Query: Evaluating geo-social influence in location-based social networks

Relevant passages:
Scaling personalized web search <=SEP=> Recent web search techniques augment traditional text matching with a global notion of "importance" based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as partial vectors. Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques.

Friendship and mobility: user movement in location-based social networks <=SEP=> Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

Influence and correlation in social networks <=SEP=> In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.
 This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.
 In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.
 We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.

The link-prediction problem for social networks <=SEP=> 

SimRank: a measure of structural-context similarity <=SEP=> The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.

Simulation on Simulink AC4 model (200hp DTC induction motor drive) using Fuzzy Logic controller <=SEP=> Classical direct torque control (DTC) has advantage in absence of coordinate transform and voltage modulator block. However, it may have disadvantage in controlling electromagnetic torque. DTC produces high ripple in electromagnetic torque as it is not directly controlled. High torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. Thus, Fuzzy Logic controller is applied to reduce electromagnetic torque ripple. This paper presents the simulation analysis of Fuzzy Logic Direct Torque Controller (FLDTC) of induction machine drives. However, only the design of the controller using built in DTC induction motor drive model of Simulink AC4 will be discussed in this paper. Using FLDTC, the resulting electromagnetic torque from produced less ripple than classical DTC.

Camera calibration with spheres: linear approaches <=SEP=> This paper addresses the problem of camera calibration from spheres. By studying the relationship between the dual images of spheres and that of the absolute conic, a linear solution has been derived from a recently proposed non-linear semi-definite approach. However, experiments show that this approach is quite sensitive to noise. In order to overcome this problem, a second approach has been proposed, where the orthogonal calibration relationship is obtained by regarding any two spheres as a surface of revolution. This allows a camera to be fully calibrated from an image of three spheres. Besides, a conic homography is derived from the imaged spheres, and from its eigenvectors the orthogonal invariants can be computed directly. Experiments on synthetic and real data show the practicality of such an approach.

Smart music player integrating facial emotion recognition and music mood recommendation <=SEP=> Songs, as a medium of expression, have always been a popular choice to depict and understand human emotions. Reliable emotion based classification systems can go a long way in helping us parse their meaning. However, research in the field of emotion-based music classification has not yielded optimal results. In this paper, we present an affective cross-platform music player, EMP, which recommends music based on the real-time mood of the user. EMP provides smart mood based music recommendation by incorporating the capabilities of emotion context reasoning within our adaptive music recommendation system. Our music player contains three modules: Emotion Module, Music Classification Module and Recommendation Module. The Emotion Module takes an image of the user's face as an input and makes use of deep learning algorithms to identify their mood with an accuracy of 90.23%. The Music Classification Module makes use of audio features to achieve a remarkable result of 97.69% while classifying songs into 4 different mood classes. The Recommendation Module suggests songs to the user by mapping their emotions to the mood type of the song, taking into consideration the preferences of the user.

Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity <=SEP=> When heckled, professional comedians frequently lament that “everyone’s a comedian!” It’s easy to see why: professional comedians don’t possess different kinds of brains from others or engage in radically different kinds of behaviors from others, and moreover, the success of their acts is predicated on others’ shared ability to understand and reason about comic situations they describe. The difference between comedians and their audience is a matter not of kind, but of degree, a difference that is reflected in the vocational emphasis they place on humor. Researchers in the field of computational creativity find themselves in a similar situation. As a subdiscipline of artificial intelligence, computational creativity explores theories and practices that give rise to a phenomenon, creativity, that all intelligent systems, human or machine, can legitimately lay claim to. Who is to say that a given AI system is not creative, insofar as it solves nontrivial problems or generates useful outputs that are not hard wired into its programming? As with comedians’ being funny, the difference between studying computational creativity and studying artificial intelligence is one of emphasis rather than one of kind: the field of computational creativity, as typified by a long-running series of workshops at AI-related conferences, places a vocational emphasis on creativity and attempts to draw together the commonalities of what Articles

Jubatus : An Open Source Platform for Distributed Online Machine Learning <=SEP=> Distributed computing is essential for handling very large datasets. Online learning is also promising for learning from rapid data streams. However, it is still an unresolved problem how to combine them for scalable learning and prediction on big data streams. We propose a general computational framework called loose model sharing for online and distributed machine learning. The key is to share only models rather than data between distributed servers. We also introduce Jubatus, an open source software platform based on the framework. Finally, we describe the details of implementing classifier and nearest neighbor algorithms, and discuss our experimental evaluations.

