ppxscal/embedding_network huggingface dataset:
Understanding file access mechanisms for embedded Ubicomp collaboration interfaces
Authors: Anthony Collins, Anastasia Bezerianos, Gregor McEwan, Markus Rittenbruch, Rainer Wasinger, Judy Kay
Venue: Proceedings of the 11th international conference on Ubiquitous computing (2009)

This paper explores the nature of interfaces to support people in accessing their files at tabletop displays embedded in the environment. To do this, we designed a study comparing people's interaction with two very different classes of file system access interface: Focus, explicitly designed for tabletops, and the familiar hierarchical Windows Explorer. In our within-subjects double-crossover study, participants collaborated on 4 planning tasks. Based on video, logs, questionnaires and interviews, we conclude that both classes of interface have a place. Notably, Focus contributed to improved collaboration and more efficient use of the workspace than with Explorer. Our results inform a set of recommendations for future interfaces enabling this important class of interaction -- supporting access to files for collaboration at tabletop devices embedded in an ubicomp environment.
A New Image Denoising Method Using Wavelet Transform
Authors: Li Hongqiao, Wang Shengqian
Venue: Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 01 (2009)

Wavelet image denoising has been widely used in the field of image noise. After taking into account the objective and subjective results of the noise image, this paper presents a new image denosing method. Firstly, this method decomposes the noisy image in order to get different sub-band image. Secondly, we remain the low-frequency wavelet coefficients unchanged, and after taking into account the relation of horizontal, vertical and diagonal high-frequency wavelet coefficients and comparing them with Donoho threshold, we make them enlarge and narrow relatively. Thirdly, we use soft-threshold denoising method to achieve image denoising. Finally, we get the denosing image by inverse wavelet transform. According to the result of experiment, this method compared to soft-threshold denosing method has a higher PSNR and visual effects.
Exploiting contextual independence in probabilistic inference
Authors: David Poole, Nevin Lianwen Zhang
Venue: Journal of Artificial Intelligence Research (2003)

Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present such a representation that exploits contextual independence in terms of parent contexts; which variables act as parents may depend on the value of other variables. The internal representation is in terms of contextual factors (confactors) that is simply a pair of a context and a table. The algorithm, contextual variable elimination, is based on the standard variable elimination algorithm that eliminates the nonquery variables in turn, but when eliminating a variable, the tables that need to be multiplied can depend on the context. This algorithm reduces to standard variable elimination when there is no contextual independence structure to exploit. We show how this can be much more efficient than variable elimination when there is structure to exploit. We explain why this new method can exploit more structure than previous methods for structured belief network inference and an analogous algorithm that uses trees.
A speech understanding framework that uses multiple language models and multiple understanding models
Authors: Masaki Katsumaru, Mikio Nakano, Kazunori Komatani, Kotaro Funakoshi, Tetsuya Ogata, Hiroshi G. Okuno
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)

The optimal combination of language model (LM) and language understanding model (LUM) varies depending on available training data and utterances to be handled. Usually, a lot of effort and time are needed to find the optimal combination. Instead, we have designed and developed a new framework that uses multiple LMs and LUMs to improve speech understanding accuracy under various situations. As one implementation of the framework, we have developed a method for selecting the most appropriate speech understanding result from several candidates. We use two LMs and three LUMs, and thus obtain six combinations of them. We empirically show that our method improves speech understanding accuracy. The performance of the oracle selection suggests further potential improvements in our system.
Exploiting Timed Automata for Conformance Testing of Power Measurements
Authors: Matthias Woehrle, Kai Lampka, Lothar Thiele
Venue: Proceedings of the 7th International Conference on Formal Modeling and Analysis of Timed Systems (2009)

For software development, testing is still the primary choice for investigating the correctness of a system. Automated testing is of utmost importance to support continuous integration and regression tests on actual hardware. For embedded systems, power consumption is a chief performance metric, which is tightly coupled to the hardware used and the software exploiting low power modes. Automated testing of power consumption requires to investigate its conformance to a specification. We employ timed automata for specifying the expected behavior of a real sensor node application, as well as for describing the power measurements obtained from its real-world implementation. Introducing computational optimizations, the presented approach allows to utilize standard model checkers for automated conformance testing of modeled systems and monitored power consumption of their implementations.
Exploring the decision forest: an empirical investigation of Occam's razor in decision tree induction
Authors: Patrick M. Murphy, Michael J. Pazzani
Venue: Journal of Artificial Intelligence Research (1993)

