ppxscal/embedding_network huggingface dataset:
On k- Versus (k + 1)-Leaf Powers
Authors: Andreas Brandstädt, Peter Wagner
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

For k ¿ 2 and a finite simple undirected graph G = (V,E), a tree T is a k-leaf root of G if V is the set of leaves of T and, for any two distinct x,y ¿ V, xy ¿ E if and only if the distance between x and y in T is at most k. G is a k-leaf power if G has a k-leaf root. Motivated by the search for underlying phylogenetic trees, the concept of k-leaf power was introduced and studied by Nishimura, Ragde and Thilikos and analysed further in many subsequent papers. It is easy to see that for all k ¿ 2, every k-leaf power is a (k + 2)-leaf power. However, it was unknown whether every k-leaf power is a (k + 1)-leaf power. Recently, Fellows, Meister, Rosamond, Sritharan and Telle settled this question by giving an example of a 4-leaf power which is not a 5-leaf power. Motivated by this result, we analyse the inclusion-comparability of k-leaf power classes and show that, for all k ¿ 4, the k- and (k + 1)-leaf power classes are incomparable. We also characterise those graphs which are simultaneously 4- and 5-leaf powers.In the forthcoming full version of this paper, we will show that for all k ¿ 6 and odd l with 3 ≤ l ≤ k ¿ 3, the k- and (k + l)-leaf power classes are incomparable. This settles all remaining cases and thus gives the complete inclusion-comparability of k-leaf power classes.
New approach in LDPC codes
Authors: Ali. A. Dargahi, Shahab Asoodeh
Venue: Proceedings of the 7th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications (2008)

recent researches have shown that in designing the LDPC coding systems, optimizing the code's performance from the error correction view, makes the implementation of decoder and encoder circuits difficult. Also emphasizing on simplifying the decoder and encoder circuits will reduce the code's performance. Therefore in the design process of an LDPC code, the code's performance and implementation constraints must be considered simultaneously. In this paper first the parity-check matrix is introduced; which helps reducing the complexity of the encoder circuit and also provides the opportunity of parallel implementation of the decoder circuit. Then the block-filling method for increasing the error correction performance is introduced and its operations on the parity-check matrix are discussed and simulated.
VLSI and New Markets Drive CG Developments
Authors: Joe Schallan
Venue: IEEE Computer Graphics and Applications (1983)

Solid modeling on an IBM PC? One exhibitor at NCGA '83¿the National Computer Graphics Association's Fourth Annual Conference and Exposition held June 26-30 in Chicago¿claimed such capability, while several other vendors demonstrated more conventional 2-D drafting capabilities on the PC. At each summer's round of graphics conferences, certain developments mark the latest direction of the industry. This year, the IBM PC-based systems indicate that graphics manufacturers are turning to a mostly untapped market¿low-end CAD. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Towards improving conceptual modeling: an examination of common errors and their underlying reasons
Authors: Sudha Ram
Venue:  (2008)

Databases are a critical part of Information Technology. Following a rigorous methodology in the database lifecycle ensures the development of an effective and efficient database. Conceptual data modeling is a critical stage in the database lifecycle. However, modeling is hard and error prone. An error could be caused by multiple reasons. Finding the reasons behind errors helps explain why the error was made and thus facilitates corrective action to prevent recurrence of that type of error in the future. We examine what errors are made during conceptual data modeling and why. In particular, this research looks at expertise-related reasons behind errors. We use a theoretical approach, grounded in work from educational psychology, followed up by a survey study to validate the model. Our research approach includes the following steps: (1) measure expertise level, (2) classify kinds of errors made, (3) evaluate significance of errors, (4) predict types of errors that will be made based on expertise level, and (5) evaluate significance of each expertise level. Hypotheses testing revealed what aspects of expertise influence different types of errors. Once we better understand why expertise related errors are made, future research can design tailored training to eliminate the errors.
Friendstore: cooperative online backup using trusted nodes
Authors: Dinh Nguyen Tran, Frank Chiang, Jinyang Li
Venue: Proceedings of the 1st Workshop on Social Network Systems (2008)

