ppxscal/embedding_network huggingface dataset:
Fast Analytic Placement using Minimum Cost Flow
Authors: Ameya R. Agnihotri, Patrick H. Madden
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

Many current integrated circuits designs, such as those released for the ISPD2005[14] placement contest, are extremely large and can contain a great deal of white space. These new placement problems are challenging; analytic placers perform well, but can suffer from high run times. In this paper, we present a newplacement tool called Vaastu. Our approach combines continuous and discrete optimization techniques. We utilize network flows, which incorporate the more realistic half-perimeter wire length objective, to facilitate module spreading in conjunction with a log-sum-exponential function based analytic approach. Our approach obtains wire length results that are competitive with the best known results, but with much lower run times.
On the Use of an Extended Relational Model to Handle Changing Incomplete Information
Authors: A. M. Keller
Venue: IEEE Transactions on Software Engineering (1985)

In this paper we consider approaches to updating databases containing null values and incomplete information. Our approach distinguishes between modeling incompletely known worlds and modeling changes in these worlds. As an alternative to the open and closed world assumptions, we propose the expanded closed world assumption. Under this assumption, we discuss how to perform updates on databases containing set nulls, marked nulls, and simple conditional tuples, and address some issues of refining incompletely specified information.
NCC '79 Wrap-up The Social Implications of Computers
Authors: W. Myers
Venue: Computer (1979)

The remarkable growth of the computing industry in the past quarter century has been the result not only of capabilities in technology, development, manufacturing, and marketing, but also of the social setting in which these abilities were exercised. This year's NCC organizers chose to emphasize these social relationships in plenary sessions, panel discussions, and a few of the printed papers. The bulk of the 120 published papers, however, continues to deal with technical matters under the broad headings of applications, architecture, data base management, technology, networking, and software techniques. 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
Probabilistic based recursive model for adaptive processing of data structures
Authors: Siu-Yeung Cho
Venue: Expert Systems with Applications: An International Journal (2008)

One of the most popular frameworks for the adaptive processing of data structures to date, was proposed by Frasconi et al. [Frasconi, P., Gori, M., & Sperduti, A. (1998). A general framework for adaptive processing of data structures. IEEE Transactions on Neural Networks, 9(September), 768-785], who used a Backpropagation Through Structures (BPTS) algorithm [Goller, C., & Kuchler, A. (1996). Learning task-dependent distributed representations by back-propagation through structures. In Proceedings of IEEE international conference on neural networks (pp. 347-352); Tsoi, A. C. (1998). Adaptive processing of data structure: An expository overview and comments. Technical report in Faculty Informatics. Wollongong, Australia: University of Wollongong] to carry out supervised learning. This supervised model has been successfully applied to a number of learning tasks that involve complex symbolic structural patterns, such as image semantic structures, internet behavior, and chemical compounds. In this paper, we extend this model, using probabilistic estimates to acquire discriminative information from the learning patterns. Using this probabilistic estimation, smooth discriminant boundaries can be obtained through a process of clustering onto the observed input attributes. This approach enhances the ability of class discrimination techniques to recognize structural patterns. The proposed model is represented by a set of Gaussian Mixture Models (GMMs) at the hidden layer and a set of ''weighted sum input to sigmoid function'' models at the output layer. The proposed model's learning framework is divided into two phases: (a) locally unsupervised learning for estimating the parameters of the GMMs and (b) globally supervised learning for fine-tuning the GMMs' parameters and optimizing weights at the output layer. The unsupervised learning phase is formulated as a maximum likelihood problem that is solved by the expectation-maximization (EM) algorithm. The supervised learning phase is formulated as a cost minimization problem, using the least squares optimization or Levenberg-Marquardt method. The capabilities of the proposed model are evaluated in several simulation platforms. From the results of the simulations, not only does the proposed model outperform the original recursive model in terms of learning performance, but it is also significantly better at classifying and recognizing structural patterns.
Characterising Range Image Features via Gradient Operators
Authors: Sonya Coleman, Shanmugalingam Suganthan, Bryan Scotney
Venue: Proceedings of the 14th International Conference on Image Analysis and Processing (2007)

Real-time extraction of features from range images can play an important role in computer vision tasks such as localisation and navigation. Developing operators that can characterise features in a range image, such as jump, crease, or smooth edges, is challenging, due to both the irregular spatial distribution of range image data and the nature of the features themselves. We present adaptive gradient operators that can automatically change shape to accommodate irregular data distribution; through appropriate analysis of the output responses, we show that the operators can also be specialised to characterise particular types of range image features.
Fuzzy Multi-attribute Decision Making Under Interval Number
Authors: Min Wang
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

