Search results for: data center

Authors: Vasudha Bhatnagar, Ahmed Sultan Al-Hegami, Naveen Kumar

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Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules lead to another data mining problem, although of reduced complexity. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules to ultimately improve the overall efficiency of KDD process. In this paper we study novelty of the discovered rules as a subjective measure of interestingness. We propose a hybrid approach that uses objective and subjective measures to quantify novelty of the discovered rules in terms of their deviations from the known rules. We analyze the types of deviation that can arise between two rules and categorize the discovered rules according to the user specified threshold. We implement the proposed framework and experiment with some public datasets. The experimental results are quite promising.

Keywords: Knowledge Discovery in Databases (KDD), Data Mining, Rule Discovery, Interestingness, Subjective Measures, Novelty Measure.

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Authors: Ana Gabriela Diaz, Luis Fermín Córdova, Jr., Roberto Lamazares

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The island of Cuba, the largest of the Greater Antilles, is located in the tropical North Atlantic. It is annually affected by numerous weather events, which have caused severe damage to our coastal areas. In the same way that many other coastlines around the world, the beautiful beaches of the Hicacos Peninsula also suffer from erosion. This leads to a structural regression of the coastline. If measures are not taken, the hotels will be exposed to the advance of the sea, and it will be a serious problem for the economy. With the aim of studying the intensity of this type of activity, specialists of group of coastal and marine engineering from CIH, in the framework of the research conducted within the project MEGACOSTAS 2, provide their research to simulate extreme events and assess their impact in coastal areas, mainly regarding the definition of flood volumes and morphodynamic changes in sandy beaches. The main objective of this work is the evaluation of the process of Varadero beach erosion (the coastal sector has an important impact in the country's economy) on the Hicacos Peninsula for different paths of hurricanes. The mathematical model XBeach, which was integrated into the Coastal engineering system introduced by the project of MEGACOSTA 2 to determine the area and the more critical profiles for the path of hurricanes under study, was applied. The results of this project have shown that Center area is the greatest dynamic area in the simulation of the three paths of hurricanes under study, showing high erosion volumes and the greatest average length of regression of the coastline, from 15- 22 m.

Keywords: Beach, erosion, mathematical model, coastal areas.

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Authors: Chien-Chun Hung, Chun-Fong Wu

Abstract:

It is important for a commander to have real-time information to aware situations and to make decision in the battlefield. Results of modern technique developments have brought in this kind of information for military purposes. Unmanned aerial vehicle (UAV) is one of the means to gather intelligence owing to its widespread applications. It is still not clear whether or not the mini UAV with short-range wireless transmission system is used as a reconnaissance system in Taiwanese. In this paper, previous experience on the research of the sort of aerial vehicles has been applied with a data-relay system using the ZigBee modulus. The mini UAV developed is expected to be able to collect certain data in some appropriate theaters. The omni-directional antenna with high gain is also integrated into mini UAV to fit the size-reducing trend of airborne sensors. Two advantages are so far obvious. First, mini UAV can fly higher than usual to avoid being attacked from ground fires. Second, the data will be almost gathered during all maneuvering attitudes.

Keywords: Mini UAV, reconnaissance, wireless transmission, ZigBee modulus.

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Authors: Ryad Zemouri, Paul Ciprian Patic

Abstract:

An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically reconfigured in real-time as new actual failure time data arrives. The performance of our proposed approach has been tested using sixteen real-time software failure data. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-steppredictability compared to existing neural network model for failure time prediction.

Keywords: Neural network, Prediction error, Recurrent RadialBasis Function Network, Reliability prediction.

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Authors: A. A. Hossam-Eldin, E. N. Abdallah, M. S. El-Nozahy

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The identification and elimination of bad measurements is one of the basic functions of a robust state estimator as bad data have the effect of corrupting the results of state estimation according to the popular weighted least squares method. However this is a difficult problem to handle especially when dealing with multiple errors from the interactive conforming type. In this paper, a self adaptive genetic based algorithm is proposed. The algorithm utilizes the results of the classical linearized normal residuals approach to tune the genetic operators thus instead of making a randomized search throughout the whole search space it is more likely to be a directed search thus the optimum solution is obtained at very early stages(maximum of 5 generations). The algorithm utilizes the accumulating databases of already computed cases to reduce the computational burden to minimum. Tests are conducted with reference to the standard IEEE test systems. Test results are very promising.

