Search results for: data security

Authors: Hang Ju, Changmin Zhu

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The development level of the BeiDou Navigation Satellite System (BDS) can strongly reflect national defense strength as an important spatial information infrastructure. BDS can be not only used for military purposes, such as intelligence gathering, nuclear explosion monitoring, emergency communications, but also for location services, transportation, mapping, precision agriculture. In order to ensure the national defense security and the wide application of BDS in civil and military areas, BDS must be autonomous and controllable. As a complex system of knowledge-intensive, knowledge transformation runs through the whole process of research and development, production, operation, and maintenance of BDS. Based on the perspective of knowledge transformation, this paper expounds on the meaning of socialization, externalization, combination, and internalization of knowledge transformation, and the coupling relationship of autonomy and control on the basis of analyzing the status quo and problems of the autonomy and control of BDS. The autonomous and controllable framework of BDS based on knowledge transformation is constructed from six dimensions of management capability, R&D capability, technical capability, manufacturing capability, service support capability, and application capability. It can provide support for the smooth implementation of information security policy, provide a reference for the autonomy and control of the upstream and downstream industrial chains in Beidou, and provide a reference for the autonomous and controllable research of aerospace components, military measurement test equipment, and other related industries.

Keywords: knowledge transformation, BeiDou Navigation Satellite System, autonomy and control, framework

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: Amanda Silva Parra, Dayra Yisel García Ramirez

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In developing countries, agricultural "modernization" has led to a loss of biodiversity and inefficiency of agricultural systems, manifested in increases in Greenhouse Gas Emissions (GHG) and the C footprint, generating the susceptibility of systems agriculture to environmental problems, loss of biodiversity, depletion of natural resources, soil degradation and loss of nutrients, and a decrease in the supply of products that affect food security for peoples and nations. Each year agriculture emits 10 to 12% (5.1 to 6.1 Gt CO2eq per year) of the total estimated GHG emissions (51 Gt CO2 eq per year). The FAO recommends that countries that have not yet done so consider declaring sustainable agriculture as an essential or strategic activity of public interest within the framework of green economies to better face global climate change. The objective of this research was to estimate the balance of GHG in agricultural systems of Cumaral, Meta (Colombia), to contribute to the recovery and sustainable operation of agricultural systems that guarantee food security and face changes generated by the climate in a more intelligent way. To determine the GHG balances, the IPCC methodologies were applied with a Tier 1 and 2 level of use. It was estimated that all the silvopastoral systems evaluated play an important role in this reconversion compared to conventional systems such as improved pastures. and degraded pastures due to their ability to capture C both in soil and in biomass, generating positive GHG balances, guaranteeing greater sustainability of soil and air resources.

Keywords: climate change, carbon capture, environmental sustainability, GHG mitigation, silvopastoral systems

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Authors: Go Bong Choi, Shin Je Lee, Sung Jin Yoo, Gibaek Lee, Jong Min Lee

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In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model.

Keywords: proportional hazard model, survival model, water main deterioration, ecological sciences

Procedia PDF Downloads 743

Authors: Anusha Konduru, Zhiyong Shan, Preethi Santhanam, Vinod Namboodiri, Rajiv Bagai

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Mobile applications are increasing in a significant amount, each to address the requirements of many users. However, the quick developments and enhancements are resulting in many underlying defects. Android apps create and handle a large variety of 'instance' data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of Android, an app can be paused, sent into the background, or killed at any time. If the instance data is not saved and restored between runs, in addition to data loss, partially-saved or corrupted data can crash the app upon resume or restart. However, it is difficult for the programmer to manually test this issue for all the activities. This results in the issue of data loss that the data entered by the user are not saved when there is any interruption. This issue can degrade user experience because the user needs to reenter the information each time there is an interruption. Automated testing to detect such data loss is important to improve the user experience. This research proposes a tool, DroidDL, a data loss detector for Android, which detects the instance data loss from a given android application. We have tested 395 applications and found 12 applications with the issue of data loss. This approach is proved highly accurate and reliable to find the apps with this defect, which can be used by android developers to avoid such errors.

