ppxscal/embedding_network huggingface dataset:
Gene Selection for Cancer Classification Using DCA
Authors: Hoai An Thi, Van Vinh Nguyen, Samir Ouchani
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Gene selection is a very important problem in microarray data analysis and has critical implications for the discovery of genes related to serious diseases. In this paper the problem of gene selection for cancer classification is considered. We develop a combined SVMs - feature selection approach based on the Smoothly Clipped Absolute Deviation penalty, minimizing directly the classifier performance. To solve our optimization problems, we apply the DCA (Difference of Convex functions Algorithms) which is a general framework for non-convex continuous optimization. This leads to a successive linear programming algorithm with finite convergence. Preliminary computational experiments on different real data demonstrate that our methods accomplish the desired goal: suppression of a large number of features with a small error of classification.
Neural Network-Based Robust Tracking Control for Nonholonomic Mobile Robot
Authors: Jinzhu Peng, Yaonan Wang, Hongshan Yu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

A robust tracking controller with bound estimation based on neural network is proposed to deal with the unknown factors of nonholonomic mobile robot, such as model uncertainties and external disturbances. The neural network is to approximate the uncertainties terms and the interconnection weights of the neural network can be tuned online. And the robust controller is designed to compensate for the approximation error. Moreover, an adaptive estimation algorithm is employed to estimate the bound of the approximation error. The stability of the proposed controller is proven by Lyapunov function. The proposed neural network-based robust tracking controller can overcome the uncertainties and the disturbances. The simulation results demonstrate that the proposed method has good robustness.
Combination and Comparison of Chemometric Methods for Determination of pH of Rice Vinegars Using Visible and Near Infrared Spectroscopy
Authors: Fei Liu, Yong He, Li Wang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Visible and near infrared (Vis/NIR) spectroscopy combined with a hybrid calibrations of partial least squares (PLS), BP neural network (BPNN) and least squares-support vector machine (LS-SVM) was investigated to determine the pH values of rice vinegars. Five varieties of rice vinegars and 300 samples were prepared. After some preprocessing, PLS was implemented for calibration as well as the extraction of principal components, which would be used as the inputs of BPNN and LS-SVM models. Finally, the BPNN and LS-SVM regression models were developed with a comparison of PLS. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.982, 0.042, and -0.012 by PLS, 0.992, 0.027, and 0.004 by BPNN, while 0.996, 0.018, and 0.005 by LS-SVM, respectively. The results indicated that Vis/NIR spectroscopy combined with chemometrics could be applied as a high precision way for the determination of pH of rice vinegars.
Evaluation of Ecological Conditions Using Bioindicators: Application of Fuzzy Modeling
Authors: Michael Arkhipov, Elena Krueger, Dmitry Kurtener
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

Exploring biological indicators as tool for evaluation of ecological conditions is one of prime interest for planning process. The focus of this paper is biological indicator based on seed characteristics and defined with the use of fuzzy sets methodology. It is considered application of fuzzy biological indicators in combination with the minimum average weighted deviation method. Finally, Adaptive Neuro-Fuzzy Inference System is utilized for categorization of biological indicators.
A Fast Method for Pricing Early-Exercise Options with the FFT
Authors: R. Lord, F. Fang, F. Bervoets, C. W. Oosterlee
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

A fast and accurate method for pricing early exercise options in computational finance is presented in this paper. The main idea is to reformulate the well-known risk-neutral valuation formula by recognizing that it is a convolution. This novel pricing method, which we name the `CONV' method for short, is applicable to a wide variety of payoffs and only requires the knowledge of the characteristic function of the model. As such the method is applicable within exponentially Lévy models, including the exponentially affine jump-diffusion models. For an M-times exercisable Bermudan option, the overall complexity is O(MNlog(N)) with N grid points used to discretize the price of the underlying asset. It is also shown that American options can be very efficiently computed by combining Richardson extrapolation to the CONV method.
Higher Education's Role in Job Training
Authors: Sorel Reisman
Venue: IT Professional (2004)

