ppxscal/embedding_network huggingface dataset:
Fostering Self-Directed Learning with Social Software: Social Network Analysis and Content Analysis
Authors: Effie Lai-Chong Law, Anh Vu Nguyen-Ngoc
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

Self-directed Learning (SDL) is an old pedagogical concept facing new challenges engendered by the emerging social software. We aim to explore the issue how SDL is facilitated in online cross-cultural collaborative settings --- iCamp Trial2 involving faculty and students from four European academic institutions. Empirical data of two relatively active groups were meticulously analyzed with the use of social network analysis and content analysis. Results showed the online collaborative learning environment empowered by social software could potentially enhance that SDL of some but not all the students. The actual impact is stipulated by several critical factors such as the students' intrinsic motivation and initial anxiety about the learning situations. Initial formal structure is essential. Revision of Henri scheme is implied.
Online Outlier Detection Based on Relative Neighbourhood Dissimilarity
Authors: Nguyen Hoang Vu, Vivekanand Gopalkrishnan, Praneeth Namburi
Venue: Proceedings of the 9th international conference on Web Information Systems Engineering (2008)

Outlier detection has many practical applications, especially in domains that have scope for abnormal behavior, such as fraud detection, network intrusion detection, medical diagnosis, etc. In this paper, we present a technique for detecting outliers and learning from data in multi-dimensional streams. Since the concept in such streaming data may drift, learning approaches should be online and should adapt quickly. Our technique adapts to new incoming data points, and incrementally maintains the models it builds in order to overcome the effect of concept drift. Through various experimental results on real data sets, our approach is shown to be effective in detecting outliers in data streams as well as in maintaining model accuracy.
Quantitative Wind Tunnel Studies Using Pressure- and Temperature Sensitive Paints
Authors: R. H. Engler, U. Fey, U. Henne, Chr. Klein, W. E. Sachs
Venue: Journal of Visualization (2005)

The pressure sensitive paint (PSP) intensity and lifetime system is an optical measurement technique to investigate absolute pressure fields on model surfaces for basic research in laboratories, industrial wind tunnels or high speed rotating turbo machines. Detailed qualitative and quantitative information and understanding of flow phenomena can be obtained in speed ranges from U$_{&infin;}$=20 m/s up to Ma=5.0. A number of projects of industrial interest has been investigated in different wind tunnels covering low speed, transonic, trisonic and cryogenic facilities. The influence of the main error sources for the components of the PSP system have been checked. Comparison of experimental pressure fields obtained by means of PSP and the results of numerical calculations have been carried out. Different wind tunnel models ranging from basic configurations such as a cropped delta wing to a complex half model of a large propeller-driven transport aircraft with all flaps, rudders and shrouds, and rotating or oscillating models as well as Reynolds number effects on models have been investigated.
Protein Folding Simulation with New Move Set in 3D Lattice Model
Authors: X. -M. Li
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

We present the lowest energy conformations for several large benchmark problems in 3D HP model. We found these solutions with MC and genetic algorithm using new move set. The new move set including rotation and mirror reflection is suitable for use in protein folding simulation. Experiment results show that new move set can find these best solutions in less time on average and dramatically superior to that of the commonly used move set.
Transitive Observation-Based Causation, Saliency, and the Markov Condition
Authors: Jean-François Bonnefon, Didier Dubois, Henri Prade
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

If A caused B and B caused C, did A caused C? Although causality is generally regarded as transitive, some philosophers have questioned this assumption, and models of causality in artificial intelligence are often agnostic with respect to transitivity: They define causation, then check whether the definition makes all, or only some, causal arguments transitive. We consider two formal models of observation-based causation, which differ in the way they represent uncertainty. The quantitative model uses a standard probabilistic definition; the qualitative model uses a definition based on nonmonotonic consequence. The two models identify different sufficient conditions for the transitivity of causation: The Markov condition on events for the quantitative model, and a Saliency condition (if B is true then generally A is true) for the qualitative model. We explore the formal relations between these sufficient conditions, and between the underlying definitions of observation-based causation. These connections shed light on the range of applicability of both models.
Investigation on Sparse Kernel Density Estimator Via Harmony Data Smoothing Learning
Authors: Xuelei Hu, Yingyu Yang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper we apply harmony data smoothing learning on a weighted kernel density model to obtain a sparse density estimator. We empirically compare this method with the least squares cross-validation (LSCV) method for the classical kernel density estimator. The most remarkable result of our study is that the harmony data smoothing learning method outperforms LSCV method in most cases and the support vectors selected by harmony data smoothing learning method are located in the regions of local highest density of the sample.
Richness Versus Parsimony Antecedents of Technology Adoption Model for E-Learning Websites
Authors: Hsiu-Li Liao, Hsi-Peng Lu
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