Keywords: Bad Data, Genetic Algorithms, Linearized Normal residuals, Observability, Power System State Estimation.

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Authors: Y. Kumru, K. Enhos, H. Köymen

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In this paper, we report the experimental results on using complementary Golay coded signals at 7.5 MHz to detect breast microcalcifications of 50 µm size. Simulations using complementary Golay coded signals show perfect consistence with the experimental results, confirming the improved signal to noise ratio for complementary Golay coded signals. For improving the success on detecting the microcalcifications, orthogonal complementary Golay sequences having cross-correlation for minimum interference are used as coded signals and compared to tone burst pulse of equal energy in terms of resolution under weak signal conditions. The measurements are conducted using an experimental ultrasound research scanner, Digital Phased Array System (DiPhAS) having 256 channels, a phased array transducer with 7.5 MHz center frequency and the results obtained through experiments are validated by Field-II simulation software. In addition, to investigate the superiority of coded signals in terms of resolution, multipurpose tissue equivalent phantom containing series of monofilament nylon targets, 240 µm in diameter, and cyst-like objects with attenuation of 0.5 dB/[MHz x cm] is used in the experiments. We obtained ultrasound images of monofilament nylon targets for the evaluation of resolution. Simulation and experimental results show that it is possible to differentiate closely positioned small targets with increased success by using coded excitation in very weak signal conditions.

Keywords: Coded excitation, complementary Golay codes, DiPhAS, medical ultrasound.

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Authors: Shilpy Sharma

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As the web continues to grow exponentially, the idea of crawling the entire web on a regular basis becomes less and less feasible, so the need to include information on specific domain, domain-specific search engines was proposed. As more information becomes available on the World Wide Web, it becomes more difficult to provide effective search tools for information access. Today, people access web information through two main kinds of search interfaces: Browsers (clicking and following hyperlinks) and Query Engines (queries in the form of a set of keywords showing the topic of interest) [2]. Better support is needed for expressing one's information need and returning high quality search results by web search tools. There appears to be a need for systems that do reasoning under uncertainty and are flexible enough to recover from the contradictions, inconsistencies, and irregularities that such reasoning involves. In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. Semi-supervised, multi-view algorithms, which reduce the amount of labeled data required for learning, rely on the assumptions that the views are compatible and uncorrelated. This paper describes the use of semi-structured machine learning approach with Active learning for the “Domain Specific Search Engines". A domain-specific search engine is “An information access system that allows access to all the information on the web that is relevant to a particular domain. The proposed work shows that with the help of this approach relevant data can be extracted with the minimum queries fired by the user. It requires small number of labeled data and pool of unlabelled data on which the learning algorithm is applied to extract the required data.

Keywords: Search engines; machine learning, Informationretrieval, Active logic.

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Authors: Jürgen Roßmann, Gerrit Alves

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In today-s world, the efficient utilization of wood resources comes more and more to the mind of forest owners. It is a very complex challenge to ensure an efficient harvest of the wood resources. This is one of the scopes the project “Virtual Forest II" addresses. Its core is a database with data about forests containing approximately 260 million trees located in North Rhine-Westphalia (NRW). Based on this data, tree growth simulations and wood mobilization simulations can be conducted. This paper focuses on the latter. It describes a discrete-event-simulation with an attached 3-D real time visualization which simulates timber harvest using trees from the database with different crop resources. This simulation can be displayed in 3-D to show the progress of the wood crop. All the data gathered during the simulation is presented as a detailed summary afterwards. This summary includes cost-benefit calculations and can be compared to those of previous runs to optimize the financial outcome of the timber harvest by exchanging crop resources or modifying their parameters.

Keywords: Timber harvest, simulation, 3-D, optimization.

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Authors: V. Girondel, L. Bonnaud, A. Caplier, M. Rombaut

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This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.

Keywords: Belief theory, classifiers comparison, data fusion, human motion analysis, real-time processing, static posture recognition.

