ppxscal/embedding_network huggingface dataset:
Power Analysis Resistant AES Implementation with Instruction Set Extensions
Authors: Stefan Tillich, Johann Großschädl
Venue: Proceedings of the 9th international workshop on Cryptographic Hardware and Embedded Systems (2007)

In recent years, different instruction set extensions for cryptography have been proposed for integration into general-purpose RISC processors. Both public-key and secret-key algorithms can profit tremendously from a small set of custom instructions specifically designed to accelerate performance-critical code sections. While the impact of instruction set extensions on performance and silicon area has been widely investigated in the recent past, the resulting security aspects (i.e. resistivity to side-channel attacks) of this particular design approach remain an open research topic. In this paper we discuss and analyze different techniques for increasing the side-channel resistance of AES software implementations using instruction set extensions. Furthermore, we propose a combination of hardware and software-related countermeasures and investigate the resulting effects on performance, cost, and security. Our experimental results show that a moderate degree of protection can be achieved with a simple software countermeasure. Hardware countermeasures, such as the implementation of security-critical functional units using a DPA-resistant logic style, lead to much higher resistance against side-channel attacks at the cost of a moderate increase in silicon area and power consumption.
A Software Solution for Accessible E-Government Portals
Authors: Dietmar Nedbal, Gerald Petz
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

The importance of e-Government web sites being accessible to people with disabilities is rising within the European Nations. Several European programs and state-laws are calling on governments and local government authorities to build accessible web sites. The paper deals with the development of a web content management system (CMS) that is already widely used by municipalities in Central Europe in accordance with the guidelines of the Web Accessibility Initiative (WAI). The project methodology is presented, followed by a discussion on the design of the database, the separation of layout and content and the implementation of the web site with Cascading Style Sheets (CSS).
An Efficient Radiosity Approach for Realistic Image Synthesis
Authors: Michael Cohen, Donald Greenberg, David Immel, Philip Brock
Venue: IEEE Computer Graphics and Applications (1986)

The radiosity method models the interaction of light between diffusely reflecting surfaces and accurately predicts the global illumination effects. Procedures are now available to simulate complex environments including occluded and textured surfaces. For accurate rendering, the environment must be discretized into a fine mesh, particularly in areas of high intensity gradients. The interdependence between surfaces implies solution techniques which are computationally intractable. This article describes new procedures to predict the global illumination function without excessive computational expense. Statistics indicate the enormous potential of this approach for realistic image synthesis, particularly for dynamic images of static environments.
The NORC and Problems in High-Speed Computing
Authors: John Von Neumann
Venue: IEEE Annals of the History of Computing (1981)

It is very fitting that the Annals of the History of Computing should reprint von Neumann's talk, which should help the reader in some small measure to feel and appreciate the brilliance and scope of the man who is now the legendary patron of our field.
Learning the Semantic Meaning of a Concept from the Web
Authors: Yang Yu, Yun Peng
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

Many researchers have used text classification method in solving the ontology mapping problem. Their mapping results heavily depend on the availability of quality exemplars used as training data. However, manual preparation of exemplars is costly. In this work, we propose to automatically extract text from web pages returned by a search engine. Search queries are formed according to the semantic information given in the ontology. We have implemented a prototype system that automates the entire process (from search query formation to conditional probability calculation) and conducted a series of experiments. We assessed the effectiveness of our approach by comparing the obtained conditional probabilities with human expectations. Our main contribution is that we explored the possibilities of utilizing web information for text classification based ontology mapping and made several valuable discoveries on its usefulness for future research.
Constructing relational diagrams in audio: the multiple perspective hierarchical approach
Authors: Oussama Metatla, Nick Bryan-Kinns, Tony Stockman
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

