ppxscal/embedding_network huggingface dataset:
Using a Bigram Event Model to Predict Causal Potential
Authors: Brandon Beamer, Roxana Girju
Venue: Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing (2009)

This paper addresses the problem of causal knowledge discovery. Using online screenplays, we generate a corpus of temporally ordered events. We then introduce a measure we call causal potential which is easily calculated with statistics gathered over the corpus and show that this measure is highly correlated with an event pair's tendency of encoding a causal relation. We suggest that causal potential can be used in systems whose task is to determine the existence of causality between temporally adjacent events, when critical context is either missing or unreliable. Moreover, we argue that our model should therefore be used as a baseline for standard supervised models which take into account contextual information.
Asymptotic expansions of test statistics for dimensionality and additional information in canonical correlation analysis when the dimension is large
Authors: Tetsuro Sakurai
Venue: Journal of Multivariate Analysis (2009)

This paper examines asymptotic expansions of test statistics for dimensionality and additional information in canonical correlation analysis based on a sample of size N=n+1 on two sets of variables, i.e., x"u;p"1x1 and x"v;p"2x1. These problems are related to dimension reduction. The asymptotic approximations of the statistics have been studied extensively when dimensions p"1 and p"2 are fixed and the sample size N tends to infinity. However, the approximations worsen as p"1 and p"2 increase. This paper derives asymptotic expansions of the test statistics when both the sample size and dimension are large, assuming that x"u and x"v have a joint (p"1+p"2)-variate normal distribution. Numerical simulations revealed that this approximation is more accurate than the classical approximation as the dimension increases.
One-mode three-way overlapping cluster analysis
Authors: Satoru Yokoyama, Atsuho Nakayama, Akinori Okada
Venue: Computational Statistics (2009)

The present paper introduces an overlapping cluster analysis model and an associated algorithm that can analyze one-mode three-way similarities. The present model is an extension of ADCLUS model, and the present algorithm is based on the MAPCLUS algorithm. In the present model, one-mode three-way similarities are represented by the sum of the numerical weights of clusters to which any triplet of objects belongs. The present model and algorithm were applied to joint purchase data, and compared the result with that of MAPCLUS to show that the present model is effective in representing one-mode three-way similarities.
Construction and Application of the Decision Tree Model for Agricultural Land Grading Based on MATLAB
Authors: Lu Zhao, Xinqi Zheng, Hongwen Yan, Shuqing Wang, Kouqiang Zhang
Venue: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining (2009)

Aiming at the insufficiencies of traditional agricultural land grading methods, this study discussed the process and technical route of agricultural land grading based on decision tree analysis method and GIS, constructed an agricultural land grading model based on MATLAB and decision tree C4.5 algorithm. Furthermore, We took Luanwan village of Pingyin county in China for the empirical study, selected seven indicators as the test attributes, predicted agricultural land grade on support of this model, and expressed the rules in the quantitative way. The results showed that agricultural land grading model based on decision tree which is coded in M-language of MATLAB doesn’t rely on the empirical knowledge. It has the ability of self-learning, and the gained rules are easy to be understood. Moreover, the high rate of accuracy will be able to meet the requirements of evaluation.
New differential approximation algorithm for k-customer vehicle routing problem
Authors: Takayuki Nagoya
Venue: Information Processing Letters (2009)

We present a differential approximation algorithm for k-customer vehicle routing problem. It is known that this problem is 12 differential approximable for k>=3. It is also known that if the triangle inequality is satisfied then this problem is 35 differential approximable for k=4 and 23 differential approximable for 5==5 without assuming the triangle inequality holds.
A DVS-assisted hard real-time I/O device scheduling algorithm
Authors: Edward T.-H. Chu, Tai-Yi Huang, Cheng-Han Tsai, Jian-Jia Chen, Tei-Wei Kuo
Venue: Real-Time Systems (2009)

The I/O subsystem has become a major source of energy consumption in a hard real-time monitoring and control system. To reduce its energy consumption without missing deadlines, a dynamic power management (DPM) policy must carefully consider the power parameters of a device, such as its break-even time and wake-up latency, when switching off idle devices. This problem becomes extremely complicated when dynamic voltage scaling (DVS) is applied to change the execution time of a task. In this paper, we present COLORS, a composite low-power scheduling framework that includes DVS in a DPM policy to maximize the energy reduction on the I/O subsystem. COLORS dynamically predicts the earliest-access time of a device and switches off idle devices. It makes use of both static and dynamic slack time to extend the execution time of a task by DVS, in order to create additional switch-off opportunities. Task workloads, processor profiles, and device characteristics all impact the performance of a low-power real-time algorithm. We also identify a key metric that primarily determines its performance. The experimental results show that, compared with previous work, COLORS achieves additional energy reduction up to 20%, due to the efficient utilization of slack time.
A protocol for evaluating computer systems for application in a physician's office
Authors: Daniel D. Butcher, Steven G. Jenks, Curtis P. McNeeley, Robert E. Mahan
Venue: Proceedings of the May 19-22, 1975, national computer conference and exposition (1975)

