Search results for: data infrastructure

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

Procedia PDF Downloads 167

Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

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With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of humans, vehicle, roadside infrastructure, and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the report proposes a distributed multi-agent C-ITS. The system consists of Roadside Sub-system, Vehicle Sub-system, and Personal Sub-system. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: distributed system, artificial intelligence, multi-agent, cooperative intelligent transportation system

Procedia PDF Downloads 193

Authors: Joel M. De La Rosa R.

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In this paper, the results obtained when monitoring the horizontal deformations of the soil mass are detailed, during each of the construction stages of several vertical shafts located in the soft soils of the Valley of Mexico, by means of the flotation method. From the analysis of these results, the magnitude and percentage relationship with respect to the diameter and depth of excavation of the horizontal deformations that occurred during the monitoring period is established. Based on the horizontal deformation monitoring system and the information provided by the supervisor's site log, the construction stages that have the greatest impact on deformations are established. Additionally, an analysis of the deformations is carried out, which takes into account the resistance and deformability characteristics of the excavated soils, as well as the prevailing hydraulic conditions. This work will allow construction engineers and institutions in charge of infrastructure works in the Valley of Mexico to establish permissible ranges for horizontal deformations that can occur in very soft and saturated soils, during the different construction stages; improving response protocols to potentially dangerous behaviors.

Keywords: vertical shaft, flotation method, very soft clays, construction supervision

Procedia PDF Downloads 173

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

Procedia PDF Downloads 334

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

Procedia PDF Downloads 81

Authors: Jo Kuys, Kirsten Day

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Improving urban transportation systems requires an emphasis on users’ end-to-end journey experience, from the moment the user steps out of their home to when they arrive at their destination. In considering such end-to-end experiences, human centred design (HCD) must be integrated from the very beginning to generate viable outcomes for the public. An HCD approach will encourage innovative outcomes while acknowledging all factors that need to be understood along the journey. We provide evidence to show that when designing for public transportation, it is not just about the physical manifestation of a particular outcome; moreover, it’s about the context and human behaviours that need to be considered throughout the design process. Humans and their behavioural factors are vitally important to successful implementation of sustainable public transport systems. Through an in-depth literature review of HCD approaches for urban transportation systems, we provide a base to exploit the benefits and highlight the importance of including HCD in public transportation projects for greater patronage, resulting in more sustainable cities. An HCD approach is critical to all public transportation projects to understand different levels of transportation design, from the setting of transport policy to implementation to infrastructure, vehicle, and interface design.

Keywords: human centred design, public transportation, urban planning, user experience

Procedia PDF Downloads 166

Authors: Wanqing You, Kai Qian, Ying Qian

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The concepts of Cloud Computing provide users with infrastructure, platform and software as service, which make those services more accessible for people via Internet. To better analysis the performance of Cloud Computing provisioning policies as well as resources allocation strategies, a toolkit named CloudSim proposed. With CloudSim, the Cloud Computing environment can be easily constructed by modelling and simulating cloud computing components, such as datacenter, host, and virtual machine. A good scheduling strategy is the key to achieve the load balancing among different machines as well as to improve the utilization of basic resources. Recently, the existing scheduling algorithms may work well in some presumptive cases in a single machine; however they are unable to make the best decision for the unforeseen future. In real world scenario, there would be numbers of tasks as well as several virtual machines working in parallel. Based on the concepts of multi-queue, this paper presents a new scheduling algorithm to schedule tasks with CloudSim by taking into account several parameters, the machines’ capacity, the priority of tasks and the history log.

Keywords: hierarchical queue, load balancing, CloudSim, information technology

Procedia PDF Downloads 405

Authors: Flavio Contrera, Paulo Cesar Gregorio

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Recent political developments and government control actions in the face of the COVID-19 pandemic draw attention to the contrast between the duties of government and the demands of democratic representation. Elected by mobilizing far-right issues, Trump and Bolsonaro moved away from the WHO guidelines but had to accommodate demands on the health and on the social protection system on the one hand and demands from the economic sector on the other. This study used the MARPOR Project method to assess the impact of the COVID-19 pandemic on the issue and ideological congruence between the electoral and governmental arena in both the Trump and Bolsonaro Administrations. Findings reveal issue congruence between arenas in "National Way of Life: Positive", "Law and Order," and "Technology and Infrastructure" for Donald Trump, and "Welfare State Expansion" for Bolsonaro. Ideological estimation results show that Trump and Bolsonaro positioned to the right in their presidential elections, initially moved to the center-right. However, welfare policies actions at high frequency during the COVID-19 pandemic moved the ideological estimations of both governments to the center-left, despite their denial rhetoric.

