ppxscal/embedding_network huggingface dataset:
Information Extraction incorporating Paragraph Feature and Hidden Markov Model
Authors: Liu Na, Lu Mingyu, Tang Huanling
Venue: Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops (2007)

With the data of Internet continuous growth, Information Extraction has become the foundational and effective means to handling the quantity of text. This paper puts forward a method of information extraction that incorporating paragraph feature and hidden Markov Model. The method takes paragraph instead of words as research object, paragraph is text sequence saved from web pages after preprocessed. Every paragraph is converted into special tokens, these tokens are the observation symbols of hidden Markov Model. The whole experiments are carried out on EBM web pages set. The information extracted includes Title, Author, Affiliation and Journal etc. The experimental results show that this method can improve precision and recall in some degree.
Egghe's construction of Lorenz curves resolved
Authors: Quentin L. Burrell
Venue: Journal of the American Society for Information Science and Technology (2007)

In a recent article (Burrell, 2006), the author pointed out that the version of Lorenz concentration theory presented by Egghe (2005a, 2005b) does not conform to the classical statistical/econometric approach. Rousseau (2007) asserts confusion on our part and a failure to grasp Egghe's construction, even though we simply reported what Egghe stated. Here the author shows that Egghe's construction rather than including the standard case, as claimed by Rousseau, actually leads to the Leimkuhler curve of the dual function, in the sense of Egghe. (Note that here we distinguish between the Lorenz curve, a convex form arising from ranking from smallest to largest, and the Leimkuhler curve, a concave form arising from ranking from largest to smallest. The two presentations are equivalent. See Burrell, 1991, 2005; Rousseau, 2007.) &copy; 2007 Wiley Periodicals, Inc.
AdaBoost is Consistent
Authors: Peter L. Bartlett, Mikhail Traskin
Venue: The Journal of Machine Learning Research (2007)

The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investigated. In particular, we consider the stopping strategy to be used in AdaBoost to achieve universal consistency. We show that provided AdaBoost is stopped after n1-ε iterations---for sample size n and ε ∈ (0,1)---the sequence of risks of the classifiers it produces approaches the Bayes risk.
A functional-link-neural network for short-term electric load forecasting
Authors: P. K. Dash, A. C. Liew, H. P. Satpathy
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (1999)

This paper presents a functional-link network based short-term electric load forecasting system for real-time implementation. The load and weather parameters are modelled as a nonlinear autoregressive moving average (ARMA) process and parameters of this model are obtained using the functional approximation capabilities of an auto-enhanced Functional Link net. Numerous and significant advantages accrue from using a flat net, including rapid quadratic optimisation in the learning of weights, simplification in the hardware as well as in computational procedures. The functional link net based load forecasting system accounts for seasonal and daily load characteristics as well as abnormal conditions, holidays and other conditions. It is capable of forecasting load with a lead time of one hour to seven days. The adaptive mechanism with a nonlinear learning rule is used to train the network on-line. The results indicate that the functional link net based load forecasting system produces robust and more accurate load forecasts in comparison to simple adaptive neural network or statistical based approaches. Testing the algorithm with load and weather data for a period of two years reveals satisfactory performance with mean absolute percentage error (MAPE) mostly less than 2% for a 24-hour ahead forecast and less than 2.5% for a 168-hour ahead forecast.
IMP: a meta-tooling platform for creating language-specific ides in eclipse
Authors: Philippe Charles, Robert M. Fuhrer, Stanley M. Sutton,  Jr.
Venue: Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering (2007)

Programming language design remains a vital field, with interest in languages targeting concurrency, scripting, and aspects, as well as in domain-specific languages. Full-featured integrated development environments (IDEs) have become critical to the adoption of new languages. A key factor in the success of these IDEs is the provision of services specifically tailored to the language. However, modern IDE frameworks are large and complex, and the cost of constructing a language-specific IDE from scratch remains prohibitive IMP is an IDE meta-tooling platform intended to relieve much of the burden of IDE development in Eclipse. IMP combines a language-independent framework, generators for partial implementations of language-specific services, and support for the completion of service implementations by programming at various levels of abstraction. Unlike much of the previous work, IMP permits signigicant customization of IDE appearance and behavior and accommodates incremental elaboration of the IDE; it also makes significant reuse of code and assists during the IDE development process. IMP-based IDEs are in use in research projects in IBM, including within IMP itself. IMP is available as an open-source release from SourceForge.net
Fast Selection of Small and Precise Candidate Sets from Dictionaries for Text Correction Tasks
Authors: K. Schulz, S. Mihov, P. Mitankin
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01 (2007)

