ppxscal/embedding_network huggingface dataset:
Refactoring with wrangler, updated: data and process refactorings, and integration with eclipse
Authors: Huiqing Li, Simon Thompson, György Orosz, Melinda Tóth
Venue: Proceedings of the 7th ACM SIGPLAN workshop on ERLANG (2008)

Wrangler is a refactoring tool for Erlang, implemented in Erlang. This paper reports the latest developments in Wrangler, which include improved user experience, the introduction of a number of data- and process-related refactorings, and also the implementation of an Eclipse plug-in which, together with Erlide, provides refactoring support for Erlang in Eclipse.
Simultaneous Imaging Techniques for Temperature and Velocity Fields in Thermal Fluid Flows
Authors: N. Fujisawa, S. Funatani, Y. Watanabe
Venue: Journal of Visualization (2008)

In this paper, non-intrusive imaging techniques for simultaneously measuring temperature and velocity fields of thermal liquid flows are described. The experimental methods for temperature imaging considered here are the thermo-sensitive liquid crystal tracers and the laser-induced fluorescence, which are combined with particle image velocimetry (PIV) to measure simultaneously the corresponding 2D and 3D velocity fields. The features of these experimental methods and characteristics are examined, covering the measurable temperature range, uncertainty of measurement, time response and so on. The successful examples of simultaneous measurement of temperature and velocity fields are described for turbulent Rayleigh-B&eacute;rnard convection of a horizontal fluid layer and turbulent buoyant plume issuing from a circular nozzle, and their physical mechanisms of transport phenomena are explained.
A Modified Adaptive Genetic BP Neural Network with Application to Financial Distress Analysis
Authors: Zhibin Xiong
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, common neural networks often suffer from long convergent processes and occasionally involve in a local optimal solution that more or less limited their applications in practice. To overcome the drawbacks of neural networks, this study develops a new modified genetic algorithm—multi-population adaptive genetic back-propagation algorithm (MAGBPA). In this paper, a hybrid system combining feed-forward neural network and MAGBPA—multi-population adaptive genetic back-propagation neural network (MAGBPNN) is proposed to overcome NN's drawbacks. Furthermore, the new model has been applied to financial distress analysis based on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of MAGBPNN model is much better than the ones of common neural network model.
Perceived Strength of Lateral Acceleration Display for Touchscreen Emulation of Mechanical Switch Transients
Authors: Chris Ullrich, Manuel Cruz
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

A lateral motion touchscreen display can produce satisfying tactile feedback reminiscent of high quality mechanical switches. This study examines the perception of lateral motion acceleration for touchpanel displays for a variety of activation thresholds. A user study directly comparing short, high magnitude normal and lateral accelerations shows that for low accelerations, lateral motion is perceived as slightly weaker than normal, but for high accelerations, lateral acceleration is perceived as stronger than normal acceleration by as much as 40%. The results indicate that for high acceleration, high activation threshold mechanical switches, such as are found in automotive dashboards, lateral motion touchpanels can provide equivalent strength to normal motion displays with significantly less fingertip acceleration.
Abstractions to Support Interactions Between Self-Managed Cells
Authors: Alberto Schaeffer-Filho, Emil Lupu
Venue: Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management (2007)

Management of pervasive systems cannot rely on human intervention nor centralised decision-making functions due to their complex and intrinsically mobile nature. In previous work, we proposed the concept of a self-managed cell (SMC) as an architectural pattern for building ubiquitous applications. A SMC consists of hardware and software components that form an autonomous administrative domain. SMCs may be realised at different scales, from body-area networks, to an entire room or larger settings. However, to scale to larger systems it is necessary for SMCs to collaborate with each other, to federate or compose in larger SMC structures. We describe here the main abstractions we have defined and explore future directions towards this goal.
A Reference Curriculum for Service Engineering
Authors: Frank C. Tung
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Described here is a proposed curriculum design for service engineering based on the six years of experience of a master’s program at the School of Software and Microelectronics, Peking University as well as the preliminary result of a joint effort in developing a service science, management and engineering curriculum between IBM and several leading universities in China. Included at the end is a brief description of the current curriculum at Peking University.
Prioritization of Incomplete Fuzzy Preference Relation
Authors: Hsuan-Shih Lee, Pei-Di Shen, Wen-Li Chyr
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

