ppxscal/embedding_network huggingface dataset:
Parameter optimization for growth model of greenhouse crop using genetic algorithms
Authors: Chunni Dai, Meng Yao, Zhujie Xie, Chunhong Chen, Jingao Liu
Venue: Applied Soft Computing (2009)

Automatic greenhouse production is quite new in China. For the development of our modern agriculture it is a significant issue to accurately formulate the simulation growth models of greenhouse plants in different environments. The objective of our study was to develop an approach to calibrate the growth model of greenhouse crop. In this paper, an adaptive genetic algorithm (GA) is proposed and evaluated for this issue. This new algorithm is composed of two GAs. The primary one is utilized to parameterize the growth model and the secondary is to determine the algorithmic parameters of the primary GA. The superior performance of this new procedure is demonstrated through its applications to three test functions and the greenhouse optimization problems compared with other two GAs. This presented technique may be a fine framework for the development of similar application for complex biological models that require parameterization when a new set of environmental conditions arises or there is a need to account for differences among subspecies or varieties.
802.11n under the microscope
Authors: Vivek Shrivastava, Shravan Rayanchu, Jongwoon Yoonj, Suman Banerjee
Venue: Proceedings of the 8th ACM SIGCOMM conference on Internet measurement (2008)

We present an experimental study of IEEE 802.11n (high throughput extension to the 802.11 standard) using commodity wireless hardware. 802.11n introduces a variety of new mechanisms including physical layer diversity techniques, channel bonding and frame aggregation mechanisms. Using measurements from our testbed, we analyze the fundamental characteristics of 802.11n links and quantify the gains of each mechanism under diverse scenarios. We show that the throughput of an 802.11n link can be severely degraded (up ≈85%) in presence of an 802.11g link. Our results also indicate that increased amount of interference due to wider channel bandwidths can lead to throughput degradation. To this end, we characterize the nature of interference due to variable channel widths in 802.11n and show that careful modeling of interference is imperative in such scenarios. Further, as a reappraisal of previous work, we evaluate the effectiveness of MAC level diversity in the presence of physical layer diversity mechanisms introduced by 802.11n.
Mapping of hydrothermally altered rocks by the EO-1 Hyperion sensor, Northern Danakil Depression, Eritrea
Authors: Ronen Gersman, Eyal Ben-Dor, Michael Beyth, Dov Avigad, Michael Abraha, Alem Kibreab
Venue: International Journal of Remote Sensing (2008)

An EO-1 Hyperion scene was used to identify and map hydrothermally altered rocks and a Precambrian metamorphic sequence at and around the Alid volcanic dome, at the northern Danakil Depression, Eritrea. Mapping was coupled with laboratory analyses, including reflectance measurements, X-ray diffraction, and petrographic examination of selected rock samples. Thematic maps were compiled from the dataset, which was carefully pre-processed to evaluate and to correct interferences in the data. Despite the difficulties, lithological mapping using narrow spectral bands proved possible. A spectral signature attributed to ammonium was detected in the laboratory measurements of hydrothermally altered rocks from Alid. This was expressed as spectral absorption clues in the atmospherically corrected cube, at the known hydrothermally altered areas. The existence of ammonium in hydrothermally altered rocks within the Alid dome has been confirmed by previous studies. Spectral information of endmember's mineralogy found in the area (e.g. dolomite) enables a surface mineral map to be produced that stands in good agreement with the known geology along the overpass. These maps are the first hyperspectral overview of the surface mineralogy in this arid terrain and may be used as a base for future studies of remote areas such as the Danakil.
Keyword search on external memory data graphs
Authors: Bhavana Bharat Dalvi, Meghana Kshirsagar, S. Sudarshan
Venue: Proceedings of the VLDB Endowment (2008)

