Search results for: Semantic Trend Detection.

Authors: Meryem Lamrani, Younès El Amrani, Aziz Ettouhami

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The ability of UML to handle the modeling process of complex industrial software applications has increased its popularity to the extent of becoming the de-facto language in serving the design purpose. Although, its rich graphical notation naturally oriented towards the object-oriented concept, facilitates the understandability, it hardly successes to report all domainspecific aspects in a satisfactory way. OCL, as the standard language for expressing additional constraints on UML models, has great potential to help improve expressiveness. Unfortunately, it suffers from a weak formalism due to its poor semantic resulting in many obstacles towards the build of tools support and thus its application in the industry field. For this reason, many researches were established to formalize OCL expressions using a more rigorous approach. Our contribution join this work in a complementary way since it focuses specifically on OCL predefined properties which constitute an important part in the construction of OCL expressions. Using formal methods, we mainly succeed in expressing rigorously OCL predefined functions.

Keywords: Formal methods, Z, OCL, predefined properties, metamodel types.

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Authors: Nadia Bensiali, Nadia Benalia, Amar Omeiri

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The recent trend in field oriented control (FOC) is towards the use of sensorless techniques that avoid the use of speed sensor and flux sensor. Sensors are replaced by estimators or observers to minimise the cost and increase the reliability. In this paper an anlyse of perfomance of a MRAS used in sensorless control of induction motors and sensitvity to machine parameters change are studied.

Keywords: Induction motor drive, adaptive observer, MRAS, stability analysis.

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Authors: Mohammad Ahmadinejad, Mohammad Reza Shakibaie, Kyvan Shams, Mohammad Khalili

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Legionella pneumophila is involved in more than 95% cases of severe atypical pneumonia. Infection is mainly by inhalation the indoor aerosols through the water-coolant systems. Because some Legionella strains may be viable but not culturable, therefore, Taq polymerase, DNA amplification and semi-nested-PCR were carried out to detect Legionella-specific 16S-rDNA sequence. For this purpose, 1.5 litter of water samples from 77 water-coolant system were collected from four different hospitals, two nursing homes and one student hostel in Kerman city of Iran, each in a brand new plastic bottle during summer season of 2006 (from April to August). The samples were filtered in the sterile condition through the Millipore Membrane Filter. DNA was extracted from membrane and used for PCR to detect Legionella spp. The PCR product was then subjected to semi-nested PCR for detection of L. pneumophila. Out of 77 water samples that were tested by PCR, 30 (39%) were positive for most species of Legionella. However, L. pneumophila was detected from 14 (18.2%) water samples by semi-nested PCR. From the above results it can be concluded that water coolant systems of different hospitals and nursing homes in Kerman city of Iran are highly contaminated with L. pneumophila spp. and pose serious concern. So, we recommend avoiding such type of coolant system in the hospitals and nursing homes.

Keywords: Legionella pneumophila, water-coolant system, semi-nested -PCR.

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Authors: Htay Htay Yi, Zin May Aye

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Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: False negative rate, intrusion detection system, machine learning methods, performance.

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Authors: F. Abubaker, F. Tortorici, M. Capogni, C. Sutera, V. Bellini

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This project concerns with the detection efficiency of the portable Triple-to-Double Coincidence Ratio (TDCR) at the National Institute of Metrology of Ionizing Radiation (INMRI-ENEA) which allows direct activity measurement and radionuclide standardization for pure-beta emitter or pure electron capture radionuclides. The dependency of the simulated detection efficiency of the TDCR, by using Monte Carlo simulation Geant4 code, on the Birks factor (kB) and defocusing parameter has been examined especially for low energy beta-emitter radionuclides such as 3H and 14C, for which this dependency is relevant. The results achieved in this analysis can be used for selecting the best kB factor and the defocusing parameter for computing theoretical TDCR parameter value. The theoretical results were compared with the available ones, measured by the ENEA TDCR portable detector, for some pure-beta emitter radionuclides. This analysis allowed to improve the knowledge of the characteristics of the ENEA TDCR detector that can be used as a traveling instrument for in-situ measurements with particular benefits in many applications in the field of nuclear medicine and in the nuclear energy industry.