The concept of incomplete fuzzy preference relation is discussed in this paper. We focus on additive consistent incomplete fuzzy preference relations, in which the correspondence between priority vector and incomplete fuzzy preference relation is investigated. We find that an additive consistent incomplete fuzzy preference relation does not imply there is a priority vector that satisfies additive transitivity.
An Efficient Deniable Key Exchange Protocol (Extended Abstract)
Authors: Shaoquan Jiang, Reihaneh Safavi-Naini
Venue: Financial Cryptography and Data Security: 12th International Conference, FC 2008, Cozumel, Mexico, January 28-31, 2008. Revised Selected Papers (2008)

A deniable key exchange allows two parties to jointly share a secret key while neither of two nor an outsider can prove to a third party that the communication between the two happened. This is an important mechanism for realizing a deniably secure channel. In this paper, we propose an efficient key exchange protocol and prove its deniable security. We compare our construction with the best known protocol with the same property and show the advantages of the new construction.
Cache Noise Prediction
Authors: Prateek Pujara, Aneesh Aggarwal
Venue: IEEE Transactions on Computers (2008)

Caches are very inefficiently utilized because not all the excess data brought into the cache, to exploit spatial locality, is utilized. Our experiments showed that Level 1 data cache has a utilization of only about 57%. Increasing the efficiency of the cache (by increasing its utilization) can have significant benefits in terms of reducing the cache energy consumption, reducing the bandwidth requirement, and making more cache space available for the useful data. In this paper, we focus on prediction mechanisms to predict the useless data in a cache block (cache noise), so that only the useful data is brought into the cache on a cache miss. The prediction mechanisms consider the words usage history of cache blocks for predicting the useful data. We obtained a predictability of about 95% with a simple last words usage predictor. When applying cache noise prediction to L1 data cache, we observed about 37% improvement in cache utilization, and about 23% and 28% reduction in cache energy consumption and bandwidth requirement, respectively. Cache noise mispredictions increased the miss rate by 0.1% and had almost no impact on instructions per cycle (IPC) count.
Bitonal image creation for automatic content conversion
Authors: Costin-Anton Boiangiu, Andrei-Iulian Dvornic
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

In this paper we address the problem of binarization of scanned documents which is a preprocessing requirement for most algorithms aimed at document image analysis. Two new approaches which focus on problem areas like low contrast documents, noise, and backside image showing through the paper sheet are presented in the following. First of all we propose a technique which is based on an initial preprocessing step followed by a conversion from the continuous space to the bitonal document. The first stage of this process focuses on document characteristics enhancement through contrast stretching for each color channel. The second step is a locally adaptive binarization process using color thresholding based on a Gaussian blur effect. Apart from that we present a noise-removal conversion technique based on combining the result of a series of threshold masks. Experimental results are given in order to verify the effectiveness of the proposed technique.
Energy-Efficient Tracking of Continuous Objects in Wireless Sensor Networks
Authors: Jung-Hwan Kim, Kee-Bum Kim, Chauhdary Sajjad Hussain, Min-Woo Cui, Myong-Soon Park
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of tracking continuous objects such as fires, bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that detects and monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with simulation results that overall size of the report message as well as the number of nodes that transmit the report message to the sink can be significantly reduced especially when the density of nodes deployed over the network field is high.
Conviviality masks in multiagent systems
Authors: Patrice Caire, Serena Villata, Guido Boella, Leendert van der Torre
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

In this paper we study tools for conviviality to develop user-friendly multiagent systems. First, we show how to use the social-cognitive concept of conviviality in multiagent system technology by relating it to agent power and social dependence networks. Second, we define conviviality masks as transformations of social dependencies by hiding power relations and social structures to facilitate social interactions. Third, we introduce dynamic dependence networks to model the creation of conviviality using conviviality masks. We illustrate the use of conviviality masks with a multiagent teleconferencing application for virtual worlds.
Comparison of Shannon, Renyi and Tsallis Entropy Used in Decision Trees
Authors: Tomasz Maszczyk, Włodzisław Duch
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

Shannon entropy used in standard top-down decision trees does not guarantee the best generalization. Split criteria based on generalized entropies offer different compromise between purity of nodes and overall information gain. Modified C4.5 decision trees based on Tsallis and Renyi entropies have been tested on several high-dimensional microarray datasets with interesting results. This approach may be used in any decision tree and information selection algorithm.
Indoor navigation of a non-holonomic mobile robot using a visual memory
Authors: Jonathan Courbon, Youcef Mezouar, Philippe Martinet
Venue: Autonomous Robots (2008)

