ppxscal/embedding_network huggingface dataset:
Front Environment Recognition of Personal Vehicle Using the Image Sensor and Acceleration Sensors for Everyday Computing
Authors: Takahiro Matsui, Takeshi Imanaka, Yasuyuki Kono
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction (2009)

In this research, we propose the method for detecting moving objects in front of the Segway by detecting running state for the Segway. Running state of the personal vehicle Segway is detected with both an image sensor and an acceleration sensor mounted on the Segway. When objects are moving in front of the Segway, the image sensor can capture the motion while the acceleration sensor shows a different result. By analyzing the difference our method successfully recognizes moving objects from environment.
A Management Accounting Model on Revenue Based on Data Integeration under EC Circumstances
Authors: Zu-guang Wu, Guo-hua Zhu, Bin Xia, Hui Liu
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

In traditional accounting,cost is an accumulating process little by little but revenue is realized and recognized on a timeline.Though revenue matches cost,there are different notion about revenue and cost in accounting,which reflects that it is a flaw of traditional accounting theory.Based on the idea of accumulating and assigning cost with the production process and combining the latent customers’ state transformation under Electronic Commerce(EC) circumstances,the author present that revenue realization be a process and not be a point-in-time. Combining the data in EC and using the Markov process,this paper establish a Revenue process model under electronic commerce(EC) circumstances, which integrates the data produced in different period of the process of revenue realization.Finally,this paper illustrate the method of forecasting revenue in management accounting under electronic commerce(EC) circumstances and the feasibility ofadvancing the time of revenue recognition in financial accounting. The use of this model can change the performance management and analysis process and change the Business-to-Business service control method.
Relationship Based Privacy Management for Ubiquitous Society
Authors: Yuan Tian, Biao Song, Eui-Nam Huh
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part I (2009)

The growing concern of users for their personal information has made it critical to implant effective technologies for privacy and data privacy management. Most of the previous studies focus on Role Based Access Control model, and another approach is setting user preference separately rather than considering them together. However, the former one would lead inconvenience for users and the later one may result in conflicts or unconscious data disclosure after users setting their preferences. This paper proposes a privacy system using graph data structure to manage privacy data, limiting disclosure of data, guaranteeing high flexibility and facilitating the management of data. An important advantage of our approach is that it not only supports privacy-aware role based access control, but also permits users to clarify their privacy preferences. A full detailed view of a proper conversion from role-based privacy management to personal privacy preference management is provided.
Analysing recognition errors in unlimited-vocabulary speech recognition
Authors: Teemu Hirsimäki, Mikko Kurimo
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers (2009)

We analyze the recognition errors made by a morph-based continuous speech recognition system, which practically allows an unlimited vocabulary. Examining the role of the acoustic and language models in erroneous regions shows how speaker adaptive training (SAT) and discriminative training with minimum phone frame error (MPFE) criterion decrease errors in different error classes. Analyzing the errors with respect to word frequencies and manually classified error types reveals the most potential areas for improving the system.
A dominance tree and its application in evolutionary multi-objective optimization
Authors: Chuan Shi, Zhenyu Yan, Kevin Lü, Zhongzhi Shi, Bai Wang
Venue: Information Sciences: an International Journal (2009)

Most contemporary multi-objective evolutionary algorithms (MOEAs) store and handle a population with a linear list, and this may impose high computational complexities on the comparisons of solutions and the fitness assignment processes. This paper presents a data structure for storing the whole population and their dominating information in MOEAs. This structure, called a Dominance Tree (DT), is a binary tree that can effectively and efficiently store three-valued relations (namely dominating, dominated or non-dominated) among vector values. This paper further demonstrates DT's potential applications in evolutionary multi-objective optimization with two cases. The first case utilizes the DT to improve NSGA-II as a fitness assignment strategy. The second case demonstrates a DT-based MOEA (called a DTEA), which is designed by leveraging the favorable properties of the DT. The simulation results show that the DT-improved NSGA-II is significantly faster than NSGA-II. Meanwhile, DTEA is much faster than SPEA2, NSGA-II and an improved version of NSGA-II. On the other hand, in regard to converging to the Pareto optimal front and maintaining the diversity of solutions, DT-improved NSGA-II and DTEA are found to be competitive with NSGA-II and SPEA2.
Study on Fault Diagnosis of Gear with Spall Using Ferrography and Vibration Analysis
Authors: Wei Feng, Xiaopeng Xie, Yibo Cao
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