Keywords: Birks constant, defocusing parameter, GEANT4 code, TDCR parameter.

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Authors: I. Parkova, A. Vališevskis, A. Viļumsone

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Nocturnal enuresis or bed-wetting is intermittent incontinence during sleep of children after age 5 that may precipitate wide range of behavioral and developmental problems. One of the non-pharmacological treatment methods is the use of a bed-wetting alarm system. In order to improve comfort conditions of nocturnal enuresis alarm system, modular moisture sensor should be replaced by a textile sensor. In this study behavior and moisture detection speed of woven and sewn sensors were compared by analyzing change in electrical resistance after solution (salt water) was dripped on sensor samples. Material of samples has different structure and yarn location, which affects solution detection rate. Sensor system circuit was designed and two sensor tests were performed: system activation test and false alarm test to determine the sensitivity of the system and activation threshold. Sewn sensor had better result in system’s activation test – faster reaction, but woven sensor had better result in system’s false alarm test – it was less sensitive to perspiration simulation. After experiments it was found that the optimum switching threshold is 3V in case of 5V input voltage, which provides protection against false alarms, for example – during intensive sweating.

Keywords: Conductive yarns, moisture textile sensor.

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Authors: Tanwarat Kajsongkram, Saowalux Rotamporn, Sirinat Limbunruang, Sirinan Thubthimthed

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High Performance Liquid Chromatography (HPLC) method was developed and validated for simultaneous estimation of 6-Gingerol(6G) and 6-Shogaol(6S) in joint pain relief gel containing ginger extract. The chromatographic separation was achieved by using C18 column, 150 x 4.6mm i.d., 5μ Luna, mobile phase containing acetonitrile and water (gradient elution). The flow rate was 1.0 ml/min and the absorbance was monitored at 282 nm. The proposed method was validated in terms of the analytical parameters such as specificity, accuracy, precision, linearity, range, limit of detection (LOD), limit of quantification (LOQ), and determined based on the International Conference on Harmonization (ICH) guidelines. The linearity ranges of 6G and 6S were obtained over 20- 60 and 6-18 μg/ml respectively. Good linearity was observed over the above-mentioned range with linear regression equation Y= 11016x- 23778 for 6G and Y = 19276x-19604 for 6S (x is concentration of analytes in μg/ml and Y is peak area). The value of correlation coefficient was found to be 0.9994 for both markers. The limit of detection (LOD) and limit of quantification (LOQ) for 6G were 0.8567 and 2.8555 μg/ml and for 6S were 0.3672 and 1.2238 μg/ml respectively. The recovery range for 6G and 6S were found to be 91.57 to 102.36 % and 84.73 to 92.85 % for all three spiked levels. The RSD values from repeated extractions for 6G and 6S were 3.43 and 3.09% respectively. The validation of developed method on precision, accuracy, specificity, linearity, and range were also performed with well-accepted results.

Keywords: Ginger, 6-gingerol, HPLC, 6-shogaol.

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial neural networks, digital image processing, pattern recognition.

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Authors: Bertalan Forstner, Dr. Hassan Charaf

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In the world of Peer-to-Peer (P2P) networking different protocols have been developed to make the resource sharing or information retrieval more efficient. The SemPeer protocol is a new layer on Gnutella that transforms the connections of the nodes based on semantic information to make information retrieval more efficient. However, this transformation causes high clustering in the network that decreases the number of nodes reached, therefore the probability of finding a document is also decreased. In this paper we describe a mathematical model for the Gnutella and SemPeer protocols that captures clustering-related issues, followed by a proposition to modify the SemPeer protocol to achieve moderate clustering. This modification is a sort of link management for the individual nodes that allows the SemPeer protocol to be more efficient, because the probability of a successful query in the P2P network is reasonably increased. For the validation of the models, we evaluated a series of simulations that supported our results.