Today, it is common for users to own more than tens of gigabytes of digital pictures, videos, experimental traces, etc. Although many users already back up such data on a cheap second disk, it is desirable to also seek off-site redundancies so that important data can survive threats such as natural disasters and operator mistakes. Commercial online backup service is expensive [1, 11]. An alternative solution is to use a peer-to-peer storage system. However, existing cooperative backup systems are plagued by two long-standing problems [3, 4, 9, 19, 27]: enforcing minimal availability from participating nodes, and ensuring that nodes storing others' backup data will not deny restore service in times of need.
Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets
Authors: Paweł Żwan, Piotr Szczuko, Bożena Kostek, Andrzej Czyżewski
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

The aim of the research study presented in this paper is the automatic singing voice recognition. For this purpose a database containing singers' sample recordings has been constructed and parameters are extracted from recorded voices of trained and untrained singers of various voice types. Parameters, which are especially designed for the analysis of the singing voice are described and their physical interpretation is given. Decision systems based on artificial neutral networks and rough sets are used for automatic voice type/voice quality classification. Results obtained in the automatic classification performed by both decision systems are then compared and conclusions are derived.
Camera Location and Aperture Characterization Using the Transformation between a 2D Plane and the Image Captured by the Camera
Authors: L. Llucia, S. Mavromatis, S. Perrotte, P. Dias, J. Sequeira
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

This paper uses as starting point the transformation matrix defined in the homogeneous space that associates the points of a 2D plane (that represents the model) with those of another 2D space (the image one), this transformation characterizing the camera capture process. This transformation (an homography from 2D to 2D) is coming from previous work and is used within the scope of the SimulFoot project. The final objective is to reconstruct a 3D model from TV soccer scenes, making it important to characterize the transformation between a 2D plane (the soccer field) and the camera image. We suppose the transformation (from image to field) is a conic projection whose center is S and projection plane is P in the model 3D space. We formulate two additional hypotheses related to the reference system of P: its origin is the orthogonal projection of S on P, and its first basis vector is parallel to the horizontal plane xOy. In fact, these conditions are often verified in soccer scenes since the camera is fixes on a tripod. In this communication, we give the camera location and aperture expressions on the only basis of the transformation matrix values.
Wavelet estimation by Bayesian thresholding and model selection
Authors: Eugenio Cinquemani, Gianluigi Pillonetto
Venue: Automatica (Journal of IFAC) (2008)

Many wavelet-based algorithms have been proposed in recent years to solve the problem of function estimation from noisy samples. In particular it has been shown that threshold approaches lead to asymptotically optimal estimation and are extremely effective when dealing with real data. Working under a Bayesian perspective, in this paper we first study optimality of the hard and soft thresholding rules when the function is modelled as a stochastic process with known covariance function. Next, we consider the case where the covariance function is unknown, and propose a novel approach that models the covariance as a certain wavelet combination estimated from data by Bayesian model selection. Simulated data are used to show that the new method outperforms traditional threshold approaches as well as other wavelet-based Bayesian techniques proposed in the literature.
Educational Resources Public Service Platform Based on Heterogeneous Data Exchange
Authors: Shuming Li, Qingtang Liu, Tao Huang, Zongkai Yang
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

There are many different e-learning system and educational resources, which have cause the problems, such as standard was not unified, resources was difficult to be shared, the supporting service need to be improved. The paper proposed an Educational Resources Public Service Platform based on Heterogeneous Data Exchange (ERPSP). ERPSP is an E-learning Resources Platform integrates all levels of educational resources by the one-stop operation pattern, and constructs the digital, integrated, and opening learning support service environment for different educational system and resources.
Modelling of Magnetic Resonance Spectra Using Mixtures for Binned and Truncated Data
Authors: Juan M. Garcia-Gomez, Montserrat Robles, Sabine Huffel, Alfons Juan-Císcar
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

Magnetic Resonance Spectroscopy (MRS) provides the biochemical composition of a tissue under study. This information is useful for the in-vivo diagnosis of brain tumours. Prior knowledge of the relative position of the organic compound contributions in the MRS suggests the development of a probabilistic mixture model and its EM-based Maximum Likelihood Estimation for binned and truncated data. Experiments for characterizing and classifying Short Time Echo (STE) spectra from brain tumours are reported.
Ubiquitous Electronic Tagging
Authors: Roy Want, Daniel M. Russell
Venue: IEEE Distributed Systems Online (2000)

