ppxscal/embedding_network huggingface dataset:
Plenary lecture 7: digital signature and hash function irregularity
Authors: Milan Tuba
Venue: Proceedings of the 8th Wseas international conference on Telecommunications and informatics (2009)

Use of computer networks is expanding in many important areas such as e-government, e-business, e-learning etc. In any such communications network it is crucial to be able to authenticate both the contents and the origin of a message. Digital signatures (based on public key schemas) are used for authentication and ideally they should provide the same guarantees as a handwritten signatures: unforgeability (only the author of a message should be able to sign his name to a message), undeniability (the author of a message should not be able to deny he signed it at a later stage) and authentication (the signature should allow the contents of the message to be authenticated). In order to provide message authentication the signature must depend on the contents of the message being signed. Two major problems with the public key-based signature schemes are that they are existentially forgeable and if the message is long then the signature will take a long time to compute. To overcome both of these problems hash functions that map a (possibly lengthy) message to a small digest h(M) are used. Among other desirable properties (the length of h(M) should be small, the function h should be a publicly known one-way function, it should destroy algebraic relationships between messages and signatures), an interesting one is that it should be 'collision-resistant', that is it should be difficult to find two messages with the same hash value. To find a collision the birthday attack is used, which shows that attacker may not need to examine too many messages before he ?nds a collision. If attacker generates random messages and computes their hash values then with probability at least ½ he ?nds a collision after generating ?2|R| messages, where |R| is the total number of possible hash values for the corresponding hash function. The real situation is even worse. In previous estimates it is always assumed that the hash function is regular, meaning that all points in the range have the same number of preimages under h. If h is not regular, fewer trials are required. Here we examine different types of irregularity of the hash function and the quantitative changes in the required number of trials to find a collision.
Voice and video capacity of a secure IEEE 802.11g wireless network
Authors: Barry E. Mullins, Jason R. Seyba, Richard A. Raines, Benjamin W.P. Ramsey, Paul D. Williams
Venue: ACM SIGMOBILE Mobile Computing and Communications Review (2009)

This paper describes an empirical evaluation of the ability of an IEEE 802.11g network to transport audio and video as well as compare audio quality in the presence and absence of an access point within an office environment using standard off-the-shelf hardware and default device configurations. The impact of securing the audio stream with WPA (WiFi Protected Access) on the perceived quality is also examined. Following the ITU-T P.800 recommendation, thirty-six human subjects assess audio and video quality using a Mean Opinion Score (MOS) on a wireless multimedia system. Experimental data suggest that securing the voice traffic has no significant effect on the quality of the audio signal received. Furthermore, an 18.4% improvement in the perceived quality of the audio signal can be achieved by routing the audio and video traffic through an access point instead of allowing the audio and video traffic to flow directly between two arbitrary nodes within a wireless local area network. Furthermore, increasing the number of conversations reduces the perceived quality of the audio signal by 23.5% and the video signal by 16.8%. Disabling video increases the perceived audio quality by 38.9%. This paper shows that the usable capacity, based on signal quality, of a standard IEEE 802.11g wireless multimedia system deployed in an office environment is two audio-only conversations or one audio/one video connection on the wireless network.
Modular Equivalence in General
Authors: Tomi Janhunen
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

The notion of modular equivalence was recently introduced in the context of a module architecture proposed for logic programs under answer set semantics [12, 6, 13]. In this paper, the module architecture is abstracted for arbitrary knowledge bases, KB-functions for short, giving rise to a universal notion of modular equivalence. A further objective of this paper is to study modular equivalence in the contexts of SAT-functions, i.e., propositional theories with a module interface, and their logic programming counterpart, known as LP-functions [6]. As regards SAT-functions, we establish the full compositionality of classical semantics. This proves modular equivalence a proper congruence relation for SAT-functions. Moreover, we address the interoperability of SAT-functions and LP-functions in terms of strongly faithful transformations in both directions. These considerations justify the proposed design of KB-functions in general and pave the way for hybrid KB-functions.
Orthogonal Quadratic Discriminant Functions for Face Recognition
Authors: Suicheng Gu, Ying Tan, Xingui He
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