Keywords: congruence, COVID-19, Donald Trump, Jair Bolsonaro

Procedia PDF Downloads 213

Authors: Bharat P. Modi, Jayesh M. Patel

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Mobile Internet is acting as a major source of data. As the number of web pages continues to grow the Mobile web provides the data miners with just the right ingredients for extracting information. In order to cater to this growing need, a special term called Mobile Web mining was coined. Mobile Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web Usage mining deals with understanding the behavior of users by making use of Mobile Web Access Logs that are generated on the server while the user is accessing the website. A Web access log comprises of various entries like the name of the user, his IP address, a number of bytes transferred time-stamp etc. A variety of Log Analyzer tools exists which help in analyzing various things like users navigational pattern, the part of the website the users are mostly interested in etc. The present paper makes use of such log analyzer tool called Mobile Web Log Expert for ascertaining the behavior of users who access an astrology website. It also provides a comparative study between a few log analyzer tools available.

Keywords: mobile web access logs, web usage mining, web server, log analyzer

Procedia PDF Downloads 344

Authors: Arjun Kumar Rath, Titus Dhanasingh

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Embedded product development requires software to test hardware functionality during development and finding issues during manufacturing in larger quantities. As the components are getting integrated, the devices are tested for their full functionality using advanced software tools. Benchmarking tools are used to measure and compare the performance of product features. At present, these tests are based on a variety of methods involving varying hardware and software platforms. Typically, these tests are custom built for every product and remain unusable for other variants. A majority of the tests goes undocumented, not updated, unusable when the product is released. To bridge this gap, a solution accelerator in the form of a framework can address these issues for running all these tests from one place, using an off-the-shelf tests library in a continuous integration environment. There are many open-source test frameworks or tools (fuego. LAVA, AutoTest, KernelCI, etc.) designed for testing embedded system devices, with each one having several unique good features, but one single tool and framework may not satisfy all of the testing needs for embedded systems, thus an extensible framework with the multitude of tools. Embedded product testing includes board bring-up testing, test during manufacturing, firmware testing, application testing, and assembly testing. Traditional test methods include developing test libraries and support components for every new hardware platform that belongs to the same domain with identical hardware architecture. This approach will have drawbacks like non-reusability where platform-specific libraries cannot be reused, need to maintain source infrastructure for individual hardware platforms, and most importantly, time is taken to re-develop test cases for new hardware platforms. These limitations create challenges like environment set up for testing, scalability, and maintenance. A desirable strategy is certainly one that is focused on maximizing reusability, continuous integration, and leveraging artifacts across the complete development cycle during phases of testing and across family of products. To get over the stated challenges with the conventional method and offers benefits of embedded testing, an embedded test framework (ETF), a solution accelerator, is designed, which can be deployed in embedded system-related products with minimal customizations and maintenance to accelerate the hardware testing. Embedded test framework supports testing different hardwares including microprocessor and microcontroller. It offers benefits such as (1) Time-to-Market: Accelerates board brings up time with prepacked test suites supporting all necessary peripherals which can speed up the design and development stage(board bring up, manufacturing and device driver) (2) Reusability-framework components isolated from the platform-specific HW initialization and configuration makes the adaptability of test cases across various platform quick and simple (3) Effective build and test infrastructure with multiple test interface options and preintegrated with FUEGO framework (4) Continuos integration - pre-integrated with Jenkins which enabled continuous testing and automated software update feature. Applying the embedded test framework accelerator throughout the design and development phase enables to development of the well-tested systems before functional verification and improves time to market to a large extent.

