ppxscal/embedding_network huggingface dataset:
Letters: Direct simplification for kernel regression machines
Authors: Wenwu He, Zhizhong Wang
Venue: Neurocomputing (2008)

Kernel machines have been widely used in learning. However, standard algorithms are often time consuming. To this end, we propose a new method, direct simplification (DS) for imposing the sparsity of kernel regression machines. Different to the existing sparse methods, DS performs approximation and optimization in a unified framework by incrementally finding a set of basis functions that minimizes the primal risk function directly. The main advantage of our method lies in its ability to form very good approximations for kernel regression machines with a clear control on the computation complexity as well as the training time. Experiments on two real time series and two benchmarks assess the feasibility of our method and show that DS can obtain better performance with fewer bases compared with two-step-type sparse method.
Integrating Folksonomies with the Semantic Web
Authors: Lucia Specia, Enrico Motta
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.
A hybrid algorithm for the single-machine total tardiness problem
Authors: T. C. E. Cheng, A. A. Lazarev, E. R. Gafarov
Venue: Computers and Operations Research (2009)

We propose a hybrid algorithm based on the ant colony optimization (ACO) meta-heuristic, in conjunction with four well-known elimination rules, to tackle the NP-hard single-machine scheduling problem to minimize the total job tardiness. The hybrid algorithm has the same running time as that of ACO. We conducted extensive computational experiments to test the performance of the hybrid algorithm and ACO. The computational results show that the hybrid algorithm can produce optimal or near-optimal solutions quickly, and its performance compares favourably with that of ACO for handling standard instances of the problem.
Categorization, analysis, and visualization of computer-mediated communication and electronic markets
Authors: Hsinchun Chen
Venue:  (2008)

Computer mediated communication (CMC) and electronic markets have seen tremendous growth due to the fast propagation of the internet. In spite of the numerous benefits of electronic communication, it is not without its pitfalls. Two characteristics of computer mediated communication have proven to be particularly problematic: online anonymity and the enormity of data present in cyber communities. This dissertation follows the design science research paradigm in MIS, by addressing issues pertaining to the design and development of an important IT artifact capable of alleviating the two aforementioned CMC concerns. We present 8 essays related to the creation of CMC systems that can provide improved text analysis capabilities by incorporating a richer set of textual information types. Using Systemic Functional Linguistic Theory (SFLT) as a kernel theory, emphasis is placed on developing techniques for analyzing textual and ideational information. A rich set of features are used to represent textual (e.g., style, genres, social cues etc.) and ideational (topics, sentiments, affects, etc.) information. The research revolves around a core set of algorithms utilized for feature selection, categorization, analysis, and visualization of CMC text. The dissertation is arranged in three parts. The first two parts attempt to develop a set of features and techniques that can effectively represent textual and ideational information. In Chapters 2-5, we leverage information types related to the textual meta-function of SFLT for enhanced identity and institutional trust. Experiments are conducted on various CMC modes prevalent in organizational settings, including email, instant messaging, forums, program code, and websites. Chapters 6-8 consider two important information types associated with the ideational meta-function of SFLT: opinions and emotions. We assess the ability to gauge consumer sentiments and affects using machine learning techniques on various CMC modes, including product review and social discussion forums. The third part relates to the design, development, and evaluation of a visualization system that can analyze the presence of the aforementioned information types in text-based CMC archives (Chapter 9). We propose a design framework for CMC text analysis systems that is grounded in SFLT. The CyberGate system is developed as an instantiation of the design framework.
An Algorithm for Binary Contour Objects Representation and Recognition
Authors: Dariusz Frejlichowski
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

Representation of an object in image for machine learning applications (recognition, retrieval, identification, etc.) has to be based on a previously chosen feature. Binary shape is a very popular and commendable one. It has many advantages and can be successfully used in many applications, especially in engineering. To achieve better characteristics, various shape transformations are used. Obviously, they should be robust to as many shape deformations as it is possible. In this paper results of exhaustive exploration of a new method are presented. This method is based on transformation from Cartesian to polar coordinates, but it overcomes few problems, that were not solved yet. Above all, the proposed transform is more robust to occlusion and noise, two the most challenging problems.
Freeform surface inference from sketches via neural networks
Authors: Usman Khan, Abdelaziz Terchi
Venue: Neurocomputing (2008)

