ppxscal/embedding_network huggingface dataset:
Automated Segmentation Using a Fast Implementation of the Chan-Vese Models
Authors: Huan Xu, Xiao-Feng Wang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In this paper, a fast implementation method of Chan-Vese models is proposed, which does not require numerical solutions of PDEs. The advantages of traditional level set methods, such as automatic handling of topological changes, are also preserved. The whole process is described as follows: First, the Otsu thresholding method is adopted to obtain the initial contours for the following level set evolution. Then, the initial curves are evolved to approach the true boundaries of objects by using the proposed fast implementation method of Chan-Vese model. Experimental results on some real and synthetic images show that our proposed approach is capable of automatically segmenting images with a low time-consumption.
A fuzzy-neural and multiple-bucket approach for estimating lot cycle time in a wafer fab with dynamic product mix
Authors: Toly Chen
Venue: Computers and Industrial Engineering (2008)

A fuzzy-neural and multiple-bucket approach is proposed in this study for lot cycle time estimation in a wafer fab with dynamic product mix, which was seldom thoroughly investigated in the past studies. The proposed methodology is composed of two parts. In the first part, the multiple-bucket approach is applied to consider the future release plan of the wafer fab. Subsequently, the FCM-FBPN approach is applied to estimate the cycle time of every lot in the wafer fab. The buckets obtained in the first part become additional inputs to the FBPN. In this way, the fluctuation in the product mix since the release of a wafer lot can be considered in estimating the cycle time of the wafer lot. According to experimental results, the estimation accuracy of the proposed methodology was significantly better than those of many existing approaches. Other findings include that a large number of buckets were beneficial to the estimation accuracy, and might not worsen the efficiency of the proposed methodology.
On the Formal Development of Safety-Critical Software
Authors: Andy Galloway, Frantz Iwu, John Mcdermid, Ian Toyn
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

We reflect on the formal development models applicable to embedded control systems in light of our experience with safety-critical applications from the aerospace domain. This leads us to propose two complementary enhancements to Parnas' four-variable model, one elaborating the structure outside the control computer, and the other elaborating the structure inside the control computer. We then identify several challenges which illustrate why formal development in this domain is difficult, and report our own progress in meeting these challenges. Finally, we outline the residual issues, which form the agenda for our future work.
Logical Analysis of Mappings between Medical Classification Systems
Authors: Elena Cardillo, Claudio Eccher, Luciano Serafini, Andrei Tamilin
Venue: Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications (2008)

Medical classification systems provide an essential instrument for unambiguously labeling clinical concepts in processes and services in healthcare and for improving the accessibility and elaboration of the medical content in clinical information systems. Over the last two decades the standardization efforts have established a number of classification systems as well as conversion mappings between them. Although these mappings represent the agreement reached between human specialists who devised them, there is no explicit formal reference establishing the precise meaning of the mappings. In this work we close this semantic gap by applying the results that have been recently reached in the area of AI and the Semantic Web on the formalization and analysis of mappings between heterogeneous conceptualizations. Practically, we focus on two classification systems which have received great widespread and preference within the European Union, namely ICPC-2 (International Classification of Primary Care) and ICD-10 (International Classification of Diseases). The particular contributions of this work are: the logical encoding in OWL of ICPC-2 and ICD-10 classifications; the formalization of the existing ICPC-ICD conversion mappings in terms of OWL axioms and further verification of its coherence using the logical reasoning; and finally, the outline of the other semantic techniques for automated analysis of implications of future mapping changes between ICPC and ICD classifications.
Vision Based Navigation Algorithm for Autonomic Landing of UAV without Heading & Attitude Sensors
Authors: Tang Daquan, Zhang Hongyue
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

A navigation algorithm completely based on machine vision for the autonomic landing of UAV without heading and attitude sensors is presented. The image of an airport runway lighting acquired by the airborne camera is determined by the aircraft’s attitude, heading and position relative to the runway. The image gradients of the centerline and threshold bar of runway lighting, the lognitudinal mean and the lateral mean of the image coordinates of the observed airport lights, etc., can be calculated and used as the measurements in a extended Kalman filter. The Kalman filter then generates the estimates of the aircraft’s motion parameters, including position and velocity relative to the ground, and attitude, heading and rotating rate. The simulation results indicate that the navigation algorithm meet the navigation accuracy requirements for various FAA categories of landing.
An Architecture for Combining Semantic Web Techniques with Intelligent Tutoring Systems
Authors: Pedro J. Muñoz Merino, Carlos Delgado Kloos
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

