ppxscal/embedding_network huggingface dataset:
Goal Algebra for IT Alignment
Authors: Shuichiro Yamamoto
Venue: Proceeding of the 2006 conference on Knowledge-Based Software Engineering: Proceedings of the Seventh Joint Conference on Knowledge-Based Software Engineering (2006)

The goal graph is used to decompose goals in Goal Oriented Requirements Engineering. It is not clear how to validate global optimality of goal graphs. In this paper, the goal algebra is proposed to formalize goal decomposition and provide graph interpretation for the goal algebra. The proposed method can reduce redundant goals and then ensure the global optimality of goal graphs using the goal dependency.
Ontology-based query processing for understanding intentions of indirect speech acts in natural-language question answering
Authors: Hideki Mima, Susumu Ota, Koji Nagatsuna
Venue: International Journal of Computer Applications in Technology (2009)

In an attempt to develop a robust method for achieving natural-language question answering, we propose a method for ontology-based query processing to infer intentions from indirect speech acts that do not express the real intentions explicitly. In this method, the real intentions of the indirect speech act are classified into one of five intention classes such as 'refusal' and 'reversal'. Furthermore, we construct an ontology for intention transition, which is composed of concepts for events and their relations attributed with the above intention classes. In addition, we construct an ontology of objects for the targets of events &ndash; e.g. 'files' and 'figures', which also contains special relations such as 'exclusive'. Given the two above ontologies, an algorithm is proposed to infer users' actual intentions. This proposed method was tested with human subjects and a prototype system. The system successfully inferred the intentions for approximately 80&#37; of the subjects' indirect speech acts.
Distributed Intelligent Learning Environment for Screening Mammography
Authors: Paul Taylor, Joao Campos, Rob Procter, Mark Hartswood, Louise Wilkinson, Elaine Anderson, Lesley Smart
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

We are developing a computer based training system to support breast cancer screening. The prototype we are developing is a distributed Intelligent Learning Environment in which the work of collecting and annotating interesting cases will be shared and the experience of using the images will be pooled. It is only by creating a network of students and mentors across different clinical sites that we can hope to obtain the breadth and variety of experience required to build robust models of the screening context. Using a pragmatic approach, our design allows for exploratory and experiential learning. Such a design is more likely to succeed in a screening environment because the system will fit the needs of film readers without being prescriptive about how and what they should learn
Supporting agent-oriented designs with models of macroscopic system behavior
Authors: Jan Sudeikat, Wolfgang Renz
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

The purposeful development of MAS, particularly when addressing complex, decentralized application architectures, demands the ability to anticipate the effects of agent coaction. Sophisticated design and modeling tools support the development of individual agent models and their arrangement in organizational structures. However, only limited support is available to pre-estimate the qualitative, macroscopic system properties that rise from agent (inter-)action. Here, we propose a systemic modeling level that allows to describe the qualitative macroscopic dynamics of MAS by modeling the impact of exhibiting agent behaviors on parts of the agent population as well as environment elements. We discuss the systematic derivation of these models from established design models/notations and outline how these models can be used to anticipate the qualitative dynamics of MAS as well as to validate MAS implementations.
Optimal Interdiction of Unreactive Markovian Evaders
Authors: Alexander Gutfraind, Aric Hagberg, Feng Pan
Venue: Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2009)

The interdiction problem arises in a variety of areas including military logistics, infectious disease control, and counter-terrorism. In the typical formulation of network interdiction, the task of the interdictor is to find a set of edges in a weighted network such that the removal of those edges would maximally increase the cost to an evader of traveling on a path through the network.Our work is motivated by cases in which the evader has incomplete information about the network or lacks planning time or computational power, e.g. when authorities set up roadblocks to catch bank robbers, the criminals do not know all the roadblock locations or the best path to use for their escape.We introduce a model of network interdiction in which the motion of one or more evaders is described by Markov processes and the evaders are assumed not to react to interdiction decisions. The interdiction objective is to find an edge set of size B, that maximizes the probability of capturing the evaders.We prove that similar to the standard least-cost formulation for deterministic motion this interdiction problem is also NP-hard. But unlike that problem our interdiction problem is submodular and the optimal solution can be approximated within 1 ¿ 1/e using a greedy algorithm. Additionally, we exploit submodularity through a priority evaluation strategy that eliminates the linear complexity scaling in the number of network edges and speeds up the solution by orders of magnitude. Taken together the results bring closer the goal of finding realistic solutions to the interdiction problem on global-scale networks.
Ambient Intelligence --From Personal Assistance to Intelligent Megacities
Authors: Michael Berger, Florian Fuchs, Michael Pirker
Venue: Proceeding of the 2007 conference on Advances in Ambient Intelligence (2007)

