ppxscal/embedding_network huggingface dataset:
A General Framework for Nondeterministic, Probabilistic, and Stochastic Noninterference
Authors: Alessandro Aldini, Marco Bernardo
Venue: Foundations and Applications of Security Analysis: Joint Workshop on Automated Reasoning for Security Protocol Analysis and Issues in the Theory of Security, ARSPA-WITS 2009, York, UK, March 28-29, 2009, Revised Selected Papers (2009)

We introduce a notion of stochastic noninterference aimed at extending the classical approach to information flow analysis with fine-grain information describing the temporal behavior of systems. In particular, we refer to a process algebraic setting that joins durational activities expressing time passing through exponentially distributed random variables, zero duration activities allowing for prioritized/ probabilistic choices, and untimed activities with unspecified duration. In this setting unifying time, priority, probability, and nondeterminism, we highlight the expressive power of stochastic noninterference with respect to the existing definitions of nondeterministic and probabilistic noninterference. From this comparison, we obtain that stochastic noninterference turns out to be very strict and limiting in real-world applications and, therefore, requires the use of relaxation techniques. Among them we advocate performance evaluation as a means for achieving a reasonable balance between security requirements and quality.
A Fast and Scalable Fuzzy-rough Nearest Neighbor Algorithm
Authors: Sun Liang-yan, Chen Li
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

In this paper, classification efficiency of the conventional K-nearest neighbor algorithm is enhanced by improving the KNN search and exploiting fuzzy-rough uncertainty. A new algorithm FFRNN (Fast Fuzzy-rough Nearest Neighbor) is proposed, which approximates a set of potential candidates of nearest neighbors by examining the absolute difference of total variation between each data of the training set and the unclassified object. Then, the k-nearest neighbors are searched from the candidate set. Moreover, fuzzy and rough uncertainties are exploited. It is shown that FFRNN is faster and higher classification accuracy than KNN and FRNN algorithm. Besides, FFRNN can distinguish between equal evidence and ignorance, thus the class confidence values do not necessarily sum up to one and the semantics of the class confidence values becomes richer.
Modeling multi-cellular behavior in epidermal tissue homeostasis via finite state machines in multi-agent systems
Authors: Thomas Sütterlin, Simone Huber, Hartmut Dickhaus, Niels Grabe
Venue: Bioinformatics (2009)

Motivation: For the efficient application of multi-agent systems to spatial and functional modeling of tissues flexible and intuitive modeling tools are needed, which allow the graphical specification of cellular behavior in a tissue context without presuming specialized programming skills. Results: We developed a graphical modeling system for multi-agent based simulation of tissue homeostasis. An editor allows the intuitive and hierarchically structured specification of cellular behavior. The models are then automatically compiled into highly efficient source code and dynamically linked to an interactive graphical simulation environment. The system allows the quantitative analysis of the morphological and functional tissue properties emerging from the cell behavioral model. We demonstrate the relevance of the approach using a recently published model of epidermal homeostasis as well as a series of cell-cycle models. Availability: The complete software is available in binary executables for MS-Windows and Linux at tiga.uni-hd.de Contact: niels.grabe@bioquant.uni-heidelberg.de
Research of Blending Learning Making Use of Web2.0 Tools
Authors: Rui Cheng
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

The research mainly focus on the probability and effectiveness of integrating multiple web2.0 tools into blending learning on the basis of analyzing the features of web2.0 tools and blending learning as well. Grounding on an empirical research a new blending learning mode making use of web2.0 tools has been put forward，and the learning procedure, features and learning environment of the mode are detailed in this paper besides an instance as well.
A Mixture of Transformed Hidden Markov Models for Elastic Motion Estimation
Authors: Huijun Di, Linmi Tao, Guangyou Xu
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

Elastic motion is a nonrigid motion constrained only by some degree of smoothness and continuity. Consequently, elastic motion estimation by explicit feature matching actually contains two correlated subproblems: shape registration and motion tracking, which account for spatial smoothness and temporal continuity, respectively. If we ignore their interrelationship, solving each of them alone will be rather challenging, especially when the cluttered features are involved. To integrate them into a probabilistic model, one straightforward approach is to draw the dependence between their hidden states. With regard to their separated states, there are, however, two different explanations of motion which are still made under the individual constraint of smoothness or continuity. Each one can be error-prone, and their coupling causes error propagation. Therefore, it is highly desirable to design a probabilistic model in which a unified state is shared by the two subproblems. This paper is intended to propose such a model, i.e., a Mixture of Transformed Hidden Markov Models (MTHMM), where a unique explanation of motion is made simultaneously under the spatiotemporal constraints. As a result, the MTHMM could find a coherent global interpretation of elastic motion from local cluttered edge features, and experiments show its robustness under ambiguities, data missing, and outliers.
Criteria for Evaluating the Privacy Protection Level of Identity Management Services
Authors: Hyangjin Lee, Inkyoung Jeun, Hyuncheol Jung
Venue: Proceedings of the 2009 Third International Conference on Emerging Security Information, Systems and Technologies (2009)