A novel approach of constructing a freeform surface from 2D planar sketches is proposed. A multilayer perceptron (MLP) neural network was employed to infer 3D freeform surfaces from 2D freehand curves. Planar boundary strokes of a surface patch produced the training set. The neural network (ANN) output mapped 2D points of the sketch curves onto 3D control points of the surface boundary. Internal points were interpolated to create the final surface. Experimentation determined the optimal parameters and ANN architecture. The methodology was applied to synthetic and realistic data. The results demonstrate successful 3D surface inference from planar sketches.
Indiscernibility Relation for Continuous Attributes: Application in Image Recognition
Authors: Krzysztof Cyran, Urszula Stanczyk
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

The paper presents an application of rough sets in a problem defined for the continuous feature space used by hybrid, high speed, pattern recognition system. The feature extraction part of this system is built as a holographic ring-wedge detector based on binary grating. Such feature extractor can be optimized and we apply for this purpose automatic knowledge acquisition and processing. Features from optimized extractor are then classified with the use of probabilistic neural network classifier. The methodology, proposed by one of the authors in earlier works, has been further enhanced here by application of modified indiscernibility relation. Modified version of this relation makes possible natural application of discrete type rough knowledge representation to problems defined in continuous space. We present an application of modified indiscernibility relation in the domain of image recognition.
Tensor correlation filter based class-dependence feature analysis for face recognition
Authors: Yan Yan, Yu-Jin Zhang
Venue: Neurocomputing (2008)

Recently, class-dependence feature analysis (CFA), which is based on the design of correlation filters in the frequency domain, has been developed for robust face recognition. Traditional CFA designs correlation filters by using two-dimensional (2D) Fourier transforms of the images. In this paper, we propose a tensor correlation filter based CFA (TCF-CFA) method to generalize traditional CFA by encoding the image data as tensors. Experimental results on four benchmark face databases show the effectiveness and robustness of TCF-CFA for face recognition.
A Realistic Coronary Artery Phantom for Particle Image Velocimetry: Featuring Injection-Molded Inclusions and Multiple Layers
Authors: J. Brunette, R. Mongrain, J. C. Tardif
Venue: Journal of Visualization (2004)

Myocardial infarction results from the rupture of an atherosclerotic plaque, which occurs in response to both mechanical stress and inflammatory processes. In order experimentally observe flow into atherosclerotic coronary artery morphologies, a novel technique for molding realistic compliant phantom featuring injection-molded inclusions and multiple layers has been developed. This transparent phantom allows for particle image velocimetry (PIV) flow analysis and can supply experimental data to validate computational fluid dynamics algorithms and hypothesis.
A Note on Refinement Operators for IE-Based ILP Systems
Authors: Alireza Tamaddoni-Nezhad, Stephen Muggleton
Venue: Proceedings of the 18th international conference on Inductive Logic Programming (2008)

ILP systems which use some form of Inverse Entailment (IE) are based on clause refinement through a hypotheses space bounded by a most specific clause. In this paper we give a new analysis of refinement operators in this setting. In particular, Progol's refinement operator is revisited and discussed. It is known that Progol's refinement operator is incomplete with respect to the general subsumption order. We introduce a subsumption order relative to a most specific (bottom) clause. This subsumption order, unlike previously suggested orders, characterises Progol's refinement space. We study the properties of this subsumption order and show that ideal refinement operators exist for this order. It is shown that efficient operators can be implemented for least generalisation and greatest specialisation in the subsumption order relative to a bottom clause. We also study less restricted subsumption orders relative to a bottom clause and show how Progol's incompleteness can be addressed.
An Efficient Association Rule Mining Algorithm for Classification
Authors: A. Zemirline, L. Lecornu, B. Solaiman, A. Ech-Cherif
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

In this paper, we propose a new Association Rule Mining algorithm for Classification (ARMC). Our algorithm extracts the set of rules, specific to each class, using a fuzzy approach to select the items and does not require the user to provide thresholds. ARMC is experimentaly evaluated and compared to state of the art classification algorithms, namely CBA, PART and RIPPER. Results of experiments on standard UCI benchmarks show that our algorithm outperforms the above mentionned approaches in terms of mean accuracy.
Design and Implementation of a WSN-Based Intelligent Light Control System
Authors: Meng-Shiuan Pan, Lun-Wu Yeh, Yen-Ann Chen, Yu-Hsuan Lin, Yu-Chee Tseng
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

