bluuebunny/crossref_metadata_2025 huggingface dataset:
A Power Monitoring System Based on a Multi-Component Power Model
Authors: Lin Weiwei , Wang Haoyu , Wu Wentai
Published: December 2017

As the increasing IT energy consumption emerged as a prominent issue, computer system energy consumption monitoring and optimization has gradually become a significant research forefront. However, most existing energy monitoring methods are limited to hardware-based measurement or coarse-grained energy consumption estimation. They cannot provide fine-grained energy consumption data (i.e., component energy consumption) and high-scalability for distributed cloud environments. In this article, the authors first study widely-used power models of CPUs, memory and hard disks. Then, following an investigation into disk power behaviors in sequential I/O and random I/O, they propose an improved I/O-mode aware disk power model with multiple variables and thresholds. They developed EnergyMeter, a monitoring software utility that can provide accurate power estimate by exploiting a multi-component power model. Experiments based on PCMark prove that the average error of EnergyMeter is merely 5% under a variety of workloads
An Enhanced Task Scheduling in Cloud Computing Based on Deadline-Aware Model
Authors: Alworafi Mokhtar A. , Mallappa Suresha
Published: December 2017

Cloud computing is the latest in distributed computing technology. The delivery mechanism between the service provider and users depends on Service Level Agreement (SLA). SLA contains Quality of Service (QoS), which has some constraints such as deadline to achieve user satisfaction. In this article, the authors propose a Deadline-Aware Priority Scheduling (DAPS) model to minimize the average makespan, and maximize resource utilization under deadline constraint. In the proposed model, the tasks are sorted based on length priority in ascending order and labeling the VM's state as successful which achieves the deadline constraint, and then mapping the tasks to the suitable VM that has minimum processing time. The authors compared their proposed model to the existing algorithms GA, Min-Min, SJF and Round Robin. The proposed model outperforms other algorithms by reducing the average of makespan, mean of total average response time, number of violations, violation ratio, and failure ratio, while increasing resource utilization, and guarantee ratio for tasks that meet deadline constraint.
GPU Implementation of Image Convolution Using Sparse Model with Efficient Storage Format
Authors: Mohammed Saira Banu Jamal , Babu M. Rajasekhara , Sriram Sumithra
Published: December 2017

With the growth of data parallel computing, role of GPU computing in non-graphic applications such as image processing becomes a focus in research fields. Convolution is an integral operation in filtering, smoothing and edge detection. In this article, the process of convolution is realized as a sparse linear system and is solved using Sparse Matrix Vector Multiplication (SpMV). The Compressed Sparse Row (CSR) format of SPMV shows better CPU performance compared to normal convolution. To overcome the stalling of threads for short rows in the GPU implementation of CSR SpMV, a more efficient model is proposed, which uses the Adaptive-Compressed Row Storage (A-CSR) format to implement the same. Using CSR in the convolution process achieves a 1.45x and a 1.159x increase in speed compared to the normal convolution of image smoothing and edge detection operations, respectively. An average speedup of 2.05x is achieved for image smoothing technique and 1.58x for edge detection technique in GPU platform usig adaptive CSR format.
Embarrassingly Parallel GPU Based Matrix Inversion Algorithm for Big Climate Data Assimilation
Authors: Varalakshmi M. , Kesarkar Amit Parashuram , Lopez Daphne
Published: December 2017

Attempts to harness the big climate data that come from high-resolution model output and advanced sensors to provide more accurate and rapidly-updated weather prediction, call for innovations in the existing data assimilation systems. Matrix inversion is a key operation in a majority of data assimilation techniques. Hence, this article presents out-of-core CUDA implementation of an iterative method of matrix inversion. The results show significant speed up for even square matrices of size 1024 X 1024 and more, without sacrificing the accuracy of the results. In a similar test environment, the comparison of this approach with a direct method such as the Gauss-Jordan approach, modified to process large matrices that cannot be processed directly within a single kernel call shows that the former is twice as efficient as the latter. This acceleration is attributed to the division-free design and the embarrassingly parallel nature of every sub-task of the algorithm. The parallel algorithm has been designed to be highly scalable when implemented with multiple GPUs for handling large matrices.
A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks
Authors: Fang Dongfeng , Ye Feng , Qian Yi , Sharif Hamid
Published: December 2017

