ppxscal/embedding_network huggingface dataset:
REFSQ'08 International Working Conference on Requirements Engineering: Foundation for Software Quality
Authors: Barbara Paech, Colette Rolland
Venue: Proceedings of the 14th international conference on Requirements Engineering: Foundation for Software Quality (2008)

The 14th Working Conference on Requirements Engineering: Foun-dation for Software Quality (REFSQ'08) will be held in the beautiful city of Montpellier, France on the 16th and 17th June 2008. This introduction gives an overview of the conference and its program.
Machine learning models on random graphs
Authors: Irwin King, Michael R. Lyu
Venue:  (2007)

In this thesis, we establish three machine learning models on random graphs: Heat Diffusion Models on Random Graphs, Predictive Random Graph Ranking, and Random Graph Dependency. The heat diffusion models on random graphs lead to Graph-based Heat Diffusion Classifiers (G-HDC) and a novel ranking algorithm on Web pages called DiffusionRank. For G-HDC, a random graph is constructed on data points. The generated random graph can be considered as the representation of the underlying geometry, and the heat diffusion model on them can be considered as the approximation to the way that heat flows on a geometric structure. Experiments show that G-HDC can achieve better performance in accuracy in some benchmark datasets. For DiffusionRank, theoretically we show that it is a generalization of PageRank when the heat diffusion coefficient tends to infinity, and empirically we show that it achieves the ability of anti-manipulation. Predictive Random Graph Ranking (PRGR) incorporates DiffusionRank. PRGR aims to solve the problem that the incomplete information about the Web structure causes inaccurate results of various ranking algorithms. The Web structure is predicted as a random graph, on which ranking algorithms are expected to be improved in accuracy. Experimental results show that the PRGR framework can improve the accuracy of the ranking algorithms such as PageRank and Common Neighbor. Three special forms of the novel Random Graph Dependency measure on two random graphs are investigated. The first special form can improve the speed of the C4.5 algorithm, and can achieve better results on attribute selection than &gamma; used in Rough Set Theory. The second special form of the general random graph dependency measure generalizes the conditional entropy because it becomes equivalent to the conditional entropy when the random graphs take their special form-equivalence relations. Experiments demonstrates that the second form is an informative measure, showing its success in decision trees on small sample size problems. The third special form can help to search two parameters in G-HDC faster than the cross-validation method. In summary, the viewpoint of random graphs indeed provides us an opportunity of improving some existing machine learning algorithms.
Coopetitive multi-camera surveillance using model predictive control
Authors: Vivek K. Singh, Pradeep K. Atrey, Mohan S. Kankanhalli
Venue: Machine Vision and Applications (2008)

We present a generic framework for enhanced active multi-sensing. We propose a coopetitive interaction approach, which combines the salient features of cooperation and competition with an aim to optimize the cooperation among sensors to achieve best results at the system level rather than redundantly implementing cooperation at each stage. We also employ model predictive control based forward state estimation method for counter-acting various delays faced in multi-sensor environments. The results obtained for two different visual surveillance adaptations with different number of cameras and different surveillance goals provide clear evidence for the improvements created by adoption of the proposed enhancements.
Making Arithmetic Accessible
Authors: John A. Gardner, Carolyn K. Gardner, Blake Jones, Elizabeth Jones
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

The authors discuss a truly universal learning system that will allow all children including blind and deaf children to do arithmetic using standard algorithms. This system uses the three main learning modalities, sight, sound and touch, allowing students to simultaneously use multiple senses when learning. The product was designed with a flexible user-interface so that students can receive and enter information in a variety of ways. This product makes use of scalable vector graphics and uses XForms to create input fields within graphics.
Complexity Reduction of Constant Matrix Computations over the Binary Field
Authors: Oscar Gustafsson, Mikael Olofsson
Venue: Proceedings of the 1st international workshop on Arithmetic of Finite Fields (2007)

In this work an algorithm for realizing a multiplication of a vector by a constant matrix over the binary field with few two-input XOR-gates is proposed. This type of problem occurs in, e.g., Galois field computations, syndrome computation for linear error correcting codes, cyclic redundancy checks (CRCs), linear feedback shift-registers (LFSRs), and implementations of the Advanced Encryption Standard (AES) algorithm. As the proposed algorithm can utilize cancellation of terms it outperforms in general previously proposed algorithms based on sub-expression sharing.
Genetic Algorithmic Topology Control for Two-Tiered Wireless Sensor Networks
Authors: Donghwan Lee, Wonjun Lee, Joongheon Kim
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