Recently, wireless sensor networks (WSNs) have been widely discussed in many applications. In this paper, we propose a WSN-based intelligent light control system for indoor environments. Wireless sensors are responsible for measuring current illuminations. Two kinds of lighting devices, namely whole lighting and local lighting devices, are used to provide background and concentrated illuminations, respectively. Users may have various illumination requirements according to their activities. An illumination requirement is as the combination of background and concentrated illumination demands and users' locations. We propose a decision algorithm to determine the proper illuminations of devices to satisfy users. Then a closed-loop device control algorithm is applied to adjust the llumination levels of lighting devices. The prototyping results verify that our ideas are practical and feasible.
How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation
Authors: Michael Zur Muehlen, Jan Recker
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

The Business Process Modeling Notation (BPMN) is an increasingly important industry standard for the graphical representation of business processes. BPMN offers a wide range of modeling constructs, significantly more than other popular languages. However, not all of these constructs are equally important in practice as business analysts frequently use arbitrary subsets of BPMN. In this paper we investigate what these subsets are, and how they differ between academic, consulting, and general use of the language. We analyzed 120 BPMN diagrams using mathematical and statistical techniques. Our findings indicate that BPMN is used in groups of several, well-defined construct clusters, but less than 20% of its vocabulary is regularly used and some constructs did not occur in any of the models we analyzed. While the average model contains just 9 different BPMN constructs, models of this complexity have typically just 4-5 constructs in common, which means that only a small agreed subset of BPMN has emerged. Our findings have implications for the entire ecosystems of analysts and modelers in that they provide guidance on how to reduce language complexity, which should increase the ease and speed of process modeling.
An Extended R-Tree Indexing Method Using Selective Prefetching in Main Memory
Authors: Hong-Koo Kang, Joung-Joon Kim, Dong-Oh Kim, Ki-Joon Han
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

Recently, researches have been performed on a general method that can improve the cache performance of the R-Tree in the main memory to reduce the size of an entry so that a node can store more entries. However, this method generally requires additional processes to reduce information of entries. In addition, the cache miss always occurs on moving between a parent node and a child node. To solve these problems, this paper proposes the SPR-Tree (Selective Prefetching R-Tree), which is an extended R-Tree indexing method using selective prefetching according to node size in the main memory. The SPR-Tree can produce wider nodes to optimize prefetching without additional modifications on the R-Tree. Moreover, the SPR-Tree can reduce the cache miss that can occur in the R-Tree. In our simulation, the search, insert, and delete performance of the SPR-Tree improved up to 40%, 10%, 30% respectively, compared with the R-Tree.
Materialized community ground models for large-scale earthquake simulation
Authors: Steven W. Schlosser, Michael P. Ryan, Ricardo Taborda, Julio López, David R. O'Hallaron, Jacobo Bielak
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

Large-scale earthquake simulation requires source datasets which describe the highly heterogeneous physical characteristics of the earth in the region under simulation. Physical characteristic datasets are the first stage in a simulation pipeline which includes mesh generation, partitioning, solving, and visualization. In practice, the data is produced in an ad-hoc fashion for each set of experiments, which has several significant shortcomings including lower performance, decreased repeatability and comparability, and a longer time to science, an increasingly important metric. As a solution to these problems, we propose a new approach for providing scientific data to ground motion simulations, in which ground model datasets are fully materialized into octress stored on disk, which can be more efficiently queried (by up to two orders of magnitude) than the underlying community velocity model programs. While octrees have long been used to store spatial datasets, they have not yet been used at the scale we propose. We further propose that these datasets can be provided as a service, either over the Internet or, more likely, in a datacenter or supercomputing center in which the simulations take place. Since constructing these octrees is itself a challenge, we present three data-parallel techniques for efficiently building them, which can significantly decrease the build time from days or weeks to hours using commodity clusters. This approach typifies a broader shift toward science as a service techniques in which scientific computation and storage services become more tightly intertwined.
An Artificial Immune System Approach for B-Spline Surface Approximation Problem
Authors: Erkan Ülker, Veysi İşler
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