Keywords: board diagnostics software, embedded system, hardware testing, test frameworks

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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

Procedia PDF Downloads 100

Authors: Hen Friman

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Various categories of aqueous solutions are discharged within residential, institutional, commercial, and industrial structures. To safeguard public health and preserve the environment, it is imperative to subject wastewater to treatment processes that eliminate pathogens (such as bacteria and viruses), nutrients (such as nitrogen and phosphorus), and other compounds. Failure to address untreated sewage accumulation can result in an array of adverse consequences. Israel exemplifies a special case in wastewater management. Appropriate wastewater treatment significantly benefits sectors such as agriculture, tourism, horticulture, and industry. Nevertheless, untreated sewage in settlements lacking proper sewage collection or transportation networks remains an ongoing and substantial threat. Notably, the process of wastewater treatment entails substantial energy consumption. Consequently, this study explores the integration of solar energy as a renewable power source within the wastewater treatment framework. By incorporating renewable energy sources into the process, costs can be minimized, and decentralized facilities can be established even in areas lacking adequate infrastructure for traditional treatment methods.

Keywords: renewable energy, solar energy, innovative, wastewater treatment

Procedia PDF Downloads 89

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

Procedia PDF Downloads 264

Authors: Adila Shafqat

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Climate change and urbanization has changed the face of many cities in developing countries. Urbanization is linked with land use and land cover change, that is further intensify by the effects of changing climates. Green infrastructures provide numerous ecosystem services which effect the physical set up of the cities in the long run. Land surface temperatures is considered as defining parameter in the studies of the thermal impact on the land cover. Current study is conducted in the semi-arid urban areas of the Bahawalpur region. Accordingly, Land Surface Temperatures and land cover maps are derived from Landsat image through remote sensing techniques. The cooling impact of green infrastructure is determined by calculating land surface temperature of buffered zones around green infrastructures. A regression model is applied for results. It is seen that land surface temperature around green infrastructures in 1 to 3 degrees lower than the built up surroundings. The result indicates that the urban green infrastructures should be planned according to the local needs and characteristics of landuse so that they can effectively tackle land surface temperatures of urban areas.

Keywords: climate change, surface temperatures, green spaces, urban planning

Procedia PDF Downloads 100

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

Procedia PDF Downloads 145

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

Procedia PDF Downloads 368

Authors: E. Azizi Asiyabar

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The phenomenon of migration to larger cities has brought about a range of consequences, including increased travel demand and the necessity for smooth traffic flow to expedite transportation. Regrettably, insufficient urban transportation infrastructure has given rise to various issues, including air pollution, heightened fuel consumption, and wasted time. To address traffic-related problems and the economic, social, and environmental challenges that ensue, a well-equipped, efficient, fast, cost-effective, and high-capacity transportation system is imperative, with a focus on reliability. This study undertakes a comprehensive examination of rail transportation systems and subsequently compares their advantages and limitations. The findings of this investigation reveal that hybrid monorails exhibit lower maintenance requirements and associated costs when compared to other types of monorails, standard trains, and urban light rail systems. Given their favorable attributes in terms of pollution reduction, increased transportation speed, and enhanced quality of service, hybrid monorails emerge as a highly recommended and suitable option.

Keywords: comparing, most efficient, selecting, urban transportation

Procedia PDF Downloads 65

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

Procedia PDF Downloads 388

Authors: Merad Boudia Omar Rafik, Feham Mohammed

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Data aggregation in wireless sensor networks (WSNs) provides a great reduction of energy consumption. The limited resources of sensor nodes make the choice of an encryption algorithm very important for providing security for data aggregation. Asymmetric cryptography involves large ciphertexts and heavy computations but solves, on the other hand, the problem of key distribution of symmetric one. The latter provides smaller ciphertexts and speed computations. Also, the recent researches have shown that achieving the end-to-end confidentiality and the end-to-end integrity at the same is a challenging task. In this paper, we propose (SA-SPKC), a novel security protocol which addresses both security services for WSNs, and where only the base station can verify the individual data and identify the malicious node. Our scheme is based on stateful public key encryption (StPKE). The latter combines the best features of both kinds of encryption along with state in order to reduce the computation overhead. Our analysis