Small sample size (SSS) problem is usually a limit to the robustness of learning methods in face recognition. Especially in the quadratic discriminant functions (QDF), too many parameters need to be estimated and covariance matrix of a class is usually singular. In order to overcome the SSS problems, we proposed a novel approach called orthogonal quadratic discriminant functions (OQDF). The OQDF assumes probability distribution functions of each two classes of face images have a uniform shape. Then, three OQDF models are developed. The Laplacian smoothing transform (LST) and Fisher's linear discriminant (FLD) are employed to preprocess the face images for the OQDF classifier. Finally, we evaluate our proposed algorithms on two face databases, ORL and Yale.
Fault Prediction in Aircraft Engines Using Self-Organizing Maps
Authors: Marie Cottrell, Patrice Gaubert, Cédric Eloy, Damien François, Geoffroy Hallaux, Jérôme Lacaille, Michel Verleysen
Venue: Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (2009)

Aircraft engines are designed to be used during several tens of years. Their maintenance is a challenging and costly task, for obvious security reasons. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too.The maintenance can be improved if an efficient procedure for the prediction of failures is implemented. The primary source of information on the health of the engines comes from measurement during flights. Several variables such as the core speed, the oil pressure and quantity, the fan speed, etc. are measured, together with environmental variables such as the outside temperature, altitude, aircraft speed, etc.In this paper, we describe the design of a procedure aiming at visualizing successive data measured on aircraft engines. The data are multi-dimensional measurements on the engines, which are projected on a self-organizing map in order to allow us to follow the trajectories of these data over time. The trajectories consist in a succession of points on the map, each of them corresponding to the two-dimensional projection of the multi-dimensional vector of engine measurements. Analyzing the trajectories aims at visualizing any deviation from a normal behavior, making it possible to anticipate an operation failure.However rough engine measurements are inappropriate for such an analysis; they are indeed influenced by external conditions, and may in addition vary between engines. In this work, we first process the data by a General Linear Model (GLM), to eliminate the effect of engines and of measured environmental conditions. The residuals are then used as inputs to a Self-Organizing Map for the easy visualization of trajectories.
Unsupervised Word Sense Disambiguation Using The WWW
Authors: Ioannis P. Klapaftis, Suresh Manandhar
Venue: Proceeding of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium (2006)

This paper presents a novel unsupervised methodology for automatic disambiguation of nouns found in unrestricted corpora. The proposed method is based on extending the context of a target word by querying the web, and then measuring the overlap of the extended context with the topic signatures of the different senses by using Bayes rule. The algorithm is evaluated on Semcor 2.0. The evaluation showed that the web-based extension of the target word's local context increases the amount of contextual information to perform semantic interpretation, in effect producing a disambiguation methodology, which achieves a result comparable to the performance of the best system in SENSEVAL 3.
Dimensionality reduction for hyperspectral data
Authors: John J. Benedetto, Wojciech Czaja
Venue:  (2008)

This thesis is about dimensionality reduction for hyperspectral data. Special emphasis is given to dimensionality reduction techniques known as kernel eigenmap methods and manifold learning algorithms. Kernel eigenmap methods require a nearest neighbor or a radius parameter be set. A new algorithm that does not require these neighborhood parameters is given. Most kernel eigenmap methods use the eigenvectors of the kernel as coordinates for the data. An algorithm that uses the frame potential along with subspace frames to create nonorthogonal coordinates is given. The algorithms are demonstrated on hyperspectral data. The last two chapters include analysis of representation systems for LIDAR data and motion blur estimation, respectively.
CPA beats &infin;-CFA
Authors: Frédéric Besson
Venue: Proceedings of the 11th International Workshop on Formal Techniques for Java-like Programs (2009)

Context-sensitive points-to analysis is the current most scalable technology for constructing a precise control-flow graph for large object-oriented programs. One appealing feature of this framework is that it is parametric thus allowing to trade time for precision. Typical instances of this framework are &kappa;-CFAs and Agesen's Cartesian Product Algorithm (CPA). It is common sense that &kappa;-CFAs (for increasing &kappa;s) form a hierarchy. Yet, what is the relative precision of &kappa;-CFA and CPA? Grove and Chambers [2] conjecture that CPA is more precise than &infin;-CFA. For a core object-oriented language, we formally compare the precision of &infin;-CFA and CPA. We prove that CPA is indeed strictly more precise than &infin;-CFA. On a theoretical level, this result confirms the findings of empiric studies concluding the superiority of object-sensitivity with respect to call-string sensitivity.
Providing Flexible Process Support to Project-Centered Learning
Authors: Stefano Ceri, Florian Daniel, Maristella Matera, Alessandro Raffio
Venue: IEEE Transactions on Knowledge and Data Engineering (2009)

