Search results for: humanitarian data ecosystem

Authors: Christopher Muhawe, Masooda Bashir

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With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.

Keywords: data breach and misuse, economic harms, privacy harms, psychological harms

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Authors: Keyur Raval, Steffen Krohn, Bruno Moerschbacher

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Chitin, the biopolymer of the N-acetylglucosamine, is the most abundant biopolymer on the planet after cellulose. Industrially, chitin is isolated and purified from the shell residues of shrimps. A deacetylated derivative of chitin i.e. chitosan has more market value and applications owing to it solubility and overall cationic charge compared to the parent polymer. This deacetylation on an industrial scale is performed chemically using alkalis like sodium hydroxide. This reaction not only is hazardous to the environment owing to negative impact on the marine ecosystem. A greener option to this process is the enzymatic process. In nature, the naïve chitin is converted to chitosan by chitin deacetylase (CDA). This enzymatic conversion on the industrial scale is however hampered by the crystallinity of chitin. Thus, this enzymatic action requires the substrate i.e. chitin to be soluble which is technically difficult and an energy consuming process. We in this project wanted to address this shortcoming of CDA. In lieu of this, we have modeled a fusion protein with CDA and an auxiliary protein. The main interest being to increase the accessibility of the enzyme towards crystalline chitin. A similar fusion work with chitinases had improved the catalytic ability towards insoluble chitin. In the first step, suitable partners were searched through the protein data bank (PDB) wherein the domain architecture were sought. The next step was to create the models of the fused product using various in silico techniques. The models were created by MODELLER and evaluated for properties such as the energy or the impairment of the binding sites. A fusion PCR has been designed based on the linker sequences generated by MODELLER and would be tested for its activity towards insoluble chitin.

Keywords: chitin deacetylase, modeling, chitin binding domain, chitinases

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Zhe Wang

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The fashion week, as a critical international fashion event in shaping world fashion cities, is one of the most significant world events that serves as the core medium for designers to stage new collections. However, its role in bringing about and shaping design ideologies of major fashion cities have long been neglected from a fashion ecosystem perspective. With the expanding scale of international fashion weeks in terms of culture and commerce, the organizational structures of these fashion weeks are becoming more complex. In the emerging fashion city, typified by Shanghai, a newly-formed 'hodgepodge' transforming the current global fashion ecosystem. A city’s legitimate fashion institutions, typically the organizers of international fashion weeks, have cultivated various cultural characteristics via rules and regulations pertaining to international fashion weeks. Under these circumstances, designers’ participatory behaviors, specifically show design and production, are influenced by the cultural ideologies of official organizers and institutions. This research compares international based Chinese (IBC) independent designers’ participatory behavior in London and Shanghai Fashion Weeks: specifically, the way designers present their clothing and show production. both of which are found to be profoundly influenced by cultural and design ideologies of fashion weeks. They are, to a large degree, manipulated by domestic institutions and organizers. Shanghai fashion week has given rise to a multiple, mass-ended entertainment carnival design and cultural ideology in Shanghai, thereby impacting the explicit cultural codes or intangible rules that IBC designers must adhere to when designing and producing fashion shows. Therefore, influenced by various cultural characteristics in the two cities, IBC designers’ show design and productions, in turn, play an increasingly vital role in shaping the design characteristic of an international fashion week. Through researching the organizational systems and design preferences of organizers of London and Shanghai fashion weeks, this paper demonstrates the embeddedness of design systems in the forming of design ideologies under various cultural and institutional contexts. The core methodology utilized in this research is ethnography. As a crucial part of a Ph.D. project on innovations in fashion shows under a cross-cultural context run by Edinburgh College of Art, School of Design, the fashion week’s organizational culture in various cultural contexts is investigated in London and Shanghai for approximately six months respectively. Two IBC designers, Angel Chen and Xuzhi Chen were followed during their participation of London and Shanghai Fashion Weeks from September 2016 to June 2017, during which two consecutive seasons were researched in order to verify the consistency of design ideologies’ associations with organizational system and culture.