We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that affect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some training data and the accuracy of the tree on test data. The experiments were performed on a massively parallel Maspar computer. The results of the experiments on several artificial and two real world problems indicate that, for many of the problems investigated, smaller consistent decision trees are on average less accurate than the average accuracy of slightly larger trees.
Health Decision Support for Biomedical Signals Monitoring System over a WSN
Authors: Sing-Hui Toh, Kyeong-Hoon Do, Wan-Young Chung, Seung-Chul Lee
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

A biomedical signals monitoring system with multiple physiological signs measurement capability in real time is designed and implemented. This system performs a local vital sign data analysis using cell phone over wireless sensor network (WSN) technology and code division multiple access (CDMA) network. Cell phone-based personal medical recorder becomes popular to facilitate self-control of chronic diseases and observe patients’ condition continuously. The risk of ignoring monitoring patients’ physiological data by doctor is concerned since large quantities of data are processed and analyzed daily. An intelligent decision support system is developed to enhance healthcare providers’ review of monitoring data.
Improving reinforcement learning function approximators via neuroevolution
Authors: Shimon Whiteson
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 4 (2005)

Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of taking each action in each state. In most problems of real-world interest, learning this value function requires a function approximator. which represents the mapping from stateaction pairs to values via a concise, parameterized function and uses supervised learning methods to set its parameters. Function approximators make it possible to use temporal difference methods on large problems but, in practice, the feasibility of doing so depends on the ability of the human designer to select an appropriate representation for the value function. My thesis presents a new approach to function approximation that automates some of these difficult design choices by coupling temporal difference methods with policy search methods such as evolutionary computation. It also presents a particular implementation which combines NEAT, a neuroevolutionary policy search method, and Q-learning, a popular temporal difference method, to yield a new method called NEAT+Q that automatically learns effective representations for neural network function approximators. Empirical results in a server job scheduling task demonstrate that NEAT+Q can outperform both NEAT and Q-learning with manually designed neural networks.
HCD Case Study for the Information Security Training System
Authors: Akira Kondo, Makoto Yoshii
Venue: Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009 (2009)

We proposed organization persona as persona scenario method for business to business content creation process.This paper introduces three projects cases which were based on HCD process. We improved design process practically and enhanced persona for organization as company.
Virtual Configuration Management: A Technique for Partial Runtime Reconfiguration
Authors: Mohamed Taher, Tarek El-Ghazawi
Venue: IEEE Transactions on Computers (2009)

Reconfigurable Computers (RCs), built from configurable processors can offer high performance in a wide range of applications. However, due to the limited reconfigurable resources, not all needed functionalities can be implemented at the same time, and runtime reconfiguration becomes an appealing solution. This work proposes techniques suitable for multitasking applications as well as applications that can change the course of processing in a nondeterministic fashion. In order to exploit both spatial and temporal locality simultaneously, the proposed model groups hardware functions into configuration blocks of fixed size (pages), variable size (segments), or hybrid (paged segments). Multiple blocks can be configured on a chip simultaneously. Data mining techniques are used to group related functions into blocks (pages or segments) and temporal locality is exploited through block replacement techniques. Simulation, as well as emulation using the Cray XD1 reconfigurable high-performance computer was used in the experimental study. Results show a significant improvement in performance using the proposed techniques.
Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations
Authors: Matthew Ferrara, Gregory Arnold, Mark Stuff
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