This paper proposes an optimized topology control scheme (GA-OTC) based on a genetic algorithm for clustering-based hierarchical wireless sensor networks (WSNs). By using a genetic algorithm, we can obtain optimal solutions to multiple objective functions according to the two criteria of both balanced energy consumption and minimized total energy consumption of cluster heads while the entire WSNs field is covered by clusters. Through performance evaluation studies, we show that GA-OTC achieves desirable properties.
Fuzzy Random Dependent-Chance Bilevel Programming with Applications
Authors: Rui Liang, Jinwu Gao, Kakuzo Iwamura
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In this paper, a two-level decentralized decision-making problem is formulated as fuzzy random dependent-chance bilevel programming. We define the fuzzy random Nash equilibrium in the lower level problem and the fuzzy random Stackelberg-Nash equilibrium of the overall problem. In order to find the equilibria, we propose a hybrid intelligent algorithm, in which neural network, as uncertain function approximator, plays a crucial role in saving computing time, and genetic algorithm is used for optimization. Finally, we apply the fuzzy random dependent-chance bilevel programming to hierarchical resource allocation problem for illustrating the modelling idea and the effectiveness of the hybrid intelligent algorithm.
Unconstrained Parametric Minimization of a Polynomial: Approximate and Exact
Authors: S. Liang, D. J. Jeffrey
Venue: Computer Mathematics: 8th Asian Symposium, ASCM 2007, Singapore, December 15-17, 2007. Revised and Invited Papers (2008)

We consider a monic polynomial of even degree with symbolic coefficients. We give a method for obtaining an expression in the coefficients (regarded as parameters) that is a lower bound on the value of the polynomial, or in other words a lower bound on the minimum of the polynomial. The main advantage of accepting a bound on the minimum, in contrast to an expression for the exact minimum, is that the algebraic form of the result can be kept relatively simple. Any exact result for a minimum will necessarily require parametric representations of algebraic numbers, whereas the bounds given here are much simpler. In principle, the method given here could be used to find the exact minimum, but only for low degree polynomials is this feasible; we illustrate this for a quartic polynomial. As an application, we compute rectifying transformations for integrals of trigonometric functions. The transformations require the construction of polynomials that are positive definite.
A Novel Naive Bayesian Text Classifier
Authors: Wang Ding, Songnian Yu, Qianfeng Wang, Jiaqi Yu, Qiang Guo
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The Naive Bayesian (NB) classifier is one of the simple but most efficient and stable classification methods. The great efficiency of NB is mainly because of the conditionally independence assumption among the attributes, which is problematic in practice especially while the attributes are strongly correlated. In this paper, we propose a novel NB text classifier, package and combined naïve Bayesian text classifier (PC-NB) that relaxes the independence assumption. The main aim of PC-NB is to make naïve bayesian classifier be more accurate without efficiency reduction. A set of experiments were performed and the results of the analysis and experiment indicate that the proposed classifier is more accurate and powerful while the attributes of an instance are strongly correlated.
Distributed Knowledge Representation on the Social Semantic Desktop: Named Graphs, Views and Roles in NRL
Authors: Michael Sintek, Ludger Elst, Simon Scerri, Siegfried Handschuh
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

The vision of the Social Semantic Desktop defines a user's personal information environment as a source and end-point of the Semantic Web: Knowledge workers comprehensively express their information and data with respect to their own conceptualizations. Semantic Web languages and protocols are used to formalize these conceptualizations and for coordinating local and global information access. From the way this vision is being pursued in the NEPOMUK project, we identified several requirements and research questions with respect to knowledge representation. In addition to the general question of the expressivity needed in such a scenario, two main challenges come into focus: i) How can we cope with the heterogeneity of knowledge models and ontologies, esp. multiple knowledge modules with potentially different interpretations? ii) How can we support the tailoring of ontologies towards different needs in various exploiting applications?In this paper, we present NRL, an approach to these two question that is based on named graphs for the modularization aspect and a view concept for the tailoring of ontologies. This view concept turned out to be of additional value, as it also provides a mechanism to impose different semantics on the same syntactical structure.We think that the elements of our approach are not only adequate for the semantic desktop scenario, but are also of importance as building blocks for the general Semantic Web.
Project management for arbitrary random durations and cost attributes by applying network approaches
Authors: Yi-Kuei Lin
Venue: Computers Mathematics with Applications (2008)