E-learning can be viewed as an innovation in information technology (IT) and learning. The Technology Acceptance Model (TAM) has previously received significant attention in the IS research field. The Perceived Characteristics of Innovating (PCI) antecedents of technology adoption decisions have not been widely researched empirically. This study explores students' perceptions of utilizing the e-learning website in their decision processes. This work also identifies which model supports a more explanation of variance in the e-learning context. Both TAM and PCI antecedents are investigated in the same context of an e-learning website. Experimental results demonstrate that the PCI constructs explain slightly more variance in users' intentions of continued use than TAM antecedents. The PCI adoption model provides increasingly rich information concerning the continued use of e-learning website.
Effects of Cultural Background on Internet Buying Behaviour: Towards a Virtual Global Village?
Authors: Efthymios Constantinides, Carlota Lorenzo, Miguel Ángel Gómez-Borja, Peter Geurts
Venue: Proceedings of the 9th international conference on E-Commerce and Web Technologies (2008)

The article identifies the combined influence of the Web Experience (or online experience) components on the online consumer's behaviour from three different angles: (1) The relative importance of the online experience factors in choosing an online vendor, (2) the actual effect of these factors on the vendor choice and (3) the influence of personal and behavioural characteristics on the virtual shopping behaviour. The results identify early symptoms of an emerging behavioural convergence among Internet users of different cultures and nationalities, suggesting that cultural and behavioural differences in the physical world could have limited influence on people's behaviour in the virtual marketplace. This outcome could suggest the emergence of a global virtual village, an issue worth of further scholastic research but also an issue of particular importance for global Web vendors and web site designers.
Simulation Level of Detail for Virtual Humans
Authors: Cyril Brom, Ondřej Šerý, Tomáš Poch
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

Graphical level of detail (LOD) is a set of techniques for coping with the issue of limited computational resources by reducing the graphical detail of the scene far from the observer. Simulation LOD reduces quality of the simulation at the places unseen. Contrary to graphical LOD, simulation LOD has been almost unstudied. As a part of our on-going effort on a large virtual-storytelling game populated by tens of complex virtual humans, we have developed and implemented a set of simulation LOD algorithms for simplifying virtual space and behaviour of virtual humans. The main feature of our technique is that it allows for several degrees of detail, i.e. for gradual varying of simulation quality. In this paper, we summarise the main lessons learned, introduce the prototype implementation called IVE and discuss the possibility of scaling our technique to other applications featuring virtual humans.
Bargaining Power in Electronic Negotiations: A Bilateral Negotiation Mechanism
Authors: Ricardo Buettner, Stefan Kirn
Venue: Proceedings of the 9th international conference on E-Commerce and Web Technologies (2008)

Bargaining power has a major influence in negotiations. Up to now, a lot of electronic negotiation models have been developed and manifold negotiation challenges have been already addressed, but mainly related to the structure and the process of the negotiation. However, research concerning bargaining power is still inadequate represented. Thus, in order to contribute to the state of the art of electronic negotiations, this paper shows a bilateral automated negotiation mechanism that considers bargaining power.
Going Weighted: Parameterized Algorithms for Cluster Editing
Authors: Sebastian Böcker, Sebastian Briesemeister, Quang B. Bui, Anke Truss
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

The goal of the Cluster Editing problem is to make the fewest changes to the edge set of an input graph such that the resulting graph is a disjoint union of cliques. This problem is NP-complete but recently, several parameterized algorithms have been proposed. In this paper we present a surprisingly simple branching strategy for Cluster Editing. We generalize the problem assuming that edge insertion and deletion costs are positive integers. We show that the resulting search tree has size O(1.82 k ) for edit cost k, resulting in the currently fastest parameterized algorithm for this problem. We have implemented and evaluated our approach, and find that it outperforms other parametrized algorithms for the problem.
Parabolic Flight Reconstruction from Multiple Images from a Single Camera in General Position
Authors: Raúl Rojas, Mark Simon, Oliver Tenchio
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

This paper shows that it is possible to retrieve all parameters of the parabolic flight trajectory of an object from a time stamped sequence of images captured by a single camera looking at the scene. Surprisingly, it is not necessary to use two cameras (stereo vision) in order to determine the coordinates of the moving object with respect to the floor. The technique described in this paper can thus be used to determine the three-dimensional trajectory of a ball kicked by a robot. The whole calculation can be done, at the limit, with just three measurements of the ball position captured in three consecutive frames. Therefore, this technique can be used to forecast the future motion of the ball a few milliseconds after the kick has taken place. The computation is fast and allows a robot goalie to move to the correct blocking position. Interestingly, this technique can also be used to self-calibrate stereo cameras.
Automated Registration of Volumes of Interest for a Combined X-Ray Tomosynthesis and Ultrasound Breast Imaging System
Authors: Mitchell M. Goodsitt, Heang-Ping Chan, Lubomir Hadjiiski, Gerald L. Lecarpentier, Paul L. Carson
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

