Search results for: network transformation

Authors: Elizelle Juanee Cilliers

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Recent planning thinking has laid the foundations for a general sense of best practice that aims to enhance the quality of life, suggesting an open and participatory process. It is accepted that integration of top-down and bottom-up approaches may lead to efficient action in environments and sustainable planning and development, although it is also accepted that such an integrated approach has various challenges of implementation. A flexible framework in which the strengths of both the top-down and bottom-up approaches were explored in this research, based on the EU Interreg VALUE Added project and five case studies where student education and student involvement played a crucial role within the participation process of the redesign of the urban environment. It was found that international student workshops were an effective tool to integrate bottom-up and top-down structures, as it acted as catalyst for communication, interaction, creative design, quick transformation from planning to implementation, building social cohesion, finding mutual ground between stakeholders and thus enhancing overall quality of life and quality of environments. It offered a good alternative to traditional participation modes and created a platform for an integrative planning approach. The role and importance of education and integration within the urban environment were emphasized.

Keywords: top-down, bottom-up, flexible, student involvement

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Rafael Vicente Salar

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Cities are steadily transforming their productive systems based on value-adding strategies, with the aim of becoming more competitive in a globalized economy. This fact is reflected in inner urban spaces which are increasingly accommodating new economic activities related to knowledge, culture, creativity, and tourism, to the detriment of traditional activities. This is the case of the Trafalgar Garment District (TGD), located in Barcelona´s Eixample Dret neighborhood, an economic urban space historically devoted to the garment wholesale trade. This district is currently experiencing the transformation of its traditional economic specialization. In the last 50 years, external and internal factors have caused, firstly, the disintegration of the Catalonian garment regional cluster. This has resulted in the closure of the majority of metropolitan garment workshops. Secondly, this has also caused either the disappearance of wholesale firms or their relocation to more suitable spaces in the metropolitan area. Specifically, the TGD's economic restructuration is related to the attraction of firms related to the lodging industry and the new economy. In addition, some of the wholesale businesses are adopting new management strategies in order to remain in the TGD. These initiatives are thought to allow them, on one hand, to upgrade their products and, on the other, to reconfigure their internal organization in order to be more competitive.

Keywords: Barcelona, garment district, new economy, tourism, garmen wholesale trade

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

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An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

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Authors: Diego De Almeida Pereira, Diana Borchenko

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Artificial neural networks are used for many applications as they are able to learn complex nonlinear relationships between input and output data. As the number of neurons and layers in a neural network increases, it is possible to represent more complex behaviors. The present study proposes that artificial neural networks are a valuable tool for architecture and engineering professionals concerned with understanding how buildings influence human and social well-being based on theories of environmental psychology.

Keywords: environmental psychology, architecture, neural networks, human and social well-being

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Authors: Sarah Reker, Christiane H. Kellner

