Computational Science and Its Applications -- ICCSA 2015

The frequency of extreme weather events has accelerated, an apparent outcome of progressive climate change. Excess water is a significant consequence of these events and is now the leading cause of insurance claims for infrastructure and property damage.

Governments recognize that plans for growth must reflect communities’ needs, strengths and opportunities while balancing the

cumulative effects

of economic growth with environmental concerns. Legislation must incorporate the cumulative effects of economic growth with adaptation to weather events to protect the environment and citizens, while ensuring that products of growth such as buildings and infrastructure are resilient. For such a process to be effective it will be necessary for the private sector to develop and operate cumulative effect decision support software (CEDSS) tools and to work closely with all levels of government including watershed management authorities (WMAs) that supply environmental data. Such cooperation and sharing will require a new

Open Data

information-sharing platform managed by the private sector. This paper outlines that platform, its operation and possible governance model.

Climate change is one of this century’s greatest unbalancing forces that affect our planet. Mining the public awareness is an essential step towards the assessment of current climate policies, dedication of sufficient resources, and construction of new policies for business planning. In this paper, we present an exploratory data mining method that compares two types of networks. The first type is constructed from a set of words collected from a Climate Change corpus, which we consider as ground-truth (i.e., base of comparison). The other type of network is constructed from a reasonably large data set of 72 million tweets; it is used to analyze the public awareness of climate change on Twitter.

The results show that the social-language used on Twitter is more complex than just single word expressions. While the term climate and the hashtag (#climate) scored a lower rank, complex terms such as (“Climate Change”) and (“Climate Engineering”) were more dominant using hashtags. More interestingly, we found the (#ClimateChange) hashtag is the top ranked term, among all other features, used on Twitter to signal climate familiarity expressions. This is indeed striking evidence that demonstrates a great deal of awareness and provides hope for a better future dealing with Climate Change issues.

The increasing volume of empty houses in historical cities constitute a challenge in times of economic crisis and acute housing needs. In order build coherent guidelines and implement effective policies, it is necessary to understand long-term patterns in city growth. The present work analyses urban dynamics at the micro level and present clues concerning transmutation in Lisbon, Portugal, using 3D geographical modelling to estimate potential housing supply. The recent availability of detailed demographic historical micro-datasets presents an opportunity to understand long-term trends.

Integrating cartographic and altimetric data, vacant houses of the city are mapped and attributes like area, volume and number of floors are estimated. Then, the potential for social housing is evaluated, based on state owned buildings morphology. Exploratory Spatial Data Analysis (ESDA) help to highlight trends at a finer scale, using advanced geovisualization techniques. The challenge of working with distinct data sources was tackled using Free and Open Source (FOSS) Geographical Database Management Systems (GDBMS) PostgreSQL (and spatial extension PostGIS); this facilitated interoperability between datasets.

The EU “Water” Directive establishes a common European framework for the environmental protection of inland, coastal and marine waters. Environmental pressures related to agri-livestock systems are still a major concern among the general public and policy makers. In this study, carried out in Umbria region, Italy, a novel spatial database for a multi-scale analysis was designed and implemented integrating different agricultural and livestock farming datasets. Beyond descriptive indicators about agricultural and livestock farming systems, this database allows to assess, at different geographic levels of investigation (cadastral sheets, municipalities, provinces, entire region, Nitrogen Vulnerable Zones, bodies of groundwater, sub-basins), the potential nitrogen crop supply, the potential nitrogen availability from livestock manure, and, by means of a scenario analysis, the total potential nitrogen load. These indicators appear to be very relevant to support decision making and to pursue the environmental objectives established by EU and national regulations.

Rust is a disease that leads to considerable losses in the worldwide coffee industry. In Colombia, the disease was first reported in 1983 in the department of Caldas. Since then, it spread rapidly through all other coffee departments in the country. Recent research efforts focus on detection of disease incidence using computer science techniques such as supervised learning algorithms. However, a number of different authors demonstrate that results are not sufficiently accurate using a single classifier. Authors in the computer field propose alternatives for this problem, making use of techniques that combine classifier results. Nevertheless, the traditional approaches have a limited performance due to dataset absence. Therefore we proposed an empirical multi-classifier for coffee rust detection in Colombian crops.

