ppxscal/embedding_network huggingface dataset:
Query by output
Authors: Quoc Trung Tran, Chee-Yong Chan, Srinivasan Parthasarathy
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

It has recently been asserted that the usability of a database is as important as its capability. Understanding the database schema, the hidden relationships among attributes in the data all play an important role in this context. Subscribing to this viewpoint, in this paper, we present a novel data-driven approach, called Query By Output (QBO), which can enhance the usability of database systems. The central goal of QBO is as follows: given the output of some query Q on a database D, denoted by Q(D), we wish to construct an alternative query Q′ such that Q(D) and Q′ (D) are instance-equivalent. To generate instance-equivalent queries from Q(D), we devise a novel data classification-based technique that can handle the at-least-one semantics that is inherent in the query derivation. In addition to the basic framework, we design several optimization techniques to reduce processing overhead and introduce a set of criteria to rank order output queries by various notions of utility. Our framework is evaluated comprehensively on three real data sets and the results show that the instance-equivalent queries we obtain are interesting and that the approach is scalable and robust to queries of different selectivities.
Solving Global Optimization Problems Using MANGO
Authors: Akın Günay, Figen Öztoprak, Ş. İlker Birbil, Pınar Yolum
Venue: Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (2009)

Traditional approaches for solving global optimization problems generally rely on a single algorithm. The algorithm may be hybrid or applied in parallel. Contrary to traditional approaches, this paper proposes to form teams of algorithms to tackle global optimization problems. Each algorithm is embodied and ran by a software agent. Agents exist in a multiagent system and communicate over our proposed MultiAgent ENvironment for Global Optimization (MANGO). Through communication and cooperation, the agents complement each other in tasks that they cannot do on their own. This paper gives a formal description of MANGO and outlines design principles for developing agents to execute on MANGO. Our case study shows the effectiveness of multiagent teams in solving global optimization problems.
Design of wideband CIC compensator filter for a digital IF receiver
Authors: Gordana Jovanovic Dolecek, fred harris
Venue: Digital Signal Processing (2009)

This paper presents the multiplierless CIC compensation filter based on the 2M-order filter and the sharpening technique. This technique proposed by Kaiser and Hamming attempts to improve the pass band and the stop band of a symmetric nonrecursive filter using the multiple copies of the same filter. We have considered the simplest sharpening polynomial that improves the frequency characteristic with minimum increase in computational complexity. The proposed filter provides wideband compensation over the specified CIC main lobe bandwidth. The design parameter is a single integer b which depends on K, the number of cascade CIC stages and is independent on the decimation factor M. The values of b tabulated here were obtained from MATLAB simulations. A number of demonstrated characteristics make the proposed structure a good candidate for software defined radio (SDR) applications.
Accuracy and efficiency enhancement in analysis and simulation in micro-technology using frequency-domain hybrid reduction method: micro-electromechanical optical filter case study
Authors: Yousof Gheisari
Venue: Proceedings of the 8th WSEAS international conference on Microelectronics, nanoelectronics, optoelectronics (2009)

A new method for model reduction is proposed. The reduction technique is based a recently developed methods, namely frequency domain balanced truncation within a frequency bound. This reduction technique is developed for model reduction of vey large dynamical systems which are frequently appear in analysis and simulation in Micro-technology as a result of finite element method. The proposed method is of interest for practical model order reduction because in this context it shows to keep the accuracy of the approximation as high as possible without sacrificing the computational efficiency. The method is implemented for simulation and analysis of micro-electromehcanical optical filter. Numerical results show the accuracy and efficiency enhancement of the method in comparison to previous techniques in this context.
Analysis of noise emissions by trains in proximity of a railway station
Authors: J. Quartieri, A. Troisi, C. Guarnaccia, T. Lenza, P. D'Agostino, S. D'Ambrosio, G. Iannone
Venue: Proceedings of the 10th WSEAS international conference on Acoustics & music: theory & applications (2009)

In this paper we consider noise impact of train transits in the framework of low velocity regimes, especially in proximity of a railway station, from the point of view of environmental impact observed in the neighbourhood of the station. In particular we will analyze such a setting by means of a predictive software confronting the algorithmic simulations with the experimental results obtained from noise measurements detected on a building façade, close to a railway station at night time. In order to opportunely model train transits and the related auxiliary phenomena with a realistic description, we consider different types of trains and of running conditions. The whole predictive scheme is relied on the timetable of train departures and arrivals of the Battipaglia station considered during a 24h working time.
Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs
Authors: Jilles S. Dibangoye, Abdel-Illah Mouaddib, Brahim Chai-draa
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 (2009)