This paper concentrates on the quality management of a project by applying network approaches and statistics methods. A large-scale project can be modeled as a project network. This paper tries to schedule the duration of the project by applying the approaches in network analysis in case the activity duration and the corresponding cost are both arbitrary random variables with a probability distribution. Subject to the project time (the deadline to complete the project) constraint and the budget constraint, this paper studies how to schedule all activity durations of the project. Two algorithms are proposed to generate all upper and lower duration vectors for the project. All feasible activity durations are among such upper and lower duration vectors. Whenever any constraint is changed during the project period, we can re-schedule the activity durations efficiently by applying the proposed algorithm. Furthermore, we can evaluate the probability that the project will be executed successfully, subject to the time and budget constraints. Such a probability is an index for measuring the quality and feasibility of the project.
Projection with Double Nonlinear Integrals for Classification
Authors: Jinfeng Wang, Kwongsak Leung, Kinhong Lee, Zhenyuan Wang
Venue: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008)

In this study, a new classification model based on projection with Double Nonlinear Integrals is proposed. There exist interactions among predictive attributes towards the decisive attribute. The contribution rate of each combination of predictive attributes, including each singleton, towards the decisive attribute can be re presented by a fuzzy measure. We use Double Nonlinear Integrals with respect to the signed fuzzy measure to project data to 2-Dimension space. Then classify the virtual value in the 2-D space projected by Nonlinear Integrals. In our experiments, we compare our classifier based on projection with Double Nonlinear Integrals with the classical method- Naïve Bayes. The results show that our classification model is better than Naïve Bayes.
LSI Modular Computers, Systems, and Networks
Authors: S. I. Kartashev
Venue: Computer (1978)

Modular LSI-based design makes possible new patterns of computer and network organization, providing greater computational power and flexibility.
The Robotics and Mechatronics Kit "qfix"
Authors: Stefan Enderle
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

Robot building projects are increasingly used in schools and universities to raise the interest of students in technical subjects. They can especially be used to teach the three mechatronics areas at the same time: mechanics, electronics, and software. However, it is hard to find reusable, robust, modular and cost-effective robot development kits in the market. Here, we present qfix, a modular construction kit for edutainment robotics and mechatronics experiments which fulfills all of the above requirements and receives strong interest from schools and universities. The outstanding advantages of this kit family are the solid aluminium elements, the modular controller boards, and the programming tools which reach from an easy-to-use graphical programming environment to a powerful C++ library for the GNU compiler collection.
Penalty methods for the numerical solution of American multi-asset option problems
Authors: Bjørn Fredrik Nielsen, Ola Skavhaug, Aslak Tveito
Venue: Journal of Computational and Applied Mathematics (2008)

We derive and analyze a penalty method for solving American multi-asset option problems. A small, non-linear penalty term is added to the Black-Scholes equation. This approach gives a fixed solution domain, removing the free and moving boundary imposed by the early exercise feature of the contract. Explicit, implicit and semi-implicit finite difference schemes are derived, and in the case of independent assets, we prove that the approximate option prices satisfy some basic properties of the American option problem. Several numerical experiments are carried out in order to investigate the performance of the schemes. We give examples indicating that our results are sharp. Finally, the experiments indicate that in the case of correlated underlying assets, the same properties are valid as in the independent case.
An Automatic Voice Query System for Bank Based on Telephone Network
Authors: Dong-ming Li, Jing Zhang, Li Zhang, Ya-ju Liu, Bo Du
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Automatic voice query system for bank by phone was a hi-tech product based on computer technology, electronic communication technology and speech synthesis technology. The hardware design of this system adopted modularization design scheme, which centered on SCM AT89C51, and controlled the phone transceiver chip MT8870 and voice chip ISD4004, realizing voice query for bank account by phone remote control. This paper mainly analyzed and designed several circuits such as ringing detection circuit, on-hook and off-hook control circuit, amplified-filter circuit, receiving circuit, voice circuit and the connection circuit between PC and SCM; it finally gave the realization process of the system’s software.
On earliest deadline first scheduling for temporal consistency maintenance
Authors: Ming Xiong, Qiong Wang, Krithi Ramamritham
Venue: Real-Time Systems (2008)

