs offer tremendous computational performance at relatively low costs. While GPUs are favorable to applications with much inherent parallelism requiring coarse-grain synchronization between processors, methods for efficiently utilizing GPUs for algorithms computing QR decomposition remain elusive. In this paper, we discuss the architectural characteristics of GPUs and explain how a high-performance implementation of QR decomposition may be implemented. We provide detailed performance analysis of the resulting implementation for real-valued matrices and offer recommendations for achieving high performance to future developers of dense linear algebra procedures for GPUs. Our implementation sustains 143 GFLOP/s, and we believe this is the fastest announced QR implementation executing entirely on the GPU.
Collective bin packing: an active learning exercise
Authors: Vincent A. Cicirello
Venue: Journal of Computing Sciences in Colleges (2009)

Active learning has become an important part of effective computer science pedagogy. Active learning helps to keep students engaged in the learning process and is useful at all levels of computer science education from introductory courses on programming through more advanced upper level topics and graduate level coursework. In this paper, we present an active learning exercise that we developed for use in courses on artificial intelligence for demonstrating heuristic search concepts as well as concepts related to swarm intelligence. We have also found the exercise useful in an interdisciplinary course for non-majors to demonstrate some of the problem solving approaches of computer science. It is also applicable to courses on discrete mathematics. The exercise that we call Collective Bin Packing is an adaptation of the well-known combinatorial optimization problem known as bin packing. During the exercise, students take turns deciding upon individual problem solving steps. A follow-up class discussion draws out the algorithmic elements of the students' collective reasoning process. An interactive Java application is used to facilitate the group problem solving exercise.
Elasticity: topological characterization of robustness in complex networks
Authors: A. Sydney, C. Scoglio, P. Schumm, R. E. Kooij
Venue: Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems (2008)

Just as a herd of animals relies on its robust social structure to survive in the wild, similarly robustness is a crucial characteristic for the survival of a complex network under attack. The capacity to measure robustness in complex networks defines a network's survivability in the advent of classical component failures and at the onset of cryptic malicious attacks. To date, robustness metrics are deficient and unfortunately the following dilemmas exist: accurate models necessitate complex analysis while conversely, simple models lack applicability to our definition of robustness. In this paper, we define robustness and present a novel metric, elasticity- a bridge between accuracy and complexity-a link in the chain of network robustness. Additionally, we "test-drive" the performance of elasticity on Internet topologies and online social networks, and articulate results.
Shrinkage Effect in Ancestral Maximum Likelihood
Authors: Elchanan Mossel, Sebastien Roch, Mike Steel
Venue: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) (2009)

Ancestral maximum likelihood (AML) is a method that simultaneously reconstructs a phylogenetic tree and ancestral sequences from extant data (sequences at the leaves). The tree and ancestral sequences maximize the probability of observing the given data under a Markov model of sequence evolution, in which branch lengths are also optimized but constrained to take the same value on any edge across all sequence sites. AML differs from the more usual form of maximum likelihood (ML) in phylogenetics because ML averages over all possible ancestral sequences. ML has long been know to be statistically consistent - that is, it converges on the correct tree with probability approaching 1 as the sequence length grows. However, the statistical consistency of AML has not been formally determined, despite informal remarks in a literature that dates back 20 years. In this short note we prove a general result that implies that AML is statistically inconsistent. In particular we show that AML can 'shrink' short edges in a tree, resulting in a tree that has no internal resolution as the sequence length grows. Our results apply to any number of taxa.
Artificial Neural Networks in Cancer Recurrence Prediction
Authors: Panrasee Ritthipravat
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 02 (2009)

This paper aims to review the use of artificial neural networks (ANNs) in prediction of cancer recurrence. The sources of publications were randomly selected from PUBMED database, IEEE explore, and the google search engine with the keywords for searching as “recurrence” or “relapse” or “disease free” + “neural network” + “cancer”. Increasing of the predictive performance was considered. In addition, handling incomplete data and feature selection techniques usually employed in this application were examined.
A framework for memetic optimization using variable global and local surrogate models
Authors: Yoel Tenne, S. W. Armfield
Venue: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2009)