Keywords: Peer-to-Peer, model, performance, networkmanagement.

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Authors: Asif Ansari, T.C.Manjunath, C. Ardil

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In today-s competitive environment, the security concerns have grown tremendously. In the modern world, possession is known to be 9/10-ths of the law. Hence, it is imperative for one to be able to safeguard one-s property from worldly harms such as thefts, destruction of property, people with malicious intent etc. Due to the advent of technology in the modern world, the methodologies used by thieves and robbers for stealing have been improving exponentially. Therefore, it is necessary for the surveillance techniques to also improve with the changing world. With the improvement in mass media and various forms of communication, it is now possible to monitor and control the environment to the advantage of the owners of the property. The latest technologies used in the fight against thefts and destruction are the video surveillance and monitoring. By using the technologies, it is possible to monitor and capture every inch and second of the area in interest. However, so far the technologies used are passive in nature, i.e., the monitoring systems only help in detecting the crime but do not actively participate in stopping or curbing the crime while it takes place. Therefore, we have developed a methodology to detect the motion in a video stream environment and this is an idea to ensure that the monitoring systems not only actively participate in stopping the crime, but do so while the crime is taking place. Hence, a system is used to detect any motion in a live streaming video and once motion has been detected in the live stream, the software will activate a warning system and capture the live streaming video.

Keywords: Motion, Detection, System, Video, Crime, Matlab, Surveillance.

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Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

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Background modeling and subtraction in video analysis has been widely used as an effective method for moving objects detection in many computer vision applications. Recently, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are the most frequently occurred problems in the practical situation. This paper presents a favorable two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean value of each RGB color channel. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the output of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate very competitive performance compared to previous models.

Keywords: Background subtraction, codebook model, local binary pattern, dynamic background, illumination changes.

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Authors: Ki Ok Choi, Sung Ho Hong, Dong Suck Kim, Don Mook Choi

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Rack type warehouses are different from general buildings in the kinds, amount, and arrangement of stored goods, so the fire risk of rack type warehouses is different from those buildings. The fire pattern of rack type warehouses is different in combustion characteristic and storing condition of stored goods. The initial fire burning rate is different in the surface condition of materials, but the running time of fire is closely related with the kinds of stored materials and stored conditions. The stored goods of the warehouse are consisted of diverse combustibles, combustible liquid, and so on. Fire detection time may be delayed because the residents are less than office and commercial buildings. If fire detectors installed in rack type warehouses are inadaptable, the fire of the warehouse may be the great fire because of delaying of fire detection. In this paper, we studied what kinds of fire detectors are optimized in early detecting of rack type warehouse fire by real-scale fire tests. The fire detectors used in the tests are rate of rise type, fixed type, photo electric type, and aspirating type detectors. We considered optimum fire detecting method in rack type warehouses suggested by the response characteristic and comparative analysis of the fire detectors.

Keywords: Fire detector, rack, response characteristic, warehouse.

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Authors: Yingying Ding

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This study presents a methodology of specialized translation with the objective of helping teachers to improve the strategies in teaching translation. In order to allow students to acquire skills to translate specialized texts, they need to become familiar with the semantic and syntactic features of source texts and target texts. The aim of our study is to use a corpus-based approach in the teaching of specialized translation between Chinese and Italian. This study proposes to construct a specialized Chinese - Italian comparable corpus that consists of 50 economic contracts from the domain of food. With the help of AntConc, we propose to compile a comparable corpus in for translation teaching purposes. This paper attempts to provide insight into how teachers could benefit from comparable corpus in the teaching of specialized translation from Italian into Chinese and through some examples of passive sentences how students could learn to apply different strategies for translating appropriately the voice.

Keywords: Corpus-based approach, translation teaching, specialized translation.