Keyword search on graph structured data has attracted a lot of attention in recent years. Graphs are a natural "lowest common denominator" representation which can combine relational, XML and HTML data. Responses to keyword queries are usually modeled as trees that connect nodes matching the keywords. In this paper we address the problem of keyword search on graphs that may be significantly larger than memory. We propose a graph representation technique that combines a condensed version of the graph (the "supernode graph") which is always memory resident, along with whatever parts of the detailed graph are in a cache, to form a multi-granular graph representation. We propose two alternative approaches which extend existing search algorithms to exploit multigranular graphs; both approaches attempt to minimize IO by directing search towards areas of the graph that are likely to give good results. We compare our algorithms with a virtual memory approach on several real data sets. Our experimental results show significant benefits in terms of reduction in IO due to our algorithms.
BioJava
Authors: R. C. G. Holland, T. A. Down, M. Pocock, A. Prlić, D. Huen, K. James, S. Foisy, A. Dräger, A. Yates, M. Heuer, M. J. Schreiber
Venue: Bioinformatics (2008)

Summary: BioJava is a mature open-source project that provides a framework for processing of biological data. BioJava contains powerful analysis and statistical routines, tools for parsing common file formats and packages for manipulating sequences and 3D structures. It enables rapid bioinformatics application development in the Java programming language. Availability: BioJava is an open-source project distributed under the Lesser GPL (LGPL). BioJava can be downloaded from the BioJava website ( http://www.biojava.org). BioJava requires Java 1.5 or higher. Contact:andreas.prlic@gmail.com. All queries should be directed to the BioJava mailing lists. Details are available at http://biojava.org/wiki/BioJava:MailingLists.
Motion detection and object tracking with discrete leaky integrate-and-fire neurons
Authors: Lon Risinger, Khosrow Kaikhah
Venue: Applied Intelligence (2008)

A biologically inspired visual system capable of motion detection and pursuit motion is implemented using a Discrete Leaky Integrate-and-Fire (DLIF) neuron model. The system consists of a visual world, a virtual retina, the neural network circuitry (DLIF) to process the information, and a set of virtual eye muscles that serve to move the input area (visual field) of the retina within the visual world. Temporal aspects of the DLIF model are heavily exploited including: spike propagation latency, relative spike timing, and leaky potential integration. A novel technique for motion detection is employed utilizing coincidence detection aspects of the DLIF and relative spike timing. The system as a whole encodes information using relative spike timing of individual action potentials as well as rate coded spike trains. Experimental results are presented in which the motion of objects is detected and tracked in real and animated video. Pursuit motion is successful using linear and also sinusoidal paths which include object velocity changes. The visual system exhibits dynamic overshoot correction heavily exploiting neural network characteristics. System performance is within the bounds of real-time applications.
Would you mind being watched by machines? Privacy concerns in data mining
Authors: Vincent C. M&#x00fc;ller
Venue: AI Society (2008)

Data mining is not an invasion of privacy because access to data is only by machines, not by people: this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people.
Contraction Maps on Ifqm-spaces with Application to Recurrence Equations of Quicksort
Authors: S. Romaguera, P. Tirado
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Recently, C. Alaca, D. Turkoglu and C. Yildiz [Chaos, Solitons and Fractals, 2006], have proved intuitionistic fuzzy versions of the celebrated Banach fixed point theorem and Edelstein fixed point theorem respectively, by means of a notion of intuitionistic fuzzy metric space which is based on the concept of fuzzy metric space due to I. Kramosil and J. Michalek [Kybernetika, 1975]. In this paper we generalize the notions of intuitionistic fuzzy metric space by Alaca, Turkoglu and Yildiz to the quasi-metric setting and we present an intuitionistic fuzzy quasi-metric version of the Banach contraction principle. We apply this approach to deduce the existence of solution for the recurrence equations associated to the analysis of Quicksort algorithm in the framework of intuitionistic fuzzy quasi-metric spaces (ifqm-spaces, in short).
Approaches to analyse corporate tags for business intelligence purposes
Authors: Céline Van Damme
Venue: Proceedings of the first international workshop on Ontology-supported business intelligence (2008)