Keywords: institutional ideologies, international fashion weeks, IBC independent designers; fashion show

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Authors: Jeter Canete, Noreen Joyce Estrella, Yedda Sachi Patrice Madelo

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Zooplankton plays a crucial role in aquatic ecosystems and a number of water parameters involved in it. Despite their relevance, there is inadequate information about zooplankton communities in Sarangani Bay, Sarangani Province: one of the most essential waterbodies in Mindanao. The aim of the present study was to determine the composition, abundance, and diversity of zooplankton as well as to provide more recent data about the physico-chemical characteristics of Sarangani Bay. Zooplankton samples were collected by vertical hauls using a zooplankton net (mouth diameter: 0.5m; mesh size opening: round, 350μm) in three stations in the coastal waters of Alabel, Malapatan, and Maasim during November 2018. A total of 74 species of zooplankton belonging mainly to Kingdom Protozoa, Phylum Arthropoda, Chaetognatha, and Chordata were identified. Results showed a total zooplankton abundance of 1,984,166 ind/m³ with the highest count recorded at Malapatan (717,169 ind/m³) and the lowest at Maasim (624,411 ind/m³). Among 22 zooplankton groups identified, subclass Copepoda was found to be the most dominant (73.10%), followed by Appendicularia (12.18%) and Vertebrata (3.54%). Diversity analysis revealed an even distribution of species and a diverse ecosystem in all stations sampled. Correlation analysis indicated a strong relationship between zooplankton abundance and physico-chemical parameters. Overall, the physico-chemical profile of Sarangani Bay did not differ from the standards set by DENR, and analysis of the zooplankton communities revealed that Sarangani Bay favorably supports marine organisms to flourish. The findings of this study provide useful knowledge on zooplankton communities and can be used to create management strategies to protect the aquatic biodiversity in Sarangani Bay.

Keywords: aquatic biomonitoring, biodiversity, physicochemical analysis, population survey, Sarangani Bay, Sarangani Province, zooplankton

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Authors: Chuping Lo

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This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

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Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

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Authors: Seda Muftugil-Yalcin

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With the proliferation of the number of social enterprises worldwide, there is now a whole ecosystem full of different organisational actors revolving around social enterprises. Impact hubs, incubation centers, and organisations (profit or non-profit) that offer consultancy services to social enterprises can be said to constitute one such cluster in the eco-system. These organisations offer a variety of services to social enterprises which desire to maximize their positive social impact. Especially with regards to impact measurement, there are numerous systems/guides/approaches/tools developed that claim to benefit social enterprises. Many organisations choose one of the existing tools and craft programs that help social enterprises to measure and to manage their social impacts. However, empirical evidence with regards to how the services of these consultancy organisations are precisely utilized on the field is scarce. This inevitably casts doubt on the impact of these organisations themselves. This research dwells on four case studies from the Netherlands and Turkey. In each country, two university-affiliated impact centers and two independent consultancy agencies that work with social entrepreneurs in the area of social impact measurement are closely examined. The overarching research question has been 'With regards to impact measurement, how do the founders/managers of these organisations perceive and make sense of their contribution to social enterprises and to the social entrepreneurship eco-system at large?' As for methodology, in-depth interviews were carried out with the managers/founders of these organisations and discourse analysis method has been used for data analysis together with grounded theory. The comparison between Turkey and Netherlands elucidate common denominators of impact measurement hype and discourses that are currently existing worldwide. In addition, it also reveals differing priorities of social enterprises in these different settings, which shape the expectations of social enterprises of consultancy organisations. Comparison between university affiliated impact hubs and independent consultancy organisations also give away important data about how different forms of consultancy organisations (in this case university based and independent) position themselves in relation to alike organisations with similar aims. The overall aim of the research is to reveal the contribution of the consultancy organisations that work with social enterprises to the social entrepreneurship field as perceived by them through a cross cultural study. The findings indicate that in both settings, the organisations that were claiming to bring positive social impact on the social entrepreneurship eco-system through their impact measurement trainings were themselves having a hard time in concretizing their own contributions; which indicated that these organisations were in need of a different impact measurement discourse than the ones they were championing.