While business process definition is becoming more and more popular as an instrument for describing human activities, there is a growing need for software tools supporting business process abstractions to help users organize and monitor their desktop work. Tools are most effective when they embed some knowledge about the process, e.g., in terms of the typical activities required by the process, so that users can execute the activities without having to define them. Tools must be lightweight and flexible, so as to enable users to create or change the process as soon as there is a new need. In this article, we first describe an application-independent approach to flexible process support by discussing the abstractions required for modeling, creating, enacting, and modifying flexible processes. Then, we show our approach at work in the context of project-centered learning. In this application, learners are challenged to perform concrete tasks in order to master specific subjects; in doing so, they have to conduct significant projects and cope with realistic (or even real-life) working conditions and scenarios. Often, students are geographically dispersed or under severe timing constraints, because these activities intertwine with their normal university activity. As a result, they need communication technology in order to interact and workflow technology in order to organize their work. The developed platform provides a comprehensible, e-learning-specific set of activities and process templates, which can be combined through a simple Web interface into project-centered collaboration processes. We discuss how the general paradigm of flexible processes was adapted to the learning concept, implemented, and experienced by students.
Concept Modeling in Multidisciplinary Research Environment
Authors: Jukka Aaltonen, Ilkka Tuikkala, Mika Saloheimo
Venue: Proceeding of the 2008 conference on Information Modelling and Knowledge Bases XIX (2008)

In a typical scientific research process there is a stage that consists of the following activities: a review of literature, an exploration of existing theories, a review of theoretical background and a definition of the terminology used. The results of these tasks are then presented through some form of conceptualization. Cross-disciplinary research settings introduce several issues when designing these conceptualizations and when sharing and communicating then between the researchers and the stakeholders of the real-world phenomena under study. In this research paper a multidisciplinary conceptualization metamodel and a practical methodology is specified to address these issues. The study demonstrates the preliminary results of utilizing this methodology in the context of an on-going business network integration research project. Additionally, it introduces the prototype of a web-based research environment providing support and an arena for collaboration.
Advances in Information Systems Development: Volume 2, 1st edition
Authors: Gabor Knapp, Gabor Knapp, Gregory Wojtkowski, Joze Zupancic, Stanislaw Wrycza
Venue:  (2007)

Information Systems Development (ISD) progresses rapidly, continually creating new challenges for the professionals involved. New concepts, approaches and techniques of systems development emerge constantly in this field. Progress in ISD comes from research as well as from practice. The aim of the Conference is to provide an international forum for the exchange of ideas and experiences between academia and industry, and to stimulate exploration of new solutions.
Speech enhancement and source separation using probabilistic models
Authors: Terrence Sejnowski, Herbert Levine
Venue:  (2008)

Statistical signal processing has been very successful. We proposed novel probabilistic models and developed efficient algorithms for two important problems: speech enhancement and source separation.Part I focused on the speech enhancement. We developed two models with efficient algorithms. The first one assumed a Gaussian Mixture Model (GMM) in the log-spectral domain for speech prior which was trained by expectation maximization (EM) algorithm. Three approximations were employed to enhance the computational efficiency. The Laplace method estimated the signal by computing the mode of the posterior distribution, either in the frequency domain or in the log-spectrum domain. The Gaussian approximation converted the GMM in the log-spectrum domain into a GMM in the frequency domain by minimizing the KL-divergency. It provided an efficient gain and noise spectrum estimation with the EM algorithm. The second one used a Gaussian scale mixture model (GSMM) as speech prior. This model specified a stochastic dependency between the log-spectra and the frequency components which can be estimated simultaneously with GSMM. The algorithms for training the model and signal estimation were developed. All these algorithms were evaluated by applying them to enhance the speeches corrupted by the speech shaped noise (SSN). The experimental results demonstrated that the proposed algorithms improved the signal-to-noise ratio and lowered the word recognition error rate.In part II, a novel probabilistic framework based on Independent Vector Analysis (IVA) was proposed to separate the convolutive mixture of sources. IVA assumed a multidimensional GMM for the source priors. The joint modeling of all frequency bins originating from the same source prevented the permutation disorder that associated with independent component analysis (ICA). The GMM source priors could adapt to the statistics of the sources and enable IVA to separate different type of signals. We developed EM algorithms for both the noiseless case and noisy case. For noiseless case, an online algorithm was developed to handle non-stationary environments. For noisy case, noise reduction was achieved together with the separation processes. The algorithms were evaluated by applying them to separate the mixtures of speech and music. The experimental results showed improved performance over other algorithms.
Agent-Based Environment for Knowledge Integration
Authors: Anna Zygmunt, Jarosław Koźlak, Leszek Siwik
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Representing knowledge with the use of ontology description languages offers several advantages arising from knowledge reusability, possibilities of carrying out reasoning processes and the use of existing concepts of knowledge integration. In this work we are going to present an environment for the integration of knowledge expressed in such a way. Guaranteeing knowledge integration is an important element during the development of the Semantic Web. Thanks to this, it is possible to obtain access to services which offer knowledge contained in various distributed databases associated with semantically described web portals. We will present the advantages of the multi-agent approach while solving this problem. Then, we will describe an example of its application in systems supporting company management knowledge in the process of constructing supply-chains.
Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging
Authors: Oscar Camara, Estanislao Oubel, Gemma Piella, Simone Balocco, Mathieu Craene, Alejandro Frangi
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