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From the perspective of people with disabilities, inequalities can also emerge from spatial segregation, the lack of social contacts or limited economic resources. In order to reduce or even eliminate these disadvantages and increase general well-being, community-based participation as well as decentralisation efforts within exclusively residential homes is essential. Therefore, the new research project “Index for participation development and quality of life for persons with disabilities”(TeLe-Index, 2014-2016), which is anchored at the Technische Universität München in Munich and at a large residential complex and service provider for persons with disabilities in the outskirts of Munich aims to assist the development of community-based living environments. People with disabilities should be able to participate in social life beyond the confines of the institution. Since a diverse society is a society in which different individual needs and wishes can emerge and be catered to, the ultimate goal of the project is to create an environment for all citizens–regardless of disability, age or ethnic background–that accommodates their daily activities and requirements. The UN-Convention on the Rights of Persons with Disabilities, which Germany also ratified, postulates the necessity of user-centered design, especially when it comes to evaluating the individual needs and wishes of all citizens. Therefore, a multidimensional approach is required. Based on this insight, the structure of the town-like center will be remodeled to open up the community to all people. This strategy should lead to more equal opportunities and open the way for a much more diverse community. Therefore, macro-level research questions were inspired by quality of life theory and were formulated as follows for different dimensions: •The user dimension: what needs and necessities can we identify? Are needs person-related? Are there any options to choose from? What type of quality of life can we identify? The economic dimension: what resources (both material and staff-related) are available in the region? (How) are they used? What costs (can) arise and what effects do they entail? •The environment dimension: what “environmental factors” such as access (mobility and absence of barriers) prove beneficial or impedimental? In this context, we have provided academic supervision and support for three projects (the construction of a new school, inclusive housing for children and teenagers with disabilities and the professionalization of employees with person-centered thinking). Since we cannot present all the issues of the umbrella-project within the conference framework, we will be focusing on one project more in-depth, namely “Outpatient Housing Options for Children and Teenagers with Disabilities”. The insights we have obtained until now will enable us to present the intermediary results of our evaluation. The most central questions pertaining to this part of the research were the following: •How have the existing network relations been designed? •What meaning (or significance) does the existing service offers and structures have for the everyday life of an external residential group? These issues underpinned the environmental analyses as well as the qualitative guided interviews and qualitative network analyses we carried out.

Keywords: decentralisation, environmental analyses, outpatient housing options for children and teenagers with disabilities, qualitative network analyses

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Authors: Julius Y Asamoah

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Mobile money has seen faster adoption in the decade. Its emergence serves as an essential driver of financial inclusion and an innovative financial service delivery channel, especially to the unbanked population. The rising importance of mobile money services has caught policymakers and regulators' attention, seeking to understand the many issues emerging from this context. At the same time, it is unlocking the potential of knowledge of this new technology. Regulatory responses and support are essential, requiring significant changes to current regulatory practices in Ghana. The article aims to answer the following research questions: "What risk does an unregulated mobile money service pose to consumers and the financial system? "What factors stimulate and hinder the introduction of mobile payments in developing countries? The sample size used was 250 respondents selected from the study area. The study has adopted an analytical approach comprising a combination of qualitative and quantitative data collection methods. Actor-network theory (ANT) is used as an interpretive lens to analyse this process. ANT helps analyse how actors form alliances and enrol other actors, including non-human actors (i.e. technology), to secure their interests. The study revealed that government regulatory policies impact mobile money as critical to mobile money services in developing countries. Regulatory environment should balance the needs of advancing access to finance with the financial system's stability and draw extensively from Kenya's work as the best strategies for the system's players. Thus, regulators need to address issues related to the enhancement of supportive regulatory frameworks. It recommended that the government involve various stakeholders, such as mobile phone operators. Moreover, the national regulatory authority creates a regulatory environment that promotes fair practices and competition to raise revenues to support a business-enabling environment's key pillars as infrastructure.

Keywords: actor-network theory (ANT), cashless future, Developing countries, Ghana, Mobile Money

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Authors: Enrique Barra, Aldo Gordillo, Juan Quemada

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This paper presents the Virtual Science Hub platform. It is an open source platform that combines a social network, an e-learning authoring tool, a video conference service and a learning object repository for science teaching enrichment. These four main functionalities fit very well together. The platform was released in April 2012 and since then it has not stopped growing. Finally we present the results of the surveys conducted and the statistics gathered to validate this approach.