For better or worse, commercial companies are goading colleges to become the training grounds of choice for their new hires.
A Declarative Framework for Constrained Search Problems
Authors: Paweł Sitek, Jarosław Wikarek
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Constrained search problems (eg. scheduling, planning, resource allocation, placement, routing etc.) appear frequently at different levels of decisions. They are usually characterized by many types of constraints, which make them unstructured and difficult to solve (NP-complete). Traditional mathematical programming approaches are deficient because their representation of constraints is artificial (using 0-1 variables). Unlike traditional approaches, constraint logic programming (CLP) provides for a natural representation of heterogeneous constraints. In CLP we state the problem requirements by constraints; we do not need to specify how to meet these requirements. In this paper we propose a declarative framework for decision support system (DSS) for constrained search problems implemented by CLP and relational SQL database. We illustrate this concept by the implementation of a DSS for scheduling problems with external resources in different production organization environments.
Towards Usability Evaluation for Smart Appliance Ensembles
Authors: Gregor Buchholz, Stefan Propp
Venue: Interactive Systems. Design, Specification, and Verification: 15th International Workshop, DSV-IS 2008 Kingston, Canada, July 16-18, 2008 Revised Papers (2008)

Smart environments comprise users and devices to form ad-hoc an ensemble and assist the users to fulfill their tasks more efficiently and more conveniently. This introduces new challenges for usability evaluations. To cope with theses issues, we propose the application of task models. Following this approach the behavior of the users can be interpreted as a trace through the corresponding task model. We discuss our method of capturing, visualizing and analyzing traces through task models within smart environments. The paper provides the first results of a prototypical implementation.
Experiment of Event Injection Technology for Network Dependability Evaluation
Authors: Yonggang Pang, Huiqiang Wang, Ye Du
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Dependability evaluation of computer network is an integrated and complex research problem. Evolving from fault injection, event injection can be better used to evaluate dependability of computer network system. According to the principle of event injection, an experiment model for network dependability evaluation was designed and correlative experiments were carried out. At last, application of event injection for dependability evaluation on computer network was discussed.
A Pairwise Key Pre-distribution Scheme for Wireless Sensor Network
Authors: Hui-Feng Huang
Venue: Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics (2008)

Wireless sensor networks have been widely used in a variety of domains which include military sensing and tracking, patient monitoring and health care, etc. When sensor networks are deployed in a hostile environment, security becomes extremely important, as they are vulnerable targets to different types of malicious attacks. Due to the wireless property of sensor devices, the sensor networks may easily be compromised by attackers who modify messages or provide misleading information to other sensor nodes. Therefore, the security of the sensor networks is very important. This paper will present a new efficient key pre-distribution scheme for secure wireless sensor networks. It provides an approach that any pair of sensor nodes can find a common pairwise secret key between them with simple calculation. Compared with previously proposed key pre-distribution schemes, the proposed method could reduce large amounts of computations and communications in both the key pre-distribution step and finding a common secret key for any pair of nodes to achieve secure connectivity.
Full body: The importance of the phenotype in evolution
Authors: George Kampis, László Gulyás
Venue: Artificial Life (2008)

This is a position paper on phenotype-based evolution modeling. It argues that evolutionary complexity is essentially a functional kind of complexity, and for it to evolve, a full body, or, in other words, a dynamically defined, deeply structured, and plasticity-bound phenotype is required. In approaching this subject, we ask and answer some key questions, which we think are interrelated. The questions we discuss and the answers we propose are: (a) How should complexity growth be measured or operationalized in natural and artificial systems? Evolutionary complexity is akin to that of machines, and to operationalize it, we need to study how machinelike organismic functions work and develop. Inspired by studies on causality, we propose the notion of mechanism. A mechanism is a simplified causal system that carries out a function. A growth of functional complexity involves interconversions between a deep (or unused) process and that of a mechanism. (b) Are the principles of natural selection, as they are currently understood, sufficient to explain the evolution of complexity? Our answer is strongly negative. Natural selection helps adapting mechanisms to carry out a given task, but will not generate a task. Hence there is a tradeoff between available tasks and mechanisms fulfilling them. To escape, we argue that competition avoidance is required for new complexity to emerge. (c) What are the environmental constraints on complexity growth in living systems? We think these constraints arise from the structure of the coevolving ecological system, and the basic frames are given by the niche structure. We consider the recently popular idea of niche construction and relate it to the plasticity of the phenotype. We derive a form of phenotype plasticity from the hidden (unused) and explicit (functional) factors discussed in the causality part. (d) What are the main hypotheses about complexity growth that can actually be tested? We hypothesize that a rich natural phenotype that supports causality-function conversions is a necessary ingredient of complexity growth. We review our work on the FATINT system, which incorporates similar ideas in a computer simulation, and shows that full-body phenotypes are sufficient for achieving functional evolution. (e) What language is most appropriate for speaking about the evolution of complexity in living systems? FATINT is developed using advanced agent-based modeling techniques, and we discuss the general relevance of this methodology for understanding and simulating the phenomena discussed.
An Adversary Aware and Intrusion Detection Aware Attack Model Ranking Scheme
Authors: Liang Lu, Rei Safavi-Naini, Jeffrey Horton, Willy Susilo
Venue: Proceedings of the 5th international conference on Applied Cryptography and Network Security (2007)