We propose a framework of memetic optimization using variable global and local surrogate-models for optimization of expensive functions. The framework employs the trust-region approach but replaces the quadratic models with the more general RBF ones. It makes an extensive use of accuracy assessment to select the models used and to improve them if necessary. It also employs several efficient and stable numerical methods to improve its performance. Rigorous performance analysis shows the proposed framework significantly outperforms several existing surrogate-assisted evolutionary algorithms.
Evaluation of a comprehensive P2P video-on-demand streaming system
Authors: Karl-André Skevik, Vera Goebel, Thomas Plagemann
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2009)

Video-on-demand (VoD) streaming has recently become a popular service on the Internet, with several companies offering videos to a global audience. However, traditional client/server based VoD streaming systems can be very bandwidth intensive and expensive to maintain, especially for high quality video content. To improve the scalability these systems, the use of peer-to-peer (P2P) networking has been proposed, but despite the efficiency of applications such as BitTorrent for downloading of large files, it is not simple to use P2P techniques for streaming. Problems such as firewalls and freeloaders reduce the efficiency of both types of P2P systems, but for real-time services such as streaming, the result can be reduced playback quality. Other issues include the traffic load imposed on ISPs by P2P networks, which can motivate ISPs to interfere with the P2P traffic. Finally, protecting against malicious modification of content can increase overhead, response times, and startup delays. We consider these issues to be fundamental to the problem of P2P based VoD, but despite the large amount of research that has been done in this field, these issues have largely been ignored. To address this, we present an evaluation of the Streaming P2P Protocol (SPP) architecture. By studying the problem as a whole we have found a simple and comprehensive solution that addresses all the four issues listed above. To show that the system is not only scalable, but also that it can be implemented efficiently, we have used both simulations and experiments on PlanetLab for evaluation. The results show that the combination of cache nodes and use of end-user resources found in the SPP architecture can give a low load on servers and ISPs, even when firewalls are taken into consideration. Furthermore, we observed low startup delays and few playback errors during the PlanetLab experiments. The scalable and low-cost distribution of content possible with the SPP architecture should be suitable for both large-scale commercial distributors and users of community networks with limited resources.
Shortest paths in linear time on minor-closed graph classes, with an application to Steiner tree approximation
Authors: Siamak Tazari, Matthias Müller-Hannemann
Venue: Discrete Applied Mathematics (2009)

We generalize the linear-time shortest-paths algorithm for planar graphs with nonnegative edge-weights of Henzinger et al. (1994) to work for any proper minor-closed class of graphs. We argue that their algorithm can not be adapted by standard methods to all proper minor-closed classes. By using recent deep results in graph minor theory, we show how to construct an appropriate recursive division in linear time for any graph excluding a fixed minor and how to transform the graph and its division afterwards, so that it has maximum degree three. Based on such a division, the original framework of Henzinger et al. can be applied. Afterwards, we show that using this algorithm, one can implement Mehlhorn's (1988) 2-approximation algorithm for the Steiner tree problem in linear time on these graph classes.
Dynamic Verification of Memory Consistency in Cache-Coherent Multithreaded Computer Architectures
Authors: Albert Meixner, Daniel J. Sorin
Venue: IEEE Transactions on Dependable and Secure Computing (2009)