Due to proliferation of smart cities and other smart services, extensive data collection needs to be accomplished by mobile sensor networks (MSNs). However, sensing and data collection are voluntary tasks for many MSN users. For example, drivers are not required to report traffic condition although their vehicles with advanced sensors have easy access to critical information. Therefore, incentive mechanisms are needed to recruit sensing users (SUs). Incentive mechanisms proposed for traditional MSNs cannot be applied directly due to limited information of SU used for recruitment. In this article, the authors propose a novel cloud-based MSN model that consists of three parties, including data request party, cloud-based platform and SUs. To better utilize information of SUs, a data quality model is proposed to measure the credit level of SUs. The proposed SU recruitment strategy takes into consideration social connections of users. According to the strategy, SUs are divided into two separate levels. Moreover, the authors propose an incentive mechanism using a Stackelberg game theoretical approach to achieve the maximum utility of each recruited SU. The simulation results demonstrate that the proposed incentive mechanism can recruit SUs more efficiently while providing data quality guarantee.
A Distributed Least-Squares Algorithm in Wireless Sensor Networks With Unknown and Limited Communications
Authors: Wang Jing , Ahn In Soo , Lu Yufeng , Yang Tianyu , Staskevich Gennady
Published: December 2017

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.
WSN Lifetime and Reliability Analysis From the Death Criterion Perspective
Authors: Nouh Sara , Elgaml Nada , Khattab Ahmed , Soliman Samy S. , Daoud Ramez M. , Amer Hassanein H.
Published: December 2017

Wireless Sensor Networks (WSNs) are widely used in numerous critical applications, and require the network to have a prolonged lifetime and high tolerance to failures. However, the battery-operated sensor nodes used in WSNs cause the network to be resource-constrained. On the one hand, there is a continuous urge to efficiently exploit the WSN energy, and hence, prolong the network lifetime. On the other hand, WSN node failures are not only attributed to battery drain. Node failures can be caused by hardware or software malfunctioning. In this article, the authors assess the impact of the death criterion on the network lifetime and reliability. It is related how the data from the different sensors are aggregated to the death criterion. Additionally, the impact of the number of sensing cycles per network master on the network lifetime and energy efficiency for the different considered death criteria. The effect of the network master selection process on the energy efficiency is also examined. Finally, the impact of the death criterion on the reliability of the WSN is evaluated.
A Study of Recursive Techniques for Robust Identification of Time-Varying Electrical Equivalent Circuit Models of Li-Ion Batteries
Authors: Mostafa Ashraf , Das Manohar
Published: December 2017

This article presents results of a comparative study of recursive techniques for robust identification of time-varying electrical equivalent circuit models of li-ion batteries. Two such methods are studied, a direct continuous time system identification method and an indirect discrete time technique. The results of this study indicate that although both methods work equally well for identification of time-invariant circuit models from clean voltage-current data, the direct continuous time method outperforms indirect discrete time technique for identification of time-varying circuit models. Similar conclusions can also be drawn for identification of equivalent circuit models in the presence of noise and/or unmodeled dynamics.
A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting
Authors: Pannakkong Warut , Pham Van-Hai , Huynh Van-Nam
Published: December 2017

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.
Construction and Application of Sentiment Lexicons in Finance
Authors: Seki Kazuhiro , Shibamoto Masahiko
Published: December 2017

This article proposes an approach to constructing sentiment lexicons in the financial domain. The approach takes advantages of news bulletins and a given financial variable, such as stock prices, to generate candidates of sentiment expressions by fusing the two data sources. The candidates are then filtered based on their co-occurrences with financial seed words and are subsequently expanded by analogical reasoning using distributed representation of words. Evaluative experiments on real-world news and stock price data shows that the resulting lexicons are mostly reasonable and capture the characteristics of the target financial variables. As a potential application, trading simulation is also carried out based on the resulting financial sentiment lexicons, demonstrating the utility of the lexicons.
A Social Media Recommender System
Authors: Sperlì Giancarlo , Amato Flora , Mercorio Fabio , Mezzanzanica Mario , Moscato Vincenzo , Picariello Antonio
Published: December 2017

Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.
A Framework to Secure Medical Image Storage in Cloud Computing Environment
Authors: Marwan Mbarek , Kartit Ali , Ouahmane Hassan
Published: December 2017