Reinforcement Learning Testbed for Power-Consumption Optimization <=SEP=> Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management. These models are difficult to design and often lead to suboptimal or unstable performance. In this paper, we show how deep reinforcement learning techniques can be used to control the cooling system of a simulated data center. In contrast to common control algorithms, those based on reinforcement learning techniques can optimize a system’s performance automatically without the need of explicit model knowledge. Instead, only a reward signal needs to be designed. We evaluated the proposed algorithm on the open source simulation platform EnergyPlus. The experimental results indicate that we can achieve 22% improvement compared to a model-based control algorithm built into the EnergyPlus. To encourage the reproduction of our work as well as future research, we have also publicly released an open-source EnergyPlus wrapper interface 1 directly compatible with existing reinforcement learning frameworks.

Social Network Structures in Open Source Software Development Teams <=SEP=> Drawing on social network theories and previous studies, this research examines the dynamics of social network structures in open source software (OSS) teams. Three projects were selected from SourceForge.net in terms of their similarities as well as their differences. Monthly data were extracted from the bug tracking systems in order to achieve a longitudinal view of the interaction pattern of each project. Social network analysis was used to generate the indices of social structure. The finding suggests that the interaction pattern of OSS projects evolves from a single hub at the beginning to a core/periphery model as the projects move forward. INtrODUctION The information system development arena has seen many revolutions and evolutions. We have witnessed the movement from structured development to object-oriented (OO) development. Modeling methods, such as data flow diagram and entity relationship diagram, are facing new OO modeling languages, such as the unified modeling language (UML) (see Siau & Cao, 2001; Siau, Erickson, & Lee, 2005; Siau & Loo, 2006) and OO methodologies, such as unified process (UP). The latest development includes agile modeling (see Erickson, Lyytinen, & Siau, 2005), extreme programming, and OSS development. While many of these changes are related to systems development paradigms, methodologies, methods, and techniques, the phenomenon of OSS development entails a different structure for software development teams. Chapter 2.17 Social Network Structures in Open Source Software Development Teams Yuan Long Colorado State University-Pueblo, USA Keng Siau University of Nebraska-Lincoln, USA Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. 646 Social Network Structures in Open Source Software Development Teams Unlike conventional software projects, the participants of OSS projects are volunteers. They are self-selected based on their interests and capability to contribute to the projects (Raymond, 2000). In addition, the developers of OSS projects are distributed all around the world. They communicate and collaborate with each other through the Internet, using e-mails or discussion boards. Therefore, effective and efficient communication and collaboration are critical to OSS success. However, little empirical research has been conducted to study the underlying interaction pattern of OSS teams, especially the dynamics of the social network structures in OSS development teams. To fill this gap, this study examines the evolvement of social structure in OSS teams. The study contributes to the enhancement of the understanding of OSS development, and provides foundation for future studies to analyze the antecedents and consequences of social networks in the OSS context. The remainder of the paper is structured as follows. First, prior studies on social network structures in OSS teams are reviewed. Second, theories related to social structure and social network theory are discussed. Third, the research methodology is presented, and the research results are reported. Next, discussions of the results, the limitations, and the implications are provided. The paper concludes with suggestions for future research.