Keywords: Android, automated testing, activity, data loss

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Authors: Alfiah Hasanah, Silvia Mendolia, Oleg Yerokhin

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This paper explores the impacts of adult child migration on the health of elderly parents left behind. The maternal and children health are a priority of health-related policy in most low and middle-income country, and so there is lack of evidence on the health of older population particularly in Indonesia. With increasing life expectancy and limited access to social security and social services for the elderly in this country, the consequences of increasing number of out-migration of adult children to parent health are important to investigate. This study use Indonesia Family Life Survey (IFLS), the only large-scale continuing longitudinal socioeconomic and health survey that based on a sample of households representing about 83 percent of the Indonesian population in its first wave. Using four waves of IFLS including the recent wave of 2014, several indicators of the self-rated health status, interviewer-rated health status and days of illness are used to estimate the impact of labour out-migration of adult children on parent health status. Incorporate both individual fixed effects to control for unobservable factors in migrant and non-migrant households and the ordered response of self-rated health, this study apply the ordered logit of “Blow-up and Cluster” (BUC ) estimator. The result shows that labour out-migration of adult children significantly improves the self-rated health status of the elderly parent left behind. Findings of this study are consistent with the view that migration increases family resources and contribute to better health care and nutrition of the family left behind.

Keywords: aging, migration, panel data, self-rated health

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Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: Frezer Okubay Weldegebriel

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The tourism sector in Ethiopia plays a significant role in the national economy. The government is granting its pledge and readiness to develop this sector through various initiatives since to eradicate poverty and encourage economic development of the country is one of the Millennium Development plans. The tourism sector has been identified as one of the priority economic sectors by many countries, and the Government of Ethiopia has planned to make Ethiopia among the top five African destinations by 2020. Nevertheless, the international tourism demand for Ethiopia currently lags behind other African countries such as South Africa, Egypt, Morocco, Tanzania, and Kenya. Meanwhile, the number of international tourists’ arrival in Ethiopia is recently increasing even if it cannot be competitive with other African countries. Therefore, to offer demand-driven tourism products, the Ethiopian government, Tourism planners, Tour & Travel operators need to understand the important factors, which affect international tourists’ decision to visit Ethiopian destinations. This study was intended to analyze Italian Tourists Demand towards Ethiopian destination. The researcher aimed to identify the demand for Italian tourists’ preference to Ethiopian destinations comparing to the top East African countries. This study uses both qualitative and quantitative research methodology, and the data is manipulating through primary data collection method using questionnaires, interviews, and secondary data by reviewing books, journals, magazines, past researches, and websites. An active and potential Italian tourist cohort, five well-functioning tour operators based in Ethiopia for Italian tourists and professionals from Ethiopian Ministry of Tourism and Culture participated. Based on the analysis of the data collected through the questionnaire, interviews, and reviews of different materials, the study disclosed that the majority of Italian tourists have a high demand on Ethiopian Tourist destination. Historical and cultural interest, safety and security, the hospitality of the people and affordable accommodation coast are the main reason for them. However, some Italian tourists prefer to visit Kenya, Tanzania, and Uganda due to the fact that they are fascinated by adventure, safari and beaches, while Ethiopia cannot provide these attractions. Most Italian tourists have little information and practical experiences on Ethiopian tourism possibilities via a tour and travel companies. Moreover, the insufficient marketing campaign and promotion by Ethiopian Government and Ministry of Tourism could also contribute to the failure of Ethiopian tourism.

Keywords: The demand of Italian tourists, Ethiopia economy, Ethiopia tourism destination, promoting Ethiopia tourism

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Kajal Attri

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Introduction: Transgender people are considered third gender in India, although they still face identification issues and alienated from society. Furthermore, they face several challenges, including discrimination in employment, resources, education, and property as a result, most transgender people make a living through begging at traffic lights, trains, and buses; attending auspicious occasions such as childbirth and weddings; and engaging in sex work, which includes both home-based and street-based sex work. During COVID-19, maintaining social distance exacerbates transgender people's circumstances and prevents them from accessing health care services, sexual reassignment surgery, identity-based resources, government security, and financial stability. Nonetheless, the pandemic raised unfavorable attitudes about transgender persons, such as unsupportive family members and trouble forming emotional relationships. This study focuses on how we missed transgender people during COVID-19 to provide better facilities to cope with this situation when they are already the most vulnerable segment of the society. Methodology: The research was conducted using secondary data from published publications and grey literature obtained from four databases: Pubmed, Psychinfo, Science direct, and Google scholar. The literature included total 25 articles that met the inclusion criteria for a review. Result and Discussion: Transgender people, who are considered the most vulnerable sector of society, have already faced several obstacles as a result of the outbreak. The analysis underscores the difficulties that transgender persons faced during COVID-19, such as, They had trouble accessing the government's social security programmes during the lockdown, which provide rations and pensions since they lack the necessary identifying cards. The impact of COVID-19 leaves transgender people at heightened risk of poverty and ill health because they exist on the margins of society, those livelihood base on sex work, begging, and participation on auspicious occasions. They had a significant risk of contracting SARS-CoV2 because they lived in congested areas or did not have permanent shelter, and they were predominantly infected with HIV, cancer, and other non-communicable illnesses. The pandemic raised unfavorable attitudes about transgender persons, such as unsupportive family members and trouble forming emotional relationships. Conclusion: The study comes forward with useful suggestions based on content analysis and information to reduce the existing woes of transgenders during any pandemic like COVID-19.

