ppxscal/embedding_network huggingface dataset:
Time-variant normal profiling for anomaly detection systems
Authors: Rex E. Gantenbein
Venue:  (2008)

Anomaly detection supports discovery of suspicious activities in a computer system by creating a normal profile of a system's behavior, then raising an alert when the system's behavior does not fit its normal profile. Unfortunately, the knowledge of what constitutes "normal" behavior is usually incomplete, and a user's usual patterns change over time; thus old normal profiles may no longer reflect a new pattern that users may have learned. For this reason, a methodology to consistently update normal profiles needs to be devised so that intrusion detection can be accomplished more effectively. The main goal of our research is to construct a time-variant normal profile for anomaly detection systems without requiring time-consuming retraining. We propose to apply a robust classification system to concurrently employed sequences of UNIX commands, which represent users' normal profiles. Subsequently, we continuously update these sequences by keeping the most recently employed patterns whose size is dynamically determined. Active window adjustment through a concept drift algorithm helps to keep relevant instances without having to contain outdated patterns as well. The ability to dynamically adapt the normal profiles provides a significant foundation for real-time intrusion detection.
Exploration and visualization of OLAP cubes with statistical tests
Authors: Carlos Ordonez, Zhibo Chen
Venue: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration (2009)

In On-Line Analytical Processing (OLAP), users explore a database cube with roll-up and drill-down operations in order to find interesting results. Most approaches rely on simple aggregations and value comparisons in order to validate findings. In this work, we propose to combine OLAP dimension lattice traversal and statistical tests to discover significant metric differences between highly similar groups. A parametric statistical test allows pair-wise comparison of neighboring cells in cuboids, providing statistical evidence about the validity of findings. We introduce a two-dimensional checkerboard visualization of the cube that allows interactive exploration to understand significant measure differences between two cuboids differing in one dimension along with associated image data. Our system is tightly integrated into a relational DBMS, by dynamically generating SQL code, which incorporates several optimizations to efficiently explore the cube, to visualize discovered cell pairs and to view associated images. We present an experimental evaluation with medical data sets focusing on finding significant relationships between risk factors and disease.
IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three Problems
Authors: Philippe Laborie
Venue: Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2009)

Since version 2.0, IBM ILOG CP Optimizer provides a new scheduling language supported by a robust and efficient automatic search. This paper illustrates both the expressivity of the modelling language and the robustness of the automatic search on three problems recently studied in the scheduling literature. We show that all three problems can easily be modelled with CP Optimizer in only a few dozen lines (the complete models are provided) and that on average the automatic search outperforms existing problem specific approaches.
Collaborative Lesson-preparing Environments: EduWiki Designing and its Applications
Authors: Yueliang Zhou, Chaohua Gong
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

Eduwiki is a version of Wiki integrated educational special needs aims to support collaborative lesson-preparing. The paper described the workflow and function of developing Eduwiki. The framework of Eduwiki and its applications were demonstrated in details, focusing on collaborative authoring, history-version comparing, Tag, modification reasons and other mechanisms. These mechanisms are effective in monitoring and recording processes of lesson-preparing. The evaluation of teachers are the important reference standards for further improving availability of Eduwiki.
The fully discrete multistep characteristic finite volume element methods for the two-dimensional generalized nerve conduction equation
Authors: Zhiyue Zhang, Zhoufeng Wang
Venue: Neural, Parallel Scientific Computations (2008)

In the paper, a fully discrete Multistep characteristic finite volume element method is introduced and analyzed for approximating the solution of a nonlinear-hyperbolic equation in 2-space variables. Piecewise quadratic trial functions and piecewise constant test functions are used to finally obtain error estimate O(Δt2+ h2). A numerical experiment is given which showed the method is practicable.
Automatic Fitting of a Deformable Face Mask Using a Single Image
Authors: Annika Kuhl, Tele Tan, Svetha Venkatesh
Venue: Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques (2009)

We propose an automatic method for person-independent fitting of a deformable 3D face mask model under varying illumination conditions. Principle Component Analysis is utilised to build a face model which is then used within a particle filter based approach to fit the mask to the image. By subdividing a coarse mask and using a novel texture mapping technique, we further apply the 3D face model to fit into lower resolution images. The illumination invariance is achieved by representing each face as a combination of harmonic images within the weighting function of the particle filter. We demonstrate the performance of our approach on the IMM Face Database and the Extended Yale Face Database B and show that it outperforms the Active Shape Models approach [6].
Enterprise-level architecture for interactive web-based 3D visualization of geo-referenced repositories
Authors: Bruno Simões, Giuseppe Conti, Stefano Piffer, Raffaele de Amicis
Venue: Proceedings of the 14th International Conference on 3D Web Technology (2009)

