ppxscal/embedding_network huggingface dataset:
Compositional Specification of Web Services Via Behavioural Equivalence of Nets: A Case Study
Authors: Filippo Bonchi, Antonio Brogi, Sara Corfini, Fabio Gadducci
Venue: Proceedings of the 29th international conference on Applications and Theory of Petri Nets (2008)

Web services represent a promising technology for the development of distributed heterogeneous software systems. In this setting, a major issue is to establish whether two services can be used interchangeably in any context. This paper illustrates -- through a concrete scenario from banking systems -- how a suitable notion of behavioural equivalence over Petri nets can be effectively employed for checking the correctness of service specifications and the replaceability of (sub)services.
Modified ART2A-DWNN for Automatic Digital Modulation Recognition
Authors: Xuexia Wang, Zhilu Wu, Yaqin Zhao, Guanghui Ren
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

A modified ART2A-DWNN for automatic digital modulation recognition is proposed in this paper. Daubechies wavelet "db9" is chosen instead of "morlet" wavelet as the mother wavelet in ART2A-DWNN because of its compactness and orthonormality. Simulations have been carried out with the modulated signals corrupted by Gaussian noise to evaluate the performance of the proposed method. Recognition capability, noise immunity and convenience of accommodating new patterns of the modified ART2A-DWNN are simulated and analyzed. The experimental results have indicated the advantages of the modified method. Comparing the performance of the two ART2A-DWNNs, the modified ART2A-DWNN has higher recognition capability than the one with "morlet" wavelet.
Automatic Composition of Services with Security Policies
Authors: Yannick Chevalier, Mohammed Anis Mekki, Michaël Rusinowitch
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Automatic composition of web services is a challenging task. Many works have considered simplified automata models that abstract away from the structure of messages exchanged by the services. For the domain of security services (such as digital signing or timestamping) we propose a novel approach to automated composition of services based on their security policies. The approach amounts to collecting the constraints on messages, parameters and control flow from the components services and the goal service requirements. A constraint solver checks the feasability of the composition — possibly adapting the message structure while preserving the semantics — and displays the service composition as a message sequence chart. The resulting composed service can be verified automatically for ensuring that it cannot be subject to active attacks from intruders. The services that are input to our system are provided in a declarative way using a high level specification language. The approach is fully automatic and we show on a case-study how it succeeds in deriving a composed service that is currently proposed as a product by OpenTrust.
Visualizing Software Architectural Design Decisions
Authors: Larix Lee, Philippe Kruchten
Venue: Proceedings of the 2nd European conference on Software Architecture (2008)

Software architecture can be represented as a set of design decisions. Exploring and analyzing architectural design decisions are difficult due to how the decisions are represented and displayed. We describe four visualization aspects that apply to architectural design decision exploration and analysis: 1) tabular listing; 2) decision structure visualization; 3) decision chronology visualization; and 4) decision impact visualization. These aspects address some situations where visualization helps people understand and utilize decisions.
Particle Swarm Optimization with Variable Population Size
Authors: Laura Lanzarini, Victoria Leza, Armando Giusti
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

At present, the optimization problem resolution is a topic of great interest, which has fostered the development of several computer methods forsolving them.Particle Swarm Optimization (PSO) is a metaheuristics which has successfully been used in the resolution of a wider range of optimization problems, including neural network training and function minimization. In its original definition, PSO makes use, during the overall adaptive process, of a population made up by a fixed number of solutions.This paper presents a new extension of PSO, called VarPSO, incorporating the concepts of age and neighborhood to allow varying the size of the population. In this way, the quality of the solution to be obtained will not be affected by the used swarm's size.The method here proposed is applied to the resolution of some complex functions, finding better results than those typically achieved using a fixed size population.
Using Patterns to Capture Architectural Decisions
Authors: Neil B. Harrison, Paris Avgeriou, Uwe Zdun
Venue: IEEE Software (2007)