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Authors: J. R. Wang, Y. Chiang, W. S. Hsu, H. C. Chen, S. H. Chen, J. H. Yang, S. W. Chen, C. Shih

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In this study, the RASCAL and ALOHA codes are used to establish an analysis methodology for hydrogen fluoride (HF) evaluation. There are three main steps in this study. First, the UF₆ data were collected. Second, one postulated case was analyzed by using the RASCAL and UF₆ data. This postulated case assumes that fire occurring and UF₆ is releasing from a building. Third, the results of RASCAL for HF mass were as the input data of ALOHA. Two postulated cases of HF were analyzed by using ALOHA code and the results of RASCAL. These postulated cases assume fire occurring and HF is releasing with no raining (Case 1) or raining (Case 2) condition. According to the analysis results of ALOHA, the HF concentration of Case 2 is smaller than Case 1. The results can be a reference for the preparing of emergency plans for the release of HF.

Keywords: RASCAL, ALOHA, UF6, hydrogen fluoride.

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Authors: K.M. Faraoun, A. Boukelif

Abstract:

This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher-s Iris dataset, and then, the KDD-99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperform the existing GP-classification methods [1],[2] and [3], and give a very accepted results compared to other existing techniques proposed in [4],[5],[6],[7] and [8].

Keywords: Genetic programming, patterns classification, intrusion detection

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Authors: A. H. Azni, Madihah Mohd Saudi, Azreen Azman, Ariff Syah Johari

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Sensor Network are emerging as a new tool for important application in diverse fields like military surveillance, habitat monitoring, weather, home electrical appliances and others. Technically, sensor network nodes are limited in respect to energy supply, computational capacity and communication bandwidth. In order to prolong the lifetime of the sensor nodes, designing efficient routing protocol is very critical. In this paper, we illustrate the existing routing protocol for wireless sensor network using data centric approach and present performance analysis of these protocols. The paper focuses in the performance analysis of specific protocol namely Directed Diffusion and SPIN. This analysis reveals that the energy usage is important features which need to be taken into consideration while designing routing protocol for wireless sensor network.

Keywords: Data Centric Approach, Directed Diffusion, SPIN WSN Routing Protocol.

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Authors: Ljerka Hrženjak-Šego, Željko Polić, Zdravka Aljinović

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In general, reports are a form of representing data in such way that user gets the information he needs. They can be built in various ways, from the simplest (“select from") to the most complex ones (results derived from different sources/tables with complex formulas applied). Furthermore, rules of calculations could be written as a program hard code or built in the database to be used by dynamic code. This paper will introduce two types of reports, defined in the DB structure. The main goal is to manage calculations in optimal way, keeping maintenance of reports as simple and smooth as possible.

Keywords: Data Definition diagram, Server Model Diagram, system modelling, reports.

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Authors: Asim Majeed, Anwar Ul Haq, Mike, Lloyd-Williams, Arshad Jamal, Usman Butt

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This study aims to investigate the impact of data leak of M&S customers on digital communities. Modern businesses are using digital communities as an important public relations tool for marketing purposes. This form of communication helps companies to build better relationship with their customers which also act as another source of information. The communication between the customers and the organizations is not regulated so users may post positive and negative comments. There are new platforms being developed on a daily basis and it is very crucial for the businesses to not only get themselves familiar with those but also know how to reach their existing and perspective consumers. The driving force of marketing and communication in modern businesses is the digital communities and these are continuously increasing and developing. This phenomenon is changing the way marketing is conducted. The current research has discussed the implications on M&S business performance since the data was exploited on digital communities; users contacted M&S and raised the security concerns. M&S closed down its website for few hours to try to resolve the issue. The next day M&S made a public apology about this incidence. This information was proliferated on various digital communities and it has impacted negatively on M&S brand name, sales and customers. The content analysis approach is being used to collect qualitative data from 100 digital bloggers including social media communities such as Facebook and Twitter. The results and finding provide useful new insights into the nature and form of security concerns of digital users. Findings have theoretical and practical implications. This research will showcase a large corporation utilizing various digital community platforms and can serve as a model for future organizations.

