ppxscal/embedding_network huggingface dataset:
The reactive simulatability (RSIM) framework for asynchronous systems
Authors: Michael Backes, Birgit Pfitzmann, Michael Waidner
Venue: Information and Computation (2007)

We define reactive simulatability for general asynchronous systems. Roughly, simulatability means that a real system implements an ideal system (specification) in a way that preserves security in a general cryptographic sense. Reactive means that the system can interact with its users multiple times, e.g., in many concurrent protocol runs or a multi-round game. In terms of distributed systems, reactive simulatability is a type of refinement that preserves particularly strong properties, in particular confidentiality. A core feature of reactive simulatability is composability, i.e., the real system can be plugged in instead of the ideal system within arbitrary larger systems; this is shown in follow-up papers, and so is the preservation of many classes of individual security properties from the ideal to the real systems. A large part of this paper defines a suitable system model. It is based on probabilistic IO automata (PIOA) with two main new features: One is generic distributed scheduling. Important special cases are realistic adversarial scheduling, procedure-call-type scheduling among colocated system parts, and special schedulers such as for fairness, also in combinations. The other is the definition of the reactive runtime via a realization by Turing machines such that notions like polynomial-time are composable. The simple complexity of the transition functions of the automata is not composable. As specializations of this model we define security-specific concepts, in particular a separation between honest users and adversaries and several trust models. The benefit of IO automata as the main model, instead of only interactive Turing machines as usual in cryptographic multi-party computation, is that many cryptographic systems can be specified with an ideal system consisting of only one simple, deterministic IO automaton without any cryptographic objects, as many follow-up papers show. This enables the use of classic formal methods and automatic proof tools for proving larger distributed protocols and systems that use these cryptographic systems.
An Expert System for measuring shear-wave splitting above small earthquakes
Authors: Ping Hao, Yuan Gao, Stuart Crampin
Venue: Computers Geosciences (2008)

As part of the development of a system for routinely measuring shear-wave splitting, this paper introduces an Expert System (ES) to measure the polarisations and time-delays of seismic shear-wave splitting in three-component seismograms above small earthquakes. Expert Systems are rule-based computer techniques designed to provide expertise in particular topics, where the rules are algorithms developed from previous knowledge and experience. The technique is tested on data recorded by the seismic network in Iceland. The statistics suggests that the ES is reasonably successful and provides appropriate initial input parameters for a more precise analysis, which leads to the success of the comprehensive Shear-Wave Analysis System (SWAS) for measuring shear-wave splitting.
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
Authors: C. T. Zahn
Venue: IEEE Transactions on Computers (1971)

A family of graph-theoretical algorithms based on the minimal spanning tree are capable of detecting several kinds of cluster structure in arbitrary point sets; description of the detected clusters is possible in some cases by extensions of the method. Development of these clustering algorithms was based on examples from two-dimensional space because we wanted to copy the human perception of gestalts or point groupings. On the other hand, all the methods considered apply to higher dimensional spaces and even to general metric spaces. Advantages of these methods include determinacy, easy interpretation of the resulting clusters, conformity to gestalt principles of perceptual organization, and invariance of results under monotone transformations of interpoint distance. Brief discussion is made of the application of cluster detection to taxonomy and the selection of good feature spaces for pattern recognition. Detailed analyses of several planar cluster detection problems are illustrated by text and figures. The well-known Fisher iris data, in four-dimensional space, have been analyzed by these methods also. PL/1 programs to implement the minimal spanning tree methods have been fully debugged.
An Improved Method of Wavelets Basis Image Denoising Using Besov Norm Regularization
Authors: Hong Yang, Yiding Wang
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