Keywords: secure data aggregation, wireless sensor networks, elliptic curve cryptography, homomorphic encryption

Procedia PDF Downloads 278

Authors: Ahmed N. Shmroukh, Gamal Tag Abdel-Jaber, Rashed D. Aldughpassi

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The feasibility of improving the performance of the proposed solar still unit which operated in very hot climate is investigated numerically and verified with experimental data. This solar desalination unit with proposed auxiliary device as seawater preheating system using petrol based textherm oil was used to produce pure fresh water from seawater. The effective evaporation area of basin is about 1 m². The unit was tested in two main operation modes which are normal and with seawater preheating system. The results showed that, there is good agreement between the theoretical data and the experimental data; this means that the numerical model can be accurately dependable for predicting the proposed solar still performance and design parameters. The results also showed that the fresh water productivity of the solar still in the modified preheating case which is higher than normal case, leads to an increase in productivity of 42%.

Keywords: improving productivity, seawater desalination, solar stills, theoretical model

Procedia PDF Downloads 126

Authors: Khadidja Belbachir, Hafida Belbachir

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subsequently the expansion of the physical supports storage and the needs ceaseless to accumulate several data, the sequential algorithms of associations’ rules research proved to be ineffective. Thus the introduction of the new parallel versions is imperative. We propose in this paper, a parallel version of a sequential algorithm “Partition”. This last is fundamentally different from the other sequential algorithms, because it scans the data base only twice to generate the significant association rules. By consequence, the parallel approach does not require much communication between the sites. The proposed approach was implemented for an experimental study. The obtained results, shows a great reduction in execution time compared to the sequential version and Count Distributed algorithm.

Keywords: association rules, distributed data mining, partition, parallel algorithms

Procedia PDF Downloads 387

Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

Procedia PDF Downloads 160

Authors: U. Waqas, M. F. Ahmed, A. Mehmood, M. A. Rashid

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In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data quality, Null hypothesis, Seismic lines, Seismic reflection survey

Procedia PDF Downloads 141

Authors: Derick M. Rakgoale, Topside E. Mathonsi, Vusumuzi Malele

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Cloud computing offers distributed online and on-demand computational services from anywhere in the world. Cloud computing services have grown immensely over the past years, especially in the past year due to the Coronavirus pandemic. Cloud computing has changed the working environment and introduced work from work phenomenon, which enabled the adoption of technologies to fulfill the new workings, including cloud services offerings. The increased cloud computing adoption has come with new challenges regarding data privacy and its integrity in the cloud environment. Previously advanced encryption algorithms failed to reduce the memory space required for cloud computing performance, thus increasing the computational cost. This paper reviews the existing encryption algorithms used in cloud computing. In the future, artificial neural networks (ANN) algorithm design will be presented as a security solution to ensure data integrity, confidentiality, privacy, and availability of user data in cloud computing. Moreover, MATLAB will be used to evaluate the proposed solution, and simulation results will be presented.

Keywords: cloud computing, data integrity, confidentiality, privacy, availability

Procedia PDF Downloads 110

Authors: Ali Alahmari

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Urban drainage in Saudi Arabia is an increasingly challenging issue due to factors such as climate change and rapid urban expansion. The existing infrastructure, based on traditional drainage systems, is not always able to cope with the increased precipitation, sometimes leading to rainwater runoff and floods causing disturbances and damage to property. Therefore, there is a need to find new ways of managing drainage, such as Sustainable Urban Drainage Systems (SUDS). The research has highlighted the main driving forces behind the need for change, revealed by the participants, to the need to adopt new ideas and approaches for urban drainage. However, while moving towards this, certain factors that may hinder the aim of using the experiences of other countries and taking advantage of innovative solutions. The research illustrates an initial conceptual model for these factors emerging from the analysis. It identifies some of the fundamental issues affecting the resistance to change towards the adoption of the concept of sustainability in Saudi Arabia, with Riyadh city as a case study. This was by using a qualitative approach, whereby, through two phases of fieldwork during 2013 and 2014, twenty-six semi-structured interviews were conducted with a number of representative officials and professionals from key government departments and organisations related to urban drainage management. Grounded Theory approach was followed to analyse the qualitative data obtained. Resistance to change was classified to: firstly: individual inertia (e.g. familiarity with the conventional solutions and approaches, lack of awareness, and considering sustainability as a marginal matter in urban planning). This resulted in not paying the desired attention, and impact on planning and setting priorities for development. Secondly: institutionalised inertia (e.g. lack of technical and design specifications for other unconventional drainage solutions, lack of consideration by decision makers in other disciplines such as contributions from environmental and geographical studies, and routine work and bureaucracy). This contributes to the weakness of decision-making, weakness in the role of research, and a lack of human resources. It seems that attitudes towards change may have reduced the ability to move forward towards sustainable development, in addition to contributing towards difficulties in some aspects of the decision-making process. Thus, the chapter provides insights into the current situation in Saudi Arabia and contributes to understanding the decisions that are made regarding change.