Interoperability, interactivity and 3D visualization are three of the most important challenges related to Geographical Information Systems. In the last few years, research on interoperability has evolved from simple exchange of spatial data files, through the establishment of standardized spatial data repositories, to the first initiatives allowing management of semantic aspects of data. Interoperability has been mainly hampered by the need to agree on standards, and to develop appropriate tools and methods. The Open Geospatial Consortium (OGC) has proposed a number of standards with the intention of promoting interoperability through the use of open services such as Web Mapping Service (WMS), Web Processing Service (WPS), Web Feature Service (WFS) and Web Coverage Service (WCS). However, there are a number of open issues regarding interactivity, 3D data visualization, fault tolerance, scalability, server-independent implementation, delayed-time transactions, to name but a few, that clearly reflect the need for further study and discussion. This paper presents a service-oriented architecture in the context of interoperable Web 3D GIS applications, designed to support features such as manipulation, processing and visual analysis of geographical information.
Supporting Awareness in Real-Time Distance Learning with a Real-World Avatar
Authors: Hiroaki Ogata, Kazuya Terai, Takaaki Shinto, Wataru Bando, Yoneo Yano
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

This paper proposes a real-world avatar in distance learning. The avatar is a very simple robot that remote students can control and supports involving two kinds of awareness for teachers and students. One is activity awareness that includes gestures such as raising the hand and nodding the head. We have developed the avatar system with Phidgets that provides a programmable interface to control motors. This paper also describes the experimentation with this system in a distant learning setting.
Protein Fragment Swapping: A Method for Asymmetric, Selective Site-Directed Recombination
Authors: Wei Zheng, Karl E. Griswold, Chris Bailey-Kellogg
Venue: Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (2009)

This paper presents a new approach to site-directed recombination, swapping combinations of selected discontiguous fragments from a source protein in place of corresponding fragments of a target protein. By being both asymmetric (differentiating source and target) and selective (swapping discontiguous fragments), our method focuses experimental effort on a more restricted portion of sequence space, constructing hybrids that are more likely to have the properties that are the objective of the experiment. Furthermore, since the source and target need to be structurally homologous only locally (rather than overall), our method supports swapping fragments from functionally important regions of a source into a target "scaffold"; e.g., to humanize an exogenous therapeutic protein. A protein fragment swapping plan is defined by the residue position boundaries of the fragments to be swapped; it is assessed by an average potential score over the resulting hybrid library, with singleton and pairwise terms evaluating the importance and fit of the swapped residues. While we prove that it is NP-hard to choose an optimal set of fragments under such a potential score, we develop an integer programming approach, which we call Swagmer, that works very well in practice. We demonstrate the effectiveness of our method in two types of swapping problem: selective recombination between beta-lactamases and activity swapping between glutathione transferases. We show that the selective recombination approach generates a better plan (in terms of resulting potential score) than a traditional site-directed recombination approach. We also show that in both cases the optimized experiment is significantly better than one that would result from stochastic methods.
Description Logic Rules
Authors: Markus Krötzsch, Sebastian Rudolph, Pascal Hitzler
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

We introduce description logic (DL) rules as a new rule-based formalism for knowledge representation in DLs. As a fragment of the Semantic Web Rule Language SWRL, DL rules allow for a tight integration with DL knowledge bases. In contrast to SWRL, however, the combination of DL rules with expressive description logics remains decidable, and we show that the DL SROIQ --the basis for the ongoing standardisation of OWL 2 --can completely internalise DL rules. On the other hand, DL rules capture many expressive features of SROIQ that are not available in simpler DLs yet. While reasoning in SROIQ is highly intractable, it turns out that DL rules can be introduced to various lightweight DLs without increasing their worst-case complexity. In particular, DL rules enable us to significantly extend the tractable DLs EL++ and DLP.
Fast algorithm for detection of reference spheres in digital panoramic radiography
Authors: Dan Brüllmann, Julia Mennickheim, Bernd d'Hoedt
Venue: Computers in Biology and Medicine (2009)

In this paper, an algorithm for detection of reference spheres from digital panoramic radiographic images is presented. The proposed algorithm was tested on a database of 107 digital panoramic radiographic images which were used for dental diagnostics. Results show that the proposed method exhibits for detection of reference spheres, a sensitivity of 97.33% and specificity of 93.85%. Performance time differed between 0.55 and 2.36s depending on image size. The aim of this work was to provide a fast ellipse detection algorithm to reduce measuring time on preoperative implant planning by lowering the computational cost.
On efficient opacity correction for over-sampled volume ray casting visualization
Authors: Timothy S. Newman
Venue:  (2008)

