ppxscal/embedding_network huggingface dataset:
Balancing TCP buffer vs parallel streams in application level throughput optimization
Authors: Esma Yildirim, Dengpan Yin, Tevfik Kosar
Venue: Proceedings of the second international workshop on Data-aware distributed computing (2009)

The end-to-end performance of TCP over wide-area may be a major bottleneck for large-scale network-based applications. Two practical ways of increasing the TCP performance at the application layer is using multiple parallel streams and tuning the buffer size. Tuning the buffer size can lead to significant increase in the throughput of the application. However using multiple parallel streams generally gives better results than optimized buffer size with a single stream. Parallel streams tend to recover from failures quicker and are more likely to steal bandwidth from the other streams sharing the network. Moreover our experiments show that proper usage of tuned buffer size with parallel streams can even increase the throughput more than the cases where only tuned buffers and only parallel streams are used. In that sense, balancing a tuned buffer size and the number of parallel streams and defining the optimal values for those parameters are very important. In this paper, we analyze the results of different techniques to balance TCP buffer and parallel streams at the same time and present the initial steps to a balanced modeling of throughput based on these optimized parameters.
A Comprehensive Analysis of Indirect Branch Prediction
Authors: Oliverio J. Santana, Ayose Falcón, Enrique Fernández, Pedro Medina, Alex Ramírez, Mateo Valero
Venue: Proceedings of the 4th International Symposium on High Performance Computing (2009)

Indirect branch prediction is a performance limiting factor for current computer systems, preventing superscalar processors from exploiting the available ILP. Indirect branches are responsible for 55.7% of mispredictions in our benchmark set, although they only stand for 15.5% of dynamic branches. Moreover, a 10.8% average IPC speedup is achievable by perfectly predicting all indirect branches.The Multi-Stage Cascaded Predictor (MSCP) is a mechanism proposed for improving indirect branch prediction. In this paper, we show that a MSCP can replace a BTB and accurately predict the target address of both indirect and non-indirect branches. We do a detailed analysis of MSCP behavior and evaluate it in a realistic setup, showing that a 5.7% average IPC speedup is achievable.
Intelligent Traffic Control on Internet-Like TopologiesDATRA-Dynamically Adjusted Traffic Rate Alterations
Authors: Antonia Katzouraki, Philippe De Wilde, Robert Ghanea Hercock
Venue: Proceeding of the 2005 conference on Self-Organization and Autonomic Informatics (I) (2005)

The growing scale and importance of technological networks throughout the world has highlighted the devastating consequences of catastrophic network failures. In this paper, we address this crucial issue through a detailed analysis of network traffic distribution across network nodes, with the aim of developing an intelligent traffic control model. Specifically, we develop and demonstrate the Dynamically Adjusted Traffic Rates model, which aims to fairly distribute traffic amongst network nodes according to their network characteristics. Our model is independent of topology and is based on dynamically adjusted traffic rates and properties similar to those observed in real Internet traffic. In this paper our model is numerically analyzed over a variety of network topologies to display its chaotic features leading to self-similarity. The model is inspired by biological evidence.
Business intelligence projects in second year information systems courses
Authors: Mike Hart
Venue: Proceedings of the 2009 Annual Conference of the Southern African Computer Lecturers' Association (2009)

In recent years Business Intelligence (BI) has been rated by international academics and chief information officers (CIOs) as one of the most, if not the most, important application areas of information systems (IS). In many undergraduate university IS curricula, however, BI is only briefly mentioned, sometimes under the topic of Decision Support Systems (DSS), but students rarely gain a practical understanding of what it means and how it is used in practice by end users. This paper describes experiences of the author over five years of introducing practical BI projects to a 2nd year class of IS majors in a database systems course, and most recently, to a 2nd year class of 650 accounting and finance majors in a general course on IT in Business. In each case only five lectures were available to cover the topic of BI, including DSS, data warehousing, data mining, OLAP, and corporate performance management. Because of its relative user-friendliness and widespread use in industry OLAP was chosen for the project. Students were given large commercial datasets to analyse, using leading BI software products. In answering a range of questions, they had to assume the roles of an international product manager as well as a country manager for a global organization. The paper describes the objectives of the exercise, how software, data and project questions were selected for the project each year, the experiences of project teams and the lecturer, constraints and challenges faced, and overall learning points. It starts with an introduction into BI and its typical components and activities, then turns to IS at the University of Cape Town. The use of projects and some related educational issues are discussed, then the history of the projects from 2004 to 2008 is outlined. The conclusion comes after discussion and some learning points.
Qos in parallel job scheduling
Authors: P. Sadayappan
Venue:  (2008)

