ppxscal/embedding_network huggingface dataset:
Learning language semantics from ambiguous supervision
Authors: Rohit J. Kate, Raymond J. Mooney
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

This paper presents a method for learning a semantic parser from ambiguous supervision. Training data consists of natural language sentences annotated with multiple potential meaning representations, only one of which is correct. Such ambiguous supervision models the type of supervision that can be more naturally available to language-learning systems. Given such weak supervision, our approach produces a semantic parser that maps sentences into meaning representations. An existing semantic parsing learning system that can only learn from unambiguous supervision is augmented to handle ambiguous supervision. Experimental results show that the resulting system is able to cope up with ambiguities and learn accurate semantic parsers.
Question utility: a novel static ranking of question search
Authors: Young-In Song, Chin-Yew Lin, Yunbo Cao, Hae-Chang Rim
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

In this paper, we propose a notion of 'question utility' for studying usefulness of questions and show how question utility can be integrated into question search as static ranking. To measure question utility, we examine three methods: (a) a method of employing the language model to estimate the probability that a question is generated from a question collection and then using the probability as question utility; (b) a method of using the LexRank algorithm to evaluate centrality of questions and then using the centrality as question utility; and (c) the combination of (a) and (b). To use question utility in question search, we employ a log linear model for combining relevance score in question search and utility score regarding question utility. Our experimental results with the questions about 'travel' from Yahoo! Answers show that question utility can be effective in boosting up ranks of generally useful questions.
Predictive Feedback Scheduling for Resource-Constrained Networks with Flexible Workload
Authors: Zuxin Li, Wenjun Hu, Wanliang Wang, Bicheng Lei
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

Resource-constrained networks usually run in an unpredictable open environment due to the workload variations. In this paper, a predictive feedback scheduler based on least squares support vector machines (LSSVM) is proposed in order to guarantee the stability of the system. It periodically monitors the network resources, predicates the next period of available bandwidth, and adopts interpolated method to calculate the next sampling period from predicative value. Consequently, the system’s bandwidth is dynamically allocated by this feedback scheduling mechanism. Two different strategies, which are fixed bandwidth allocation and predictive feedback scheduling strategy based on LSSVM, are compared respectively. The results of simulation indicate that the proposed strategy can guarantee the stability of the system with flexible workload, and prove that the predictive feedback scheduling is an effective tradeoff method between quality of control and quality of service.
A Tsallis-Entropy Image Thresholding Method Based on Two-Dimensional Histogram Obique Segmentation
Authors: Xiaoguang Tian, Xiaorong Hou
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01 (2009)

Image Segmentation is one of important tasks in conventional or document image processing. Tsallis-entropy based image thresholding method has been considered one of the most efficient ways for image segmentation. At present one useful way in entropy-thresholding segmentation is based 2-D vertical segmentation, but obvious limitations exist in this approach. In this paper, a method 2-D obique segmentation is applied. Meanwhile motivate by Kullback-Leibler (KL) distance which measures the information discrepance between two different sources, a new optimization based on minimizing imitational Kullback-Leibler distance (MIKL) criterion function is proposed. It is a new technique that image threshold value by 2-D Tsallis-entropy obique segmentation based on MIKL. Some typical results show the superiorty of the proposed technique over references, achieving better effect wherever in the document or conventional image segmentation.
Integrating Learning Styles and Personality Traits into an Affective Model to Support Learner's Learning
Authors: Makis Leontidis, Constantin Halatsis
Venue: Proceedings of the 8th International Conference on Advances in Web Based Learning (2009)

The aim of this paper is to present a model in order to integrate the learning style and the personality traits of a learner into an enhanced Affective Style which is stored in the learner's model. This model which can deal with the cognitive abilities as well as the affective preferences of the learner is called Learner Affective Model (LAM). The LAM is used to retain learner's knowledge and activities during his interaction with a Web-based learning environment and also to provide him with the appropriate pedagogical guidance. The proposed model makes use of an ontological approach in combination with the Bayesian Network model and contributes to the efficient management of the LAM in an Affective Module.
Policy implications of market segmentation as a determinant of fixed-mobile service substitution: What it means for carriers and policy makers
Authors: Amit M. Schejter, Alexander Serenko, Ofir Turel, Mehdi Zahaf
Venue: Telematics and Informatics (2010)