This dissertation focuses on investigating the correction of an artifact in oversampled volume ray casting visualizations. Specifically, new correction techniques for over-composited opacities are presented for two sampling scenarios commonly used in volume ray casting visualizations. While the only existing opacity correction technique in the literature follows a dataset homogeneity assumption, the new opacity correction techniques described in this dissertation are fast, generalized cell-by-cell approaches which introduce new opacity correction factors to avoid assuming dataset homogeneity. In addition, efficient approaches using commodity hardware to accelerate the opacity correction techniques are explored. One class of approaches exploits a programmable graphics processing unit (GPU) in performing the arithmetic operations necessary to correct the opacities. Another class exploits cluster computing environments lacking programmable GPUs. In particular, the cluster-based computation exploits multi-processing and multi-threading that utilize data parallelism. A third class is a hybrid approach which employs multi-threading using a dual core processor and a GPU. For comparison, the efficiency approaches are also extended to the existing opacity correction technique. Experimental results and analyses using volumetric datasets to measure the effectiveness of the approaches developed in the dissertation are also presented.
Complete and accurate clone detection in graph-based models
Authors: Nam H. Pham, Hoan Anh Nguyen, Tung Thanh Nguyen, Jafar M. Al-Kofahi, Tien N. Nguyen
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

Model-Driven Engineering (MDE) has become an important development framework for many large-scale software. Previous research has reported that as in traditional code-based development, cloning also occurs in MDE. However, there has been little work on clone detection in models with the limitations on detection precision and completeness. This paper presents ModelCD, a novel clone detection tool for Matlab/Simulink models, that is able to efficiently and accurately detect both exactly matched and approximate model clones. The core of ModelCD is two novel graph-based clone detection algorithms that are able to systematically and incrementally discover clones with a high degree of completeness, accuracy, and scalability. We have conducted an empirical evaluation with various experimental studies on many real-world systems to demonstrate the usefulness of our approach and to compare the performance of ModelCD with existing tools.
Active Learning with Automatic Soft Labeling for Induction of Decision Trees
Authors: Jiang Su, Sayyad Shirabad Jelber, Stan Matwin, Jin Huang
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

Decision trees have been widely used in many data mining applications due to their interpretable representation. However, learning an accurate decision tree model often requires a large amount of labeled training data. Labeling data is costly and time consuming. In this paper, we study learning decision trees with lesser labeling cost from two perspectives: data quality and data quantity. At each step of active learning process we learn a random forest and then use it to label a large quantity of unlabeled data. To overcome the large tree size caused by the machine labeling, we generate weighted (soft) labeled data using the prediction confidence of the labeling classifier. Empirical studies show that our method can significantly improve active learning in terms of labeling cost for decision tree learning, and the improvement does not sacrifice the size of decision trees.
Direct methods for numerical solution of singular integro-differential equations in classical Hölder spaces (case γ ≠ 0)
Authors: Iurie Caraus, Nikos E. Mastorakis
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

In this article we elaborate the numerical schemes of the collocation and quadrature- interpolation methods for the approximate solution of the singular integro-differential equations when the kernel has a weak singularity. The equations are defined on the arbitrary smooth closed contour of complex plane. The collocation and quadrature-interpolation methods are based on the descritization using Fejér points. Theoretical background for these methods is to be laid in classical Hölder spaces.
Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing
Authors: Ervin Ramollari, Dimitrios Kourtesis, Dimitris Dranidis, Anthony J. Simons
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

Recent years have seen the utilisation of Semantic Web Service descriptions for automating a wide range of service-related activities, with a primary focus on service discovery, composition, execution and mediation. An important area which so far has received less attention is service validation, whereby advertised services are proven to conform to required behavioural specifications. This paper proposes a method for validation of service-oriented systems through automated functional testing. The method leverages ontology-based and rule-based descriptions of service inputs, outputs, preconditions and effects (IOPE) for constructing a stateful EFSM specification. The specification is subsequently utilised for functional testing and validation using the proven Stream X-machine (SXM) testing methodology. Complete functional test sets are generated automatically at an abstract level and are then applied to concrete Web services, using test drivers created from the Web service descriptions. The testing method comes with completeness guarantees and provides a strong method for validating the behaviour of Web services.
Autonomous sensor and action model learning for mobile robots
Authors: Peter Stone
Venue:  (2008)