Mermaid: Integrating Vertex-Centric with Edge-Centric for Real-World Graph Processing <=SEP=> There has been increasing interests in processing large-scale real-world graphs, and recently many graph systems have been proposed. Vertex-centric GAS (Gather-Apply-Scatter) and Edge-centric GAS are two graph computation models being widely adopted, and existing graph analytics systems commonly follow only one computation model, which is not the best choice for real-world graph processing. In fact, vertex degrees in real-world graphs often obey skewed power-law distributions: most vertices have relatively few neighbors while a few have many neighbors. We observe that vertex-centric GAS for high-degree vertices and edge-centric GAS for low-degree vertices is a much better choice for real-world graph processing. In this paper, we present Mermaid, a system for processing large-scale real-world graphs on a single machine. Mermaid skillfully integrates vertex-centric GAS with edge-centric GAS through a novel vertex-mapping mechanism, and supports streamlined graph processing. On a total of 6 practical natural graph processing tasks, we demonstrate that, on average, Mermaid achieves 1.83x better performance than the state-of-the-art graph system on a single machine.

Bayesian Optimization Algorithm <=SEP=> In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed To esti mate the distribution techniques for model ing multivariate data by Bayesian networks are used The proposed algorithm identi es reproduces and mixes building blocks up to a speci ed order It is independent of the ordering of the variables in the strings rep resenting the solutions Moreover prior in formation about the problem can be incor porated into the algorithm However prior information is not essential Preliminary ex periments show that the BOA outperforms the simple genetic algorithm even on decom posable functions with tight building blocks as a problem size grows

Activity recognition from acceleration data based on discrete consine transform and SVM <=SEP=> This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.

Space-Efficient Online Computation of Quantile Summaries <=SEP=> An ∈-approximate quantile summary of a sequence of <i>N</i> elements is a data structure that can answer quantile queries about the sequence to within a precision of ∈<i>N</i>.
We present a new online algorithm for computing∈-approximate quantile summaries of very large data sequences. The algorithm has a worst-case space requirement of <i>&Ogr;</i>(1÷∈ log(∈<i>N</i>)). This improves upon the previous best result of <i>&Ogr;</i>(1÷∈ log<sup>2</sup>(∈<i>N</i>)). Moreover, in contrast to earlier deterministic algorithms, our algorithm does not require a priori knowledge of the length of the input sequence.
Finally, the actual space bounds obtained on experimental data are significantly better than the worst case guarantees of our algorithm as well as the observed space requirements of earlier algorithms.

Load Disaggregation Based on Aided Linear Integer Programming <=SEP=> Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP)-based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear-programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures and, hence, works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.

Information flow and cooperative control of vehicle formations <=SEP=> We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability.

Enhanced Visibility of MoS 2 , MoSe 2 , WSe 2 and Black-Phosphorus : Making Optical Identification of 2 D Semiconductors Easier <=SEP=> We explore the use of Si3N4/Si substrates as a substitute of the standard SiO2/Si substrates employed nowadays to fabricate nanodevices based on 2D materials. We systematically study the visibility of several 2D semiconducting materials that are attracting a great deal of interest in nanoelectronics and optoelectronics: MoS2, MoSe2, WSe2 and black-phosphorus. We find that the use of Si3N4/Si substrates provides an increase of the optical contrast up to a 50%–100% and also the maximum contrast shifts towards wavelength values optimal for human eye detection, making optical identification of 2D semiconductors easier. OPEN ACCESS Electronics 2015, 4 848

Scaling Similarity Joins over Tree-Structured Data <=SEP=> Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-ofthe-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this paper, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. In order to scale up the join, the computed subgraphs are managed in a two-layer index. Our experimental results on real and synthetic data collections show that our approach outperforms the state-of-the-art methods by up to an order of magnitude.

