ppxscal/embedding_network huggingface dataset:
A practical analytic single scattering model for real time rendering
Authors: Bo Sun, Ravi Ramamoorthi, Srinivasa G. Narasimhan, Shree K. Nayar
Venue: ACM SIGGRAPH ASIA 2008 courses (2008)

We consider real-time rendering of scenes in participating media, capturing the effects of light scattering in fog, mist and haze. While a number of sophisticated approaches based on Monte Carlo and finite element simulation have been developed, those methods do not work at interactive rates. The most common real-time methods are essentially simple variants of the OpenGL fog model. While easy to use and specify, that model excludes many important qualitative effects like glows around light sources, the impact of volumetric scattering on the appearance of surfaces such as the diffusing of glossy highlights, and the appearance under complex lighting such as environment maps. In this paper, we present an alternative physically based approach that captures these effects while maintaining realtime performance and the ease-of-use of the OpenGL fog model. Our method is based on an explicit analytic integration of the single scattering light transport equations for an isotropic point light source in a homogeneous participating medium. We can implement the model in modern programmable graphics hardware using a few small numerical lookup tables stored as texture maps. Our model can also be easily adapted to generate the appearances of materials with arbitrary BRDFs, environment map lighting, and precomputed radiance transfer methods, in the presence of participating media. Hence, our techniques can be widely used in real-time rendering.
A shift of mind - Introducing a concept creation model
Authors: Jun Nakamura, Yukio Ohsawa
Venue: Information Sciences: an International Journal (2009)

The ability to construct concepts is indispensable to both individual and evolutionary development. Our model involves the use of ambiguous stimuli to facilitate decision-making by promoting analogical reasoning. Toward this end, we have developed Web-based exercises in word categorization for the purpose of engaging participants in analogical reasoning that contributes to the integration of words and leads to the construction of new concepts. 12 graduate students and 20 junior high school students were presented with ambiguous information for the purpose of comparison between the senior and the junior students. We hypothesized that the senior students tend to behave with more insight rather than junior students with less activation of thought process. Our results suggested that the presentation of the ambiguous stimuli were associated with unique thought processes, which are consistent with approaches to word categorization that reflect either the experience of insight or the operation of a trial and error strategy, depending on the junior or the senior students. We showed that the senior students tend to be more like insight into categorization design, while the junior as rather try and error behavior, in consideration of needed time and actions in analogical thinking.
Collaborative Filtering for Implicit Feedback Datasets
Authors: Yifan Hu, Yehuda Koren, Chris Volinsky
Venue: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (2008)

A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.
Reactivity in Online Auctions: Understanding Bidding Behavior through Reactive Transitions
Authors: Adriano Pereira, Leonardo Rocha, Fernando Mourão, Wagner Meira, Paulo Góes
Venue: Proceedings of the 2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services (2008)

Internet systems are a typical scenario where sequences of interactions arise. Modeling the factors that drive the dynamics of an online auction, for example, is complex, since successive interactions become a loop-feedback mechanism, that we call reactivity, that is, the user behavior affects the auction negotiation and vice-versa. In this paper we briefly describes our methodology for characterizing online auctions, considering reactivity. We present the reactive transitions, that is the approach we adopt to model reactivity in online auctions. The reactive transition models the reactivity function, providing a way to discover the bidding behavior's patterns. We also validate our model using actual bidding data from eBay. The results show rich details to understand bidding behavior, that can be used to design support-decision agents and simulate e-markets.
Single-chip microcomputers can be easy to program
Authors: Bill Huston
Venue: Proceedings of the June 7-10, 1982, national computer conference (1982)

Most single-chip microcomputers (MCUs) use the split-memory Harvard architecture. A few single-chips trace their architectural heritage to large computers due to the common-memory Von Neumann organization. The major differences are that a Harvard-based MCU costs less in its undistorted form, and a Von Neumann-based MCU is more expandable and easier to program. Since the traits of Harvard-based single-chips are quite well known, though perhaps not by that name, the focus is placed on the programming benefits of a Von Neumann MCU. Programming costs can be lowered while increasing program reliability. Data organizations can be more flexible in both RAM and ROM. Program changes can be incorporated more quickly. The generalized instruction set is easier to understand. The M6805 family of MCUs is used to illustrate these benefits. 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
SCS: A New Similarity Measure for Categorical Sequences
Authors: Abdellali Kelil, Shengrui Wang
Venue: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (2008)

