Search results for: user data security

Authors: David Ahiamadia, Ramilan Thiagarajah, Peter Tozer

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In Sub Saharan Africa, farm typologies have been used as a practical way to address heterogeneity among farming systems which is mostly done by grouping farms into subsets with similar characteristics. Due to the complexity in farming systems among farm households, it is not possible to formulate policy recommendations for individual farmers. As a result, this study employs a multivariate statistical approach using Principal Component Analysis (PCA) coupled with cluster analysis to reduce heterogeneity in a 615-household data set from the Africa Rising Baseline Evaluation Survey for 25 farming communities in Northern Ghana. Variables selected for the study were mostly socio-economic, production potential, production intensity, production orientation, crop diversity, food security, resource endowments, and climate risk variables. To avoid making some individuals in the subpopulation worse off when aclimate risk intervention is broadly implemented, the findings of the study also account for diversity in climate risk perception among the different farm types identified and their response strategies towards climate risk. The climate risk variables used in this study involve the most severeclimate shock types perceived by the household, household response to climate shock type, and reason for crop failure (i.e., maize, rice, and groundnut). Eventually, four farm types, each with an adequate level of homogeneity in climate risk perception and response strategies, were identified. Farm type 1 and 3 were wealthy with a lower degree of climate risk perception compared to farm type 2 and 4. Also, relatively wealthy farmers used asset liquidation as a climate risk management strategy, whereas poor farmers resorted to engaging in spiritual activities such as prayers, sacrifices, and divine consultations.

Keywords: smallholder, households, climate risk, variables, typologies

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Authors: Ahmad Shayeq Azizi, Yuji Toda

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In Afghanistan, floods are the most frequent and recurrent events among other natural disasters. On the other hand, lack of monitoring data is a severe problem, which increases the difficulty of making the appropriate flood countermeasures of flood forecasting. This study is carried out to simulate the flood inundation in Harirud River Basin by application of distributed hydrological model, Integrated Flood Analysis System (IFAS) and 2D hydrodynamic model, International River Interface Cooperative (iRIC) based on satellite rainfall combined with historical peak discharge and global accessed data. The results of the simulation can predict the inundation area, depth and velocity, and the hardware countermeasures such as the impact of levee installation can be discussed by using the present method. The methodology proposed in this study is suitable for the area where hydrological and geographical data including river survey data are poorly observed.

Keywords: distributed hydrological model, flood inundation, hydrodynamic model, ungauged basins

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Authors: Daniel Bulanda, Janusz A. Starzyk, Adrian Horzyk

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The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms.

Keywords: characteristic points, electrocardiogram, ECG, machine learning, signal compression

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Authors: Abraham Terán Salcedo, Didier Samayoa Ochoa

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This work presents a numerical implementation of a method for estimating the box-counting dimension of self-avoiding curves on a planar space, fractal objects captured on digital images; this method is named fractioning estimator. Classical methods of digital image processing, such as noise filtering, contrast manipulation, and thresholding, among others, are used in order to obtain binary images that are suitable for performing the necessary computations of the fractioning estimator. A user interface is developed for performing the image processing operations and testing the fractioning estimator on different captured images of real-life fractal objects. To analyze the results, the estimations obtained through the fractioning estimator are compared to the results obtained through other methods that are already implemented on different available software for computing and estimating the box-counting dimension.

Keywords: box-counting, digital image processing, fractal dimension, numerical method

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Authors: Matej Komanec, Jan Bohata, Stanislav Zvanovec, Tomas Nemecek, Jan Broucek, Josef Beran