Considerable research has focused on the problem of scheduling dynamically arriving independent parallel jobs on a given set of resources to improve the performance with respect to various system and user metrics. However, there has been little work on provision of Quality of Service (QoS) in space-shared parallel job scheduling, in the form of hard deadline guarantees and service differentiation. Both of these functionalities offered by system providers are very desirable to the user. On the other hand, revenue maximization along with the optimal management of resources is appealing to a service provider. This dissertation addresses these seemingly orthogonal aspects of parallel job scheduling in stages. At first, a new scheme called QoPS is developed, to provide QoS in the form of response time guarantees. Essentially, QoPS implements an admission control mechanism for jobs, and provides deadline guarantees for all accepted jobs. Secondly, a pioneer model is proposed to enable proportional service differentiation (PSD) in job scheduling. A PSD framework would basically allow proportional allocation of resources across users based on relative priorities. In addition, new schemes are designed to offer PSD to satisfy the varied expectations of users without hurting traditional performance metrics. In order to address the revenue issue, two different charging models are investigated, determined by the resource provider and user respectively. Since no QoS-enabled charging model is currently deployed at any supercomputer center, a new provider-determined charging model is proposed. In this context, the impact of user tolerance towards missed deadlines is studied, as well as various techniques to further improve the overall revenue. Alternatively, a user-centric and market-based revenue approach originally proposed for non-QoS scheduling is adapted for QoS-aware scheduling. Using this charging model, an extension to QoPS called DVQoPS is being developed, that considers the opportunity cost using a history-based predictive technique and thus maximizes the overall revenue while maintaining the deadline guarantees in an integrated way.
Good vibrations: Asymmetric vibrations for directional haptic cues
Authors: Hanns W. Tappeiner, Roberta L. Klatzky, Bert Unger, Ralph Hollis
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

In this paper we present a new concept for haptic guidance in multiple dimensions, in the form of asymmetric vibrations. We show that by adding asymmetry, vibrations from a single source can, like continuous forces, provide effective, high-resolution directional haptic cues in multiple dimensions. Unlike feedback via continuous forces, vibrations can be generated in an almost arbitrarily small workspace. After describing the concept in detail, we describe a user study that assesses the effects of such vibrations - applied to subjects' hands - on their ability to distinguish between different directions. The results indicate that asymmetric vibrations can provide accurate directional haptic feedback.
Interactive engagement with embodied agents: an empirically validated framework
Authors: H. C. van Vugt, J. F. Hoorn, E. A. Konijn
Venue: Computer Animation and Virtual Worlds (2009)

This paper presents an empirically tested theoretical framework to explain user engagement and end-user satisfaction with interactive agents. Such a framework is not only important from a scientific point of view; application designers may find a set of dos and don'ts that help them create more satisfying embodied agents in different task domains and social settings. From a multidisciplinary perspective, we have conducted a series of experiments to verify underlying mechanisms in the processes of interacting and engaging with embodied agents in various task domains. Our results show that the most commonly held views are not always tenable; sometimes other factors provide better explanations for liking an embodied agent or end-user satisfaction. For example, it is not realism but rather affordances and ethics that are key for understanding user responses, and a beautiful design is not always the most preferable. From our results, guidelines for designers and future research are reflected upon. Copyright &copy; 2009 John Wiley & Sons, Ltd. Systematic experimentation showed that user engagement with embodied agents is not only established through the traditional dimensions of aesthetics and realism but more so through the agent's action possibilities (affordances). Reversely, willingness to use the embodied agent is not only fueled by usability issues but equally so by the moral intentions designed into the agent (ethics). Results are compiled in the theoretical framework called Interactively Perceiving and Experiencing Fictional Characters (I-PEFiC).
Essential Testing: A Use Case Driven Approach
Authors: Greg Fournier
Venue:  (2007)