Measuring the similarity between categorical sequences is a fundamental process in many data mining applications. A key issue is to extract and make use of significant features hidden behind the chronological and structural dependencies found in these sequences. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but such sequences often have similar structural features in chronologically different positions. In this paper we propose SCS, a novel method for measuring the similarity between categorical sequences, based on an original pattern matching scheme that makes it possible to capture chronological and non-chronological dependencies. SCS captures significant patterns that represent the natural structure of sequences, and reduces the influence of those representing noise. It constitutes an effective approach for measuring the similarity of data such as biological sequences, natural language texts and financial transactions. To show its effectiveness, we have tested SCS extensively on a range of datasets, and compared the results with those obtained by various mainstream algorithms.
Query optimization for spatio-temporal data stream management systems
Authors: Hicham G. Elmongui
Venue: SIGSPATIAL Special (2009)

Location-detection devices are used ubiquitously in moving objects due to the everyday decreasing cost and simplified technology. Usually, these devices will send the moving objects' location information to a spatio-temporal data stream management system that will be then responsible for answering spatio-temporal queries related to these moving objects. Most of the existing work focused on the continuous spatio-temporal query execution. However, several outstanding challenges have been either addressed partially or not at all in the existing literature. In this paper, we focus on the optimization of multi-predicate spatio-temporal queries on moving objects. In particular, we provide a costing mechanism for continuous spatio-temporal queries. We provide for the optimization of the parameters of the spatio-temporal operators. Finally, we propose the adaptive execution of the continuous queries for spatio-temporal data stream management systems.
Debellor: A Data Mining Platform with Stream Architecture
Authors: Marcin Wojnarski
Venue: Transactions on Rough Sets IX (2008)

This paper introduces Debellor (www.debellor.org) --- an open source extensible data mining platform with stream-based architecture, where all data transfers between elementary algorithms take the form of a stream of samples. Data streaming enables implementation of scalable algorithms, which can efficiently process large volumes of data, exceeding available memory. This is very important for data mining research and applications, since the most challenging data mining tasks involve voluminous data, either produced by a data source or generated at some intermediate stage of a complex data processing network.Advantages of data streaming are illustrated by experiments with clustering time series. The experimental results show that even for moderate-size data sets streaming is indispensable for successful execution of algorithms, otherwise the algorithms run hundreds times slower or just crash due to memory shortage.Stream architecture is particularly useful in such application domains as time series analysis, image recognition or mining data streams. It is also the only efficient architecture for implementation of online algorithms.The algorithms currently available on Debellor platform include all classifiers from Rseslib and Weka libraries and all filters from Weka.
Programming with data frames for everyday data items
Authors: David W. Embley
Venue: Proceedings of the May 19-22, 1980, national computer conference (1980)

Processing everyday data items such as dollar amounts, time, dates, and account numbers constitutes a significant portion of real-world computer applications. Programmers involved with everyday data items confront the drudgery of writing routines to recognize, validate, transform, store, retrieve, manipulate, and display these items and also the challenge to develop user-friendly data-entry systems and insure data integrity. They usually meet these challenges using various and sundry ad hoc techniques.
Developing a coordinated vendor-buyer model in two-stage supply chains with stochastic lead-times
Authors: Mohsen S. Sajadieh, Mohammad R. Akbari Jokar, Mohammad Modarres
Venue: Computers and Operations Research (2009)

This paper develops an approach to determine the optimal production and shipment policy for an integrated vendor-buyer problem. The vendor manufactures the product in batches at a finite rate and ships the output to the buyer. All shipments to the buyer are equal-sized batches. Despite previous papers in the literature, we assume that the supply lead-time between vendor and buyer is stochastic and shortage is also allowed. The objective is to minimize the expected total cost of both buyer and vendor. We derive the expected annual integrated total cost function and propose an analytic solution procedure to determine the optimal policy. To illustrate the significance of cost-reduction of the integrated approach in comparison with independent decisions by buyer or vendor, some numerical examples are also presented.
Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier
Authors: Loris Nanni, Alessandra Lumini
Venue: Neural Computing and Applications (2009)