Keywords: Digital, communities, performance, dissemination, implications, data, exploitation.

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Authors: J. R. Wang, Y. Chiang, W. Y. Li, H. T. Lin, H. C. Chen, C. Shih, S. W. Chen

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The dry-storage systems of nuclear power plants (NPPs) in Taiwan have become one of the major safety concerns. There are two steps considered in this study. The first step is the verification of the TRACE by using VSC-17 experimental data. The results of TRACE were similar to the VSC-17 data. It indicates that TRACE has the respectable accuracy in the simulation and analysis of the dry-storage systems. The next step is the application of TRACE in the dry-storage system of Kuosheng NPP (BWR/6). Kuosheng NPP is the second BWR NPP of Taiwan Power Company. In order to solve the storage of the spent fuels, Taiwan Power Company developed the new dry-storage system for Kuosheng NPP. In this step, the dry-storage system model of Kuosheng NPP was established by TRACE. Then, the steady state simulation of this model was performed and the results of TRACE were compared with the Kuosheng NPP data. Finally, this model was used to perform the safety analysis of Kuosheng NPP dry-storage system. Besides, FRAPTRAN was used tocalculate the transient performance of fuel rods.

Keywords: BWR, TRACE, FRAPTRAN, Dry-Storage.

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Authors: Cesar Hernández, Diego Giral, Fernando Santa

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Spectrum handover is a significant topic in the cognitive radio networks to assure an efficient data transmission in the cognitive radio user’s communications. This paper proposes a comparison between three spectrum handover models: VIKOR, SAW and MEW. Four evaluation metrics are used. These metrics are, accumulative average of failed handover, accumulative average of handover performed, accumulative average of transmission bandwidth and, accumulative average of the transmission delay. As a difference with related work, the performance of the three spectrum handover models was validated with captured data of spectrum occupancy in experiments performed at the GSM frequency band (824 MHz - 849 MHz). These data represent the actual behavior of the licensed users for this wireless frequency band. The results of the comparison show that VIKOR Algorithm provides a 15.8% performance improvement compared to SAW Algorithm and, it is 12.1% better than the MEW Algorithm.

Keywords: Cognitive radio, decision making, MEW, SAW, spectrum handover, VIKOR.

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Authors: Jan Newmarch

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With the increasing spread of computers and the internet among culturally, linguistically and geographically diverse communities, issues of internationalization and localization and becoming increasingly important. For some of the issues such as different scales for length and temperature, there is a well-developed measurement theory. For others such as date formats no such theory will be possible. This paper fills a gap by developing a measurement theory for a class of scales previously overlooked, based on discrete and interval-valued scales such as spanner and shoe sizes. The paper gives a theoretical foundation for a class of data representation problems.

Keywords: Data representation, internationalisation, localisation, measurement theory.

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Authors: J. Madureira, R. Lagido, I. Sousa

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Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU. 

Keywords: Inertial Measurement Unit (IMU), Global Positioning System (GPS), smartphone, surfing performance.

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Authors: U. Sakthi, R. Hemalatha, R. S. Bhuvaneswaran

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In Virtual organization, Knowledge Discovery (KD) service contains distributed data resources and computing grid nodes. Computational grid is integrated with data grid to form Knowledge Grid, which implements Apriori algorithm for mining association rule on grid network. This paper describes development of parallel and distributed version of Apriori algorithm on Globus Toolkit using Message Passing Interface extended with Grid Services (MPICHG2). The creation of Knowledge Grid on top of data and computational grid is to support decision making in real time applications. In this paper, the case study describes design and implementation of local and global mining of frequent item sets. The experiments were conducted on different configurations of grid network and computation time was recorded for each operation. We analyzed our result with various grid configurations and it shows speedup of computation time is almost superlinear.

Keywords: Association rule, Grid computing, Knowledge grid, Mobility prediction.

