ppxscal/embedding_network huggingface dataset:
Geodesic Generative Topographic Mapping
Authors: Raúl Cruz-Barbosa, Alfredo Vellido
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

Nonlinear dimensionality reduction (NLDR) methods aim to provide a faithful low-dimensional representation of multivariate data. The manifold learning family of NLDR methods, in particular, do this by defining low-dimensional manifolds embedded in the observed data space. Generative Topographic Mapping (GTM) is one such manifold learning method for multivariate data clustering and visualization. The non-linearity of the mapping it generates makes it prone to trustworthiness and continuity errors that would reduce the faithfulness of the data representation, especially for datasets of convoluted geometry. In this study, the GTM is modified to prioritize neighbourhood relationships along the generated manifold. This is accomplished through penalizing divergences between the Euclidean distances from the data points to the model prototypes and the corresponding geodesic distances along the manifold. The resulting Geodesic GTM model is shown to improve not only the continuity and trustworthiness of the representation generated by the model, but also its resilience in the presence of noise.
Roadmapping and i-Systems
Authors: Tieju Ma, Hongbin Yan, Yoshiteru Nakamori
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

The abstract should summarize the contents of the paper and should contain at least 70 and at most 150 words. It should be set in 9-point font size and should be inset 1.0 cm from the right and left margins. There should be two blank (10-point) lines before and after the abstract. This document is in the required format. Roadmapping, as a strategic planning tool, is attracting increasing applications in industries. By applying the principles of Interactive Planning (IP), this paper puts forward a new solution for making personal academic research roadmaps. Then this paper introduces an ongoing project that is to develop a roadmapping support system based on the solution, and gives some considerations about applying the i-System Methodology for enhancing the knowledge creation in a roadmapping process.
Assigning Ontology-Based Semantics to Process Models: The Case of Petri Nets
Authors: Pnina Soffer, Maya Kaner, Yair Wand
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

Syntactically correct process models are not necessarily meaningful or represent processes that are feasible to execute. Specifically, when executed, the modeled processes might not be guaranteed to reach their goals. We propose that assigning ontological semantics to process modeling constructs can result in more meaningful models. Furthermore, the ontological semantics can impose constraints on the allowed process models which in turn can provide rules for developing process models. In particular, such models can be designed to be valid in the senses that the process can accomplish its goal when executed. We demonstrate this approach for Petri Net based process models.
ICT Development Indices - UNCTAD 2004 Report
Authors: Staff
Venue: I-WAYS - The Journal of E-Government Policy and Regulation (2005)

Information and communications technologies (ICTs) offer unique opportunities for developing countries to narrow the development gap with industrialized countries. They have the potential to assist developing countries 'leapfrog' entire stages of developments. This is the theme of a UN Conference on Trade and Development (UNCTAD) report entitled The Digital Divide: ICT Development Indices 2004. UNCTAD notes that despite the potential benefits offered by ICTs, significant barriers to their effective use exist in both developed and developing countries. These barriers must be addressed to allow the realization of the full potential of ICTs. Developing countries have to deal with problems of telecom infrastructure, poor computer and general literacy, lack of awareness of the Internet and regulatory inadequacy. Benchmarking the extent of ICT development is an important tool for policy-makers. It allows comparisons between countries and indicates how well countries are doing compared to others in terms of adaptation, mastery and development. Compared with better-performing countries helps identify policies for further improvement and progression. The WSIS Plan of Action calls for "realistic international performance evaluation and benchmarking through comparable statistical indicators and research results." This report updates UNCTAD's ICT Development Indices to benchmark and review trends in the digital divide. It presents a summary of options that countries can adopt as well as country case studies, benchmarking examples and analyzes policy that drives the quality of its implementation. The following are the overview and benchmarking ICT developments sections of the report. A complete text of the report is available from http://www.unctad.org. 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
Kernel-Based Grouping of Histogram Data
Authors: Tilman Lange, Joachim M. Buhmann
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Organizing objects into groups based on their co-occurrence with a second, relevance variable has been widely studied with the Information Bottleneck (IB) as one of the most prominent representatives. We present a kernel-based approach to pairwise clustering of discrete histograms using the Jensen-Shannon (JS) divergence, which can be seen as a two-sample test. This yields a cost criterion with a solid information-theoretic justification, which can be approximated in polynomial time with arbitrary precision. In addition to that, a relation to optimal hard clustering IB solutions can be established. To our knowledge, we are the first to devise algorithms for the IB with provable approximation guaranties. In practice, one obtains convincing results in the context of image segmentation using fast optimization heuristics.
Study on the Time-Frequency Characteristics of Engine Induction Noise in Acceleration Based on S Transform
Authors: Zhiyong Hao, Hongmei Xu, Guangtai Zheng, Guoxi Jing
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