Multithreaded servers with cache-coherent shared memory are the dominant type of machines used to run critical network services and database management systems. To achieve the high availability required for these tasks, it is necessary to incorporate mechanisms for error detection and recovery. Correct operation of the memory system is defined by the memory consistency model. Errors can therefore be detected by checking if the observed memory system behavior deviates from the specified consistency model. Based on recent work, we design a framework for dynamic verification of memory consistency (DVMC). The framework consists of mechanisms to verify three invariants that are proven to guarantee that a specified memory consistency model is obeyed. We describe an implementation of the framework for the SPARCv9 architecture, and we experimentally evaluate its performance using full-system simulation of commercial workloads.
High-Performance Query Processing of a Real-World OLAP Database with ParGRES
Authors: Melissa Paes, Alexandre A. Lima, Patrick Valduriez, Marta Mattoso
Venue: High Performance Computing for Computational Science - VECPAR 2008: 8th International Conference, Toulouse, France, June 24-27, 2008. Revised Selected Papers (2008)

Typical OLAP queries take a long time to be processed so speeding up the execution of each single query is imperative to decision making. ParGRES is an open-source database cluster middleware for high performance OLAP query processing. By exploiting intra-query parallelism on PC clusters, ParGRES has shown excellent performance using the TPC-H benchmark. In this paper, we evaluate ParGRES on a real-world OLAP database. Through adaptive virtual partitioning of the database, ParGRES yields linear and very often super-linear speedup for frequent queries. This shows that ParGRES is a very cost-effective solution for OLAP query processing in real settings.
The impact of the field time on response, retention, and response completeness in list-based Web surveys
Authors: Anja S. Göritz, Stefan Stieger
Venue: International Journal of Human-Computer Studies (2009)

A short field time is an often-cited benefit of Web-based surveys that rely on pre-recruited people. However, it has never been examined how different field times as implemented through different deadlines for participation influence response behavior. Four experiments were conducted in which the deadline for taking part in the study was varied across several days, and there was a control group who was not told any deadline. We examined the impact of both stating a deadline versus not stating a deadline and the length of the deadline on the response rate, the retention rate, and response completeness. It was found that response rises with the number of days a study is in the field. There is tentative evidence that the more generous the deadline, the smaller the retention rate and clear evidence that response completeness is lower. Moreover, in a quasi-experimental fashion it was explored whether responding late to a study request is associated with being retained until the end of the study and with the completeness of filling out the questionnaire. There is no straightforward association between responding late to a study request on the one hand and retention and response completeness on the other hand.
A Formal Mapping between Program Slicing and Z Specifications
Authors: Fangjun Wu
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 01 (2009)

This paper represents a research effort towards the formal mapping between program slicing and Z specifications. General aspects of program slicing are considered, i.e. definitions of program slicing, nodes and edges of program dependence graphs, and program slicing algorithms. With this approach, we provide not only a precise semantic basis for program slicing but also a sound mechanism for reasoning and verification about program slicing. 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
Proceedings of the second ACM conference on Wireless network security
Venue: Conference On Wireless Network Security (2009)

It is our great pleasure to welcome you to the 2nd ACM Conference on Wireless Network Security (WiSec) at ETH Zürich, Switzerland. ACM WiSec addresses all aspects of wireless network security. This includes the security of networks such as cellular, metropolitan, local area, vehicular, ad hoc, satellite, underwater, and sensor networks, as well as RFID. The first WiSec, was held in Alexandria (USA) in 2008, where the conference merged three existing workshops: ESAS (European Workshop on Security of Ad Hoc and Sensor Networks), SASN (ACM Workshop on Security in Ad Hoc and Sensor Networks), and WiSec (ACM Wireless Security Workshop). This year's conference follows the success of the first WiSec and continues to be a premier forum for wireless security research. This year's call for papers attracted 107 submissions. Papers were evaluated based on their relevance, technical quality, and novelty, using a double-blind review process. Each paper was reviewed by three reviewers and the final paper selection was done at a program committee meeting that took place over a phone conference. The program committee accepted 14 full papers and 14 short papers for presentation at the conference, yielding an acceptance rate of 13% for full papers and 26% for all accepted papers. The program also includes two invited keynotes, given by Ross Anderson and Bart Preneel.