Keywords: consultancy organisations, social entrepreneurship, social impact measurement, social impact discourse

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: R Palson Kennedy, P.Kiran Sai

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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

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Authors: Tsedekech Gebremeskel Weldmichael, Tamas Szegi, Lubangakene Denish, Ravi Kumar Gangwar, Erika Micheli, Barbara Simon

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Following the 1992 Earth Summit in Rio de Janeiro, soil biodiversity has been recognized globally as a crucial player in guaranteeing the functioning of soil and a provider of several ecosystem services essential for human well-being. The microbial fraction of the soil is a vital component of soil fertility as soil microbes play key roles in soil aggregate formation, nutrient cycling, humification, and degradation of pollutants. Soil fauna, such as earthworms, have huge impacts on soil organic matter dynamics, nutrient cycling, and infiltration and distribution of water in the soil. Currently, land-use change has been a global concern as evidence accumulates that it adversely affects soil biodiversity and the associated ecosystem goods and services. In this study, we examined the patterns of soil microbial respiration (SMR) and earthworm (abundance, biomass, and species richness) across three land-use types (grassland, arable land, and forest) in Hungary. The objectives were i) to investigate whether there is a significant difference in SMR and earthworm (abundance, biomass, and species richness) among land-use types. ii) to determine the key soil properties that best predict the variation in SMR and earthworm communities. Soil samples, to a depth of 25 cm, were collected from the surrounding areas of seven soil profiles. For physicochemical parameters, soil organic matter (SOM), pH, CaCO₃, E₄/E₆, available nitrogen (NH₄⁺-N and NO₃⁻-N), potassium (K₂O), phosphorus (P₂O₅), exchangeable Ca²⁺, Mg²⁺, soil moisture content (MC) and bulk density were measured. The analysis of SMR was determined by basal respiration method, and the extraction of earthworms was carried out by hand sorting method as described by ISO guideline. The results showed that there was no statistically significant difference among land-use types in SMR (p > 0.05). However, the highest SMR was observed in grassland soils (11.77 mgCO₂ 50g⁻¹ soil 10 days⁻¹) and lowest in forest soils (8.61 mgCO₂ 50g⁻¹ soil 10 days⁻¹). SMR had strong positive correlations with exchangeable Ca²⁺ (r = 0.80), MC (r = 0.72), and exchangeable Mg²⁺(r = 0.69). We found a pronounced variation in SMR among soil texture classes (p < 0.001), where the highest value in silty clay loam soils and the lowest in sandy soils. This study provides evidence that agricultural activities can negatively influence earthworm communities, in which the arable land had significantly lower earthworm communities compared to forest and grassland respectively. Overall, in our study, land use type had minimal effects on SMR whereas, earthworm communities were profoundly influenced by land-use type particularly agricultural activities related to tillage. Exchangeable Ca²⁺, MC, and texture were found to be the key drivers of the variation in SMR.

Keywords: earthworm community, land use, soil biodiversity, soil microbial respiration, soil property

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

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Authors: Henning Lahmann, Benjamyn I. Scott, Bart Custers

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Suboptimal adoption of AI in defence organisations carries risks for the protection of the freedom, safety, and security of society. Despite the vast opportunities that defence AI-technology presents, there are also a variety of ethical, legal, and societal concerns. To ensure the successful use of AI technology by the military, ethical, legal, and societal aspects (ELSA) need to be considered, and their concerns continuously addressed at all levels. This includes ELSA considerations during the design, manufacturing and maintenance of AI-based systems, as well as its utilisation via appropriate military doctrine and training. This raises the question how defence organisations can remain strategically competitive and at the edge of military innovation, while respecting the values of its citizens. This paper will explain the set-up and share preliminary results of a 4-year research project commissioned by the National Research Council in the Netherlands on the ethical, legal, and societal aspects of AI in defence. The project plans to develop a future-proof, independent, and consultative ecosystem for the responsible use of AI in the defence domain. In order to achieve this, the lab shall devise a context-dependent methodology that focuses on the ‘analysis’, ‘design’ and ‘evaluation’ of ELSA of AI-based applications within the military context, which include inter alia unmanned aircraft. This is bolstered as the Lab also recognises and complements the existing methods in regards to human-machine teaming, explainable algorithms, and value-sensitive design. Such methods will be modified for the military context and applied to pertinent case-studies. These case-studies include, among others, the application of autonomous robots (incl. semi- autonomous) and AI-based methods against cognitive warfare. As the perception of the application of AI in the military context, by both society and defence personnel, is important, the Lab will study how these perceptions evolve and vary in different contexts. Furthermore, the Lab will monitor – as they may influence people’s perception – developments in the global technological, military and societal spheres. Although the emphasis of the research project is on different forms of AI in defence, it focuses on several case studies. One of these case studies is on unmanned aircraft, which will also be the focus of the paper. Hence, ethical, legal, and societal aspects of unmanned aircraft in the defence domain will be discussed in detail, including but not limited to privacy issues. Typical other issues concern security (for people, objects, data or other aircraft), privacy (sensitive data, hindrance, annoyance, data collection, function creep), chilling effects, PlayStation mentality, and PTSD.