Identity Management is the one of web services that manages the digital identity and the personally identifiable information of the user who subscribed for various web services in Internet. It was developed to provide user with an easy way to use and manage various user's digital identities that were provided from each web service. If the user subscribes to an Identity Management service, the user can access the other web sites affiliated with the Identity Management service and use their web services by using the identity issued by the Identity Management service. And the user can manage the user's personally identifiable information distributed among various web sites in an integrated way through this service. However, if the identity provider, which provides this Identity Management service, discloses the user’s identity and personal identifiable information, identity theft can happen throughout the entire affiliated web sites. As a result, the privacy protection level of the Identity provider, that is, the level of protection for personally identifiable information, is the critical factor of successful Identity Management service. Therefore, Identity Provider should provide an easy way to the internal or external auditor of them for assessing the privacy protection level. This paper describes privacy threats for each identity life cycle, such as Identity provision, propagation, use andmaintain, and destruction, and proposes the criteria that evaluate the privacy protection level provided by the Identity provider as a countermeasure against these threats. The internal or external auditor can use the criteria described in this paper, as a way of assessing the privacy protection level of Identity Provider
Functional Reachability
Authors: C.-H. Luke Ong, Nikos Tzevelekos
Venue: Proceedings of the 2009 24th Annual IEEE Symposium on Logic In Computer Science (2009)

What is reachability in higher-order functional programs? We formulate reachability as a decision problem in the setting of the prototypical functional language PCF, and show that even in the recursion-free fragment generated from a finite base type, several versions of the reachability problem are undecidable from order 4 onwards, and several other versions are reducible to each other. We characterise a version of the reachability problem in terms of a new class of tree automata introduced by Stirling at FoSSaCS 2009, called Alternating Dependency Tree Automata (ADTA). As a corollary, we prove that the ADTA non-emptiness problem is undecidable, thus resolving an open problem raised by Stirling. However, by restricting to contexts constructible from a finite set of variable names, we show that the corresponding solution set of a given instance of the reachability problem is regular. Hence the relativised reachability problem is decidable.
How to Learn from Intelligent Products; The Structuring of Incoherent Field Feedback Data in Two Case Studies
Authors: Renate Bruin, Yuan Lu, Aarnout Brombacher
Venue: Proceedings of the Symposium on Human Interface 2009 on ConferenceUniversal Access in Human-Computer Interaction. Part I: Held as Part of HCI International 2009 (2009)

A growing number of products - particularly highly innovative and intelligent products - are being returned by customers, while analysis shows that many of these products are in fact functioning according to their technical specifications. Product developers are recognizing the need for information that gives more detail about the reason for these product returns, in order to find the root cause of the problems. Traditionally a lot of information from the field is stored in departments like sales and service (helpdesks and repair centers). Combining these data sources with new field feedback data sources, like customer experiences on the internet and product log files, could provide product developers with more in-depth and accurate information about the actual product performance and the context of use. Case studies were done at two different industries: a company from consumer electronic industry and a company from professional medical system industry. The Maturity Index on Reliability (MIR) method, a method to assess capability for businesses to respond to product reliability related issues, was combined with Contextual Design, a method for user context mapping and requirement definition, and explored as a means to analyze the structure and capability of the current field feedback process in supporting the development of complex and innovative products .
Numerical simulation of structure response outfitted with a tuned liquid damper
Authors: M. Marivani, M. S. Hamed
Venue: Computers and Structures (2009)

An integrated fluid-structure numerical model has been developed to simulate the response of a single degree of freedom (SDOF) structure outfitted with a Tuned Liquid Damper (TLD). The structure is exposed to random external excitations. A non-linear, two-dimensional, flow model has been developed using the finite-difference method. Unlike most existing flow models, the present model does not include any linearization assumptions; it rather solves the entire nonlinear, moving boundary, flow problem under conditions leading to large interfacial deformations. The free surface has been reconstructed using the volume of fluid method and the donor-acceptor algorithm. The Duhamel integral method has been used to determine the response of the structure. The effectiveness and accuracy of the flow model has been validated using a set of benchmark problems and experimental data. The numerical results of this model have been compared with results of an equivalent TMD model. The present fluid-structure model can be used as a valuable tool for performance evaluation and design of more effective tuned liquid dampers.
The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines
Authors: Nahla Barakat, Andrew P. Bradley
Venue: Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (2009)