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With the exponential growth of social networks, video streaming and increasing demands on data rates, the number of newly built data centers rises proportionately. The data centers, however, have to adjust to the rapidly increased amount of data that has to be processed. For this purpose, multi-mode (MM) fiber based infrastructures are often employed. It stems from the fact, the connections in data centers are typically realized within a short distance, and the application of MM fibers and components considerably reduces costs. On the other hand, the usage of MM components brings specific requirements for installation service conditions. Moreover, it has to be taken into account that MM fiber components have a higher production tolerance for parameters like core and cladding diameters, eccentricity, etc. Due to the high demands for the reliability of data center components, the determination of properly excited optical field inside the MM fiber core belongs to the key parameters while designing such an MM optical system architecture. Appropriately excited mode field of the MM fiber provides optimal power budget in connections, leads to the decrease of insertion losses (IL) and achieves effective modal bandwidth (EMB). The main parameter, in this case, is the encircled flux (EF), which should be properly defined for variable optical sources and consequent different mode-field distribution. In this paper, we present detailed investigation and measurements of the mode field distribution for short MM links purposed in particular for data centers with the emphasis on reliability and safety. These measurements are essential for large MM network design. The various scenarios, containing different fibers and connectors, were tested in terms of IL and mode-field distribution to reveal potential challenges. Furthermore, we focused on estimation of particular defects and errors, which can realistically occur like eccentricity, connector shifting or dust, were simulated and measured, and their dependence to EF statistics and functionality of data center infrastructure was evaluated. The experimental tests were performed at two wavelengths, commonly used in MM networks, of 850 nm and 1310 nm to verify EF statistics. Finally, we provide recommendations for data center systems and networks, using OM3 and OM4 MM fiber connections.

Keywords: optical fiber, multi-mode, data centers, encircled flux

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Authors: Eun Hak Lee, Young-Hyun Seo, Hosuk Shin, Seung-Young Kho

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Among traffic crashes, driving under the influence (DUI) of alcohol is the most dangerous behavior in Seoul, South Korea. In 2016 alone 40 deaths occurred on of 2,857 cases of DUI. Since DUI is one of the major factors in increasing the severity of crashes, the intensive management of DUI required to reduce traffic crash deaths and the crash damages. This study aims to investigate the relationship between DUI and traffic safety in order to establish countermeasures for traffic safety improvement. The analysis was conducted on the habitual drivers who drove under the influence. Information of habitual drivers is matched to crash data and fine data. The descriptive statistics on data used in this study, which consists of driver license acquisition, traffic fine, and crash data provided by the Korean National Police Agency, are described. The drivers under the influence are classified by statistically significant criteria, such as driver’s age, license type, driving experience, and crash reasons. With the results of the analysis, we propose some countermeasures to enhance traffic safety.

Keywords: driving under influence, traffic safety, traffic crash, traffic fine

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Authors: J. Wicks

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Aim: To develop a simple methodology to allow collected heart rate (HR) data from inexpensive wearable devices to be expressed in a suitable format (METs) to quantitate occupational (and recreational) activity. Introduction: Assessment of occupational activity is commonly done by utilizing questionnaires in combination with prescribed MET levels of a vast range of previously measured activities. However for any individual the intensity of performing a specific activity can vary significantly. Ideally objective measurement of individual activity is preferred. Though there are a wide range of HR recording devices there is a distinct lack methodology to allow processing of collected data to quantitate energy expenditure (EE). The HR index equation expresses METs in relation to relative HR i.e. the ratio of activity HR to resting HR. The use of this equation provides a simple utility for objective measurement of EE. Methods: During a typical occupational work period of approximately 8 hours HR data was recorded using a Polar RS 400 wrist monitor. Recorded data was downloaded to a Windows PC and non HR data was stripped from the ASCII file using ‘Notepad’. The HR data was exported to a spread sheet program and sorted by HR range into a histogram format. Three HRs were determined, namely a resting HR (the HR delimiting the lowest 30 minutes of recorded data), a mean HR and a peak HR (the HR delimiting the highest 30 minutes of recorded data). HR indices were calculated (mean index equals mean HR/rest HR and peak index equals peak HR/rest HR) with mean and peak indices being converted to METs using the HR index equation. Conclusion: Inexpensive HR recording devices can be utilized to make reasonable estimates of occupational (or recreational) EE suitable for large scale demographic screening by utilizing the HR index equation. The intrinsic value of the HR index equation is that it is independent of factors that influence absolute HR, namely fitness, smoking and beta-blockade.