All of software design involves developers making decisions and reifying those decisions in code. The decisions made during software architecting are particularly significant, as they have systemwide implications, especially on quality attributes. However, architects often fail to document their decisions well. This leads to architectural erosion: decisions made during later development might conflict with the original architectural decisions and thus cause significant system disruptions. Researchers are investigating various methods and tools to help architects effectively document their decisions. However, such documentation remains difficult, so we often lose knowledge of key architectural decisions. Architecture patterns can be an important way to capture and recover this knowledge. They are easy to use and provide a rich set of information about rationale, consequences, and related decisions. This article is part of a special issue on software patterns.
Approximate Parameter Learning in Conditional Random Fields: An Empirical Investigation
Authors: Filip Korč, Wolfgang Förstner
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the parameter likelihood gradient. We show, as opposed to [1][2], that these parameter learning methods can be improved and evaluate the resulting performance employing different inference techniques. We show that the approximation based on penalized pseudo-likelihood (PPL) in combination with the Maximum A Posteriori (MAP) inference yields results comparable to other state of the art approaches, while providing advantages to formulating parameter learning as a convex optimization problem. Eventually, we demonstrate applicability on the task of detecting man-made structures in natural images.
Fine hierarchies and m-reducibilities in theoretical computer science
Authors: Victor L. Selivanov
Venue: Theoretical Computer Science (2008)

This is a survey of results about versions of fine hierarchies and many-one reducibilities that appear in different parts of theoretical computer science. These notions and related techniques play a crucial role in understanding complexity of finite and infinite computations. We try not only to present the corresponding notions and facts from the particular fields but also to identify the unifying notions, techniques and ideas.
Data Hiding in Binary Images by Pixel-Value Weighting
Authors: Ki-Hyun Jung, Keum-Sook Ha, Kee-Young Yoo
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

This paper proposes a new data hiding method for binary images using pixel-value weighting. The binary cover image is partitioned into non-overlapping sub-blocks and finds the most suitable position to embed a secret bit for each sub-block. The proposed method calculates a weighted value for a sub-block to pivot a pixel to be changed. This improves the image quality of the stego-image. The experimental results show that the proposed method achieves good visual quality.
Turtles all the way down: research challenges in user-based attestation
Authors: Jonathan M. McCune
Venue: Proceedings of the 2nd workshop on Recent advances on intrusiton-tolerant systems (2008)

Current trusted computing technologies allow computing devices to verify each other using attestation, but in a networked world, there is no reason to trust one computing device any more than another. Treating these devices as turtles, the user who seeks a trustworthy system from which to verify others quickly realizes that it's "turtles all the way down" because of the endless loop of trust dependencies. We need to provide the user with one initial turtle (the iTurtle) which is axiomatically trustworthy, thereby breaking the dependency loop. Further, the size of the software trusted computing base on today's computing devices is overwhelming. We argue that a mechanism for reducing the size is essential for extracting meaning from attestations and enabling an iTurtle to do its job. This talk will present some of the research challenges involved in designing and using an iTurtle, and in architecting systems to provide meaningful attestations to an iTurtle.
A Variant of N-Gram Based Language Classification
Authors: Andrija Tomović, Predrag Janičić
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Rapid classification of documents is of high-importance in many multilingual settings (such as international institutions or Internet search engines). This has been, for years, a well-known problem, addressed by different techniques, with excellent results. We address this problem by a simple n-grams based technique, a variation of techniques of this family. Our n-grams-based classification is very robust and successful, even for 20-fold classification, and even for short text strings. We give a detailed study for different lengths of strings and size of n-grams and we explore what classification parameters give the best performance. There is no requirement for vocabularies, but only for a few training documents. As a main corpus, we used a EU set of documents in 20 languages. Experimental comparison shows that our approach gives better results than four other popular approaches.
A New Architecture of Sensitive File Management Based on Dual-Core and EFI
Authors: Zijian Deng, Xuejia Lai
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

How to protect the sensitive information from unauthorized disclosure is one of the problems in computer security. In this paper we present a new method for preventing the sensitive file from unauthorized disclosure, Security File Management (SFM). SFM relies on Dual-Core technology, Extensible Firmware Interface and Trusted computing to provide a much high level of security than current solutions. All operations of sensitive files are run in Trusted Boundary. In this paper, we present the architecture of SFM, the initial designer and the security analysis.
Xtreme illustrations
Authors: Dan Sawyer
Venue: Linux Journal (2008)

Check out Xara Extreme, a Linux-compatible alternative to Inkscape.
Accessible lectures: moving towards automatic speech recognition models based on human methods
Authors: Miltiades Papadopoulos, Elaine Pearson
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

The traditional lecture remains the most common method of teaching and while it is the most convenient from a delivery point of view, it is the least flexible and accessible. This paper responds to the challenge of meeting the needs and access requirements of students with disabilities by urging further adaptations in the learning environment. The aim of this work is to explore the way speech recognition technology can be employed in the University classroom to make lectures more flexible and accessible. The concluding section explores the concept of an ASR model, based on principles derived from studies of human methods of recognition, in order to increase their performance and efficiency.
Editorial: Reviewer merits and review control in an age of electronic manuscript management systems
Authors: Gary Marchionini
Venue: ACM Transactions on Information Systems (TOIS) (2008)