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Authors: Igor Guatelli

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The universe of aesthetic perception entails impasses about sensitive divergences that each text or visual object may be subjected to. If approached through intertextuality that is not based on the misleading notion of kinships or similarities a priori admissible, the possibility of anachronistic, heterogeneous - and non-diachronic - assemblies can enhance the emergence of interval movements, intermediate, and conflicting, conducive to a method of reading, interpreting, and assigning meaning that escapes the rigid antinomies of the mere being and non-being of things. In negative, they operate in a relationship built by the lack of an adjusted meaning set by their positive existences, with no remainders; the generated interval becomes the remnant of each of them; it is the opening that obscures the stable positions of each one. Without the negative of absence, of that which is always missing or must be missing in a text, concept, or image made positive by history, nothing is perceived beyond what has been already given. Pairings or binary oppositions cannot lead only to functional syntheses; on the contrary, methodological disturbances accumulated by the approximation of signs and entities can initiate a process of becoming as an opening to an unforeseen other, transformation until a moment when the difficulties of [re]conciliation become the mainstay of a future of that sign/entity, not envisioned a priori. A counter-history can emerge from these unprecedented, misadjusted approaches, beginnings of unassigned injunctions and disjunctions, in short, difficult alliances that open cracks in a supposedly cohesive history, chained in its apparent linearity with no remains, understood as a categorical historical imperative. Interstices are minority fields that, because of their opening, are capable of causing opacity in that which, apparently, presents itself with irreducible clarity. Resulting from an incomplete and maladjusted [at the least dual] marriage between the signs/entities that originate them, this interval may destabilize and cause disorder in these entities and their own meanings. The interstitials offer a hyphenated relationship: a simultaneous union and separation, a spacing between the entity’s identity and its otherness or, alterity. One and the other may no longer be seen without the crack or fissure that now separates them, uniting, by a space-time lapse. Ontological, semantic shifts are caused by this fissure, an absence between one and the other, one with and against the other. Based on an improbable approximation between some conceptual and semantic shifts within the design production of architect Rem Koolhaas and the textual production of the philosopher Jacques Derrida, this article questions the notion of unity, coherence, affinity, and complementarity in the process of construction of thought from these ontological, epistemological, and semiological fissures that rattle the signs/entities and their stable meanings. Fissures in a thought that is considered coherent, cohesive, formatted are the negativity that constitutes the interstices that allow us to move towards what still remains as non-identity, which allows us to begin another story.

Keywords: Clearing, interstice, negative, remnant, spectrum.

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Authors: A.S.A. Khiar, A.K. Arof

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Starch/chitosan blend have been prepared via the solution casting technique. Ionic conductivity for the system was conducted over a wide range of frequency between 50 Hz-1 MHz and at temperatures between 303 K and 373 K. Sample with 35 wt% of NH4NO3 shows the highest conductivity of 3.89 ± 0.79 x 10-5 Scm-1 at room temperature. Conductivity-temperature relationship suggests that samples are Arrhenian. Power law exponent was obtained through dielectric loss variation and the trend suggests that the conduction mechanism of the ions can be represented by the correlated barrier hopping (CBH) model.

Keywords: starch, chitosan, permittivity, power exponent law

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Authors: Maria C. Proença, Marco Aniceto, Pedro N. Santos, José C. Freitas

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A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed.

Keywords: Automatic detection, wavelets, defects, fracture lines.

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Authors: Abdelaziz Guerrouat, Harald Richter

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In this paper test generation methods and appropriate fault models for testing and analysis of embedded systems described as (extended) finite state machines ((E)FSMs) are presented. Compared to simple FSMs, EFSMs specify not only the control flow but also the data flow. Thus, we define a two-level fault model to cover both aspects. The goal of this paper is to reuse well-known FSM-based test generation methods for automation of embedded system testing. These methods have been widely used in testing and validation of protocols and communicating systems. In particular, (E)FSMs-based specification and testing is more advantageous because (E)FSMs support the formal semantic of already standardised formal description techniques (FDTs) despite of their popularity in the design of hardware and software systems.

Keywords: Formal methods, testing and validation, finite state machines, formal description techniques.