Prior knowledge about a problem domain can be utilized to bias Support Vector Machines (SVMs) towards learning better hypothesis functions. To this end, a number of methods have been proposed that demonstrate improved generalization performance after the application of domain knowledge; especially in the case of scarce training data. In this paper, we propose an extension to the virtual support vectors (VSVs) technique where only a subset of the support vectors (SVs) is utilized. Unlike previous methods, the purpose here is to compensate for noise and uncertainty in the training data. Furthermore, we investigate the effect of domain knowledge not only on the quality of the SVM model, but also on rules extracted from it; hence the learned pattern by the SVM. Results on five benchmark and one real life data sets show that domain knowledge can significantly improve both the quality of the SVM and the rules extracted from it.
Mobile Interaction with the Real World
Authors: Andreas Zimmermann, Niels Henze, Xavier Righetti, Enrico Rukzio
Venue: Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services (2009)

The workshop on Mobile Interaction with the Real World (MIRW 2009) will invite papers which focus on new mobile and wearable input and output interfaces which allow simpler and straightforward interactions with mobile services and applications. An inherit problem of current mobile devices are their limited output and input capabilities. This workshop continues a successful series of workshops (2006--2008) that focus on new approaches to overcome these issues. Examples are the usage of external visual interfaces (e.g. projector phones, public displays, interactive surfaces) and additional input capabilities (e.g. gestures, on-body interfaces, pointing) and innovative feedback mechanisms (e.g. tactile feedback). The workshop combines technical presentations with the presentation of prototypes and focused discussions to drive interaction between participants.
Transfer Learning with Data Edit
Authors: Yong Cheng, Qingyong Li
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

We often face the situation where very limited labeled data are available to learn an effective classifier in target domain while there exist large amounts of labeled data with similar feature or distribution in certain relevant domains. Transfer learning aims at improving the performance of a learner in target domain given labeled data in one or more source domains. In this paper, we present an algorithm to learn effective classifier without or a few labeled data on target domain, given some labeled data with same features and similar distribution in source domain. Our algorithm uses the data edit technique to approach distribution from the source domain to the target domain by removing "mismatched" examples in source domain and adding "matched" examples in target domain. Experimental results on email classification problem have confirmed the effectiveness of the proposed algorithm.
Emotion Detection: Application of the Valence Arousal Space for Rapid Biological Usability Testing to Enhance Universal Access
Authors: Christian Stickel, Martin Ebner, Silke Steinbach-Nordmann, Gig Searle, Andreas Holzinger
Venue: Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Addressing Diversity. Part I: Held as Part of HCI International 2009 (2009)

Emotion is an important mental and physiological state, influencing cognition, perception, learning, communication, decision making, etc. It is considered as a definitive important aspect of user experience (UX), although at least well developed and most of all lacking experimental evidence. This paper deals with an application for emotion detection in usability testing of software. It describes the approach to utilize the valence arousal space for emotion modeling in a formal experiment. Our study revealed correlations between low performance and negative emotional states. Reliable emotion detection in usability tests will help to prevent negative emotions and attitudes in the final products. This can be a great advantage to enhance Universal Access.
RenderAnts: interactive Reyes rendering on GPUs
Authors: Kun Zhou, Qiming Hou, Zhong Ren, Minmin Gong, Xin Sun, Baining Guo
Venue: ACM Transactions on Graphics (TOG) (2009)

We present RenderAnts, the first system that enables interactive Reyes rendering on GPUs. Taking RenderMan scenes and shaders as input, our system first compiles RenderMan shaders to GPU shaders. Then all stages of the basic Reyes pipeline, including bounding/splitting, dicing, shading, sampling, compositing and filtering, are executed on GPUs using carefully designed data-parallel algorithms. Advanced effects such as shadows, motion blur and depth-of-field can also be rendered. In order to avoid exhausting GPU memory, we introduce a novel dynamic scheduling algorithm to bound the memory consumption during rendering. The algorithm automatically adjusts the amount of data being processed in parallel at each stage so that all data can be maintained in the available GPU memory. This allows our system to maximize the parallelism in all individual stages of the pipeline and achieve superior performance. We also propose a multi-GPU scheduling technique based on work stealing so that the system can support scalable rendering on multiple GPUs. The scheduler is designed to minimize inter-GPU communication and balance workloads among GPUs. We demonstrate the potential of RenderAnts using several complex RenderMan scenes and an open source movie entitled Elephants Dream. Compared to Pixar's PRMan, our system can generate images of comparably high quality, but is over one order of magnitude faster. For moderately complex scenes, the system allows the user to change the viewpoint, lights and materials while producing photorealistic results at interactive speed.
Removing All Silent Transitions from Timed Automata
Authors: Cătălin Dima, Ruggero Lanotte
Venue: Proceedings of the 7th International Conference on Formal Modeling and Analysis of Timed Systems (2009)