The proposed research compares the feasibility of three well known machine learning algorithms on the detection of malware on the Android platform. Once accuracy is at an acceptable level, these algorithms performance are further enhanced to decrease analysis time, which can lead to faster detection rates. The framework makes use of powerful GPU’s (Graphics Processing Unit) in order to reduce the time spent on computation for malware detection. Utilizing MATLAB’s parallel computing kit, we can execute analysis at a much higher speed due to the increased cores in the GPU. A reduced computation time allows for quick updates to the user about zero day malware, resulting in a decreased impact. With the increase in mobile devices unending, quick detection will become necessary to combat mobile malware, and with Android alone reaching its 50 billionth app downloads will be no small task.

Data Mining is a popular knowledge discovery technique. In data mining decision trees are of the simple and powerful decision making models. One of the limitations in decision trees is towards the data source which they tackle. If data sources which are given as input to decision tree are of imbalance nature then the efficiency of decision tree drops drastically, we propose a decision tree structure which mimics human learning by performing balance of data source to some extent. In this paper, we propose a novel method based on sampling strategy. Extensive experiments, using C4.5 decision tree as base classifier, show that the performance measures of our method is comparable to state-of-the-art methods.

Most of existing designated verifier proxy signature (DVPSt) schemes which are proved to be secure in the standard model is constructed based on Water’s identity-based encryption. Therefore, security reduction efficiency of these schemes is very low and it may decrease these schemes’ security. In this study, we propose a new DVPSt scheme and present a detailed security proof of the proposed DVPSt in the standard model. We also address a tight security reduction of the proposed scheme based on the gap bilinear Diffie-Hellman assumption. Compared with other DVPSt schemes, our proposed DVPSt has two advantages, i.e., the computational cost is lower and security reduction is tighter. Therefore, our proposed DVPSt scheme is very suitable for application in some communication network situations in where the resources are limited.

Risk analysis is always the pivotal part of emergency preparedness for critical infrastructure protection such as power grid and traffic network. The main contribution of this paper is to employ case-based reasoning (CBR) method (combines case representation and retrieval) to illustrate a risk assessment framework for protecting power grid. It focuses on two core key parts: (1) Using ontology model to express precursors of risk, the described semantic network contains sub-concepts to outline selected precursors from hazards, environment, responders and physical system and (2) by analysis of emergency scenario precursors with sub-concept similarity and fuzzy value similarity calculation, the potential risks could be recognized to assist to retrieve the past knowledge, and effective and feasible actions would be taken to decrease the threats or cut disaster loss for power grid. Via a case study, the result shows that the proposed method extends the applicability of conventional CBR technique to numbers of real-world settings.

Power network is usually the vital part of critical infrastructure protection. Confirming the level of vulnerability of the power network forms the basis of making a sound protection mechanism. This paper attempts to build a power network vulnerability analysis framework by introducing the multi-attribute analysis logic into power network vulnerability assessment and studying the power network vulnerability under the multi-attribute, dynamic and static combined conditions. Compared to one-attribute vulnerability analysis, multi-attribute analysis proves that a comprehensive physical vulnerability could reveal the power network vulnerability in the case of multi-attribute integration as a whole, and then we can use the result to confirm the hazard level for different parts of the power network. Finally, this paper illustrated the feasibility of the method through assessing the power network vulnerability of the city of Q.

To develop an effective teaching-learning process for a group of students respecting their individual learning pace is a challenging task for teachers. To assist students individually, it is necessary to identify each student’s difficulty and take appropriate teaching action. This paper presents an assisted learning tool based on the web that monitors and reports the student’s learning behavior for the teacher. This tool, called eTutor, also performs preconfigured actions (i.e., displays a video or text) according to the current state of student learning. We tested this tool in two different topics for two groups of students. The evaluation showed that this tool promotes student assistance, helping the teachers to be closer to their students.

Constraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.

This work focuses on software applications designed using service-oriented architectural model. These applications consist of several Web Services from different sources working together, to obtain complex Web Services. Several events occur during the interaction between Web Services, such as degradation of QoS, breakdown of deployed services, etc. These events disrupt the communications. In our work we have developed a service-oriented application for a Smart City, we have deployed a monitoring mechanisms to examine a service oriented application architecture, monitor interactions and build the graphical representation of the architecture using graphs to view the interactions events.