Design and simulation of a Sierpinski carpet fractal antenna for 5G commercial applications <=SEP=> This article presents the design and simulation of a multi-band microstrip patch antenna, using Sierpinski carpet fractal concept. The proposed antenna is designed on a FR-4 substrate with a relative permittivity of 4.4 and excited using a microstrip feed line at the edge of the patch. Sierpinski carpet fractal with iteration-3 is applied to the radiating patch. After the application of fractal, this patch antenna resonates at 14.80 GHz, 17.92 GHz, 23.12 GHz, and 27.92 GHz with impedance bandwidth of 5800 MHz, 3040 MHz, 2960 MHz, and 3840 MHz respectively. With these four bands, this antenna finds application in commercial 5G wireless communications. To illustrate the effectiveness of the fractal concept, four patch antennas are designed independently at those four resonating frequencies and compared with the single fractal antenna in terms of their size and other performance parameters. All the antennas proposed here, are designed using HFSS15 and results are analyzed in terms of return loss, VSWR, gain, and bandwidth.

OpenCV based disease identification of mango leaves <=SEP=> This paper aims in classifying and identifying the diseases of mango leaves for Indian agriculture. K-means algorithm is chosen for the disease segmentation, and the disease classification and identification is carried out using the SVM classifier. Disease identification based on analysis of patches or discoloring of leaf will hold good for some of the plant diseases, but some other diseases which will deform the leaf shape cannot be identified based on the same method. In this case leaf shape based disease identification has to be performed. Based on this analysis two topics are addressed in this research paper. (1) Disease identification using the OpenCV libraries (2) Leaf shape based disease identification. Keywordk-means,Principal Component Analysis (PCA), feature extraction, shape detection, disease identification, Elliptic fourier analysis, Support Vector Machine(SVM), Artificial Neural Network (ANN)

Assessing the benefits of computational offloading in mobile-cloud applications <=SEP=> This paper presents the results of a formative study conducted to determine the effects of computation offloading in mobile applications by comparing “application performance” (chiefly energy consumption and response time). The study examined two general execution scenarios: (1) computation is performed locally on a mobile device, and (2) when it is offloaded entirely to the cloud. The study also carefully considered the underlying network characteristics as an important factor affecting the performance. More specifically, we refactored two mobile applications to offload their computationally intensive functionality to execute in the cloud. We then profiled these applications under different network conditions, and carefully measured “application performance” in each case. The results were not as conclusive as we had expected. On fast networks, offloading is almost always beneficial. However, on slower networks, the offloading cost-benefit analysis is not as clear cut. The characteristics of the data transferred between the mobile device and the cloud may be a deciding factor in determining whether offloading a computation would improve performance.

A Batteryless Sensor ASIC for Implantable Bio-Impedance Applications <=SEP=> The measurement of the biological tissue's electrical impedance is an active research field that has attracted a lot of attention during the last decades. Bio-impedances are closely related to a large variety of physiological conditions; therefore, they are useful for diagnosis and monitoring in many medical applications. Measuring living tissues, however, is a challenging task that poses countless technical and practical problems, in particular if the tissues need to be measured under the skin. This paper presents a bio-impedance sensor ASIC targeting a battery-free, miniature size, implantable device, which performs accurate 4-point complex impedance extraction in the frequency range from 2 kHz to 2 MHz. The ASIC is fabricated in 150 nm CMOS, has a size of 1.22 mm × 1.22 mm and consumes 165 μA from a 1.8 V power supply. The ASIC is embedded in a prototype which communicates with, and is powered by an external reader device through inductive coupling. The prototype is validated by measuring the impedances of different combinations of discrete components, measuring the electrochemical impedance of physiological solution, and performing ex vivo measurements on animal organs. The proposed ASIC is able to extract complex impedances with around 1 Ω resolution; therefore enabling accurate wireless tissue measurements.