The definition of S transform was respectively deduced from short time Fourier transform and continuous wavelet transform. The simulation experiment indicates that S transform behaves itself with better time-frequency analysis and positioning capability. It is feasible to adopt S transform to investigate the time-frequency characteristics of engine induction noise in acceleration. Engine induction noise was recorded in acceleration. S transform was employed to analyze the recorded signals. The analysis results show that the signal energy of induction noise is mainly concentrated nearby the linearly modulated frequency band defined by the fundamental frequency, which increases with engine speed. The analysis results are helpful to engine induction noise control.
Data density and trend reversals in auditory graphs: Effects on point-estimation and trend-identification tasks
Authors: Michael A. Nees, Bruce N. Walker
Venue: ACM Transactions on Applied Perception (TAP) (2008)

Auditory graphs&mdash;displays that represent quantitative information with sound&mdash;have the potential to make data (and therefore science) more accessible for diverse user populations. No research to date, however, has systematically addressed the attributes of data that contribute to the complexity (the ease or difficulty of comprehension) of auditory graphs. A pair of studies examined the role of data density (i.e., the number of discrete data points presented per second) and the number of trend reversals for both point-estimation and trend-identification tasks with auditory graphs. For the point-estimation task, more trend reversals led to performance decrements. For the trend-identification task, a large main effect was again observed for trend reversals, but an interaction suggested that the effect of the number of trend reversals was different across lower data densities (i.e., as density increased from 1 to 2 data points per second). Results are discussed in terms of data sonification applications and rhythmic theories of auditory pattern perception.
Classifications of Liver Diseases from Medical Digital Images
Authors: Lequan Min, Yongan Ye, Shubiao Gao
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

Hepatitis B/C virus (HBV/HCV) infections are serious problems of world-wide, which cause over million die each year. Most of HBV/HCV patients need long term therapy. Side effects and virus mutations make difficult to determine the durations and endpoints of treatments. Medical images of livers provide evaluating tools for effectiveness of anti-virus treatments. This paper presents a liver hepatitis progression model. Each class C i in the model consists of three characteristic qualities: gray-scale characteristic interval I G, i , non-homogenous degree N h, i and entropy Entro i . This model aims to describe both digitally and visually a patient's liver damage. Examples are given to explain how to use the liver hepatitis progress model to classify people with normal livers, healthy HBV carriers, light chronic HBV patients and chronic cirrhosis HBV patients. The results show that our analysis results are in agreement with the clinic diagnoses and provide quantitative and visual interpretations.
Dynamic quorums for DHT-based enterprise infrastructures
Authors: Roberto Baldoni, Ricardo Jiménez-Peris, Marta Patiño-Martínez, Leonardo Querzoni, Antonino Virgillito
Venue: Journal of Parallel and Distributed Computing (2008)

Peer-to-peer systems (P2P) have become a popular technique to design large-scale distributed applications in unmanaged inter-domain settings, such as file sharing or chat systems, thanks to their capabilities to self-organize and evenly split the load among peers. Recently, enterprises owning a large IT hardware and software infrastructure started looking at these P2P technologies as a means both to reduce costs and to help their technical divisions to manage huge number of devices characterized by a high level of cooperation and a relatively low churn. Gaining a quick exclusive access to the system for maintenance or auditing purposes in these enterprise infrastructures is a fundamental operation to be implemented. Conversely, this kind of operation is usually not an issue in the previously mentioned inter-domain setting, where peers are inherently independent and cannot be managed. In the context of classical distributed applications, quorum systems have been considered as a major building block for implementing many paradigms, from distributed mutual exclusion to data replication management. In this paper, we explore how to architect decentralized protocols implementing quorum systems in Distributed Hash Table based cooperative P2P networks. Our results show that quorum systems taken ''as is'' from the literature and directly applied to such networks are not scalable due to the high load imposed onto the underlying network. This paper introduces some design principles for both quorum systems and protocols using them that boost their scalability and performance. These design principles consist in a dynamic and decentralized selection of quorums and in the exposition and exploitation of internals of the DHT. As a third design principle it is also shown how to redesign quorum systems to enable efficient decentralization. We show that by combining these design principles in a cooperative environment with relatively low churn it is possible to minimize the imposed load in the system, in terms of sites contacted to obtain a quorum, and the latency of quorum acquisition.
Discovering Web Services to Improve Requirements Specifications: Does It Help?
Authors: Konstantinos Zachos, Neil Maiden, Rhydian Howells-Morris
Venue: Proceedings of the 14th international conference on Requirements Engineering: Foundation for Software Quality (2008)