Multi-attribute decision making (MADM) is important in many domains where fuzzy and uncertain information are involved. Usually decision makers are willing or able to provide interval information, because of lack of knowledge or data, their limited experience or time pressure. The research about fuzzy MADM under interval number has become an active branch in decision making science. After introducing some primary concepts, this paper discuss two methods that deal with fuzzy MADM, whose attribute weights and attribute values are expressed in the form of interval number. The first method based on programming model is a typical method, having a more complicated calculation procedure, compared with the second method based on stochastic simulation. In order to illuminate these two methods clearly, principles and calculation steps of both methods are introduced. In the end of each method's discussion, one real application example in choosing shipping modes for the adaptability of satisfying cargo owners' requirements is given, in order to testify each method's feasibility.
RoonRoon: A Wearable Teddy as Social Interface for Contextual Notification
Authors: Fahim Kawsar, Kaori Fujinami, Susanna Pirttikangas, Keisuke Hayashi, Tatsuo Nakajima
Venue: Proceedings of the The 2007 International Conference on Next Generation Mobile Applications, Services and Technologies (2007)

In this paper we present a wearable teddy, RoonRoon (a physical embodiment artefact) that acts as a user interface for information services. It can monitor user's physical activity state (walking, standing, running and sitting) and can provide personalized information in a contextual manner minimizing interruptions. RoonRoon has four notification mechanisms: audible beeps, human voice, ambient sound and vibration. Since RoonRoon is intended primarily for mobile use, interaction is managed completely by voice using natural language. This paper describes the design and implementation of RoonRoon. We also present an informal study that reveals end users' impressions towards wearable toys as social interfaces.
A note on two fixed point problems
Authors: Ch. Boonyasiriwat, K. Sikorski, Ch. Xiong
Venue: Journal of Complexity (2007)

We extend the applicability of the Exterior Ellipsoid Algorithm for approximating n-dimensional fixed points of directionally nonexpanding functions. Such functions model many practical problems that cannot be formulated in the smaller class of globally nonexpanding functions. The upper bound 2n^2ln(2/@?) on the number of function evaluations for finding @?-residual approximations to the fixed points remains the same for the larger class. We also present a modified version of a hybrid bisection-secant method for efficient approximation of univariate fixed point problems in combustion chemistry.
Variable Rate Fertilizer Distributor in Precision Farming Based on PLC Technology
Authors: Limin Shao, Xiu Wang
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

In order to improve the fertilization rationality of crop and decrease pollution and waste, the variable rate fertilizer applicator integrated optical sensors was designed based on PLC technology. NDVI (Normalized Difference Vegetative Index) value was a scale to measure the status health and the stand or fall of the crop. According to the real-time data from the NDVI equipment, combined fuzzy control algorithm and the tractor speed, it applied fertilizer through electric operator. The experiment indicated that NDVI could instruct variable rate fertilizer, and it could satisfy with the requirement with of the range of the error permission.
Distributed application management using Plush
Authors: J. Albrecht, C. Tuttle, A. Snoeren, A. Vahdat
Venue: Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium (2005)

Recent computing trends have shown an increase in the demand for large-scale, distributed, federated computing environments. Two of the more popular environments that have emerged are the grid and PlanetLab. At a high level, these systems are similar in many ways; both are comprised of a set of heterogeneous interconnected machines that allows secure resource sharing for a variety of different users and applications. However, at a lower level, the systems are very distinct in the sense that they were designed to solve different types of problems, and therefore have fundamental differences that make it difficult to develop and deploy applications on both platforms. As a result, application designers and researchers create software that runs on either the grid or PlanetLab, but not both. We propose to solve this problem by describing a common abstraction for both PlanetLab and grid applications. Further, we present Plush - a tool that implements the distributed application abstraction by providing a pluggable and extensible infrastructure allowing users to customize their environment for running experiments on both PlanetLab and the grid.
SAR Image Segmentation Based on Mixture Context and Wavelet Hidden-Class-Label Markov Random Field
Authors: Ming Li, Yan Wu, Shunjun Wu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03 (2007)

Because of the property that Synthetic Aperture Radar (SAR) image includes plenty of multiplicative speckle noise, a new segmentation algorithm is proposed based on the mixture context and the wavelet hidden-class-label Markov Random Field (MRF). In our paper, a wavelet mixture heavy-tailed model is constructed, then the hidden-class-label MRF is extended to the wavelet domain to suppress the effect of speckle noise. Since the multi-scale segmentation with overlapping window is presented here, this segmentation method is utilized at the finest scale of stationary wavelet transform (SWT) domain, and the classical segmentation method is also utilized at the coarse scales of discrete wavelet transform (DWT) domain. Finally, a mixture context model is proposed to estimate inter-scale parameters and the optimal segmentation result is derived from a new maximum a posteriori (MAP) classification. The experimental results show that our method achieves accurate SAR image segmentation result and preserves detailed boundary effectively.
Representing Images Using Nonorthogonal Haar-Like Bases
Authors: Feng Tang, Ryan Crabb, Hai Tao
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