In this paper we present some of our experiences in the development of protocols for the evaluation of manual and automated medical information systems. The objective of this work has been to determine the feasibility of implementing computer technology in the office practices of private practicing physicians in Clackamas County, Oregon.
The improvement of deformations and charateristics of HGA during clamping using finite element analysis
Authors: Thoatsanope Kamnerdtong, Surachate Chutima, Jukkraphun Parirukvijit
Venue: Proceedings of the 40th Conference on Winter Simulation (2008)

This paper presents the study of Head Gimbal Assembly (HGA) clamping for characteristic parameters measurement which is an important stage of hard disk drive assembly process. During clamping, HGA is deformed. The values of static attitude and gram load which are significant characteristics of HGA are affected and could exceed acceptable values. Consequently, a lot of HGAs are rejected or these might decrease the performance and the read-write precision of hard disk drive. In this research, finite element analysis is performed to study the behaviors of HGA under various clamping forces and configurations for the process development. Results show that using lower clamping force and having rectangular shape for the upper part and with fillet for the lower part of clamping unit have less deformation than the other configurations of clamping. These make considerable improvement of parameter measurement during HGA clamping process efficiently and effectively.
An Ensemble Approach for Semantic Assessment of Summary Writings
Authors: Yulan He, Siu Cheung Hui, Tho Thanh Quan
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 01 (2009)

Computer-assisted assessment of summary writings is a challenging area which has recently attracted much interest from the research community. This is mainly due to the advances in other areas such as information extraction and natural language processing which have made automatic summary assessment possible. Different techniques such as Latent Semantic Analysis, $n$-gram co-occurrence and BLEUhave been proposed for automatic evaluation of summaries. However, these techniques are unable to achieve good performance. In this paper, we propose an ensemble approach, that integrates two of the most effective summary evaluation techniques, LSA and n-gram co-occurrence, for improving the accuracy of automatic summary assessment. The performance of the proposed ensemble approach has shown that it is able to achieve high accuracy and improve theperformance quite substantially compared with other existingtechniques.
Effect of Si capping layer on the interface quality and NBTI of high mobility channel Ge-on-Si pMOSFETs
Authors: Ook Sang Yoo, Jungwoo Oh, Kyung Seok Min, Chang Yong Kang, B. H. Lee, Kyong Taek Lee, Min Ki Na, Hyuk-Min Kwon, P. Majhi, H-H Tseng, Raj Jammy, J. S. Wang, Hi-Deok Lee
Venue: Microelectronic Engineering (2009)

The effects of a Si capping layer on the device characteristics and negative bias temperature instability (NBTI) reliability were investigated for Ge-on-Si pMOSFETs. A Ge pMOSFET with a Si cap shows a lower subthreshold slope (SS), higher transconductance (G"m) and enhanced drive current. In addition, lower threshold voltage shift and G"m","m"a"x degradation are observed during NBTI stress. The primary reason for these characteristics is attributed to the improved interface quality at the high-k dielectric/substrate interface. Charge pumping was used to verify the presence of lower density of states in Ge pMOSFETs with a Si cap.
Discovering Flow Anomalies: A SWEET Approach
Authors: James M. Kang, Shashi Shekhar, Christine Wennen, Paige Novak
Venue: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (2008)

Given a percentage-threshold and readings from a pair of consecutive upstream and downstream sensors, flow anomaly discovery identifies dominant time intervals where the fraction of time instants of significantly mis-matched sensor readings exceed the given percentage-threshold. Discovering flow anomalies (FA) is an important problem in environmental flow monitoring networks and early warning detection systems for water quality problems. However, mining FAs is computationally expensive because of the large (potentially infinite) number of time instants of measurement and potentially long delays due to stagnant (e.g. lakes) or slow moving (e.g. wetland) water bodies between consecutive sensors. Traditional outlier detection methods (e.g. t-test) are suited for detecting transient FAs (i.e., time instants of significant mis-matches across consecutive sensors) and cannot detect persistent FAs (i.e., long variable time-windows with a high fraction of time instant transient FAs) due to a lack of a pre-defined window size. In contrast, we propose a Smart Window Enumeration and Evaluation of persistence-Thresholds (SWEET) method to efficiently explore the search space of all possible window lengths. Computation overhead is brought down significantly by restricting the start and end points of a window to coincide with transient FAs, using a smart counter and efficient pruning techniques. Experimental evaluation using a real dataset shows our proposed approach outperforms Naıve alternatives.
Fabric defect detection based on multiple fractal features and support vector data description
Authors: Hong-gang Bu, Jun Wang, Xiu-bao Huang
Venue: Engineering Applications of Artificial Intelligence (2009)