Ambient Intelligence as a vision for future information systems is currently receiving more and more attention in research and industry. In this article, we investigate how related technology domains, such as Artificial Intelligence, Multi-Agent Systems and the Semantic Web can provide methods and IT infrastructures to support Ambient Intelligence middleware and applications. By means of three case studies ranging from personal assistance to megacity management we identify functional and non-functional requirements. Based on this study we propose AmIRA as an Ambient Intelligence Reference Architecture with a focus on methods, architectures and components. We illustrate the instantiation of AmIRA by describing the implementation of three industrial applications related to the previously presented case studies.
Dealing with Inconsistencies in DL-Lite Ontologies
Authors: Liping Zhou
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

As a shared conceptualization of a particular domain, ontologies play an important role for the success of the Semantic Web. However it is often difficult to create an absolutely consistent ontology. Inconsistency can occur due to several reasons, such as modeling errors, migration or merging ontologies, and ontology evolution. So it is essential to study how to deal with inconsistent ontologies. Many approaches have been proposed to solve this problem. These approaches are mainly used for dealing with inconsistency in expressive Description Logics(DLs). In our work, we consider inconsistency handling in the DL-Lite family, which is a family of DLs that preserve tractable reasoning and are specifically tailored to deal with large amounts of data. Like other DLs, inconsistencies in DL-Lite can also easily occur because disjoint axioms are allowed. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Efficient ID-based multi-decrypter encryption with short ciphertexts
Authors: Zhen-Chuan Chai, Zhen-Fu Cao, Yuan Zhou
Venue: Journal of Computer Science and Technology (2007)

Multi-decrypter encryption is a typical application in multi-user cryptographic branches. In multi-decrypter encryption, a message is encrypted under multiple decrypters' public keys in the way that only when all the decrypters cooperate, can the message be read. However, trivial implementation of multi-decrypter encryption using standard approaches leads to heavy computation costs and long ciphertext which grows as the receiver group expands. This consumes much precious bandwidth in wireless environment, such as mobile ad hoc network. In this paper, we propose an efficient identity based multi-decrypter encryption scheme, which needs only one or zero (if precomputed) pairing computation and the ciphertext contains only three group elements no matter how many the receivers are. Moreover, we give a formal security definition for the scheme, and prove the scheme to be chosen ciphertext secure in the random oracle model, and discuss how to modify the scheme to resist chosen ciphertext attack.
Marketing Planning by Design: Systematic Planning for Successful Marketing Strategy
Authors: Ralf Strauss
Venue:  (2009)

Marketing is under immense pressure to perform: required to submit reports to management, judged by the sales department based on whether it helps sales, scrutinized by financial controlling regarding how efficiently it uses budgets, and last but not least, under constant review by customers, markets and the public. Marketing faces more dilemmas and conflicts of interest than any other part of a company. The reason for this lies in the lack of a plan for marketing planning. This book not only identifies numerous examples of this problem as experienced by businesses, it also offers ways of solving the problem. Ralf Strauss highlights a 7 phase process for marketing planning, where the potential marketing can reach is demonstrated. Useful check lists included in this book allow the readers to readily create their own plans for a marketing planning. With insights drawn from more than 150 case studies included in the book, Marketing Planning by Design covers areas such as: How to overcome existing hurdles of marketing planning and marketing strategy. How to set up a project for managing the marketing planning cycle. How to develop a really target group and content driven marketing planning, which is stepwise cascaded from a program, campaign down to a tactical level. How to make marketing accountable in terms of performance measurement. How to implement an enhanced marketing planning in the organisation. How to systematically integrate Web 2.0 into marketing planning, or how to link marketing with modern IT. This highly practical book is destined to be a must-have reference work on any marketers desk.
One-click white balance using human skin reflectance
Authors: Jeremy Long, Amy A. Gooch
Venue: Proceedings of Graphics Interface 2009 (2009)

