ppxscal/embedding_network huggingface dataset:
Fuzzy c-means clustering with prior biological knowledge
Authors: Luis Tari, Chitta Baral, Seungchan Kim
Venue: Journal of Biomedical Informatics (2009)

We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.
Block Kalman Filtering for Large-Scale DSGE Models
Authors: Ingvar Strid, Karl Walentin
Venue: Computational Economics (2009)

In this paper block Kalman filters for Dynamic Stochastic General Equilibrium models are presented and evaluated. Our approach is based on the simple idea of writing down the Kalman filter recursions on block form and appropriately sequencing the operations of the prediction step of the algorithm. It is argued that block filtering is the only viable serial algorithmic approach to significantly reduce Kalman filtering time in the context of large DSGE models. For the largest model we evaluate the block filter reduces the computation time by roughly a factor 2. Block filtering compares favourably with the more general method for faster Kalman filtering outlined by Koopman and Durbin (J Time Ser Anal 21:281---296, 2000) and, furthermore, the two approaches are largely complementary.
The Monoculture Risk Put into Context
Authors: Kenneth P. Birman, Fred B. Schneider
Venue: IEEE Security and Privacy (2009)

Conventional wisdom holds that software monocultures are exceptionally vulnerable to malware outbreaks. The authors argue that this oversimplifies and misleads. An analysis based on attacker reactions likely to be evoked by successive generations of defenses suggests that deploying a monoculture in conjunction with automated diversity is indeed a very sensible defense today.
The influence of learning on evolution: A mathematical framework
Authors: Ingo Paenke, Tadeusz J. Kawecki, Bernhard Sendhoff
Venue: Artificial Life (2009)

The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.
Multicomponent image segmentation: a comparative analysis between a hybrid genetic algorithm and self-organizing maps
Authors: M. M. Awad, K. Chehdi, A. Nasri
Venue: International Journal of Remote Sensing (2009)

Image segmentation is an essential process in image analysis. Several methods have been developed to segment multicomponent images and the success of these methods depends on the characteristics of the acquired image and the percentage of imperfections in the process of its acquisition. Many of the segmentation methods are parametric, which means that many parameters need to be computed or provided before the segmentation process, and any method that works on one type of multicomponent image cannot necessarily work on another. In addition, many segmentation methods are supervised, where a priori knowledge is needed, such as the number of classes. To overcome these obstacles, a self-organizing map (SOM), which is an unsupervised nonparametric method, was used to segment four different types of multicomponent images (Landsat, SPOT, IKONOS and CASI), and the results compared to those of a new nonparametric unsupervised genetic algorithm (GA) for image segmentation. To improve the performance of the GA, a hill-climbing process and another random heuristic module were added to escape the local-minima trap and to improve the speed of the GA; the new algorithm is called the hybrid genetic algorithm (HGA). Verification of the results was performed using two different techniques: field verification and the functional model. These verification techniques show that the HGA is more accurate in multicomponent image segmentation than the SOM.
Advances in Government Enterprise Architecture
Authors: Pallab Saha
Venue:  (2008)

Over the past two decades, the government sector has emerged as the area of largest implementation of enterprise architecture - a critical success factor for all types, scales, and intensities of e-government programs.Advances in Government Enterprise Architecture is a seminal publication in the emerging and evolving discipline of enterprise architecture (EA). Presenting current developments, issues, and trends in EA, this critical resource provides IT managers, government CIOs, researchers, educators, and professionals with insights into the impact of effective EA on IT governance, IT portfolio management, and IT outsourcing, creating a must-have holding for academic libraries and organizational information centers.
An Analysis of Suburb Tourism Trend Based on System Dynamics Method
Authors: Xia Jin
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 02 (2009)

This paper analyzes the suburb tourism trend based on system dynamics method. It applies SD (System Dynamics) method in system analysis, which starts with problem’s structure and discarding trivialities. An empirical study is made to find the trend of suburb tourism due to new holiday policy. The finding of this paper is very useful for local authorities who want to facilitate the development of local tourism.
Dependable VLSI: device, design and architecture: how should they cooperate?
Authors: Shuichi Sakai, Hidetoshi Onodera, Hiroto Yasuura, James C. Hoe
Venue: Proceedings of the 2009 Asia and South Pacific Design Automation Conference (2009)

