54 research outputs found
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Building Adaptive Capacity: An Analysis of Innovations in Information and Communication Technology in Post-Earthquake Haiti
New information and communication technology (ICT) platforms that emerged in the humanitarian response to the 2010 earthquake in Port-au-Prince, Haiti, have been hyped for their ability to spatialize and coordinate disaster relief efforts and, more broadly, to assist short-term mitigation efforts. This research examines the role of recent innovations in ICT in augmenting the adaptive capacity of the Haitian state to carry out mitigation and adaptation planning. Case studies, analysis of communication flows, and interviews were used to assess the state uptake of three different mobile and open source platforms, including: Ushahidi/Noula, SIS-KLOR, and OpenStreetMap. Findings suggest while these new platforms do offer the potential to increase state efficiencies, assess community need, and produce geospatial information, a combination of fear of responsibility, limited resources, lack of local ownership, and path dependency render foreign technologies unsuitable for Haitian use and impede institutionalization. Recommendations focus on shifting practice from investing in foreign technologies to promoting domestic innovation in ICT by strengthening and updating intellectual property legislation and investing in domestic tech firms. This research is relevant to strategic planners, policy-makers, and international organizations working in the IT secto
Introduction to development engineering: a framework with applications from the field
This open access textbook introduces the emerging field of Development Engineering and its constituent theories, methods, and applications. It is both a teaching text for students and a resource for researchers and practitioners engaged in the design and scaling of technologies for low-resource communities. The scope is broad, ranging from the development of mobile applications for low-literacy users to hardware and software solutions for providing electricity and water in remote settings. It is also highly interdisciplinary, drawing on methods and theory from the social sciences as well as engineering and the natural sciences.
The opening section reviews the history of âtechnology-for-developmentâ research, and presents a framework that formalizes this body of work and begins its transformation into an academic discipline. It identifies common challenges in development and explains the bookâs iterative approach of âinnovation, implementation, evaluation, adaptation.â Each of the next six thematic sections focuses on a different sector: energy and environment; market performance; education and labor; water, sanitation and health; digital governance; and connectivity. These thematic sections contain case studies from landmark research that directly integrates engineering innovation with technically rigorous methods from the social sciences. Each case study describes the design, evaluation, and/or scaling of a technology in the field and follows a single form, with common elements and discussion questions, to create continuity and pedagogical consistency. Together, they highlight successful solutions to development challenges, while also analyzing the rarely discussed failures. The book concludes by reiterating the core principles of development engineering illustrated in the case studies, highlighting common challenges that engineers and scientists will face in designing technology interventions that sustainably accelerate economic development.
Development Engineering provides, for the first time, a coherent intellectual framework for attacking the challenges of poverty and global climate change through the design of better technologies. It offers the rigorous discipline needed to channel the energy of a new generation of scientists and engineers toward advancing social justice and improved living conditions in low-resource communities around the world
Introduction to Development Engineering
This open access textbook introduces the emerging field of Development Engineering and its constituent theories, methods, and applications. It is both a teaching text for students and a resource for researchers and practitioners engaged in the design and scaling of technologies for low-resource communities. The scope is broad, ranging from the development of mobile applications for low-literacy users to hardware and software solutions for providing electricity and water in remote settings. It is also highly interdisciplinary, drawing on methods and theory from the social sciences as well as engineering and the natural sciences. The opening section reviews the history of âtechnology-for-developmentâ research, and presents a framework that formalizes this body of work and begins its transformation into an academic discipline. It identifies common challenges in development and explains the bookâs iterative approach of âinnovation, implementation, evaluation, adaptation.â Each of the next six thematic sections focuses on a different sector: energy and environment; market performance; education and labor; water, sanitation and health; digital governance; and connectivity. These thematic sections contain case studies from landmark research that directly integrates engineering innovation with technically rigorous methods from the social sciences. Each case study describes the design, evaluation, and/or scaling of a technology in the field and follows a single form, with common elements and discussion questions, to create continuity and pedagogical consistency. Together, they highlight successful solutions to development challenges, while also analyzing the rarely discussed failures. The book concludes by reiterating the core principles of development engineering illustrated in the case studies, highlighting common challenges that engineers and scientists will face in designing technology interventions that sustainably accelerate economic development. Development Engineering provides, for the first time, a coherent intellectual framework for attacking the challenges of poverty and global climate change through the design of better technologies. It offers the rigorous discipline needed to channel the energy of a new generation of scientists and engineers toward advancing social justice and improved living conditions in low-resource communities around the world