The determination whether mobile and fixed telecommunications services operate in the same market not only affects business decision of service providers, but also has wide implications on public policy decisions pertaining to the means by which they should be regulated in the United States. This study conducts a two-stage cluster analysis implementing the American Customer Satisfaction Model on two datasets of 3251 and 5060 data points pertaining to mobile and fixed-line phone services, respectively, collected by the National Quality Research Center at the University of Michigan in order to determine whether the way consumers perceive these services can indicate as to their levels of substitutability. It concludes that different types of customers with different needs consume these products and offers policy makers some insight on how to further the penetration of mobile services.
MixIn3D: 3D Mixed Reality with ToF-Camera
Authors: Reinhard Koch, Ingo Schiller, Bogumil Bartczak, Falko Kellner, Kevin Köser
Venue: Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging (2009)

This work discusses an approach to seamlessly integrate real and virtual scene content by on-the-fly 3D scene modeling and dynamic scene interaction. The key element is a ToF-depth camera, accompanied by color cameras, mounted on a pan-tilt head. The system allows to scan the environment for easy 3D reconstruction, and will track and model dynamically moving objects like human actors in 3D. This allows to compute mutual occlusions between real and virtual objects and correct light and shadow generation with mutual light interaction. No dedicated studio is required, as virtually any room can be turned into a virtual studio with this approach. Since the complete process operates in 3D and produces consistent color and depth sequences, this system can be used for full 3D TV production.
Hierarchic Genetic Algorithm for Designated Multi-nodes Routing
Authors: Xiaoli Xiao, Min Huang
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

This paper proposed a hierarchic genetic algorithm used for solving the shortest path problem via designated multi-nodes routing of wireless networks. The hierarchy of solutions was plotted according to designated nodes, Variable-length chromosomes were employed in our genetic algorithm and the stochastic universal sampling was adopted as the selective operation. Crossover and mutation used the topological information from network to operate. A repair function was used to eliminate the unfeasible solution produced by genetic operations. The algorithm separated the solutions of feasible regions of objective function to reduce the size of populations and chromosome length, and each sub-population can run independently and simultaneously. Using this method can decompose complex routing issues into many sub-issues. By solving sub-issues the shortest path via designated multi-nodes can be obtained. The authors made simulation experiments on the algorithm by establishing a random network topology. The results indicated that the algorithm can effectively get the shortest path via designated multi-nodes , exhibits a much higher rate of convergence and is relatively less subject to network scale and the problem’s complexity than traditional methods.
SEO: Search Engine Optimization Bible, 2nd edition
Authors: Jerri Ledford
Venue:  (2009)

Detailed, practical guide to increasing your Web traffic through better search results Wonder how some companies pop up high in search engine rankings? It's all about search appeal. Master the strategies, techniques, and shortcuts in this detailed guide and you can improve your Web site's search rankings and drive the targeted traffic you want to your virtual door. Learn new ways to add social media to the SEO mix, make your site mobile Web-friendly, write SEO tags for maximum exposure, and more. Search Engine Optimization (SEO) is hot; the online advertising market is expected to grow at 34% CAGR between 2005 and 2010, and nine out of ten companies are estimated to be implementing SEO strategies Find out how to get listed in the major search engines, directories, and indexes, and learn strategies for planning and implementing a successful SEO campaign Take advantage of the case studies of readers who implemented the SEO techniques outlined in the first edition of this book and significantly improved search rankings Discover how to target and reach the customers you really want; optimize your site specifically for Google, MSN, or Yahoo!; demystify the role of links and linking in search; implement social media and mobile search optimization; and analyze your SEO efforts to see what works If you want to make SEO work for you, the new edition of this practical book is what you need to succeed.
A New Strategic Partnerships Method for Virtual Enterprise Based on PSO
Authors: Neng Wang
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