This paper proposes an improved image denoising algorithm which bases on wavelets thresholding- and uses the Besov norm regularization. Given a noisy image u_0 and suppose the target image u belongs to Besov space B_q^\alpha(L^p ) , we need to solve the optimization problem: min \left\| u \right\|_{B_q^\alpha(L^p )}^q+ \frac{\lambda } {2}\left\| {u - u_0 } \right\|_{L^2 }^2 . The existing algorithms used the fixed parameters p, q, \alpha of B_q^\alpha(L^p )to determine the threshold of wavelets reconstruction. Since different parts of an image may have different smoothness properties, and wavelet coefficients denote different frequency subbands of an image, the subimages at each wavelets scale level may have distinct smoothness properties. The larger the \alpha is, the smoother the images are in B_q^\alpha(L^p ). Taking the smoothness index \alpha into account, we try to optimize the \alpha_j at different wavelet scale j with p,q fixed. Experimental results show that our method achieves better denoising effect with higher PSNR than the \alpha fixed method.
Evolving artificial cell signaling networks using molecular classifier systems
Authors: James Decraene, George Mitchell, Barry McMullin
Venue: Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems (2006)

Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of Cell Signaling Networks focuses on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs.
Memory model sensitive bytecode verification
Authors: Thuan Quang Huynh, Abhik Roychoudhury
Venue: Formal Methods in System Design (2007)

Modern concurrent programming languages like C# and Java have a programming language level memory model, which captures the set of all allowed behaviors of programs on any implementation platform--uni- or multi-processor. Such a memory model is typically weaker than Sequential Consistency and allows reordering of operations within a program thread. Therefore, programs verified correct by assuming Sequential Consistency (that is, each thread proceeds in program order) may not behave correctly on certain platforms! One solution to this problem is to develop program checkers which are memory model sensitive. In this paper, we develop a bytecode level invariant checker for the programming language C#. Our checker identifies program states which are reached only because the C# memory model is more relaxed than Sequential Consistency. It employs partial order reduction strategies to speed up the search. These strategies are different from standard partial order reduction methods since our search also considers execution traces containing bytecode re-orderings. Furthermore, our checker identifies (a) operation re-orderings which cause undesirable states to be reached, and (b) simple program modifications--by inserting memory barrier operations--which prevent such undesirable re-orderings.
From the Area Editor: Search-The New Incarnations
Authors: Naren Ramakrishnan
Venue: Computer (2007)

Search is a thriving research topic with novel incarnations continuing to materialize.
A Hardware Redundancy Reconfiguration Scheme for Tolerating Multiple Module Failures
Authors: S. Y. H. Su
Venue: IEEE Transactions on Computers (1980)

This paper deals with a method for designing a digital system which will remain operational in spite of the failure of some of its components. A scheme and its realization are presented for automatically reconfiguring a 5MR (five modular redundancy system or 5-input majority voting system) into a triple modular redundancy (TMR) system under a single or double module failures. The scheme can tolerate a double fault followed by a single fault which can neither be tolerated by a 5MR nor by a hybrid redundancy system with a TMR core. It uses no spare units and the circuit realization is relatively simple. The modular structure of the logic design for the proposed scheme should make the testing of the system easier. The scheme can be used in both binary and multivalued systems.
A parameterized direct LDA and its application to face recognition
Authors: Fengxi Song, David Zhang, Jizhong Wang, Hang Liu, Qing Tao
Venue: Neurocomputing (2007)

In this paper, we propose a new feature extraction method-parameterized direct linear discriminant analysis (PD-LDA) for small sample size problems. Similar to direct LDA (D-LDA), PD-LDA is a modification of KLB (the Karhunen-Loeve expansion based on the between-class scatter matrix). As an improvement of D-LDA and KLB, PD-LDA inherits two important advantages of them. That is, it can be directly applied to high-dimensional input spaces and implemented with great efficiency. Meanwhile, experimental results conducted on two benchmark face image databases, i.e., AR and FERET, demonstrate that PD-LDA is much more effective and robust than D-LDA. In addition, it outperforms state-of-the-art facial feature extraction methods such as KLB, eigenfaces, and Fisherfaces.
The MEG registry and SCART: complementary tools for creation, discovery and re-use of metadata schemas
Authors: Rachel Heery, Pete Johnston, Dave Beckett, Damian Steer
Venue: Proceedings of the 2002 international conference on Dublin core and metadata applications: Metadata for e-communities: supporting diversity and convergence (2002)