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Authors: Houman Tamaddon, Mehrdad Behnia, Masud Behnia

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An accurate study of blood flow is associated with an accurate vascular pattern and geometrical properties of the organ of interest. Due to the complexity of vascular networks and poor accessibility in vivo, it is challenging to reconstruct the entire vasculature of any organ experimentally. The objective of this study is to introduce an innovative approach for the reconstruction of a full vascular tree from available morphometric data. Our method consists of implementing morphometric data on those parts of the vascular tree that are smaller than the resolution of medical imaging methods. This technique reconstructs the entire arterial tree down to the capillaries. Vessels greater than 2 mm are obtained from direct volume and surface analysis using contrast enhanced computed tomography (CT). Vessels smaller than 2mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray’s Laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray’s Laws to every vessel bifurcation simultaneously, lead to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. During the simulation, we used the averaged flow rate for each order to predict the pressure drop and once the pressure drop is predicted, the flow rate is corrected to match the computed pressure drop for each vessel. The final results for 3 cardiac cycles is presented and compared to the clinical data.

Keywords: Blood flow, Morphometric data, Vascular tree, Strahler ordering system.

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Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: Aero-thermo-elasticity, elastic deformation, structural temperature, multi-field coupling.

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Authors: Tae in Seol, Sun-Tae Chung, Seongwon Cho

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Realistic 3D face model is desired in various applications such as face recognition, games, avatars, animations, and etc. Construction of 3D face model is composed of 1) building a face shape model and 2) rendering the face shape model. Thus, building a realistic 3D face shape model is an essential step for realistic 3D face model. Recently, 3D morphable model is successfully introduced to deal with the various human face shapes. 3D dense correspondence problem should be precedently resolved for constructing a realistic 3D dense morphable face shape model. Several approaches to 3D dense correspondence problem in 3D face modeling have been proposed previously, and among them optical flow based algorithms and TPS (Thin Plate Spline) based algorithms are representative. Optical flow based algorithms require texture information of faces, which is sensitive to variation of illumination. In TPS based algorithms proposed so far, TPS process is performed on the 2D projection representation in cylindrical coordinates of the 3D face data, not directly on the 3D face data and thus errors due to distortion in data during 2D TPS process may be inevitable. In this paper, we propose a new 3D dense correspondence algorithm for 3D dense morphable face shape modeling. The proposed algorithm does not need texture information and applies TPS directly on 3D face data. Through construction procedures, it is observed that the proposed algorithm constructs realistic 3D face morphable model reliably and fast.

Keywords: 3D Dense Correspondence, 3D Morphable Face Shape Model, 3D Face Modeling.

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Authors: Angelo Lerro, Manuela Battipede, Piero Gili, Alberto Brandl

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Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.

Keywords: Neural network, aerodynamic angles, virtual sensor, unmanned aerial vehicle, air data system, flight test.

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Authors: Kanda Runapongsa Saikaew, Patcharaporn Jiranuwattanawong, Patinya Taearak

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Currently, there is excessively growing information about places on Facebook, which is the largest social network but such information is not explicitly organized and ranked. Therefore users cannot exploit such data to recommend places conveniently and quickly. This paper proposes a Facebook application and an Android application that recommend places based on the number of check-ins of those places, the distance of those places from the current location, the number of people who like Facebook page of those places, and the number of talking about of those places. Related Facebook data is gathered via Facebook API requests. The experimental results of the developed applications show that the applications can recommend places and rank interesting places from the most to the least. We have found that the average satisfied score of the proposed Facebook application is 4.8 out of 5. The users’ satisfaction can increase by adding the app features that support personalization in terms of interests and preferences.

Keywords: Mobile computing, location-based services, recommendation system, social network analysis.

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Authors: E. Bakkaloğlu, N. Torunbalcı