The automatic identification industry is growing rapidly. Tags used for identification exist in many forms and appear on such items as luggage, dogs, trains, and fruit. As with many advancing technologies, a variety of standards exist that do not interoperate and thus hinder widespread deployment.
An Iterative local-search framework for solving constraint satisfaction problem
Authors: Mohamed Tounsi, Samir Ouis
Venue: Applied Soft Computing (2008)

In this article, we introduce a new solving framework based on using alternatively two local-search algorithms to solve constraint satisfaction and optimization problems. The technique presented is based on the integration of local-search algorithm as a mechanism to diversify the search instead of using a build on diversification mechanisms. Thus, we avoid tuning the multiple parameters to escape from a local optimum. This technique improves the existing methods: it is generic especially when the given problem can be expressed as a constraint satisfaction problem. We present the way the local-search algorithm can be used to diversify the search in order to solve real examination timetabling problems. We describe how the local-search algorithm can be used to assist any other specific local-search algorithm to escape from local optimality. We showed that such framework is efficient on real benchmarks for timetabling problems.
Kernel design for isolation and assurance of physical memory
Authors: Dhammika Elkaduwe, Philip Derrin, Kevin Elphinstone
Venue: Proceedings of the 1st workshop on Isolation and integration in embedded systems (2008)

Embedded systems are evolving into increasingly complex software systems. One approach to managing this software complexity is to divide the system into smaller, tractable components and provide strong isolation guarantees between them. This paper focuses on one aspect of the system's behaviour that is critical to any such guarantee: management of physical memory resources. We present the design of a kernel that has formally demonstrated the ability to make strong isolation guarantees of physical memory. We also present the macro-level performance characteristics of a kernel implementing the proposed design.
Differences in Fitts' Law Task Performance Based on Environment Scaling
Authors: Gregory S. Lee, Bhavani Thuraisingham
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

Haptics research has begun implementing haptic feedback in tasks of great precision and skill, such as robotic surgery. Haptic displays can represent task environments with arbitrary scaling. Fitts' Law suggests differences in the scale of a workspace rendered on a visual display and in a haptic display should not affect performance of those tasks. However, interactions of great precision and skill may require understanding and verifying the influence of perceiving an environment when the visual and haptic displays represent those environments with differing scales. This experiment measured the influence that mismatched haptic and visual display scalings had on movement times in. Each of five treatments used different scales in the visual and the haptic displays. A Friedman rank test showed a significant difference across all treatments. A post hoc pairwise comparison showed a nearly significant difference between two treatments. These findings suggest the need for further study using more participants and parametric statistics to measure the magnitude of the possible influences.
PAM: a novel performance/power aware meta-scheduler for multi-core systems
Authors: Mohammad Banikazemi, Dan Poff, Bulent Abali
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

Sharing resources such as caches and main memory bandwidth in multi-core systems requires a more sophisticated scheduling scheme. PAM is a low-overhead, user-level meta-scheduler which does not require any hardware or software changes. In particular, it operates by detecting resource congestions and providing guidelines to the standard system scheduler by limiting the assignment of processes to subsets of available cores. PAM contains a cache model that it uses to predict the impact of new schedules. PAM can be used to improve the system along three dimensions: performance, power, and energy consumption (and any combination of these three). On our prototype, we show individual benchmarks can improve by up to 33% and the overall system performance can be improved by as much as 14%.
The Need of Intelligent Driver Training Systems for Road Safety
Authors: Husnain Malik, Andry Rakotonirainy
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

Automobiles have deeply impacted the way in which people travel but they have also caused a number of deaths and injury due to crashes. Driver inattention, inexperience, poor judgment and/or fatigue etc., are the major contributing factors in crashes. In order to address these complex issues, scientists have developed a number of technological systems ranging from Anti-lock Braking System (ABS), early warning systems to complete autonomous driving solutions. Driver’s driving competencies also play a vital role in judging the road environment and reacting in-time to avoid any possible collision. Therefore driver’s perceptual and motor skills remain a key factor impacting on-road safety. Currently, there is a need for researchers to develop and evaluate Intelligent Transport Systems (ITS) that assess driving competencies. These tools should be designed to help driver instructors and drivers to accurately evaluate the driving behaviour. This paper outlines a vision for future research in the area of intelligent driver training systems for enhancing road safety and also presents preliminary implementation.
Symbiosis and Software Evolvability
Authors: Liguo Yu, Srini Ramaswamy, John Bush
Venue: IT Professional (2008)