Nowadays, modern healthcare providers create massive medical images every day because of the recent progress in imaging tools. This is generally due to the increasing number of patients demanding medical services. This has resulted in a continuous demand of a large storage space. Unfortunately, healthcare domains still use local data centers for storing medical data and managing business processes. This has significant negative impacts on operating costs associated with licensing fees and maintenance. To overcome these challenges, healthcare organizations are interested in adopting cloud storage rather than on-premise hosted solutions. This is mainly justified by the scalability, cost savings and availability of cloud services. The primary objective of this model is to outsource data and delegate IT computations to an external party. The latter delivers needed storage systems via the Internet to fulfill client's demands. Even though this model provides significant cost advantages, using cloud storage raises security challenges. To this aim, this article describes several solutions which were proposed to ensure data protection. The existing implementations suffer from many limitations. The authors propose a framework to secure the storage of medical images over cloud computing. In this regard, they use multi-region segmentation and watermarking techniques to maintain both confidentiality and integrity. In addition, they rely on an ABAC model to ensure access control to cloud storage. This solution mainly includes four functions, i.e., (1) split data for privacy protection, (2) authentication for medical dataset accessing, (3) integrity checking, and (4) access control to enforce security measures. Hence, the proposal is an appropriate solution to meet privacy requirements.
Arabic Stemmer Based Big Data
Authors: Madani Youness , Erritali Mohammed , Bengourram Jamaa
Published: December 2017

By its morphological and syntactic richness, the Arabic language is considered among the most difficult languages to deal with it in the field of information search. This is due; in particular, to the various difficulties encountered in its Stemming, which has not yet experienced a standard approach. The Stemming algorithm for Arabic words has been an important topic in Arabic information retrieval. The intention of this article is to parallelize a stemming algorithm for Arabic by proposing a distributed stemming algorithm in a big data system. This is by using the Hadoop framework, the MapReduce programming model for the development of the algorithm, and the distributed file system HDFS for the Storage of stemming result.
Optimizing Ontology Alignments by Using Neural NSGA-II
Authors: Biniz Mohamed , El Ayachi Rachid
Published: December 2017

In this article, the authors propose a new hybrid approach based on a continuous Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a neural network to refine the alignment results. This approach consists of three phases: (i) pre-alignment phase which allows to identify the formats of input ontologies, to adapt them and to transform them into Ontology Web Language (OWL) in order to solve the problem of heterogeneity of representation. (ii) alignment phase which combines syntactic and linguistic matching techniques and methods, based on the relevant attributes per different points of syntactic and structural technic. (iii) The post-alignment phase which optimizes the matching by a hybrid technique of continuous NSGA-II and networks of neurons. This approach is compared with the greatest systems per the Ontology Alignment Evaluation Initiative (OAEI) standard. The experimental results appear that the proposed approach is effective.
Two-Dimensional Face Surface Analysis Using Facial Feature Points Detection Approaches
Authors: Ahdid Rachid , Azougaghe Es-said , Safi Said , Manaut Bouzid
Published: December 2017

Geometrical features are widely used to descript human faces. Generally, they are extracted punctually from landmarks, namely facial feature points. The aims are various, such as face recognition, facial expression recognition, face detection. In this article, the authors present two feature extraction methods for two-dimensional face recognition. Their approaches are based on facial feature points detection by compute the Euclidean Distance between all pairs of this points for a first method (ED-FFP) and Geodesic Distance in the second approach (GD-FFP). These measures are employed as inputs to commonly used classification techniques such as Neural Networks (NN), k-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test the methods and evaluate its performance, a series of experiments were performed on two-dimensional face image databases (ORL and Yale). The experimental results also indicated that the extraction of image features is computationally more efficient using Geodesic Distance than Euclidean Distance.
Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate
Authors: Outanoute M. , Lachhab A. , Selmani A. , Oubehar H. , Snoussi A. , Guerbaoui M. , Ed-dahhak A. , Bouchikhi B.
Published: December 2017

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.
A Content Based Image Retrieval Method Based on K-Means Clustering Technique
Authors: Ouhda Mohamed , El Asnaoui Khalid , Ouanan Mohammed , Aksasse Brahim
Published: December 2017

With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.
Segmentation of Optic Disc in Fundus Images Using an Active Contour
Authors: Elbalaoui A. , Ouadid Y. , Merbouha A.
Published: December 2017