There are several possible applications of Semantic Web techniques in Intelligent Tutoring Systems (ITSs) and important advantages can be obtained from their application. This paper presents an architecture to combine Semantic Web techniques with ITSs, explaining its elements, relationships, challenges, and the different design criterions, offering some guidelines to make decisions when different implementation solutions are possible. We have implemented an instance of this architecture using the XTutor ITS and the CWM (Closed World Machine) Semantic Web reasoner. The implemented framework permits personalization of problems with hints for students. The paper also describes this specific implementation of the general architecture as well as some user adaptation examples with the implemented framework.
The Problem of False Alarms: Evaluation with Snort and DARPA 1999 Dataset
Authors: Gina C. Tjhai, Maria Papadaki, Steven M. Furnell, Nathan L. Clarke
Venue: Proceedings of the 5th international conference on Trust, Privacy and Security in Digital Business (2008)

It is a common issue that an Intrusion Detection System (IDS) might generate thousand of alerts per day. The problem has got worse by the fact that IT infrastructure have become larger and more complicated, the number of generated alarms that need to be reviewed can escalate rapidly, making the task very difficult to manage. Moreover, a significant problem facing current IDS technology now is the high level of false alarms. The main purpose of this paper is to investigate the extent of false alarms problem in Snort, using the 1999 DARPA IDS evaluation dataset. A thorough investigation has been carried out to assess the accuracy of alerts generated by Snort IDS. Significantly, this experiment has revealed an unexpected result; with 69% of total generated alerts are considered to be false alarms.
Summary-based pointer analysis framework for modular bug finding
Authors: Stephen A. Edwards, Alfred V. Aho
Venue:  (2008)

Modern society is irreversibly dependent on computers and, consequently, on software. However, as the complexity of programs increase, so does the number of defects within them. To alleviate the problem, automated techniques are constantly used to improve software quality. Static analysis is one such approach in which violations of correctness properties are searched and reported. Static analysis has many advantages, but it is necessarily conservative because it symbolically executes the program instead of using real inputs, and it considers all possible executions simultaneously. Being conservative often means issuing false alarms, or missing real program errors. Pointer variables are a challenging aspect of many languages that can force static analysis tools to be overly conservative. It is often unclear what variables are affected by pointer-manipulating expressions, and aliasing between variables is one of the banes of program analysis. To alleviate that, a common solution is to allow the programmer to provide annotations such as declaring a variable as unaliased in a given scope, or providing special constructs such as the "never-null" pointer of Cyclone. However, programmers rarely keep these annotations up-to-date. The solution is to provide some form of pointer analysis, which derives useful information about pointer variables in the program. An appropriate pointer analysis equips the static tool so that it is capable of reporting more errors without risking too many false alarms. This dissertation proposes a methodology for pointer analysis that is specially tailored for "modular bug finding." It presents a new analysis space for pointer analysis, defined by finer-grain "dimensions of precision," which allows us to explore and evaluate a variety of different algorithms to achieve better trade-offs between analysis precision and efficiency. This framework is developed around a new abstraction for computing points-to sets, the Assign-Fetch Graph, that has many interesting features. Empirical evaluation shows promising results, as some unknown errors in well-known applications were discovered.
Self-Monitoring of Non-functional Requirements in Reactive Autonomic Systems Framework: A Multi-agent Systems Approach
Authors: Heng Kuang, Olga Ormandjieva
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

The growing trend toward developing complex software systems has highlighted the need to build software Non-Functional Requirements (NFR) into the system model. This research proposes a Multi-Agent Systems (MAS) approach in Reactive Autonomic Systems (RAS) whose architecture, configuration, and self-monitoring of the NFR at runtime are specified in a single formal framework. The proposed Reactive Autonomic System Framework (RASF) in this paper has required expressive power to model the RAS, NFR and their runtime self-monitoring mechanism, which mechanism is implemented by an intelligent control loop to collect data, analyze them and provide timely feedback on the NFR satisfaction.
Recursive Algorithms for a Novel Identification Criterion
Authors: Yue Zhao, Guiming Luo, Wook-Hyun Kwon
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

