ppxscal/embedding_network huggingface dataset:
A Mixed Reality Teaching and Learning Environment
Authors: Victor Callaghan, Michael Gardner, Bernard Horan, John Scott, Liping Shen, Minjuan Wang
Venue: Proceedings of the 1st international conference on Hybrid Learning and Education (2008)

This work in progress paper describes collaborative research, taking place on three continents, towards creating a `mixed reality teaching learning environment' (MiRTLE) that enables teachers and students participating in realtime mixed and online classes to interact with avatar representations of each other. The longer term hypothesis that will be investigated is that avatar representations of teachers and students will help create a sense of shared presence, engendering a sense of community and improving student engagement in online lessons. This paper explores the technology that will underpin such systems by presenting work on the use of a massively multi-user game server, based on Sun's Project Darkstar and Project Wonderland tools, to create a shared teaching environment, illustrating the process by describing the creation of a virtual classroom. We describe the Shanghai NEC eLearning system that will form the platform for the deployment of this work. As these systems will take on an increasingly global reach, we discuss how cross cultural issues will effect such systems. We conclude by outlining our future plans to test our hypothesis by deploying this technology on a live system with some 15,000 online users.
Characterizing Dark DNS Behavior
Authors: Jon Oberheide, Manish Karir, Z. Morley Mao
Venue: Proceedings of the 4th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment (2007)

Security researchers and network operators increasingly rely on information gathered from honeypots and sensors deployed on darknets, or unused address space, for attack detection. While the attack traffic gleaned from such deployments has been thoroughly scrutinized, little attention has been paid to DNS queries targeting these addresses. In this paper, we introduce the concept of dark DNS, the DNS queries associated with darknet addresses, and characterize the data collected from a large operational network by our dark DNS sensor. We discuss the implications of sensor evasion via DNS reconnaissance and emphasize the importance of reverse DNS authority when deploying darknet sensors to prevent attackers from easily evading monitored darknets. Finally, we present honeydns, a tool that complements existing network sensors and low-interaction honeypots by providing simple DNS services.
The Use of Support Vector Machine for Phase Balancing in the Distribution Feeder
Authors: M. W. Siti, A. A. Jimoh, J. A. Jordaan, D. V. Nicolae
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Phase voltage and current unbalances in power system distribution networks are major factors leading to extra losses, communication interference, equipment overloading, and malfunctioning of the protective relay, which consequently results in service quality and operation efficiency being reduced. As a better alternative to the traditional practices of manual trial and error, and the contemporary solution technique of network reconfiguration or load rearrangement, this paper investigates and proposes a novel method that is based on the use of the historical data and artificial intelligence for eliminating or minimizing phase unbalance problems. The proposed method is based on support vector machine.
Text Categorization in Non-linear Semantic Space
Authors: Claudio Biancalana, Alessandro Micarelli
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed by using a set of manually classified documents, i.e. a training collection. Term-based representation of documents has found widespread use in TC. However, one of the main shortcomings of such methods is that they largely disregard lexical semantics and, as a consequence, are not sufficiently robust with respect to variations in word usage. In this paper we design, implement, and evaluate a new text classification technique. Our main idea consists in finding a series of projections of the training data by using a new, modified LSI algorithm, projecting all training instances to the low-dimensional subspace found in the previous step, and finally inducing a binary search on the projected low-dimensional data. Our conclusion is that, with all its simplicity and efficiency, our approach is comparable to SVM accuracy on classification.
Active linear guidances for micro actuators: alternative concepts and first prototypes
Authors: Berend Denkena, Stefan Brehmeier, Hans-Christian M&#x00f6;hring
Venue: Microsystem Technologies (2008)

In micro actuated systems, active guidances provide guiding a runner frictionless and fail-safe. Thus, the research objectives within the Collaborative Research Centre CRC516 sub-project C3 are related to aerostatic and magnetic principles for active guidances. Concerning guidances using the aerostatic principle, one version with thin-film techniques as well as one without were developed, manufactured with aid of other sub-projects, and tested. The force characteristics of the prototypes were determined and the dynamic behaviour of the aerostatic guidances was analysed within experimental investigations. Furthermore, an integrated capacitive displacement measurement system was developed and successfully validated within its prototypic functionality. Concerning the magnetic principle, guidances based on repulsive forces as well as on attractive forces were analysed. Hence, different types of controllers were developed and the behaviour of the controlled system was simulated by means of Matlab/Simulink and VisualNastran.
A Visual Object Recognition System Invariant to Scale and Rotation
Authors: Yasuomi D. Sato, Jenia Jitsev, Christoph Malsburg
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