Recent scaling up of decentralized partially observable Markov decision process (DEC-POMDP) solvers towards realistic applications is mainly due to approximate methods. Of this family, Memory Bounded Dynamic Programming (MBDP), which combines in a suitable manner top-down heuristics and bottom-up value function updates, can solve DEC-POMDPs with large horizons. The performances of MBDP, can be, however, drastically improved by avoiding the systematic generation and evaluation of all possible policies which result from the exhaustive backup. To achieve that, we suggest a heuristic search method, namely Point Based Incremental Pruning (PBIP), which is able to distinguish policies with different heuristic estimates. Taking this insight into account, PBIP searches only among the most promising policies, finds those useful, and prunes dominated ones. Doing so permits us to reduce clearly the amount of computation required by the exhaustive backup. The computation experiment shows that PBIP solves DEC-POMDP benchmarks up to 800 times faster than the current best approximate algorithms, while providing solutions with higher values.
A system-oriented analysis model to enhance patient safety in healthcare organizations
Authors: Sheu-Wen Chuang, Chung-Yu Pan, Chin-Yin Huang
Venue: Systems Engineering (2009)

Applications of system approach and changes in &ldquo;systems&rdquo; of care have been recognized as a key for the successful implementation of patient safety; however, the performances of patient safety have neither been satisfactory nor acceptable. The lack of knowledge and understanding of errors is one of the major barriers in blocking the success. In this paper, a four-step System Oriented Event Analysis model is proposed based on system theory and risk management model for social-technical systems. It reflects both a philosophical approach and a mechanism to guide the investigation team to build up the ability of controlling safety in the way of system thinking. A pilot study in a 50-bed hospital was performed. The system knowledge of errors for health care professionals has been strengthened during the model implementation. The system hierarchy and safety control matrix for vulnerable system components in the outpatient clinic consultation system has been established to prevent patient identification errors. A reduction of this type of error and the increase of incidents reported were found after ten months of implementation. Health care institutions can use this model as a training instrument for system knowledge enhancement, and as an analysis mechanism for the sustainability of patient safety. &copy; 2008 Wiley Periodicals, Inc. Syst Eng
Model evolution by run-time parameter adaptation
Authors: Ilenia Epifani, Carlo Ghezzi, Raffaela Mirandola, Giordano Tamburrelli
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

Models can help software engineers to reason about design-time decisions before implementing a system. This paper focuses on models that deal with non-functional properties, such as reliability and performance. To build such models, one must rely on numerical estimates of various parameters provided by domain experts or extracted by other similar systems. Unfortunately, estimates are seldom correct. In addition, in dynamic environments, the value of parameters may change over time. We discuss an approach that addresses these issues by keeping models alive at run time and feeding a Bayesian estimator with data collected from the running system, which produces updated parameters. The updated model provides an increasingly better representation of the system. By analyzing the updated model at run time, it is possible to detect or predict if a desired property is, or will be, violated by the running implementation. Requirement violations may trigger automatic reconfigurations or recovery actions aimed at guaranteeing the desired goals. We illustrate a working framework supporting our methodology and apply it to an example in which a Web service orchestrated composition is modeled through a Discrete Time Markov Chain. Numerical simulations show the effectiveness of the approach.
Mathematical modelling in software reliability
Authors: Omar Shatnawi, Mohammad Taib Shatnawi
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

A mathematical model based on stochastic and statistics theories is useful to describe the software fault-removal phenomena or the software failure-occurrence phenomena and estimate the software reliability quantitatively. A mathematical tool which describes software reliability aspect is a software reliability growth model (SRGM). Discrete time models in software reliability are important and a little effort has been made in this direction. Their importance cannot be underestimated since the number of test cases is more appropriate measure of the fault removal/detection period than the CPU/calendar time used by continuous time model. These models generally provide a better fit than their continuous time counterparts. It is important to note that due to the complexity of software design, it is not expected that any single model can incorporate all factors which are thought to influence software reliability. In this paper, we show how beginning with very simple assumptions, non-homogenous Poisson process (NHPP) type of discrete time SRGMs, are gradually made more realistic with the incorporation of imperfect debugging, involvement of a learning-process in debugging and introduction of new faults. The applicability of the resultant generalized model is demonstrated through several actual software reliability data sets obtained from different software development projects. The proposed generalized model is also checked against different components of the model, including existing one, thus highlighting its applicability.
A Push---Pull Network with Infinite Supply of Work
Authors: Anat Kopzon, Yoni Nazarathy, Gideon Weiss
Venue: Queueing Systems: Theory and Applications (2009)