The problem addressed in this paper concerns the ensembling generation for evidential k-nearest-neighbour classifier. An efficient method based on particle swarm optimization (PSO) is here proposed. We improve the performance of the evidential k-nearest-neighbour (EkNN) classifier using a random subspace based ensembling method. Given a set of random subspace EkNN classifier, a PSO is used for obtaining the best parameters of the set of evidential k-nearest-neighbour classifiers, finally these classifiers are combined by the &#x201c;vote rule&#x201d;. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.
A method based on rank-ordered filter to detect edges in cellular image
Authors: Xiaoyin Xu, Zhong Yang, Yaming Wang
Venue: Pattern Recognition Letters (2009)

To extract morphological features about nuclei from microscopy cellular image, it is usually required to find the edges of nuclei at first. Standard edge detection methods may not produce satisfactory results due to the varying brightness and background in cellular image. It is important to extract close, smooth, and correct edges in order to compute features like compactness, convexity, roundness, and etc. We present a new method to detect edges of nuclei in microscopy images. The method is based on using median filtering to compute the total variation with respect to the central pixel in a filter window. This step exploits one important feature of median filter, i.e., within the filter window, the total variation (TV) with respect to the median is always less than or equal to the TV with respect to the original center pixel. In other words, median filtering looks for the output that minimizes the total variation within the filtering window. The resulting image has enhanced contrast along the boundary of nuclei. As the final step, we use popular edge detection methods such as Canny detector and Laplacian of Gaussian to find edges of nuclei in the image. Examples from processing real cellular image obtained by light microscope show that the method obtains better edges in terms of connectivity, smoothness, and closely following the boundary of nuclei.
A Novel RS-based Key Frame Representation for Video Mining in Compressed-Domain
Authors: Xiang-wei Li, Ming-xin Zhang, Ya-lin Zhu, Jin-hong Xin
Venue: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining (2009)

It is a challenging issue to analyze video content for video mining tasks due to lacking of effective representation of video. In this paper, we propose a novel key frame representation algorithm based on Rough Sets (RS) in Discrete Cosine Transform (DCT) compressed-domain. Firstly, we extract DCT coefficients in compressed-domain, select and preprocess the DC coefficients that derived from DCT coefficients. Secondly, we construct Information System with DC coefficients. Finally, we reduce Information System using attributes reduced theory of RS, and obtained the representation of the video frames by reduced DC coefficients. Experimental results show that the proposed algorithm is fast and effective. Compared to conventional algorithm, our algorithm enjoys the following advantages: (1) the numbers of the key frame extracted using our algorithm become more scientific; (2) the algorithm can avoid the expensive computations in decompression processes.
Planning and Scheduling Using Microsoft Office Project 2007, 4th edition
Authors: Paul E. Harris
Venue:  (2007)

A user guide written for Project Management Professionals in any industry who wish to learn or improve their skills in Microsoft Project 20007 and discover how to get the most out of the software up to an intermediate level in a single project environment using Standard or Professional versions.
QuickBooks 2009 For Dummies
Authors: Stephen L.,  CPA,  MBA,  MS Nelson
Venue: For Dummies (2008)

Got a business to run? QuickBooks makes small-business accounting easier, and QuickBooks 2009 For Dummies shows you how! Bookkeeping may not be your favorite part of running a small business, so learning to use QuickBooks might pay off in more ways than one.QuickBooks helps you keep financial records, prepare invoices and record sales, pay bills, settle up at the end of the year, and more. In this guide, a top accounting and tax planning consultant tells you what you need to do to be ready for QuickBooks, then how to install and set up the program for your business. Discover how to:Use the latest version of QuickBooks to maintain your accounting records in compliance with tax laws Create invoices and credit memos, record sales receipts, set up inventory items, and monitor your inventory Record and pay bills, print checks, and process payroll, including withholding Balance accounts and generate financial reports Set up a business budget and reconcile your account Perform weekly, monthly, and annual record-keeping Create job estimates that can be easily converted into invoices or compared with actual costs Track accounts receivable and payable Produce reports that help you understand your customer list, vendors, job expenses, inventory, payroll, budget, and more Use job-costing procedure that help you estimate, bill, and track jobs Decide whether to use accrual-basis accounting or cash-basis accounting Handling the financial chores with QuickBooks can put the fun back into owning your own business. QuickBooks 2009 For Dummies makes it easier!
Modeling of Thin Film Process Data Using a Genetic Algorithm-Optimized Initial Weight of Backpropagation Neural Network
Authors: Byungwhan Kim, Hwajune Lee, Donghwan Kim
Venue: Applied Artificial Intelligence (2009)