LIPIcs, Volume 277, GIScience 2023, Complete Volume
LIPIcs, Volume 277, GIScience 2023, Complete Volum
Introduction to Development Engineering
This open access textbook introduces the emerging field of Development Engineering and its constituent theories, methods, and applications. It is both a teaching text for students and a resource for researchers and practitioners engaged in the design and scaling of technologies for low-resource communities. The scope is broad, ranging from the development of mobile applications for low-literacy users to hardware and software solutions for providing electricity and water in remote settings. It is also highly interdisciplinary, drawing on methods and theory from the social sciences as well as engineering and the natural sciences. The opening section reviews the history of âtechnology-for-developmentâ research, and presents a framework that formalizes this body of work and begins its transformation into an academic discipline. It identifies common challenges in development and explains the bookâs iterative approach of âinnovation, implementation, evaluation, adaptation.â Each of the next six thematic sections focuses on a different sector: energy and environment; market performance; education and labor; water, sanitation and health; digital governance; and connectivity. These thematic sections contain case studies from landmark research that directly integrates engineering innovation with technically rigorous methods from the social sciences. Each case study describes the design, evaluation, and/or scaling of a technology in the field and follows a single form, with common elements and discussion questions, to create continuity and pedagogical consistency. Together, they highlight successful solutions to development challenges, while also analyzing the rarely discussed failures. The book concludes by reiterating the core principles of development engineering illustrated in the case studies, highlighting common challenges that engineers and scientists will face in designing technology interventions that sustainably accelerate economic development. Development Engineering provides, for the first time, a coherent intellectual framework for attacking the challenges of poverty and global climate change through the design of better technologies. It offers the rigorous discipline needed to channel the energy of a new generation of scientists and engineers toward advancing social justice and improved living conditions in low-resource communities around the world
Risks of Discrimination through the Use of Algorithms. A study compiled with a grant from the Federal Anti-Discrimination Agency
Algorithms, including artificial intelligence, are used in a variety of ways to differentiate people, services, products or positions. This study uses examples to illustrate the technical and organisational causes of discrimination risks and analyses the resulting forms of discrimination. Its particular focus is on the social risks from algorithmic differentiation and automated decision-making, including injustice by generalisation, treatment of people as mere objects, restrictions on the free development of personality and informational self-determination, accumulation effects and growing inequality as well as risks to societal goals of equality or social policy. In these cases, there is a need for reforms of the anti-discrimination and data protection law, but also for societal considerations and definitions of which kinds of algorithmic differentiations are considered acceptable in a society in order to protect fundamental rights and values. Last but not least, the study discusses tasks for anti-discrimination agencies and equality bodies, ranging from the identification and proof of algorithm-based discrimination to preventive and cooperative actions
Demystifying security and compatibility issues in Android Apps
Never before has any OS been so popular as Android. Existing mobile phones
are not simply devices for making phone calls and receiving SMS messages, but
powerful communication and entertainment platforms for web surfing, social
networking, etc. Even though the Android OS offers powerful communication and
application execution capabilities, it is riddled with defects (e.g., security
risks, and compatibility issues), new vulnerabilities come to light daily, and
bugs cost the economy tens of billions of dollars annually. For example,
malicious apps (e.g., back-doors, fraud apps, ransomware, spyware, etc.) are
reported [Google, 2022] to exhibit malicious behaviours, including privacy
stealing, unwanted programs installed, etc. To counteract these threats, many
works have been proposed that rely on static analysis techniques to detect such
issues. However, static techniques are not sufficient on their own to detect
such defects precisely. This will likely yield false positive results as static
analysis has to make some trade-offs when handling complicated cases (e.g.,
object-sensitive vs. object-insensitive). In addition, static analysis
techniques will also likely suffer from soundness issues because some
complicated features (e.g., reflection, obfuscation, and hardening) are
difficult to be handled [Sun et al., 2021b, Samhi et al., 2022].Comment: Thesi
FIN-DM: finantsteenuste andmekaeve protsessi mudel
Andmekaeve hĂ”lmab reeglite kogumit, protsesse ja algoritme, mis vĂ”imaldavad ettevĂ”tetel iga pĂ€ev kogutud andmetest rakendatavaid teadmisi ammutades suurendada tulusid, vĂ€hendada kulusid, optimeerida tooteid ja kliendisuhteid ning saavutada teisi eesmĂ€rke. Andmekaeves ja -analĂŒĂŒtikas on vaja hĂ€sti mÀÀratletud metoodikat ja protsesse. Saadaval on mitu andmekaeve ja -analĂŒĂŒtika standardset protsessimudelit. KĂ”ige mĂ€rkimisvÀÀrsem ja laialdaselt kasutusele vĂ”etud standardmudel on CRISP-DM. Tegu on tegevusalast sĂ”ltumatu protsessimudeliga, mida kohandatakse sageli sektorite erinĂ”uetega. CRISP-DMi tegevusalast lĂ€htuvaid kohandusi on pakutud mitmes valdkonnas, kaasa arvatud meditsiini-, haridus-, tööstus-, tarkvaraarendus- ja logistikavaldkonnas. Seni pole aga mudelit kohandatud finantsteenuste sektoris, millel on omad valdkonnapĂ”hised erinĂ”uded.