Service-centric systems pose new opportunities for when engineering requirements. This paper reports an evaluation of software tools with which to exploit discovered services to improve the completeness of requirements specifications. Although these tools had been evaluated previously in facilitated industrial workshops, industrial users had not used the tools directly. In this paper we report 2 industrial uses and evaluations in which experienced analysts used the tools directly on 2 real-world requirements projects. Results reveal that analysts used the tools to retrieve web services that could implement specified requirements, but analysts were less able to improve these requirements in light of the retrieved services. Results have implications for iterative service discovery processes and service discovery algorithms.
Transaction Safe Nonblocking Data Structures
Authors: Virendra J. Marathe, Michael F. Spear, Michael L. Scott
Venue: Proceedings of the 21st international symposium on Distributed Computing (2007)

This brief announcement focuses on interoperability of software transactions with ad hoc nonblocking algorithms. Specifically, we modify arbitrary nonblocking operations so that (1) they can be used both inside and outside transactions, (2) external uses serialize with transactions, and (3) internal uses succeed if and only if the surrounding transaction commits. Interoperability enables seemless integration with legacy code, atomic composition of nonblocking operations, and the equivalent of hand-optimized, closed nested transactions.
A Fuzzy Cluster Algorithm Based on Mutative Scale Chaos Optimization
Authors: Chaoshun Li, Jianzhong Zhou, Qingqing Li, Xiuqiao Xiang
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

The traditional Fuzzy C-Means (FCM) algorithm has some disadvantages in optimization method, which makes the algorithm liable to fall into local optimum, thus failing to get the optimal clustering results. According to the defect of FCM algorithm, a new Fuzzy Clustering algorithm based on Chaos Optimization (FCCO) is proposed in this paper, which combines mutative scale chaos optimization strategy and gradient method together. Moreover, a fuzzy cluster validity index (PBMF) is introduced to make the FCCO algorithm capable of clustering automatically. Three other fuzzy cluster validity indices, namely XB, PC and PE, are utilized to compare the performances of FCCO, FCM and another algorithm, when applied to artificial and real data sets classification. Experiment results show FCCO algorithm is more likely to obtain the global optimum and achieve better performances on validity indices than other algorithms.
Extreme Value Based Adaptive Operator Selection
Authors: Álvaro Fialho, Luís Costa, Marc Schoenauer, Michèle Sebag
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Credit Assignment is an important ingredient of several proposals that have been made for Adaptive Operator Selection. Instead of the average fitness improvement of newborn offspring, this paper proposes to use some empirical order statistics of those improvements, arguing that rare but highly beneficial jumps matter as much or more than frequent but small improvements. An extreme value based Credit Assignment is thus proposed, rewarding each operator with the best fitness improvement observed in a sliding window for this operator. This mechanism, combined with existing Adaptive Operator Selection rules, is investigated in an EC-like setting. First results show that the proposed method allows both the Adaptive Pursuit and the Dynamic Multi-Armed Bandit selection rules to actually track the best operators along evolution.
CryptMT3 Stream Cipher
Authors: Makoto Matsumoto, Mutsuo Saito, Takuji Nishimura, Mariko Hagita
Venue: New Stream Cipher Designs: The eSTREAM Finalists (2008)

CryptMT version 3 (CryptMT3) is a stream cipher obtained by combining a large LFSR and a nonlinear filter with memory using integer multiplication. Its period is proved to be no less than 219937¿ 1, and the 8-bit output sequence is at least 1241-dimensionally equidistributed. It is one of the fastest stream ciphers on a CPU with SIMD operations, such as Intel Core 2 Duo.
Chirplet Transform Applied to Simulated and Real Blue Whale (Balaenoptera musculus) Calls
Authors: Mohammed Bahoura, Yvan Simard
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