Lexical text correction relies on a central step where ap- proximate search in a dictionary is used to select the best correction suggestions for an ill-formed input token. In pre- vious work we introduced the concept of a universal Lev- enshtein automaton and showed how to use these automata for efficiently selecting from a dictionary all entries within a fixed Levenshtein distance to the garbled input word. In this paper we look at refinements of the basic Levenshtein distance that yield more sensible notions of similarity in distinct text correction applications, e.g. OCR. We show that the concept of a universal Levenshtein automaton can be adapted to these refinements. In this way we obtain a method for selecting correction candidates which is very ef- ficient, at the same time selecting small candidate sets with high recall.
Early DoS Attack Detection using Smoothened Time-Series andWavelet Analysis
Authors: Pravin Shinde, Srinivas Guntupalli
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

Denial of Service(DoS) attacks are ubiquitous to computer networks. Flood based attacks are a common class of DoS attacks. DoS detection mechanisms that aim at detecting floods mainly look for sudden changes in the traffic and mark them anomalous. In this paper, we propose a method that considers the traffic in a network as a time-series and smoothens it using exponential moving average and analyzes the smoothened wave using energy distribution based on wavelet analysis. The parameters we used to represent the traffic are number of bytes received per unit time and the proportion between incoming and outgoing bytes. By analyzing the energy distribution in the wavelet form of a smoothened time-series, growth in the traffic, which is the result of a DoS attack can be detected very early. As the parameters we considered represent different properties of the network, the accuracy of the detection will be very high and with less false positives.
The Need for Improved Programming Language
Authors: C. T. Zahn
Venue: Computer (1975)

I adopt the point of view that an ideal language contains features well-matched to the abstract concepts which the problem-solving programmer uses in his own thinking. I also assume that the modifiability of programs is of paramount importance. The features below are not necessarily listed in order of importance; an ideal general-purpose programming language should probably have all of them because each one encourages a more systematic structured writing of programs. Most of these features are contained in some form in at least one implemented language, but unfortunately the best known and most used languages contain few of them.
Cache Organizations for H.264/AVC Motion Compensation
Authors: Ju-Hyun Kim, Gyoung-Hwan Hyun, Hyuk-Jae Lee
Venue: Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2007)

A large demand on memory bandwidth puts limitation on the performance improvement of an H.264/AVC decoder. In order to reduce the amount of frame memory access, this paper proposes a new memory architecture that uses a cache memory to store the data for motion compensation. Optimized for the data access pattern of motion compensation, two new cache organizations are proposed to reduce cache conflict misses. A split-index direct mapped cache maps a page of a frame memory into two consecutive cache lines by turns. Another cache organization, called a circular cache, does not fetch the entire line of a cache but reads only the necessary part of a cache line. A block pre-fetching scheme is proposed to prefetch a block of data which is likely to be used by the motion compensation of the next block. With the circular cache and block pre-fetching, a frame memory bandwidth requirement is reduced by an average of 72% which is 5% better than the previous work. In addition, the number of the non-sequential data accesses is reduced by an average of 68%.
Efficient Delay-Based Schedulig Scheme for Supporting Real-Time Traffic in HSDPA Networks
Authors: Samreen Riaz Husain, Nidal Nasser, Hossam Hassanein
Venue: Proceedings of the 32nd IEEE Conference on Local Computer Networks (2007)

HSDPA is a 3.5G wireless cellular system that can offer data rates of up to 14.4 Mbps which is much beyond what 2.5G and 3G cellular systems could offer. Due to its high supportable data rate, HSDPA can support a wide range of applications with diverse QoS. Data generated by these applications consists of different traffic classes; including conversational, streaming, interactive and background; each having certain QoS requirements. HSDPA scheduling schemes need to accommodate these traffic classes as well as provide certain QoS provisions for them. This paper proposes a Delay-Based Scheduler (DBS) that prioritizes traffic based on its delay requirements. In addition, the DBS is designed to achieve a high throughput and a fair allocation of resources among users. Simulation results indicate that the DBS achieves a lower average queuing delay while maintaining other performance metrics when compared with existing HSDPA scheduling schemes.
Lerna: an active storage framework for flexible data access and management
Authors: S. V. Anastasiadis, R. G. Wickremesinghe, J. S. Chase
Venue: Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium (2005)