A new identification criterion is concerned for disturbed system with color noise in this paper. The cost function considers both the errors between the actual outputs and the estimation result and the difference of each estimation error. Under this performance function, a new recursive algorithm DSCN (Disturbed System with Color Noise) is proposed. It could reduce the influence of noise in system identification, further decrease the prediction error. Several simulation examples are given to illustrate the method's anti-disturbance performance.
Exploiting SIMD Parallelism with the CGiS Compiler Framework
Authors: Nicolas Fritz, Philipp Lucas, Reinhard Wilhelm
Venue: Languages and Compilers for Parallel Computing: 20th International Workshop, LCPC 2007, Urbana, IL, USA, October 11-13, 2007, Revised Selected Papers (2007)

Today's desktop PCs feature a variety of parallel processing units. Developing applications that exploit this parallelism is a demanding task, and a programmer has to obtain detailed knowledge about the hardware for efficient implementation. CGiS is a data-parallel programming language providing a unified abstraction for two parallel processing units: graphics processing units (GPUs) and the vector processing units of CPUs. The CGiS compiler framework fully virtualizes the differences in capability and accessibility by mapping an abstract data-parallel programming model on those targets. The applicability of CGiS for GPUs has been shown in previous work; this work presents the extension of the framework for SIMD instruction sets of CPUs. We show how to overcome the obstacles in mapping the abstract programming model of CGiS to the SIMD hardware. Our experimental results underline the viability of this approach: Real-world applications can be implemented easily with CGiS and result in efficient code.
Serial Processing of Emotional Type and Intensity: Evidence from an ERP Study
Authors: Nugraha P. Utama, Atsushi Takemoto, Yasuharu Koike, Katsuki Nakamura
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

ERP responses were examined while subjects were identifying type of facial emotion as well as assessing intensity of facial emotion. We found a significant correlation between the magnitude of P100 response and the correct identification of type of facial emotion over the right posterior region and that between the magnitude of N170 response and the assessment of intensity of facial emotion over the right posterior and left frontal regions. Finding of these significant correlations from the same right occipital region suggested that the human brain processes information about facial emotion serially; type of facial emotion is processed first and thereafter its saliency or intensity level.
A Supercompiler for Core Haskell
Authors: Neil Mitchell, Colin Runciman
Venue: Implementation and Application of Functional Languages: 19th International Workshop, IFL 2007, Freiburg, Germany, September 27-29, 2007. Revised Selected Papers (2008)

Haskell is a functional language, with features such as higher order functions and lazy evaluation, which allow succinct programs. These high-level features present many challenges for optimising compilers. We report practical experiments using novel variants of supercompilation, with special attention to let bindings and the generalisation technique.
Virtual policy communities and responsive governance: Redesigning on-line debates
Authors: Victor Bekkers
Venue: Information Polity (2004)

In this article has investigated how we can improve the use of the Internet to design new virtual governance arrangements that take into account the somewhat disappointing results of on-line debates and use the experiences of more virtual community-based approaches of deliberation and learning. Four sources of inspiration are explored: a) experiences with on-line debates, b) the functioning of communities on the internet, especially the Linux community, c) experiences with community of practice and d) experiences with so-called virtual policy communities. The article ends with a number of design principles that can be used to shape governance arrangements in which public participation, deliberation and learning are important political values and which can help us to redesign on-line debates.
Multifractal Analysis on the Sphere
Authors: Emilie Koenig, Pierre Chainais
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

A new generation of instruments in astrophysics or vision now provide spherical data. These spherical data may present a self-similarity property while no spherical analysis tool is yet available to characterize this property. In this paper we present a first numerical study of the extension of multifractal analysis onto the sphere using spherical wavelet transforms. We use a model of multifractal spherical textures as a reference to test this approach. The results of the spherical analysis appear qualitatively satisfactory but not as accurate as those of the usual 2D multifractal analysis.
Learning Disjointness
Authors: Johanna Völker, Denny Vrandečić, York Sure, Andreas Hotho
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

An increasing number of applications benefits from light-weight ontologies, or to put it differently "a little semantics goes a long way". However, our experience indicates that more expressiveness can offer significant advantages. Introducing disjointness axioms, for instance, greatly facilitates consistency checking and the automatic evaluation of ontologies. In an extensive user study we discovered that proper modeling of disjointness is a difficult and very time-consuming task. We therefore developed an approach to automatically enrich learned or manually engineered ontologies with disjointness axioms. This approach relies on several methods for obtaining syntactic and semantic evidence from different sources which we believe to provide a solid base for learning disjointness. After thoroughly evaluating the implementation of our approach we think that in future ontology engineering environments the automatic discovery of disjointness axioms may help to increase the richness, quality and usefulness of any given ontology.
Fast track article: Secure data aggregation in wireless sensor networks: A watermark based authentication supportive approach
Authors: Wei Zhang, Yonghe Liu, Sajal K. Das, Pradip De
Venue: Pervasive and Mobile Computing (2008)