Efficient and compact representation of images is a fundamental problem in computer vision. In this paper, we propose methods that use Haar-like binary box functions to represent a single image or a set of images. A desirable property of these box functions is that their inner product operation with an image can be computed very efficiently. We propose two closely related novel subspace methods to model images: the non-orthogonal binary subspace (NBS) method and binary principal component analysis (B-PCA) algorithm. NBS is spanned directly by binary box functions and can be used for image representation, fast template matching and many other vision applications. B-PCA is a structure subspace that inherits the merits of both NBS (fast computation) and PCA (modeling data structure information). B-PCA base vectors are obtained by a novel PCA guided NBS method. We also show that BPCA base vectors are nearly orthogonal to each other. As a result, in the non-orthogonal vector decomposition process, the computationally intensive pseudo-inverse projection operator can be approximated by the direct dot product without causing significant distance distortion. Experiments on real image datasets show promising performance in image matching, reconstruction and recognition tasks with significant speed improvement.
Towards a Comprehensive View of Secure Software Engineering
Authors: Mehrez Essafi, Lamia Labed, Henda Ben Ghezala
Venue: Proceedings of the The International Conference on Emerging Security Information, Systems, and Technologies (2007)

Secure software engineering is a big challenge. This is mainly due to the increasing complexity, openness and extensibility of modern applications, which make a complete analysis of security requirements very hard. The overall problem space is consequently no longer easily comprehensible for developers. This paper is an attempt to explore some of these issues underlying secure software engineering. We propose a secure software engineering framework, which suggests considering secure software engineering along four different, but complementary, views. Each view is capturing a particular relevant aspect of secure software engineering. Our motivations for developing this framework are to: (a) help understand and clarify the secure software engineering domain, (b) guide in classifying and comparing both secure software and securing approaches and (c) help researchers to identify new research axes.
Intelligent Fault Diagnosis Using Entropy-Based Rough Decision Tree Method
Authors: Chao Zhang, Cunbao Ma, Dong Song
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

The fault diagnosis on large complex system is a difficult problem due to the complex structure of the system and the presence of high dimensional fault datasets. To solve this problem, integrating minimize entropy principle approach (MEPA), rough sets theory and C4.5 algorithm, an entropy-based rough decision tree method is proposed to extract fault diagnosis rules. The diagnosis example of a 4153 diesel demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis method, and verified the feasibility of engineering application.
Hybridized Swarm Metaheuristics for Evolutionary Random Forest Generation
Authors: Miroslav Bursa, Lenka Lhotska, Martin Macas
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining Ant Colony metaheuristics and Evolutionary computing technique. The method benefits from the stochastic process and population approach, which allows the algorithm to evolve more efficiently than each method alone. As the method is similar to random forest generation, it can be also used for feature selection. The paper also discusses the parameter estimation for the method. Tests on real data (UCI and real biomedical data) have been performed and evaluated. The average accuracy of the method over MIT-BIH database with normalized data and equalized classes is sensitivity 93.22 % and specificity 87.13 %.
Economic ordering policy for an item with imperfect quality subject to the in-house inspection
Authors: I. Konstantaras, S. K. Goyal, S. Papachristos
Venue: International Journal of Systems Science (2007)

This article considers a production/inventory system where each lot of items received or produced contains a random proportion of defective units, items of imperfect quality. The purchaser contacts a 100% inspection in order to identify the perfect (acceptable) quality items. The model examines the following two options for the imperfect quality items: sell them to a secondary market, as a single batch and at a price lower to that of new ones, or rework them at some cost and then use them as new ones to satisfy demand. After inspection, the good quality items are sent to the working inventory warehouse in batches of equal size. For both of these cases, the optimal ordering lot size and the optimal number of batches are obtained. A numerical example illustrates the solution procedure and sensitivity analysis results are reported.
Adaptive recurrent cerebellar model articulation controller for linear ultrasonic motor with optimal learning rates
Authors: Ya-Fu Peng, Chih-Min Lin
Venue: Neurocomputing (2007)