Keywords: climate change, new knowledge and approaches, resistance to change, Saudi Arabia, SUDS, urban drainage, urban expansion

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Authors: Ahmed Elrewainy

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Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no information about the given data cube. Sparsity is one of the recent approaches used in the source recovery or unmixing techniques. The l₁-norm optimization problem “basis pursuit” could be used as a sparsity-based approach to solve this unmixing problem where the endmembers is assumed to be sparse in an appropriate domain known as dictionary. This optimization problem is solved using proximal method “iterative thresholding”. The l₁-norm basis pursuit optimization problem as a sparsity-based unmixing technique was used to unmix real and synthetic hyperspectral data cubes.

Keywords: basis pursuit, blind source separation, hyperspectral imaging, spectral unmixing, wavelets

Procedia PDF Downloads 183

Authors: Nihed Bahria El Asghar, Imen Jouili, Mounir Frikha

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Inter-datacenter transport network is very bandwidth and delay demanding. The data transferred over such a network is also highly QoS-exigent mostly because a huge volume of data should be transported transparently with regard to the application user. To avoid the data transfer failure, a backup path should be reserved. No re-routing delay should be observed. A dedicated 1+1 protection is however not applicable in inter-datacenter transport network because of the huge spare capacity. In this context, we propose a survivable virtual network with minimal backup based on network coding (1 ⊕ 1) and solve it using a modified Dijkstra-based heuristic.

Keywords: network coding, dedicated protection, spare capacity, inter-datacenters transport network

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Authors: Prachi Buche Marathe

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Karanja Laad is a city located in the Vidarbha region in the state of Maharashtra, India. It has a huge amount of tangible and intangible heritage in the form of monuments, precincts, a group of structures, festivals and procession route, which is neglected and lost with time. Three different religions Hinduism, Islam and Jainism along with associations of being a birthplace of Swami Nrusinha Saraswati, an exponent of Datta Sampradaya sect and the British colonial layer have shaped the culture and society of the place over the period. The architecture of the town Karanja Laad has enhanced its unique historic and cultural value with a combination of all these historic layers. Karanja Laad is also a traditional trading historic town with unique hybrid architectural style and has a good potential for developing as a tourist place along with the present image of a pilgrim destination of Datta Sampradaya. The aim of the research is to prepare a conservation proposal for the historic town along with the management framework. Objectives of the research are to study the evolution of Karanja town, to identify the cultural resources along with issues of the historic core of the city, to understand Datta sampradaya, and contribution of Saint Nrusinha Saraswati in the religious sect and his association as an important personality with Karanja. The methodology of the research is site visits to the Karanja city, making field surveys for documentation and discussions and questionnaires with the residents to establish heritage and identify potential and issues within the historic core thereby establishing a case for conservation. Field surveys are conducted for town level study of land use, open spaces, occupancy, ownership, traditional commodity and community, infrastructure, streetscapes, and precinct activities during the festival and non-festival period. Building level study includes establishing various typologies like residential, institutional commercial, religious, and traditional infrastructure from the mythological references like waterbodies (kund), lake and wells. One of the main issues is that the loss of the traditional footprint as well as the traditional open spaces which are getting lost due to the new illegal encroachments and lack of guidelines for the new additions to conserve the original fabric of the structures. Traditional commodities are getting lost since there is no promotion of these skills like pottery and painting. Lavish bungalows like Kannava mansion, main temple Wada (birthplace of the saint) have a huge potential to be developed as a museum by adaptive re-use which will, in turn, attract many visitors during festivals which will boost the economy. Festival procession routes can be identified and a heritage walk can be developed so as to highlight the traditional features of the town. Overall study has resulted in establishing a heritage map with 137 heritage structures identified as potential. Conservation proposal is worked out on the town level, precinct level and building level with interventions such as developing construction guidelines for further development and establishing a heritage cell consisting architects and engineers for the upliftment of the existing rich heritage of the Karanja city.