The set covering problem is a classical model in the subject of combinatorial optimization for service allocation, that consists in finding a set of solutions for covering a range of needs at the lowest possible cost. In this paper, we report various approximate methods to solve this problem, such as Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms. We illustrate experimental results of these metaheuristics for solving a set of 65 non-unicost set covering problems from the Beasley’s OR-Library.

Semi-supervised learning methods employ both labeled and unlabeled data in their training process. Therefore, they are commonly applied to interactive image processing tasks, where a human specialist may label a few pixels from the image and the algorithm would automatically propagate them to the remaining pixels, classifying the entire image. The particle competition and cooperation model is a recently proposed graph-based model, which was developed to perform semi-supervised classification. It employs teams of particles walking in a undirected and unweighed graph in order to classify data items corresponding to graph nodes. Each team represents a class problem, they try to dominate the unlabeled nodes in their neighborhood, at the same time that they try to avoid invasion from other teams. In this paper, the particle competition and cooperation model is applied to the task of interactive image segmentation. Image pixels are converted to graph nodes. Nodes are connected if they represent pixels with visual similarities. Labeled pixels generate particles that propagate their labels to the unlabeled pixels. Computer simulations are performed on some real-world images to show the effectiveness of the proposed approach. Images are correctly segmented in regions of interest, including non-contiguous regions.

Lately, the demand for mobile applications development has increased significantly mainly due to growth of use of mobile devices and the need to port existing web applications. To reduce development’s time and cost, Software Product Lines (SPLs) have also been used in the context of mobile applications. However, the existing SPLs do not worry about supporting the development of mobile applications corresponding to the existing Web applications, as it is desirable to have access to the information and main features of these applications in mobile devices. In face of this problem, this paper discusses the motivation and presents the evolution from a SPL in the e-Gov Web (e-Gov Web SPL) domain to a SPL in the mobile domain (e-Gov Mobile SPL) having in mind the need to supply market demand. The conducted evolution was supported by the PLUS approach (Product Line UML-Based Software Engineering) and by the features model. Furthermore, this work debates the main results obtained through some e-Gov Mobile SPL instantiations in the precision livestock domain.

The main objective of this article is to make use of the induction variable elimination in the matrix multiplication task. The main obstacle to this aim is iterating through a matrix column, because it requires jumping over tables. As a solution to this trouble we propose a shifting window in a form of a table of auxiliary double pointers. The ready-to-use C++ source code is presented. Finally, we performed thorough time execution tests of the new C++ matrix multiplication algorithm. Those tests proved the high efficiency of the proposed optimization.

Cross-lingual annotation projection methods can benefit from rich-resourced languages to improve the performance of Natural Language Processing (NLP) tasks in less-resourced languages. In this research, Malay is experimented as the less-resourced language and English is experimented as the rich-resourced language. The research is proposed to reduce the deadlock in Malay computational linguistic research due to the shortage of Malay tools and annotated corpus by exploiting state-of-the-art English tools. This paper proposes an alignment method known as MEWA (

M

alay-

E

nglish

W

ord

A

ligner) that integrates a Dice Coefficient and bigram string similarity measure with little supervision to automatically recognize three common named entities – person (PER), organization (ORG) and location (LOC). Firstly, the test collection of Malay journalistic articles describing on Indonesian terrorism is established in three volumes – 646, 5413 and 10002 words. Secondly, a comparative study between selected state-of-the-art tools is conducted to evaluate the performance of the tools against the test collection. Thirdly, MEWA is experimented to automatically induced annotations using the test collection and the identified English tool. A total of 93% accuracy rate is achieved in a series of NE annotation projection experiment.

Reconstruct and quantify the RNA molecules in a cell at a given moment is an important problem in molecular biology that allows one to know which genes are being expressed and at which intensity level. Such problem is known as Transcriptome Reconstruction and Quantification Problem (TRQP). Although several approaches were already designed that solve the TRQP, none of them model it as a combinatorial optimization problem. In order to narrow this gap, we present here a new combinatorial optimization problem called Maximum Similarity Partitioning Problem (MSPP) that models the TRQP. In addition, we prove that the MSPP is NP-complete in the strong sense and present a greedy heuristic for it.