Artificial neural network, particularly the backpropagation neural network (BPNN), has been used to construct a prediction model of plasma processes. In building a BPNN model, many training factors are typically involved and the most difficult factor is the initial weight distribution (IWD). In this study, a technique to optimize the IWD effect on BPNN prediction performance is presented. This was accomplished by using genetic algorithm (GA). The experimental data were collected from the etching of silica thin films in a CHF3-CF4 inductively coupled plasma. The etch process was statistically characterized and the etch responses to model include silica etch rate, Al etch rate, Al selectivity, and silica profile angle denoted as anisotropy. The effect of GA parameters (mutation and crossover probabilities) was also evaluated by conducting a 42 full factorial experiment. The performances of GA-BPNN models were compared to those for conventional models. The comparison revealed an improved prediction of GA-BPNN models for all etch responses. The improvement was even more than 15% for all but the Al etch rate data. The proven improvements support the finding that the presented technique is effective in optimizing an IWD effect on BPNN modeling.
Intelligent virus detection on mobile devices
Authors: Deepak Venugopal, Guoning Hu, Nicoleta Roman
Venue: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services (2006)

In this paper, we describe a new solution for detecting mobile phone viruses. The solution is based on Bayesian decision theory using heuristic rules derived from common functionalities among different virus samples. Specifically, we detect viruses according to the DLL usage of a program, which is directly linked to the functionality of this program. Our solution is able to detect unknown viruses, especially the variants of existing ones. We evaluate our solution on the Symbian platform, where most viruses are present in the wild. We constructed a virus detector based on DLL functions from a small set of virus samples. It detects 95% of mobile viruses and yields no false alarm.
Mining the customer credit using hybrid support vector machine technique
Authors: Weimin Chen, Chaoqun Ma, Lin Ma
Venue: Expert Systems with Applications: An International Journal (2009)

Credit scoring has become a critical and challenging management science issue, as the credit industry has been facing fiercer competition in recent years. Many methods have been suggested to tackle this problem in the literature. In this paper, we proposed hybrid support vector machine technique based on three strategies: (1) using CART to select input features, (2) using MARS to select input features, (3) using grid search to optimize model parameters. In order to verify the feasibility and effectiveness of the proposed hybrid SVM model, one credit card dataset provided by a local bank in China is used in this study. Analytic results demonstrate that the hybrid SVM technique not only has the best classification rate, but also has the lowest Type II error in comparison with CART, MARS and SVM and justify the presumptions that SVM having better capability of capturing nonlinear relationship among variables.
Classifications of digital medical images with multifractal analysis
Authors: Jelena Andjelkovic, Natasa Zivic, Branimir Reljin, Vladimir Celebic, Iva Salom
Venue: Proceedings of the 8th conference on Signal, Speech and image processing (2008)

The purpose of this research was finding differences between medical images in order of their classifications in terms of separation malign tissue from normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability. The results of computer analysis of images have been presented and discussed.
Effect of Different Coding Patterns on Compressed Frequency Domain Based Universal JPEG Steganalysis
Authors: Bin Li, Fangjun Huang, Shunquan Tan, Jiwu Huang, Yun Q. Shi
Venue: Proceedings of the 6th International Workshop on Digital Watermarking (2008)

Current steganalytic schemes for JPEG steganography are in favor of extracting features in DCT (Discrete Cosine Transform) domain and/or spatial domain. A recently proposed compressed frequency domain based universal steganalytic algorithm [21] showed concern over the statistics of Huffman compressed stream. The authors claimed that it was very effective in detecting stego images embedded by JPEG steganographic tools, including JPHide, F5 and OutGuess. Even though only several bytes were embedded, the scheme was still able to work, thus demonstrating astonishing steganalysis capability. By carefully controlled studies on the factors which may have impact on the new steganalytic method, we find out the truly cause of the powerfulness of this "payload-independent" steganalyzer. Experimental results reveal that different coding patterns used in cover and stego images, rather than the Avalanche Criterion [24] explained by its authors, have led to great detection efficiency. When the same coding pattern is employed in both cover and stego images, the performance of the newly devised steganalyzer has greatly dropped. Implication from this paper is that we should ensure the difference between the cover and stego images is only caused by data embedding itself in steganography and steganalysis.