Keywords: e-learning, platform, authoring tool, science teaching, educational sciences

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Authors: Ademola Ibukunolu Atanda

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Research undertakings are meant to proffer solutions to issues and challenges in society. This justifies the need for research in ivory towers. Multinational and non-governmental organisations, as well as foundations, commit financial resources to support research endeavours. In recent times, the direction and dimension of research undertaking encourage collaborations, whereby experts from different disciplines or specializations would bring their expertise in addressing any identified problem, whether in humanities or sciences. However, the extent to which collaborative research undertakings are perceived and embraced by academic staff would determine the impact collaborative research would have on society. To this end, this study investigated academic staff’s perception and willingness to be involved in collaborative research for the purpose of proffering solutions to societal problems. The study adopted a descriptive research design. The population comprised academic staff in southern Nigeria. The sample was drawn through a convenient sampling technique. The data were collected using a questionnaire titled “Perception and Willingness to Participate in Collaborative Research Questionnaire (PWPCRQ)’ using Google Forms. Data collected were analyzed using descriptive statistics of simple percentages, mean and charts. The findings showed that Academic Staff’s readiness to participate in collaborative research is to a great extent (89%) and they participate in collaborative research very often (51%). The Academic Staff was involved more in collaboration research among their colleagues within their universities (1.98) than participation in inter-disciplines collaboration (1.47) with their colleagues outside Nigeria. Collaborative research was perceived to impact on development (2.5). Collaborative research offers the following benefits to members’ aggregation of views, the building of an extensive network of contacts, enhancement of sharing of skills, facilitation of tackling complex problems, increased visibility of research network and citations and promotion of funding opportunities. The study concluded that Academic staff in universities in the South-West of Nigeria participate in collaborative research but with their colleagues within Nigeria rather than outside the country. Based on the findings, it was recommended that the management of universities in South-West Nigeria should encourage collaborative research with some incentives.

Keywords: collaboration, research, development, participation

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Authors: Nino Grigolaia

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Introduction - The paper discusses the role of industrial policy in the development of the economy in the country. The main challenges on the way to the implementation of industrial policy are analyzed: the long-term period of industrial policy, the risk of changes in priorities, the limited scope and external shocks. Methodology - Various research methods are used in the paper. The methods of induction, deduction, analysis, synthesis, analogy, correlation and statistical observation are used. Main Findings - Based on the analysis of the current situation in Georgia, the obstacles to the country's industrialization and its supporting factors are identified. Also, the challenges of the country's core industrial policies are revealed. Specific industry development strategies, ways of state support and main directions of new industrial policies are identified. Conclusion - The paper concludes that the development of the industrial sector is critical for the future growth and development of the Georgian economy, which will accelerate the industrialization and structural transformation processes, reduce the trade deficit, increase the exports and create more jobs in the country. The listed changes will guarantee the improvement of the socio-economic situation of the population. Accordingly, it is revealed that the study of industrial policy in Georgia is still actual. Based on the analysis, relevant conclusions in the field of industrialization of the country are developed and recommendations are proposed.

Keywords: industrialization , industrial policy, industrialization of the economy, Georgia priorities

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Authors: Chao Wang

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Under rapid innovation of information technology, the media plays a very important role in the dissemination of information, and it has a totally different analogy generations face. However, the involvement of narrative images provides more possibilities of narrative text. "Images" through the process of aperture, a camera shutter and developable photosensitive processes are manufactured, recorded and stamped on paper, displayed on a computer screen-concretely saved. They exist in different forms of files, data, or evidence as the ultimate looks of events. By the interface of media and network platforms and special visual field of the viewer, class body space exists and extends out as thin as sebum layer, extremely soft and delicate with real full tension. The physical space of sebum layer of confuses the fact that physical objects exist, needs to be established under a perceived consensus. As at the scene, the existing concepts and boundaries of physical perceptions are blurred. Sebum layer physical simulation shapes the “Topical-Similarity" immersing, leading the contemporary social practice communities, groups, network users with a kind of illusion without the presence, i.e. a non-real illusion. From the investigation and discussion of literatures, digital movies editing manufacture and produce the variability characteristics of time (for example, slices, rupture, set, and reset) are analyzed. Interactive eBook has an unique interaction in "Waiting-Greeting" and "Expectation-Response" that makes the operation of image narrative structure more interpretations functionally. The works of digital editing and interactive technology are combined and further analyze concept and results. After digitization of Interventional Imaging and interactive technology, real events exist linked and the media handing cannot be cut relationship through movies, interactive art, practical case discussion and analysis. Audience needs more rational thinking about images carried by the authenticity of the text.

