ppxscal/embedding_network huggingface dataset:
A perceptually relevant no-reference blockiness metric based on local image characteristics
Authors: Hantao Liu, Ingrid Heynderickx
Venue: EURASIP Journal on Advances in Signal Processing (2009)

A novel no-reference blockiness metric that provides a quantitative measure of blocking annoyance in block-based DCT coding is presented. The metric incorporates properties of the human visual system (HVS) to improve its reliability, while the additional cost introduced by the HVS is minimized to ensure its use for real-time processing. This is mainly achieved by calculating the local pixel-based distortion of the artifact itself, combined with its local visibility by means of a simplified model of visual masking. The overall computation efficiency and metric accuracy is further improved by including a grid detector to identify the exact location of blocking artifacts in a given image. The metric calculated only at the detected blocking artifacts is averaged over all blocking artifacts in the image to yield an overall blockiness score. The performance of this metric is compared to existing alternatives in literature and shows to be highly consistent with subjective data at a reduced computational load. As such, the proposed blockiness metric is promising in terms of both computational efficiency and practical reliability for real-life applications.
Efficient AUC Maximization with Regularized Least-Squares
Authors: Tapio Pahikkala, Antti Airola, Hanna Suominen, Jorma Boberg, Tapio Salakoski
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

Area under the receiver operating characteristics curve (AUC) is a popular measure for evaluating the quality of binary classifiers, and intuitively, machine learning algorithms that maximize an approximation of AUC should have a good AUC performance when classifying new examples. However, designing such algorithms in the framework of kernel methods has proven to be challenging. In this paper, we address AUC maximization with the regularized least-squares (RLS) algorithm also known as the least-squares support vector machine. First, we introduce RLS-type binary classifier that maximizes an approximation of AUC and has a closed-form solution. Second, we show that this AUC-RLS algorithm is computationally as efficient as the standard RLS algorithm that maximizes an approximation of the accuracy. Third, we compare the performance of these two algorithms in the task of assigning topic labels for newswire articles in terms of AUC. Our algorithm outperforms the standard RLS in every classification experiment conducted. The performance gains are most substantial when the distribution of the class labels is unbalanced. In conclusion, modifying the RLS algorithm to maximize the approximation of AUC does not increase the computational complexity, and this alteration enhances the quality of the classifier.
Locality kernels for sequential data and their applications to parse ranking
Authors: Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski
Venue: Applied Intelligence (2009)

We propose a framework for constructing kernels that take advantage of local correlations in sequential data. The kernels designed using the proposed framework measure parse similarities locally, within a small window constructed around each matching feature. Furthermore, we propose to incorporate positional information inside the window and consider different ways to do this. We applied the kernels together with regularized least-squares (RLS) algorithm to the task of dependency parse ranking using the dataset containing parses obtained from a manually annotated biomedical corpus of 1100 sentences. Our experiments show that RLS with kernels incorporating positional information perform better than RLS with the baseline kernel functions. This performance gain is statistically significant.
Detection of unique temporal segments by information theoretic meta-clustering
Authors: Shin Ando, Einoshin Suzuki
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

The central challenge in temporal data analysis is to obtain knowledge about its underlying dynamics. In this paper, we address the observation of noisy, stochastic processes and attempt to detect temporal segments that are related to inconsistencies and irregularities in its dynamics. Many conventional anomaly detection approaches detect anomalies based on the distance between patterns, and often provide only limited intuition about the generative process of the anomalies. Meanwhile, model-based approaches have difficulty in identifying a small, clustered set of anomalies. We propose Information-theoretic Meta-clustering (ITMC), a formalization of model-based clustering principled by the theory of lossy data compression. ITMC identifies a 'unique' cluster whose distribution diverges significantly from the entire dataset. Furthermore, ITMC employs a regularization term derived from the preference for high compression rate, which is critical to the precision of detection. For empirical evaluation, we apply ITMC to two temporal anomaly detection tasks. Datasets are taken from generative processes involving heterogeneous and inconsistent dynamics. A comparison to baseline methods shows that the proposed algorithm detects segments from irregular states with significantly high precision and recall.
A Grid of Remote Laboratories for Teaching Electronics
Authors: Andrea Bagnasco, Anna Marina Scapolla
Venue: Proceeding of the 2005 conference on Towards the Learning Grid: Advances in Human Learning Services (2005)