SCART is an RDF schema creation tool designed for use by implementers working within digital library and learning environments. This schema creation and registration tool is being developed to work in conjunction with registry software. SCART will provide implementers with a simple tool to declare their schemas, including local usage and adaptations, in a machine understandable way based on the RDF Schema specification. This tool is optimised for use by the Metadata for Education Group, projects and services within the UK providing resource discovery in the domain of education. By providing a complementary creation tool and registry the aim is to facilitate easy discovery of existing schemas already registered in a schemas registry, and to enable implementers to re-use these existing schemas where appropriate.
RTOS and Codesign Toolkit for Multiprocessor Systems-on-Chip
Authors: S. Honda
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

Multiprocessor designs have become popular in embedded domains for achieving the power and performance requirements. In this paper, we present principles and techniques for design and implementation of RTOS for embedded multiprocessor systems. We also present a system-level design toolkit for rapid design and evaluation of embedded multiprocessor systems.
Hybrid results merging
Authors: Georgios Paltoglou, Michail Salampasis, Maria Satratzemi
Venue: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007)

The problem of results merging in distributed information retrieval environments has been approached by two different directions in research. Estimation approaches attempt to calculate the relevance of the returned documents through ad-hoc methodologies (weighted score merging, regression etc) while download approaches, download all the documents locally, partially or completely, in order to estimate "first hand" their relevance. Both have their advantages and disadvantages. It is assumed that download algorithms are more effective but they are very expensive in terms of time and bandwidth. Estimation approaches on the other hand, usually rely on document relevance scores being returned by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging, rely on a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced reconciles the above two approaches, combining their strengths, while minimizing their weaknesses. It is based on downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. The proposed algorithm is tested in a variety of settings and its performance is found to be better than estimation approaches, while approximating that of download.
Spectrally Efficient Frequency Hopping System Design forWireless Networks
Authors: Tongtong Li, Qi Ling, Jian Ren
Venue: Proceedings of the International Conference on Wireless Algorithms,Systems and Applications (2007)

Frequency hopping systems have been widely used in military communications to prevent hostile jamming, interception and detection. In traditional frequency hopping (FH) systems, the transmitter hops in a pseudo-random manner among available frequencies according to a prespecified algorithm, and the receiver operates accordingly in exact synchronization with the transmitter's hopping pattern. In multiple access systems, a collision may happen when more than one users transmit in the same frequency band simultaneously. Two major limitations with the conventional frequency hopping systems are: strict requirement on frequency acquisition/synchronization, and very low spectral efficiency due to inefficient utilization of the available bandwidth. In this paper, we introduce a new concept - collision-free frequency hopping (CFFH). Based on the OFDM framework and the secure subcarrier assignment algorithm, the proposed CFFH system can achieve high information capacity through collision-free multiple access, and can successfully resolve the strict synchronization limitation. At the same time, as each user still transmits through a pseudo-random frequency hopping scheme, CFFH can maintain the inherent anti-jamming, anti-interception security features of the conventional FH system. The proposed CFFH scheme can be used for both civilian and military applications where secure high speed information transmission is needed.
Early slip detection with a tactile sensor based on retina
Authors: Rocío Maldonado-López, Fernando Vidal-Verdú, Gustavo Liñán, Elisenda Roca, Ángel Rodríguez-Vázquez
Venue: Analog Integrated Circuits and Signal Processing (2007)