In the present paper, we examine the problem of supporting application-specific computation within a network file server. Our objectives are (i) to introduce an easy to use yet powerful architecture for executing both custom-developed and legacy applications close to the stored data, (ii) to investigate the performance improvement that we get from data proximity in I/O-intensive processing, and (in) to exploit the I/O-traffic information available within the file server for more effective resource management. One main difference from previous active storage research is our emphasis on the expressive power and usability of the network server interface. We describe an extensible active storage framework that we built in order to demonstrate the feasibility of the proposed system design. We show that accessing large datasets over a wide-area network through a regular file system can penalize the system performance, unless application computation is moved close to the stored data. Our conclusions are substantiated through experimentation with a popular multilayer map warehouse application.
State and events for Web services: a comparison of five WS-resource framework and WS-notification implementations
Authors: M. Humphrey, G. Wasson
Venue: Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium (2005)

The Web services resource framework defines conventions for managing state in distributed systems based on Web services, and WS-notification defines topic-based publish/subscribe mechanisms. We analyze five independent and quite different implementations of these specifications from the perspectives of architecture, functionality, standards compliance, performance, and interoperability. We identify both commonalities among the different systems (e.g., similar dispatching and SOAP processing mechanisms) and differences (e.g., security, programming models, and performance). Our results provide insights into effective implementation approaches. Our results may also provide application developers, system architects, and deployers with guidance in identifying the right implementation for their requirements and in determining how best to use that implementation and what to expect with regard to performance and interoperability.
What does web 2.0 have to do with databases?
Authors: Sihem Amer-Yahia, Alon Halevy
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

Web 2.0 is a buzzword we have been hearing for over 2 years. According to Wikipedia, it hints at an improved form of the World Wide Web where technologies such as weblogs, social bookmarking, RSS feeds, photo and video sharing, based on an architecture of participation and democracy that encourages users to add value to the application as they use it. Web 2.0 enables social networking on the Web by allowing users to contribute content, share it, rate it, create a network of friends, and decide what they like to see and how they want it to look like.
Least-squared ordered weighted averaging operator weights: Research Articles
Authors: Byeong Seok Ahn, Haechurl Park
Venue: International Journal of Intelligent Systems (2008)

The ordered weighted averaging (OWA) operator by Yager (IEEE Trans Syst Man Cybern 1988; 18; 183&ndash;190) has received much more attention since its appearance. One key point in the OWA operator is to determine its associated weights. Among numerous methods that have appeared in the literature, we notice the maximum entropy OWA (MEOWA) weights that are determined by taking into account two appealing measures characterizing the OWA weights. Instead of maximizing the entropy in the formulation for determining the MEOWA weights, a new method in the paper tries to obtain the OWA weights that are evenly spread out around equal weights as much as possible while strictly satisfying the orness value provided in the program. This consideration leads to the least-squared OWA (LSOWA) weighting method in which the program is to obtain the weights that minimize the sum of deviations from the equal weights since entropy is maximized when all the weights are equal. Above all, the LSOWA method allocates the positive and negative portions to the equal weights that are identical but opposite in sign from the middle point in the number of criteria. Furthermore, interval LSOWA weights can be constructed when a decision maker specifies his or her orness value in uncertain numerical bounds and we present a method, with those uncertain interval LSOWA weights, for prioritizing alternatives that are evaluated by multiple criteria. &copy; 2008 Wiley Periodicals, Inc.
HVS-based Imperceptibility Measure of Watermark in Watermarked Color Image
Authors: Hongmei Yang, Yongquan Liang, Lianshan Liu, Haibin Sun
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Imperceptibility of embedded watermark reflects the fidelity of watermarked image. Based on the color masking and Watson's JND(Just Noticeable Differences) model, a calculation method of JNCD(Just Noticeable Color Differences) is proposed. Then the masking weight for each element of color image is calculated using the JNCD and a new quality measure of watermarked color image is proposed, i.e. CPSNR (Color image Peak Signal to Noise Ratio). The quality of watermarked color image calculated by CPSNR is closer to the sensitivity of human vision than PSNR.
A Model for Tuberculosis with Various Latent Periods
Authors: Zhong-wei Jia, Xiao-wen Li, Zhen Jin, Dan Feng, Wu-chun Cao
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

