ppxscal/embedding_network huggingface dataset:
Information Distance from a Question to an Answer
Authors: Ming Li
Venue: Proceedings of the 13th Annual international conference on Computing and Combinatorics (2007)

We know how to measure distance from Beijing to Toronto. However, do you know how to measure the distance between two information carrying entities? For example: two genomes, two music scores, two programs, two articles, two emails, or from a question to an answer? Furthermore, such a distance measure must be application-independent, must be universal in the sense it is provably better than all other distances, and must be applicable.From a simple and accepted assumption in thermodynamics, we have developed such a theory. I will present this theory and will present one of the new applications of this theory: a question answering system.
Equal-area spherical maps for computer graphics
Authors: Tien-Tsin Wong
Venue:  (2007)

Spherical map is the foundation for many applications in computer graphics, such as environment mapping, precomputed radiance transfer, radiosity and image-based rendering. For these applications that involve intensive computation on spherical domain, it is generally desirable to employ equal-area and uniform spherical maps as the underlying parameterization. However, equal-area spherical maps are less exploited in graphics. In this dissertation, we first introduce an equal-area spherical map, HEALPix, which is borrowed from astrophysics. Its associated sample pattern is uniformly distributed over the sphere surface. Then we discuss its application in environment mapping. Although the HEALPix representation is more balanced than the traditional cubemap, it cannot utilize the built-in hardware operations like cubemap, and the mipmapping construction is more complicated. Therefore, its rendering speed is not comparable to that of the cubemap. This motivates us to invent a new six-face spherical map, called isocube. Unlike cubemap, isocube is an equal-area mapping, i.e., each texel spans the same area and is equally important. Due to the six-face representation, isocube can fit nicely into the cubemap hardware and hence can fully exploit the hardware operations tailored for cubemap. In addition, its mapping computation only involves a small overhead. Therefore the look-up speed for isocube is very fast. Next we discuss another application of HEALPix map, the dynamic environment sequence sampling. Previous work only considers the static case where the environment map is approximated by finite directional light sources. In the dynamic case, the individual regeneration of samples for each frame may introduce abrupt, changes in the rendering animation. To handle this temporal inconsistency problem, we propose spherical q2-tree based on IIEALPix map. The adaptiveness of the quadtree can suppress the abrupt changes between consecutive frames, and hence a rather smooth rendering can be produced. This method, however, generates sample pattern independently for each environment frame, and therefore it may still cause unexpected, lumps in some situations. To fully utilize the temporal coherence in the sequence, we present a global sampling approach which treats the dynamic environment sequence as a all volume and performs sampling in the volume. The volumetric sampling adapts the sample number according to each frame and restricts the temporal/spatial changes within small subdivided volumes, and hence ensures a smooth sampling sequence. Within the framework, we present a volumetric importance metric and develop a binary-quad tree to perform stratification. Experimental results show that our volumetric sampling method can generate light samples with a better temporal consistency.
Compartment Lines Forming Emergent Alternative Configurations of Vehicles on Weaving Sections
Authors: R. Nishi, H. Miki, A. Tomoeda, K. Nishinari
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

Drawing Compartment lines on weaving sections is planed for easing the traffic congestion through forming emergent alternative configurations of vehicles before weaving. The degree of the alternative configurations was quantified in weaving sections partitioned into identical cells. A new stochastic Cellular Automaton (CA) model was ruled for the dynamics along the line with the interactions between vehicles neighboring each other. The longitudinal formation of the zigzag pattern along the line was obtained for the first time through simulations and the four cluster approximation. Furthermore, both results fit to each other.
A generalization of Fibonacci and Lucas matrices
Authors: Predrag Stanimirović, Jovana Nikolov, Ivan Stanimirović
Venue: Discrete Applied Mathematics (2008)