Testing theories through practice is an important approach to teaching, especially for scientific and technical curricula. Lack of resources and logistic problems often make practice impossible. During the last few years, several attempts to find an alternative to in-laboratory experiments have been made by many researchers. In this paper, we present our approach to the challenge of remote laboratories. We outline the model we have created and the prototype we have implemented and validated. Finally, we propose a grid-oriented view of the remote laboratory that could help to support experimental activity in e-learning efforts in a scattered community of users.
A Computational Model of Argumentation in Everyday Conversation: A Problem-Centred Approach
Authors: Jean-Louis Dessalles
Venue: Proceeding of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008 (2008)

Human beings share a common competence for generating relevant arguments. We therefore hypothesize the existence of a cognitive procedure that enables them to determine the content of their arguments. The originality of the present approach is to analyse spontaneous argument generation as a process in which arguments either signal problems or aim at solving previously acknowledged problems.
Learning with a non-exhaustive training dataset: a case study: detection of bacteria cultures using optical-scattering technology
Authors: M. Murat Dundar, E. Daniel Hirleman, Arun K. Bhunia, J. Paul Robinson, Bartek Rajwa
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

For a training dataset with a nonexhaustive list of classes, i.e. some classes are not yet known and hence are not represented, the resulting learning problem is ill-defined. In this case a sample from a missing class is incorrectly classified to one of the existing classes. For some applications the cost of misclassifying a sample could be negligible. However, the significance of this problem can better be acknowledged when the potentially undesirable consequences of incorrectly classifying a food pathogen as a nonpathogen are considered. Our research is directed towards the real-time detection of food pathogens using optical-scattering technology. Bacterial colonies consisting of the progeny of a single parent cell scatter light at 635 nm to produce unique forward-scatter signatures. These spectral signatures contain descriptive characteristics of bacterial colonies, which can be used to identify bacteria cultures in real time. One bottleneck that remains to be addressed is the nonexhaustive nature of the training library. It is very difficult if not impractical to collect samples from all possible bacteria colonies and construct a digital library with an exhaustive set of scatter signatures. This study deals with the real-time detection of samples from a missing class and the associated problem of learning with a nonexhaustive training dataset. Our proposed method assumes a common prior for the set of all classes, known and missing. The parameters of the prior are estimated from the samples of the known classes. This prior is then used to generate a large number of samples to simulate the space of missing classes. Finally a Bayesian maximum likelihood classifier is implemented using samples from real as well as simulated classes. Experiments performed with samples collected for 28 bacteria subclasses favor the proposed approach over the state of the art.
Bug localization by constructing reduced traces
Authors: S. G. Groshev
Venue: Programming and Computing Software (2009)

A method, based on the existing UniTESK test, which finds a bug in a system under test, is proposed to construct the minimal test that finds the same bug. This test can be used to localize this bug in the source code of the system. Two strategies for constructing such a test are considered, a comparative analysis of their advantages and disadvantages is performed, and the optimal strategy is proposed. A mathematical justification of the proposed method is given. An algorithm implementing this method is described, and its correctness is proved. An implementation of the proposed method for the CTESK testing tool is described.
Optimization as a function of the phase-lag order of nonlinear explicit two-step P-stable method for linear periodic IVPs
Authors: S. Stavroyiannis, T. E. Simos
Venue: Applied Numerical Mathematics (2009)

We elaborate on a nonlinear explicit two-step P-stable method of fourth algebraic order and varying phase-lag order for solving one-dimensional second-order linear periodic initial value problems (IVPs) of ordinary differential equations. Using special vector arithmetic with respect to an analytic function, the method can be extended to be vector applicable for multidimensional problems. Numerical results to illustrate the efficiency of the method are presented.
Security challenges in seamless mobility: how to "handover" the keys?
Authors: Katrin Hoeper, Lidong Chen, Antonio Izquierdo, Nada Golmie
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