The strategic partnerships and optimization problem is an important area of virtual enterprise. The model of strategic partnerships is analyzed in this paper. In a virtual enterprise, the whole task can be accomplished by the cooperation among those candidate partners. The optimal objective is to minimize the total cost and completing time. To improve the searching performance for particle swarm optimization (PSO), and satisfy the optimal target, a discrete PSO (DPSO) algorithm is presented and applied to solve the strategic partnerships problem of virtual enterprise. In DPSO, each particle represents a candidate solution, and its component takes an integer number between 1 and the number of alternative enterprises of the certain sub-task. A series of experiments are performed and the simulation results are compared with the genetic algorithm (GA) to show the efficiency of the proposed DPSO algorithm. Moreover, the results obtained through the DPSO can be confirmed by the method of exhaustion.
Empirical study on the performance stability of named entity recognition model across domains
Authors: Hong Lei Guo, Li Zhang, Zhong Su
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

When a machine learning-based named entity recognition system is employed in a new domain, its performance usually degrades. In this paper, we provide an empirical study on the impact of training data size and domain information on the performance stability of named entity recognition models. We present an informative sample selection method for building high quality and stable named entity recognition models across domains. Experimental results show that the performance of the named entity recognition model is enhanced significantly after being trained with these informative samples.
Hiding sensitive knowledge without side effects
Authors: Aris Gkoulalas-Divanis, Vassilios S. Verykios
Venue: Knowledge and Information Systems (2009)

Sensitive knowledge hiding in large transactional databases is one of the major goals of privacy preserving data mining. However, it is only recently that researchers were able to identify exact solutions for the hiding of knowledge, depicted in the form of sensitive frequent itemsets and their related association rules. Exact solutions allow for the hiding of vulnerable knowledge without any critical compromises, such as the hiding of nonsensitive patterns or the accidental uncovering of infrequent itemsets, amongst the frequent ones, in the sanitized outcome. In this paper, we highlight the process of border revision, which plays a significant role towards the identification of exact hiding solutions, and we provide efficient algorithms for the computation of the revised borders. Furthermore, we review two algorithms that identify exact hiding solutions, and we extend the functionality of one of them to effectively identify exact solutions for a wider range of problems (than its original counterpart). Following that, we introduce a novel framework for decomposition and parallel solving of hiding problems, which are handled by each of these approaches. This framework improves to a substantial degree the size of the problems that both algorithms can handle and significantly decreases their runtime. Through experimentation, we demonstrate the effectiveness of these approaches toward providing high quality knowledge hiding solutions.
Building Secure Block Ciphers on Generic Attacks Assumptions
Authors: Jacques Patarin, Yannick Seurin
Venue: Selected Areas in Cryptography: 15th International Workshop, SAC 2008, Sackville, New Brunswick, Canada, August 14-15, Revised Selected Papers (2009)

Up to now, the design of block ciphers has been mainly driven by heuristic arguments, and little theory is known to constitute a good guideline for the development of their architecture. Trying to remedy this situation, we introduce a new type of design for symmetric cryptographic primitives with high self-similarity. Our design strategy enables to give a reductionist security proof for the primitive based on plausible assumptions regarding the complexity of the best distinguishing attacks on random Feistel schemes or other ideal constructions. Under these assumptions, the cryptographic primitives we obtain are perfectly secure against any adversary with computational resources less than a given bound. By opposition, other provably secure symmetric primitives, as for example C [3] and KFC [4], designed using information-theoretic results, are only proved to resist a limited (though significant) range of attacks. Our construction strategy leads to a large expanded key size, though still usable in practice (around 1 MB).
Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-supervised Extension
Authors: Yu Zhang, Dit-Yan Yeung
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (2009)

Linear discriminant analysis (LDA) is a commonly used method for dimensionality reduction. Despite its successes, it has limitations under some situations, including the small sample size problem, the homoscedasticity assumption that different classes have the same Gaussian distribution, and its inability to produce probabilistic output and handle missing data. In this paper, we propose a semi-supervised and heteroscedastic extension of probabilistic LDA, called S2HPLDA, which aims at overcoming all these limitations under a common principled framework. Moreover, we apply automatic relevance determination to determine the required dimensionality of the low-dimensional space for dimensionality reduction. We empirically compare our method with several related probabilistic subspace methods on some face and object databases. Very promising results are obtained from the experiments showing the effectiveness of our proposed method.
Threshold Determining Method for Feature Selection
Authors: Yanling Li, Li Song
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