VLSI dependability is one of the most significant issues in the modern world. Here the panelists will discuss the key technologies for it as well as the cost optimization among device, design and architecture.
A link mining algorithm for earnings forecast and trading
Authors: Germán Creamer, Sal Stolfo
Venue: Data Mining and Knowledge Discovery (2009)

The objective of this paper is to present and discuss a link mining algorithm called CorpInterlock and its application to the financial domain. This algorithm selects the largest strongly connected component of a social network and ranks its vertices using several indicators of distance and centrality. These indicators are merged with other relevant indicators in order to forecast new variables using a boosting algorithm. We applied the algorithm CorpInterlock to integrate the metrics of an extended corporate interlock (social network of directors and financial analysts) with corporate fundamental variables and analysts' predictions (consensus). CorpInterlock used these metrics to forecast the trend of the cumulative abnormal return and earnings surprise of S P 500 companies. The rationality behind this approach is that the corporate interlock has a direct effect on future earnings and returns because these variables affect directors and managers' compensation. The financial analysts engage in what the agency theory calls the "earnings game": Managers want to meet the financial forecasts of the analysts and analysts want to increase their compensation or business of the company that they follow. Following the CorpInterlock algorithm, we calculated a group of well-known social network metrics and integrated with economic variables using Logitboost. We used the results of the CorpInterlock algorithm to evaluate several trading strategies. We observed an improvement of the Sharpe ratio (risk-adjustment return) when we used "long only" trading strategies with the extended corporate interlock instead of the basic corporate interlock before the regulation Fair Disclosure (FD) was adopted (1998---2001). There was no major difference among the trading strategies after 2001. Additionally, the CorpInterlock algorithm implemented with Logitboost showed a significantly lower test error than when the CorpInterlock algorithm was implemented with logistic regression. We conclude that the CorpInterlock algorithm showed to be an effective forecasting algorithm and supported profitable trading strategies.
An adaptive compiler method for scheduling and place-and-route for VLIW-based dynamic reconfigurable processor
Authors: Ryuji Hada, Kazuya Tanigawa, Akira Kojima, Tetsuo Hironaka
Venue: Proceedings of the 12th WSEAS international conference on Computers (2008)

We have proposed and developed a compiler for dynamic reconfigurable processor based on VLIW model. VLIW model, which is our compiler targets, fetches and executes one configuration data as VLIW instruction. For this model, our compiler schedules mapping elements as operations and live variables in program, with consideration of hardware resources. Next, it places and routes the elements to hardware resources. In place-and-route, if the number of mapping elements in one configuration data is large, place-and-route condition is so difficult that the number of final steps might be worse. The number of mapping elements is significant important, because output result is widely different by depending on the number of mapping elements, and the optimal number of them is also different depending on the nature of applications. To search the optimal number of them and improve scheduling and place-and-route results, we propose a novel mapping method which dynamically changes the maximum number of mapping elements as parameters. To solve the previous problem, we adopt heuristic approach. As one of target processors based on VLIW model, we use PARS processor which dynamically reconfigurates and executes one configuration data as one VLIW instruction every cycle. We evaluate our method by the number of generated configuration data. And the comparative method is ones which uses constant parameters which is statically set by depending on the hardware resources of target processor. From the results, our method can find more optimal parameters for each application, and decrease 22.6% of the number of configuration data in maximum (13.4% in average), compared with the comparative method.
Using Face Quality Ratings to Improve Real-Time Face Recognition
Authors: Karl Axnick, Ray Jarvis, Kim C. Ng
Venue: Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology (2009)

A Face Quality Rating (FQR) is a value derived from a face image that indicates the probability that the face image will be successfully recognized by a specific face recognition method. The FQR can be used as a pre-filter in real-time environments where thousands of face images can be captured every second by multiple surveillance cameras. With so many captured face images, face recognition methods need to strategically decide which face images to attempt recognition on, as it is prohibitively difficult to attempt recognition on all of the images. The FQR pre-filter optimizes processor time utilization resulting in more people being recognized (faster and more accurately) before they leave the surveillance cameras' views. We generate FQR values using Multiple Layered Perceptron (MLP) neural networks. We then use these MLPs in a real-time environment to experimentally prove that FQR pre-filtering improves the speed and accuracy of any real-time face recognition method...
The Research of Granular Computing Applied in Image Mosaic
Authors: Gang Xu, Jing Cai
Venue: Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics (2009)

