ppxscal/embedding_network huggingface dataset:
Feature-selection ability of the decision-tree algorithm and the impact of feature-selection/extraction on decision-tree results based on hyperspectral data
Authors: Y. Y. Wang, J. Li
Venue: International Journal of Remote Sensing (2008)

The decision-tree (DT) algorithm is a very popular and efficient data-mining technique. It is non-parametric and computationally fast. Besides forming interpretable classification rules, it can select features on its own. In this article, the feature-selection ability of DT and the impacts of feature-selection/extraction on DT with different training sample sizes were studied by using AVIRIS hyperspcetral data. DT was compared with three other feature-selection methods; the results indicated that DT was an unstable feature selector, and the number of features selected by DT was strongly related to the sample size. Trees derived with and without feature-selection/extraction were compared. It was demonstrated that the impacts of feature selection on DT were shown mainly as a significant increase in the number of tree nodes (14.13-23.81%) and moderate increase in tree accuracy (3.5-4.8%). Feature extraction, like Non-parametric Weighted Feature Extraction (NWFE) and Decision Boundary Feature Extraction (DBFE), could enhance tree accuracy more obviously (4.78-6.15%) and meanwhile a decrease in the number of tree nodes (6.89-16.81%). When the training sample size was small, feature-selection/extraction could increase the accuracy more dramatically (6.90-15.66%) without increasing tree nodes.
Relaxation in text search using taxonomies
Authors: Marcus Fontoura, Vanja Josifovski, Ravi Kumar, Christopher Olston, Andrew Tomkins, Sergei Vassilvitskii
Venue: Proceedings of the VLDB Endowment (2008)

In this paper we propose a novel document retrieval model in which text queries are augmented with multi-dimensional taxonomy restrictions. These restrictions may be relaxed at a cost to result quality. This new model may be applicable in many arenas, including multifaceted, product, and local search, where documents are augmented with hierarchical metadata such as topic or location. We present efficient algorithms for indexing and query processing in this new retrieval model. We decompose query processing into two sub-problems: first, an online search problem to determine the correct overall level of relaxation cost that must be incurred to generate the top k results; and second, a budgeted relaxation search problem in which all results at a particular relaxation cost must be produced at minimal cost. We show the latter problem is solvable exactly in two hierarchical dimensions, is NP-hard in three or more dimensions, but admits efficient approximation algorithms with provable guarantees. We present experimental results evaluating our algorithms on both synthetic and real data, showing order of magnitude improvements over the baseline algorithm.
Product evaluation and development process using a fuzzy compromise-based goal programming approach
Authors: Ethem Tolga, S. Emre Alptekin
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2008)

Quality function deployment (QFD) is a product/service design and improvement tool used to achieve higher customer satisfaction. Basically, QFD is a transformation of vague and imprecise customer needs into measurable product/service attributes. This article integrates compromise programming based goal programming into the QFD process to determine to what extent the product/service attributes should be improved. The fuzzy set theory is applied to the model to deal with the imprecise nature of data. Differing from existing QFD applications, our proposed methodology applies analytic network process (ANP) to evaluate the inner dependencies among customer needs, among product attributes and also the relationships between them. Furthermore, it determines the best product/service in the market as the goal employing compromise programming. Finally, the methodology ends with the goal programming method which consists of this predefined goal and the product/service provider's budget limitation. The aim is to improve the performance values of the selected product/service according to the best one. A real-world application on e-learning products provided by the higher education institutions in Turkey illustrates the applicability of our proposed methodology.
A First Study on Clustering Collections of Workflow Graphs
Authors: Emanuele Santos, Lauro Lins, James P. Ahrens, Juliana Freire, Cláudio T. Silva
Venue: Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008. Revised Selected Papers (2008)

As workflow systems get more widely used, the number of workflows and the volume of provenance they generate has grown considerably. New tools and infrastructure are needed to allow users to interact with, reason about, and re-use this information. In this paper, we explore the use of clustering techniques to organize large collections of workflow and provenance graphs. We propose two different representations for these graphs and present an experimental evaluation, using a collection of 1,700 workflow graphs, where we study the trade-offs of these representations and the effectiveness of alternative clustering techniques.
Mining and Analyzing Organizational Social Networks Using Minimum Spanning Tree
Authors: Victor Ströele A. Menezes, Ricardo Tadeu Da Silva, Moisés Ferreira De Souza, Jonice Oliveira, Carlos E. Mello, Jano Moreira De Souza, Geraldo Zimbrão
Venue: Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS (2008)