Keywords: sebum layer, topical and similarity, interactive technology, image narrative

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Dean Sinković, Tea Golja, Morena Paulišić

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Upon the end of the vicious war in former Yugoslavia in 1995, international development community widely promoted microfinance as the key development framework to eradicate poverty, create jobs, increase income. Widespread claims were made that microfinance institutions would play vital role in creating a bedrock for sustainable ‘bottom-up’ economic development trajectory, thus, helping newly formed states to find proper way from economic post-war depression. This uplifting neoliberal narrative has no empirical support in the Republic of Croatia. Firstly, the type of enterprises created via microfinance sector are small, unskilled, labor intensive, no technology and with huge debt burden. This results in extremely high failure rates of microenterprises and poor individuals plunging into even deeper poverty, acute indebtedness and social marginalization. Secondly, evidence shows that microcredit is exact reflection of dangerous and destructive sub-prime lending model with ‘boom-to-bust’ scenarios in which benefits are solely extracted by the tiny financial and political elite working around the microfinance sector. We argue that microcredit providers are not proper financial structures through which developing countries should look way out of underdevelopment and poverty. In order to achieve sustainable long-term growth goals, public policy needs to focus on creating, supporting and facilitating the small and mid-size enterprises development. These enterprises should be technically sophisticated, capable of creating new capabilities and innovations, with managerial expertise (skills formation) and inter-connected with other organizations (i.e. clusters, networks, supply chains, etc.). Evidence from South-East Europe suggest that such structures are not created via microfinance model but can be fostered through various forms of social enterprises. Various legal entities may operate as social enterprises: limited liability private company, limited liability public company, cooperative, associations, foundations, institutions, Mutual Insurances and Credit union. Our main hypothesis is that cooperatives are potential agents of social and economic transformation and community development in the region. Financial cooperatives are structures that can foster more efficient allocation of financial resources involving deeper democratic arrangements and more socially just outcomes. In Croatia, pioneers of the first social enterprises were civil society organizations whilst forming a separated legal entity. (i.e. cooperatives, associations, commercial companies working on the principles of returning the investment to the founder). Ever since 1995 cooperatives in Croatia have not grown by pursuing their own internal growth but mostly by relying on external financial support. The greater part of today’s registered cooperatives tend to be agricultural (39%), followed by war veterans cooperatives (38%) and others. There are no financial cooperatives in Croatia. Due to the above mentioned we look at the historical developments and the prevailing social enterprises forms and discuss their advantages and disadvantages as potential agents for social and economic transformation and community development in the region. There is an evident lack of understanding of this business model and of its potential for social and economic development followed by an unfavorable institutional environment. Thus, we discuss the role of governance and management in the formation of social enterprises in Croatia, stressing the challenges for the governance of the country’s social enterprise movement.

Keywords: financial cooperatives, governance and management models, microfinance institutions, social enterprises

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Authors: Mehmet Karaata

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Given two distinct vertices (nodes) source s and target t of a graph G = (V, E), the two node-disjoint paths problem is to identify two node-disjoint paths between s ∈ V and t ∈ V . Two paths are node-disjoint if they have no common intermediate vertices. In this paper, we present an algorithm with O(m)-time complexity for finding two node-disjoint paths between s and t in arbitrary graphs where m is the number of edges. The proposed algorithm has a wide range of applications in ensuring reliability and security of sensor, mobile and fixed communication networks.