Keywords: autonomous weapon systems, unmanned aircraft, human-machine teaming, meaningful human control, value-sensitive design

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Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim

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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Muttamas Wongwanich, John R. Bryson, Catherine E. Harris

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Science parks are an essential component of localized innovation ecosystems. Science Parks have played a critical role in enhancing local innovation ecosystems in developed market economies. Attempts have been made to replicate best practice in other national contexts. To our best knowledge, the study about the development of Science Parks has not been undertaken on the economic impact on the developing countries. Further research is required to understand the adoption of Science Park policies in developing and emerging economies. This study explores the implementation of Science Park policy and its impacts on economic growth and development in Thailand, focusing on the relationship between universities and businesses. The Thailand context is essential. Thailand’s economy is dominated by agriculture and tourism. The Science Park policy is trying to develop an agriculturally orientated innovative ecosystem. Thailand established four Science Parks based on a policy that highlighted the importance of cooperation between government, HEIs, and businesses. These Science Parks are intended to increase small and medium enterprises’ (SMEs) innovativeness, employment, and regional economic growth by promoting collaboration and knowledge transfer between HEIs and the private sector. This study explores one regional Science Park in Thailand with an emphasis on understanding the implementation and operation of a triple helix innovation policy. The analysis explores the establishment of the Science Park and its impacts on firms and the regional economy through interviews with Science Parks directors, firms, academics, universities, and government officials. The analysis will inform Science Park policy development in Thailand to support the national objective to develop an innovation ecosystem based on the integration of technology with innovation policy, supporting technology-based SMEs in the creation of local jobs. The finding shows that the implementation of the Science Park policy in Thailand requires support and promotion from the government. The regional development plan must be related to the regional industry development strategy, considering the strengths and weaknesses of local entrepreneurs. The long time in granting a patent is the major obstacle in achieving the government’s aim in encouraging local economic activity. The regional Science Parks in Thailand are at the early stage of the operation plan. Thus, the impact on the regional economy cannot be measured and need further investigation in a more extended period. However, local businesses realize the vital of research and development (R&D). There have been more requests for funding support in doing R&D. Furthermore, there is the creation of linkages between businesses, HEIs, and government authorities as expected.

Keywords: developing country, emerging economy, regional development, science park, Thailand, triple helix

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Authors: Elena Valionienė, Genutė Kalvaitienė

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The issue of resilience of an individual, an organisation, or an entire ecosystem of organisations has recently become an integral part of the education system, where the uncertainties that lead to societal development in the short term create economic, social, and psycho-emotional instability. The Maritime Transport Sector (MTS) is no exception, and the aim of the article is to model the possibilities of enhancing the professional, sociocultural, and psycho-emotional resilience of MTS specialists to proactively respond to crises caused by uncertainties. The research consists of theoretical model creation that helps to identify general maritime business resilience factors and critical success factors. This can develop high resilience and achieve business excellence in a highly volatile, uncertain, complex, and ambiguous (VUCA) environment.