In this paper, we discuss key management challenges for seamless handovers across heterogeneous wireless networks. We focus on utilizing existing keying material from previous access authentications to expedite network entry. For a seamless handover, keys must be available at the target network at the time of the handover. Currently, industry is still exploring possible ways to handle keys for mobility. This paper identifies the challenges of secure derivation and timely distribution of such keys. We discuss solutions for intra- and inter-technology handovers within the same network and between networks with roaming agreements. The presented solutions include different types of handover key hierarchies and key distribution protocols. In addition, we analyze the tradeoffs between security and performance in the discussed solutions.
Analysis of transaction logs for insights into use of life oral histories
Authors: Michael G. Christel, Bryan S. Maher, Huan Li
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

A digital video library of over 900 hours of video and 18000 stories from The HistoryMakers was used by 214 students, faculty, librarians, and life-long learners interacting with a system providing multiple search and viewing capabilities over a trial period of several months. User demographics and actions were logged, providing metrics on how the system was used. This poster overviews a few highlights from these transaction logs of the Informedia digital video library system for life oral histories.
Fundamentals of Digital Logic with Verilog Design, 2 edition
Authors: Stephen Brown, Zvonko Vranesic
Venue:  (2007)

Fundamentals of Digital Logic With Verilog Design teaches the basic design techniques for logic circuits. It emphasizes the synthesis of circuits and explains how circuits are implemented in real chips. Fundamental concepts are illustrated by using small examples.Use of CAD software is well integrated into the book. A CD-ROM that contains Altera's Quartus CAD software comes free with every copy of the text. The CAD software provides automatic mapping of a design written in Verilog into Field Programmable Gate Arrays (FPGAs) and Complex Programmable Logic Devices (CPLDs). Students will be able to try, firsthand, the book's Verilog examples (over 140) and homework problems. Engineers use Quartus CAD for designing, simulating, testing and implementing logic circuits. The version included with this text supports all major features of the commercial product and comes with a compiler for the IEEE standard Verilog language. Students will be able to: enter a design into the CAD systemcompile the design into a selected devicesimulate the functionality and timing of the resulting circuitimplement the designs in actual devices (using the school's laboratory facilities) Verilog is a complex language, so it is introduced gradually in the book. Each Verilog feature is presented as it becomes pertinent for the circuits being discussed. To teach the student to use the Quartus CAD, the book includes three tutorials. Table of contentsChapter 1 Design Concepts Chapter 2 Introduction to Logic Circuits Chapter 3 Implementation Technology Chapter 4 Optimized Implementation of Logic Functions Chapter 5 Number Representation and Arithmetic Circuits Chapter 6 Combinational-Circuit Building Blocks Chapter 7 Flip-Flop, Registers, Counters, and a Simple Processor Chapter 8 Synchronous Sequential Circuits Chapter 9 Asynchronous Sequential Circuits Chapter 10 Digital System Design Chapter 11 Testing of Logic Circuits Chapter 12 Computer Aided Design Tools Appendix A Verilog Reference Appendix B Tutorial 1-Using Quartus II CAD Software Appendix C Tutorial 2-Implementing Circuits in Altera Devices Appendix D Tutorial 3-Physical Implementation in a FPGA Appendix E Commercial Devices Answers Index
Implementation of the GRP scheme for computing radially symmetric compressible fluid flows
Authors: Jiequan Li, Tiegang Liu, Zhongfeng Sun
Venue: Journal of Computational Physics (2009)

The study of radially symmetric compressible fluid flows is interesting both from the theoretical and numerical points of view. Spherical explosion and implosion in air, water and other media are well-known problems in application. Typical difficulties lie in the treatment of singularity in the geometrical source and the imposition of boundary conditions at the symmetric center, in addition to the resolution of classical discontinuities (shocks and contact discontinuities). In the present paper we present the implementation of direct generalized Riemann problem (GRP) scheme to resolve this issue. The scheme is obtained directly by the time integration of the fluid flows. Our new contribution is to show rigorously that the singularity is removable and derive the updating formulae for mass and energy at the center. Together with the vanishing of the momentum, we obtain new numerical boundary conditions at the center, which are then incorporated into the GRP scheme. The main ingredient is the passage from the Cartesian coordinates to the radially symmetric coordinates.
A Knowledge-driven Data Warehouse Model for Analysis Evolution
Authors: Cécile Favre, Fadila Bentayeb, Omar Boussaid
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