This article proposes a new characteristic-based image mosaic algorithm that used the granular computing theory. Firstly, establish a granular computing model of the image waiting to be spliced to obtain the image's edge segmentation; Secondly, extract the characteristic points in the edge graph, then carry on the related operation to these characteristic points to find matching characteristic points, and eliminate the mismatches to find the most superior matching characteristics; Thirdly, work out the spot transformation relations of two images according to the matching characteristic points, and mapped the two images on the same geometric plane surface by transformation matrix; Finally, smooth transition processing will be carried on the overlapped region to achieve the purpose of seamless image mosaic. The experimental result indicates that the algorithm in this article is a stable and steady method that superior than the traditional algorithm, and it gets well effectiveness.
Playback Delay in Mesh-Based Peer-to-Peer Systems with Random Packet Forwarding
Authors: Viktoria Fodor, Ilias Chatzidrossos
Venue: Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies (2008)

In meshed-based peer-to-peer streaming systems peers forward packets to their neighbors based on local scheduling decisions. The local decisions affect how packets get distributed in the mesh, the probability of duplicates and the distribution of the transmission delays. In this paper we propose an analytic framework that allows the evaluation of various scheduling algorithms. We consider two solutions where scheduling is performed at the forwarding peer, based on the knowledge of the playout buffer content at the neighbors. We evaluate the effectiveness of the solutions in terms of the probability that a peer can play out a packet versus the playback delay, and the scalability of the solutions with respect to the size of the overlay.
Simulated minimum Hellinger distance estimation of stochastic volatility models
Authors: Teruko Takada
Venue: Computational Statistics Data Analysis (2009)

A simultaneously efficient and robust approach for distribution-free parametric inference, called the simulated minimum Hellinger distance (SMHD) estimator, is proposed. In the SMHD estimation, the Hellinger distance between the nonparametrically estimated density of the observed data and that of the simulated samples from the model is minimized. The method is applicable to the situation where the closed-form expression of the model density is intractable but simulating random variables from the model is possible. The robustness of the SMHD estimator is equivalent to the minimum Hellinger distance estimator. The finite sample efficiency of the proposed methodology is found to be comparable to the Bayesian Markov chain Monte Carlo and maximum likelihood Monte Carlo methods and outperform the efficient method of moments estimators. The robustness of the method to a stochastic volatility model is demonstrated by a simulation study. An empirical application to the weekly observations of foreign exchange rates is presented.
Warm rainy clouds and droplet size distribution
Authors: Nazario D. Ramirez-Beltran, Robert J. Kuligowski, Melvin J. Cardona, Sandra Cruz-Pol
Venue: WSEAS TRANSACTIONS on SYSTEMS (2009)

This paper presents the analysis of cloud droplet size distribution (DSD) and the use of a proxy variable for the parameters of the DSD to explore the possibility of detecting warm rainy clouds. Cloud DSD plays a paramount role in the parameterization of cloud microphysics in climate models. The droplet size distribution also plays a crucial role in determining the radiative properties of clouds and is usually obtained using satellite retrieval algorithms. This paper presents an analysis of the modified Gamma model which is used to represent the cloud DSD. The analysis of the DSD is conducted through the derivation of the distribution moments and their physical interpretation. The analysis includes the discussion of the mean, variance, effective radius, and effective variance of the DSD. Usually the effective radius is extracted from radiance measurements. However, since the modified Gamma distribution has two parameters it requires estimates of both the effective radius and effective variance to properly estimate cloud microphysics from radiance information.
Robust reservoir generation by correlation-based learning
Authors: Tadashi Yamazaki, Shigeru Tanaka
Venue: Advances in Artificial Neural Systems (2009)