The information overload in business organizations hampers the information analysis process. Business intelligence tools can be used to analyse large amounts of information, however in most cases they only focus on structured information. More and more companies annotate tags to unstructured information to improve the information retrieval. We propose to exploit tags and tagging data to generate business intelligence. We believe that the analysis of large amounts of unstructured information for business intelligence purposes can be reduced to analysing tags and tag data. In the paper, we suggest that (1) tags and tag data can produce business intelligence from large amounts of unstructured information provided that some prerequisites are taken into account, (2) propose two step-by-step approaches of how existing mining and statistical techniques can be applied on tags and tagged data (3) by means of a tag data set from a European company, we provide preliminary evidence that the proposed approaches applied on corporate tags produce promising results regarding business intelligence.
Binary waypoint geographical routing in wireless mesh networks
Authors: Eryk Schiller, Paul Starzetz, Franck Rousseau, Andrzej Duda
Venue: Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems (2008)

We propose Binary Waypoint Routing, a novel geographical routing protocol for wireless mesh networks. Its idea is to learn and maintain source routes to a small number of nodes called binary waypoints that are placed in subspaces constructed as a result of binary space partitioning. A source node sends a packet to a waypoint for a given destination and intermediate nodes try to adapt the packet route by aiming at waypoints that are closer to the destination. Our simulation results show that the proposed scheme achieves high packet delivery rate with a traffic pattern similar to the Optimal Shortest Path Routing.
Predicting failures with developer networks and social network analysis
Authors: Andrew Meneely, Laurie Williams, Will Snipes, Jason Osborne
Venue: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering (2008)

Software fails and fixing it is expensive. Research in failure prediction has been highly successful at modeling software failures. Few models, however, consider the key cause of failures in software: people. Understanding the structure of developer collaboration could explain a lot about the reliability of the final product. We examine this collaboration structure with the developer network derived from code churn information that can predict failures at the file level. We conducted a case study involving a mature Nortel networking product of over three million lines of code. Failure prediction models were developed using test and post-release failure data from two releases, then validated against a subsequent release. One model's prioritization revealed 58% of the failures in 20% of the files compared with the optimal prioritization that would have found 61% in 20% of the files, indicating that a significant correlation exists between file-based developer network metrics and failures.
Compressed B+-trees
Authors: Hung-Yi Lin
Venue: WSEAS Transactions on Computers (2008)

The B+-tree and its variants have been reported as the good index structures for retrieving data. Database systems frequently establish the B+-tree style indices for fast access to data records. However, traditional B+-tree index could be a performance bottleneck because of its inflatable hierarchy. Many works focus on improving indexing techniques. In fact, the optimization of data organization inside index nodes is the most critical factor to improve retrieval quality. Some handles like pre-partition of data space, node splitting by force, node splitting with unbalanced partition, and node splitting upon overflow loading always burden index structures with plenty of storage space and building overhead. In this paper, we propose a new index scheme to highly aggregate the external structure in a B+-tree. It also adopts a better splitting policy to completely remove the suffering from data insertion orders. Our new index technique can compress data records in leaves and in turn reduce index size to improve query performance. In addition, the entire index's space utilization is promoted to a higher level; thereby the index's space requirement becomes smaller and easily resides in memory.
Experiments on automatic recognition of nonnative Arabic speech
Authors: Yousef Ajami Alotaibi, Sid-Ahmed Selouani, Douglas O'Shaughnessy
Venue: EURASIP Journal on Audio, Speech, and Music Processing (2008)