Keywords: COVID-19, transgender, lockdown, transwomen, stigmatization

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Authors: Temitayo Tosin Alawiye

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Plant disease control is important in maintaining plant vigour, grain quantity, abundance of food, feed, and fibre produced by farmers all over the world. Farmers make use of different methods in controlling these diseases but one of the commonly used method is the use of chemicals. However, the continuous and excessive usages of these agrochemicals pose a danger to the environment, man and wildlife. The more the population growth the more the food security challenge which leads to more pressure on agronomic growth. Agricultural waste also known as green waste are the residues from the growing and processing of raw agricultural products such as fruits, vegetables, rice husk, corn cob, mushroom growth medium waste, coconut husk. They are widely used in land bioremediation, crop production and protection which include disease control. These agricultural wastes help the crop by improving the soil fertility, increase soil organic matter and reduce in many cases incidence and severity of disease. The objective was to review the agricultural waste that has worked effectively against certain soil-borne diseases such as Fusarium oxysporum, Pythiumspp, Rhizoctonia spp so as to help minimize the use of chemicals. Climate change is a major problem of agriculture and vice versa. Climate change and agriculture are interrelated. Change in climatic conditions is already affecting agriculture with effects unevenly distributed across the world. It will increase the risk of food insecurity for some vulnerable groups such as the poor in Sub Saharan Africa. The food security challenge will become more difficult as the world will need to produce more food estimated to feed billions of people in the near future with Africa likely to be the biggest hit. In order to surmount this hurdle, smallholder farmers in Africa must embrace climate-smart agricultural techniques and innovations which includes the use of green waste in agriculture, conservative agriculture, pasture and manure management, mulching, intercropping, etc. Training and retraining of smallholder farmers on the use of green energy to mitigate the effect of climate change should be encouraged. Policy makers, academia, researchers, donors, and farmers should pay more attention to the use of green energy as a way of reducing incidence and severity of soilborne plant diseases to solve looming food security challenges.

Keywords: agricultural waste, climate change, green energy, soil borne plant disease

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Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

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Authors: Saul Ngarava, Leocadia Zhou, Nomakhaya Monde

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Water crises have the fourth most significant societal impact after weapons of mass destruction, climate change, and extreme weather conditions, ahead of natural disasters. Intricacies between women and water are central to achieving the 2030 Sustainable Development Goals (SDGs). The majority of the 1.2 billion poor people worldwide, with two-thirds being women, and mostly located in Sub Sahara Africa (SSA) and South Asia, do not have access to safe and reliable sources of water. There exist gendered differences in water security based on the division of labour associating women with water. Globally, women and girls are responsible for water collection in 80% of the households which have no water on their premises. Women spend 16 million hours a day collecting water, while men and children spend 6 million and 4 million per day, respectively, which is time foregone in the pursuit of other livelihood activities. Due to their proximity and activities concerning water, women are vulnerable to water insecurity through exposures to water-borne diseases, fatigue from physically carrying water, and exposure to sexual and physical harassment, amongst others. Proximity to treated water and their wellbeing also has an effect on their sensitivity and adaptive capacity to water insecurity. The great distances, difficult terrain and heavy lifting expose women to vulnerabilities of water insecurity. However, few studies have quantified the vulnerabilities and burdens on women, with a few taking a phenomenological qualitative approach. Vulnerability studies have also been scanty in the water security realm, with most studies taking linear forms of either quantifying exposures, sensitivities or adaptive capacities in climate change studies. The current study argues for the need for a water insecurity vulnerability assessment, especially for women into research agendas as well as policy interventions, monitoring, and evaluation. The study sought to identify and provide pathways through which female-headed households were water insecure in South Africa, the 30th driest country in the world. This was through linking the drinking water decision as well as the vulnerability frameworks. Secondary data collected during the 2016 General Household Survey (GHS) was utilised, with a sample of 5928 female-headed households. Principal Component Analysis and Structural Equation Modelling were used to analyse the data. The results show dynamic relationships between water characteristics and water treatment. There were also associations between water access and wealth status of the female-headed households. Association was also found between water access and water treatment as well as between wealth status and water treatment. The study concludes that there are dynamic relationships in water insecurity (exposure, sensitivity, and adaptive capacity) for female-headed households in South Africa. The study recommends that a multi-prong approach is required in tackling exposures, sensitivities, and adaptive capacities to water insecurity. This should include capacitating and empowering women for wealth generation, improve access to water treatment equipment as well as prioritising the improvement of infrastructure that brings piped and safe water to female-headed households.