This work focuses on using data mining techniques to identify intra and inter organization groups of people with similar profiles and that could have relationships among them. Our clustering method identifies clusters with a link mining-based technique that uses the minimum spanning tree to construct group hierarchies. In this paper we analyze the scientific scenario in Computing Science in Brazil, assessing how researchers in the best universities and research centers collaborate and relate to each other.
Asynchronous congestion control in multi-hop wireless networks with maximal matching-based scheduling
Authors: Loc Bui, Atilla Eryilmaz, R. Srikant, Xinzhou Wu
Venue: IEEE/ACM Transactions on Networking (TON) (2008)

We consider a multi-hop wireless network shared by many users. For an interference model that constrains a node to either transmit to or receive from only one other node at a time, and not to do both, we propose an architecture for fair resource allocation that consists of a distributed scheduling algorithm operating in conjunction with an asynchronous congestion control algorithm. We show that the proposed joint congestion control and scheduling algorithm supports at least one-third of the throughput supportable by any other algorithm, including centralized algorithms.
Data description and noise filtering based detection with its application and performance comparison
Authors: Hyun-Woo Cho
Venue: Expert Systems with Applications: An International Journal (2009)

Abnormalities should be detected reliably so that corrective actions can be taken to maintain a high quality level of products. Fault or outlier detection provides early warning for a fault and identification of its assignable cause. The availability of large real-time datasets has motivated the study of data-driven approaches to fault detection. Recently, many powerful kernel-based nonlinear learning techniques have been developed and shown to be very effective tools. As one of one-classification methods, SVDD is able to define a boundary around samples with a volume as small as possible. This paper proposes a data description-based detection method combined with orthogonal filtering for enhanced detection abilities. The orthogonal filtering as a preprocessing step is executed before SVDD modeling to remove unwanted variation of data. The performance of the proposed method was demonstrated using data of two test processes. The case study has shown that the proposed method produces reliable detection results. In addition, the use of SVDD combined with orthogonal filtering step outperformed other one-class classification-based detection methods.
Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry
Authors: Lukas Käll, John D. Storey, William Stafford Noble
Venue: Bioinformatics (2008)

Motivation: A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more dif.cult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution. Results: We demonstrate how to apply non-parametric logistic regression to this problem. The method makes no explicit assumptions about the form of the underlying score distribution; instead, the method relies upon decoy PSMs, produced by searching the spectra against a decoy sequence database, to provide a model of the null score distribution. We show that our non-parametric logistic regression method produces accurate PEP estimates for six different commonly used PSM score functions. In particular, the estimates produced by our method are comparable in accuracy to those of PeptideProphet, which uses a parametric or semiparametric model designed speci.cally to work with SEQUEST. The advantage of the non-parametric approach is applicability and robustness to new score functions and new types of data. Availability: C++ code implementing the method as well as supplementary information is available at http://noble.gs.washington.edu/proj/qvality Contact: noble@gs.washington.edu
SMS-based human-hosted interactive tv in Finland
Authors: Pauliina Tuomi
Venue: Proceeding of the 1st international conference on Designing interactive user experiences for TV and video (2008)

Interactive TV entertainment has brought to life a new kind of TV game show host culture in Finland. A qualitative study of SMS-to-TV human-hosted interactive TV games, specifically, TV-mobile games and call quizzes, was conducted by recording sample interactive TV programs and corresponding discussion forums on the Internet and analyzing the content. The role of the human host in these programs was analyzed and discussed to answer these questions: Why is this interactive entertainment popular? What different dimensions can be found? How could this field be used more effectively and what are the aspects developers should pay attention to while designing iTV entertainment? This research is important beyond Finland since Finland tends to pioneer interactive entertainment that later spreads out to other countries
Fuzzy individual and moving range control charts with &alpha;-cuts
Authors: Nihal Erginel
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2008)