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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: Rajendra M. Sonar

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The recent trend has been using hybrid approach rather than using a single intelligent technique to solve the problems. In this paper, we describe and discuss a framework to develop enterprise solutions that are backed by intelligent techniques. The framework not only uses intelligent techniques themselves but it is a complete environment that includes various interfaces and components to develop the intelligent solutions. The framework is completely Web-based and uses XML extensively. It can work like shared plat-form to be accessed by multiple developers, users and decision makers.

Keywords: Intelligent System Development Framework, WebbasedIntelligent Systems, Retail Banking.

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Authors: Nhon Van Do

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In the artificial intelligence field, knowledge representation and reasoning are important areas for intelligent systems, especially knowledge base systems and expert systems. Knowledge representation Methods has an important role in designing the systems. There have been many models for knowledge such as semantic networks, conceptual graphs, and neural networks. These models are useful tools to design intelligent systems. However, they are not suitable to represent knowledge in the domains of reality applications. In this paper, new models for knowledge representation called computational networks will be presented. They have been used in designing some knowledge base systems in education for solving problems such as the system that supports studying knowledge and solving analytic geometry problems, the program for studying and solving problems in Plane Geometry, the program for solving problems about alternating current in physics.

Keywords: Artificial intelligence, artificial intelligence and education, knowledge engineering, knowledge representation.

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Authors: Hyun-Koo Kim, Ju H. Park, Ho-Youl Jung

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This paper presents an effective traffic lights recognition method at the daytime. First, Potential Traffic Lights Detector (PTLD) use whole color source of YCbCr channel image and make each binary image of green and red traffic lights. After PTLD step, Shape Filter (SF) use to remove noise such as traffic sign, street tree, vehicle, and building. At this time, noise removal properties consist of information of blobs of binary image; length, area, area of boundary box, etc. Finally, after an intermediate association step witch goal is to define relevant candidates region from the previously detected traffic lights, Adaptive Multi-class Classifier (AMC) is executed. The classification method uses Haar-like feature and Adaboost algorithm. For simulation, we are implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and rural roads. Through the test, we are compared with our method and standard object-recognition learning processes and proved that it reached up to 94 % of detection rate which is better than the results achieved with cascade classifiers. Computation time of our proposed method is 15 ms.

Keywords: Traffic Light Detection, Multi-class Classification, Driving Assistance System, Haar-like Feature, Color SegmentationMethod, Shape Filter

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Authors: Ahmet Denker

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The trend in the world of Information Technology (IT) is getting increasingly large and difficult projects rather than smaller and easier. However, the data on large-scale IT project success rates provide cause for concern. This paper seeks to answer why large-scale IT projects are different from and more difficult than other typical engineering projects. Drawing on the industrial experience, a compilation of the conditions that influence failure is presented. With a view to improve success rates solutions are suggested.

Keywords: Software engineering, software economics, project management, large-scale projects.

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Authors: Salima Behdenna, Fatiha Barigou, Ghalem Belalem

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Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic ABSA approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: Sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity.

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Authors: A. Tellaeche, X. P. Burgos-Artizzu, G. Pajares, A. Ribeiro

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One important objective in Precision Agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. In order to reach this goal, two major factors need to be considered: 1) the similar spectral signature, shape and texture between weeds and crops; 2) the irregular distribution of the weeds within the crop's field. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. The proposed system involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and the weeds. From these attributes, a hybrid decision making approach determines if a cell must be or not sprayed. The hybrid approach uses the Support Vector Machines and the Fuzzy k-Means methods, combined through the fuzzy aggregation theory. This makes the main finding of this paper. The method performance is compared against other available strategies.

Keywords: Fuzzy k-Means, Precision agriculture, SupportVectors Machines, Weed detection.