Existing methods for white balancing photographs tend to rely on skilled interaction from the user, which is prohibitive for most amateur photographers. We propose a minimal interaction system for white balancing photographs that contain humans. Many of the pictures taken by amateur photographers fall into this category. Our system matches a user-selected patch of skin in a photograph to an entry in a skin reflectance function database. The estimate of the illuminant that emerges from the skin matching can be used to white balance the photograph, allowing users to compensate for biased illumination in an image with a single click. We compare the quality of our results to output from three other low-interaction methods, including commercial approaches such as Google Picasa's one-click relighting [19], a whitepoint-based algorithm [16], and Ebner's localized gray-world algorithm [7]. The comparisons indicate that our approach offers several advantages for amateur photographers.
A Hybrid Neural Network Method for UAV Attack Route Integrated Planning
Authors: Nan Wang, Xueqiang Gu, Jing Chen, Lincheng Shen, Min Ren
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

This paper proposes a hybrid neural network method to solve the UAV attack route planning problem considering multiple factors. In this method, the planning procedure is decomposed by two planners: penetration planner and attack planner. The attack planner determines a candidate solution set, which adopts Guassian Radial Basis Function Neural Networks (RBFNN) to give a quick performance evaluation to find the optimal candidate solutions. The penetration planner adopts an alterative Hopfield Neural Network (NN) to refine the candidates in a fast speed. The combined effort of the two neural networks efficiently relaxes the coupling in the planning procedure and is able to generate a near-optimal solution within low computation time. The algorithms are simple and can easily be accelerated by parallelization techniques. Detailed experiments and results are reported and analyzed.
Exploiting Quaternion PCA in Virtual Character Motion Analysis
Authors: Tomasz Grudzinski
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

Animating virtual human-like characters has been a challenge in computer graphics for many years. Until now no single technique exists which addresses all of the arising problems. This paper presents a short overview of the methods and directions that were taken around the world to develop a solution to the subject. Following that, the paper concentrates on example-based algorithms and statistical analysis of the virtual character motion recorded using motion capture class devices or hand-made animations. The application of PCA technique on joint orientation correlations is introduced and two approaches to the crucial issue of quaternion linearization are compared with experimental results given for each of them. Then the summary of current state of research is provided and conclusions and future work possibilities are discussed.
Statistical inference with learning for temporal data analysis in visual computing
Authors: Xinyu Xu
Venue:  (2008)

Analyzing temporal visual data is an important task in computer vision. The fundamental challenges of the task mainly come from three aspects: the high dimensionality of the data, the complexity of the temporal dynamics, and the imperfect image measurements. This research focuses on developing novel computational methods for addressing the challenges in the context of motion tracking and classification in video. In 2D visual tracking, this research proposes to explicitly model the dependency among different state variables so as to reduce the sampling burden of the Sequential Monte Carlo (SMC) approaches. This results in a novel statistical tracking framework, Rao-Blackwellised Particle Filter (RBPF), which exploits the dependency to marginalize the tractable state components, leaving only the remaining state variables being estimated by the SMC method, leading to increased estimation accuracy and efficiency. Meanwhile, the approach greatly alleviates the "curse of dimensionality" and degeneracy problems associated with the traditional Particle Filter. Further, for tracking articulated human motion in 3D, this dissertation presents a new learning and inference approach, in which the motion correlation between the left-side and the right-side joint angles is exploited to increase the sampling efficiency, with a learning approach based on Partial Least Square Regression being employed to model the left-right motion constraints. A new RBPF algorithm is designed to integrate the learned motion prior with the online measurements. Experiments show that the approach outperforms the state-of-the-art approaches. Finally, to address the problem of classification of temporal data with strong underlying dynamics, this work proposes a Discriminative Gaussian Process Dynamical Model (D-GPDM) that learns a discriminative probabilistic low dimensional representation of the high-dimensional data. The resultant novel algorithm is found to be able to recover the latent-space dynamics of the data while keeping maximum separation of different classes and thus high classification accuracy can be obtained. The main contributions of this dissertation lie in the explicit modeling of the dependency among the state variables with learning, the design of the RBPF tracking framework exploiting the dependency models, and the method of temporal data classification that combines dimensionality reduction with dynamic modeling using a latent space approach.
Modified Histogram Based Fuzzy Filter
Authors: Ayyaz Hussain, M. Arfan Jaffar, Abdul Basit Siddiqui, Muhammad Nazir, Anwar M. Mirza
Venue: Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques (2009)

