ppxscal/embedding_network huggingface dataset:
Data mining for the diagnosis of type II diabetes from three-dimensional body surface anthropometrical scanning data
Authors: Chad-Ton Su, Chien-Hsin Yang, Kuang-Hung Hsu, Wen-Ko Chiu
Venue: Computers Mathematics with Applications (2006)

Diabetes mellitus has become a general chronic disease as a result of changes in customary diets. Impaired fasting glucose (IFG) and fasting plasma glucose (FPG) levels are two of the indices which physicians use to diagnose diabetes mellitus. Although this is a fairly accurate approach, the tests are expensive and time consuming. This study attempts to construct a prediction model for Type II diabetes using anthropometrical body surface scanning data. Four data mining approaches, including backpropagation neural network, decision tree, logistic regression, and rough set, were used to select the relevant features from the data to predict diabetes. Accuracy of classification was evaluated for these approaches. The result showed that volume of trunk, left thigh circumference, right thigh circumference, waist circumference, volume of right leg, and subjects' age were associated with the condition of diabetes. The accuracy of the classification of decision tree and rough set was found to be superior to that of logistic regression and backpropagation neural network. Several rules were then extracted based on the anthropometrical data using decision tree. The result of implementing this method is not only useful for the physician as a tool for diagnosing diabetes, but it is sophisticated enough to be used in the practice of preventive medicine.
Visual Thinking with an Interactive Diagram
Authors: Colin Ware, Anne T. Gilman, Robert J. Bobrow
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

To investigate the process of reasoning with an interactive diagram, we recorded eye movements and mouse clicks of 28 users as they investigated social relationships in a 313-node network diagram. The MEgraph application used to display this network combines techniques such as topological range searching and motion highlighting to enable interactive exploration of complex network diagrams. Long-term memory encoding was assessed with a surprise recall protocol one week later, with and without lightweight visual history traces. Frequent video-game players relied more on peripheral vision, moving their gaze less often. History support was also associated with more efficient visual strategies. History traces improved users' ability to reconstruct prior work on retest.
Functional Pattern Recognition of 3D Laser Scanned Images of Wood-Pulp Chips
Authors: Marcos López, José M. Matías, José A. Vilán, Javier Taboada
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

We evaluate the appropriateness of applying a functional rather than the typical vectorial approach to a pattern recognition problem. The problem to be resolved was to construct an online system for controlling wood-pulp chip granulometry quality for implementation in a wood-pulp factory. A functional linear model and a functional logistic model were used to classify the hourly empirical distributions of wood-chip thicknesses estimated on the basis of images produced by a 3D laser scanner. The results obtained using these functional techniques were compared to the results of their vectorial counterparts and support vector machines, whose input consisted of several statistics of the hourly empirical distribution. We conclude that the empirical distributions have sufficiently rich functional traits so as to permit the pattern recognition process to benefit from the functional representation.
Identification in the Limit of k,l-Substitutable Context-Free Languages
Authors: Ryo Yoshinaka
Venue: Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications (2008)

Recently Clark and Eyraud (2005, 2007) have shown that substitutable context-free languages are polynomial-time identifiable in the limit from positive data. Substitutability in context-free languages can be thought of as the analogue of reversibility in regular languages. While reversible languages admit a hierarchy, namely k-reversible regular languages for each nonnegative integer k, Clark and Eyraud targeted the subclass of context-free languages that corresponds to zero-reversible regular languages only. Following Clark and Eyraud's proposal, this paper introduces a hierarchy of substitutable context-free languages as the analogue of that of k-reversible regular languages and shows that each class in the hierarchy is also polynomial-time identifiable in the limit from positive data.
Towards Machine Learning of Grammars and Compilers of Programming Languages
Authors: Keita Imada, Katsuhiko Nakamura
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

This paper discusses machine learning of grammars and compilers of programming languages from samples of translation from source programs into object codes. This work is an application of incremental learning of definite clause grammars (DCGs) and syntax directed translation schema (SDTS), which is implemented in the Synapse system. The main experimental result is that Synapse synthesized a set of SDTS rules for translating extended arithmetic expressions with function calls and assignment operators into object codes from positive and negative samples of the translation. The object language is a simple intermediate language based on inverse Polish notation. These rules contain an unambiguous context free grammar for the extended arithmetic expressions, which specifies the precedence and associativity of the operators. This approach can be used for designing and implementing a new programming language by giving the syntax and semantics in the form of the samples of the translation.
Semicomplete Multipartite Digraphs Whose Every Arc Is Contained in a Hamiltonian Path
Authors: Wei Meng, Shengjia Li
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