Essential Testing provides detailed insight into bringing testing agility to any software project including ones with lots of rigidity. It introduces a realistic view of software testing that includes the concepts and methods needed to get the software testing job done in an efficient manner. It is based on practical Use Case driven testing techniques that work on any software development project, even those where Use Cases aren't front and center. Skipping the ceremony testing concepts are presented and tied together in a sequential and straightforward fashion, while injecting real world, less than perfect examples in the form of 'war stories'. Testing methods and techniques are described in a common sense manner that is easy to understand This is a book for testers looking for hands on tools and help - and for software managers and developers looking for a different approach to software testing, one that focuses on being agile no matter what type of project. 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
Efficient regularized solution path algorithms with applications in machine learning and data mining
Authors: Michael D. Gordon, Ji Zhu
Venue:  (2008)

The topic of this dissertation is based on regularization methods and efficient solution path algorithms for machine learning and data mining.The first essay proposes the doubly regularized support vector machine (DrSVM) for classification. The DrSVM uses the elastic-net penalty, a mixture of the L2-norm and the L1-norm penalties. By doing so, the DrSVM performs automatic variable selection in a way similar to the L1-norm SVM. In addition, the DrSVM encourages highly correlated variables to be selected (or removed) together, which is called the grouping effect. It also develops efficient algorithms to compute the whole solution paths of the DrSVM.Based on the DrSVM, the second essay proposes the hybrid huberized support vector machine (HHSVM). The HHSVM uses the elastic-net penalty and the huberized hinge loss function. Similar to the DrSVM, the HHSVM performs automatic variable selection and has the grouping effect. However its computational cost is significantly reduced due to its loss function. The third essay proposes two models for image denoising. In this essay, the L1-norm of the pixel updates is used as the penalty. The L1-norm penalty has the advantage of changing only the noisy pixels, while leaving the non-noisy pixels untouched. Efficient algorithms are designed to compute entire solution paths of these L1-norm penalized models, which facilitate the selection of a balance between the "loss" and the "penalty."The last essay proposes a two-step kernel learning method based on the support vector regression (SVR) for financial time series forecasting. Given a number of candidate kernels, our method learns a sparse linear combination of these kernels so that the resulting kernel can be used to predict well on future data. The L1-norm regularization approach is used to achieve kernel learning. Since the regularization parameter must be carefully selected, to facilitate parameter tuning, we develop an efficient solution path algorithm that solves the optimal solutions for all possible values of the regularization parameter. Our kernel learning method is applied to forecast the S P500 and the NASDAQ market indices and shows promising results.
Supporting Diagnostics of Coronary Artery Disease with Multi-resolution Image Parameterization and Data Mining
Authors: Matjaž Kukar, Luka Šajn
Venue: Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques (2009)

Coronary artery disease has been described as one of the curses of the western world, as it is one of the most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics is typically performed in a stepwise manner. The four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and ECG (electrocardiogram) at rest, (2) sequential ECG testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and finally (4) coronary angiography, that is considered as the "gold standard" reference method. Our study focuses on improving diagnostic performance of the third diagnostic level. Myocardial scintigraphy is non invasive; it results in a series of medical images that are relatively inexpensively obtained. In clinical practice, these images are manually described (parameterized) by expert physicians. In the paper we present an innovative alternative to manual image evaluation --- an automatic image parameterization in multiple resolutions, based on texture description with specialized association rules. Extracted image parameters are combined into more informative composite parameters by means of principle component analysis, and finally used to build automatic classifiers with machine learning methods. Our experiments with synthetic datasets show that association-rule-based multi-resolution image parameterization equals or surpasses other state-of-the-art methods for finding multiple informative resolutions. Experimental results in coronary artery disease diagnostics confirm these results as our approach significantly improves the clinical results in terms of quality of image parameters as well as diagnostic performance.
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Authors: Erzsébet Merényi, Kadim Tasdemir, Lili Zhang
Venue: Similarity-Based Clustering: Recent Developments and Biomedical Applications (2009)

In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics. We call such data manifolds highly structured. We describe approaches to structure identification through self-organized learning, in the context of such data. We present some of our recently developed methods to show that self-organizing neural maps contain a great deal of information that can be unleashed and put to use to achieve detailed and accurate learning of highly structured manifolds, and we also offer some comparisons with existing clustering methods on real data.
Enhancing user experience while gaming in archaeological parks with cellular phones
Authors: Carmelo Ardito, Maria Francesca Costabile, Rosa Lanzilotti
Venue: Proceedings of the 8th International Conference on Interaction Design and Children (2009)