Keywords: maritime transport sector, resilience, uncertainties, VUCA

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: Shah Fahad

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Managing the Urban Heat Island (UHI) effects is a pressing concern for achieving sustainable urban development and ensuring thermal comfort in major cities of developing nations, such as Lahore, Pakistan. The current UHI effect is mostly triggered by climate change and rapid urbanization. This study explored UHI over the Lahore district and its adjoining urban and rural-urban fringe areas. Landsat satellite data was utilized to investigate spatiotemporal patterns of Land Use and Land Cover changes (LULC), Land Surface Temperature (LST), UHI, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Urban Thermal Field Variance Index (UTFVI). The built-up area increased very fast, with a coverage of 22.99% in 2000, 36.06% in 2010, and 47.17% in 2020, while vegetation covered 53.21 % in 2000 and 46.16 % in 2020. It also revealed a significant increase in the mean LST, from 33°C in 2000 to 34.8°C in 2020. The results indicated a significantly positive correlation between LST and NDBI, a weak correlation was also observed between LST and NDVI. The study used scatterplots to show the correlation between NDBI and NDVI with LST, results revealed that the NDBI and LST had an R² value of 0.6831 in 2000 and 0.06541 in 2022, while NDVI and LST had an R² value of 0.0235 in 1998 and 0.0295 in 2022. Proper environmental planning is vital in specific locations to enhance quality of life, protect the ecosystem, and mitigate climate change impacts.

Keywords: land use land cover, spatio-temporal analysis, remote sensing, land surface temperature, urban heat island, lahore pakistan

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Authors: Claudia Donoso

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This study compares the restriction of mobility of migrants and asylum seekers during the COVID-19 pandemic in the United States and Ecuador. Based on the discourse analysis of anti-migrant rhetoric in press articles, migrant stories in the press, reports, and border control practices, the study examines the Ecuadorian government’s response to the migration flow of Venezuelans and the United States enforcement practices against Latin American asylum seekers. By exploring Giorgio Agamben’s concept of bare life, the article argues that this failure to protect mobility rights is due to the United States and Ecuador’s views of forced migrants as bare life and carriers of the virus, justifying xenophobia, resistance to humanitarian international law, and exceptionalism. By drawing on a feminist intersectional approach, the study adds to recent research on the securitization of forced migration and challenge the race/ethnicity, immigration status, class, and nationality-based discrimination of the measures undertaken during the pandemic. The article illustrates how the treatment of forced migrants as bare life was aggravated by their intersectional inequalities. It concludes by providing recommendations that could be enforced by the US and Ecuadorian governments to protect the right to freedom of mobility.

Keywords: bare life, intersectionality, mobility rights, COVID-19, Ecuador, United States

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Eui-Jin Kim, Hasik Lee, Su-Jin Park, Dong-Kyu Kim

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Smart-card data are expected to provide information on activity pattern as an alternative to conventional person trip surveys. The focus of this study is to propose a method for training the person trip surveys to supplement the smart-card data that does not contain the purpose of each trip. We selected only available features from smart card data such as spatiotemporal information on the trip and geographic information system (GIS) data near the stations to train the survey data. XGboost, which is state-of-the-art tree-based ensemble classifier, was used to train data from multiple sources. This classifier uses a more regularized model formalization to control the over-fitting and show very fast execution time with well-performance. The validation results showed that proposed method efficiently estimated the trip purpose. GIS data of station and duration of stay at the destination were significant features in modeling trip purpose.

Keywords: activity pattern, data fusion, smart-card, XGboost

Procedia PDF Downloads 237

Authors: Smita Singh

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The paper develops a critical framework to the hegemonic discourses resorted to by the dominant powers in the global security architecture. Within this framework, security is viewed as a discourse through which identities and threats are represented and produced to legitimize the security concerns of few at the cost of others. International security have long been driven and dominated by power relations. Since the end of the Cold War, the global transformations have triggered contestations to the idea of security at both theoretical and practical level. These widening and deepening of the concept of security have challenged the existing power hierarchies at the theoretical level but not altered the substance and actors defining it. When discourses are introduced into security studies, several critical questions erupt: how has power shaped security policies of the globe through language? How does one understand the meanings and impact of those discourses? Who decides the agenda, rules, players and outliers of the security? Language as a symbolic system and form of power is fluid and not fixed. Over the years the dominant Western powers, led by the United States of America have employed various discursive practices such as humanitarian intervention, responsibility to protect, non proliferation, human rights, war on terror and so on to reorient the constitution of identities and interests and hence the policies that need to be adopted for its actualization. These power relations are illustrated in this paper through the narratives used in the nonproliferation regime. The hierarchical security dynamics is a manifestation of the global power relations driven by many factors including discourses.