In this paper, a fuzzy based impulse noise removal technique has been proposed. The proposed filter is based on noise detection, fuzzy set construction, histogram estimation and fuzzy filtering process. Noise detection process is used to identify the set of noisy pixels which are used for estimating the histogram of the original image. Estimated histogram of the original image is used for fuzzy set construction using fuzzy number construction algorithm. Fuzzy filtering process is the main component of the proposed technique. It consists of fuzzification, defuzzification and predicted intensity processes to remove impulse noise. Sensitivity analysis of the proposed technique has been performed by varying the number of fuzzy sets. Experimental results demonstrate that the proposed technique achieves much better performance than state-of-the-art filters. The comparison of the results is based on global error measure as well as local error measures i.e. mean square error (MSE) and structural similarity index measure (SSIM).
Understanding collective content: purposes, characteristics and collaborative practices
Authors: Thomas Olsson
Venue: Proceedings of the fourth international conference on Communities and technologies (2009)

User-created media content is being increasingly shared with the communities people belong to. The content has a role of a motivator in social interaction within the communities. In fact, the content creation and management can be often seen as a collective effort where group members participate to create common memories and maintain relationships. We studied how four communities interact with content that is collectively created and used, i.e. collective content. The aim was to explore communities' collaborative interaction activities and the purposes of the content to be able to specify what collective content actually is. We report users' motivations for creating the collective content and its role in community interaction. We determine the factors and characteristics by which collectivity (i.e. the extent to which something is collective) of the content can be described: the community's contribution, the relevance of the content and the level of sharing. Based on the results, we present a new dimension of collectivity for categorizing media content and thus being able to better illustrate the community aspects in content interaction.
Crystallization and silicon diffusion nanoscale effects on the electrical properties of Al2O3 based devices
Authors: M. Lanza, M. Porti, M. Nafria, X. Aymerich, G. Benstetter, E. Lodermeier, H. Ranzinger, G. Jaschke, S. Teichert, L. Wilde, P. Michalowski
Venue: Microelectronic Engineering (2009)

In this work, Atomic Force Microscopy (AFM) based techniques are used to study, at the nanoscale, the dependence of the electrical properties of Al"2O"3 stacks for Flash memories on the percent of diffused Silicon and material crystallization after being annealed at different temperatures.
Hardware Accelerated Segmentation of Complex Volumetric Filament Networks
Authors: David Mayerich, John Keyser
Venue: IEEE Transactions on Visualization and Computer Graphics (2009)

We present a framework for segmenting and storing filament networks from scalar volume data. Filament networks are encountered more and more commonly in biomedical imaging due to advances in high-throughput microscopy. These data sets are characterized by a complex volumetric network of thin filaments embedded in a scalar volume field. High-throughput microscopy volumes are also difficult to manage since they can require several terabytes of storage, even though the total volume of the embedded structure is much smaller. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet these networks can span large regions of the data set. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and undersampled data. We use graphics hardware to accelerate the tracing algorithm, making it more useful for large data sets. After the initial network is traced, we use an efficient encoding scheme to store volumetric data pertaining to the network.
Prediction of grid-connected photovoltaic system output using evolutionary programming-ANN models
Authors: Shahril Irwan Sulaiman, Titik Khawa Abdul Rahman, Ismail Musirin
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

This paper presents the evolutionary training of a feed-forward Artificial Neural Network (ANN) using the Evolutionary Programming (EP). Besides optimizing the ANN regression performance, EP is employed to optimize the architecture and training parameters of a two-hidden layer ANN model for the prediction of total AC power output from a grid-connected photovoltaic system. The Evolutionary Programming-ANN (EPANN) model utilizes solar radiation and ambient temperature as its inputs while the output is the total AC power produced from the grid connected PV system. EP is used to optimize the regression performance of each model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for training. The performance of EPANN is tested using ?wo different training algorithms with similar input and output settings. The training algorithms are the Levenberg-Marquardt algorithm and scaled conjugate gradient algorithm. It is found that the Levenberg-Marquardt training algorithm produces better regression performance during training and testing compared to the scaled conjugate gradient training algorithm. Besides that, it could also be implemented faster compared to the scaled conjugate gradient algorithm. Nevertheless, the EPANN with scaled conjugate gradient algorithm could be accomplished using a smaller architecture compared to the EPANN with Levenberg-Marquardt algorithm.