Keywords: energy expenditure, heart rate histograms, heart rate index, occupational activity

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Authors: Weisi Guo

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It is well established that there may be running correlations in sequential exam marks due to students sitting in the order of course registration patterns. As such, a random and non-sequential sampling of exam marks is a standard recommended practice. Here, the paper examines a large number of exam data stretching several years across different modules to see the degree to which it is true. Using the real mark distribution as a generative process, it was found that random simulated data had no more sequential randomness than the real data. That is to say, the running correlations that one often observes are statistically identical to chance. Digging deeper, it was found that some high running correlations have students that indeed share a common course history and make similar mistakes. However, at the statistical scale of a module question, the combined effect is statistically similar to the random shuffling of papers. As such, there may not be the need to take random samples for marks, but it still remains good practice to mark papers in a random sequence to reduce the repetitive marking bias and errors.

Keywords: data analysis, empirical study, exams, marking

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: K. Tejasri, K. Suvarna Vani, S. Prathyusha, S. Ramya

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Proteins are chained sequences of amino acids which are brought together by the peptide bonds. Many varying formations of the chains are possible due to multiple combinations of amino acids and rotation in numerous positions along the chain. Protein structure prediction is one of the crucial goals worked towards by the members of bioinformatics and theoretical chemistry backgrounds. Among the four different structure levels in proteins, we emphasize mainly the secondary level structure. Generally, the secondary protein basically comprises alpha-helix and beta-sheets. Multi-class classification problem of data with disparity is truly a challenge to overcome and has to be addressed for the beta strands. Imbalanced data distribution constitutes a couple of the classes of data having very limited training samples collated with other classes. The secondary structure data is extracted from the protein primary sequence, and the beta-strands are predicted using suitable machine learning algorithms.

Keywords: proteins, secondary structure elements, beta-sheets, beta-strands, alpha-helices, machine learning algorithms

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Authors: Laura Roe

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This study draws on twelve months of ethnographic fieldwork conducted in 2017 with heroin and poly-substance users in Scotland and explores experiences of time and temporality as factors in continuing drug use. The research largely took place over the year in which drug-related deaths in Scotland reached a record high, and were statistically recorded as the highest in Europe. This qualitative research is therefore significant in understanding both evolving patterns of drug use and the experiential lifeworlds of those who use heroin and other substances in high doses. Methodologies included participant observation, structured and semi-structured interviews, and unstructured conversations with twenty-two regular participants. The fieldwork was conducted in two needle exchanges, a community recovery group and in the community. The initial aim of the study was to assess evolving patterns of drug preferences in order to explore a clinical and user-reported rise in the use of novel psychoactive substances (NPS), which are typically considered to be highly potent, synthetic substances, often available at a low cost. It was found, however, that while most research participants had experimented with NPS with varying intensity, those who used every day regularly consumed heroin, methadone, and alcohol with benzodiazepines such as diazepam or anticonvulsants such as gabapentin. The research found that many participants deliberately pursued the non-fatal effects of overdose, aiming to induce states of dissociation, detachment and uneven consciousness, and did so by both mixing substances and experimenting with novel modes of consumption. Temporality was significant in the decision to consume cocktails of substances, as users described wishing to sever themselves from time; entering into states of ‘non-time’ and insensibility through specific modes of intoxication. Time and temporality similarly impacted other aspects of addicted life. Periods of attempted abstinence witnessed a slowing of time’s passage that was tied to affective states of boredom and melancholy, in addition to a disruptive return of distressing and difficult memories. Abject past memories frequently dominated and disrupted the present, which otherwise could be highly immersive due to the time and energy-consuming nature of seeking drugs while in financial difficulty. There was furthermore a discordance between individual user temporalities and the strict time-based regimes of recovery services and institutional bodies, and the study aims to highlight the impact of such a disjuncture on the efficacy of treatment programs. Many participants had difficulty in adhering to set appointments or temporal frameworks due to their specific temporal situatedness. Overall, exploring increasing tendencies of heroin users in Scotland towards poly-substance use, this study draws on experiences and perceptions of time, analysing how temporality comes to bear on the ways drugs are sought and consumed, and how recovery is imagined and enacted. The study attempts to outline the experiential, intimate and subjective worlds of heroin and poly-substance users while explicating the structural and historical factors that shape them.