We consider a two-node multiclass queueing network with two types of jobs moving through two servers in opposite directions, and there is infinite supply of work of both types. We assume exponential processing times and preemptive resume service. We identify a family of policies which keep both servers busy at all times and keep the queues between the servers positive recurrent. We analyze two specific policies in detail, obtaining steady state distributions. We perform extensive calculations of expected queue lengths under these policies. We compare this network with the Kumar---Seidman---Rybko---Stolyar network, in which there are two random streams of arriving jobs rather than infinite supply of work.
A least squares formulation for a class of generalized eigenvalue problems in machine learning
Authors: Liang Sun, Shuiwang Ji, Jieping Ye
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Many machine learning algorithms can be formulated as a generalized eigenvalue problem. One major limitation of such formulation is that the generalized eigenvalue problem is computationally expensive to solve especially for large-scale problems. In this paper, we show that under a mild condition, a class of generalized eigenvalue problems in machine learning can be formulated as a least squares problem. This class of problems include classical techniques such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Linear Discriminant Analysis (LDA), as well as Hypergraph Spectral Learning (HSL). As a result, various regularization techniques can be readily incorporated into the formulation to improve model sparsity and generalization ability. In addition, the least squares formulation leads to efficient and scalable implementations based on the iterative conjugate gradient type algorithms. We report experimental results that confirm the established equivalence relationship. Results also demonstrate the efficiency and effectiveness of the equivalent least squares formulations on large-scale problems.
Interactive spatio-temporal cluster analysis of VAST challenge 2008 datasets
Authors: Gennady Andrienko, Natalia Andrienko
Venue: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration (2009)

We describe a visual analytics method supporting the analysis of two different types of spatio-temporal data, point events and trajectories of moving agents. The method combines clustering with interactive visual displays, in particular, map and space-time cube. We demonstrate the use of the method by applying it to two datasets from the VAST Challenge 2008: evacuation traces (trajectories of people movement) and landings and interdictions of migrant boats (point events).
Strengthening Admissible Coalitions
Authors: Guido Boella, Luigi Sauro, Leendert van der Torre
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

We develop a criterion for coalition formation among goal-directed agents, the indecomposable do-ut-des property. The indecomposable do-ut-des property refines the do-ut-des property (literally give to get) by considering the fact that agents prefer to form coalitions whose components cannot be formed independently. A formal description of this property is provided as well as an analysis of algorithms and their complexity.
Differences in beliefs and attitudes about computer science among students and faculty of the bachelor program
Authors: Jacob Perrenet
Venue: ACM SIGCSE Bulletin (2009)

Becoming a (computer) scientist involves more than learning knowledge and skills. The development of the professional attitude towards the discipline and the professional beliefs can be seen as a process of enculturation. In this study attitudes and beliefs of Computer Science students of the first, second and third Bachelor year, are compared with the beliefs and attitudes of the faculty of the department. The study shows enculturation at some aspects: attitudes and beliefs change into the direction of those of the faculty. At some other aspects, however, change occurs in the opposite direction. For a series of aspects, students halfway the first year already have attitudes and beliefs similar to those of the faculty.
Concept of a Multi-Agent System for Assisting in Real Estate Appraisals
Authors: Tadeusz Lasota, Zbigniew Telec, Bogdan Trawiński, Krzysztof Trawiński
Venue: Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (2009)

The general architecture of a multi-agent system for real estate appraisal (MAREA) is presented in the paper. The appraisal data warehouse is filled with data drawn from source cadastral databases. Data driven appraisal models are created using different machine learning algorithms. Architecture of the MAREA system in the JADE platform was also proposed. Several experiments aimed to assess the usefulness of different machine learning algorithms for the system were conducted using the KEEL tool.
Long-term digital preservation: preserving authenticity and usability of 3-D data
Authors: Julie Doyle, Herna Viktor, Eric Paquet
Venue: International Journal on Digital Libraries (2009)