Software systems need to evolve to adapt to either a new environment or a new requirement. This position paper discusses the evolution of a software system from the viewpoint of an ecosystem and asserts that the evolution of a software system is not a standalone process but an aggregate process of other related software systems that forms a software ecosystem. This paper describes several of our ongoing projects in studying different types of symbiotic relationships between software systems and their effects on software evolvability.
An Update Propagation Algorithm for P2P File Sharing over Wireless Mobile Networks
Authors: Haengrae Cho
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Peer-to-peer (P2P) file sharing systems often replicate files to multiple nodes. File replication is beneficial in the sense that it can achieve good query latency, load balance, and reliability. However, it introduces another problem of maintaining mutual consistency among replica when a file is updated. The new file has to be propagated to all of its replica. In this paper, we propose an update propagation algorithm for P2P file sharing over wireless mobile networks (MONET). Compared to the previous algorithms proposed in wired P2P file sharing systems, our algorithm has low communication overhead. It also guarantees safe delivery of updates even when the underlying network is unreliable. This means that our algorithm is well matched to the characteristics of MONET such as limited battery power of mobile nodes, lower network reliability, and frequent disconnection of mobile nodes.
Introductory C Programming Language Learning with Game-Based Digital Learning
Authors: Wen-Chih Chang, Yu-Min Chou
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

Educational game, motivates learners, is full of entertaining. Learners can practice and challenge themselves in an interactive game. In order to become formal tools in everyday teaching/learning process, we create Bomberman game which is included most concepts in introductory C programming language. It brings modern education concept in concert with the classical teaching and laboratory work. Learners can view the learning materials, reading/writing C codes, solving problems with C codes to control the movement of the Bomberman accompany with game music. Bomberman game combines continuous challenge, interesting storyline, fun and realism. This vivid learning environment can engage students spending their precious time for extensive practice since students have highly motivation to win the game. We hope that we have shown a new method for educational practice with game-based digital learning for teaching C programming course.
Ludic aspects of the generative audiovisual narrative system
Authors: Iro Laskari
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

This paper documents a research project that deals with the application of an artificial life (AL) approach for creating cinematographic narration. The project investigates the possibility of producing autonomous audiovisual sequences, in which meaning results from a kind of hypermontage, conditioned by genetic algorithms. Firstly a relevant literature review lead to the formulation of a theoretical thesis regarding generative narratives and consequently, a system was created to support and evaluate this thesis. During the presentation of this paper, the system and its audiovisual results will be demonstrated. Such approach may also hold potential for Pervasive Awareness application scenarios, and could produce subtle behavior changes to the awareness applications.
Learning Similarity Measures from Pairwise Constraints with Neural Networks
Authors: Marco Maggini, Stefano Melacci, Lorenzo Sarti
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

This paper presents a novel neural network model, called Similarity Neural Network (SNN), designed to learn similarity measures for pairs of patterns exploiting binary supervision. The model guarantees to compute a non negative and symmetric measure, and shows good generalization capabilities even if a small set of supervised examples is used for training. The approximation capabilities of the proposed model are also investigated. Moreover, the experiments carried out on some benchmark datasets show that SNNs almost always outperform other similarity learning methods proposed in the literature.
REALM: Replication of Data for a Logical Group Based MANET Database
Authors: Anita Vallur, Le Gruenwald, Nick Hunter
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

Mobile Ad-Hoc Networks, or MANETs, provide communication between free-roaming mobile hosts without a fixed infrastructure. These MANETs operate under conditions of limited battery power and may also experience frequent network partitioning. This paper introduces a data replication technique called REALM (REplication of data for A Logical group based MANET database). REALM takes advantage of application semantics when determining where to place replicas of data items. It also tries to predict network partitioning and disconnection ahead of time and creates copies of data items accordingly. Experiments show that REALM increases the percentage of successful transactions with or without the presence of network disconnection and partitioning.
eCAT: a tool for automating test cases generation and execution in testing multi-agent systems
Authors: Cu D. Nguyen, Anna Perini, Paolo Tonella
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers (2008)

We introduce eCAT, a tool that supports deriving test cases semi-automatically from goal-based analysis diagrams, generates meaningful test inputs based on agent interaction ontology, and more importantly it can evolve and execute test cases automatically and continuously on a multi-agent system (MAS). Our experiments have shown that the proposed tool can exercise MAS more extensively and effectively than manual testing under the usual time constraints.