Keywords: built heritage, conservation, Datta Sampradaya, Karanja Laad, Swami Nrusinha Saraswati, procession route

Procedia PDF Downloads 149

Authors: Benjamin Okike

Abstract:

There is a need to hide information along the superhighway. Today, information relating to the survival of individuals, organizations, or government agencies is transmitted from one point to another. Adversaries are always on the watch along the superhighway to intercept any information that would enable them to inflict psychological ‘injuries’ to their victims. But with information encryption, this can be prevented completely or at worst reduced to the barest minimum. There is no doubt that so many encryption techniques have been proposed, and some of them are already being implemented. However, adversaries always discover loopholes on them to perpetuate their evil plans. In this work, we propose the enhanced data encryption standard (EDES) that would deploy randomly generated numbers as an encryption method. Each time encryption is to be carried out, a new set of random numbers would be generated, thereby making it almost impossible for cryptanalysts to decrypt any information encrypted with this newly proposed method.

Keywords: encryption, enhanced data encryption, encryption techniques, information security

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Authors: Antonella Falanga, Armando Cartenì

Abstract:

"Big data" refers to extremely vast and complex sets of data, encompassing extraordinarily large and intricate datasets that require specific tools for meaningful analysis and processing. These datasets can stem from diverse origins like sensors, mobile devices, online transactions, social media platforms, and more. The utilization of big data is pivotal, offering the chance to leverage vast information for substantial advantages across diverse fields, thereby enhancing comprehension, decision-making, efficiency, and fostering innovation in various domains. Big data, distinguished by its remarkable attributes of enormous volume, high velocity, diverse variety, and significant value, represent a transformative force reshaping the industry worldwide. Their pervasive impact continues to unlock new possibilities, driving innovation and advancements in technology, decision-making processes, and societal progress in an increasingly data-centric world. The use of these technologies is becoming more widespread, facilitating and accelerating operations that were once much more complicated. In particular, big data impacts across multiple sectors such as business and commerce, healthcare and science, finance, education, geography, agriculture, media and entertainment and also mobility and logistics. Within the transportation sector, which is the focus of this study, big data applications encompass a wide variety, spanning across optimization in vehicle routing, real-time traffic management and monitoring, logistics efficiency, reduction of travel times and congestion, enhancement of the overall transportation systems, but also mitigation of pollutant emissions contributing to environmental sustainability. Meanwhile, in public administration and the development of smart cities, big data aids in improving public services, urban planning, and decision-making processes, leading to more efficient and sustainable urban environments. Access to vast data reservoirs enables deeper insights, revealing hidden patterns and facilitating more precise and timely decision-making. Additionally, advancements in cloud computing and artificial intelligence (AI) have further amplified the potential of big data, enabling more sophisticated and comprehensive analyses. Certainly, utilizing big data presents various advantages but also entails several challenges regarding data privacy and security, ensuring data quality, managing and storing large volumes of data effectively, integrating data from diverse sources, the need for specialized skills to interpret analysis results, ethical considerations in data use, and evaluating costs against benefits. Addressing these difficulties requires well-structured strategies and policies to balance the benefits of big data with privacy, security, and efficient data management concerns. Building upon these premises, the current research investigates the efficacy and influence of big data by conducting an overview of the primary and recent implementations of big data in transportation systems. Overall, this research allows us to conclude that big data better provide to enhance rational decision-making for mobility choices and is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, public transport, sustainable mobility, transport demand, transportation planning

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