Although research on non-visual access to visually represented information is steadily growing, very little work has investigated how such forms of representation could be constructed through non-visual means. We discuss in this paper our approach for providing audio access to relational diagrams using multiple perspective hierarchies, and describe the design of two interaction strategies for constructing and manipulating such diagrams through this approach. A comparative study that we conducted with sighted users showed that a non-guided strategy allowed for significantly faster interaction times, and that both strategies supported similar levels of diagram comprehension. Overall, the reported study revealed that using multiple perspective hierarchies to structure the information encoded in a relational diagram enabled users construct and manipulate such information through an audio-only interface, and that combining aspects from the guided and the non-guided strategies could support greater usability.
Context-Sensitive Mobile Services for Police Officers: A Re-assessment of TAM
Authors: Harry Bouwman, Lidwien van de Wijngaert, Henny de Vos
Venue: Proceedings of the 2008 7th International Conference on Mobile Business (2008)

This paper focuses on the explanatory value of context parameters and individual characteristics for preferences of police officers for mobile applications. Starting from adoption and acceptance (DoI and TAM), media choice and task-technology fit (TTF) theories, we collected data from police officers, who were asked which specific communication devices they preferred in specific situations. Our analyses show that the explanatory value of individual parameters is high when tested in a model that focuses exclusively on individual characteristics. In addition, context characteristics also play an important role in the preference with regard to specific communication technologies, as conjoint analysis shows. When we include context and individual characteristics into a single model, the results show that the significance of the TAM concepts is low and the context-related issues become more important. TAM models are too generic to understand behavior with regard to mobile technologies. Research should take contextual factors into account
Classification of Anti-learnable Biological and Synthetic Data
Authors: Adam Kowalczyk
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

We demonstrate a binary classification problem in which standard supervised learning algorithms such as linear and kernel SVM, naive Bayes, ridge regression, k-nearest neighbors, shrunken centroid, multilayer perceptron and decision trees perform in an unusual way. On certain data sets they classify a randomly sampled training subset nearly perfectly, but systematically perform worse than random guessing on cases unseen in training. We demonstrate this phenomenon in classification of a natural data set of cancer genomics microarrays using cross-validation test. Additionally, we generate a range of synthetic datasets, the outcomes of 0-sum games, for which we analyse this phenomenon in the i.i.d. setting.Furthermore, we propose and evaluate a remedy that yields promising results for classifying such data as well as normal datasets. We simply transform the classifier scores by an additional 1-dimensional linear transformation developed, for instance, to maximize classification accuracy of the outputs of an internal cross-validation on the training set. We also discuss the relevance to other fields such as learning theory, boosting, regularization, sample bias and application of kernels.
Deterministic Sampling Algorithms for Network Design
Authors: Anke Zuylen
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

For several NP-hard network design problems, the best known approximation algorithms are remarkably simple randomized algorithms called Sample-Augment algorithms in [11]. The algorithms draw a random sample from the input, solve a certain subproblem on the random sample, and augment the solution for the subproblem to a solution for the original problem. We give a general framework that allows us to derandomize most Sample-Augment algorithms, i.e. to specify a specific sample for which the cost of the solution created by the Sample-Augment algorithm is at most a constant factor away from optimal. Our approach allows us to give deterministic versions of the Sample-Augment algorithms for the connected facility location problem, in which the open facilities need to be connected by either a tree or a tour, the virtual private network design problem, 2-stage rooted stochastic Steiner tree problem with independent decisions, the a priori traveling salesman problem and the single sink buy-at-bulk problem. This partially answers an open question posed in Gupta et al. [11]. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A Wavelet-Based Neural Network Applied to Surface Defect Detection of LED Chips
Authors: Hong-Dar Lin, Chung-Yu Chung
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This research explores the automated detection of surface defects that fall across two different background textures in a light-emitting diode (LED) chip. Water-drop defects, commonly found on chip surface, impair the appearance of LEDs as well as their functionality and security. Automated inspection of a water-drop defect is difficult because the defect has a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the blemish may fall across two different background textures, which further increases the difficulties of defect detection. We first use the one-level Haar wavelet transform to decompose a chip image and extract four wavelet characteristics. Then, the Multi-Layer Perceptron (MLP) neural network with back-propagation (BPN) algorithm is applied to integrate the multiple wavelet characteristics. Finally, the wavelet-based neural network approach judges the existence of water-drop defects. Experimental results show that the proposed method achieves an above 96.8% detection rate and a below 4.8% false alarm rate.
Application of the neural nets for forecasting the electricity demand
Authors: Iliyana Vincheva
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