In this work, we present a registration framework for cardiac cine MRI (cMRI), tagged (tMRI) and delay-enhancement MRI (deMRI), where the two main issues to find an accurate alignment between these images have been taking into account: the presence of tags in tMRI and respiration artifacts in all sequences. A steerable pyramid image decomposition has been used for detagging purposes since it is suitable to extract high-order oriented structures by directional adaptive filtering. Shift correction of cMRI is achieved by firstly maximizing the similarity between the Long Axis and Short Axis cMRI. Subsequently, these shift-corrected images are used as target images in a rigid registration procedure with their corresponding tMRI/deMRI in order to correct their shift. The proposed registration framework has been evaluated by 840 registration tests, considerably improving the alignment of the MR images (mean RMS error of 2.04mm vs. 5.44mm).
Multi-walk Parallel Pattern Search Approach on a GPU Computing Platform
Authors: Weihang Zhu, James Curry
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

This paper studies the efficiency of using Pattern Search (PS) on bound constrained optimization functions on a Graphics Processing Unit (GPU) computing platform. Pattern Search is a direct search optimization technique that does not require derivative information on non-linear programming problems. Pattern Search is ideally suited to a GPU computing environment due to its low memory requirement and no communication between threads in a multi-walk setting. To adapt to a GPU environment, traditional Pattern Search is modified by terminating based on iterations instead of tolerance. This research designed and implemented a multi-walk Pattern Search algorithm on a GPU computing platform. Computational results are promising with a computing speedup of 100+ compared to a corresponding implementation on a single CPU.
Feasibility of Motion Planning on Directed Graphs
Authors: Zhilin Wu, Stéphane Grumbach
Venue: Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation (2009)

Because of irreversibility of movements, motion planning on directed graphs is much more intricate than that on graphs. Recently we showed that the feasibility of motion planning on acyclic and strongly connected directed graphs can be decided in time O(nm) (n,m are respectively the number of vertices and arcs of the directed graph), but left the feasibility of motion planning on (general) directed graphs open. In this paper, we complete the solution by showing that the feasibility of motion planning on directed graphs can be decided in time O(n 2 m).
College community and computer-mediated communication: the student voice
Authors: Ed Taylor
Venue:  (2008)

This qualitative dissertation study examined how students conceptualized community, and how they connected their use of communication technologies to specific aspects of community life. The data derived from a series of interviews with 11 undergraduates at a large research university in the Northwest. Results show that diversity can play a much stronger role in creating a sense of community than previously indicated in the literature; community was felt most strongly within student sub-communities, principally due to the diversity of the student population. Within sub-communities students used a variety of modes to manage their roles in the communication. However, face-to-face communication was still central to conversations revolving around serious matters, and this suggests a strong and continuing role for face-to-face communication in student development.
Promoting Critical Reflective Capabilities in a CSCL Environment
Authors: Ada W. W. Ma
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

CSCL is believed to be a powerful pedagogical process to equip students with critical reflective capabilities to be more sensitive to cultural diversity, stereotypes and multiple perspectives. The elements of collaborative learning are drawn from research findings and come up with a new model to be implemented for our learners in the coming semester using the Web as an avenue for on-line self-reflection and peer on-going critical debate. Participants are learners of Bachelor Degrees who will be assigned with group work project. They will be required to undertake a critical analysis on fashion advertisements to question the taken-for-granted assumptions that have been perpetuated inequity and injustice, accompanying with reflective journals and a reflective paper. Findings of this project will examine if the created peer support system promote learners' critical reflection.