A successful optic disc (OD) segmentation is an important task for automated detection white lesions related to diabetic retinopathy. Therefore, exudate detection is the authors' major purpose, but they must extract the OD prior to the process because it appears with similar color, intensity and contrast to other characteristics of the retinal image. The retinal image consists of blood vessels that emerge from the OD. The presence of these blood vessels may act as a disturbance for the detection of OD. This article presents a novel method for segmentation of the OD in retinal images. The methodology includes localization of the OD center, followed by elimination of vascular structure using an inpainting method. Finally, an active contour model was applied to boundary OD segmentation. The results are compared with a ground truth image from the ophthalmologist. The source retinal image for performing this work was obtained from the publicly available DRIVE and MESSIDOR databases. This method offers a successful segmentation of OD which may help in diagnosis of various retinal abnormalities.
Systematic Literature Reviews in Social Sciences and Humanities
Authors: Mangas-Vega Almudena , Dantas Taísa , Sánchez-Jara Javier Merchán , Gómez-Díaz Raquel
Published: December 2017

The objective of this article is to analyze the factors that may influence the results t of a systematic literature review (SLR) in Social Sciences and Humanities. It is a case study focused on the analysis of a SLR centered on reading research and digital reading. Through this analysis it was possible to detect errors commonly found in bibliographic reference information provided by different sources. In addition, the diversity of sources treated permitted an understanding of the disparity in the data provided by sources of information. The difficulty in managing non-standard data, and the need to include different standardization techniques in order to reduce the number of errors and allows avoiding the distortion in interpretations of the data. The results indicated that SLR is a suitable technique to apply it in the Social Sciences and Humanities although was possible to recognize that carries more difficulties than in other areas.
An Open Perspective for Educational Games
Authors: Silveira Ismar Frango , Villalba-Condori Klinge Orlando
Published: December 2017

In the field of computers in education, educational digital games have potential to involve more issues of motivation and involvement, considering their possibilities for higher level of interaction and engagement. However, years of research have shown that the impact of educational games is lower than expected, especially the difficulty to adapt them to different educational contexts, such as with different educational, linguistic, cultural and social aspects. In that sense, this article presents an open perspective on the development of educational games, emphasizing the challenges related to their development and their effective potential for use in education, proposing that they be designed as Open Educational Resources (OER). From this perspective, it is expected to support communities that would aggregate developers (programmers, game designers, media producers, etc.) and users (teachers and students) so they can work collaboratively in creating educational games in an open way.
A Systematic Mapping Study on Requirements Engineering in Software Ecosystems
Authors: Vegendla Aparna , Duc Anh Nguyen , Gao Shang , Sindre Guttorm
Published: December 2017

Software ecosystems (SECOs) and open innovation processes have been claimed as a way forward for the software industry. A proper understanding of requirements is as important for SECOs as for more traditional ones. This article presents a mapping study on the issues of RE and quality aspects in SECOs. Our findings indicate that among the various phases or subtasks of RE, most of the SECO specific research has been accomplished on elicitation, analysis, and modeling. On the other hand, requirement selection, prioritization, verification, and traceability has attracted few published studies. Among the various quality attributes, most of the SECOs research has been performed on security, performance and testability. On the other hand, reliability, safety, maintainability, transparency, usability attracted few published studies. The article provides a review of the academic literature about SECO-related RE activities, modeling approaches, and quality attributes, positions the source publications in a taxonomy of issues and identifies gaps where there has been little research.
Web Knowledge Turbine as a Proposal for Personal and Professional Self-Organisation in Complex Times
Authors: Royo Enrique Rubio , McKay Susan Cranfield , Nelson-Santana Jose Carlos , Rodríguez Ramiro N Delgado , Ocon-Carreras Antonio A.
Published: December 2017

This article describes a proposal for sustainable way to adapt to current complex process of global transformation, using the ‘Web Knowledge Turbine' (WKT) as a self-organised ecosystem for the co-creation of personal and collective narratives. The authors contemplate all human social systems as Complex Adaptive Systems with the capacity for self-organisation derived from a permanent learning process. Accordingly, a shift in the focus of teaching programmes from mere mechanisms of knowledge transmission, to a process focused on learning and in particular, a process of self-directed, connected, and deep learning which has at its core the profile of the eLearner as the central protagonist. The cornerstone of this process is a Complex Ecosystem of Personal Knowledge (CEPK) which will support teaching at an undergraduate level, progressively and transversely, from its outset. Considering the classroom as a networked community of learners whose objective is not only to gain a command of a particular subject (WHAT content do they need to learn?), but also HOW and WHY they need to learn it.
Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier
Authors: Singh Geetika , Chhabra Indu
Published: December 2017