Keywords: addiction, poly-substance use, temporality, timelessness

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Authors: Dina Magnaye

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In the Philippines, the performance of the agricultural sector is gauged through crop productivity and returns from farm production rather than the biodiversity in the agricultural ecosystem. Agricultural development hinges on the overall goal of increasing productivity through intensive agriculture, monoculture system, utilization of high yielding varieties in plants, and genetic upgrading in animals. This merits an analysis of the role of agro-biodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. These enterprises conserve biodiversity while equitably sharing production income in the utilization of biological resources. The study aims to determine how community-based pro-biodiversity enterprises become instrumental in local climate change adaptation and agro-biodiversity conservation as input to local green economic development planning. It also involves an assessment of the role of agrobiodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. The perceptions of the local community members both in urban and upland rural areas on community-based pro-biodiversity enterprises were evaluated. These served as a basis in developing a planning modality that can be mainstreamed in the management of local green economic enterprises to benefit the environment, provide local income opportunities, conserve species diversity, and sustain environment-friendly farming systems and practices. The interviews conducted with organic farmer-owners, entrepreneur-organic farmers, and organic farm workers revealed that pro-biodiversity enterprise such as organic farming involved the cyclic use of natural resources within the carrying capacity of a farm; recognition of the value of tradition and culture especially in the upland rural area; enhancement of socio-economic capacity; conservation of ecosystems in harmony with nature; and climate change mitigation. The suggested planning modality for community-based pro-biodiversity enterprises for a green economy encompasses four (4) phases to include community resource or capital asset profiling; stakeholder vision development; strategy formulation for sustained enterprises; and monitoring and evaluation.

Keywords: agro-biodiversity, agro-biodiversity conservation, local green economy, organic farming, pro-biodiversity enterprise

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Authors: Pui-Lai To, E-Ping Sung

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With the flourishing development of online shopping, an increasing number of customers see online shopping as an entertaining experience. Because the online consumer has a double identity as a shopper and an Internet user, online shopping should offer hedonic values of shopping and Internet usage. The purpose of this study is to investigate hedonic online shopping motivations from the perspectives of traditional hedonic value and flow theory. The study adopted a focus group interview method, including two online and two offline interviews. Four focus groups of shoppers consisted of online professionals, online college students, offline professionals and offline college students. The results of the study indicate that traditional hedonic values and dimensions of flow theory exist in the online shopping environment. The study indicated that online shoppers seem to appreciate being able to learn things and grow to become competitive achievers online. Comparisons of online hedonic motivations between groups are conducted. This study serves as a basis for the future growth of Internet marketing.

Keywords: flow theory, hedonic motivation, internet shopping

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Authors: Aadityaa Singla

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The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

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Authors: Yu Pan, Jing Wu, Lingwan Shen

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Since the reform and opening, China's cities have developed rapidly, and the industrial structure has been constantly adjusted and optimized. A large number of industrial plants have lost their production functions and become idle buildings. The renovation projects for the old factory buildings are important parts of the urban renewal, and most of them are the cultural and creative industrial park projects. In this paper, a statistical analysis of renovation projects of the representative cultural and creative industrial parks in recent years was conducted. According to the user's spatial experience satisfaction survey, the physical and spatial factors affecting the space regeneration of the old factory were concluded. Thus the relationship between space regeneration and material, structure, internal and external space design has been derived. Finally, we summarized the general spatial processing model in which the contradiction between ‘new’ and ‘old’ can be grafted and transformed.