Chirplet transform performance to identify low-frequency blue whale calls is tested with simulations and observations from North-West Atlantic. The three different calls are simulated using linear or quadratic frequency sweeping chirps and a hanning window. The performance of Chirplet transform to accurately estimate the chirp parameters with or without noise is first assessed. Then the performance to classify the real vocalizations from the test dataset using the three features best estimated from the simulations is then assessed. The method has a high classification rate and appears promising to efficiently identify these blue whale signature vocalizations with a reduced number of parameters, even under low signal to noise ratios.
Adaptive Network-Based Fuzzy Inference Model of Plasma Enhanced Chemical Vapor Deposition Process
Authors: Byungwhan Kim, Seongjin Choi
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this study, a prediction model of plasma enhanced chemical deposition (PECVD) data was constructed by using an adaptive network-based fuzzy inference system (ANFIS). The PECVD process was characterized by means of a Box Wilson statistical experiment. The film characteristics modeled are deposition rate and stored charge. The prediction performance of ANFIS models was evaluated as a function of training factors, including the step-size, type of membership functions, and normalization factor of inputs-output pairs. The effects of each training factor were sequentially optimized. The root mean square errors of optimized deposition rate and charge models were 11.94 Å/min and 1.37 ×1012/cm2, respectively. Compared to statistical regression models, ANFIS models yielded an improvement of more than 20%. This indicates that ANFIS can effectively capture nonlinear plasma dynamics.
Using Place and Transition Fault Net Models for Sequential Diagnosis Time Assessment in Discrete Event Systems
Authors: Iwan Tabakow
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

The diagnosis-time assessment is studied below in the case of discrete event systems. It is assumed that any such system is described by a live, bounded, and reversible place-transition Petri net. There are assumed some deterministically given delays associated with the transitions of N and hence N is assumed to be deterministic timed. For this purpose two different type of (single) fault models are used, i.e. place fault and transition fault models and the corresponding diagnosis process is assumed to be sequential. Hence, the diagnosis-time assessment is realised by computing the absolute value of the time difference between the minimum cycle time of N and the worst-case fault-isolation time according to the obtained diagnostic tree. 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
Ranking and Sorting in Unreliable Single Hop Radio Network
Authors: Marcin Kik
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

We propose simple and efficient sorting algorithm for unreliable single hop radio network. (In such network each listening station receives transmitted message with some probability p < 1.) We also propose a method of periodic transmission of a sorted sequence that allows for efficient and energetically safe ranking in this sequence.
A broader picture of the complexity of strategic behavior in multi-winner elections
Authors: Reshef Meir, Ariel D. Procaccia, Jeffrey S. Rosenschein
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

Recent work by Procaccia, Rosenschein and Zohar [14] established some results regarding the complexity of manipulation and control in elections with multiple winners, such as elections of an assembly or committee; that work provided an initial understanding of the topic. In this paper, we paint a more complete picture of the topic, investigating four prominent multi-winner voting rules. First, we characterize the complexity of manipulation and control in these voting rules under various kinds of formalizations of the manipulator's goal. Second, we extend the results about complexity of control to various well-known types of control. This work enhances our comprehension of which multi-winner voting rules should be employed in various settings.
Analysis of Doppler Ultrasound Blood Flow Signals of Wrist Radial Artery for Discriminating Healthy People from 3 Kinds of Patients
Authors: Kuanquan Wang, Chao Xu, Dongyu Zhang, Naimin Li
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Doppler Ultrasound Blood Flow Signal (DUBFS) is a non-stationary signal that is widely used in the study of the clinical diagnosis of cardiovascular diseases. According to the theory of pulse diagnosis of Traditional Chinese Medicine, in this paper, a feature extraction method based on Hilbert_Huang transform is proposed in order to investigate the relationship between the DUBFS of wrist radial artery and the pathological changes of certain organs. The extracted features have applied in classification experiments on 4 groups of data, which are healthy persons, gastritis patients, cholecystitis patients and nephritis patients, respectively. Experimental results with high recognition rates demonstrate that Hilbert_Huang transform is an effective method of time-frequency analysis for DUBFS, and extracted features by proposed method have promising discriminating ability between the healthy people and 3 kinds of patients.
High-order mimetic finite difference method for diffusion problems on polygonal meshes
Authors: Vitaliy Gyrya, Konstantin Lipnikov
Venue: Journal of Computational Physics (2008)

The mimetic finite difference (MFD) methods mimic important properties of physical and mathematical models. As a result, conservation laws, solution symmetries, and the fundamental identities of the vector and tensor calculus are held for discrete models. The MFD methods retain these attractive properties for full tensor coefficients and arbitrary polygonal meshes which may include non-convex and degenerate elements. The existing MFD methods for solving diffusion-type problems are second-order accurate for the conservative variable (temperature, pressure, energy, etc.) and only first-order accurate for its flux. We developed new high-order MFD methods which are second-order accurate for both scalar and vector variables. The second-order convergence rates are demonstrated with a few numerical examples on randomly perturbed quadrilateral and polygonal meshes.