This paper describes an invariant-based shape- and motion reconstruction algorithm for 3D-to-1D orthographically projected range data taken from unknown viewpoints. The algorithm exploits the object-image relation that arises in echo-based range data and represents a simplification and unification of previous work in the literature. Unlike one proposed approach, this method does not require uniqueness constraints, which makes its algorithmic form independent of the translation removal process (centroid removal, range alignment, etc.). The new algorithm, which simultaneously incorporates every projection and does not use an initialization in the optimization process, requires fewer calculations and is more straightforward than the previous approach. Additionally, the new algorithm is shown to be the natural extension of the approach developed by Tomasi and Kanade for 3D-to-2D orthographically projected data and is applied to a realistic inverse synthetic aperture radar imaging scenario, as well as experiments with varying amounts of aperture diversity and noise.
An Algorithm for Sensory Area Coverage by Mobile Robots Operating in Complex Arenas
Authors: Mattias Wahde, David Sandberg
Venue: Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics (2009)

In this paper, a novel algorithm for sensory area coverage by mobile robots is proposed, with applications in, for example, mapping and exploration. The algorithm generates a sequence of nodes for the robot to visit, such that it covers as much as possible of the arena using a laser range finder. A crucial part of the exploration behavior implementing this algorithm is a deadlock avoidance procedure, which allows the robot to handle the inevitable problems (such as coping with narrow passages and obstacles of different size and height) that occur when navigating in complex arenas. Despite the simplicity of the algorithm, the robot is generally able to cover (with its laser range finder) 98% or more of an initially unexplored arena.
Classifier Chains for Multi-label Classification
Authors: Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (2009)

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
Privacy-Aware Attribute-Based Encryption with User Accountability
Authors: Jin Li, Kui Ren, Bo Zhu, Zhiguo Wan
Venue: Proceedings of the 12th International Conference on Information Security (2009)

As a new public key primitive, attribute-based encryption (ABE) is envisioned to be a promising tool for implementing fine-grained access control. To further address the concern of user access privacy, privacy-aware ABE schemes are being developed to achieve hidden access policy recently. For the purpose of secure access control, there is, however, still one critical functionality missing in the existing ABE schemes, which is user accountability. Currently, no ABE scheme can completely prevent the problem of illegal key sharing among users. In this paper, we tackle this problem by firstly proposing the notion of accountable, anonymous, and ciphertext-policy ABE (CP-A3BE, in short) and then giving out a concrete construction. We start by improving the state-of-the-art of anonymous CP-ABE to obtain shorter public parameters and ciphertext length. In the proposed CP-A3BE construction, user accountability can be achieved in black-box model by embedding additional user-specific information into the attribute private key issued to that user, while still maintaining hidden access policy. The proposed constructions are provably secure.
Song search and retrieval by tapping
Authors: Geoffrey Peters, Caroline Anthony, Michael Schwartz
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 4 (2005)

We present an interactive, web based system for musical song search and retrieval, using rhythmic tapping as the primary means of query input. Our approach involves encoding the input rhythm as a contour string, and using approximate string matching to determine the most likely match with songs in the database.
Introduction to classification: likelihoods, margins, features, and kernels: tutorial for NAACL-HLT 2007
Authors: Dan Klein
Venue: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Tutorial Abstracts on XX (2007)

Statistical methods in NLP have exploited a variety of classification techniques as core building blocks for complex models and pipelines. In this tutorial, we will survey the basic techniques behind classification. We first consider the basic principles, including the principles of maximum likelihood and maximum margin. We then discuss several core classification technologies: naive Bayes, perceptrons, logistic regression, and support vector machines. The discussion will include the key optimization ideas behind their training and the empirical trade-offs between the various classifiers. Finally, we consider the extension to kernels and kernelized classification: what can kernels offer and what is their cost? The presentation is targeted to NLP researchers new to these methods or those wanting to understand more about how these techniques are interconnected.
Multilingual video and audio news alerting
Authors: David D. Palmer, Patrick Bray, Marc Reichman, Katherine Rhodes, Noah White, Andrew Merlino, Francis Kubala
Venue: Demonstration Papers at HLT-NAACL 2004 on XX (2004)

This paper describes a fully-automated realtime broadcast news video and audio processing system. The system combines speech recognition, machine translation, and cross-lingual information retrieval components to enable real-time alerting from live English and Arabic news sources.
Study on the Application of Multi-objective Grey Situation Decision-Making Theory for Transportation Mode Choice
Authors: Dazhi Jiang, Wei Deng, Xiaoli Zhang
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