We here address the problem of scale and orientation invariant object recognition, making use of a correspondence-based mechanism, in which the identity of an object represented by sensory signals is determined by matching it to a representation stored in memory. The sensory representation is in general affected by various transformations, notably scale and rotation, thus giving rise to the fundamental problem of invariant object recognition. We focus here on a neurally plausible mechanism that deals simultaneously with identification of the object and detection of the transformation, both types of information being important for visual processing. Our mechanism is based on macrocolumnar units. These evaluate identity- and transformation-specific feature similarities, performing competitive computation on the alternatives of their own subtask, and cooperate to make a coherent global decision for the identity, scale and rotation of the object.
Extension of the Titchmarsh representation of the Mertens function M(x), and numerical support of the Riemann hypothesis
Authors: M. Aslam Chaudhry
Venue: Computers Mathematics with Applications (2008)

A closed form representation of the Mertens function, without assuming simplicity of the non-trivial zeros of the zeta function, is proved and the Titchmarsh representation of the function is recovered as a special case. We exploit a representation of the Mertens function and provide numerical support of the Riemann hypothesis and the simplicity of the zeros of the zeta function.
Knowledge Exploitation through Modeling, Navigation and Querying, an Ontology-Based Integrated Architecture
Authors: Giorgos Kourlimpinis, Giorgos Gyftodimos, John Psarras, Dimitris Askounis
Venue: Proceedings of the 2008 Panhellenic Conference on Informatics (2008)

Knowledge Management has received great and ongoing attention during the last decades. The amount of knowledge and the interest for knowledge exploitation has been the spark for numerous attempts and proposals concerning knowledge representation, storage, usage, querying, acquisition and deduction. Ontologies offer an intuitive and expressive tool for these objectives. In this paper we propose an integrative approach and we present a software tool architecture that supports dynamic knowledge representation and exploration in a simple yet expressive manner, offering advanced querying and navigation abilities supporting the goal of knowledge exploitation and development, without presupposing deep modeling experience.
Task Segmentation in a Mobile Robot by mnSOM and Clustering with Spatio-temporal Contiguity
Authors: M. Aziz Muslim, Masumi Ishikawa, Tetsuo Furukawa
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

In our previous study, task segmentation by mnSOM implicitly assumes that winner modules corresponding to subsequences in the same class share the same label. This paper proposes to do task segmentation by applying various clustering methods to the resulting mnSOM without using the above assumption. Firstly we use the conventional hierarchical clustering. It assumes that the distances between any pair of modules are provided with precision, but this is not exactly true. Accordingly, this is followed by a clustering based on only the distance between spatially adjacent modules with modification by their temporal contiguity. This clustering with spatio-temporal contiguity provides superior performance to the conventional hierarchical clustering and comparable performance with mnSOM using the implicit assumption.
Photoconsistent Relative Pose Estimation between a PMD 2D3D-Camera and Multiple Intensity Cameras
Authors: Christian Beder, Ingo Schiller, Reinhard Koch
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

Active range cameras based on the Photonic Mixer Device (PMD) allow to capture low-resolution depth images of dynamic scenes at high frame rates. To use such devices together with high resolution optical cameras (e.g. in media production) the relative pose of the cameras with respect to each other has to be determined. This task becomes even more challenging, if the camera is to be moved and the scene is highly dynamic.We will present an efficient algorithm for the estimation of the relative pose between a single 2D3D-camera with respect to several optical cameras. The camera geometry together with an intensity consistency criterion will be used to derive a suitable cost function, which will be optimized using gradient descend. It will be shown, how the gradient of the cost function can be efficiently computed from the gradient images of the high resolution optical cameras.We will show, that the proposed method allows to track and to refine the pose of a moving 2D3D-camera for fully dynamic scenes.
A PTAS for Static Priority Real-Time Scheduling with Resource Augmentation
Authors: Friedrich Eisenbrand, Thomas Rothvoß
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We present a polynomial time approximation scheme for the real-time scheduling problem with fixed priorities when resource augmentation is allowed. For a fixed &epsilon;> 0, our algorithm computes an assignment using at most (1 + &epsilon;)&middot;OPT + 1 processors in polynomial time, which is feasible if the processors have speed 1 + &epsilon;. We also show that, unless P = NP, there does not exist an asymptotic FPTAS for this problem.
Efficient Similarity Search for Tree-Structured Data
Authors: Guoliang Li, Xuhui Liu, Jianhua Feng, Lizhu Zhou
Venue: Proceedings of the 20th international conference on Scientific and Statistical Database Management (2008)