This paper presents a mathematical model to evaluate the impact of different latent periods on the epidemic of tuberculosis. Analysis results show that the disease-free equilibrium P_0 is globally asymptotically stable and disease is eliminated from population if R_0 \leqslant1. However, the proof of stability of the unique endemic equilibrium P* is still a challege. Numerical simulation demonstrates that P* is globally asymptotically stable if R_0 \ge 1.
Incremental and robust learning of subspace representations
Authors: Danijel Skočaj, Aleš Leonardis
Venue: Image and Vision Computing (2008)

Learning is a fundamental capability of any cognitive system. To enable efficient operation of a cognitive agent in a real-world environment, visual learning has to be a continuous and robust process. In this article, we present a method for subspace learning, which takes these considerations into account. We present an incremental method, which sequentially updates the principal subspace considering weighted influence of individual images as well as individual pixels within an image. We further extend this approach to enable determination of consistencies in the input data and imputation of the inconsistent values using the previously acquired knowledge, resulting in a novel method for incremental, weighted, and robust subspace learning. We demonstrate the effectiveness of the proposed concept in several experiments on learning of object and background representations.
Scalable Hybrid Unstructured and Structured Grid Raycasting
Authors: Philipp Muigg Markus Hadwiger Helmut Doleisch, Helwig Hauser
Venue: IEEE Transactions on Visualization and Computer Graphics (2007)

This paper presents a scalable framework for real-time raycasting of large unstructured volumes that employs a hybrid bricking approach. It adaptively combines original unstructured bricks in important (focus) regions, with structured bricks that are resampled on demand in less important (context) regions. The basis of this focus+context approach is interactive specification of a scalar degree of interest (DOI) function. Thus, rendering always considers two volumes simultaneously: a scalar data volume, and the current DOI volume. The crucial problem of visibility sorting is solved by raycasting individual bricks and compositing in visibility order from front to back. In order to minimize visual errors at the grid boundary, it is always rendered accurately, even for resampled bricks. A variety of different rendering modes can be combined, including contour enhancement. A very important property of our approach is that it supports a variety of cell types natively, i.e., it is not constrained to tetrahedral grids, even when interpolation within cells is used. Moreover, our framework can handle multi-variate data, e.g., multiple scalar channels such as temperature or pressure, as well as time-dependent data. The combination of unstructured and structured bricks with different quality characteristics such as the type of interpolation or resampling resolution in conjunction with custom texture memory management yields a very scalable system.
Data Mining Based on Clonal Selection Wavelet Network
Authors: Zhenguo Chen
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Recently there has been significant development in the use of wavelet network methods in various data mining processes, but how to select the proper structure and parameter of wavelet network is a difficulty. This paper offers a kind of solution, in which the clonal selection Algorithm is proposed and the hierarchical structure is used in the coding scheme, thus a novel algorithm of clonal selection wavelet network is presented, meanwhile the evolution of topologic structure and the parameter learning of the wavelet network can be gotten. To evaluate the performance we apply this algorithm to data mining. After illustrating our method with a representative dataset, simulations show that the data mining based on clonal selection wavelet network achieves the better prediction accuracy than other traditional methods.
Acquiring Innovative Software Systems: Experiences from the Field
Authors: Onur Demirors, N. Alpay Karagoz, Cigdem Gencel
Venue: Proceedings of the 33rd EUROMICRO Conference on Software Engineering and Advanced Applications (2007)

Managing acquisition of software projects, containing innovative components, brings many challenges to acquirer organizations. Innovative software projects usually have unclear requirements and scope. They require stakeholders such as research institutes, software development organizations and the domain experts who work together and need a flexible budget and time frame to be successful. As a result, the acquisition management activities such as estimation of the product size and the required development effort, acquisition planning and control, determining proper structures for the management of organizations, and requirements management require unique approaches. In this study, experience gained from the acquisition of an innovative military software development project is presented.
Detection of Static and Dynamic Obstacles Based on Fuzzy Data Association with Laser Scanner
Authors: Jinxia Yu, Zixing Cai, Zhuohua Duan
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

Aimed at the detection of static and dynamic obstacles in environmental mapping of mobile robot, an unsupervised clustering algorithm is presented to realize feature extraction of obstacles based on the analysis of ranging data obtained from 2D laser scanner. Considering the unknown clustering number in advance, the validation index function is introduced into the self-learning mechanism to determine the accurate clustering number automatically. At the same time, fuzzy logic is integrated into incremental data association of obstacle features to make the static or dynamic obstacles classification decision to reduce the uncertain influence. Using our office as the operating environment to implement the experiment of feature extraction and obstacles classification, the results verify the effectiveness of this approach.