Long-term digital preservation, the process of maintaining digital objects through time to ensure continued access, has become a crucial issue in recent years. Whilst the amount of digitised information is constantly increasing, so too is the pace of progress in information technology, resulting in obsolescence of the software and hardware required to access and view digital information. Despite many organisations recognising this threat and the resulting need for preservation action, more work is required to effectively address the issue. We present in this article a framework for the long-term digital preservation of 3-D data. This framework is based on two pertinent preservation practices, emulation and metadata which ensure that the authenticity and usability, respectively, of a preserved digital object remain intact through time. An evaluation of our framework is presented which illustrates the viability of our approach in retaining accessibility, authenticity and usability for future end users.
Qualitative modeling and monitoring of the selected ecosystem violated with parasitic dehumidifying and dehydrating
Authors: J. Bila, J. Jura, I. Bukovsky
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

The paper proposes two data mining methods used for modeling and monitoring of the selected ecosystems. The first method consists in the following stages: 1. Formation of monitoring states - the design of a state model - the formation of monitoring super-classes - the design of a rule-based system on the set of monitoring super-classes - the design of an algorithm for the detection of unexpected situations. The second method has these stages: the construction of the qualitative matrix of state dependencies - the construction of the matroid on the set of states - the extraction of the matroid bases - special operations with matroid bases. The methodology is developed with data support of a real ecosystem in the East Bohemia. The frame structure of the measurement centers network and the associated structure of the system database is introduced. The demonstration of the methods is presented in a database segment acquired from one of the measurement center.
Managing data using neighbour replication on a triangular-grid structure
Authors: Mustafa Mat Deris, Jemal H. Abawajy, David Taniar, Ali Mamat
Venue: International Journal of High Performance Computing and Networking (2009)

Data in grid research deals with storage, replication, and management of large data sets in a distributed environment. The all-data-to-all-sites replication schemes, like Read-One Write-All (ROWA) and Tree Grid Structure (TGS), are the popular techniques in grid. However, these techniques have a weakness in data storage capacity and data access times. In this paper, we propose the all-data-to-some-sites scheme called the 'Neighbour Replication on Triangular Grid' (NRTG) technique. The proposed scheme minimises the storage capacity as well as data access time with high update availability. It also tolerates failures such as server and site failures.
Machine listening for context-aware computing
Authors: Alex Waibel
Venue:  (2006)

Machine listening is an area of study which is rapidly increasing in importance. The proliferation of massive sensory corpora, together with the perceptual needs of smart computational devices and smart spaces has lead to this increase. Machine listening provides both a computationally cheap alternative to machine vision, and a source of information that is complementary to visual information; hence, perceptual systems which lack the ability to process auditory information will in general perform less well than those which can process auditory information. Machine listening is also interesting in its own right, as research into computational auditory processing can help to shed light on general principles of perception, and on how our own perceptual systems work. This thesis describes machine listening research designed to solve real-world problems in perceptual and context-aware computing. This thesis makes two claims. First, it claims that machine listening technologies are well-suited to the task of providing context awareness in real-world computational systems, whether these systems are intended to provide operational cues to smart devices or spaces, or to segment, summarize, or select segments of interest in multimedia corpora to make them more useful to human users. Second, it claims that the use of the core principle of perception, redundancy reduction, can guide the design of practical systems to provide context awareness in this way. The validity of these claims is supported by evidence from three application areas: multimedia gisting, acoustic environment recognition, and estimation of user interruptibility for the CHIL Connector service, a smart mobile telephone.
KiWi --- A Platform for Semantic Social Software (Demonstration)
Authors: Sebastian Schaffert, Julia Eder, Szaby Grünwald, Thomas Kurz, Mihai Radulescu
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

Semantic Wikis have demonstrated the power of combining Wikis with Semantic Web technology. The KiWi system goes beyond Semantic Wikis by providing a flexible and adaptable platform for building different kinds of Social Semantic Software, powered by Semantic Web technology. While the KiWi project itself is primarily focussed on the knowledge management domain, this demonstration shows how KiWi aspects like the Wiki Principles and Content Versatility can be used to build completely different kinds of Social Software applications. The first application we show is an "ordinary" Semantic Wiki system preloaded with content from a online news site. The second application called TagIT is a map-based system where locations and routes on a map can be "tagged" by users with textual descriptions, SKOS categories, and multimedia material. Both applicatons are built on top of the same KiWi platform and actually share the same content.
Help in Modelling with Visual Languages
Authors: Kai Herrmann, Ulrich Hoppe, Markus Kuhn
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

In this paper we describe how a constraint checking system can provide advanced help for a visual language modelling process, not only taking into account single elements, but also the context of the element for which help is needed.