Peer review is an important resource of scholarly communities and must be managed and nurtured carefully. Electronic manuscript management systems have begun to improve some aspects of workflow for conferences and journals but also raise issues related to reviewer roles and reputations and the control of reviews over time. Professional societies should make their policies related to reviews and reviewer histories clear to authors and reviewers, develop strategies and tools to facilitate good and timely reviews, and facilitate the training of new reviewers.
Identifying Major Components of Pictures by Audio Encoding of Colours
Authors: Guido Bologna, Benoît Deville, Thierry Pun, Michel Vinckenbosch
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

The goal of the See ColOr project is to achieve a non-invasive mobility aid for blind users that will use the auditory pathway to represent in real-time frontal image scenes. More particularly, we have developed a prototype which transforms HSL coloured pixels into spatialized classical instrument sounds lasting for 300 ms. Hue is sonified by the timbre of a musical instrument, saturation is one of four possible notes, and luminosity is represented by bass when luminosity is rather dark and singing voice when it is relatively bright. Our first experiments are devoted to static images on the computer screen. Six participants with their eyes covered by a dark tissue were trained to associate colours with musical instruments and then asked to determine on several pictures, objects with specific shapes and colours. In order to simplify the protocol of experiments, we used a tactile tablet, which took the place of the camera. Overall, experiment participants found that colour was helpful for the interpretation of image scenes.
Meta Level Reasoning and Default Reasoning
Authors: Yi Zhou, Yan Zhang
Venue: Proceedings of the 11th European conference on Logics in Artificial Intelligence (2008)

In this paper, we propose a logic framework for meta level reasoning as well as default reasoning in a general sense, based on an arbitrary underlying logic. In this framework, meta level reasoning is the task of how to deduce new meta level rules by giving a set of rules, whilst default reasoning is the problem of what are the possible candidate beliefs by giving them. We define the semantics for both meta level reasoning and default reasoning and investigate their relationships. We show that this framework captures various nonmonotonic paradigms, including answer set programming, default logic, contextual default reasoning, by applying the underlying logic to different classes. Finally, we show that this framework can be reduced into answer set programming.
Extension Robust Control of a Three-Level Converter for High-Speed Railway Tractions
Authors: Kuei-Hsiang Chao
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

In this paper, the dynamic model of a three-level neutral point clamped (NPC) converter with neutral-point voltage-balance control (NPVBC) and input power factor correction for high-speed railway tractions is derived. And accordingly, a feedback controller is designed to meet the given voltage regulation control specifications. As the variation of converter parameters occurs, a compensation signal is yielded by an extension robust controller (ERC) to preserve the prescribed response. The compensation signal is adaptively tuned by a model error driven extension weighting controller. Some simulation results are presented to demonstrate the effectiveness of the proposed controller.
Comparison of Dimension Reduction Methods for Database-Adaptive 3D Model Retrieval
Authors: Ryutarou Ohbuchi, Jun Kobayashi, Akihiro Yamamoto, Toshiya Shimizu
Venue: Adaptive Multimedial Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers (2007)

Distance measures, along with shape features, are the most critical components in a shape-based 3D model retrieval system. Given a shape feature, an optimal distance measure will vary per query, per user, or per database. No single, fixed distance measure would be satisfactory all the time. This paper focuses on a method to adapt distance measure to the database to be queried by using learning-based dimension reduction algorithms. We experimentally compare six such dimension reduction algorithms, both linear and non-linear, for their efficacy in the context of shape-based 3D model retrieval. We tested the efficacy of these methods by applying them to five global shape features. Among the dimension reduction methods we tested, non-linear manifold learning algorithms performed better than the other, e.g. linear algorithms such as principal component analysis. Performance of the best performing combination is roughly the same as the top finisher in the SHREC 2006 contest.
DHTC: an effective DXTC-based HDR texture compression scheme
Authors: Wen Sun, Yan Lu, Feng Wu, Shipeng Li
Venue: Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware (2008)