Keywords: gender, principal component analysis, structural equation modelling, vulnerability, water insecurity

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

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Authors: Sven Gehrke, Johannes Ruhland

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Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.

Keywords: trust, data mining, CRISP DM, stakeholder management

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Authors: Romeo Botes, Imelda Smit

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In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.

Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list

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Authors: Tatiana Rizova

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The migrant crisis, which peaked in 2015-2016, posed an unprecedented challenge to the European Union’s (EU) newest member states, including Bulgaria and Hungary. Their governments had to formulate sound migration policies with expediency and sensitivity to the needs of millions of people fleeing violent conflicts in the Middle East and failed states in North Africa. Political leaders in post-communist countries had to carefully coordinate with other EU member states on joint policies and solutions while minimizing the risk of alienating their increasingly anti-migrant domestic constituents. Post-communist member states’ governments chose distinct policy responses to the crisis, which were dictated by factors such as their governments’ partisan stances on migration, their views of the European Union, and the decision to frame the crisis as a security or a humanitarian issue. This paper explores how two Bulgarian governments (Boyko Borisov’s second and third government formed during the 43rd and 44th Bulgarian National Assembly, respectively) navigated the processes of EU migration policy making and managing the expectations of their electorates. Based on a comparative analysis of refugee policies in Bulgaria and Hungary during the height of the crisis (2015-2016) and a temporal analysis of refugee policies in Bulgaria (2015-2018), the paper advances the following conclusions. Drawing on insights of the Copenhagen school of security studies, the paper argues that cultural concerns dominated domestic debates in both Bulgaria and Hungary; both governments framed the issue predominantly as a matter of security rather than humanitarian disaster. Regardless of the similarities in issue framing, however, the two governments sought different paths of tackling the crisis. While the Bulgarian government demonstrated its willingness to comply with EU decisions (such as the proposal for mandatory quotas for refugee relocation), the Hungarian government defied EU directives and became a leading voice of dissent inside the EU. The current Bulgarian government (April 2017 - present) appears to be committed to complying with EU decisions and accepts the strategy of EU burden-sharing, while the Hungarian government has continually snubbed the EU’s appeals for cooperation despite the risk of hefty financial penalties. Hungary’s refugee policies have been influenced by the parliamentary representation of the far right-wing party Movement for a Better Hungary (Jobbik), which has encouraged the majority party (FIDESZ) to adopt harsher anti-migrant rhetoric and more hostile policies toward refugees. Bulgaria’s current government is a coalition of the center-right Citizens for a European Development of Bulgaria (GERB) and its far right-wing junior partners – the United Patriots (comprised of three nationalist political parties). The parliamentary presence of Jobbik in Hungary’s parliament has magnified the anti-migrant stance, rhetoric, and policies of Mr. Orbán’s Civic Alliance; we have yet to observe a substantial increase in the anti-migrant rhetoric and policies in Bulgaria’s case. Analyzing responses to the migrant/refugee crisis is a critical opportunity to understand how issues of cultural identity and belonging, inclusion and exclusion, regional integration and disintegration are debated and molded into policy in Europe’s youngest member states in the broader EU context.