A data warehouse is built by collecting data from external sources. Several changes on contents and structures can usually happen on these sources. Therefore, these changes have to be reflected in the data warehouse using schema updating or versioning. However a data warehouse has also to evolve according to new users' analysis needs. In this case, the evolution is rather driven by knowledge than by data. In this paper, we propose a Rule-based Data Warehouse (R-DW) model, in which rules enable the integration of users' knowledge in the data warehouse. The R-DW model is composed of two parts: one fixed part that contains a fact table related to its first level dimensions, and a second evolving part, defined by means of rules. These rules are used to dynamically create dimension hierarchies, allowing the analysis contexts evolution, according to an automatic and concurrent way. Our proposal provides flexibility to data warehouse's evolution by increasing users' interaction with the decision support system.
Exploratory Analysis of Co-Change Graphs for Code Refactoring
Authors: Hassan Khosravi, Recep Colak
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

Version Control Systems (VCS) have always played an essential role for developing reliable software. Recently, many new ways of utilizing the information hidden in VCS have been discovered. Clustering layouts of software systems using VCS is one of them. It reveals groups of related artifacts of the software system, which can be visualized for easier exploration. In this paper we use an Expectation Maximization (EM) based probabilistic clustering algorithm and visualize the clustered modules using a compound node layout algorithm. Our experiments with repositories of two medium size software tools give promising results indicating improvements over many previous approaches.
Patch Learning for Incremental Classifier Design
Authors: Rudy Sicard, Thierry Artières, Eric Petit
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 present a learning algorithm for nominal data. It builds a classifier by adding iteratively a simple patch function that modifies the current classifier. Its main advantage lies in the possibility to learn every patch function parameters optimally from the Bayesian point of view hence avoiding overtraining.
A Research Agenda for the Science and Design of Assessment for Deep Learning
Authors: Michael J. Strait
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

In this paper, I argue for use-inspired basic research on assessment for deep learning of core abilities in interdisciplinary fields of study essential to meet the scientific, technological, educational, and workforce challenges of the 21st century. I emphasize the critical role of technology as an object of research and development, and as a means of organizing, conducting, and communicating research and applications.
Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences
Authors: Frank Schweitzer
Venue: Springer Series In Synergetics (2007)

"This book lays out a vision for a coherent framework for understanding complex systems'' (from the foreword by J. Doyne Farmer). By developing the genuine idea of Brownian agents, the author combines concepts from informatics, such as multiagent systems, with approaches of statistical many-particle physics. This way, an efficient method for computer simulations of complex systems is developed which is also accessible to analytical investigations and quantitative predictions. The book demonstrates that Brownian agent models can be successfully applied in many different contexts, ranging from physicochemical pattern formation, to active motion and swarming in biological systems, to self-assembling of networks, evolutionary optimization, urban growth, economic agglomeration and even social systems.
Textile Flaw Classification by Wavelet Reconstruction and BP Neural Network
Authors: Yean Yin, Ke Zhang, Wenbing Lu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

This paper proposes an approach of textile flaw classification based on histogram and BP neural network. The common two types of textile flaws, namely oil stain and hole, can be extracted and classified. The method can detect flaws for two types of texture fabrics: statistical textures with isotropic patterns and structural textures with oriented patterns. For the extraction of flaw features, histograms of "hole" and "oil stain" are computed as the input of BP neural network. Some samples are selected for testing, the results show that the method can effectively detect defects and classify the types of defects with high recognition correct rate.
Collaboration Patterns for Adaptive Software Engineering Processes
Authors: Andrea Freßmann, Thomas Sauer, Ralph Bergmann
Venue: Proceeding of the 2005 conference on Self-Organization and Autonomic Informatics (I) (2005)

In the software engineering domain, workflow systems are an established technology for team coordination and cooperation. However, software development processes tend to be unknown, unrepeatable, and knowledge intensive, with changes occurring frequently. Thus, strategies to distribute best practices and lessons learned on how to collaborate effectively within this ever-changing environment are required, especially when project participants may work autonomously. In this paper, collaboration patterns are introduced to describe collaboration strategies explicitly. The presented approach is based on adaptive workflows, allowing to tailor the collaboration patterns as changes occur. An example of software project planning and enactment shows how collaboration patterns can be put into practice.