Computer-vision-based automatic detection of fabric defects is one of the difficult one-class classification tasks in the real world. To overcome the incapacity of a single fractal feature in dealing with this task, multiple fractal features have been extracted in the light of the theory of and problems present in the box-counting method as well as the inherent characteristics of woven fabrics. Based on statistical learning theory, the up-to-date support vector data description (SVDD) is an excellent approach to the problem of one-class classification. A robust new scheme is presented in this paper for optimally selecting values of the parameters especially that of the scale parameter of the Gaussian kernel function involved in the training of the SVDD model. Satisfactory experimental results are finally achieved by jointly applying the extracted multiple fractal features and SVDD to the detection of defects from several datasets of fabric samples with different texture backgrounds.
Issues in alerting: medication order entry in real practice
Authors: Jennifer Cloud-Buckner, Jennie J. Gallimore, Peter K. Wong
Venue: Proceedings of the 12th WSEAS international conference on Computers (2008)

Medication Order Entry (MOE) and Computerized Physician Order Entry (CPOE) systems have been recommended by a variety of medical organizations to decrease human error in patient care delivery. However, when these systems are implemented in real practice, several organizational and technological factors can make the system actually add to human error and lack of patient safety. This article proposes research questions for examining the need for improving effectiveness of alerts in MOE systems in order to prevent error and reduce alert fatigue.
Design of ECG instrumentation and implementation of digital filter for noise reduction
Authors: Mahesh S. Chavan, Ra. Agarwala, M. D. Uplane
Venue: Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision (2008)

The biomedical topic that attracted the early interest of engineers is that of electrocardiography. It is the most useful noninvasive medical diagnostic tests and is in very wide use (an estimated 200 million ECG s taken each year). Its technological challenge is to improve performance by increasing the number of simultaneously recorded signal, raising the signal to noise ratio. The increase in data acquisition rate also provides new challenges in the design of recording systems including the application of methods of data compression. The electrocardiogram has the considerable diagnostic significance, and applications of ECG monitoring are diverse and in wide use. Difrence types of noise due do which ECG gets corrupted.The various noise types considered were 50Hz power line interference, base line drift due to respiration, abrupt base line shifts, and a composite noise constructed from all of the other noise types. Different digital filter structures are presented in the paper to eliminate these diverse forms of noise sources and their performance is discussed with reference to efficiency, distortion of the signal. The ECG signals used in this study were obtained by designing twelve lead instrumentation amplifier. The design system leads to prototype device for the ECG. To remove low frequency and Power line intereference in the ECG signal digital filter based on least mean squares has been implemented and presented in the paper.
Optical Tone: dynamic color composition
Authors: Tsutomu Mutoh
Venue: ACM SIGGRAPH ASIA 2008 artgallery: emerging technologies (2008)

Optical Tone is an installation that proposes and proves a technique for interactive, real-time, 3D colour composition in accordance with human perception of colour consistency.
A Predictive Video-on-Demand Bandwidth Management Using the Kalman Filter over Heterogeneous Networks
Authors: Chung-Ming Huang, Chung-Wei Lin, Xin-Ying Lin
Venue: The Computer Journal (2009)

In order to adapt the quality of an on-demand video stream over a time-varying bandwidth channel, a network-aware bandwidth estimation and rate control scheme are required. This paper proposes a predictive video-on-demand (VoD) bandwidth management and a feedback-based buffer control scheme for streaming fine granular scalability videos over wired/WLAN/3G networks. The predictive VoD bandwidth management includes two parts: bandwidth estimation and rate adaptation. According to the measured information of packet round-trip-time, loss-rate, delay jitter and received bit-rate, an improved Kalman filter is proposed to predict an available bandwidth recursively, and to determine a proper transmission rate in consideration of buffer fullness of a decoder. The optimal parameters of the Kalman filter, e.g. a transition matrix and error covariances, can be initialized, converged and adapted to characteristics of the current network. In our experiments, distinct network traffic models are simulated in comparison with pathChirp and one Republic of China patent. The corresponding estimation results with respect to network information are also exhibited in the real networks.
The Ferry Cover Problem
Authors: Michael Lampis, Valia Mitsou
Venue: Theory of Computing Systems (2009)