Doktoritöös kĂ€sitletakse seda lĂŒnka finantsteenuste sektoripĂ”hise andmekaeveprotsessi (FIN-DM) kavandamise, arendamise ja hindamise kaudu. Samuti uuritakse, kuidas kasutatakse andmekaeve standardprotsesse eri tegevussektorites ja finantsteenustes. Uurimise kĂ€igus tuvastati mitu tavapĂ€rase raamistiku kohandamise stsenaariumit. Lisaks ilmnes, et need meetodid ei keskendu piisavalt sellele, kuidas muuta andmekaevemudelid tarkvaratoodeteks, mida saab integreerida organisatsioonide IT-arhitektuuri ja Ă€riprotsessi. Peamised finantsteenuste valdkonnas tuvastatud kohandamisstsenaariumid olid seotud andmekaeve tehnoloogiakesksete (skaleeritavus), Ă€rikesksete (tegutsemisvĂ”ime) ja inimkesksete (diskrimineeriva mĂ”ju leevendus) aspektidega. SeejĂ€rel korraldati tegelikus finantsteenuste organisatsioonis juhtumiuuring, mis paljastas 18 tajutavat puudujÀÀki CRISP- DMi protsessis.
Uuringu andmete ja tulemuste abil esitatakse doktoritöös finantsvaldkonnale kohandatud CRISP-DM nimega FIN-DM ehk finantssektori andmekaeve protsess (Financial Industry Process for Data Mining). FIN-DM laiendab CRISP-DMi nii, et see toetab privaatsust sĂ€ilitavat andmekaevet, ohjab tehisintellekti eetilisi ohte, tĂ€idab riskijuhtimisnĂ”udeid ja hĂ”lmab kvaliteedi tagamist kui osa andmekaeve elutsĂŒklisData mining is a set of rules, processes, and algorithms that allow companies to increase revenues, reduce costs, optimize products and customer relationships, and achieve other business goals, by extracting actionable insights from the data they collect on a day-to-day basis. Data mining and analytics projects require well-defined methodology and processes. Several standard process models for conducting data mining and analytics projects are available. Among them, the most notable and widely adopted standard model is CRISP-DM. It is industry-agnostic and often is adapted to meet sector-specific requirements. Industry- specific adaptations of CRISP-DM have been proposed across several domains, including healthcare, education, industrial and software engineering, logistics, etc. However, until now, there is no existing adaptation of CRISP-DM for the financial services industry, which has its own set of domain-specific requirements.
This PhD Thesis addresses this gap by designing, developing, and evaluating a sector-specific data mining process for financial services (FIN-DM). The PhD thesis investigates how standard data mining processes are used across various industry sectors and in financial services. The examination identified number of adaptations scenarios of traditional frameworks. It also suggested that these approaches do not pay sufficient attention to turning data mining models into software products integrated into the organizations' IT architectures and business processes. In the financial services domain, the main discovered adaptation scenarios concerned technology-centric aspects (scalability), business-centric aspects (actionability), and human-centric aspects (mitigating discriminatory effects) of data mining. Next, an examination by means of a case study in the actual financial services organization revealed 18 perceived gaps in the CRISP-DM process.
Using the data and results from these studies, the PhD thesis outlines an adaptation of
CRISP-DM for the financial sector, named the Financial Industry Process for Data Mining
(FIN-DM). FIN-DM extends CRISP-DM to support privacy-compliant data mining, to tackle AI ethics risks, to fulfill risk management requirements, and to embed quality assurance as part of the data mining life-cyclehttps://www.ester.ee/record=b547227
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