The people’s choice behaviors on travel mode always change according to the different factors such as the destination, trip purpose, type of goods, time sensitive, cost-sensitive, the reliability of transportation system, etc. The Multi-objective Grey Situation Decision-making theory (MGSD) has never been adopted in transportation professional field. So it’s the first time to apply this method to analyze the people’s mode choice problems in comprehensive transportation system. A concrete example is also given at the end of this paper and the analysis results show that MGSD has its practicality and reliability in transportation research field.
Planning with problems requiring temporal coordination
Authors: Andrew Coles, Maria Fox, Derek Long, Amanda Smith
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

We present the first planner capable of reasoning with both the full semantics of PDDL2.1 (level 3) temporal planning and with numeric resources. Our planner, CRIKEY3, employs heuristic forward search, using the start-and-end semantics of PDDL2.1 to manage temporal actions. The planning phase is interleaved with a scheduling phase, using a Simple Temporal Network, in order to ensure that temporal constraints are met. To guide search, we introduce a new temporal variant of the Relaxed Planning Graph heuristic that is capable of reasoning with the features of this class of domains, along with the Timed Initial Literals of PDDL2.2. CRIKEY3 extends the state-of-the-art in handling the full temporal expressive power of PDDL2.1, including numeric temporal domains.
A Trust Model Combining Reputation and Credential
Authors: Jiawei Gong, Jun Chen, Haojiang Deng, Jinlin Wang
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01 (2009)

Most current reputation models fail to calculate peers’ reputation accurately when there is little transaction history between the peers. To this end, this paper presents a trust model combing reputation and credential which accepts inputs of both feedback and credential. The model is based on reputation-based trust model, and according to the correlation of credential and reputation, the model can derive credential trust from the credential adaptively, then combine the credential trust and reputation to get the overall trust value. Analyses and simulations show that this model can improve the accuracy of the trust value computation significantly.
Hunting elusive metaphors using lexical resources
Authors: Saisuresh Krishnakumaran, Xiaojin Zhu
Venue: Proceedings of the Workshop on Computational Approaches to Figurative Language (2007)

In this paper we propose algorithms to automatically classify sentences into metaphoric or normal usages. Our algorithms only need the WordNet and bigram counts, and does not require training. We present empirical results on a test set derived from the Master Metaphor List. We also discuss issues that make classification of metaphors a tough problem in general.
Universal XForms for Dynamic XQuery Generation
Authors: Susan Malaika, Keith Wells
Venue: Proceedings of the 6th International XML Database Symposium on Database and XML Technologies (2009)

This demonstration illustrates how a variety of queries can be built dynamically by examining XML stored in databases through general purpose XForms. The forms are called the Universal XForms for XQuery, and abbreviated to the Universal XForms. The Universal XForms help users to construct XQueries through the provision of prompts, and do not require prior knowledge of the structure of the data to be queried. Sample XML documents from more than twenty industry formats constitute the base for illustrating the building of queries in the demonstration.
Distributions and Schedules of CPU Time in a Multiprocessor System When the Users' Utility Functions Are Linear
Authors: Alexander Khutoretskij, Sergei Bredikhin
Venue: Proceedings of the 10th International Conference on Parallel Computing Technologies (2009)

We consider a market of CPU time in a multiprocessor system providing the payable services. We suggest a market model under the following assumptions: (i) processors may be of different capacity; (ii) the processors' owners (suppliers) may have the different reservation prices; (iii) every user (consumer) has a linear utility function and one task within the considered period; (iv) a task can use different processors, but it cannot use two processors at the same moment. The main result is that all equilibrium CPU time distributions can be obtained as solutions to some linear programming problem, and equilibrium prices can be determined on the base of shadow prices for the same problem. An equilibrium distribution shows, what time each task should use each processor, but, generally speaking, it doesn't determine a scheduling. If the most labour-intensive job can be completed within the time-period to be distributed, using only the slowest processor, then the simple algorithm exists for constructing a scheduling corresponding to some equilibrium distribution. In general case such algorithm is unknown.