Feature selection is a key step in text categorization, its results has direct influence on the classification accuracy. Evaluation function is usually adopted in feature selection methodto calculate the value of feature words,and the feature words which assessed value is higher than setted threshold are maintained as the final feature subset.So the threshold is the important factors of feature selection. However, the threshold is very difficult to determine. In theory, there is no good solution. In practice, people often use their experience to set a initial value, then debug threshold repeatedly according to the results of the classification. In such case, debugging scope is often too great to be easy to determine the threshold. Aimming at the difficulties of threshold determining, this paper mainly studied threshold determining methods for feature selection.First, based on the analysis of several common feature selection methods£¬the key questions ofthreshold determining are defined ,and the idea of threshold determining is put forward.Then,in accordance with the idea, four methods are designed for threshold detemining based on the characteristics of the different feature selection methods. Experimental results show that the proposed methods are effective in improving classification performance. After analyzing the results, this paper gets expressly some useful conclusions .
Approximate minimum spanning tree clustering in high-dimensional space
Authors: Chih Lai, Taras Rafa, Dwight E. Nelson
Venue: Intelligent Data Analysis (2009)

Minimum spanning tree (MST) clustering sequentially inserts the nearest points in the R$^{d}$ space into a list which is then divided into clusters by using desired criteria. This insertion order, however, can be relaxed provided approximately nearby points in a condensed area are adjacently inserted into a list before distant points in other areas. Based on this observation, we propose an approximate clustering method in which a new Approximate MST (AMST) is repeatedly built in the maximum (d+1) iterations from two sources: a new Hilbert curve created from carefully shifted N data points, and a previous AMST which holds cumulative vicinity information derived from earlier iterations. Although the final AMST may not completely match to a true MST built from an $O(N^{2})$ algorithm, most mismatches occur locally within individual data groups which are unimportant for clustering. Our experiments on synthetic datasets and animal motion vectors extracted from surveillance videos show that high-quality clusters can be efficiently obtained from this approximation method.
Spatial Topology Relationships Concrete Domain and Consistency Determination
Authors: Ping Guo, Rui-xia Zhang
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01 (2009)

At the present time, the research of qualitative spatial relationships primary base on first-order predicate logic, but its shortcoming becomes obvious. Description logic is a formal knowledge representation language, and it has strong expression and reasoning ability. This paper band the description logic together with the qualitative spatial topology relationship representation and reasoning. Also, it gives the description of qualitative spatial topology relationships concrete domain, the topology relationship aggregation, and an algorithm of consistency determination.
Point-based policy iteration
Authors: Shihao Ji, Ronald Parr, Hui Li, Xuejun Liao, Lawrence Carin
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

We describe a point-based policy iteration (PBPI) algorithm for infinite-horizon POMDPs. PBPI replaces the exact policy improvement step of Hansen's policy iteration with point-based value iteration (PBVI). Despite being an approximate algorithm, PBPI is monotonic: At each iteration before convergence, PBPI produces a policy for which the values increase for at least one of a finite set of initial belief states, and decrease for none of these states. In contrast, PBVI cannot guarantee monotonic improvement of the value function or the policy. In practice PBPI generally needs a lower density of point coverage in the simplex and tends to produce superior policies with less computation. Experiments on several benchmark problems (up to 12,545 states) demonstrate the scalability and robustness of the PBPI algorithm.
N-Dimensions, Parallel Realities, And Their Relations To Human Perception And Development
Authors: Majid M. Naini, Joan F. Naini
Venue: Journal of Integrated Design Process Science (2009)

This paper delineates a new conceptualization of the integration of technology, psychology, medicine, and science, with an emphasis on quantum physics. The focus is on developing a new paradigm to comprehend the ultimate reality and its relation to the human journey in time, space, and beyond. As we learn more through scientific and technological advancements, we recognize that the human being has been ignoring his higher self. An increased understanding of the human brain, body, mind, and spirit is leading to the recognition of the extraordinary, yet often concealed, abilities we all possess. In addition, our increased understanding of the existing realities in parallel dimensions utilizing the superstring theory, is leading to a broader perception of the whole reality and its effect on the human development and condition. The relationship between electromagnetic frequencies and energies and human physiology and psychology requires further study to produce a more definitive understanding of the human makeup. As we increase our understanding and measuring of electromagnetic frequencies and energy in human beings, we will be better able to manipulate this energy. Through this integration of the sciences we will finally develop a more precise map for human composition, development, health, and recovery from diseases.