An intuitive display method was developed to enable a user to select and visualize corresponding volumes of interest in combined digital breast tomosynthesis (DBT) and automated breast ultrasound images. Details of this method are described including experiments performed to assess the accuracy of the calibration for coordinate transformation. The distance between an object in the ultrasound images and the center of a volume of interest transferred to the ultrasound images from DBT is 4 mm after first stage calibration and 0.8 mm after second stage calibration which corrects for experimentally determined offsets. The display is being used in reader studies, and preliminary results of those studies will be discussed.
The ArguGRID Platform: An Overview
Authors: Francesca Toni, Mary Grammatikou, Stella Kafetzoglou, Leonidas Lymberopoulos, Symeon Papavassileiou, Dorian Gaertner, Maxime Morge, Stefano Bromuri, Jarred Mcginnis, Kostas Stathis, Vasa Curcin, Moustafa Ghanem, Li Guo
Venue: Proceedings of the 5th international workshop on Grid Economics and Business Models (2008)

The ArguGRID project aims at supporting service selection and composition in distributed environments, including the Grid and Service-oriented architectures, by means of argumentative agents, an agent environment, a service-composition environment, Peer-to-Peer technology and Grid middleware. Agents are argumentative in that they use argumentation-based decision-making and argumentation-supported negotiation of services and contracts. The integration of all technologies gives rise to the overall ArguGRID platform. In this paper we outline the main components and the overall functionalities of the ARGUGRID platform.
Plan on Obstacle-Avoiding Path for Mobile Robots Based on Artificial Immune Algorithm
Authors: Yen-Nien Wang, Tsai-Sheng Lee, Teng-Fa Tsao
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle; AIA has a strong parallel processing, learning and memorizing ability. This study will design and control a mobile robot within a limited special scale. Through a research method based on the AIA, this study will find out the optimum obstacle-avoiding path. The main purpose of this study is to make it possible for the mobile robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle and best learning efficiency. In the end, through the research method proposed and the experimental results, it will become obvious that the application of the AIA after improvement in the obstacle-avoiding path planning for mobile robots is really effective.
A monolithic based NIRS front-end wireless sensor
Authors: Hervé F. Achigui, Mohamad Sawan, Christian J. B. Fayomi
Venue: Microelectronics Journal (2008)

This paper concerns a novel analog front-end of a wireless brain oxymeter smart sensoring instrument based on near-infrared spectroreflectometry (NIRS). The NIRS sensor makes use of dynamic threshold transistors (DTMOS) for low voltage (1V), low power and low noise enhancement. The design is composed of a transimpedance amplifier (TIA) and an operational transconductance amplifier (OTA). The OTA differential input pairs use DTMOS devices for input common mode range enhancement. The OTA was fabricated in a standard 0.18@mm CMOS process technology. Measurements under a 5pF capacitive load for the OTA gave a DC open loop gain of 67dB, unity frequency gain bandwidth of 400kHz, input and output swings of 0.58 and 0.7V, a power consumption of 18@mW, and an input referred noise of 134nV/@/Hz at 1kHz without any extra noise reduction techniques. The achieved features of the proposed oxymeter front-end will allow ultra low-light level measurements, high resolution and good temperature stability.
Optimal statistical model for forecasting ozone
Authors: M. Abdollahian, R. Foroughi, N. Debnath
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

The objective of this paper is to apply time series analysis and multiple regression method to ozone data in order to obtain the optimal statistical model for forecasting next day ozone level. The best estimated model is then used to produce one-step ahead point and interval estimates of future values of the ozone series. Ozone data is analyzed using time series analysis, which resulted in an Auto Regressive Moving Average, ARMA (20, 2) with Mean Absolute Percentage Error (MAPE)=42%. Applying multiple regression method and examination of several possible contributing factors, showed that Wind speed, Mixing height where the complex chemical reactions that produce ozone take place, current and predicted next day temperatures and current ozone concentration are influential on the next day ozone concentration levels. Diagnostics tests and statistics including R-square, residual analysis and Durbin-Watson Statistic (DW) were applied in order to select the best fitted model and finally the best prediction model was found using MAPE and Mean Absolute Deviation (MAD) as predictive criteria. Regression analysis of this data, using tomorrow's and today's maximum temperature, today's wind speed and tomorrow's maximum height at 10 am, as explanatory variables results in R-square of 50.7% with MAD=12.423, MAPE=30% and DW=1.66
Anticipating Hidden Text Salting in Emails
Authors: Christina Lioma, Marie-Francine Moens, Juan-Carlos Gomez, Jan Beer, Andre Bergholz, Gerhard Paass, Patrick Horkan
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