In surface fitting problems, the selection of knots in order to get an optimized surface for a shape design is well-known. For large data, this problem needs to be dealt with optimization algorithms avoiding possible local optima and at the same time getting to the desired solution in an iterative fashion. Many computational intelligence optimization techniques like evolutionary optimization algorithms, artificial neural networks and fuzzy logic have already been successfully applied to the problem. This paper presents an application of another computational intelligence technique known as "Artificial Immune Systems (AIS)" to the surface fitting problem based on B-Splines. Our method can determine appropriate number and locations of knots automatically and simultaneously. Numerical examples are given to show the effectiveness of our method. Additionally, a comparison between the proposed method and genetic algorithm is presented.
Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering
Authors: Jonathan Gemmell, Andriy Shepitsen, Bamshad Mobasher, Robin Burke
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

The popularity of collaborative tagging, otherwise known as "folksonomies", emanate from the flexibility they afford users in navigating large information spaces for resources, tags, or other users, unencumbered by a pre-defined navigational or conceptual hierarchy. Despite its advantages, social tagging also increases user overhead in search and navigation: users are free to apply any tag they wish to a resource, often resulting in a large number of tags that are redundant, ambiguous, or idiosyncratic. Data mining techniques such as clustering provide a means to overcome this problem by learning aggregate user models, and thus reducing noise. In this paper we propose a method to personalize search and navigation based on unsupervised hierarchical agglomerative tag clustering. Given a user profile, represented as a vector of tags, the learned tag clusters provide the nexus between the user and those resources that correspond more closely to the user's intent. We validate this assertion through extensive evaluation of the proposed algorithm using data from a real collaborative tagging Web site.
A General Approach for Scenario Integration
Authors: Hongzhi Liang, Zinovy Diskin, Juergen Dingel, Ernesto Posse
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

An approach to integrating UML Sequence Diagrams is presented. It rests on a well-established theory, is generalizable to a large class of requirements engineering models, and supports many different kinds of scenario integration operations. An implementation of the approach as an Eclipse extension is described. Lessons learned from the implementation and during first, preliminary experiments to study the practical aspects of the approach, are discussed.
Single machine scheduling models with deterioration and learning: handling precedence constraints via priority generation
Authors: V. S. Gordon, C. N. Potts, V. A. Strusevich, J. D. Whitehead
Venue: Journal of Scheduling (2008)

We consider various single machine scheduling problems in which the processing time of a job depends either on its position in a processing sequence or on its start time. We focus on problems of minimizing the makespan or the sum of (weighted) completion times of the jobs. In many situations we show that the objective function is priority-generating, and therefore the corresponding scheduling problem under series-parallel precedence constraints is polynomially solvable. In other situations we provide counter-examples that show that the objective function is not priority-generating.
Traffic Flow Forecasting Model of Typical Multi-Intersection for Urban Trunk Road Based on Dissimilation Particle Swarm Optimization
Authors: Jianyu Zhao, Hui Gao, Lei Jia
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Based on the analysis of the standard Particle Swarm Optimization and the characteristic of typical multi-intersection for urban trunk road, a traffic flow forecasting model using dynamic recursion neural network is presented. The feature of this network is that the output of the hidden layer connects to the input of itself through the delay and storage of the context layer. The method of self-connection makes it sensitive to the historical data and improves the ability to process dynamic information. The initial inputs of the context unit and all the weights of the model are optimized by Dissimilation Particle Swarm Optimization. The algorithm mutates the local optimum based on the judging of local or global optimal value, and helps it jump out local convergence, so as to solve the defects which the found value are local optimal value and fall into the problem of prematurity of traditional Particle Swarm Optimization. An example demonstrates that this algorithm is simple, efficient, and fast.
Pose Estimation for Evaluating Standing Long Jumps via Dynamic Bayesian Networks
Authors: Hui-Huang Hsu, Yao-Bao Yen, Chu-Song Chen, Chun-Ta Ho
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

A system is developed for analyzing poses in a standing long jump automatically. In the system, silhouette of the jumper is segmented from the background first. A thinning algorithm is then used to find a rough skeleton from the silhouette. Some image processing techniques are applied to make the resulted skeleton smoother and simpler. Key points are extracted from the skeleton. Finally, the dynamic Bayesian network (DBN) is used to determine the corresponding pose from the key points. The experimental result shows that pose estimation accuracy is quite good. According to the standing long jump standards, incorrect movements at different stages of the jump can thus be identified.