In-network processing presents a critical challenge for data authentication in wireless sensor networks (WSNs). Current schemes relying on Message Authentication Code (MAC) cannot provide natural support for this operation since even a slight modification to the data invalidates the MAC. Although some recent works propose using privacy homomorphism to support in-network processing, they can only work for some specific query-based aggregation functions, e.g. SUM, average, etc. In this paper, based on digital watermarking, we propose an end-to-end, statistical approach for data authentication that provides inherent support for in-network processing. In this scheme, authentication information is modulated as watermark and superposed on the sensory data at the sensor nodes. The watermarked data can be aggregated by the intermediate nodes without incurring any en route checking. Upon reception of the sensory data, the data sink is able to authenticate the data by validating the watermark, thereby detecting whether the data has been illegitimately altered. In this way, the aggregation-survivable authentication information is only added at the sources and checked by the data sink, without any involvement of intermediate nodes. Furthermore, the simple operation of watermark embedding and complex operation of watermark detection provide a natural solution of function partitioning between the resource limited sensor nodes and the resource abundant data sink. In addition, the watermark can be embedded in both spatial and temporal domains to provide the flexibility between the detection time and detection granularity. The simulation results show that the proposed scheme can successfully authenticate the sensory data with high confidence.
On the Selection of a Classification Technique for the Representation and Recognition of Dynamic Gestures
Authors: Héctor H. Avilés, Wendy Aguilar, Luis A. Pineda
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

Previous evaluations of gesture recognition techniques have been focused on classification performance, while ignoring other relevant issues such as knowledge description, feature selection, error distribution and learning performance. In this paper, we present an empirical comparison of decision trees, neural networks and hidden Markov models for visual gesture recognition following these criteria. Our results show that none of these techniques is a definitive alternative for all these issues. While neural nets and hidden Markov models show the highest recognition rates, they sacrifice clarity of its knowledge; decision trees, on the other hand, are easy to create and analyze. Moreover, error dispersion is higher with neural nets. This information could be useful to develop a general computational theory of gestures. For the experiments, a database of 9 gestures with more than 7000 samples taken from 15 people was used.
Manufacturing machines: a holonic approach
Authors: A. Epureanu, F. B., V. Marinescu, M. Banu, I. Constantin
Venue: Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation (2008)

Our paper objective is to propose a new concept of holonic control applied to reconfigurable machines. The research aims by holonic approach of reconfigurable manufacturing system to obtain radically new, self-adaptive, rapidly reconfigurable, holonic machines for batch industrial production in open-ended and rapid changing real-market environments. The holonic concept can be applied on both hardware and software levels. The generic holon is defined as a computational entity or a computational entity associated with a physical part. In the paper is show an example of how holonic approach can be implemented by developing the next generation of manufacturing machines.
Using Trust for Secure Collaboration in Uncertain Environments
Authors: Vinny Cahill, Elizabeth Gray, Jean-Marc Seigneur, Christian D. Jensen, Yong Chen, Brian Shand, Nathan Dimmock, Andy Twigg, Jean Bacon, Colin English, Waleed Wagealla, Sotirios Terzis, Paddy Nixon, Giovanna di Marzo Serugendo, Ciaran Bryce, Marco Carbone, Karl Krukow, Mogens Nielsen
Venue: IEEE Pervasive Computing (2003)

The SECURE project is investigating the design of security mechanisms for pervasive computing based on the human notion of trust. Our work addresses how entities that encounter each other in unfamiliar pervasive computing environments can overcome initial suspicion to allow secure collaboration to take place.
Local Maximal Matching and Local 2-Approximation for Vertex Cover in UDGs
Authors: Andreas Wiese, Evangelos Kranakis
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

We present 1 ¿ ¿ approximation algorithms for the maximum matching problem in location aware unit disc graphs and in growth-bounded graphs. The algorithm for unit disk graph is local in the sense that whether or not an edge is in the matching depends only on other vertices which are at most a constant number of hops away from it. The algorithm for growth-bounded graphs needs at most $O\left(\log\triangle\log^{*}n\right.+$ $\left.\frac{1}{\epsilon}^{O(1)}\cdot\log^{*}n\right)$ communication rounds during its execution. Using these matching algorithms we can compute vertex covers of the respective graph classes whose size are at most twice the optimal.