Reservoir computing (RC) is a new framework for neural computation. A reservoir is usually a recurrent neural network with fixed random connections. In this article, we propose an RCmodel in which the connections in the reservoir are modifiable. Specifically, we consider correlation-based learning (CBL), which modifies the connection weight between a given pair of neurons according to the correlation in their activities. We demonstrate that CBL enables the reservoir to reproduce almost the same spatiotemporal activity patterns in response to an identical input stimulus in the presence of noise. This result suggests that CBL enhances the robustness in the generation of the spatiotemporal activity pattern against noise in input signals. We apply our RC model to trace eyeblink conditioning. The reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response.
Service Differentiation in IEEE 802.11e HCF Access Method
Authors: Whe-Dar Lin, Der-Jiunn Deng
Venue: Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing (2008)

As the demand for broadband multimedia wireless is increasing, there is a great need for quality of service (QoS). The IEEE 802.11e workgroup proposes the hybrid coordination function (HCF) channel access mechanisms, which includes two mechanisms: enhanced distributed channel access (EDCA) and HCF control channel access (HCCA). The EDCA is a contention-based scheme and supports service differentiation through prioritiesed access to the wireless medium. The HCCA works under the control of a hybrid coordinator (HC) and provides a centralised polling scheme. In this paper, we evaluate and compare the performance of HCCA and EDCA for constant bit rate (CBR) traffic transmission. The results show that both access methods are able to carry the prioritized traffic.
Artificial neural networks as support for leaf area modelling in crop canopies
Authors: Daniel Dunea, Virgil Moise
Venue: Proceedings of the 12th WSEAS international conference on Computers (2008)

This paper investigates the feed forward neural networks utilization in improving some crop growth parameters (e.g. Leaf Area Index) forecasting based on cumulated Photosynthetically Active Radiation (PAR) time series. PAR data recorded by the DAQ system were used as input in a nonlinear sigmoid function with 3 parameters. LAI series of the red clover canopies represented the outputs of the nonlinear identified model. These outputs (N=257 for each set) were used to train different network topologies of feed-forward ANN using Quickprop and Rprop learning algorithms. The ANN output represented the one LAI ahead forecasted value. Best fitting results were obtained using QuickProp algorithm with 6 units in the input layer, 4 or 8 neurons in the hidden layer and one output neuron for unfertilized variants, and 10-4-1 and 6-8-1 network topologies for foliar fertilized variants. Advantages of neural computing techniques relied on faster computation, learning ability and noise rejection.
Trading-off security and performance in barrier slicing for remote software entrusting
Authors: Mariano Ceccato, Mila Dalla Preda, Jasvir Nagra, Christian Collberg, Paolo Tonella
Venue: Automated Software Engineering (2009)

Network applications often require that a trust relationship is established between a trusted host (e.g., the server) and an untrusted host (e.g., the client). The remote entrusting problem is the problem of ensuring the trusted host that whenever a request from an untrusted host is served, the requester is in a genuine state, unaffected by malicious modifications or attacks.Barrier slicing helps solve the remote entrusting problem. The computation of the sensitive client state is sliced and moved to the server, where it is not possible to tamper with it. However, this solution might involve unacceptable computation and communication costs for the server, especially when the slice to be moved is large. In this paper, we investigate the trade-off between security loss and performance overhead associated with moving only a portion of the barrier slice to the server and we show that this trade-off can be reduced to a multi-objective optimization problem. We describe how to make decisions in practice with reference to a case study, for which we show how to choose among the alternative options.
Study on processing for seam image based on CCD vision sensing
Authors: Kai Li, Xiaotian Guan, Hong Yue, Yi Zhang, Hegao Cai
Venue: Proceedings of the 6th conference on Applications of electrical engineering (2007)

In automatically seam tracking system with structured light vision sensor in girth-welding of pipeline, there is a lot of noise in seam images. To extract seam information reliably, it is important to process the original images to eliminate influence of noise. A novel compounding process method is presented. Firstly, one dimension Laplacian of Gaussian filter was applied to transform noises whose width was larger than laser light stripe to isolated and small noise. Then the image was smoothed with neighborhood median filter. Due to gray distribution variety of seam image, binary image was created with maximal square difference adaptive threshold within selected section based on priori information. Acnode noise was cleared up by region labeling and selected threshold, consequently a binary image of seam is obtained. Finally, smooth and consecutive skeleton of seam is extracted with morphologic thinning method. Experiments indicate that this process method satisfies requirement of real-time seam tracking.