Selection and implementation of a face descriptor that is both discriminative and computationally efficient is crucial. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) have been proven effective for face recognition. LBPs are fast to compute and are easy to extract the texture features. OC-LBP descriptors have been proposed to reduce the dimensionality of LBP while increasing the discrimination power. HOG features capture the edge features that are invariant to rotation and light. Owing to the fact that both texture and edge information is important for face representation, this article proposes a framework to combine OC-LBP and HOG. First, OC-LBP and HOG features are extracted, normalized and fused together. Next, classification is achieved using a histogram-based chi-square, square-chord and extended-canberra metrics and SVM with a normalized chi-square kernel. Experiments on three benchmark databases: ORL, Yale and FERET show that the proposed method is fast to compute and outperforms other similar state-of-the-art methods.
Cognition of a Robotic Manipulator Using the Q-Learning Based Situation-Operator Model
Authors: Dubey Akash Dutt , Mishra Ravi Bhushan
Published: December 2017

In this article, we have applied cognition on robot using Q-learning based situation operator model. The situation operator model takes the initial situation of the mobile robot and applies a set of operators in order to move the robot to the destination. The initial situation of the mobile robot is defined by a set of characteristics inferred by the sensor inputs. The Situation-Operator Model (SOM) model comprises of a planning and learning module which uses certain heuristics for learning through the mobile robot and a knowledge base which stored the experiences of the mobile robot. The control and learning of the robot is done using q-learning. A camera sensor and an ultrasonic sensor were used as the sensory inputs for the mobile robot. These sensory inputs are used to define the initial situation, which is then used in the learning module to apply the valid operator. The results obtained by the proposed method were compared to the result obtained by Reinforcement-Based Artificial Neural Network for path planning.
Metaheuristic Techniques for Test Case Generation
Authors: Sahoo Rashmi Rekha , Ray Mitrabinda
Published: December 2017

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.
Editorial
Authors: Shantha A. Aruna
Published: December 2017

No abstract available
Tourism Under Military: A Critique on Land Utilization and Tourism in Postwar Sri Lanka
Authors: Ratnayake Iraj , Hapugoda Mahesh
Published: December 2017

This critique argues that the intervention of military in land utilization and recreational tourism in the Northern and Eastern provinces of Sri Lanka was instigated by marker-driven geo-political negligence. It discovers that the nature of the introduction of post-catastrophictourism (Zizek, 2014) has been affected by profound non-articulation of political significance to the traumatic historical memory of the inhabitants who were affected, which has resulted in generating a degree of dark tourism in the area concerned. The southern invasions in the form of usual pilgrims which ‘combined battlefield and leisure tourism practices’ (Pieris, 2014: 266) has characterized ‘the presentation and the consumption of real and commoditized death and disaster area’ (Foley and Lennon, 1996: 198) which has originated in consequences of a long term conflict. Correspondingly, the apparent mass tourism promotion by the government between 2009 and 2014 too has significantly disregarded the definitive symbolic principle of ‘the visitation to places where tragedies or historically noteworthy death has occurred and that continue to impact our lives’ (Tarlow, 2005: 48) before obvious profit motives. The study observes that serious memories and sensitivities of the thirty years of the war-affected community have not been paid attention to and has caused dangerous humanitarian negligence in a wider political sense. While the function of tourism at present as an agent of development in improving the living condition of the community seems evidently true in this context, the trivialization of the symbolic significance of a historical memory and ownership of a community, which has turned ‘the suffering into a leisure experience for contemporary tourists’ (Smith et al., 2010: 38) cannot also be easily overlooked.
Implications of Policies and Procedures in Sustainable Rural Tourism Development (SRTD): A Case Study of Sri Lanka
Authors: Aslam M. S. M. , Bin Awang Khairil Wahidin
Published: December 2017

Developing tourism in rural areas is found as a panacea for many developing countries to alleviate poverty and overcome many other developmental challenges. Although tourism, a non-traditional developmental intervention accrue some economic benefits for local settings, negative impacts of tourism exacerbates the positive impacts. On the one hand Tourism is politically sound development strategy in recapturing and regenerating the development in rural areas on the other hand it challenges the sustainability in the rural development process. This study intended to appraise the role of policies and procedures in Sustainable Rural Tourism Development (SRTD) with the naturalistic evidences from tourism development in rural Sri Lanka. It employed critical qualitative research design to bring forth the interpretations from natural setting through personal interviews, direct observation and field notes in addition to a wide documental review. Documental reviews illuminate a number of ideals of SRTD in supporting local community empowerment, regenerating rural livelihoods, preserving and conserving socio-culture and environment. However, in reality socially constructed world of rural Sri Lanka reveals that the sustainable rural tourism development is curtailed by the inappropriate and unsteady policy formulation and implementation. Weaken policy formulation and implementation in real world of rural tourism development averts the ideals of SRTD.
Balancing the Conflict between Right to Information and Right to Privacy under Sri Lankan Fundamental Rights Perspective
Authors: Sooriyabandara Vishaka
Published: December 2017