Keywords: Copenhagen School, migrant crisis, refugees, security

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Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph

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In this paper, temperature extremes are forecast by employing the block maxima method of the generalized extreme value (GEV) distribution to analyse temperature data from the Cameroon Development Corporation (CDC). By considering two sets of data (raw data and simulated data) and two (stationary and non-stationary) models of the GEV distribution, return levels analysis is carried out and it was found that in the stationary model, the return values are constant over time with the raw data, while in the simulated data the return values show an increasing trend with an upper bound. In the non-stationary model, the return levels of both the raw data and simulated data show an increasing trend with an upper bound. This clearly shows that although temperatures in the tropics show a sign of increase in the future, there is a maximum temperature at which there is no exceedance. The results of this paper are very vital in agricultural and environmental research.

Keywords: forecasting, generalized extreme value (GEV), meteorology, return level

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Authors: Abdulrahman K. Alshegaifi, Chun-Hsi Huang

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Treating data based on its location in memory has received much attention in recent years due to its different properties, which offer important aspects for cache utilization. Stack data and non-stack data may interfere with each other’s locality in the data cache. One of the important aspects of stack data is that it has high spatial and temporal locality. In this work, we simulate non-unified cache design that split data cache into stack and non-stack caches in order to maintain stack data and non-stack data separate in different caches. We observe that the overall hit rate of non-unified cache design is sensitive to the size of non-stack cache. Then, we investigate the appropriate size and associativity for stack cache to achieve high hit ratio especially when over 99% of accesses are directed to stack cache. The result shows that on average more than 99% of stack cache accuracy is achieved by using 2KB of capacity and 1-way associativity. Further, we analyze the improvement in hit rate when adding small, fixed, size of stack cache at level1 to unified cache architecture. The result shows that the overall hit rate of unified cache design with adding 1KB of stack cache is improved by approximately, on average, 3.9% for Rijndael benchmark. The stack cache is simulated by using SimpleScalar toolset.

Keywords: hit rate, locality of program, stack cache, stack data

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Authors: Mohammed R. Elkobaisi, Abdelsalam Maatuk

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The search engines are the large scale information retrieval tools from the Web that are currently freely available to all. This paper explains how to convert the raw resulted number of search engines into useful information. This represents a new method for data gathering comparing with traditional methods. When a query is submitted for a multiple numbers of keywords, this take a long time and effort, hence we develop a user interface program to automatic search by taking multi-keywords at the same time and leave this program to collect wanted data automatically. The collected raw data is processed using mathematical and statistical theories to eliminate unwanted data and converting it to usable data.

Keywords: search engines, information extraction, agent system

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Authors: Anushtha Saxena

Abstract:

This paper examines the process of data monetisation bye-commerce companies operating in India. Data monetisation is collecting, storing, and analysing consumers’ data to use further the data that is generated for profits, revenue, etc. Data monetisation enables e-commerce companies to get better businesses opportunities, innovative products and services, a competitive edge over others to the consumers, and generate millions of revenues. This paper analyses the issues and challenges that are faced due to the process of data monetisation. Some of the issues highlighted in the paper pertain to the right to privacy, protection of data of e-commerce consumers. At the same time, data monetisation cannot be prohibited, but it can be regulated and monitored by stringent laws and regulations. The right to privacy isa fundamental right guaranteed to the citizens of India through Article 21 of The Constitution of India. The Supreme Court of India recognized the Right to Privacy as a fundamental right in the landmark judgment of Justice K.S. Puttaswamy (Retd) and Another v. Union of India . This paper highlights the legal issue of how e-commerce businesses violate individuals’ right to privacy by using the data collected, stored by them for economic gains and monetisation and protection of data. The researcher has mainly focused on e-commerce companies like online shopping websitesto analyse the legal issue of data monetisation. In the Internet of Things and the digital age, people have shifted to online shopping as it is convenient, easy, flexible, comfortable, time-consuming, etc. But at the same time, the e-commerce companies store the data of their consumers and use it by selling to the third party or generating more data from the data stored with them. This violatesindividuals’ right to privacy because the consumers do not know anything while giving their data online. Many times, data is collected without the consent of individuals also. Data can be structured, unstructured, etc., that is used by analytics to monetise. The Indian legislation like The Information Technology Act, 2000, etc., does not effectively protect the e-consumers concerning their data and how it is used by e-commerce businesses to monetise and generate revenues from that data. The paper also examines the draft Data Protection Bill, 2021, pending in the Parliament of India, and how this Bill can make a huge impact on data monetisation. This paper also aims to study the European Union General Data Protection Regulation and how this legislation can be helpful in the Indian scenarioconcerning e-commerce businesses with respect to data monetisation.