Given a rectilinear polygon

P

and a point

$$p \in P$$

p

∈

P

, what is a largest axis-parallel square in

P

that contains

p

? This question arises in VLSI design from physical limitations of manufacturing processes. Related problems with disks instead of squares and point sets instead of polygons have been studied previously.

We present an efficient algorithm to preprocess

P

in time

O

(

n

) for simple polygons or

$$O(n \log n)$$

O

(

n

log

n

)

if holes are allowed. The resulting data structure of size

O

(

n

) can be used to answer largest square queries for any point in

P

in time

$$O(\log n)$$

O

(

log

n

)

. Given a set of points

Q

instead of a rectilinear polygon, the same algorithm can be used to find a largest square containing a given query point but not containing any point in

Q

in its interior.

In the context of the human action recognition problem, we propose a tensor descriptor based on sparse trajectories extracted via Variable Size Block Matching. Compared to other action recognition descriptors, our method runs fast and yields a compact descriptor, due to its simplicity and the coarse representation of movement provided by block matching. We validate our method using the KTH dataset, showing improvements over a previous block matching based descriptor. The recognition rates are comparable to those of state-of-the-art methods with the additional feature of having frame rates close to real-time computation.

Many communication problems may appear during requirements elicitation causing that final products do not accomplish client expectations. This paper analyzes the impact of using business processes management notation (BPMN) instead of user stories during requirements analysis in agile methodologies. For analyzing the effectiveness of our approach, we compare the use of user stories vs. BP models in eleven software projects during requirements elicitation phase. Experiments evidence that BPMN models improve quality and quantity of information collected during requirements elicitation and ease that clients specify clearly their needs and business goals.

The essential criterion for stability and fast convergence of CFD-solvers (CFD - computational fluid dynamics) is a good quality of the mesh. Based on results of [

30

] in this paper we use the so-called centroidal Voronoi tessellation (CVT) not only for mesh generation and optimization. The CVT is applied to develop a new mesh motion method. The CVT provides an optimal distribution of generating points with respect to a cell density function. For a uniform cell density function the CVT results in high-quality isotropic meshes. The non-uniform cases lead to a trade-off between isotropy and fulfilling cell density function constraints. The idea of the proposed approach is to start with the CVT-mesh and apply for each time step of transient simulation the so-called Lloyd’s method in order to correct the mesh as a response to the boundary motion. This leads to the motion of the whole mesh as a reaction to movement. Furthermore, each step of Lloyd’s method provides a further optimization of the underlying mesh, thus the mesh remains close to the CVT-mesh. Experience has shown that it is usually sufficient to apply a few iterations of the Lloyd’s method per time step in order to achieve high-quality meshes during the whole transient simulation. In comparison to previous methods our method provides high-quality and nearly isotropic meshes even for large deformations of computational domains.

This paper presents a proposal of deploying secure communication services in the cloud for software factory university UNB (University of Brasília - Brazil). The deployment of these services will be conducted in a private cloud, allocated in the CESPE (Centro de Seleção e de Promoção de Eventos) servers. The main service that will be available is the Expresso, which is a system maintained by SERPRO (Serviço Federal de Processamento de Dados). These services increase the productivity of the factory members and increase their collaboration in projects developed internally

The development of tools for Information Retrieval Systems or Expertise Finding Systems has a common task: the understanding of the information need. Since the information need is usually expressed through natural language, the computational processing of the information need involves several NLP techniques. Fortunately, there is a vast set of tools for English, but others languages have been marginalized. Thus, in this paper, we present a Web Service that offers to clients NLP treatment for interrogative sentences written in Brazilian Portuguese. The Web Service receives the question as input e returns its meta-information. The main differential of our proposal is that we offer a full analysis of the question text using a single function. We evaluate a feature of the Web Service named Category labeler, responsible for automatic discover the subject of the question, and we found that it has a true positive rate higher than 50% (

$$\alpha $$

α

=10%).