Keywords: renovation of factory buildings, urban renewal, the cultural and creative industrial park, space regeneration, reconstruction mode

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Authors: Olivier Mills, Bernard McDonell, Laura A. S. MacDonald

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The increasing availability and quality of internet access throughout the developing world provides an opportunity to utilize online spaces to disseminate water, sanitation and hygiene (WASH) knowledge to practitioners. Since 2001, CAWST has provided in-person education, training and consulting services to thousands of WASH practitioners all over the world, supporting them to start, troubleshoot, improve and expand their WASH projects. As CAWST continues to grow, the organization faces challenges in meeting demand from clients and in providing consistent, timely technical support. In 2012, CAWST began utilizing online spaces to expand its reach by developing a series of resources websites and webinars. CAWST has developed a WASH Education and Training resources website, a Biosand Filter (BSF) Knowledge Base, a Household Water Treatment and Safe Storage Knowledge Base, a mobile app for offline users, a live chat support tool, a WASH e-library, and a series of webinar-style online training sessions to complement its in-person capacity development services. In order to determine the preliminary outcomes of providing these online services, CAWST has monitored and analyzed registration to the online spaces, downloads of the educational materials, and webinar attendance; as well as conducted user surveys. The purpose of this analysis was to find out who was using the online spaces, where users came from, and how the resources were being used. CAWST’s WASH Resources website has served over 5,800 registered users from 3,000 organizations in 183 countries. Additionally, the BSF Knowledge Base has served over 1000 registered users from 68 countries, and over 540 people from 73 countries have attended CAWST’s online training sessions. This indicates that the online spaces are effectively reaching a large numbers of users, from a range of countries. A 2016 survey of the Biosand Filter Knowledge Base showed that approximately 61% of users are practitioners, and 39% are either researchers or students. Of the respondents, 46% reported using the BSF Knowledge Base to initiate a BSF project and 43% reported using the information to train BSF technicians. Finally, 61% indicated they would like even greater support from CAWST’s Technical Advisors going forward. The analysis has provided an encouraging indication that CAWST’s online spaces are contributing to its objective of engaging and supporting WASH practitioners to start, improve and expand their initiatives. CAWST has learned several lessons during the development of these online spaces, in particular related to the resources needed to create and maintain the spaces, and respond to the demand created. CAWST plans to continue expanding its online spaces, improving user experience of the sites, and involving new contributors and content types. Through the use of online spaces, CAWST has been able to increase its global reach and impact without significantly increasing its human resources by connecting WASH practitioners with the information they most need, in a practical and accessible manner. This paper presents on CAWST’s use of online spaces through the CAWST-developed platforms discussed above and the analysis of the use of these platforms.

Keywords: education and training, knowledge sharing, online resources, water and sanitation

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Authors: DEVASHISH KHULBE, MANU PATHAK

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The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-temporal popularity of various cuisines can be analyzed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.

Keywords: Web Mining, Geographic Information Systems, Business popularity, Spatial Data Analyses

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Authors: Florence Ncube

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This paper examines the military and post-military experiences of soldiers who deserted from the Rwanda Defence Force (RDF) and tried to make a living in South Africa. Because they are deserters, they try to hide their military identity, yet it is simultaneously somewhat coercively ascribed to them by the Rwandan state and can put them in potential danger. The paper attends to the constructions, experiences, practices, and subjective understanding of the deserters’ being in exile to examine how, under circumstances of perceived threat, these men navigate real or perceived state-sponsored surveillance and threat in non-military settings in South Africa where they have become potential political and disciplinary targets. To make sense of the deserters’ experiences in these circumstances, the paper stitches together a number of useful theoretical concepts, including Bourdieu’s (1992) theory of practice and Vigh’s (2009; 2018) concept of social navigation because no single approach can coherently analyze the specificity of this study. Conventional post-military literature privileges an understanding of army desertion as a malignancy and somewhat problematic. Little is known about the military and post-military experiences of deserters who believe that army desertion is in fact a building block towards achieving subjective peace, even in the context of exile. The paper argues that the presence of Rwandan state agents in South Africa strips the context of the exile of its capacity to provide the deserters with peace, safety, and security. This paper recenters army desertion in analyses of militarism, soldiering, and transition in African contexts and complicates commonsense understandings of army desertion which assume that it is entirely problematic. This paper is drawn from an ethnography conducted among 30 junior-rank Rwandan army deserters exiled in Johannesburg and Cape Town. The researcher employed life histories, in-depth interviews, and deep hangouts to collect data.