Ferrographic technique and vibration analysis are the two main identification techniques for condition monitoring and fault diagnosis in machinery. Wear mechanisms and changes are investigated for gear with spall fault using ferrographic analysis technique. Furthermore, the features of time and frequency spectrums have been investigated using the time and frequency spectrums of vibration analysis technology in this study. Analysis results show that quantity and size of wear debris and four eigenvalues of the time spectrum gradually become greater in order of the gear with minor spall, the gear with moderate spall and the gear with severe spall. Frequency spectrums of spalling gear appear some sidebands that meshing frequency of output terminal as centre frequency, the rotating frequencies of output terminal as modulating frequency occur. The investigation on wear and vibration characteristics of simulating spall fault gear provides the auxiliary basis to determine fault for the fatiguing spall failure of the actual using gear.
A Parallel Chaos Particle Swarm Optimization
Authors: Yang Dao Ping, Zhang Kai, Fan Lin Bo, Zhao Ming
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03 (2009)

To solve Particle Swarm Optimization (PSO) to plunge into the local extremums, this paper introduces parallel calculation and chaos operator and puts forward a parallel chaos particle swarm optimization. This method divides particle population into subpopulations for asynchronous and parallel chaos optimization. So it improves the search speed and keeps population diversity to avoid premature. The simulation results show this method is better than PSO.
Evaluation Measures for Multi-class Subgroup Discovery
Authors: Tarek Abudawood, Peter Flach
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (2009)

Subgroup discovery aims at finding subsets of a population whose class distribution is significantly different from the overall distribution. It has previously predominantly been investigated in a two-class context. This paper investigates multi-class subgroup discovery methods. We consider six evaluation measures for multi-class subgroups, four of them new, and study their theoretical properties. We extend the two-class subgroup discovery algorithm CN2-SD to incorporate the new evaluation measures and a new weighting scheme inspired by AdaBoost. We demonstrate the usefulness of multi-class subgroup discovery experimentally, using discovered subgroups as features for a decision tree learner. Not only is the number of leaves of the decision tree reduced with a factor between 8 and 16 on average, but significant improvements in accuracy and AUC are achieved with particular evaluation measures and settings. Similar performance improvements can be observed when using naive Bayes.
Superstitious Learning with Rare Strategic Decisions: Theory and Evidence from Corporate Acquisitions
Authors: Maurizio Zollo
Venue: Organization Science (2009)

In this paper, the notion of superstitious learning is applied to the context of rare and complex strategic decisions. I argue that superstitious learning is a particularly relevant problem for these types of decisions not only because causal linkages between actions and outcomes might be poorly inferred, but also, more basically, because their performance outcomes are often very difficult to assess in objective ways. I test these arguments with a sample of U.S. bank mergers and find evidence that managers' perceptions of success in previous acquisitions is negatively related to the actual performance of the focal merger, and that this effect increases, rather than decreases, as managers accumulate experience. Consistent with the theoretical arguments developed, the effect is significantly attenuated as the knowledge is systematically articulated and codified and the stock of experience becomes more heterogeneous.
Bag-of-Words Vector Quantization Based Face Identification
Authors: Di Liu, Dong-mei Sun, Zheng-ding Qiu
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

This paper investigates the possibility that uses Scale-Invariance Feature Transform (SIFT) feature for face identification. However, it is impossible to employ these SIFT keys,i.e. feature vectors, for identification directly, due to the space incompatible of such SIFT keys. To this end, the Bag-of-words (Bow) vector quantization introduced from scene or text classification is conducted for unifying them. And a novel distance, Cauchy-Schwartz Inequality Distance (CSID), is performed for determining which cluster each keypoint of image belongs to after quantization, in order to compose a histogram vector as our feature. To summarize, this approach solves the problem of space incompatible and avoids a high computation that gives rises to a "dimensional curse". The experiment shows a reasonable result using SVM classifier by ORL database.
Memory intensive AND/OR search for combinatorial optimization in graphical models
Authors: Radu Marinescu, Rina Dechter
Venue: Artificial Intelligence (2009)

In this paper we explore the impact of caching during search in the context of the recent framework of AND/OR search in graphical models. Specifically, we extend the depth-first AND/OR Branch-and-Bound tree search algorithm to explore an AND/OR search graph by equipping it with an adaptive caching scheme similar to good and no-good recording. Furthermore, we present best-first search algorithms for traversing the same underlying AND/OR search graph and compare both algorithms empirically. We focus on two common optimization problems in graphical models: finding the Most Probable Explanation (MPE) in belief networks and solving Weighted CSPs (WCSP). In an extensive empirical evaluation we demonstrate conclusively the superiority of the memory intensive AND/OR search algorithms on a variety of benchmarks.
On Weight-Noise-Injection Training
Authors: Kevin Ho, Chi-Sing Leung, John Sum
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