In Statistical Process Control (SPC), both variable and attribute control charts are widely used techniques to monitor and evaluate the process with respect to the related quality characteristics. In variable control charts, quality characteristics are measured on a numerical scale, using two main parameters mean and standard deviation. When data are composed of individuals from a process, individual (X) control charts are used for mean and moving range (MR) control charts are used for standard deviation. Generally, the data in variable control charts are assumed to be composed of crisp values. But a measurement system including operator, gage, and environmental conditions may produce "uncertain" or "vague" data. In this case, the fuzzy set theory is an available tool for evaluating the vague data. In this study, fuzzy control limits for individual (X) and moving range (MR) control charts with &alpha;-cuts are constructed by using &alpha;-level fuzzy median transformation techniques. The real-world data are used and the process is evaluated by the developed fuzzy control charts.
Non-Mercer hybrid kernel for linear programming support vector regression in nonlinear systems identification
Authors: Zhao Lu, Jing Sun
Venue: Applied Soft Computing (2009)

As a new sparse kernel modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In [V.N. Vapnik, The Nature of Statistical Learning Theory, second ed., Springer-Verlag, 2000], Vapnik developed the @?-insensitive loss function for the support vector regression as a trade-off between the robust loss function of Huber and one that enables sparsity within the support vectors. The use of support vector kernel expansion provides us a potential avenue to represent nonlinear dynamical systems and underpin advanced analysis. However, in the standard quadratic programming support vector regression (QP-SVR), its implementation is often computationally expensive and sufficient model sparsity cannot be guaranteed. In an attempt to mitigate these drawbacks, this article focuses on the application of the soft-constrained linear programming support vector regression (LP-SVR) with hybrid kernel in nonlinear black-box systems identification. An innovative non-Mercer hybrid kernel is explored by leveraging the flexibility of LP-SVR in choosing the kernel functions. The simulation results demonstrate the ability to use more general kernel function and the inherent performance advantage of LP-SVR to QP-SVR in terms of model sparsity and computational efficiency.
Interactivity and user participation in the television lifecycle: creating, sharing, and controlling content
Authors: Pablo Cesar, Konstantinos Chorianopoulos
Venue: Proceeding of the 1st international conference on Designing interactive user experiences for TV and video (2008)

Interactive TV research encompasses a rather diverse body of work (e.g. multimedia, HCI, CSCW, UIST, user modeling, media studies) that has accumulated over the past 20 years. In this article, we highlight the state-of-the-art and consider two basic issues: What is interactive TV research? Can it help us reinvent the practices of creating, sharing and watching TV? We survey the literature and identify three concepts that have been inherent in interactive TV research: 1) interactive TV as content creation, 2) interactive TV as a content and experience sharing process, and 3) interactive TV as control of audiovisual content. We propose this simple taxonomy (create-share-control) as an evolutionary step over the traditional hierarchical produce-distribute-consume paradigm. Moreover, we highlight the importance of sociability in all phases of the create-share-control model.
Improved Newton's method with exact line searches to solve quadratic matrix equation
Authors: Jian-hui Long, Xi-yan Hu, Lei Zhang
Venue: Journal of Computational and Applied Mathematics (2008)

In this paper, we study the matrix equation AX^2+BX+C=0, where A,B and C are square matrices. We give two improved algorithms which are better than Newton's method with exact line searches to calculate the solution. Some numerical examples are reported to illustrate our algorithms.
Intonation modeling for Indian languages
Authors: K. Sreenivasa Rao, B. Yegnanarayana
Venue: Computer Speech and Language (2009)

In this paper we propose models for predicting the intonation for the sequence of syllables present in the utterance. The term intonation refers to the temporal changes of the fundamental frequency (F"0). Neural networks are used to capture the implicit intonation knowledge in the sequence of syllables of an utterance. We focus on the development of intonation models for predicting the sequence of fundamental frequency values for a given sequence of syllables. Labeled broadcast news data in the languages Hindi, Telugu and Tamil is used to develop neural network models in order to predict the F"0 of syllables in these languages. The input to the neural network consists of a feature vector representing the positional, contextual and phonological constraints. The interaction between duration and intonation constraints can be exploited for improving the accuracy further. From the studies we find that 88% of the F"0 values (pitch) of the syllables could be predicted from the models within 15% of the actual F"0. The performance of the intonation models is evaluated using objective measures such as average prediction error (@m), standard deviation (@s) and correlation coefficient (@c). The prediction accuracy of the intonation models is further evaluated using listening tests. The prediction performance of the proposed intonation models using neural networks is compared with Classification and Regression Tree (CART) models.
Meeting points: using thread criticality to adapt multicore hardware to parallel regions
Authors: Qiong Cai, José González, Ryan Rakvic, Grigorios Magklis, Pedro Chaparro, Antonio González
Venue: Proceedings of the 17th international conference on Parallel architectures and compilation techniques (2008)