We define the matrix U"n^(^a^,^b^,^s^) of type s, whose elements are defined by the general second-order non-degenerated sequence and introduce the notion of the generalized Fibonacci matrix F"n^(^a^,^b^,^s^), whose nonzero elements are generalized Fibonacci numbers. We observe two regular cases of these matrices (s=0 and s=1). Generalized Fibonacci matrices in certain cases give the usual Fibonacci matrix and the Lucas matrix. Inverse of the matrix U"n^(^a^,^b^,^s^) is derived. In partial case we get the inverse of the generalized Fibonacci matrix F"n^(^a^,^b^,^0^) and later known results from [Gwang-Yeon Lee, Jin-Soo Kim, Sang-Gu Lee, Factorizations and eigenvalues of Fibonaci and symmetric Fibonaci matrices, Fibonacci Quart. 40 (2002) 203-211; P. Sta@?nica@?, Cholesky factorizations of matrices associated with r-order recurrent sequences, Electron. J. Combin. Number Theory 5 (2) (2005) #A16] and [Z. Zhang, Y. Zhang, The Lucas matrix and some combinatorial identities, Indian J. Pure Appl. Math. (in press)]. Correlations between the matrices U"n^(^a^,^b^,^s^), F"n^(^a^,^b^,^s^) and the generalized Pascal matrices are considered. In the case a=0,b=1 we get known result for Fibonacci matrices [Gwang-Yeon Lee, Jin-Soo Kim, Seong-Hoon Cho, Some combinatorial identities via Fibonacci numbers, Discrete Appl. Math. 130 (2003) 527-534]. Analogous result for Lucas matrices, originated in [Z. Zhang, Y. Zhang, The Lucas matrix and some combinatorial identities, Indian J. Pure Appl. Math. (in press)], can be derived in the partial case a=2,b=1. Some combinatorial identities involving generalized Fibonacci numbers are derived.
Type-2 Fuzzy Decision Trees
Authors: Łukasz Bartczuk, Danuta Rutkowska
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

This paper presents type-2 fuzzy decision trees (T2FDTs) that employ type-2 fuzzy sets as values of attributes. A modified fuzzy double clustering algorithm is proposed as a method for generating type-2 fuzzy sets. This method allows to create T2FDTs that are easy to interpret and understand. To illustrate performace of the proposed T2FDTs and in order to compare them with results obtained for type-1 fuzzy decision trees (T1FDTs), two benchmark data sets, available on the internet, have been used.
Sensing Attacks in Computers Networks with Hidden Markov Models
Authors: Davide Ariu, Giorgio Giacinto, Roberto Perdisci
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

In this work, we propose an Intrusion Detection model for computer newtorks based on Hidden Markov Models. While stateful techniques are widely used to detect intrusion at the operating system level, by tracing the sequences of system calls, this issue has been rarely researched for the analysis of network traffic. The proposed model aims at detecting intrusions by analysing the sequences of commands that flow between hosts in a network for a particular service (e.g., an ftp session). First the system must be trained in order to learn the typical sequences of commands related to innocuous connections. Then, intrusion detection is performed by indentifying anomalous sequences. To harden the proposed system, we propose some techniques to combine HMM. Reported results attained on the traffic acquired from a European ISP shows the effectiveness of the proposed approach.
A robot in every classroom: robots and functional programming across the curriculum
Authors: David Wakeling
Venue: Proceedings of the 2008 international workshop on Functional and declarative programming in education (2008)

It has been suggested that the fall in the number of young people wishing to study computer science might be arrested by repackaging the current material into new modules which set it in a context that appeals and motivates. In this paper, we try this idea out by repackaging some introductory material into a "Robotics" module using a functional programming language. The advantages of our module are that its problem-based learning bridges the gap between the classroom and the laboratory, and that it allows everyone to concentrate on "computer science" rather than "machine" and "language" details. The disadvantages of our module are that its skills are not obviously those expected elsewhere, and that it has high setup and support costs.
Light-weight online performance monitoring and tuning with embedded gossip
Authors: Patrick G. Bridges
Venue:  (2007)

Understanding the performance of large-scale, long-running applications is difficult. Traditional trace-based performance monitoring can provide detailed performance data, but the high cost of correctly merging of traced data from all processes and interpreting the resulting traces makes this technique only useful post-mortem. Statistical methods have lower overhead on monitoring, but correlating raw performance data between processes is even more difficult than in trace-based systems, and is again not appropriate for runtime execution. This dissertation describes a new online performance monitoring approach called Embedded Gossip (EG). EG uses an application's natural communication to propagate and gather performance data at each process. By piggybacking performance information on each outgoing message and extracting the information from each incoming message, each application process is eventually aware of performance problems on nodes with which they directly or indirectly communicate. This approach is useful for large-scale, long-running, dynamic applications, where static configuration may not be optimal and traditional detailed performance monitoring is expensive. To demonstrate the viability of Embedded Gossip for global system monitoring and adaptation, this dissertation describes a general architecture for implementing EG-based monitoring systems, including a new distributed agreement protocol, 3-wait, for EG-based global system adaptations. It then presents experimental results for two monitoring systems and one global adaptation system implemented using this architecture, including global critical path profiling, global progress counting, and global scheduling of load balancing actions. The results show that Embedded Gossip can be used to implement a wide range of runtime monitoring systems. The resulting monitoring systems also have low runtime overhead, and the information gathered using these systems can effectively drive global system adaptation decisions online.
Researches on Multicriterion Optimization Approach to Image Reconstruction
Authors: Dai Weihui, Liang Shuyi, Zhou Xuan
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