Keywords: disjoint paths, distributed systems, fault-tolerance, network routing, security

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Authors: Shankaran Sitarama

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Teamwork is an extremely effective pedagogical tool in engineering education. New Product Development (NPD) has been an effective strategy of companies to streamline and bring innovative products and solutions to customers. Thus, Engineering curriculum in many schools, some collaboratively with business schools, have brought NPD into the curriculum at the graduate level. Teamwork is invariably used during instruction, where students work in teams to come up with new products and solutions. There is a significant emphasis of grade on the semester long teamwork for it to be taken seriously by students. As the students work in teams and go through this process to develop the new product prototypes, their effectiveness and learning to a great extent depends on how they function as a team and go through the creative process, come together, and work towards the common goal. A core attribute of a successful NPD team is their creativity and innovation. The team needs to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas resulting in a POC (proof-of-concept) implementation or a prototype of the product. The simultaneous requirement of teams to be creative and at the same time also converge and work together imposes different types of tensions in their team interactions. These ideational tensions / conflicts and sometimes relational tensions / conflicts are inevitable. Effective teams will have to deal with the Team dynamics and manage it to be resilient enough and yet be creative. This research paper provides a computational analysis of the teams’ communication that is reflective of the team dynamics, and through a superimposition of latent semantic analysis with social network analysis, provides a computational methodology of arriving at patterns of visual interaction. These team interaction patterns have clear correlations to the team dynamics and provide insights into the functioning and thus the effectiveness of the teams. 23 student NPD teams over 2 years of a course on Managing NPD that has a blend of engineering and business school students is considered, and the results are presented. It is also correlated with the teams’ detailed and tailored individual and group feedback and self-reflection and evaluation questionnaire.

Keywords: team dynamics, social network analysis, team interaction patterns, new product development teamwork, NPD teams

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Authors: Doaa El-Shereef

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The Canaanite civilization was one of the early great civilizations of the Near East, they influenced and been influenced from the civilizations of the ancient world especially the Egyptian and Mesopotamia civilizations. The development of the Canaanite trade started from the Chalcolithic Age to the Iron Age through the oldest trade route in the Middle East. This paper will focus on defining the Canaanites and from where did they come from and the meaning of the term Canaan and how the Ancient Manuscripts define the borders of the land of Canaan and this essay will describe the Canaanite trade route and their exported goods such as cedar wood, and pottery.

Keywords: archaeology, bronze age, Canaanite, colonies, Massilia, pottery, shipwreck, vineyards

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Authors: Sunil Chandra, Triparna Barman, Vikas Gusain, Himanshu Rawat

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within the reserved forest, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differential burnt normalized ratio index (dNBR) approach that uses the burnt ratio values generated using Short Wave Infra Red (SWIR) band and Near Infra Red (NIR) bands of the Sentinel-2A image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel 2A bands. The training and testing data are generated from the sentinel-2A data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated in rugged terrain using spectral unmixing methods which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: dNBR, spectral unmixing, neural network, forest stratum

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Vladimir Korovkin, Albert Park, Evgeny Kaganer

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The paper studies the second-level digital divide (the one defined by the way how digital technology is used in everyday life) between regions of the Russian Federation. The paper offers a systemic review of literature on the measurement of the digital divide; based upon this it suggests a composite Digital Life Index, that captures the complex multi-dimensional character of the phenomenon. The model of the index studies separately the digital supply and demand across seven independent dimensions providing for 14 subindices. The Index is based on Internet-borne data, a distinction from traditional research approaches that rely on official statistics or surveys. Regression analysis is used to determine the relative importance of factors like income, human capital, and policy in determining the digital divide. The result of the analysis suggests that the digital divide is driven more by the differences in demand (defined by consumer competencies) than in supply; the role of income is insignificant, and the quality of human capital is the key determinant of the divide. The paper advances the existing methodological literature on the issue and can also inform practical decision-making regarding the strategies of national and regional digital development.