A semicomplete multipartite digraph is obtained by replacing each edge of a complete multipartite graph by an arc or by a pair of two mutually opposite arcs. In 2002, L. Volkmann [5] raised a problem: determine some sufficient conditions for semicomplete multipartite digraphs such that every arc is contained in a Hamiltonian path. In this paper, we give a new sufficient condition and describe some semicomplete multipartite digraphs demonstrating that this sufficient condition is independent and in some sense, it is best possible.
Adaptive E---Learning: An Architecture Based on PROSA P2P Network
Authors: V. Carchiolo, A. Longheu, G. Mangioni, V. Nicosia
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Effective E---Learning requires ability to adapt in order to provide students with personalised learning path, based on specified goals and personal profiles, as well as the possibility of re-evaluating learning paths during the learning process based on actually learned topics. Moreover, shared teaching materials spread over the network, requiring an effective and efficient management solution for their storage and retrieval. In this paper we propose an E---Learning architecture where personalisation and dynamic adaption is achieved through the construction of learning paths tailored to each student's knowledge and needs, also allowing dynamically re-evaluation of such paths during the learning process, according to personal test results and/or needs. In addition, we arrange teaching materials into PROSA (P2P Resource Organisation by Social Acquaintances), a P2P network that tries to mimic the way social links among peers are established and evolve, in order to build a self organising network for an effective and efficient resource sharing and retrieval.
Specifics of Hidden Markov Model Modifications for Large Vocabulary Continuous Speech Recognition
Authors: Darius Šilingas, Laimutis Telksnys
Venue: Informatica (2004)

Specifics of hidden Markov model-based speech recognition are investigated. Influence of modeling simple and context-dependent phones, using simple Gaussian, two and three-component Gaussian mixture probability density functions for modeling feature distribution, and incorporating language model are discussed. Word recognition rates and model complexity criteria are used for evaluating suitability of these modifications for practical applications. Development of large vocabulary continuous speech recognition system using HTK toolkit and WSJCAM0 English speech corpus is described. Results of experimental investigations are presented.
Selecting Neural Network Forecasting Models Using the Zoomed-Ranking Approach
Authors: Patrícia M. Santos, Teresa B. Ludermir, Ricardo B. C. Prudêncio
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

In this work, we propose to use the Zoomed-Ranking approach to ranking and selecting Artificial Neural Network (ANN) models for time series forecasting. Given a time series to forecast, the Zoomed-Ranking provides a ranking of the candidate models, by aggregating accuracy and execution time obtained by the models in similar series. The best ranked model is then returned as the selected one. In order to evaluate this proposal, we implemented a prototype to rank three ANN models for forecasting time series from different domains. In the experiments, the rankings of models recommended by Zoomed-Ranking were significantly correlated to the ideal rankings.
Resonant Spike Propagation in Coupled Neurons with Subthreshold Activity
Authors: Belén Sancristóbal, José M. Sancho, Jordi García-Ojalvo
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Chemical coupling between neurons is only active when the pre-synaptic neuron is firing, and thus it does not allow for the propagation of subthreshold activity. Electrical coupling via gap junctions, on the other hand, is also ubiquitous and, due to its diffusive nature, transmits both subthreshold and suprathreshold activity between neurons. We study theoretically the propagation of spikes between two neurons that exhibit strong subthreshold oscillations, and which are coupled via both chemical synapses and gap junctions. Due to the electrical coupling, the periodic subthreshold activity is synchronized in the two neurons, and affects propagation of spikes in such a way that for certain values of the delay in the synaptic coupling, propagation is not possible. This effect could provide a mechanism for the modulation of information transmission in neuronal networks.
Word Sense Disambiguation in biomedical ontologies with term co-occurrence analysis and document clustering
Authors: Bill Andreopoulos, Dimitra Alexopoulou, Michael Schroeder
Venue: International Journal of Data Mining and Bioinformatics (2008)

With more and more genomes being sequenced, a lot of effort is devoted to their annotation with terms from controlled vocabularies such as the GeneOntology. Manual annotation based on relevant literature is tedious, but automation of this process is difficult. One particularly challenging problem is word sense disambiguation. Terms such as 'development' can refer to developmental biology or to the more general sense. Here, we present two approaches to address this problem by using term co-occurrences and document clustering. To evaluate our method we defined a corpus of 331 documents on development and developmental biology. Term co-occurrence analysis achieves an F-measure of 77%. Additionally, applying document clustering improves precision to 82%. We applied the same approach to disambiguate 'nucleus', 'transport', and 'spindle', and we achieved consistent results. Thus, our method is a viable approach towards the automation of literature-based genome annotation.
Capturing and optimizing the interactions between prefetching and cache line turnoff
Authors: Ismail Kadayif, Ayhan Zorlubas, Selcuk Koyuncu, Olcay Kabal, Davut Akcicek, Yucel Sahin, Mahmut Kandemir
Venue: Microprocessors Microsystems (2008)