Tree-structured data are becoming ubiquitous nowadays and manipulating them based on similarity is essential for many applications. Although similarity search on textual data has been extensively studied, searching for similar trees is still an open problem due to the high complexity of computing the similarity between trees, especially for large numbers of tress. In this paper, we propose to transform tree-structured data into strings with a one-to-one mapping. We prove that the edit distance of the corresponding strings forms a bound for the similarity measures between trees, including tree edit distance, largest common subtrees and smallest common super-trees. Based on the theoretical analysis, we can employ any existing algorithm of approximate string search for effective similarity search on trees. Moreover, we embed the bound into a filter-and-refine framework for facilitating similarity search on tree-structured data. The experimental results show that our algorithm achieves high performance and outperforms state-of-the-art methods significantly. Our method is especially suitable for accelerating similarity query processing on large numbers of trees in massive datasets.
Advanced method for cryptographic protocol verification
Authors: Nabil El Kadhi, Hazem El-Gendy
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

Based on a previous work using abstract interpretation for secret property verification, we propose a new method for protocol verification combining abstract interpretation for Input/Output set description and finite state machine for property propagation and verification. Generating Input/Output set for FSM is a bit difficult specially when one want to apply such technique on cryptographic protocol implementation. It is true that one can verify protocol specification, but in such case one cannot discover any significant logical follows introduced by implementation. To overcome this, we develop a particular semantic defined for cryptographic protocol verification, that facilitates verifying or detecting a large set of faults and attacks. Faults and attacks will be described as scenarios with a set of preconditions and post conditions. The FSM based method is used to discover a transition is possible from an initial set (inputs) to a final state describing a fault or a global attack state. This semantic along with the model and the verification tool SSPV allow the prove of more key security properties.
Voyagers and voyeurs: Supporting asynchronous collaborative visualization
Authors: Jeffrey Heer, Fernanda B. Viégas, Martin Wattenberg
Venue: Communications of the ACM (2009)

This article describes mechanisms for asynchronous collaboration in the context of information visualization, recasting visualizations as not just analytic tools, but social spaces. We contribute the design and implementation of sense.us, a Web site supporting asynchronous collaboration across a variety of visualization types. The site supports view sharing, discussion, graphical annotation, and social navigation and includes novel interaction elements. We report the results of user studies of the system, observing emergent patterns of social data analysis, including cycles of observation and hypothesis, and the complementary roles of social navigation and data-driven exploration.
Dynamics of Window-Based Network Congestion Control System
Authors: Hong-Yong Yang, Fu-Sheng Wang, Xun-Lin Zhu, Si-Ying Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

A class of window-based network congestion control system with communication delays is studied. By analyzing the network system with communication delay, a critical value of the window size to ensure the stability of network is obtained, and a critical value of the delay to ensure the system stability is presented. Enlarging the delay across the critical value, we find that the congestion control system exhibits Hopf bifurcation.
A HMM Speech Recognition System Based on FPGA
Authors: Sujuan Ke, Yibin Hou, Zhangqin Huang, Hui Li
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

This paper introduces an embedded speech recognition system, including both hardware and software frameworks, based on an FPGA platform and the HMM algorithm. The paper also proposes an approach to the design and implementation of an IP core for the forward algorithm, as well as simulating the algorithm. The main advantage of this approach is the reduction of processing time in speech recognition because the algorithm is implemented by dedicated hardware. The experimental result shows the system performs well and is helpful for speech recognition research based on an IP core in an embedded platform.
Improved Approximation Guarantees through Higher Levels of SDP Hierarchies
Authors: Eden Chlamtac, Gyanit Singh
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

For every fixed ¿ ¿ 0, we give an algorithm that, given an n-vertex 3-uniform hypergraph containing an independent set of size ¿n, finds an independent set of size $n^{\Omega(\gamma^2)}$. This improves upon a recent result of Chlamtac, which, for a fixed ¿> 0, finds an independent set of size n ¿ in any 3-uniform hypergraph containing an independent set of size $(\frac12-\varepsilon)n$. The main feature of this algorithm is that, for fixed ¿, it uses the ¿(1/¿ 2)-level of a hierarchy of semidefinite programming (SDP) relaxations. On the other hand, we show that for at least one hierarchy which gives such a guarantee, 1/¿ levels yield no non-trivial guarantee. Thus, this is a first SDP-based algorithm for which the approximation guarantee improves indefinitely as one uses progressively higher-level relaxations.
Interactive Learning of Data Structures and Algorithmic Schemes
Authors: Clara Segura, Isabel Pita, Rafael Vado Vírseda, Ana Isabel Saiz, Pablo Soler
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