We show that all ¿-transitions can be removed from timed automata if we allow transitions to be labeled with periodic clock constraints and with periodic clock updates. This utilizes a representation of the reachability relation in timed automata in a generalization of Difference Logic with periodic constraints. We also show that periodic updates are necessary for the removal of ¿-transitions.
Learning and research in the web 2 era: opportunities for research
Authors: Vanessa Peters, James D. Slotta, Andrea Forte, Amy Bruckman, Joey J. Lee, Matthew Gaydos, Christopher Hoadley, Jody Clarke
Venue: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3 (2008)

Researchers in the learning sciences have long recognized the potential of online spaces to support learning activities; however, the pervasiveness of social media construction typically associated with "Web 2.0" represents a new context for the research of learning and instruction. Wikis, for example, have popularized a social approach to constructing knowledge that was very difficult to achieve with previous technologies. Social networking applications like Facebook that interconnect people according to social or semantic relationships have become integral features of student life. Multi-user synchronous environments like Second Life provide rich, immersive experiences as well as new forms of online community. Such applications are blooming in every corner of society, influencing the ways in which people learn and exchange with one another. These four papers present distinct research applications of such technologies, illustrating how they influence not only our methods, but affect the very nature of our questions and theoretical perspectives.
Querying sequential and concurrent horn transaction logic programs using tabling techniques
Authors: Paul Fodor
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

In this poster we describe the tabling techniques for Sequential and Concurrent Horn Transaction Logic. Horn Transaction Logic is an extension of classical logic programming with state updates and it has a SLD-style evaluation algorithm. This SLD-style algorithm enters into infinite loops when computing answers to many recursive programs when they change the underlying state of the knowledge base. We solve this problem by tabling (caching) the calls, call states and answers (unifications and return states) in a searchable structure for the Sequential Transaction Logic, or building a graph for the query and memoize the "hot" vertices (vertices, currently, possible to execute) for the Propositional Concurrent Transaction Logic, so that the same call is not re-executed ad infinum. With these techniques, we can efficiently compute queries to transaction logic programs, and when the underlying programs have the bounded term-depth property (Transaction Datalog) the techniques are guaranteed to terminate. The applications of these techniques promise termination and great improvements in the uses of transaction logic: state-changing systems, artificial intelligence planning, dynamic constraints on transaction execution, workflow modeling and verification, and systems involving financial transactions.
An adaptive architecture for modular Q-learning
Authors: Takayuki Kohri, Kei Matsubayashi, Mario Tokoro
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

Reinforcement learning is a technique to learn suitable action policies that maximize utility, via the clue of reinforcement signals: reward or punishment. Q-learning, a widely used reinforcement learning method, has been analyzed in much research on autonomous agents. However, as the size of the problem space increases, agents need more computational resources and require more time to learn appropriate policies. Whitehead proposed an architecture called modular Q-learning, that decomposes the whole problem space into smaller subproblem spaces, and distributes them among multiple modules. Thus, each module takes charge of part of the whole problem. In modular Q-learning, however, human designers have to decompose the problem space, and create a suitable set of modules manually. Agents with such a fixed module architecture cannot adapt themselves to dynamic environments. Here, we propose a new architecture for reinforcement learning called AMQL (Automatic Modular Q-Learning), that enables agents to obtain a suitable set of modules by themselves using a selection method. Through experiments, we show that agents can automatically obtain suitable modules to gain a reward. Furthermore, we show that agents can adapt themselves to dynamic environments efficiently, through reconstructing modules.
Implementation of the MLP Kernel
Authors: Cheng-Yuan Liou, Wei-Chen Cheng
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

This paper presents a MLP kernel. It maps all patterns in a class into a single point in the output layer space and maps different classes into different points. These widely separated class points can be used for further classifications. It is a layered feed-forward network. Each layer is trained using the class differences and trained independently layer after layer using a bottom-up construction. The class labels are not used in the training process. It can be used in separating multiple classes.