While injecting weight noise during training has been proposed for more than a decade to improve the convergence, generalization and fault tolerance of a neural network, not much theoretical work has been done to its convergence proof and the objective function that it is minimizing. By applying the Gladyshev Theorem, it is shown that the convergence of injecting weight noise during training an RBF network is almost sure. Besides, the corresponding objective function is essentially the mean square errors (MSE). This objective function indicates that injecting weight noise during training an radial basis function (RBF) network is not able to improve fault tolerance. Despite this technique has been effectively applied to multilayer perceptron, further analysis on the expected update equation of training MLP with weight noise injection is presented. The performance difference between these two models by applying weight injection is discussed.
Latent Dirichlet Allocation with topic-in-set knowledge
Authors: David Andrzejewski, Xiaojin Zhu
Venue: Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing (2009)

Latent Dirichlet Allocation is an unsupervised graphical model which can discover latent topics in unlabeled data. We propose a mechanism for adding partial supervision, called topic-in-set knowledge, to latent topic modeling. This type of supervision can be used to encourage the recovery of topics which are more relevant to user modeling goals than the topics which would be recovered otherwise. Preliminary experiments on text datasets are presented to demonstrate the potential effectiveness of this method.
Epistemic formulae, argument structures, and a narrative on identity and deception: a formal representation from the AJIT subproject within AURANGZEB
Authors: Ephraim Nissan
Venue: Annals of Mathematics and Artificial Intelligence (2008)

The modelling of deception is an active field in artificial intelligence. Episodic formulae were introduced, applied and refined in several previous articles, some of them concerned with identity, and some, with tasks of guessing agents' minds. The AURANGZEB project is specifically concerned with combining this kind of representation with argument structure data (in particular, Toulmin's). Two previous articles were concerned with the MURAD subproject, whereas this paper introduces the AJIT model. In MURAD, the kind of deception consisted of having a victim misunderstand a perpetrator's real intentions. In AJIT, instead, a powerful agent is outsmarted by means of exchanged identities.
Clustering XML Documents Using Frequent Subtrees
Authors: Sangeetha Kutty, Tien Tran, Richi Nayak, Yuefeng Li
Venue: Advances in Focused Retrieval: 7th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008, Dagstuhl Castle, Germany, December 15-18, 2008. Revised and Selected Papers (2009)

This paper presents an experimental study conducted over the INEX 2008 Document Mining Challenge corpus using both the structure and the content of XML documents for clustering them. The concise common substructures known as the closed frequent subtrees are generated using the structural information of the XML documents. The closed frequent subtrees are then used to extract the constrained content from the documents. A matrix containing the term distribution of the documents in the dataset is developed using the extracted constrained content. The k-way clustering algorithm is applied to the matrix to obtain the required clusters. In spite of the large number of documents in the INEX 2008 Wikipedia dataset, the proposed frequent subtree-based clustering approach was successful in clustering the documents. This approach significantly reduces the dimensionality of the terms used for clustering without much loss in accuracy.
Efficient datalog abduction through bounded treewidth
Authors: Georg Gottlob, Reinhard Pichler, Fang Wei
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

Abductive diagnosis is an important method for identifying possible causes which explain a given set of observations. Unfortunately, abduction suffers from the fact that most of the algorithmic problems in this area are intractable. We have recently obtained very promising results for a strongly related problem in the database area. Specifically, the PRIMALITY problem becomes efficiently solvable and highly parallelizable if the underlying functional dependencies have bounded treewidth (Gottlob, Pichler, & Wei 2006b). In the current paper, we show that these favorable results can be carried over to logic-based abduction. In fact, we even show a further generalization of these results.
Fast License Plate Localization Using Discrete Wavelet Transform
Authors: Yuh-Rau Wang, Wei-Hung Lin, Shi-Jinn Horng
Venue: Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing (2009)