Recent studies in different fields of science caused emergence of needs for high performance computing systems like Cloud. A critical issue in design and implementation of such systems is resource allocation which is directly affected by internal and external factors like the number of nodes, geographical distance and communication latencies. Many optimizations took place in resource allocation methods in order to achieve better performance by concentrating on computing, network and energy resources. Communication latencies as a limitation of network resources have always been playing an important role in parallel processing (especially in fine-grained programs). In this paper, we are going to have a survey on the resource allocation issue in Cloud and then do an optimization on common resource allocation method based on the latencies of communications. Due to it, we added a table to Resource Agent (entity that allocates resources to the applicants) to hold the history of previous allocations. Then, a probability matrix was constructed for allocation of resources partially based on the history of latencies. Response time was considered as a metric for evaluation of proposed method. Results indicated the better response time, especially by increasing the number of tasks. Besides, the proposed method is inherently capable for detecting the unavailable resources through measuring the communication latencies. It assists other issues in cloud systems like migration, resource replication and fault –tolerance.

Many recent applications in several domains such as sensor networks, financial applications, network monitoring and click-streams generate continuous, unbounded, rapid, time varying datasets which are called data streams. In this paper we propose the optimized and elastic query mesh (OEQM) framework for data streams processing based on cloud computing to suit the changeable nature of data streams. OEQM processes the streams tuples over multiple query plans, each plan is suitable for a sub-set of data with the nearest properties and it provides elastic processing of data streams on the cloud environment. We also propose the Auto Scaling Cloud Query Mesh (AS-CQM) algorithm that supports streams processing with multiple plans and provides elastic scaling of the processing resources on demand. Our experimental results show that, the proposed solution OEQM reduces the cost for data streams processing on the cloud environment and efficiently exploits cloud resources.

There are billions of web pages available on the Internet. Search Engines always have a challenge to find the best ranked list to the user’s query from those huge numbers of pages. A lot of search results that correspond to a user’s query are not relevant to the user’s needs. Most of the page ranking algorithms use Link-based ranking (web structure) or Content-based ranking to calculate the relevancy of the information to the user’s need, but those ranking algorithms might be not enough to provide a good ranked list for the Arabic search. So, in this paper we proposed an efficient Arabic information retrieval system using a new hybrid usage-based ranking algorithm called EHURA. The objective of this algorithm is to overcome the drawbacks of the ranking algorithms and improve the efficiency of web searching. EHURA was applied to 242 Arabic Corpus to measure its performance. The result shows our proposed EHURA algorithm improves the precision over the Content-Based ranking algorithm representation, as well as the recall is affected too in this improvement.

Given an

$$n \times n$$

n

×

n

array

A

of integers, with at least one positive value, the maximum subarray sum problem consists in finding the maximum sum among the sums of all rectangular subarrays of

A

. The maximum subarray problem appears in several scientific applications, particularly in Computer Vision. The algorithms that solve this problem have been used to help the identification of the brightest regions of the images used in astronomy and medical diagnosis. The best known sequential algorithm that solves this problem has

$${\text {O}}\bigl (n^{3}\bigr )$$

O

(

n

3

)

time complexity. In this work we revisit the BSP/CGM parallel algorithm that solves this problem and we present BSP/CGM algorithms for the following related problems: the maximum largest subarray sum, the maximum smallest subarray sum, the number of subarrays of maximum sum, the selection of the subarray with

k

- maximum sum and the location of the subarray with the maximum relative density sum. To the best of our knowledge there are no parallel BSP/CGM algorithms for these related problems. Our algorithms use

p

processors and require

$${\text {O}}\bigl (n^{3}/p\bigr )$$

O

(

n

3

/

p

)

parallel time with a constant number of communication rounds. In order to show the applicability of our algorithms, we have implemented them on a cluster of computers using MPI and on a machine with GPGPU using CUDA and OpenMP. We have obtained good speedup results in both environments. We also tested the maximum relative density sum algorithm with a image of the cancer imaging archive.

This work introduces a new class of group similarity where different measures are parameterized with respect to a basic similarity defined on the elements of the sets. Group similarity measures are of great interest for many application domains, since they can be used to evaluate similarity of objects in term of the similarity of the associated sets, for example in multimedia collaborative repositories where images, videos and other multimedia are annotated with meaningful tags whose semantics reflects the collective knowledge of a community of users. The group similarity classes are formally defined and their properties are described and discussed. Experimental results, obtained in the domain of images semantic similarity by using search engine based tag similarity, show the adequacy of the proposed approach in order to reflect the collective notion of semantic similarity.