Traditional visits to archaeological parks, which are usually organized by schools, tend to generate little interest in young students, especially when they are faced with the ruins of ancient settlements whose current appearance no longer reflects their initial purpose. We have designed Explore!, an m-learning system which allows students to visit the park while playing a game. By exploiting the imaging and multimedia capabilities of the latest generation cell phone, the game improves young visitors' experience. In particular, contextual sounds have been integrated to recreate the historical atmosphere: users can hear sounds generated by ancient people engaged in their daily activities. The slim architecture of the system absolves the archaeological park from any need to invest in hardware infrastructure. An Authoring Tool can be used by history experts to implement in Explore! games to be played in different parks. In this demo paper we briefly present the game and the main components of Explore!: 1) the Game Application running on cellular phones, to be used during the game, 2) the Master Application running on a notebook, used by the game master (i.e. a teacher) to perform a reflection phase, which follows the game and 3) the Authoring Tool to modify/create new games.
Lift-based search for significant dependencies in dense data sets
Authors: W. Hämäläinen
Venue: Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics (2009)

Dependency analysis is an important but computationally demanding problem in all empirical science. It is especially problematic in bioinformatics, where data sets are often high dimensional, dense and/or strongly correlated. As a solution, we introduce a new algorithm which searches the most significant association rules expressing positive dependencies. The algorithm uses several effective pruning principles, which enable search without any minimum frequency thresholds. According to our initial experiments, the algorithm suits especially well for typical biological and medical data sets.
Decidability of $\mathcal{SHI}$ with Transitive Closure of Roles
Authors: Chan Duc
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

This paper investigates a Description Logic, namely $\mathcal{SHI}_+$, which extends $\mathcal{SHI}$ by adding transitive closure of roles. The resulting logic $\mathcal{SHI}_+$ allows transitive closure of roles to occur not only in concept inclusion axioms but also in role inclusion axioms. We show that $\mathcal{SHI}_+$ is decidable by devising a terminating, sound and complete algorithm for deciding satisfiability of concepts in $\mathcal{SHI}_+$ with respect to a set of concept and role inclusion axioms.
Model Checking Merged Program Traces
Authors: Paulo Salem da Silva, Ana C. V. de Melo
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

During a program's execution, state information can be collected and stored in the form of program traces. With such traces, one can analyze dynamic properties of the program. In this paper, we consider the problem of merging multiple traces from the same program in order to compose an approximate temporal model of its behavior. With such a model one can perform model checking based on both linear- and branching-time logics. To this end, we formally define what we mean by program trace and present some algorithms to perform trace merging. We show that each of these algorithms yield a different kind of temporal model, appropriate for different kinds of analyses. Our method is motivated by the possibility of analyzing simulations in a way that has not been done so far, and thus is developed with the needs of such a domain in mind. To demonstrate the practical feasibility of the proposed theoretical approach, we explain how to actually perform model checking of our temporal models using the NuSMV tool. Moreover, we provide proof-of-concept Java implementations of the proposed trace merging algorithms, which output NuSMV specifications. We also describe a simple case study using this implementation.
Optimal tracking of distributed heavy hitters and quantiles
Authors: Ke Yi, Qin Zhang
Venue: Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2009)

We consider the the problem of tracking heavy hitters and quantiles in the distributed streaming model. The heavy hitters and quantiles are two important statistics for characterizing a data distribution. Let A be a multiset of elements, drawn from the universe U={1,...,u}. For a given 0 ≤ Φ ≤ 1, the Φ-heavy hitters are those elements of A whose frequency in A is at least Φ |A|; the Φ-quantile of A is an element x of U such that at most Φ|A| elements of A are smaller than A and at most (1-Φ)|A| elements of A are greater than x. Suppose the elements of A are received at k remote sites over time, and each of the sites has a two-way communication channel to a designated coordinator, whose goal is to track the set of Φ-heavy hitters and the Φ-quantile of A approximately at all times with minimum communication. We give tracking algorithms with worst-case communication cost O(k/ε ⋅ log n) for both problems, where n is the total number of items in A, and ε is the approximation error. This substantially improves upon the previous known algorithms. We also give matching lower bounds on the communication costs for both problems, showing that our algorithms are optimal. We also consider a more general version of the problem where we simultaneously track the Φ-quantiles for all 0 ≤ Φ ≤ 1.