The automatic recognition of foreign-accented Arabic speech is a challenging task since it involves a large number of nonnative accents. As well, the nonnative speech data available for training are generally insufficient. Moreover, as compared to other languages, the Arabic language has sparked a relatively small number of research efforts. In this paper, we are concerned with the problem of nonnative speech in a speaker independent, large-vocabulary speech recognition system for modern standard Arabic (MSA). We analyze some major differences at the phonetic level in order to determine which phonemes have a significant part in the recognition performance for both native and nonnative speakers. Special attention is given to specific Arabic phonemes. The performance of an HMM-based Arabic speech recognition system is analyzed with respect to speaker gender and its native origin. The West Point modern standard Arabic database from the language data consortium (LDC) and the hidden Markov Model Toolkit (HTK) are used throughout all experiments. Our study shows that the best performance in the overall phoneme recognition is obtained when nonnative speakers are involved in both training and testing phases. This is not the case when a language model and phonetic lattice networks are incorporated in the system. At the phonetic level, the results show that female nonnative speakers perform better than nonnative male speakers, and that emphatic phonemes yield a significant decrease in performance when they are uttered by both male and female nonnative speakers.
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Authors: Steffen Rendle, Lars Schmidt-Thieme
Venue: Proceedings of the 2008 ACM conference on Recommender systems (2008)

Regularized matrix factorization models are known to generate high quality rating predictions for recommender systems. One of the major drawbacks of matrix factorization is that once computed, the model is static. For real-world applications dynamic updating a model is one of the most important tasks. Especially when ratings on new users or new items come in, updating the feature matrices is crucial. In this paper, we generalize regularized matrix factorization (RMF) to regularized kernel matrix factorization (RKMF). Kernels provide a flexible method for deriving new matrix factorization methods. Furthermore with kernels nonlinear interactions between feature vectors are possible. We propose a generic method for learning RKMF models. From this method we derive an online-update algorithm for RKMF models that allows to solve the new-user/new-item problem. Our evaluation indicates that our proposed online-update methods are accurate in approximating a full retrain of a RKMF model while the runtime of online-updating is in the range of milliseconds even for huge datasets like Netflix.
As go the feet...: on the estimation of attentional focus from stance
Authors: Francis Quek, Roger Ehrich, Thurmon Lockhart
Venue: Proceedings of the 10th international conference on Multimodal interfaces (2008)

The estimation of the direction of visual attention is critical to a large number of interactive systems. This paper investigates the cross-modal relation of the position of one's feet (or standing stance) to the focus of gaze. The intuition is that while one CAN have a range of attentional foci from a particular stance, one may be MORE LIKELY to look in specific directions given an approach vector and stance. We posit that the cross-modal relationship is constrained by biomechanics and personal style. We define a stance vector that models the approach direction before stopping and the pose of a subject's feet. We present a study where the subjects' feet and approach vector are tracked. The subjects read aloud contents of note cards in 4 locations. The order of visits' to the cards were randomized. Ten subjects read 40 lines of text each, yielding 400 stance vectors and gaze directions. We divided our data into 4 sets of 300 training and 100 test vectors and trained a neural net to estimate the gaze direction given the stance vector. Our results show that 31% our gaze orientation estimates were within 5°, 51% of our estimates were within 10°, and 60% were within 15°. Given the ability to track foot position, the procedure is minimally invasive.
FlexStem
Authors: Xiang Chen, Si-Min He, Dongbo Bu, Fa Zhang, Zhiyong Wang, Runsheng Chen, Wen Gao
Venue: Bioinformatics (2008)