Test purposes are today key-elements for making the formal testing approach applicable to software. They are abstractions for a set of test cases to be observed in programs and some tools for formal testing concentrate their effort to apply test purposes to programs. Defining test purposes for actual systems, however, are not very straightforward. We must first realize what is the property behind the set of test cases we want to address. To help in defining the test purposes for a system under test, this paper presents a classification for test purposes based on the testable properties patterns. First, we analyze the meaning of properties patterns in the light of test purposes and then check the testability of these patterns. As a result, we provide a classification for testable patterns applicable to test purposes and users can choose which pattern better fits his/her set of test cases to be observed. Moreover, since test purposes are defined as standard properties, a model checker can be used to find executions for which test purposes can be satisfied.

This paper presents an improvement to an existing method used in security data transmission based on discrete time hyperchaotic cryptography. The technique is implemented for a Rössler hyperchaotic generator. The improvement consists in modifying the structure of the existing generator in order to increase the robustness of the new cryptosystem with respect to known plain text attack, particularly the "identification technique".

A microblogging, such as the Twitter, is a Social Networking Service that allows the publication of short messages. Currently, Twitter has more than 270 million monthly active users, and it is widely used to discuss the most variety of topics. Due to the large amount of information circulating on Twitter, and the facility to publish and read messages through the web or mobile devices, Twitter has attracted the interest of the general public, companies, media etc. By analyzing the Twitter’s stream of data, one can identify trends, events, or even the feelings of its users. Here, we introduce a dataset of tweets about the World Cup 2014, collected from January to August of 2014; present some descriptive statistics about the data; and, finally, we show a sentiment analysis study about the Brazilian population regarding to the Brazilian national team.

Nowadays, cloud computing has become a common computing infrastructure. As the computing paradigm has been shifted to cloud computing, devices can utilize computing resource any-where/any-time/any-device. Many research papers in mobile cloud called ‘cloud offloading’ which migrates a device’s workload to a server or to other devices have been proposed. However, previous cloud offloading methods are mainly focusing on the cloud offloading between a device and a server. Furthermore, these proposed methods have rarely commonly used because the proposed methods were very complex - difficulty of partitioning application tasks and maintaining execution status sync between a device and a server in the cloud. In this paper, I proposed the adaptive framework for cloud offloading based on the web application standard - HTML5 specification - for web applications based on flexible resource in servers as well as in devices. In HTML5 specification, there is the method for the parallel execution of the task named ‘Web Worker’ and the method for the communication between a device and a server named ‘Web Socket’. Utilizing the property of this specification, I proposed a seamless method to do the cloud offloading for parallelized tasks of the web applications among devices as well as between a device and a server.. Based on proposed method, a device can seamlessly migrate a part of web application workload with the Web Worker to other resource owners - devices and servers - with a little modification of web applications. As a result, I can successfully build the environment where a device which has a HTML5 browser such as a mobile phone and a smart TV can share the workload among devices and servers in various situations – out-of-battery, good network connection, more powerful computing needs.

Current web search engines, such as Google, Bing, and Yahoo!, rank the set of documents

S

retrieved in response to a user query and display the URL of each document

D

in

S

with a title and a snippet, which serves as an abstract of

D

. Snippets, however, are not as useful as they are designed for, which is supposed to assist its users to quickly identify results of interest, if they exist. These snippets fail to (i) provide distinct information and (ii) capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is

ambiguous

, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional inputs. Furthermore, a document title is not always a good indicator of the content of the corresponding document. All of these design problems can be solved by our proposed query-based cluster and labeler, called

QClus

.

QClus

generates concise clusters of documents covering various subject areas retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest without having to browse through the documents one by one. Experimental results show that

QClus

is

effective

and

efficient

in generating high-quality clusters of documents on specific topics with informative labels.

GrabCut is one of the powerful color image segmentation techniques. One main disadvantage of GrabCut is the need for initial user interaction to initialize the segmentation process which classifies it as a semi-automatic technique. The paper presents the use of Fuzzy C-means clustering as a replacement of the user interaction for the GrabCut automation. Several researchers concluded that no single color space model can produce the best results of every image segmentation problem. This paper presents a comparative study of different color space models using automatic GrabCut for the problem of color image segmentation. The comparative study includes the test of five color space models; RGB, HSV, XYZ, YUV and CMY. A dataset of different 30 images are used for evaluation. Experimental results show that the YUV color space is the one generating the best segmentation accuracy for the used dataset of images.