Protecting the peace and human wellbeing of a particular society is the main purpose of existing a law. However, it is problematic to say exactly whether the real purpose of law can be achieved through the established law enforcement approaches like legislations, judgments, standing orders, circulars, regulations etc. Thus, to create a most effective legal system, finding an answer to that issue is very essential, because if the real purpose of law cannot be achieved properly from these approaches, other solutions should be found out to protect peace and human wellbeing of society.Human rights are the universally accepted rights which should be granted to each and every person to color-up their public and private lives. Right to information and right to privacy takes a vast attention nowadays though they make a continuous conflict with the development of the modern communication technology, because when right to information attempts to provide maximum public information to get an extensive knowledge on the public sphere, right to privacy attempts to conceal one’s personal information like family life, relationships, physical and mental integrity, etc. from others. It points out that both rights are very essential to properly arrange people’s public and private lives.Although Sri Lankan current legal system shows a satisfactory movement in protecting right to information, such a movement cannot be seen to protect people’s privacy rights. Sure enough, in spite of having vast numbers of black and white written laws, an effective practical enforcement of those laws also cannot be seen. Thus, enforcing the protection of both right to information and right to privacy effectively and striking a balance between these two conflicting rights to prevent overlapping is essential to establish the real purpose of existing a law in Sri Lanka.
Comparación de los productos de TSM L3 generados a partir de los sensores AVHRR y MODIS frente al Golfo San Jorge, Argentina
Authors: Allega L. , Cozzolino E. , Pisoni J. P. , Piccolo M. C.
Published: December 2017

Since the early 1980s, the AVHRR sensor on board the NOAA satellites has provided SST estimates to the scientific community in general. Towards the end of the 1990s, a new generation of radiometers was added, combining a wider range of spectral measurements with improvements in technology, such as the MODIS sensor on board the Terra and Aqua satellites. The development of this last sensor was based on the years of NOAA/AVHRR series. Understanding the relationship between products derived from different sensors is critical to the continuous long-term monitoring of any variable and the construction of continuous time series. The objective of this work was to make a comparison of the monthly SST values for the 2003-2006 period calculated from data from the NOAA/ AVHRR series versus Aqua-MODIS on a sector of the middle Argentina Continental shelf off the San Jorge Gulf. For both sensors, the isotherms show a similar distribution pattern in all months. Pearson’s correlation coefficient was high, both in the seasonal (> 0.9) and monthly (> 0.8) analyzes. The comparative analysis of the temperature estimated by both sensors shows that SST products are similar and can be used to analyze large and continuous time series.
Anomalías de vegetación asociadas con el fenómeno del ENOS en el valle geográfico del río Cauca, Colombia
Authors: Valencia J. M. , García C. E. , Montero D.
Published: December 2017

The main factors affecting the production and yield of sugarcane are variety, agronomic management, soil type and climate, of which the first three there is some control, while the climate is one factor of which you cannot have any control, therefore, it should be monitored. Colombia, being located in the equatorial pacific, is affected by two atmospheric oceanic phenomena known as “El Niño” and “La Niña”, which make up the climatic phenomenon of ENSO (El Niño-Southern Oscillation) and affect the quantity and the number of days with rainfall and influences the production of sugarcane. The objective of this work is to identify spatially and temporally the zones with greater and lower impact of the ENSO phenomenon in the cultivation of sugarcane in Colombia through the use of the Standard Vegetation Index (SVI) and the Rainfall Anomally Index (RAI) using EVI/MODIS images and precipitation data from meteorological stations on a quarterly basis for the period 2000-2015. A similar trend was found between both indices in the “El Niño” and “Neutral” seasons, while in the “La Niña” season the RAI tended to rise while the SVI decreased when the RAI was very high, this tendency being much more marked in areas with floods caused by the overflow of the main rivers. In addition, a comparison was made between the SVI index and a productivity anomaly index (IAP), finding a direct correlation between both (R2 = 0.4, p<0.001). This work showed that through the use of vegetation indexes, a temporal analysis of the impact of climate on an agricultural crop can be carried out, especially with ENSO conditions.
Reflectances of SPOT multispectral images associated with the turbidity of the Upper Gulf of California
Authors: Aguilar-Maldonado J. A. , Santamaría-del-Ángel E. , Sebastiá-Frasquet M. T.
Published: December 2017