Keywords: army deserter, military, identity, exile, peacebuilding, South Africa

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Authors: Wafa' Slaibi Alsharafat

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Cloud computing is a modern approach in network environment. According to increased number of network users and online systems, there is a need to help these systems to be away from unauthorized resource access and detect any attempts for privacy contravention. For that purpose, Intrusion Detection System is an effective security mechanism to detect any attempts of attacks for cloud resources and their information. In this paper, Cloud Intrusion Detection System has been proposed in term of reducing or eliminating any attacks. This model concerns about achieving high detection rate after conducting a set of experiments using benchmarks dataset called KDD'99.

Keywords: IDS, cloud computing, anticipating classifier system, intrusion detection

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Authors: Vineetha Bettaiah, Heggere S. Ranganath

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This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity.

Keywords: data mining, dimensionality reduction, piecewise linear representation, time series representation

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Authors: Hary Aprianto Wijaya Siahaan, Bayu Edo Pratama

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Sea fishery's potential to become one of the nation's assets which very contributed to Indonesia's economy. This fishery potential not in spite of the availability of the chlorophyll in the territorial waters of Indonesia. The research was conducted using three methods, namely: statistics, comparative and analytical. The data used include MODIS sea temperature data imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, MODIS data of chlorophyll-a imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, and Imaging results data ASCAT on MetOp and NOAA satellites with 27 km resolution in 2002-2015. The results of the processing of the data show that the incidence of upwelling in the south of East Java Sea began to happen in June identified with sea surface temperature anomaly below normal, the mass of the air that moves from the East to the West, and chlorophyll-a concentrations are high. In July the region upwelling events are increasingly expanding towards the West and reached its peak in August. Chlorophyll-a concentration prediction using multiple linear regression equations demonstrate excellent results to chlorophyll-a concentrations prediction in 2002 until 2015 with the correlation of predicted chlorophyll-a concentration indicate a value of 0.8 and 0.3 with RMSE value. On the chlorophyll-a concentration prediction in 2016 indicate good results despite a decline in the value of the correlation, where the correlation of predicted chlorophyll-a concentration in the year 2016 indicate a value 0.6, but showed improvement in RMSE values with 0.2.

Keywords: satellite, sea surface temperature, upwelling, wind stress

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Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

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In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

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Authors: Doga Erisik, Ahmet Karaman, Gulfem Alptekin, Ozlem Durmaz Incel

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Augmented reality (AR) is a type of virtual reality aiming to duplicate real world’s environment on a computer’s video feed. The mobile application, which is built for this project (called SARAS), enables annotating real world point of interests (POIs) that are located near mobile user. In this paper, we aim at introducing a robust and simple algorithm for placing labels in an augmented reality system. The system places labels of the POIs on the mobile device screen whose GPS coordinates are given. The proposed algorithm is compared to an existing one in terms of energy consumption and accuracy. The results show that the proposed algorithm gives better results in energy consumption and accuracy while standing still, and acceptably accurate results when driving. The technique provides benefits to AR browsers with its open access algorithm. Going forward, the algorithm will be improved to more rapidly react to position changes while driving.

Keywords: accurate tagging algorithm, augmented reality, localization, location-based AR

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Authors: Pardeep Singh, N. S. Johal

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In this research paper the level of subjectivity and objectivity adopted by journalists of different newspapers of the two provinces of the Jammu and Kashmir state has been analysed. This research paper emphasized upon the professionalism of the journalists of two provinces in catering to readers of particular province. In this study it was found that Kashmir based reporters are subjective in their reporting while covering Kashmir sentiments and use hard language against New Delhi, whereas Jammu based reporters are subjective only when it comes to defend security forces and are also bitterly critical of Pakistan, accusing it of being a sponsor of violence in Kashmir.