In this paper is discussed the application of neural networks for time series forecasting. This was illustrated with a single hidden layer feed - forward network, which is the most widely used neural network for forecasting. Short-term load forecasts are required for the control and scheduling of power systems. The focus varies from minutes to several hours ahead.
Metric Adaptation and Representation Upgrade in an Emotion-Based Agent Model
Authors: Rodrigo Ventura, Carlos Pinto-Ferreira
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

The research presented here follows a biologically inspired approach, based on the hypothesis that emotions contribute decisively for humans to cope with complex and dynamic environments. This hypothesis is founded on neurophysiological findings showing that damage in the emotion circuitry of the brain cause inability to handle simple, common life tasks [1]. Inspired by these findings, an emotion-based agent model was previously presented [2], proposing a double-processing of stimuli: a simple representation termed perceptual image, designed for fast processing and immediate response to urgent situations, and a complex representation termed cognitive image, thus slow to process, are extracted from each stimulus reaching the agent. These two representations are extracted and processed, simultaneously, by the two levels of the architecture: the perceptual and the cognitive levels. The parallelism of the processing is essential, so that quick response to urgent situations is not compromised by the slow processing of the cognitive level. These two representations are then associated and stored in memory. Once the agent faces a new situation, it matches the incoming stimulus with the agent memory, thus retrieving the associated images.
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
Authors: Elke Achtert, Hans-Peter Kriegel, Arthur Zimek
Venue: Proceedings of the 20th international conference on Scientific and Statistical Database Management (2008)

In order to establish consolidated standards in novel data mining areas, newly proposed algorithms need to be evaluated thoroughly. Many publications compare a new proposition --- if at all --- with one or two competitors or even with a so called "naïve" ad hoc solution. For the prolific field of subspace clustering, we propose a software framework implementing many prominent algorithms and, thus, allowing for a fair and thorough evaluation. Furthermore, we describe how new algorithms for new applications can be incorporated in the framework easily.
An Impulse Noise Image Filter Using Fuzzy Sets
Authors: Sun Zhong-gui, Chen Jie, Meng Guang-wu
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The successful application of fuzzy set theoryoperators on several domains, along with theincreasing need for processing digital images, havebeen the main motives behind the efforts concentratedon fuzzy sets. Fuzzy set theory, comparing with someother theories, can provide us with knowledge-basedand robust tools for image processing. By computingthe fuzziness of the pixels’ corrupted degree and takingcorresponding filter parameters, a new image filter forimpulse noise is presented in this paper. Comparingwith the median filter, which is excellent for impulsenoise, this new filter is more effective. In the end,simulation results show that the new algorithm isfeasible.
A Distributed Spatio-temporal EEG/MEG Inverse Solver
Authors: Wanmei Ou, Polina Golland, Matti Hämäläinen
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

We propose a novel ¿1¿2-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard ¿1-norm inverse solver, the proposed sparse distributed inverse solver integrates the ¿1-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and "spiky" reconstructed signals often produced by the original solvers. The joint spatio-temporal model leads to a cost function with an ¿1¿2-norm regularizer whose minimization can be reduced to a convex second-order cone programming problem and efficiently solved using the interior-point method. Validation with simulated and real MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the ¿1¿2-norm solver achieves fewer false positives and a better representation of the source locations than the conventional ¿2 minimum-norm estimates.
SLAWs: Towards a Conceptual Architecture for SLA Enforcement
Authors: Jose Antonio Parejo, Pablo Fernandez, Antonio Ruiz-Cortés, José María García
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Current technologies in Service Oriented Computing (SOC) provide a solid framework to drive the interaction of organizations from a functional point of view. In order to introduce non-functional properties in this scenario, the concept of Service Level Agreement (SLAs) comes into play as a key element. SLAs can be seen as containers of the functional and non-functional properties that both parties (the service consumer and the service provider) agree specifying its rights and obligations during the interaction. However this SLAs represent an additional responsibility for the service provider since it motivates the need of a SLA-Enforcement process in its infrastructure. A proper SLA-enforcement implies optimized resource usage that meet SLAs established with consumer, making it possible to the provider afford a larger number of customers to maximize its benefits. Current approaches to SLA enforcement are domain-specific approaches and/or based on monolithic platforms (from a SOC point of view).In this paper we propose a conceptual architecture (SLAWs) for SLA enforcement. The main goal of the proposed architecture is to be used as a conceptual framework to build a flexible SLA enforcement layer. This layer could be integrated in a seamless way in the pre-existing provider infrastructure when is based upon the service oriented architecture principles
An Enzyme-Inspired Approach to Surmount Barriers in Graph Bisection
Authors: Yong-Hyuk Kim
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