The use of satellite images for the observation and measurement of marine turbidity has been developed mainly with ocean colour sensors, such as MODIS. These images have a maximum spatial resolution of 250 m in their visible and infrared bands. In this research, images of the SPOT sensors were chosen as an alternative to overcome this limited spatial resolution. The objective was to prove the suitability of SPOT to measure turbidity in areas with great spatial variability. As a first step, all the images were standardized and the SPOT wavelength that had the largest association in the Principal Component Analysis was chosen (PCA). The results show that the bands of a SPOT multispectral image are highly redundant. The wavelength of the 610-680 nm (S2610-680) obtained the best association in 89% of the 73 images analysed. The SPOT reflectance (Rrs) (S2610-680) was compared with MODIS 620-670 nm (M1620-670), which has already been tested in other research and has proved to be adequate for measuring turbidity. Both sensors performance was similar for low and moderate reflectance but for high reflectance, SPOT (S2610-680) had a better performance than MODIS (M1620-670). Additionally, the SPOT Rrs (S2610-680) was associated with standardized Secchi disk depth data, which were measured in situ, to check SPOT suitability. SPOT Rrs (S2610-680)images were classified into: 1) cold or warm season, 2) spring tide or neap tide and 3) water flux or reflux. These constructed scenarios allowed to see the result of the Standardized Space Anomalies, which showed the continuous presence of low and medium values in the most oceanic region of the Upper Gulf of California (UGC) and very high values in all the scenarios in the intertidal zone. This research has shown that SPOT Rrs (S2610-680) is useful for observing, differentiating and measuring turbidity patterns in areas with very high spatial variability.
Mapeo de la recuperación de la vegetación quemada mediante la clasificación de índices espectrales pre- y post-incendio
Authors: Peña M. A , Ulloa J.
Published: December 2017

This study analyzed the state of recovery of the burnt vegetation in the National Park of Torres del Paine between December, 2011 and March, 2012. The calculation and comparison of the NVDI (normalized difference vegetation index) of the burnt area throughout a time series of 24 Landsat images acquired before, during and after the fire (2009- 2015), showed the temporal variation in the biomass levels of the burnt vegetation. The subsequent classification and comparison of the spectral indices: NDVI, NBR (normalized burnt ratio) and NDWI (normalized difference water index) on a full-data available and phenologically matched pre- and post-fire image pair (acquired in October 2009 and 2014), enabled to analyze and mapping the state of recovery of the burnt vegetation. The results show that the area of the lowest classes of all the spectral indices of the pre-fire date became the most dominant on the post-fire date. The pre- and post- fire NDVI class crossing by a confusion matrix showed that the highest and most prevailing pre-fire NDVI classes, mostly corresponding to hydromorphic forests and Andean scrubs, turned into the lowest class in 2014. The remaining area, comprising Patagonian steppe, reestablished its biomass levels in 2014, mostly exhibiting the same pre-fire NDVI classes. These results may provide guidelines to monitor and manage the regeneration of the vegetation impacted by this fire.
Aplicación de imágenes Sentinel-1 y Sentinel-2 en la detección y delineación de información de crisis de desastres naturales en el marco de los servicios Copernicus EMS
Authors: Donezar-Hoyos U. , Larrañaga Urien A. , Tamés-Noriega A. , Sánchez-Gil C. , Albizua-Huarte L. , Ciriza-Labiano R. , Del Barrio-Arellano F.
Published: December 2017