Keywords: conflict, Jammu and Kashmir, print media, reporter, critical, violence

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Authors: Gizem Kodak

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The Strait of Çanakkale, together with the Strait of Istanbul and the Sea of Marmara, form the Turkish Straits System. In other words, the Strait of Çanakkale is the southern gate of the system that connects the Black Sea countries with the other countries of the world. Due to the heavy maritime traffic, it is important to scientifically examine the accident characteristics in the region. In particular, the results indicated by the descriptive statistics are of critical importance in order to strengthen the safety of navigation. At this point, exploratory data analysis offers strategic outputs in terms of defining the problem and knowing the strengths and weaknesses against possible accident risk. The study aims to determine the accident characteristics in the Strait of Çanakkale with temporal and spatial analysis of historical data, using Exploratory Data Analysis (EDA) as the research method. The study's results will reveal the general characteristics of maritime accidents in the region and form the infrastructure for future studies. Therefore, the text provides a clear description of the research goals and methodology, and the study's contributions are well-defined.

Keywords: maritime accidents, EDA, Strait of Çanakkale, navigational safety

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Authors: Saima Nazir, Mustansar Ali Ghazanfar, Sanay Muhammad Umar Saeed, Muhammad Awais Azam, Saad Ali Alahmari

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Terrorism is an important and challenging concern. The entire world is threatened by only few sophisticated terrorist groups and especially in Gulf Region and Pakistan, it has become extremely destructive phenomena in recent years. Predicting the pattern of attack type, attack group and target type is an intricate task. This study offers new insight on terrorist group’s attack type and its chosen target. This research paper proposes a framework for prediction of terrorist attacks using the historical data and making an association between terrorist group, their attack type and target. Analysis shows that the number of attacks per year will keep on increasing, and Al-Harmayan in Saudi Arabia, Al-Qai’da in Gulf Region and Tehreek-e-Taliban in Pakistan will remain responsible for many future terrorist attacks. Top main targets of each group will be private citizen & property, police, government and military sector under constant circumstances.

Keywords: data mining, counter terrorism, machine learning, SVM

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Authors: Rung-Shiang Cheng, Wei-Jun Hong, Jheng-Syun Wang, Kawuu W. Lin

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Users rely increasingly on Location-Based Services (LBS) and automated navigation/guidance systems nowadays. However, while such services are easily implemented in outdoor environments using Global Positioning System (GPS) technology, a requirement still exists for accurate localization and guidance schemes in indoor settings. Accordingly, the present study presents a methodology based on GPS, Bluetooth Low Energy (BLE) beacons, and Near Field Communication (NFC) technology. Through establishing graphic information and the design of algorithm, this study develops a guidance system for indoor and outdoor on smartphones, with aim to provide users a smart life through this system. The presented system is implemented on a smartphone and evaluated on a student campus environment. The experimental results confirm the ability of the presented app to switch automatically from an outdoor mode to an indoor mode and to guide the user to the requested target destination via the shortest possible route.

Keywords: beacon, indoor, BLE, Dijkstra algorithm

Procedia PDF Downloads 285

Authors: Navid Mirzaei Varzeghani, Mahmoud Saffarzadeh, Ali Naderan, Amirhossein Taheri

Abstract:

Countries are progressing, and the world's busiest airports see year-on-year increases in travel demand. Passenger acceptability of an airport depends on the airport's appeals, which may include one of these routes between the city and the airport, as well as the facilities to reach them. One of the critical roles of transportation planners is to predict future transportation demand so that an integrated, multi-purpose system can be provided and diverse modes of transportation (rail, air, and land) can be delivered to a destination like an airport. In this study, 356 questionnaires were filled out in person over six days. First, the attraction of business and non-business trips was studied using data and a linear regression model. Lower travel costs, a range of ages more significant than 55, and other factors are essential for business trips. Non-business travelers, on the other hand, have prioritized using personal vehicles to get to the airport and ensuring convenient access to the airport. Business travelers are also less price-sensitive than non-business travelers regarding airport travel. Furthermore, carrying additional luggage (for example, more than one suitcase per person) undoubtedly decreases the attractiveness of public transit. Afterward, based on the manner and purpose of the trip, the locations with the highest trip generation to the airport were identified. The most famous district in Tehran was District 2, with 23 visits, while the most popular mode of transportation was an online taxi, with 12 trips from that location. Then, significant variables in separation and behavior of travel methods to access the airport were investigated for all systems. In this scenario, the most crucial factor is the time it takes to get to the airport, followed by the method's user-friendliness as a component of passenger preference. It has also been demonstrated that enhancing public transportation trip times reduces private transportation's market share, including taxicabs. Based on the responses of personal and semi-public vehicles, the desire of passengers to approach the airport via public transportation systems was explored to enhance present techniques and develop new strategies for providing the most efficient modes of transportation. Using the binary model, it was clear that business travelers and people who had already driven to the airport were the least likely to change.