In most cases, ill-posedness exists inherently in the solution process of inverse image problems (such as X-ray CT image reconstruction from projections, ECG and EEG inverse mapping), and affects seriously the quality, stability and accuracy of the reconstructed images with the susceptibility to the errors in measurement data and numerical solution of forward problems. This paper gave a study on the ill-posedness of image reconstruction from projections and discuss the characteristics of the ghost function which affects imaging quality and accuracy in the reconstruction process. Multicriterion regularization approach, a new approach to solve the ill-posed inverse problems, was presented with its theory basis and the experiment results. The solution stability and accuracy were studied using singular value decomposition (SVD) theory, and main factors affecting the reconstruction quality are discussed.
Parallelizing an edge detection algorithm for image recognition to classify paddy and weeds leaf on Sun Fire Cluster system
Authors: Mohd Azam Osman, Muqhtar Yassin Mohamad, Rosni Abdullah
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

Paddy is grown widely in Malaysia. The problem of paddy growing is that weeds tend to grow with the paddy. Image recognition and classification application can be developed based on image taken to distinguish between the paddy and the weeds so that pesticide spraying and forecasting for the yield can be done. However the challenge now is that how efficient is the classification when the image is huge. In this paper, a discussion on the parallelisation of image recognition algorithm will be presented. The methodology of parallelising the Canny Edge Detection algorithm will be explained. Issues discussed includes parallelization strategy for the edge detection, followed by the implementation and the results.
Implementing And Managing a CAD System Based on Vendor-Supplied Tools
Authors: Donald Lacy, Lawrence Drenan
Venue: IEEE Design Test (1985)

The VLSI CAD system built and supported by Western Digital Corporation since 1981 is composed primarily of vendor-suppliedtools; a number of in-house tools, mostly translators, have been developed to tie the system together. The main criteria forselecting tools are ease of use and performance; tool selection is supported by a committee that includes users as well asproviders. New tools are introduced through a close Partnership between the CAD Systems Department and the users. To supportthe system, a skilled staff of CAD software applications engineers is on hand, consulting with users, augmenting the documentation,interfacing with the vendors, installing new releases, preparing user interfaces and standard files, managing the hardwaresystem, and developing the soft-ware required to integrate the vendor tools. The disadvantages to using a vendor-orientedsystem are that (1) tools are too general purpose and may be poorly adapted to the company's environment and (2) new and possiblymore beneficial tools and techniques may not be available on the commercial market.
An Enhanced Histogram Matching Approach Using the Retinal Filter's Compression Function for Illumination Normalization in Face Recognition
Authors: Ahmed Salah-Eldin, Khaled Nagaty, Taha Elarif
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

Although many face recognition techniques have been proposed, recent evaluations in FRVT2006 conclude that relaxing the illumination condition has a dramatic effect on their recognition performance. Among many illumination normalization approaches, histogram matching (HM) is considered one of the most common image-processing-based approaches to cope with illumination. This paper introduces a new illumination normalization approach based on enhancing the image resulting from the HM using the gamma correction and the Retinal filter's compression function; we call it GAMMA-HM-COMP approach. Rather than many other approaches, the proposed one proves its flexibility to different face recognition methods and the suitability for real-life systems in which perfect aligning of the face is not a simple task. The efficiency of the proposed approach is empirically demonstrated using both a PCA-based (Eigenface) and a frequency-based (Spectroface) face recognition methods on both aligned and non-aligned versions of Yale B database. It leads to average increasing in recognition rates ranges from 4 ~ 7 % over HM alone.
An Artificial Immune System for Evolving Amino Acid Clusters Tailored to Protein Function Prediction
Authors: A. Secker, M. N. Davies, A. A. Freitas, J. Timmis, E. Clark, D. R. Flower
Venue: Proceedings of the 7th international conference on Artificial Immune Systems (2008)

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem, where the protein functions to be predicted correspond to classes that are arranged in a hierarchical structure, in the form of a class tree. The main contribution of this paper is to propose a new Artificial Immune System that creates a new representation for proteins, in order to maximize the predictive accuracy of a hierarchical classification algorithm applied to the corresponding protein function prediction problem.
Filtering Documents with a Hybrid Neural Network Model
Authors: Guido Bologna, Mathieu Boretti, Paul Albuquerque
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