While numerous prior studies focused on performance and energy optimizations for caches, their interactions have received much less attention. This is unfortunate since in general the performance oriented techniques influence energy behavior of the cache, and the energy oriented techniques usually increase program execution cycles. The overall energy and performance behavior of caches in embedded systems when multiple techniques co-exist remains an open research problem. This paper first studies this interaction and demonstrates how performance and energy optimizations can affect each other. We then propose three optimization schemes that turn-off cache lines in a prefetching-sensitive manner. Specifically, these schemes treat prefetched cache lines differently from the lines brought to the cache in a normal way (i.e., through a load operation) in turning off the cache lines. Our experiments with five randomly selected codes from the SPEC2000 suite indicate that the proposed approaches save significant leakage energy. Our results also show that the performance degradations incurred by the proposed approaches are very small.
A Comparative Study of Machine Learning Methods for Detecting Promoters in Bacterial DNA Sequences
Authors: Leonardo G. Tavares, Heitor S. Lopes, Carlos R. Erig Lima
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Machine Learning methods have been widely used in bioinformatics, mainly for data classification and pattern recognition. The detection of genes in DNA sequences is still an open problem. Identifying the promoter region laying prior the gene itself is an important aid to detect a gene. This paper aims at applying several Machine Learning methods to the construction of classifiers for detection of promoters in the DNA of Escherichia coli. A thorough comparison of methods was done. In general, probabilistic and neural network-based methods were those that performed better regarding accuracy rate.
Multi-objective network design and optimization for wireless local area networks
Authors: Chutima Prommak
Venue: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering (2008)

This paper presents a multi-objective wireless local area networks design and optimization. The proposed model combines three problems together, including the optimal access point placement, the frequency channel assignment and the power level assignment. In addition, it accounts for user population density in the service area, traffic demand characteristics and the physical structure of the service area. The design model aims to determine a network configuration that optimizes the network quality of services in term of the radio signal coverage and the data rate capacity to serve expected user traffic demand in the service area. Numerical results and sensitivity analysis is performed to analyze the network quality of service.
Simulation of Active Cardiac Electromechanical Dynamics
Authors: Ken C. Wong, Linwei Wang, Heye Zhang, Huafeng Liu, Pengcheng Shi
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

While finite element methods have been extensively used for computational cardiology, the complicated and computationally expensive meshing procedures largely increase the difficulties of improving numerical accuracy. Furthermore, as the complexity of the element structure increases with the order of polynomial can be handled, the finite element procedures are either simple but numerically inaccurate or accurate but labor intensive. In view of these problems, we adopt the meshfree methods for computational cardiology. Using the meshfree methods, the heart is represented by a set of nodes distributed in the myocardium without any mesh, thus spatial refinements only involve distribution of extra nodes to the area of interest. Furthermore, as the order of polynomial is not limited by elements, it can be increased with relatively ease. These are desirable features as they provide the flexibility for improving numerical accuracies. In this paper, the simulation of the cardiac electromechanical dynamics using the meshfree methods will be introduced. Experiments have been done on a cubical object to provide an insight into the electromechanical dynamics, and also on a canine heart model to show the physiological plausibility of the simulation.
Distributed authentication for low-cost wireless networks
Authors: Sridhar Machiraju, Hao Chen, Jean Bolot
Venue: Proceedings of the 9th workshop on Mobile computing systems and applications (2008)

Cost is one of the key challenges facing the deployment of wireless networks. Though 802.11-based networks have shown that costly, licensed spectrum is not always necessary, the costs of other components especially backhaul and network equipment continue to impede the growth of mobile wireless networks. In this paper, we provide some insights into how such costs can be reduced by designing a novel, low-cost authentication infrastructure for wireless networks. Our authentication scheme relies on base stations to collectively store authentication information. Thus, it eliminates the need to maintain costly infrastructure required by the traditional centralized scheme. Moreover, our scheme is optimized for mobility-induced handover "re-authentication" and, hence, reduces the authentication overhead.
Fusers Based on Classifier Response and Discriminant Function --- Comparative Study
Authors: Michal Wozniak, Konrad Jackowski
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