In this work, a novel smoothing method based on weighted Laplacian formulation is applied to resolve the heat conduction equation by finite-volume discretizations with Voronoi diagram. When a minimum number of vertices is obtained, the mesh is smoothed by means of a new approach to the weighted Laplacian formulation. The combination of techniques allows to solve the resulting linear system by the Conjugate Gradient Method. The new approach to the weighted Laplacian formulation within the set of techniques is compared to other 4 approaches to the weighted Laplacian formulation. Comparative analysis of the results shows that the proposed approach allows to maintain the approximation and presents smaller number of vertices than any of the other 4 approaches. Thus, the computational cost of the resolution is lower when using the proposed approach than when applying any of the other approaches and it is also lower than using only Delaunay refinements.

Resource constraints, such as time and network bandwidth, hinder modern search engine providers to keep local database completely synchronize with the Web. In this paper, we propose an adaptive clustering based change prediction approach to refresh the local web repository. Especially, we first group the existing web pages in the current repository into web clusters based on their similar change characteristics. We then sample and examine some pages in each cluster to estimate their change patterns. Selected cluster of web pages with higher change probability will be later downloaded to update the current repository. Finally, the effectiveness of the current download cycle will be examined; either auxiliary (non-downloaded), reward (correct change prediction), or penalty (wrong change prediction) score will be assigned to a web page. This score will later be used to reinforce the consecutive web clustering as well as the change prediction processes. To evaluate the performance of the proposed approach, we run extensive experiments on snapshots of real Web dataset of about 282,000 distinct URLs which are belonging to more than 12,500 websites. The results clearly show that the proposed approach outperforms the existing state-of-the-art on clustering-based web crawling policy in that it can provide fresher local web repository with limited resource.

An Ontology once developed needs to be kept up-to-date preferably as a collaborative process which will require web based tools. We have developed a large user centered ontology for Sri Lankan agriculture domain to represent agricultural information and relevant knowledge that can be queried in user context. We have generalized our design approach. In doing so we have identified various processes that are required to manage an ontology as a collaborative process. Based on these processes we developed an ontology management system to manage the ontology life cycle. The main processes such as modify, extend and prune the ontology components as required are included. It also has facilities to capture users’ information needs in context for modifications, search domain information, reuse and share the ontological knowledge. This is a semi-automatic ontology management system that helps to develop and manage complex real-world applications based ontologies collaboratively.

The continuing advances in cloud computing technology, infrastructures, applications, and hybrid cloud have led to provide solutions to challenges in big data and high performance computing applications. The increasing number of cloud service providers offering cloud services with non-uniform descriptions has made it time consuming to find the best match service with the user’s requirements.

This paper is an effort to speed up the service discovery and selection of IaaS cloud services which is „best-match“ to the user requirements. Preliminary experiments provided promising results which demonstrates the viability of the approach.

This paper investigates into IaaS service provisioning in hybrid cloud which comprises private and public clouds. It proposes a hybrid cloud framework in order to improve reliability and availability of IaaS services by taking into account alternative services which are available through public clouds. However, provisioning of alternative services in hybrid cloud involves complex processing, intelligent decision making and reliability and consistency issues. In the proposed framework, we develop an agent-based system using cloud ontology in order to identify and rank alternative cloud services which users can acquire in the event of failures or unavailability of desired services. The proposed framework also exploits transactional techniques in order to ensure the reliability and consistency of the service acquisition process. The proposed framework is evaluated through various experiments which show that it improves service availability and reliability in hybrid cloud.

In the present work we propose a methodology for improving tele-rehabilitation practices adopting mixed reality techniques. The implemented system analyzes the scene of a tele-rehabilitation practice acquired by a RGB-D optical sensor, and detects the objects present in the scene, identifying the type of object, its position and rotation. Furthermore we adopted Mixed Reality techniques to implement more complex rehabilitation exercises.

After an initial training period, in which the set of objects are classified by the system, the method analyzes the acquired images in real time and the identified objects (which are included in the set of preliminarily identified objects) are evidenced with a rectangle, the size and location of which are variable. All regions of the image are analyzed and objects of different shape and size are identified. In addition in a file associated to the object, the most relevant object features are stored using XML format.

The implemented method is able to identify a vast set of objects used regularly in tele-rehabilitation exercises and allows the therapist to perform the quantitative assessment of the patient practices.