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Authors: Pei-Jeng Kuo, Terumasa Aoki, Hiroshi Yasuda

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PARIS (Personal Archiving and Retrieving Image System) is an experiment personal photograph library, which includes more than 80,000 of consumer photographs accumulated within a duration of approximately five years, metadata based on our proposed MPEG-7 annotation architecture, Dozen Dimensional Digital Content (DDDC), and a relational database structure. The DDDC architecture is specially designed for facilitating the managing, browsing and retrieving of personal digital photograph collections. In annotating process, we also utilize a proposed Spatial and Temporal Ontology (STO) designed based on the general characteristic of personal photograph collections. This paper explains PRAIS system.

Keywords: Ontology, Databases and Information Retrieval, MPEG-7, Spatial-Temporal, Digital Library Designs l, metadata, Semantic Web, semi-automatic annotation

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: Social networks, community detection, modularity optimization, geographically dispersed communities.

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Authors: S. Kannimuthu, K. Premalatha

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This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to identify itemsets whose utility satisfies a given threshold. Although, the approach of mining HUIs in a distributed environment and mining of the same from XML data have not explored yet. In this work, a novel approach is proposed to mine HUIs from the XML based data in a distributed environment. This work utilizes Service Oriented Computing (SOC) paradigm which provides Knowledge as a Service (KaaS). The interesting patterns are provided via the web services with the help of knowledge server to answer the queries of the consumers. The performance of the approach is evaluated on various databases using execution time and memory consumption.

Keywords: Data mining, Knowledge as a Service, service oriented computing, utility mining.

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Authors: Soe Winn, May Thwe Oo

Abstract:

Business process automation is an important task in an enterprise business environment software development. The requirements of processing acceleration and automation level of enterprises are inherently different from one organization to another. We present a methodology and system for automation of business process management system architecture by multi-agent collaboration based on SOA. Design layer processes are modeled in semantic markup language for web services application. At the core of our system is considering certain types of human tasks to their further automation across over multiple platform environments. An improved abnormality processing with model for automation of BPMS architecture by multi-agent collaboration based on SOA is introduced. Validating system for efficiency of process automation, an application for educational knowledge base instance would also be described.

Keywords: Business process management system, businessprocess automation, multi-agent collaboration, Service OrientedArchitecture, extensible service application

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Authors: H. Unger, H. Coltzau

Abstract:

In Peer-to-Peer service networks, where peers offer any kind of publicly available services or applications, intuitive navigation through all services in the network becomes more difficult as the number of services increases. In this article, a concept is discussed that enables users to intuitively browse and use large scale P2P service networks. The concept extends the idea of creating virtual 3D-environments solely based on Peer-to-Peer technologies. Aside from browsing, users shall have the possibility to emphasize services of interest using their own semantic criteria. The appearance of the virtual world shall intuitively reflect network properties that may be of interest for the user. Additionally, the concept comprises options for load- and traffic-balancing. In this article, the requirements concerning the underlying infrastructure and the graphical user interface are defined. First impressions of the appearance of future systems are presented and the next steps towards a prototypical implementation are discussed.

Keywords: Internet Operating System, Peer-To-Peer, Service Exploration

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Authors: J. Prakash, K. Rajesh

Abstract:

In this paper we present a new method for coin identification. The proposed method adopts a hybrid scheme using Eigenvalues of covariance matrix, Circular Hough Transform (CHT) and Bresenham-s circle algorithm. The statistical and geometrical properties of the small and large Eigenvalues of the covariance matrix of a set of edge pixels over a connected region of support are explored for the purpose of circular object detection. Sparse matrix technique is used to perform CHT. Since sparse matrices squeeze zero elements and contain only a small number of non-zero elements, they provide an advantage of matrix storage space and computational time. Neighborhood suppression scheme is used to find the valid Hough peaks. The accurate position of the circumference pixels is identified using Raster scan algorithm which uses geometrical symmetry property. After finding circular objects, the proposed method uses the texture on the surface of the coins called texton, which are unique properties of coins, refers to the fundamental micro structure in generic natural images. This method has been tested on several real world images including coin and non-coin images. The performance is also evaluated based on the noise withstanding capability.

Keywords: Circular Hough Transform, Coin detection, Covariance matrix, Eigenvalues, Raster scan Algorithm, Texton.

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