Motivation: RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is proved to be NP-hard. Due to kinetic reasons the real RNA secondary structure often has local instead of global minimum free energy. This implies that we may improve the performance of RNA secondary structure prediction by taking kinetics into account and minimize free energy in a local area. Result: we propose a novel algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Still based on MFE criterion, FlexStem adopts comprehensive energy models that allow complex pseudoknots. Unlike classical thermodynamic methods, our approach aims to simulate the RNA folding process by successive addition of maximal stems, reducing the search space while maintaining or even improving the prediction accuracy. This reduced space is constructed by our maximal stem strategy and stem-adding rule induced from elaborate statistical experiments on real RNA secondary structures. The strategy and the rule also reflect the folding characteristic of RNA from a new angle and help compensate for the deficiency of merely relying on MFE in RNA structure prediction. We validate FlexStem by applying it to tRNAs, 5SrRNAs and a large number of pseudoknotted structures and compare it with the well-known algorithms such as RNAfold, PKNOTS, PknotsRG, HotKnots and ILM according to their overall sensitivities and specificities, as well as positive and negative controls on pseudoknots. The results show that FlexStem significantly increases the prediction accuracy through its local search strategy. Availability: Software is available at http://pfind.ict.ac.cn/FlexStem/ Contact:xchen@jdl.ac.cn; wgao@pku.edu.cn Supplementary information:Supplementary data are available at Bioinformatics online.
Information spillover effects of IPOs using 2SLS
Authors: Jao-Hong Cheng, Huei-Ping Chen
Venue: WSEAS Transactions on Information Science and Applications (2008)

There are several Initial Public Offerings (IPOs) regulatory changes implemented in Taiwan stock markets since 2005. One of those new mechanisms is to release the stock IPOs pricing rules to underwriters. The underwriters and listed companies have been growing up rapidly in recent years, and the whole offer size has become full-scaled. However, foreign scholars found that pioneer companies have many uncertainties in IPOs due to there isn't any information about offer price and proceeds to refer. When companies want to decide the appropriate offer price and proceeds, they have to spend a lot of time and money on information gathering. If there existed information spillover, and the information would be acquired from the other companies which had finished IPOs. Therefore, we try to research whether there existed information spillover in Taiwan IPOs market using 2SLS and Probit to analyze. By empirical results, there exist positive information spillover in offer price and listing price revision. For underwriters, underwriter rank is based on underwriter reputation, and the underwriters which have better rank usually couldn't get the high initial return.
Iterative Denoising
Authors: Kendall E. Giles, Michael W. Trosset, David J. Marchette, Carey E. Priebe
Venue: Computational Statistics (2008)

One problem in many fields is knowledge discovery in heterogeneous, high-dimensional data. As an example, in text mining an analyst often wishes to identify meaningful, implicit, and previously unknown information in an unstructured corpus. Lack of metadata and the complexities of document space make this task difficult. We describe Iterative Denoising, a methodology for knowledge discovery in large heterogeneous datasets that allows a user to visualize and to discover potentially meaningful relationships and structures. In addition, we demonstrate the features of this methodology in the analysis of a heterogeneous Science News corpus.
Simulation of the continuous time random walk of the space-fractional diffusion equations
Authors: E. A. Abdel-Rehim, R. Gorenflo
Venue: Journal of Computational and Applied Mathematics (2008)

In this article, we discuss the solution of the space-fractional diffusion equation with and without central linear drift in the Fourier domain and show the strong connection between it and the @a-stable Levy distribution, 0
Limitations of current 4G systems and its substitute schemes with TDD/TDMA
Authors: Lu Zhaogan, Zhang Taiyi, Shen Xiaodong, Li Xiaohe
Venue: WSEAS TRANSACTIONS on COMMUNICATIONS (2007)

In current 4G system given in recent literatures, channel estimation overhead and complexity of multi-user detectors (MUD), may lead to bad performance in fast fading channel scenarios when large number of users exists. So, a novel 4G system with TDD/TDMA as duplex and wireless access is designed to reduce channel estimation spending and avoid MUD, as only one user can be active to communicate with base station. Under the requirement of 4G systems, radio frame structure is elaborately designed to fit for fast fading channel scenarios. Construction of training sequences for channel estimation is also detailed together with a novel eigenmodes coupled universal space-time codes. The system architecture with consideration of link adaptations is given and evaluated for performance of TDD/TDMA 4G systems. Results show the proposed TDD/TDMA 4G can meet the requirement of 4G system under the classical ITU channel profiles.