Skeleton-based Adaptive In-between Generation for Hand-drawn Key Frames <=SEP=> For improving the efficiency of 2D animation production, this paper presents a method to create in-between frames based on hand-drawn key-frames. The outlines of characters or objects on two key-frames are used as inputs. First, a skeleton linkage of the target object is automatically constructed by rasterizing each of input key-frames and then applying a pixel-based skeleton extraction method. Secondary, a pair of skeleton linkages having corresponding structure between the current key-frame and the next key-frame is constructed by applying the stroke matching algorithm. After these processes, motion transitions between the skeleton linkages are generated based on our simulation model. When the in-between frames are created only in the 2D plane, the outlines at in-between frames can be generated by a 2D deformation. In case that the in-between transitions are containing a rotation around an axis which is no perpendicular to the drawing plane, however, a 3D structure is required. For achieving such in-between transitions, our method constructs a 3D structure by inflating 2D mesh based on the input outlines. Finally, the contours from the view-point for the created 3D structure are projected onto the 2D plane during in-between transitions. In our method, we adopt the Photic Extremum Lines (PEL) to extract the 2D contours from the obtained 3D shape. In this way, we achieve the in-between creation containing spatial rotation such as hand-flipping, which has not been achieved by general ways of in-between creation method.

Memory-Efficient Adaptive Optimization for Large-Scale Learning <=SEP=> Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each parameter, thus doubling the memory footprint of the optimizer. In behemoth-size applications, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel, simple, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity while allowing for larger models and mini-batches. We give convergence guarantees for our method and demonstrate its effectiveness in training very large deep models.

Better Approximation of Betweenness Centrality <=SEP=> Centrality indices are used to classify a graph in important and unimportant vertices. A commonly used index is betweenness centrality which is based on shortest paths. For large graphs it is almost impossible to calculate exact betweenness centrality in appropriate time. The best known algorithm today is based on solving n SSSPs and requires O(nm+ n2 log(n)) time. But in most cases, e.g. on a home computer, resources are limited. Current approximation algorithms extrapolate values by solving only k ≪ n SSSPs. Vertices near the source of a SSSP are often overestimated. We introduce improvements which take the distance to the source into account. Euclidean distance between approximation and exact values is reduced by factor 4 with same runtime. Or runtime is 16 times faster with same Euclidean distance in a standard example, the movie actor network. Other real-world networks show similar promising results.

Am I wasting my time organizing email?: a study of email refinding <=SEP=> We all spend time every day looking for information in our email, yet we know little about this refinding process. Some users expend considerable preparatory effort creating complex folder structures to promote effective refinding. However modern email clients provide alternative opportunistic methods for access, such as search and threading, that promise to reduce the need to manually prepare. To compare these different refinding strategies, we instrumented a modern email client that supports search, folders, tagging and threading. We carried out a field study of 345 long-term users who conducted over 85,000 refinding actions. Our data support opportunistic access. People who create complex folders indeed rely on these for retrieval, but these preparatory behaviors are inefficient and do not improve retrieval success. In contrast, both search and threading promote more effective finding. We present design implications: current search-based clients ignore scrolling, the most prevalent refinding behavior, and threading approaches need to be extended.

Partially supervised clustering for image segmentation <=SEP=> All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tendency to recommend solutions that equalize cluster populations. The semi-supervised c-means algorithms introduced in this paper attempt to overcome these three problems for problem domains where a few data from each class can be labeled. Segmentation of magnetic resonance images is a problem of this type and we use it to illustrate the new algorithm. Our examples show that the semi-supervised approach provides MRI segmentations that are superior to ordinary fuzzy c-means and to the crisp k-nearest neighbor rule and further, that the new method ameliorates (P1)-(P3). Cluster analysis Fuzzy c-means Partial supervision Image segmentation Magnetic resonance images

Design and Implementation of a Network Security Model for Cooperative Network <=SEP=> In this paper a design and implementation of a network security model was presented, using routers and firewall. Also this paper was conducted the network security weakness in router and firewall network devices, type of threats and responses to those threats, and the method to prevent the attacks and hackers to access the network. Also this paper provides a checklist to use in evaluating whether a network is adhering to best practices in network security and data confidentiality. The main aim of this research is to protect the network from vulnerabilities, threats, attacks, configuration weaknesses and security policy weaknesses. KeywordsNetwork security, network management, threats, security policy Received December 17, 2008; Accepted March 2 2009