In this paper we define and study a family of optimization problems called Ferry problems, which may be viewed as generalizations of the classical wolf-goat-cabbage puzzle. We present the Ferry Cover problem (FC), where the objective is to determine the minimum required boat size to safely transport n items represented by a graph&#x00a0;G and demonstrate a close connection with Vertex Cover which leads to hardness and approximation results. We also completely solve the problem on trees. Then we focus on a variation of the same problem with the added constraint that only&#x00a0;1 round-trip is allowed (FC1). We present a reduction from MAX-NAE-{3}-SAT which shows that this problem is NP-hard and APX-hard. We also provide an approximation algorithm for bipartite graphs with a factor asymptotically equal to&#x00a0; $\frac{4}{3}$and a 1.56-approximation algorithm for planar graphs. Finally, we generalize the above problem to define FCm , where at most m round-trips are allowed, and MFTk , which is the problem of minimizing the number of round-trips when the boat capacity is k. We present some preliminary lemmata for both, which provide bounds on the value of the optimal solution, and relate them to&#x00a0;FC.
Linear Precoding of Unitary Space-Time Code for GLRT Decoder
Authors: Yongliang Guo, Shihua Zhu, Zhonghua Liang
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

For unitary space-time code (USTC), the impact of spatial correlation on error performance is investigated. A tighter and simpler upper bound is derived for generalized likelihood ratio test decoder. We establish that the spatial correlation does not change the diversity gain, whereas it degrades the error performance of USTC. Motivated by the precoding of space-time block code, we designed a precoder for USTC to handle the case of the joint transmit-receive correlation. Numerical results show that the degradation in performance due to spatial correlation can be considerably compensated by the proposed algorithm.
Grid Data Management: Open Problems and New Issues from the P2P Perspective
Authors: Esther Pacitti
Venue: High Performance Computing for Computational Science - VECPAR 2008: 8th International Conference, Toulouse, France, June 24-27, 2008. Revised Selected Papers (2008)

Initially developed for the scientific community, Grid computing is now gaining much interest in important areas such as enterprise information systems. This makes data management critical since the techniques must scale up while addressing the autonomy, dynamicity and heterogeneity of the data sources. In this paper, we discuss the main open problems and new issues related to Grid data management. We first recall the main principles behind data management in distributed systems and the basic techniques. Next we make precise the requirements for Grid data management and introduce the main techniques needed to address these requirements mainly by using P2P techniques. To illustrate the use of these techniques that may be implemented in Grid infrastructures, we present a peer to peer multi-master replication approach for collaborative applications, and continuous stream processing algorithms built over distributed hash tables using gossiping.
Bayesian learning of graphical vector autoregressions with unequal lag-lengths
Authors: Pekka Marttinen, Jukka Corander
Venue: Machine Learning (2009)

Graphical modelling strategies have been recently discovered as a versatile tool for analyzing multivariate stochastic processes. Vector autoregressive processes can be structurally represented by mixed graphs having both directed and undirected edges between the variables representing process components. To allow for more expressive vector autoregressive structures, we consider models with separate time dynamics for each directed edge and non-decomposable graph topologies for the undirected part of the mixed graph.Contrary to static graphical models, the number of possible mixed graphs is extremely large even for small systems, and consequently, standard Bayesian computation based on Markov chain Monte Carlo is not in practice a feasible alternative for model learning. To obtain a numerically efficient approach we utilize a recent Bayesian information theoretic criterion for model learning, which has attractive properties when the potential model complexity is large relative to the size of the observed data set. The performance of our method is illustrated by analyzing both simulated and real data sets. Our simulation experiments demonstrate the gains in predictive accuracy which can obtained by considering structural learning of vector autoregressive processes instead of unstructured models. The analysis of the real data also shows that the understanding of the dynamics of a multivariate process can be improved significantly by considering more flexible model classes.
An Experimental Study on Distance-Based Graph Drawing
Authors: Ulrik Brandes, Christian Pich
Venue: Graph Drawing: 16th International Symposium, GD 2008, Heraklion, Crete, Greece, September 21-24, 2008. Revised Papers (2009)

In numerous application areas, general undirected graphs need to be drawn, and force-directed layout appears to be the most frequent choice. We present an extensive experimental study showing that, if the goal is to represent the distances in a graph well, a combination of two simple algorithms based on variants of multidimensional scaling is to be preferred because of their efficiency, reliability, and even simplicity. We also hope that details in the design of our study help advance experimental methodology in algorithm engineering and graph drawing, independent of the case at hand.
An algorithm for multivariate function estimation based on hierarchically refined sparse grids
Authors: Marc Brendel, Wolfgang Marquardt
Venue: Computing and Visualization in Science (2009)

An adaptive function estimation approach is presented to recover an unknown, multivariate functional relation from noisy data. Using a sparse grid combination approach, both discretization and Tikhonov regularization need to be selected appropriately to resolve functional details whilst suppressing measurement noise. An initially coarse, multivariate grid is adaptively refined using sensitivity analysis, creating a sequence of hierarchically refined grids. The problem of choosing a multi-dimensional discretization level is thus transformed to the identification of a suitable refinement step, giving rise to a nested approach for the selection of both discretization and Tikhonov regularization. Validation on multivariate test functions shows good approximation results.