Keywords: digital transformation, second-level digital divide, composite index, digital policy, regional development, Russia

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: Thulane Paepae, Tumisang Seodigeng

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This paper demonstrates the use of a method of synthesizing process flowsheets using a graphical tool called the GH-plot and in particular, to look at how it can be used to compare the reactions of a combined simultaneous process with regard to their thermodynamics. The technique uses fundamental thermodynamic principles to allow the mass, energy and work balances locate the attainable region for chemical processes in a reactor. This provides guidance on what design decisions would be best suited to developing new processes that are more effective and make lower demands on raw material and energy usage.

Keywords: attainable regions, dimethyl ether, optimal reaction network, GH Space

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Authors: Lawrence Williams

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As the mechanism which converts domains to internet protocol (IP) addresses, Domain Name System (DNS) is an essential part of internet usage. It was not designed securely and can be subject to attacks. DNS attacks have become more frequent and sophisticated and the need for detecting and preventing them becomes more important for the modern network. DNS tunnelling attacks are one type of attack that are primarily used for distributed denial-of-service (DDoS) attacks and data exfiltration. Discussion of different techniques to detect and prevent DNS tunneling attacks is done. The methods, models, experiments, and data for each technique are discussed. A proposal about feasibility is made. Future research on these topics is proposed.

Keywords: DNS, tunneling, exfiltration, botnet

Procedia PDF Downloads 62

Authors: Muhamad Nugraha

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Economic growth always correlate positively with the magnitude of the unemployment rate. This is caused by labor which one of important variable to keep growth in the real sector of the region. Meanwhile, the economic structure in districts of Jember showed an increase of economic activity began to shift towards the industrial sector and some other economic sectors, so they have an affects to considerations for policy makers to increase economic growth in Jember as an autonomous region in East Java Province. At the fact, SMEs is among the factors driving economic growth in the region. This is shown by the high amount of SMEs. However, employment in the sector grew slightly slowed. It is caused by a lack of productivity in SMEs. Through the analysis of the transformation of economic structure theory, and the theory of Triple Helix using descriptive analytical method Location Quotient and Shift - Share, found that the results of the economic structure in Jember slowly shifting from the agricultural sector to the industrial sector, because it is dominated by trade sector, hotel and restaurant sector. In addition, SMEs is the potential sector of economic growth in Jember. While to maximizing role and functions of the institution's Research and Development Center of SMEs, there are three points to be known, that are Business Landscape, Business Architecture and Value Added.

Keywords: economic growth, SMEs, labor, Research and Development Center of SMEs

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Authors: Elena G. Salina, Vadim A. Makarov

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With the emergence of primary resistance to the current drugs and wide distribution of latent tuberculosis infection, a need for new compounds with a novel mode of action is growing steadily. Copper-mediated innate immunity and antibacterial toxicity propose novel strategies in TB drug discovery and development. Transcriptome of M. tuberculosis was obtained by RNA-seq, intracellular copper content was measured by ISP MS and complexes of 1-hydroxy-2-thiopyridines with copper were detected by HPLC.1-hydroxy-2-thiopyridine derivatives were found to be highly active in vitro against both actively growing and dormant non-culturable M. tuberculosis. Transcriptome response to 1-hydroxy-2-thiopyridines revealed signs of copper toxicity in M. tuberculosis bacilli. Indeed, Cu was found to accumulate inside cells treated with 1-hydroxy-2-thiopyridines. These compounds were found to form stable charged lipophylic complexes with Cu²⁺ ions which transport into mycobacterial cell. Subsequent metabolic destruction of the complex led to transformation of 1-hydroxy-2-thiopyridines into 2-methylmercapto-2-ethoxycarbonylpyridines, which did not possess antitubercular activity and releasing of free Cu²⁺ in the cytoplasm. 1-hydroxy-2-thiopyridines are a potent class of Cu-dependent inhibitors of M. tuberculosis which may control M. tuberculosis infection by impairment of copper homeostasis. Acknowledgment: This work was financially supported by the Ministry of Education and Science of the RussianFederation (Agreement No 14.616.21.0065; unique identifier RFMEFI61616X0065).