In this paper, we propose a simple yet effective scheme (named DHTC) for HDR (high dynamic range) texture compression based on the popular LDR (low dynamic range) texture compression scheme -- S3TC/DXTC. In the proposed scheme, the original HDR texture is first pre-processed with adaptive color transform and local dynamic range reduction. Then a color distribution linearization process and a joint-channel texture coding process are applied iteratively to generate the compressed HDR texture at 8 bpp. These techniques lead to near lossless visual quality comparable to and even better than the state-of-the-art HDR texture compression schemes. Since DHTC is built upon the ubiquitous DXTC, dedicated DHTC hardware design only needs moderate extension on current hardware. Furthermore, the proposed DHTC format is not only suitable for HDR textures, but also LDR textures with alpha channels. We believe this paper provides a unique solution to meet all the practical requirements for HDR and LDR texture compression.
An Efficient and Provable Secure Identity-Based Identification Scheme in the Standard Model
Authors: Ji-Jian Chin, Swee-Huay Heng, Bok-Min Goi
Venue: Proceedings of the 5th European PKI workshop on Public Key Infrastructure: Theory and Practice (2008)

We present an efficient and provable secure identity-based identification scheme in the standard model. Our proposed scheme is secure against impersonation under passive attack based on the Computational Diffie-Hellman assumption, and secure under active and concurrent attacks based on the One-More Computational Diffie-Hellman assumption.
Air Pollutant Level Estimation Applying a Self-organizing Neural Network
Authors: J. M Barron-Adame, J. A. Herrera Delgado, M. G. Cortina-Januchs, D. Andina, A. Vega-Corona
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

This paper presents a novel Neural Network application in order to estimate Air Pollutant Levels. The application considers both Pollutant concentrations and Meteorological variables. In order to compute the Air Pollutant Level the method considers three important stages. In first stage, A process to validate data information and built a threedimensional Information Feature Vector with Pollutant concentrations and both wind speed and wind direction meteorological variables is developed. The information Feature Vector is orderly like a time series to estimate the Air Pollutant Level. In second stage, considering the behavior space knowledge a priori about pollutant and meteorological variables distribution a threedimensional Representative Vector is built in order to reduces the computational cost in Neural Network training process. In last stage, a Neural Network is designed and trained with the Threedimensional Representative Vector, then using the Threedimensional Information Feature Vector the Air Pollutant Level is estimated. This paper considers a real time series from an Automatic Environmental Monitoring Network from Salamanca, Guanajuato, Mexico, and therefore in this proposal a real Air Pollutant Level is also estimated.
Self-stabilizing Distributed Algorithms for Networks
Authors: Pradip K. Srimani
Venue: Proceedings of the 5th international symposium on Parallel and Distributed Processing and Applications (2007)

Many essential fundamental services for networked distributed systems (ad hoc, wireless or sensor) involve maintaining a global predicate over the entire network (defined by some invariance relation on the global state of the network) by using local knowledge at each of the participating nodes. The participating nodes can no longer keep track of even a small fraction of the knowledge about the global network due to limited storage. We need a new paradigm of localized distributed algorithms, where a node takes simple actions based on local knowledge of only its immediate neighbors and yet the system achieves a global objective. Self-stabilization is a relatively new paradigm for designing such localized distributed algorithms for networks; it is an optimistic way of looking at system fault tolerance and scalable coordination; it provides a cost effective built-in safeguard against transient failures that might corrupt data in a distributed system. We introduce self-stabilizing protocol design with the example of a total dominating set in a network graph and discuss some open problems.
The Effects of Hand Motion on Haptic Perception of Force Direction
Authors: Xing-Dong Yang, Walter F. Bischof, Pierre Boulanger
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

Most studies on the haptic perception of force direction have been conducted without hand movements, whereas hand movements are normally required in real-world applications. This paper reports a study on the perception of haptic force direction during hand movement. Discrimination thresholds for force direction were determined for two hand movement speeds, slow and fast, and for five reference force directions. The results show that the perception of force direction is not affected by hand movement speed. We also found that the perception of force direction was not impaired by the hand motion, nor by the direction of the reference force.
Privacy-preserving data mining
Authors: Wei Zhao, Jianer Chen
Venue:  (2006)

In the research of privacy-preserving data mining, we address issues related to extracting knowledge from large amounts of data without violating the privacy of the data owners. In this study, we first introduce an integrated baseline architecture, design principles, and implementation techniques for privacy-preserving data mining systems. We then discuss the key components of privacy-preserving data mining systems which include three protocols: data collection, inference control, and information sharing. We present and compare strategies for realizing these protocols. Theoretical analysis and experimental evaluation show that our protocols can generate accurate data mining models while protecting the privacy of the data being mined.