The interest in tactile sensors is increasing as their use in complex unstructured environments is demanded, like in telepresence, minimal invasive surgery, robotics etc. The array of pressure data provided by these devices can be treated with different image processing algorithms to extract miscellaneous information. However, as in the case of vision chips or artificial retinas, problems arise when the size of the array and the computational complexity increase. Having a look at the skin, the information collected by every mechanoreceptor is not sent to the brain for its processing, but some complex pre-processing is performed to fit the limited throughput of the nervous system. This is specially important for high bandwidth demanding tasks, as the case of slip detection with tactile sensors, which is demanding in computing requirements. Here we show some results from a tactile processor based on circuitry proposed for an artificial retina that has been modified to mimic the way the biological skin works.
The spherical constraint in Boolean quadratic programs
Authors: Jérôme Malick
Venue: Journal of Global Optimization (2007)

We propose a new approach to bound Boolean quadratic optimization problems. The idea is to re-express the Boolean constraints as one "spherical" constraint, whose dualization amounts to semidefinite least-squares problems. Studying this dualization provides an alternative interpretation of the sdp relaxation. It also reveals a new class of non-convex problems with no duality gap.
Learning to Transform Time Series with a Few Examples
Authors: Ali Rahimi, Ben Recht, Trevor Darrell
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account.
Visualization of competitive market structure by means of choice data
Authors: Werner Kunz
Venue: Computational Statistics (2007)

This paper presents a method for visualizing competitive market structures based on scanner panel data where asymmetries are taken into account. For this, I combined consumer choice models based on mixed logit models with three-mode principal component analysis. This approach can be used to unfold a competitive market structure map. The methodology presented is able to quantify the clout and receptivity of various brands. The results can then be visualized over time. Using this approach, guidelines for promotional activities of new brands can be provided, and possible threats from the competition detected.
Computing Sharp Lower and Upper Bounds for the Minimum Latency Problem
Authors: Joao Sarubbi, Henrique Pacca Luna, Gilberto de Miranda, Ricardo Saraiva de Camargo
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

The Minimum Latency Problem, also known as Traveling Repairman Problem, the Deliveryman Problem and the Traveling Salesman Problem with Cumulative Costs is a variant of the Traveling Salesman Problem in which a repairman is required to visit customers located on each node of a graph in such a way that the overall waiting times of these customers is minimized. In the present work, an algorithm based on tight different linear programming lowerbounds and a specialized GRASP procedure are presented. The linear programming based lower-bounds are based on the Quadratic Assignment Problem with the aid of side constraints. Instances from 10 up to 60 nodes are solved very close to optimality in reasonable time.
Data Mining Prediction of Shovel Cable Service Lifespan
Authors: Lizhen Wang, Ao Yang, Hong Zhang
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Using data mining technology (fuzzy mining technology), a reasonable and effective method to predict the lifespan of shovel cables is proposed. Shovel cables are expected to last approximately 2000 hours of operation. However, current lifespan range from 400 to over 1800 hours over an entire shovel fleet. The proposed approach can discover the correlation (i.e., the degree of fuzzy association) between a cable's lifespan and operating variables. The degree of fuzzy association is defined based on the distribution of the variables for the lifespan and the concept of semantic proximity (SP) between two lifespan. In addition we adopt the inverse document frequency (IDF) weight function to measure the weights of the variables in order to superpose the association degrees. Given the proximity degree (PD) between two time-series, the time-series can be successfully classified using the fuzzy equivalence partition method. To implement the method, we introduce "growing window", "scaling", and approximate computation pruning techniques in order to reduce both I/O and CPU costs. Extensive experiments on real datasets are conducted, and the experimental results are analyzed thoroughly.
Genetic algorithms as global random search methods: An alternative perspective
Authors: Charles C. Peck, Atam P. Dhawan
Venue: Evolutionary Computation (1995)

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
The Predictive Control of Sintering Temperature in Rotary Kiln Based on Image Feedback and Soft Computing
Authors: Xiaogang Zhang, Hua Chen, Jing Zhang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

The sintering temperature in rotary kiln is measured through image processing algorithm as feedback of its predictive control. The predictive model based on radial basis function (RBF) network is designed by the image and other process data optimized by genetic algorithm. The final results of application at an alumina sintering kiln show that this intelligent predictive control strategy can achieve higher stability in normal condition.