This study shows the inclusion of Sentinel-1 and Sentinel-2 images in the workflows to obtain of crisis information of different types of events and their applicability in the detection and monitoring of those events. Sentinel is an Earth Observation (EO) program that is currently being developed by the European Space Agency (ESA) in the scope of the Copernicus program operative since April 2012, formerly known as Global Monitoring for Environment and Security (GMES). This program comprises six missions, out of which three are active, Sentinel-1 that provides radar images, Sentinel-.2 providing High Resolution optical images and Sentinel-3 developed to support GMES ocean, land, atmospheric, emergency, security and cryospheric applications. The present paper describes the use of Sentinel-1 radar to detect and delineate flooded areas, and the MultiTemporal Coherence (MTC) analysis applied with pre and post-event images to delimit and monitor burnt areas and lava flows. With respect to Sentinel-2, its high spectral resolution bands allowed the delineation of burnt areas by calculating differences of vegetation and burnt indices using pre and postevent images. Results using Sentinel-1 and Sentinel-2 data were compared with results using higher spatial resolution images, both optical and radar. In all cases, the usability of Sentinel images was proven.
Evolución de la evapotranspiración y el estrés hídrico de la vegetación de dehesa en la Península Ibérica (2001-2015)
Authors: González-Dugo M. P. , Chen X. , Andreu A. , Carpintero E. , Gómez-Giráldez P. , Su Z.
Published: December 2017

The objective of this work is to provide new insights about the effect of soil water deficit on the vegetation of Mediterranean oak savanna. The evolution of evapotranspiration (ET) and vegetation water stress over this ecosystem, in the Iberian Peninsula, has been monitored for fifteen years through the application of a remote sensing thermal-based energy balance model. The Surface Energy Balance System (SEBS) has been applied, on a monthly timescale from January 2001 to December 2015, using input satellite and meteorological reanalysis databases. The model performance has been evaluated under these conditions by comparison with field measurements. The estimation of energy fluxes yielded reasonable agreements with observations (RMSD=14-20 W·m–2 for the radiative fluxes and RMSD=26-29 W·m–2 for the turbulent ones) and have led to characterize the main drought events occurred during the study period, and to quantify their effects on the vegetation coverage and production. The most generalized and severe events (2004/2005 and 2011/2012) and their impact on different vegetation strata, oaks and grasslands, are further analyzed.
Uso de herramientas de teledetección para el análisis de la severidad y estimación de gases de efecto invernadero (GEI) en incendios forestales de gran magnitud. Estudio de caso incendio La Rufina, VI Región del L. G. B. O’Higgins, Chile
Authors: Vidal P. , De Santis A. , Pérez W. , Honeyman P.
Published: December 2017

Wildfires destroy thousands of hectares of vegetation every year in Chile, a phenomenon that has steadily increased over time, both in terms of the number of fires and the area affected. Since 1985 until 2016 have occurred 1,476 wildfires severe in intensity (> 200 ha), that burned a total of about 1,243,407 ha of vegetation, and an average of 40,000 ha affected per year. Depending on the type and intensity of the fire, there are different levels of severity with which the fire affects the vegetation, a variation that is crucial for the estimation GEI in the event. The purpose of this research was to analyze the burn severity of Rufina wildfires occurred in 1999, in the VI Region of L. G. B. O’Higgins in Chile, south of the capital Santiago, using Landsat 5 TM and Landsat 7 ETM+ imagery, including in the analysis the estimated greenhouse gases emitted in relation to with the vegetation and burn severity. Burn severity was estimated through the Normalized Burn Ratio (dNBR) and GEI with the equation proposed by IPCC in 2006, which was adjusted with the combustion efficiency coefficients proposed by De Santis et al. (2010). The results show that around 16,783 ha were affected by fires of different severity and the native forest and tree plantations were affected by high severity. The ton of GEI for each level of burn severity and type of vegetation was estimated, being carbon dioxide (CO2 ) the main GEI emitted to the atmosphere in the fire. The highest emissions occurred in the areas of grasslands and scrublands, with high severity, with values ranging between 186 and 170 t/ha respectively
O Tratamento da Lepra e o saber-poder Médico e Político
Authors: SILVA Leicy Francisca da
Published: December 2017

Na segunda metade do século XIX no Brasil, a lepra começou a ser percebida como um importante problema sanitário. Médicos se voltaram para a análise da ação empreendida por diversos profissionais, não médicos, no tratamento e cura da doença. O objetivo desse artigo é observar como podemos perceber, nas diversas experiências empíricas avaliadas pelos médicos da Faculdade de Medicina do Rio de Janeiro, a relação entre médicos e ‘charlatães’ quanto ao problema da enfermidade. Avalio a divergência dos interesses de médicos e administradores no que se relaciona a essas mesmas questões. Os relatórios médicos são, portanto, tomados enquanto documentos centrais para a compreensão de como o saber/poder médico intervinha avaliando, questionando e criticando as diversas experiências de assistência e tratamento aos doentes.