Autonomous mobile robots have the potential to be extremely beneficial to society due to their ability to perform tasks that are difficult or dangerous for humans. These robots will necessarily interact with their environment through the two fundamental processes of acting and sensing. Robots learn about the state of the world around them through their sensations, and they influence that state through their actions. However, in order to interact with their environment effectively, these robots must have accurate models of their sensors and actions: knowledge of what their sensations say about the state of the world and how their actions affect that state. A mobile robot's action and sensor models are typically tuned manually, a brittle and laborious process. The robot's actions and sensors may change either over time from wear or because of a novel environment's terrain or lighting. It is therefore valuable for the robot to be able to autonomously learn these models. This dissertation presents a methodology that enables mobile robots to learn their action and sensor models starting without an accurate estimate of either model. This methodology is instantiated in three robotic scenarios. First, an algorithm is presented that enables an autonomous agent to learn its action and sensor models in a class of one-dimensional settings. Experimental tests are performed on a four-legged robot, the Sony Aibo ERS-7, walking forward and backward at different speeds while facing a fixed landmark. Second, a probabilistically motivated model learning algorithm is presented that operates on the same robot walking in two dimensions with arbitrary combinations of forward, sideways, and turning velocities. Finally, an algorithm is presented to learn the action and sensor models of a very different mobile robot, an autonomous car.
Projected Gradient Based Color Image Decomposition
Authors: Vincent Duval, Jean-François Aujol, Luminita Vese
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

This work deals with color image processing, with a focus on color image decomposition. The problem of image decomposition consists in splitting an original image f into two components u and v = f ¿ u. u contains the geometric information of the original image, while v is made of the oscillating patterns of f, such as textures. We propose a numerical scheme based on a projected gradient algorithm to compute the solution of various decomposition models for color images or vector-valued images. A direct convergence proof of the scheme is provided, and some analysis on color texture modeling is given.
Ant based clustering using case based reasoning
Authors: Zahra Sadeghi, Mohammad Teshnehlab
Venue: Proceedings of the 2nd conference on European computing conference (2008)

In this paper a new ant clustering algorithm based on case based reasoning (CBR) is presented. Every ant has a case base which is updated iteratively by the process of CBR. Each ant can use its case base to find best places for dropping its load. Also ants can take advantage of the knowledge of other ants' case bases by the process of cooperation. Our simulation results demonstrated better performance in terms of accuracy and compactness of generated clusters than previous approaches of ant based clustering.
Advanced Gate Stacks for High-Mobility Semiconductors, 1st edition
Authors: A. Dimoulas, E. Gusev, P. C. McIntyre, M. Heyns
Venue:  (2007)

Will nanoelectronic devices continue to scale according to Moores law? At this moment, there is no easy answer since gate scaling is rapidly emerging as a serious roadblock for the evolution of CMOS technology. Channel engineering based on high-mobility semiconductor materials (e.g. strained Si, alternative orientation substrates, Ge or III-V compounds) could help overcome the obstacles since they offer performance enhancement. There are several concerns though. Do we know how to make complex engineered substrates (e.g. Germanium-on-Insulator)? Which are the best interface passivation methodologies and (high-k) gate dielectrics on Ge and III-V compounds? Can we process these materials in short channel transistors using flows, toolsets and know how similar to that in Si technology? How do these materials and devices behave at the nanoscale? The reader will get a clear view of what has been done so far, what is the state-of-the-art and which are the main challenges ahead before we come any close to a viable Ge and III-V MOS technology.
A NEO-SCHUMPETERIAN MODEL OF ENERGY MARKETS
Authors: Ramon Scholz, Andreas Pyka
Venue: Cybernetics and Systems (2009)

The article deals with a Neo-Schumpeterian growth model which in addition to the traditional production factors labor and capital, also includes energy. Contrary to mainstream neoclassical approaches, which assume a representative optimizing actor, the heterogeneous actors in our model behave experimentally and in their interactions path dependencies might appear. In particular, the experimental behavior focuses on product innovations in the preliminary product industries which we conceive as the energy industries. In line with the standard literature, the following questions are addressed: What are the effects of energy-saving, and what are the effects of a singular reduction of energy consumption on the economy, both in the short-and long-term? Our Neo-Schumpeterian approach allows us to generate results that are directly comparable with mainstream neoclassical models. The model is designed in a way that by switching off the Neo-Schumpeterian innovation process allows the reproduction of traditional results. Considering, however, uncertain product innovations and path dependencies alters these results and generates new insights.