We present an interactive environment called Vedya for the visualization of data structures and algorithmic schemes which can be used as a very useful educational tool in Computer Science. The integration of Vedya and the Virtual Campus of the Complutense University of Madrid has allowed us to manage the whole administration of the individual students' homework, including generating exercises, tests, grading delivered homework, and storing the achieved results. The part of the system concerning data structures has been evaluated during the last academic course 2006/07. By means of the Vedya tool, the students benefited from complementary and interactive material, facilitating the intuitive comprehension of most typical operations of classical data structures without any restriction of time or material.
Classification of Sporadic and BRCA1 Ovarian Cancer Based on a Genome-Wide Study of Copy Number Variations
Authors: Anneleen Daemen, Olivier Gevaert, Karin Leunen, Vanessa Vanspauwen, Geneviève Michils, Eric Legius, Ignace Vergote, Bart Moor
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

Motivation: Although studies have shown that genetic alterations are causally involved in numerous human diseases, still not much is known about the molecular mechanisms involved in sporadic and hereditary ovarian tumorigenesis.Methods: Array comparative genomic hybridization (array CGH) was performed in 8 sporadic and 5 BRCA1 related ovarian cancer patients.Results: Chromosomal regions characterizing each group of sporadic and BRCA1 related ovarian cancer were gathered using multiple sample hidden Markov Models (HMM). The differential regions were used as features for classification. Least Squares Support Vector Machines (LS-SVM), a supervised classification method, resulted in a leave-one-out accuracy of 84.6%, sensitivity of 100% and specificity of 75%.Conclusion: The combination of multiple sample HMMs for the detection of copy number alterations with LS-SVM classifiers offers an improved methodological approach for classification based on copy number alterations. Additionally, this approach limits the chromosomal regions necessary to distinguish sporadic from hereditary ovarian cancer.
Idea and Practice for Paperless Education
Authors: Yiming Chen, Lianghai Wu
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

This article introduces the concept and style of E-Learning, analyzes the Blog's characteristic while being applied in teaching, then designs an E-Learning platform model based on Blog. At last, it also discusses the model's function, characteristic and existing practice problems.
Information Entropy and Co---entropy of Crisp and Fuzzy Granulations
Authors: Daniela Bianucci, Gianpiero Cattaneo, Davide Ciucci
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

The standard approach to information entropy applied to partitions of a universe is equivalently formulated as the entropy of the corresponding crisp identity resolutions, interpreted as crisp granulations, by the corresponding characteristic functionals. Moreover, in this crisp context the co---entropy notion is introduced. The extension to the case of fuzzy identity resolutions, a particular case of fuzzy granulation, is studied.
Tabu Search Algorithm for Two-Layer Networks Design Problem
Authors: Krzysztof Lenarski, Bartosz Czajka, Iwona Pozniak-Koszalka, Andrzej Kasprzak
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

This paper concerns Tabu Search approach for solving two-layer networks design problem. We present a modular case of two-layer network dimensioning in networks with non-bifurcated flows, what cause stated problem NP complete. Hardness of presented problem makes it impossible (for large networks) to attain optimal results with deterministic methods. An estimate solution may be reachable in satisfying time using heuristic methods. The main goal of this paper is to examine Tabu Search parameters and structures to achieve the best effect. To carry out researches we designed computer experimentation system. The created application allows to make simulations along with the complex experiments showing obtained results in convenient way utilizing presentation module. The results of research are shown on examples and discussed.
Motion Robust Magnetic Susceptibility and Field Inhomogeneity Estimation Using Regularized Image Restoration Techniques for fMRI
Authors: Desmond Teck Yeo, Jeffrey A. Fessler, Boklye Kim
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

In functional MRI, head motion may cause dynamic nonlinear field-inhomogeneity changes, especially with large out-of-plane rotations. This may lead to dynamic geometric distortion or blurring in the time series, which may reduce activation detection accuracy. The use of image registration to estimate dynamic field inhomogeneity maps from a static field map is not sufficient in the presence of such rotations. This paper introduces a retrospective approach to estimate magnetic susceptibility induced field maps of an object in motion, given a static susceptibility induced field map and the associated object motion parameters. It estimates a susceptibility map from a static field map using regularized image restoration techniques, and applies rigid body motion to the former. The dynamic field map is then computed using susceptibility voxel convolution. The method addresses field map changes due to out-of-plane rotations during time series acquisition and does not involve real time field map acquisitions.