Keywords: data monetization, e-commerce companies, regulatory framework, GDPR

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

Abstract:

Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Neda Seyyedi, Reza Berangi

Abstract:

Voice over IP (VOIP) network, also known as Internet telephony, is growing increasingly having occupied a large part of the communications market. With the growth of each technology, the related security issues become of particular importance. Taking advantage of this technology in different environments with numerous features put at our disposal, there arises an increasing need to address the security threats. Being IP-based and playing a signaling role in VOIP networks, Session Initiation Protocol (SIP) lets the invaders use weaknesses of the protocol to disable VOIP service. One of the most important threats is denial of service attack, a branch of which in this article we have discussed as flooding attacks. These attacks make server resources wasted and deprive it from delivering service to authorized users. Distributed denial of service attacks and attacks with a low rate can mislead many attack detection mechanisms. In this paper, we introduce a mechanism which not only detects distributed denial of service attacks and low rate attacks, but can also identify the attackers accurately. We detect and prevent flooding attacks in SIP protocol using Shannon (FDP-S), Renyi (FDP-R) and Tsallis (FDP-T) entropy. We conducted an experiment to compare the percentage of detection and rate of false alarm messages using any of the Shannon, Renyi and Tsallis entropy as a measure of disorder. Implementation results show that, according to the parametric nature of the Renyi and Tsallis entropy, by changing the parameters, different detection percentages and false alarm rates will be gained with the possibility to adjust the sensitivity of the detection mechanism.

Keywords: VOIP networks, flooding attacks, entropy, computer networks

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Authors: Dražen Cvitanić, Biljana Maljković

Abstract:

This paper presents models for predicting operating speeds on tangent sections of two-lane rural roads developed on continuous speed data. The data corresponds to 20 drivers of different ages and driving experiences, driving their own cars along an 18 km long section of a state road. The data were first used for determination of maximum operating speeds on tangents and their comparison with speeds in the middle of tangents i.e. speed data used in most of operating speed studies. Analysis of continuous speed data indicated that the spot speed data are not reliable indicators of relevant speeds. After that, operating speed models for tangent sections were developed. There was no significant difference between models developed using speed data in the middle of tangent sections and models developed using maximum operating speeds on tangent sections. All developed models have higher coefficient of determination then models developed on spot speed data. Thus, it can be concluded that the method of measuring has more significant impact on the quality of operating speed model than the location of measurement.

Keywords: operating speed, continuous speed data, tangent sections, spot speed, consistency

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Authors: S. Nickolas, Shobha K.

Abstract:

The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Samuel B. Dulay, Sandra Julich, Herbert Tomaso, Ciara K. O'Sullivan

Abstract:

An early, rapid and definite detection for the presence of biowarfare agents, pathogens, viruses and toxins is required in different situations which include civil rescue and security units, homeland security, military operations, public transportation securities such as airports, metro and railway stations due to its harmful effect on the human population. In this work, an electrochemical genosensor array that allows simultaneous detection of different biowarfare agents within an integrated microsystem that provides an easy handling of the technology which combines a microfluidics setup with a multiplexing genosensor array has been developed and optimised for the following targets: Bacillus anthracis, Brucella abortis and melitensis, Bacteriophage lambda, Francisella tularensis, Burkholderia mallei and pseudomallei, Coxiella burnetii, Yersinia pestis, and Bacillus thuringiensis. The electrode array was modified via co-immobilisation of a 1:100 (mol/mol) mixture of a thiolated probe and an oligoethyleneglycol-terminated monopodal thiol. PCR products from these relevant biowarfare agents were detected reproducibly through a sandwich assay format with the target hybridised between a surface immobilised probe into the electrode and a horseradish peroxidase-labelled secondary reporter probe, which provided an enzyme based electrochemical signal. The potential of the designed microsystem for multiplexed genosensor detection and cross-reactivity studies over potential interfering DNA sequences has demonstrated high selectivity using the developed platform producing high-throughput.

Keywords: biowarfare agents, genosensors, multipled detection, microsystem

Procedia PDF Downloads 272