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Earthquakes are inevitable natural disasters in Turkey. Therefore, buildings must be prepared for this natural hazard. Especially in hospital buildings, earthquake resistance is an essential point because hospitals are one of the first places where people come after earthquake. Although hospital buildings are more suitable for horizontal architecture, it is necessary to construct and expand multi-story hospital buildings due to difficulties in finding suitable places as a result of excessive urbanization, difficulties in obtaining appropriate size land and decrease in suitable places and increase in land values. In Turkey, using seismic isolators in public hospitals, which are placed in first degree earthquake zone and have more than 100 beds, is made obligatory by general instruction. As a result of this decision, it may sometimes be necessary to construct seismic isolated multi-story hospital buildings in cities where those problems are experienced. Although there is widespread use of seismic isolators in Japan, there are few multi-story buildings in which seismic isolators are used in Turkey. As it is known, base isolation systems are the most effective methods of earthquake resistance, as the number of floors increases, the center of gravity moves away from the base in multi-story buildings, increasing the overturning effect and limiting use of these systems. In this context, it is aimed to investigate structural systems of multi-story buildings which are built using seismic isolation methods in the world. In addition to this, a working principle is suggested for the disseminating seismic isolator used in multi-story hospital buildings. The results to be obtained from the study will guide architects who design multi-story hospital buildings in their architectural designs, and engineers in terms of structural system design.

Keywords: Earthquake, energy absorbing systems, hospital, seismic isolation systems.

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Authors: R.M.Rani

Abstract:

Chess is one of the indoor games, which improves the level of human confidence, concentration, planning skills and knowledge. The main objective of this paper is to help the chess players to improve their chess openings using data mining techniques. Budding Chess Players usually do practices by analyzing various existing openings. When they analyze and correlate thousands of openings it becomes tedious and complex for them. The work done in this paper is to analyze the best lines of Blackmar- Diemer Gambit(BDG) which opens with White D4... using data mining analysis. It is carried out on the collection of winning games by applying association rules. The first step of this analysis is assigning variables to each different sequence moves. In the second step, the sequence association rules were generated to calculate support and confidence factor which help us to find the best subsequence chess moves that may lead to winning position.

Keywords: Blackmar-Diemer Gambit(BDG), Confidence, sequence Association Rules, Support.

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Authors: Madihah Mohd Saudi, Andrea J Cullen, Mike E Woodward

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This paper presents a new STAKCERT KDD processes for worm detection. The enhancement introduced in the data-preprocessing resulted in the formation of a new STAKCERT model for worm detection. In this paper we explained in detail how all the processes involved in the STAKCERT KDD processes are applied within the STAKCERT model for worm detection. Based on the experiment conducted, the STAKCERT model yielded a 98.13% accuracy rate for worm detection by integrating the STAKCERT KDD processes.

Keywords: data mining, incident response, KDD processes, security metrics and worm detection.

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Authors: Swetha Kurup, Sridhar G., Sridhar V.

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In this paper we present a novel technique for data hiding in binary document images. We use the concept of entropy in order to identify document specific least distortive areas throughout the binary document image. The document image is treated as any other image and the proposed method utilizes the standard document characteristics for the embedding process. Proposed method minimizes perceptual distortion due to embedding and allows watermark extraction without the requirement of any side information at the decoder end.

Keywords: Entropy, Steganography, Watermarking.

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Authors: Julia Moehrmann, Gunter Heidemann, Oliver Siemoneit, Christoph Hubig, Uwe-Philipp Kaeppeler, Paul Levi

Abstract:

Image data holds a large amount of different context information. However, as of today, these resources remain largely untouched. It is thus the aim of this paper to present a basic technical framework which allows for a quick and easy exploitation of context information from image data especially by non-expert users. Furthermore, the proposed framework is discussed in detail concerning important social and ethical issues which demand special requirements in system design. Finally, a first sensor prototype is presented which meets the identified requirements. Additionally, necessary implications for the software and hardware design of the system are discussed, rendering a sensor system which could be regarded as a good, acceptable and justifiable technical and thereby enabling the extraction of context information from image data.

Keywords: Context-aware computing, ethical and social issues, image recognition, requirements in system design.

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

Abstract:

Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as physiological vital signs, images and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful to influence the judgement of clinical sentiment in ICU clinical notes. This paper presents two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of a clinical transformer model that can reliably predict clinical sentiment for notes of COVID patients in ICU. We train the model on clinical notes for COVID-19 patients, ones not previously seen by Bio_ClinicalBERT or Bio_Discharge_Summary_BERT. The model which was based on Bio_ClinicalBERT achieves higher predictive accuracy than the one based on Bio_Discharge_Summary_BERT (Acc 93.33%, AUC 0.98, and Precision 0.96). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and Precision 0.92).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation.

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