In this study, an adaptive recurrent cerebellar model articulation controller (ARCMAC) is investigated for the motion control of linear ultrasonic motor (LUSM). The proposed ARCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the ARCMAC parameters. Moreover, the analytical method based on a Lyapunov function is proposed to determine the learning-rates of ARCMAC so that the stability of the system can be guaranteed. Furthermore, the variable optimal learning-rates are derived to achieve the fastest convergence of tracking error. Finally, the effectiveness of the proposed control system is verified by the experiments of LUSM motion control. Experimental results show that high-precision tracking response can be achieved by using the proposed ARCMAC.
Integration of HfxTayN metal gate with SiO2 and HfOxNy gate dielectrics for MOS device applications
Authors: Chang-Ta Yang, Kuei-Shu Chang-Liao, Hsin-Chun Chang, B. S. Sahu, Tzu-Chen Wang, Tien-Ko Wang, Wen-Fa Wu
Venue: Microelectronic Engineering (2007)

Interaction of Hf"xTa"yN metal gate with SiO"2 and HfO"xN"y gate dielectrics has been extensively studied. Metal-oxide-semiconductor (MOS) device formed with SiO"2 gate dielectric and Hf"xTa"yN metal gate shows satisfactory thermal stability. Time-of-flight secondary ion mass spectroscopy (TOF-SIMS) analysis results show that the diffusion depths of Hf and Ta are less significant in SiO"2 gate dielectric than that in HfO"xN"y. Compared to HfO"xN"y gate dielectric, SiO"2 shows better electrical properties, such as leakage current, hysteresis, interface trap density and stress-induced flat-band voltage shift. With an increase in post metallization annealing (PMA) temperature, the electrical characteristics of the MOS device with SiO"2 gate dielectric remain almost unchanged, indicating its superior thermal and electrical stability.
Clotho: decoupling memory page layout from storage organization
Authors: Minglong Shao, Jiri Schindler, Steven W. Schlosser, Anastassia Ailamaki, Gregory R. Ganger
Venue: Proceedings of the Thirtieth international conference on Very large data bases - Volume 30 (2004)

As database application performance depends on the utilization of the memory hierarchy, smart data placement plays a central role in increasing locality and in improving memory utilization. Existing techniques, however, do not optimize accesses to all levels of the memory hierarchy and for all the different workloads, because each storage level uses different technology (cache, memory, disks) and each application accesses data using different patterns. Clotho is a new buffer pool and storage management architecture that decouples in-memory page layout from data organization on non-volatile storage devices to enable independent data layout design at each level of the storage hierarchy. Clotho can maximize cache and memory utilization by (a) transparently using appropriate data layouts in memory and non-volatile storage, and (b) dynamically synthesizing data pages to follow application access patterns at each level as needed. Clotho creates in-memory pages individually tailored for compound and dynamically changing workloads, and enables efficient use of different storage technologies (e.g., disk arrays or MEMS-based storage devices). This paper describes the Clotho design and prototype implementation and evaluates its performance under a variety of workloads using both disk arrays and simulated MEMS-based storage devices.
A New Nonlinear Pseudorandom Number Generator
Authors: J. Gait
Venue: IEEE Transactions on Software Engineering (1977)

During the next few years a new pseudorandom number generator will become available on many computer systems. A concern for the security of computer data has led to the adoption of a Data Encryption Standard (DES) by the National Bureau of Standards. This standard specifies a nonlinear cryptographic algorithm which can be used inter alia as a source of pseudorandom numbers in software applications, such as those involving order statistics, where the usual linear congruential and generalized feedback shift register generators seem to be inadequate. Results of testing the DES as a pseudorandom number generator indicate that the algorithm is more than satisfactory for this purpose.
Approximation algorithm for the temperature-aware scheduling problem
Authors: Sushu Zhang, Karam S. Chatha
Venue: Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design (2007)

The paper addresses the problem of performance optimization for a set of periodic tasks with discrete voltage/frequency states under thermal constraints. We prove that the problem is NP-hard, and present a pseudo-polynomial optimal algorithm and a fully polynomial time approximation technique (FPTAS) for the problem. The FPTAS technique is able to generate solutions in polynomial time that are guaranteed to be within a designer specified quality bound (QB) (say within 1% of the optimal). We evaluate our techniques by experimentation with multimedia and synthetic benchmarks mapped on the 70nm CMOS technology processor. The experimental results demonstrate our techniques are able to match optimal solutions when QB is set at 5%, can generate solutions that are quite close to optimal (< 5%) even when QB is set at higher values (50%), and executes in few seconds (with QB > 25%) for large task sets with 120 nodes (while the optimal solution takes several hundred seconds). We also analyze the effect of different thermal parameters, such as the initial temperature, the final temperature and the thermal resistance.