Some previous works use discrete wavelet transform (DWT) to extract license plate (LP), however, most of them are not capable of dealing with complex environments such as the low-contrast source images and the dynamic-range problems. In this paper, we propose a license plate localization (LPL) algorithm based on DWT. The LP can be extracted from complex environments and different quality of source images by using two frequency subbands. We first use the HL subband to search the features of LP and then verify the features by checking whether a horizontal line around the feature exists in the LH subband or not.The proposed method can extract both front and back LPs of various vehicles. The experiments show that the proposed method can achieve good LPL results with both short run-time and high accurate detection rate.
Unsupervised shilling detection for collaborative filtering
Authors: Bhaskar Mehta
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

Collaborative Filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation. Lies and Propaganda may be spread by malicious users who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious shilling user profiles can be injected into a collaborative recommender system which can significantly affect the robustness of a recommender system. While current detection algorithms are able to use certain characteristics of shilling profiles to detect them, they suffer from low precision, and require a large amount of training data. The aim of this work is to explore simpler unsupervised alternatives which exploit the nature of shilling profiles, and can be easily plugged into collaborative filtering framework to add robustness. Two statistical methods are developed and experimentally shown to provide high accuracy in shilling attack detection.
Modeling, Computation and Optimization: Statistical Science and Interdisciplinary Research
Authors: S. K. Neogy, S. K. Neogy, A. K. Das, R. B. Bapat
Venue: Platinum Jubilee Series (2009)

This volume provides recent developments and a state-of-the-art review in various areas of mathematical modeling, computation and optimization. It contains theory, computation as well as the applications of several mathematical models to problems in statistics, games, optimization and economics for decision making. It focuses on exciting areas like models for wireless networks, models of Nash networks, dynamic models of advertising, application of reliability models in economics, support vector machines, optimization, complementarity modeling and games.
Logic for Reasoning about Components of Persuasive Actions
Authors: Katarzyna Budzynska, Magdalena Kacprzak, Pawel Rembelski
Venue: Proceedings of the 18th International Symposium on Foundations of Intelligent Systems (2009)

The aim of the paper is to propose an extension for a model of logic $\mathcal AG_n$. Thus far, $\mathcal AG_n$ was applied for reasoning about persuasiveness of actions in multi-agent systems, i.e., we examined which arguments, provided by agents, are successful and how big such a success is. Now we enrich our approach in order to study why these arguments are efficient and what attributes cause their success. Therefore, we propose to specify persuasive actions with three parameters: content, goal and means of sending messages. As a result, we can formally express what an agent wants to achieve by executing an action, whether this action can be successful, and if not, recognize the reasons which can cause the failure.
A planning approach for message-oriented semantic web service composition
Authors: Zhen Liu, Anand Ranganathan, Anton Riabov
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

In this paper, we consider the problem of composing a set of web services, where the requirements are specified in terms of the input and output messages of the composite workflow. We propose a semantic model of messages using RDF graphs that encode OWL ABox assertions. We also propose a model of web service operations where the input message requirements and output message characteristics are modeled using RDF graph patterns. We formulate the message-oriented semantic web service composition problem and show how it can be translated into a planning problem. There are, however, significant challenges in scalably doing planning in this domain, especially since DL reasoning may be performed to check if an operation can be given a certain input message. We propose a two-phase planning algorithm that incorporates DLP reasoning and evaluate the performance of this planning algorithm.
Parallel Scalability of Video Decoders
Authors: Cor Meenderinck, Arnaldo Azevedo, Ben Juurlink, Mauricio Alvarez Mesa, Alex Ramirez
Venue: Journal of Signal Processing Systems (2009)

An important question is whether emerging and future applications exhibit sufficient parallelism, in particular thread-level parallelism, to exploit the large numbers of cores future chip multiprocessors (CMPs) are expected to contain. As a case study we investigate the parallelism available in video decoders, an important application domain now and in the future. Specifically, we analyze the parallel scalability of the H.264 decoding process. First we discuss the data structures and dependencies of H.264 and show what types of parallelism it allows to be exploited. We also show that previously proposed parallelization strategies such as slice-level, frame-level, and intra-frame macroblock (MB) level parallelism, are not sufficiently scalable. Based on the observation that inter-frame dependencies have a limited spatial range we propose a new parallelization strategy, called Dynamic 3D-Wave. It allows certain MBs of consecutive frames to be decoded in parallel. Using this new strategy we analyze the limits to the available MB-level parallelism in H.264. Using real movie sequences we find a maximum MB parallelism ranging from 4000 to 7000. We also perform a case study to assess the practical value and possibilities of a highly parallelized H.264 application. The results show that H.264 exhibits sufficient parallelism to efficiently exploit the capabilities of future manycore CMPs.