Keywords: copper toxicity, drug discovery, M. tuberculosis inhibitors, 2-thiopyridines

Procedia PDF Downloads 154

Authors: M. Emmanouilidou

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The Internet of Things (IoT) is a disruptive technological paradigm that is at the center of the digital evolution by integrating physical and virtual worlds leading to the creation of extended interconnected ecosystems that are characterized as smart environments. The concept of the IoT has a broad range of applications in different industries including the healthcare sector. The Internet of Healthcare Things (IoHT), a branch of the IoT, is expected to bring promising benefits to all involved stakeholders and accelerate the revolution of the healthcare sector through a transition towards preventive and personalized medicine. The socio-economic challenges that the healthcare sector is facing further emphasize the need for a radical transformation of healthcare systems in both developed and developing countries with the role of pervasive technological innovations, such as IoHT, recognized as key to counteract the relevant challenges. Besides the number of potential opportunities that IoHT presents, there are fundamental ethical concerns that need to be considered and addressed in relation to the application of IoHT. This paper contributes to the discussion of the emerging topic of IoHT by providing an overview of the role and potential of IoHT, highlighting the characteristics of the current and future healthcare landscape, reporting on the up-to-date status of IoHT in Europe and reflecting upon existing research in the ethics of IoHT by incorporating additional ethical dimensions that have been ignored which can provide pathways for future research in the field.

Keywords: ethics, Europe, healthcare, Internet of Things

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Authors: Md. Shah Alam

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Mushfiq Ahmad has defined a Mixed Number, which is the sum of a scalar and a Cartesian vector. He has also defined the elementary group operations of Mixed numbers i.e. the norm of Mixed numbers, the product of two Mixed numbers, the identity element and the inverse. It has been observed that Mixed Number is consistent with Pauli matrix algebra and a handy tool to work with Dirac electron theory. Its use as a mathematical method in Physics has been studied. (1) We have applied Mixed number in Quantum Mechanics: Mixed Number version of Displacement operator, Vector differential operator, and Angular momentum operator has been developed. Mixed Number method has also been applied to Klein-Gordon equation. (2) We have applied Mixed number in Electrodynamics: Mixed Number version of Maxwell’s equation, the Electric and Magnetic field quantities and Lorentz Force has been found. (3) An associative transformation of Mixed Number numbers fulfilling Lorentz invariance requirement is developed. (4) We have applied Mixed number algebra as an extension of Complex number. Mixed numbers and the Quaternions have isomorphic correspondence, but they are different in algebraic details. The multiplication of unit Mixed number and the multiplication of unit Quaternions are different. Since Mixed Number has properties similar to those of Pauli matrix algebra, Mixed Number algebra is a more convenient tool to deal with Dirac equation.

Keywords: mixed number, special relativity, quantum mechanics, electrodynamics, pauli matrix

Procedia PDF Downloads 343

Authors: Mei-Chun Chang

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Due to digital transformation and social media development in recent years, various real-time interactive digital tools have been developed to meet the design demands for virtual reality avatars, which also promote digital content learners' active participation in the creation process. As a result, new social media design tools have the characteristics of intuitive operation with a simplified interface for fast production, from which works can be simply created. This study carried out observations, records, questionnaire surveys, and interviews on the creation and learning of visual avatars made by students of the National Taiwan University of Science and Technology (NTUST) with the VRoid Studio 3D modeling tool so as to explore their learning effectiveness on the design of visual avatars. According to the results of this study, the VRoid Studio 3D character modeling tool has a positive impact on the learners and helps to improve their learning effectiveness. Students with low academic achievements said that they could complete the conceived modeling with their own thinking by using the design tool, which increased their sense of accomplishment. Conclusions are drawn according to the results, and relevant future suggestions are put forward.

Keywords: virtual avatar, character design, social media, vroid studio, creation, digital learning

Procedia PDF Downloads 176