Keywords: multimodal transportation, demand modeling, travel behavior, statistical models

Procedia PDF Downloads 156

Authors: Zahra Afshordi

Abstract:

Instagram, as an everyday life social network, hosts from the ultrasound image of an unborn fetus to the pictures of newly placed gravestones and funerals. It is a platform that allows its users to create a second identity independently from and at the same time in relation to the real space identity. The motives behind this identification are what this article is about. This article studies the motivations of Instagram users mourning for celebrities with a focus on the death of MohammadReza Shajarian. The Shajarian’s death had a wide reflection on Instagram Persian-speaking users. The purpose of this qualitative survey is to comprehend and study the user’s motivations in posting mourning and memorializing content. The methodology of the essay is a hybrid methodology consisting of content analysis and open-ended interviews. The results highlight that users' motives are more than just simple sympathy and include political protest, gaining cultural capital, reaching social status, and escaping from solitude.

Keywords: case study, celebrity, identity, Instagram, mourning, qualitative survey

Procedia PDF Downloads 141

Authors: Joel Rookwood

Abstract:

Sports mega events have become increasingly significant in sporting, political and economic terms, with analysis often focusing on issues including resource expenditure, development, legacy and sustainability. Transnational tournaments can inspire interest from a variety of demographics, and the operational management of such events can involve contributions from a range of personnel. In addition to television audiences events also attract attending spectators, and in football contexts the temporary migration of fans from potentially rival nations and teams can present event organising committees and security personnel with various challenges in relation to crowd management. The behaviour, interaction and control of supporters has previously led to incidents of disorder and hooliganism, with damage to property as well as injuries and deaths proving significant consequences. The Heysel tragedy at the 1985 European Cup final in Brussels is a notable example, where 39 fans died following crowd disorder and mismanagement. Football disasters and disorder, particularly in the context of international competition, have inspired responses from police, law makers, event organisers, clubs and associations, including stadium improvements, legislative developments and crowd management practice to improve the effectiveness of spectator safety. The growth and internationalisation of fandom and developments in event management and tourism have seen various responses to the evolving challenges associated with hosting large numbers of visiting spectators at mega events. In football contexts ‘fan parks’ are a notable example. Since the first widespread introduction in European football competitions at the 2006 World Cup finals in Germany, these facilities have become a staple element of such mega events. This qualitative, longitudinal, multi-continent research draws on extensive semi-structured interview and observation data. As a frame of reference, this work considers football events staged before and after the development of fan parks. Research was undertaken at four World Cup finals (Japan 2002, Germany 2006, South Africa 2010 and Brazil 2014), four European Championships (Portugal 2004, Switzerland/Austria 2008, Poland/Ukraine 2012 and France 2016), four other confederation tournaments (Ghana 2008, Qatar 2011, USA 2011 and Chile 2015), and four European club finals (Istanbul 2005, Athens 2007, Rome 2009 and Basle 2016). This work found that these parks are typically temporarily erected, specifically located zones where supporters congregate together irrespective of allegiances to watch matches on large screens, and partake in other forms of organised on-site entertainment. Such facilities can also allow organisers to control the behaviour, confine the movement and monitor the alcohol consumption of supporters. This represents a notable shift in policy from previous football tournaments, when the widely assumed causal link between alcohol and hooliganism which frequently shaped legislative and police responses to disorder, also dissuaded some authorities from permitting fans to consume alcohol in and around stadia. It also reflects changing attitudes towards modern football fans. The work also found that in certain contexts supporters have increasingly engaged with such provision which impacts fan behaviour, but that this is relative to factors including location, facilities, management and security.

Keywords: event, facility, fan, management, park

Procedia PDF Downloads 304