Finding optimal solutions in the graph bisection problem is a notoriously hard task. One of the main reasons is the barriers which prevent search algorithms from reaching the optimal solutions. Given an algorithm for finding optimal solutions, the search process can be represented by a Markov chain. Every two neighboring solutions has a connection in the chain with a transition probability. If the algorithm is deterministic, many of the connections are set to the probability zero since they can never be chosen in the algorithm. It thus may happen that there is no path with a positive probability. We suggest a method to open paths with zero or near-zero transition probability by implicitly changing the chain, which we believe will eventually make the search more flexible. Experimental results showed significant improvement over traditional representative partitioning methodologies, the Fiduccia-Mattheyses algorithm and its two-phase variant.
Analysis of speech properties of neurotypicals and individuals diagnosed with autism and down
Authors: Mohammed E. Hoque
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

Many individuals diagnosed with autism and Down syndrome have difficulties producing intelligible speech. Systematic analysis of their voice parameters could lead to better understanding of the specific challenges they face in achieving proper speech production. In this study, 100 minutes of speech data from natural conversations between neurotypicals and individuals diagnosed with autism/Down-syndrome was used. Analyzing their voice parameters indicated new findings across a variety of speech parameters. An immediate extension of this work would be to customize this technology allowing participants to visualize and control their speech parameters in real time and get live feedback.
SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model
Authors: Jörg Wicker, Lothar Richter, Kristina Kessler, Stefan Kramer
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

In this demonstration, we will present the concepts and an implementation of an inductive database --- as proposed by Imielinski and Mannila --- in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
Panel Session: What Are the Key Challenges in Distributed Security?
Authors: Steve Barker, David Chadwick, Jason Crampton, Emil Lupu, Bhavani Thuraisingham
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

The principal motivation for organizing a panel session at DBSEC'08 was to invite a number of distinguished researchers in data security to present their thoughts and to stimulate conference debate on a question of major importance: what are the key future challenges in distributed data security? The thoughts of the panellists on this issue are summarized in this article.
Reduction of distributed payload bridge crane oscillations
Authors: Raymond Manning, Dooroo Kim, William Singhose
Venue: Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation (2008)

The large amplitude oscillation of crane payloads is detrimental to safe and efficient operation. With certain payloads, the problem is complicated when a double-pendulum effect is created. However, most crane control research to date has focused on single-mode dynamics. For example, several researchers have shown that signal-mode oscillations can be greatly reduced by properly shaping the inputs to the crane motors. This paper builds on those previous developments to create a method for suppressing double-pendulum oscillations of a distributed payload. Theoretical models are initially used to develop and evaluate an input-shaping control algorithm. Then, experiments performed on a portable bridge and a 10-ton industrial bridge crane are used to verify the practical effectiveness of the method.
Sustainable Digital Library Systems over the DRIVER Repository Infrastructure
Authors: Michele Artini, Leonardo Candela, Donatella Castelli, Paolo Manghi, Marko Mikulicic, Pasquale Pagano
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

The DRIVER Infrastructure is an e-infrastructure providing an environment where organizations find the tools to aggregate heterogeneous content sources into uniform shared Information Spaces and then build and customize their Digital Library Systems to operate over them. In this paper, we shall show the benefits for organizations embracing the infrastructural approach by presenting the DRIVER infrastructure, its current status of maintenance, its participating organizations, and the first two systems built on top of its Information Space.