A successful computer system intrusion is often resulted from an attacker combining exploits of individual vulnerability. This can be modelled by attack models and attack graphs to provide a global view on system security against attacker's goal. However, as the size and complexity of attack models and attack graphs usually greatly exceeds human ability to visualize, understand and analyze, a scheme is required to identify important portions of attack models and attack graphs. Mehta et al. proposed to rank states of an attack model by the probability of an adversary reaching a state by a sequence of exploiting individual vulnerabilities in a previous scheme. Important portions can hence be identified by ranks of states. However, Mehta et al.'s ranking scheme is based on the PageRank algorithm which models a web surfing scenario, but has not considered much on the dissimilarity between web surfing scenarios and computer system intrusion scenarios. In this paper, we extend Mehta et al.'s scheme by taking into consideration dissimilarity between web surfing scenarios and computer system intrusion scenarios. We experiment with the same network model used in Mehta et al.'s scheme and have the results compared. The experiments yielded promising results that demonstrated consistent ranks amongst varying parameters modelled by our ranking scheme.
Improving the Usability of Novel Web Software: An Industrial Case Study of an Institutional Repository
Authors: Dana Mckay, Shaun Burriss
Venue: Proceedings of the 2008 international workshops on Web Information Systems Engineering (2008)

In this paper we discuss usability improvements made to institutional repository software in an industrial setting. The novelty of institutional repository software as a class and the industrial setting present special challenges to traditional usability evaluation; here examine these problems and present a case study which demonstrates a combination of heuristic analysis, empirical methods, background research and interface best practice to make significant usability improvements to an institutional repository.
Emotional Agents with Team Roles to Support Human Group Training
Authors: Raúl A. Aguilar, Angélica Antonio, Ricardo Imbert
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

In the teamwork research area there is an increasing interest about the principles behind team effectiveness and effective team training; for Intelligent Virtual Agents (IVAs) Team Training is an excellent application area; nevertheless, the few reported works about IVAs in team training, illustrate both the use for the individualized teaching (Pedagogical Agents) of procedural tasks and the substitution of missing team members (Teammate Agents) to promote the practice of team tasks in relation to functional roles (Taskwork) [1].Our interest on Intelligent Virtual Environments for Training (IVETs) has led us to propose a Team Training Strategy (TTS) whose purpose is to promote social skills as well as knowledge and skills related to tasks of socio technical nature. The alternatives that we are evaluated to improve the performance of human groups and to promote effective teams deal with: the use of scaffolding as the best tutoring approach, the promotion of social skills before technical skills, and especially, the selection of the best nonfunctional roles (team roles) balance according to the task.In addition, our aim is to incorporate into an IVA called Pancho (Pedagogical AgeNt to support Colaborative Human grOups) the particular behaviors of Team Roles defined by Belbin [2]; Pancho, with a selected team role --according to a team model-- will join the human group with the intention of improving the performance of the team (Teamwork) and providing scaffolding to the trainees (Taskwork). The Belbin's categorization is the earliest and still the most popular. He states that the team role can be defined as a tendency to behave, contribute and interrelate with each others at work in a certain distinctive ways; he also states that in teamwork, a good mix of team roles in the group is necessary for groups to use their technical skills optimally. The team roles defined by Belbin have very particular behaviors; we have selected a generic cognitive architecture for agents with emotionally influenced behaviors --called COGNITIVA-- to realize those roles [3]. The constructs provided by this architecture (Personal traits, Concerns, Moods, Attitudes and Physical states) are being properly instantiated to generate the desired behaviors.
A New Certificateless Electronic Cash Scheme with Multiple Banks Based on Group Signatures
Authors: Shangping Wang, Zhiqiang Chen, Xiaofeng Wang
Venue: Proceedings of the 2008 International Symposium on Electronic Commerce and Security (2008)

Most of the proposed E-cash systems require that the shop and customer belong to the same bank, which becomes the bottle-neck of E-cash’s wide application. Fangguo Zhang et al. proposed a model of electronic cash using multi-banks, but a trusted third party is needed in their system. To solve this problem, we propose a new electronic cash scheme based on group signature from bilinear pairings. In the new scheme, the shop and customer need not belong to the same bank, and a trusted third party is not needed. The identity based public key cryptosystem is used, the KGC (key generator center, or central bank) can only control partial private key of the customer, the customer known all private key of himself, so the private key escrow problem in the identity based public key cryptosystem is solved in the system. As the scheme is certificateless, the management and maintenance of the public key is much simplified.
Plagiarism Detection Based on Singular Value Decomposition
Authors: Zdenek Ceska
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