Salting is the intentional addition or distortion of content, aimed to evade automatic filtering. Salting is usually found in spam emails. Salting can also be hidden in phishing emails, which aim to steal personal information from users. We present a novel method that detects hidden salting tricks as visual anomalies in text. We solely use these salting tricks to successfully classify emails as phishing (F-measure >90%).
Orientation Extraction and Identification of the Opponent Robots in RoboCup Small-Size League
Authors: Saori Umemura, Kazuhito Murakami, Tadashi Naruse
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

In RoboCup small-size league, it is necessary to analyze the opponent robots' behavior in order to make a strategy of the own team. However, it is difficult to prepare image processing methods in advance in order to detect opponent robots' sub-markers used for the orientation detection and identification of the robots, because there is no limitation in the rule in shape, color, arrangement, and the number. This paper proposes a new method to select the most specific sub-marker attached on the top of the robot based on the features such as the size, area, and color values by using the discriminant analysis, and also explains how to extract opponent robots' orientations with some experimental results.
A Three-Dimensional Display System for Diagnostic Imaging Applications
Authors: Larry Cook, Samuel Dwyer, Solomon Batnitzky, Kyo Lee
Venue: IEEE Computer Graphics and Applications (1983)

Digitally formatted 3-D imaging will become increasingly important in medical diagnosis. What is the best trade-off of speed, picture quality, and storage requirements?
The Dynamics of Self-regulatory Processes within Self-and Externally Regulated Learning Episodes During Complex Science Learning with Hypermedia
Authors: Amy M. Witherspoon, Roger Azevedo, Sidney D'Mello
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

This paper examines the dynamics of college students' self-regulatory processes within self-regulated learning (SRL) and externally-regulated learning (ERL) episodes during hypermedia learning. We re-analyzed and extended the results from an original study recently conducted by Azevedo and colleagues [1] to address four questions related to adaptivity, based on the temporal and dynamic deployment of self-regulatory processes by learners and human tutors in fostering complex science learning with hypermedia. Our questions include: (1) How does access to a human tutor affect the deployment of various SRL processes during learning?; (2) Which transitions between self-regulatory processes are more likely to occur within SRL and ERL?; (3) Which transitions between SRL classes are more likely or less likely to occur with SRL and ERL learning episodes?; (4) Are there significant correlations between learners' observed likelihood of transitions (between SRL processes) and learning outcomes? Lastly, we discuss implications for the design of MetaTutor, an adaptive hypermedia learning environment.
FPGA-Based Real-Time Super-Resolution on an Adaptive Image Sensor
Authors: Maria E. Angelopoulou, Christos-Savvas Bouganis, Peter Y. Cheung, George A. Constantinides
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

Recent technological advances in imaging industry have lead to the production of imaging systems with high density pixel sensors. However, their long exposure times limit their applications to static images due to the motion blur effect. This work presents a system that reduces the motion blurring using a time-variant image sensor. This sensor can combine several pixels together to form a larger pixel when it is necessary. Larger pixels require shorter exposure times and produce high frame-rate samples with reduced motion blur. An FPGA is employed to enhance the spatial resolution of these samples employing Super Resolution (SR) techniques in real-time. This work focuses on the spatial resolution enhancement block and presents an FPGA implementation of the Iterative Back Projection (IBP) SR algorithm. The proposed architecture achieves 25 fps for VGA input and can serve as a general purpose real-time resolution enhancement system.
Dimensionality Reduction Using Rough Set Approach for Two Neural Networks-Based Applications
Authors: Mohammed Sammany, T. Medhat
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

In this paper, Rough Sets approach has been used to reduce the number of inputs for two neural networks-based applications that are, diagnosing plant diseases and intrusion detection. After the reduction process, and as a result of decreasing the complexity of the classifiers, the results obtained using Multi-Layer Perceptron (MLP) revealed a great deal of classification accuracy without affecting the classification decisions.
A Version of Lebesgue Decomposition Theorem for Non-additive Measure
Authors: Jun Li, Masami Yasuda, Jianzeng Li
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

In this paper, Lebesgue decomposition type theorems for non-additive measure are shown under the conditions of null-additivity, converse null-additivity, weak null-additivity and ¿-null-additivity, etc.. In our discussion, the monotone continuity of set function is not required.