When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled mobile robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the robot is controlled by a visual servoing law adapted to its nonholonomic constraint. Based on the regulation of successive homographies, this control guides the robot along the reference visual route without explicitly planning any trajectory. The proposed framework has been designed for the entire class of central catadioptric cameras (including conventional cameras). It has been validated onto two architectures. In the first one, algorithms have been implemented onto a dedicated hardware and the robot is equipped with a standard perspective camera. In the second one, they have been implemented on a standard PC and an omnidirectional camera is considered.
Synthesis of Optimal Control Policies for Some Infinite-State Transition Systems
Authors: Michel Sintzoff
Venue: Proceedings of the 9th international conference on Mathematics of Program Construction (2008)

We develop a symbolic, logic-based technique for constructing optimal control policies in some transition systems where state spaces are large or infinite. These systems are presented as iterations of finite sets of guarded assignments which have costs. The optimality objective is to minimize the total costs of system executions reaching the set characterized by a given target predicate. Guards are predicates and control policies are expressed by tuples of guards. The optimal control policy refines the control policy of the given system. It is generated from the target predicate by an iteration based on backwards induction. This iterative procedure amounts to a variant of the symbolic algorithm generating the reachability precondition; the latter characterizes the states from which some system execution reaches the target set. The main difference is the introduction of greedy and cost-dependent iteration steps.
Synthesis of Nets with Step Firing Policies
Authors: Philippe Darondeau, Maciej Koutny, Marta Pietkiewicz-Koutny, Alex Yakovlev
Venue: Proceedings of the 29th international conference on Applications and Theory of Petri Nets (2008)

The unconstrained step semantics of Petri nets is impractical for simulating and modelling applications. In the past, this inadequacy has been alleviated by introducing various flavours of maximally concurrent semantics, as well as priority orders. In this paper, we introduce a general way of controlling step semantics of Petri nets through step firing policies that restrict the concurrent behaviour of Petri nets and so improve their execution and modelling features. In a nutshell, a step firing policy disables at each marking a subset of enabled steps which could otherwise be executed. We discuss various examples of step firing policies and then investigate the synthesis problem for Petri nets controlled by such policies. Using generalised regions of step transition systems, we provide an axiomatic characterisation of those transition systems which can be realised as reachability graphs of Petri nets controlled by a given step firing policy. We also provide a decision and synthesis algorithm for PT-nets and step firing policies based on linear rewards of steps, where fixing the reward of elementary transitions is part of the synthesis problem. The simplicity of the algorithm supports our claim that the proposed approach is practical.
Inline bandwidth measurement techniques for gigabit networks
Authors: Cao Le Thanh Man, Go Hasegawa, Masayuki Murata
Venue: International Journal of Internet Protocol Technology (2008)

We introduce an inline measurement method that can overcome difficulties in measurement task in high-speed network environment, such as short packet transmission intervals and Interrupt Coalescence function deployed in the high-speed Network Interface Cards. The method adjusts the number of packets involved in a packet burst of an active TCP connection, and utilises the inter-intervals of the bursts of the corresponding ACK packets for bandwidth measurement. Experiment results show that the proposed inline measurement method can measure the bandwidth in the network paths at least 1-Gbps or higher.
Interoperable Competencies Characterizing Learning Objects in Mathematics
Authors: Erica Melis, Arndt Faulhaber, Anja Eichelmann, Susanne Narciss
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

Cognitive task analysis has been used in ITSs to predict students' performance, improve curricula and to determine appropriate feedback. Typically, the learning factors/knowledge components have been determined only for the use in one ITS or curriculum and therefore, general frameworks were not applied. Moreover, the result is sometimes rather unsystematic and not reusable across domains. However, for making learning environments interoperable and comparable and to be able to reuse learning objects, the competency hierarchies have to be usable for different learning environments and across domains. In this paper, we propose an approach to competencies represented as pairs of knowledge and cognitive process whose ontologies extend and revise existing taxonomies. A goal is to make these competencies a quasi-standard that enables interoperability and reuse. Moreover, we briefly describe, how the competency ontology can be employed for different purposes.
Cascaded crystal optical parametric amplifier model using measured complex beam profiles and its experimental validation
Authors: Joseph W. Haus, Peter E. Powers
Venue:  (2008)