This work presents an application example of text document filtering. We compare the DIMLP neural hybrid model to several machine learning algorithms. The clear advantage of this neural hybrid system is its transparency. In fact, the classification strategy of DIMLPs is almost completely encoded into the extracted rules. During cross-validation trials and in the majority of the situations, DIMLPs demonstrated to be at least as accurate as support vector machines, which is one of the most accurate classifiers of the text categorization domain. In the future, in order to further increase DIMLP accuracy, we believe that common sense knowledge could be easily inserted and refined with the use of symbolic rules.
The Determinants of the Effectiveness of Online Discussion Board Systems in eLearning: A Case Study
Authors: Habin Lee, Jong Woo Kim, Ray Hackney
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

This paper aims to reveal the determinants of the effectiveness of online discussion board systems (ODBSs) in eLearning environments to foster the interactions among the learners and/or instructors. A case in which an ODBS failed to foster the interactions among learners / instructors for knowledge sharing is introduced and hypotheses to explain the failure are developed based on thorough literature review in technology acceptance model (TAM) and knowledge hoarding. The hypotheses are tested via statistical analysis on the data collected from a questionnaire survey against the students who actually involved in the case study. The result shows that the low perceived usefulness of the ODBS by the students played major role in the failure of the system. Also it is hinted that network externalities as an intrinsic motivator is more effective than extrinsic motivators to increase the students' activities on the ODBS. Finally the paper provides the designers of eLearning systems with advice for successful operation of ODBS in eLearning.
Data Mining with Neural Networks for Wheat Yield Prediction
Authors: Georg Ruß, Rudolf Kruse, Martin Schneider, Peter Wagner
Venue: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008)

Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays' technology to agriculture. Due to the use of sensors and GPS technology, in today's agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. In this paper we deal with neural networks and their usage in mining these data. Our particular focus is whether neural networks can be used for predicting wheat yield from cheaply-available in-season data. Once this prediction is possible, the industrial application is quite straightforward: use data mining with neural networks for, e.g., optimizing fertilizer usage, in economic or environmental terms.
Geometric Deformable Model Driven by CoCRFs: Application to Optical Coherence Tomography
Authors: Gabriel Tsechpenakis, Brandon Lujan, Oscar Martinez, Giovanni Gregori, Philip J. Rosenfeld
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

We present a geometric deformable model driven by dynamically updated probability fields. The shape is defined with the signed distance function, and the internal (smoothness) energy consists of a C 1 continuity constraint, a shape prior, and a term that forces the zero-level of the shape distance function towards a connected form. The image probability fields are estimated by our collaborative Conditional Random Field (CoCRF), which is updated during the evolution in an active learning manner: it infers class posteriors in pixels or regions with feature ambiguities by assessing the joint appearance of neighboring sites and using the classification confidence. We apply our method to Optical Coherence Tomography fundus images for the segmentation of geographic atrophies in dry age-related macular degeneration of the human eye.
Digital Signal Processing Techniques for Gene Finding in Eukaryotes
Authors: Mahmood Akhtar, Eliathamby Ambikairajah, Julien Epps
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

In this paper, we investigate the effects of window shape and length on a DFT-based method for gene and exon prediction in eukaryotes. We then propose a new gene finding method which combines the selected time-domain and frequency-domain methods, by employing the most effective DNA symbolic-to-numeric representation examined to date in conjunction with suitable window shape and length parameters and a signal boosting technique. It is shown herein that the new method outperforms major existing approaches. By comparison with the existing methods, the proposed method reveals relative improvements of 15.1% to 55.9% over different methods in terms of prediction accuracy of exonic nucleotides at a 5% false positive rate using the GENSCAN test set.
Clustering of Leaf-Labelled Trees on Free Leafset
Authors: Jakub Koperwas, Krzysztof Walczak
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

This paper focuses on the clustering of leaf-labelled trees on free leafset. It extends the previously proposed algorithms, designed for trees on the same leafset. The term z-equality is proposed and all the necessary consensus and distance notions are redefined with respect to z-equality. The clustering algorithms that focus on maximizing the quality measure for two representative trees are described, together with the measure itself. Finally, the promising results of experiments on tandem duplication trees are presented.
Robust Facial Attribute-Specific Subspace-Based Principal Component Analysis for Face Recognition
Authors: Chun-ming Xu, Hai-bo Jiang, Cai-geng Zhou, Jian-jiang Yu
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Facial attribute-specific subspace-based PCA (FASS-based PCA) considers the information of class labels, and the discriminant power can be improved. However, it doesn't consider the outliers which are common in realistic training sets. To address this problem, we propose robust facial attribute-specific subspace-based PCA (Robust FASS-based PCA) algorithm in this paper, which gives a new weighted method for the evaluation of the total squared error of each class. The detailed algorithm for robust FASS-based PCA is also given. The results of experiments conducted on Yale databases show the effectiveness of the new feature extraction algorithm.