The Multiple Classifier Systems are nowadays one of the most promising directions in pattern recognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers' decisions. This work presents methods of classifier combination, where neural networks plays a role of fuser block. Fusion on level of recognizer responses or values of their discriminant functions is applied. The qualities of proposed methods are evaluated via computer experiments on generated data and two benchmark databases.
Succinct Data Structures for Retrieval and Approximate Membership (Extended Abstract)
Authors: Martin Dietzfelbinger, Rasmus Pagh
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

The retrieval problem is the problem of associating data with keys in a set. Formally, the data structure must store a function $f\colon U\to \{0,1\}^r$ that has specified values on the elements of a given set S &sube; U, |S| = n, but may have any value on elements outside S. All known methods (e. g. those based on perfect hash functions), induce a space overhead of &theta;(n) bits over the optimum, regardless of the evaluation time. We show that for any k, query time O(k) can be achieved using space that is within a factor 1 + e -k of optimal, asymptotically for large n. The time to construct the data structure is O(n), expected. If we allow logarithmic evaluation time, the additive overhead can be reduced to O(loglogn) bits whp. A general reduction transfers the results on retrieval into analogous results on approximate membership, a problem traditionally addressed using Bloom filters. Thus we obtain space bounds arbitrarily close to the lower bound for this problem as well. The evaluation procedures of our data structures are extremely simple. For the results stated above we assume free access to fully random hash functions. This assumption can be justified using space o(n) to simulate full randomness on a RAM.
A Class of Random Fuzzy Programming and Its Hybrid PSO Algorithm
Authors: Yankui Liu, Xuejie Bai, Fang-Fang Hao
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

This paper presents a new class of two-stage random fuzzy programming with recourse (RFPR) problems. Since the RFPR problem usually includes random fuzzy parameters with infinite supports, it is inherently an infinite dimensional optimization problem that can rarely be solved directly by the conventional optimization algorithms. To overcome this difficulty, this paper developed an approximation method for the original RFPR problem, and turn it into a finite-dimensional one. We also establish a convergence relation between the objective values of the original problem and its approximating problem. To solve a general RFPR problem, we design a hybrid algorithm by integrating the approximation method, neural network (NN) and particle swarm optimization (PSO) algorithm. Finally, one numerical example is presented to demonstrate the effectiveness of the designed algorithm.
Autobiographical memory and patterns of brain atrophy in fronto-temporal lobar degeneration
Authors: Margaret C. McKinnon, Elena I. Nica, Pheth Sengdy, Natasa Kovacevic, Morris Moscovitch, Morris Freedman, Bruce L. Miller, Sandra E. Black, Brian Levine
Venue: Journal of Cognitive Neuroscience (2008)

Autobiographical memory paradigms have been increasingly used to study the behavioral and neuroanatomical correlates of human remote memory. Although there are numerous functional neuroimaging studies on this topic, relatively few studies of patient samples exist, with heterogeneity of results owing to methodological variability. In this study, fronto-temporal lobar degeneration (FTLD), a form of dementia affecting regions crucial to autobiographical memory, was used as a model of autobiographical memory loss. We emphasized the separation of episodic (recollection of specific event, perceptual, and mental state information) from semantic (factual information unspecific in time and place) autobiographical memory, derived from a reliable method for scoring transcribed autobiographical protocols, the Autobiographical Interview [Levine, B., Svoboda, E., Hay, J., Winocur, G., & Moscovitch, M. Aging and autobiographical memory: Dissociating episodic from semantic retrieval. Psychology and Aging, 17, 677--689, 2002]. Patients with the fronto-temporal dementia (FTD) and mixed fronto-temporal and semantic dementia (FTD/SD) variants of FTLD were impaired at reconstructing episodically rich autobiographical memories across the lifespan, with FTD/SD patients generating an excess of generic semantic autobiographical information. Patients with progressive nonfluent aphasia were mildly impaired for episodic autobiographical memory, but this impairment was eliminated with the provision of structured cueing, likely reflecting relatively intact medial-temporal lobe function, whereas the same cueing failed to bolster the FTD and FTD/SD patients' performance relative to that of matched comparison subjects. The pattern of episodic, but not semantic, autobiographical impairment was enhanced with disease progression on 1-to 2-year follow-up testing in a subset of patients, supplementing the cross-sectional evidence for specificity of episodic autobiographical impairment with longitudinal data. This behavioral pattern covaried with volume loss in a distributed left-lateralized posterior network centered on the temporal lobe, consistent with evidence from other patient and functional neuroimaging studies of autobiographical memory. Frontal lobe volumes, however, did not significantly contribute to this network, suggesting that frontal contributions to autobiographical episodic memory may be more complex than previously appreciated.