A real-time object is one whose state may become invalid with the passage of time. A temporal validity interval is associated with the object state, and the real-time object is temporally consistent if its temporal validity interval has not expired. Clearly, the problem of maintaining temporal consistency of data is motivated by the need for a real-time system to track its environment correctly. Hence, sensor transactions must be able to execute periodically and also each instance of a transaction should perform the relevant data update before its deadline.Unfortunately, the period and deadline assignment problem for periodic sensor transactions has not received the attention that it deserves. An exception is the More-Less scheme, which uses the Deadline Monotonic (DM) algorithm for scheduling periodic sensor transactions. However, there is no work addressing this problem from the perspective of dynamic priority scheduling. In this paper, we examine the problem of temporal consistency maintenance using the Earliest Deadline First (EDF) algorithm in three steps:First, the problem is transformed to another problem with a sufficient (but not necessary) condition for feasibly assigning periods and deadlines. An optimal solution for the problem can be found in linear time, and the resulting processor utilization is characterized and compared to a traditional approach. Second, an algorithm to search for the optimal periods and deadlines is proposed. The problem can be solved for sensor transactions that require any arbitrary deadlines. However, the optimal algorithm does not scale well when the problem size increases. Hence, thirdly, we propose a heuristic search-based algorithm that is more efficient than the optimal algorithm and is capable of finding a solution if one exists.
Shared and Searchable Encrypted Data for Untrusted Servers
Authors: Changyu Dong, Giovanni Russello, Naranker Dulay
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

Current security mechanisms pose a risk for organisations that outsource their data management to untrusted servers. Encrypting and decrypting sensitive data at the client side is the normal approach in this situation but has high communication and computation overheads if only a subset of the data is required, for example, selecting records in a database table based on a keyword search. New cryptographic schemes have been proposed that support encrypted queries over encrypted data but all depend on a single set of secret keys, which implies single user access or sharing keys among multiple users, with key revocation requiring costly data re-encryption. In this paper, we propose an encryption scheme where each authorised user in the system has his own keys to encrypt and decrypt data. The scheme supports keyword search which enables the server to return only the encrypted data that satisfies an encrypted query without decrypting it. We provide two constructions of the scheme giving formal proofs of their security. We also report on the results of a prototype implementation.
Resettable Zero Knowledge with Concurrent Soundness in the Bare Public-Key Model under Standard Assumption
Authors: Yi Deng, Dongdai Lin
Venue: Information Security and Cryptology: Third SKLOIS Conference, Inscrypt 2007, Xining, China, August 31 - September 5, 2007, Revised Selected Papers (2007)

In this paper we present the first constant round resettable zero knowledge arguments with concurrent soundness for $\mathcal{NP}$ in the bare public-key (BPK for short) model assuming only collision-resistant hash functions against polynomial-time adversaries. This resolves the problem whether there exist such protocols for $\mathcal{NP}$ in BPK model without assuming sub-exponential hardness. In our protocol, the resettable zero knowledge is demonstrated via a black-box simulator, while the concurrent soundness is proved by using the malicious prover strategy in non-black-box manner.
Efficient algorithms for discrete time-frequency distributions
Authors: John M. O'Toole, Mostefa Mesbah, Boualem Boashash
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

Time-frequency distributions (TFDs) are computational costly to compute. This paper presents algorithms to reduce this computational load. Before we can compute the TFDs, however, we must define a discrete version of the TFD. Defining a discrete TFD (DTFD) is not a straightforward process--for example, a popular DTFD definition does not satisfy all desirable mathematical properties that are inherent to the TFD. In this paper, we define a new DTFD definition, the DTFD-C. This definition is closely related to another DTFD definition which we recently proposed, the DTFD-B. The DTFD-B and DTFD-C satisfy all desirable properties. We provide algorithms for both DTFD definitions. We find that the DTFD-C has the advantage: the DTFD-C requires only 50% of the computational load required to compute the DTFD-B.
Approximating Single Machine Scheduling with Scenarios
Authors: Monaldo Mastrolilli, Nikolaus Mutsanas, Ola Svensson
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

In the field of robust optimization, the goal is to provide solutions to combinatorial problems that hedge against variations of the numerical parameters. This constitutes an effort to design algorithms that are applicable in the presence of uncertainty in the definition of the instance. We study the single machine scheduling problem with the objective to minimize the weighted sum of completion times. We model uncertainty by replacing the vector of numerical values in the description of the instance by a set of possible vectors, called scenarios. The goal is to find the schedule with minimum value in the worst-case scenario.We first show that the general problem is intractable by proving that it cannot be approximated within O(log1 ¿ ¿ n) for any ¿> 0, unless NP has quasi-polynomial algorithms. We then study more tractable special cases and obtain an LP based 2-approximation algorithm for the unweighted case. We show that our analysis is tight by providing a matching lower bound on the integrality gap of the LP. Moreover, we prove that the unweighted version is NP-hard to approximate within a factor less than 6/5. We conclude by presenting a polynomial time algorithm based on dynamic programming for the case when the number of scenarios and the values of the instance are bounded by some constant.