We present a novel mechanism, called meeting point thread characterization, to dynamically detect critical threads in a parallel region. We define the critical thread the one with the longest completion time in the parallel region. Knowing the criticality of each thread has many potential applications. In this work, we propose two applications: thread delaying for multi-core systems and thread balancing for simultaneous multi-threaded (SMT) cores. Thread delaying saves energy consumptions by running the core containing the critical thread at maximum frequency while scaling down the frequency and voltage of the cores containing non-critical threads. Thread balancing improves overall performance by giving higher priority to the critical thread in the issue queue of an SMT core. Our experiments on a detailed microprocessor simulator with the Recognition, Mining, and Synthesis applications from Intel research laboratory reveal that thread delaying can achieve energy savings up to more than 40% with negligible performance loss. Thread balancing can improve performance from 1% to 20%.
Model-based view planning
Authors: William R. Scott
Venue: Machine Vision and Applications (2008)

This paper presents a multi-phase, model-based approach to view planning for automated, high fidelity object inspection or reconstruction by means of laser scanning range sensors. We describe the critical phase, fine modeling, in detail. Quality objectives and performance measures are defined. Camera and positioning system performance is modeled statistically. A theoretical framework is presented. The method is applicable to a broad class of objects with reasonable geometry and reflectance properties. Sampling of object surface and viewpoint space is characterized, including measurement and pose errors. The technique is generalizable for common range cameras and positioning systems.
A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network
Authors: Jian-Da Wu, Yu-Hsuan Wang, Peng-Hsin Chiang, Mingsian R. Bai
Venue: Expert Systems with Applications: An International Journal (2009)

An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions.
Delay composition in preemptive and non-preemptive real-time pipelines
Authors: Praveen Jayachandran, Tarek Abdelzaher
Venue: Real-Time Systems (2008)

Uniprocessor schedulability theory made great strides, in part, due to the simplicity of composing the delay of a job from the execution times of higher-priority jobs that preempt it. In this paper, we bound the end-to-end delay of a job in a multistage pipeline as a function of job execution times on different stages under preemptive as well as non-preemptive scheduling. We show that the end-to-end delay is bounded by that of a single virtual "bottleneck" stage plus a small additive component. This contribution effectively transforms the pipeline into a single stage system. The wealth of schedulability analysis techniques derived for uniprocessors can then be applied to decide the schedulability of the pipeline. The transformation does not require imposing artificial per-stage deadlines, but rather models the pipeline as a whole and uses the end-to-end deadlines directly in the single-stage analysis. It also does not make assumptions on job arrival patterns or periodicity and thus can be applied to periodic and aperiodic tasks alike. We show through simulations that this approach outperforms previous pipeline schedulability tests except for very short pipelines or when deadlines are sufficiently large. The reason lies in the way we account for execution overlap among stages. We discuss how previous approaches account for overlap and point out interesting differences that lead to different performance advantages in different cases. Further, we also show that in certain cases non-preemptive scheduling can result in higher system utilization than preemptive scheduling in pipelined systems. We hope that the pipeline delay composition rule, derived in this paper, may be a step towards a general schedulability analysis foundation for large distributed systems.
The protein information and property explorer
Authors: H. Ramos, P. Shannon, R. Aebersold
Venue: Bioinformatics (2008)

Motivation: Mass spectrometry experiments in the field of proteomics produce lists containing tens to thousands of identified proteins. With the protein information and property explorer (PIPE), the biologist can acquire functional annotations for these proteins and explore the enrichment of the list, or fraction thereof, with respect to functional classes. These protein lists may be saved for access at a later time or different location. The PIPE is interoperable with the Firegoose and the Gaggle, permitting wide-ranging data exploration and analysis. The PIPE is a rich-client web application which uses AJAX capabilities provided by the Google Web Toolkit, and server-side data storage using Hibernate. Availability: http://pipe.systemsbiology.net Contact: pshannon@systemsbiology.org