Keywords: hegemonic discourse, global security, non-proliferation regime, power politics

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

Procedia PDF Downloads 132

Authors: Feng Huang, Carlos G. Ochoa, Haitao Zhao

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Poyang Lake, the largest freshwater lake in China, is located at the middle-lower reaches of the Yangtze River basin. It has great value in socioeconomic development and is internationally recognized as an important lacustrine and wetland ecosystem with abundant biodiversity. Impacted by ongoing climate change and anthropogenic activities, especially the regulation of the Three Gorges Reservoir since 2003, Poyang Lake has experienced significant water regime evolution, resulting in challenges for the management of water resources and the environment. Quantifying the contribution of hydrologic and topographic changes to water regime alteration is necessary for policymakers to design effective adaption strategies. Long term hydrologic data were collected and the back-propagation neural networks were constructed to simulate the lake water level. The impacts of hydrologic and topographic changes were differentiated through scenario analysis that considered pre-impact and post-impact hydrologic and topographic scenarios. The lake water regime was characterized by hydrologic indicators that describe monthly water level fluctuations, hydrologic features during flood and drought seasons, and frequency and rate of hydrologic variations. The results revealed different contributions of hydrologic and topographic changes to different features of the lake water regime.Noticeable changes were that the water level declined dramatically during the period of reservoir impoundment, and the drought was enhanced during the dry season. The hydrologic and topographic changes exerted a synergistic effect or antagonistic effect on different lake water regime features. The findings provide scientific reference for lacustrine and wetland ecological protection associated with water regime alterations.

Keywords: back-propagation neural network, scenario analysis, water regime, Poyang Lake

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Authors: Milind Chaudhari, Suhail Balasinor

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Due to massive deforestation and an ever-increasing population, the organic content of the soil is depleting at a much faster rate. Due to this, there is a big chance that the entire food production in the world will drop by 40% in the next two decades. Vertical farming can help in aiding food production by leveraging big data and cloud computing to ensure plants are grown naturally by providing the optimum nutrients sunlight by analyzing millions of data points. This paper outlines the most important parameters in vertical farming and how a combination of big data and AI helps in calculating and analyzing these millions of data points. Finally, the paper outlines how different organizations are controlling the indoor environment by leveraging big data in enhancing food quantity and quality.

Keywords: big data, IoT, vertical farming, indoor farming

Procedia PDF Downloads 166

Authors: Deike Janssen

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In the scenery of the Tyrolean mountains, at an altitude of 1300 meters, stands a building. Residents and activists call it a prison. However, it is not a prison -according to authorities, it is a 'Return Counseling Facility' where migrants and refugees should be "motivated" to return "voluntary" to their countries of origin. This paper argues that the geographical location of the camp functions as a site of exclusion, isolation, and coercion where no one can decide “voluntary” to return, but where people are brought to despair to leave Austria. Through a qualitative case study, this paper documents the heavy impact of offshore detention on the mental, physical and social state of the residents and a variety of human rights problems in the centre. Different developments at the Return Counselling Facility and the law that back up the centre uncover a worrying dynamic that deliberately accepts human rights problems in order to enforce borders, a policy that disregards humanitarian, legal, and ethical stands in order to deport people at all hazards. It, therefore, can be seen as a creative and ultimate exercise of state power, which uses isolated locations to control migration. While the analysis revises the micro and macro implications of the facility and, therefore, the legal and political facets, it also sheds light on the role of the civil society, which tries to increase through constant and collective efforts the human rights efforts of the government.

Keywords: deportation, human rights, migration, refugee detention, voluntary return

Procedia PDF Downloads 130

Authors: Kgomotso Matobole, Natania De Wet, Tefo Mbambo, Hilary Rutto, Tumisang Seodigeng

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Nitrogen and phosphorus are nutrients which are required in the ecosystem, however, at high levels, these nutrients contribute to the process of eutrophication in the receiving water bodies, which threatens aquatic organisms. Hence it is vital that they are removed before the water is discharged. This phenomenon increases the cost related to wastewater treatment. This raises the need for the development of processes that are cheaper. Activated biocarbon was used in batch and filtration system to remove nitrates and phosphates. The batch system has higher nutrients removal capabilities than the filtration system. For phosphate removal, 93 % removal is achieved at the adsorbent of 300 g while for nitrates, 84 % removal is achieved when 200 g of activated carbon is loaded.

Keywords: waste water treatment, phosphates, nitrates, activated carbon, agricultural waste

Procedia PDF Downloads 406