This dissertation presents a numerical model and experiments for cascaded-crystal nonlinear optical devices where the output beams from the first stage is used as an input to a second stage. In most experimental situations intervening optics are placed in the path between the crystals to reformat the beam and this has been included in the analysis. As a concrete example, ZnGeP 2 crystals were used in the numerical simulations with first and second stage amplification. The model predicts an increase in the idler beam energy by more than a factor of 3. Following this purely numerical simulation, the model was compared with experiment by studying a cascaded-crystal optical system based on periodically poled lithium niobate (PPLN) in the laboratory. The numerical model and experimental results show close agreement. Additionally, the model helps to explain noncollinear interactions in the second stage that yield an efficient idler output over a broad range of temperatures. The model was extended further still by including real beam profiles instead of assumed shapes such as Gaussian or super-Gaussian. The phase information of the pump and signal beams was recovered using a phase retrieval method. The phase and amplitude extracted from the experiments were then used in the nonlinear optical numerical modeling. This enabled real laboratory conditions to be modeled. First the pump and signal beam amplitude and phase were retrieved using the Fresnel Phase retrieval method. Second the phases were used in the nonlinear optical numerical model as the input of the nonlinear PPLN crystal. The numerical model output based on the measured amplitude and phase information of the inputs is in close agreement with experimental results.
Independent component analysis based on marginal density estimation using weighted Parzen windows
Authors: Jiann-Ming Wu, Meng-Hong Chen, Zheng-Han Lin
Venue: Neural Networks (2008)

This work proposes a novel algorithm for independent component analysis (ICA) based on marginal density estimation. The proposed ICA algorithm aims to search for an effective demixing matrix as well as weighted Parzen window (WPW) representations for marginal densities of independent components so as to express a factorial joint density for high dimensional observations. Following the linear mixture assumption, independent component analysis is mathematically translated to minimizing the Kullback-Leibler (KL) divergence of independent components. By using Potts encoding, we express the KL divergence in an approximating form, which is shown to be tractable with respect to the WPW parameters as well as the demixing matrix and can be minimized by two interactive dynamic modules derived by the annealed expectation-maximization method and the natural gradient descent method, respectively. By numerical simulations, we test the proposed ICA algorithm with observations separately sampled from linear mixtures of independent sources and real world signals, including fetal electrocardiograms, mixed facial images and event-related potentials, extensively showing its accuracy and reliability for independent component analysis in comparison with some other popular ICA algorithms.
Searching for Interacting Features for Spam Filtering
Authors: Chuanliang Chen, Yunchao Gong, Rongfang Bie, Xiaozhi Gao
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

In this paper, we introduce a novel feature selection method--INTERACT to select relevant words of emails for spam email filtering, i.e. classifying an email as spam or legitimate. Four traditional feature selection methods in text categorization domain, Information Gain, Gain Ratio, Chi Squared, and ReliefF, are also used for performance comparison. Three classifiers, Support Vector Machine (SVM), Naïve Bayes and a novel classifier--Locally Weighted learning with Naïve Bayes (LWNB) are discussed in this paper. Four popular datasets are employed as the benchmark corpora in our experiments to examine the capabilities of these five feature selection methods and the three classifiers. In our simulations, we discover that the LWNB improves the Naïve Bayes and gain higher prediction results by learning local models, and its performance is sometimes better than that of the SVM. Our study also shows the INTERACT can result in better performances of classifiers than the other four traditional methods for the spam email filtering.
Efficient Privacy-Preserving k-Nearest Neighbor Search
Authors: Yinian Qi, Mikhail J. Atallah
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

We give efficient protocols for secure and private k-nearest neighbor (k-NN) search, when the data is distributed between two parties who want to cooperatively compute the answers without revealing to each other their private data. Our protocol for the single-step k-NN search is provably secure and has linear computation and communication complexity. Previous work on this problem had a quadratic complexity, and also leaked information about the parties' inputs. We adapt our techniquesto also solve the general multi-step k-NN search, and describe a specific embodiment of it for the case of sequence data. The protocols and correctness proofs can be extended to suit other privacy-preserving data mining tasks, such as classification and outlier detection.