Plagiarism is a widely spread problem that is the main focus of interest these days. In this paper, we propose a new method solving associations of phrases contained in text documents. This method, called SVDPlag, employs Singular Value Decomposition (SVD) for this purpose. Further, we discuss other approaches to plagiarism detection and compare them with our method. To examine the efficiency of plagiarism detection methods, we used an experimental corpus of 950 text documents about politics, which were created from the standard CTK corpus. The experiments indicate that our approach significantly improves the accuracy of plagiarism detection and overcomes other methods.
Optimization of a Dual-Energy Contrast-Enhanced Technique for a Photon Counting Digital Breast Tomosynthesis System
Authors: Ann-Katherine Carton, Christer Ullberg, Karin Lindman, Tom Francke, Andrew Maidment
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

We have developed a dual-energy technique for contrast-enhanced digital breast tomosynthesis using a photon-counting imaging system whereby low- and high-energy images are acquired in a single scan. A W target x-ray source is employed at 49 kV. Sn and Cu filters are used to obtain low- and high-energy images. Iodine enhanced images are calculated by applying a weighted subtraction to the logarithm of the Sn and Cu images. The optimal system configuration and Sn-Cu filter pair were assessed using theoretical modeling. The mean glandular dose was kept to 1 mSv. Optimal weighting factors (w t ) for the subtraction and SDNR between iodine-enhanced and non-enhanced breast background were calculated. Optimal w t and SDNR depend on the Sn-Cu combination and breast thickness. SDNR varies with the dose allocated among the Sn and Cu images.The optimal configuration uses Sn and Cu filters of sufficient thickness to require the maximum mAs achievable by the x-ray tube.
Orthogonal Least Squares Based on QR Decomposition for Wavelet Networks
Authors: Min Han, Jia Yin
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper proposes an orthogonal least square algorithm based on QR decomposition (QR-OLS) for the neurons selection of the hidden layer of wavelet networks. This new algorithm divides the original neurons matrix into several parts to avoid comparing among the poor ones and uses QR decomposition to select the significant ones. It can avoid lots of meaningless calculation. This algorithm is applied to the wavelet network with the analysis of variance (ANOVA) expansion and one-step-ahead predictions, respectively, for the Mackey-Glass delay-differential equation and the annual sunspot data set. The results show that the QR-OLS algorithm can relieve the load of the heave calculation and has a good performance.
On the Capacity of MIMO Rayleigh Fading Channels with Log-Normal Shadowing
Authors: Liang Yang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

In this paper, taking into account the multiple-input multiple-output (MIMO) systems experiencing log-normal shadowing fading, we investigate the ergodic capacity of such MIMO systems. For the MIMO fading channels with shadowing, using the Gauss Hermite integration, we present an approximated capacity expression. We also give a simplified and improved lower bound on the ergodic capacity by applying Jensen’s inequality. Numerical results demonstrate the accuracy of our analytical expressions and the tightness of our lower bounds.
Recursion on Nested Datatypes in Dependent Type Theory
Authors: Ralph Matthes
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

Nested datatypes are families of datatypes that are indexed over all types and where the datatype constructors relate different members of the family. This may be used to represent variable binding or to maintain certain invariants through typing.In dependent type theory, a major concern is the termination of all expressible programs, so that types that depend on object terms can still be type-checked mechanically. Therefore, we study iteration and recursion schemes that have this termination guarantee throughout. This is not based on syntactic criteria (recursive calls with "smaller" arguments) but just on types ("type-based termination"). An important concern are reasoning principles that are compatible with the ambient type theory, in our case induction principles.In previous work, the author has proposed an abstract description of nested datatypes together with a mapping operation (like map for lists) and an iterator on the term side and an induction principle on the logical side that could all be implemented within the Coq system (with impredicative Set that is just needed for the justification, not for the definition and the examples). For verification purposes, it is important to have naturality theorems for the obtained iterative functions. Although intensional type theory does not provide naturality in general, criteria for naturality could be established that are met in case studies on "bushes" and representations of lambda terms (also with explicit flattening).The new contribution is an extension of this abstract description to full primitive recursion and its illustration by way of examples that have been carried out in Coq. Unlike the iterative system, we do not yet have a justification within Coq.