OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Gaussian Neural Processes for Regression
Venue: arXiv cs.LG 2021
Year: 2021

Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test time, and are trainable via a simple maximum likelihood procedure. A limitation of CNPs is their inability to model dependencies in the outputs. This significantly hurts predictive performance and renders it impossible to draw coherent function samples, which limits the applicability of CNPs in down-stream applications and decision making. Neural Processes (NPs; Garnelo et al., 2018) attempt to alleviate this issue by using latent variables, relying on these to model output dependencies, but introduces difficulties stemming from approximate inference. One recent alternative (Bruinsma et al., 2021), which we refer to as the FullConvGNP, models dependencies in the predictions while still being trainable via exact maximum-likelihood. Unfortunately, the FullConvGNP relies on expensive 2D-dimensional convolutions, which limit its applicability to only one-dimensional data. In this work, we present an alternative way to model output dependencies which also lends itself maximum likelihood training but, unlike the FullConvGNP, can be scaled to two- and three-dimensional data. The proposed models exhibit good performance in synthetic experiments.
Spatial Transformer Networks for Curriculum Learning
Venue: arXiv cs.CV 2021
Year: 2021

Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the convergence rate or obtained accuracy. The main concept in curriculum learning is to start the training with simpler tasks and gradually increase the level of difficulty. Therefore, a natural question is how to determine or generate these simpler tasks. In this work, we take inspiration from Spatial Transformer Networks (STNs) in order to form an easy-to-hard curriculum. As STNs have been proven to be capable of removing the clutter from the input images and obtaining higher accuracy in image classification tasks, we hypothesize that images processed by STNs can be seen as easier tasks and utilized in the interest of curriculum learning. To this end, we study multiple strategies developed for shaping the training curriculum, using the data generated by STNs. We perform various experiments on cluttered MNIST and Fashion-MNIST datasets, where on the former, we obtain an improvement of $3.8$pp in classification accuracy compared to the baseline.
Resilient ICT4D: Building and Sustaining our Community in Pandemic Times
Venue: arXiv cs.CY 2021
Year: 2021

The impacts of the COVID-19 pandemic, disproportionally affecting vulnerable people and deepening pre-existing inequalities (Dreze, 2020; Qureshi, 2021), have interested the very same "development" processes that the IFIP Working Group 9.4 on the Implications of Information and Digital Technologies for Development has dealt with over time. A global development paradigm (Oldekop et al., 2020) has emerged in response to the global nature of the crisis, infusing new meaning in the spirit of "making a better world" with ICTs (Walsham, 2012) that always have characterised ICT4D research. Such a new meaning contextualises our research in the landscape of the first pandemic of the datafied society (Milan & Trere, 2020), coming to terms with the silencing of narratives from the margins within the pandemic (Milan et al., 2021) - in Qureshi's (2021) words, a "pandemics within the pandemic" producing new socio-economic inequities in a state of global emergency.
Digital Global Public Goods
Venue: arXiv cs.CY 2021
Year: 2021

The purpose of this paper is to define and conceptualize digital global public goods (DGPGs) and illustrate the importance of contextual relevance in ICT4D projects. Recent studies have examined the importance of digital artefacts with public goods traits, emphasizing the significant potential for socio-economic development. However, we know little about the theoretical and practical dimensions of how we can align the public goods traits of such artefacts to create relevance in the context they are implemented. To address this gap we review the literature firstly to develop a definition and conceptual basis of DGPGs and then to illustrate the importance of relevance: how to align DGPGs with context to meet local needs. The illustration draws from a case study of the District Health Information systems (DHIS2). The paper advances both the theoretical and practical understanding of DPGs in development processes.
Deconstructing the Dichotomous Relationship Between "IT analysts and End-users": A Case of Implementing Standard Indicators in Cameroon
Venue: arXiv cs.CY 2021
Year: 2021

Différance and supplément are post-structuralist concepts for analyzing language in text and are most often associated with the work of Jacque Derrida. The findings after the implementation of standard health indicators in Cameroon show that staff at the peripheral level encounter multiple challenges, including lack of participation during the implementation process, and tension between staff at the peripheral level and IT staff at the central level, which result in non-use of the system. We use deconstruction to understand the root cause and the findings reveal that IT professionals and end-users are embedded in a relation of domination. That is, IT professionals are différance from end-users and end-users are supplément of IT professionals. Although end-users are portrayed as supplementary, they are supposed to manage the system, which is contradictory. This led to IT professionals having more privilege and authority over end-users. This dichotomous relation is a derivative of the organizational structure. The notion of portraying IT professionals in charge and having more authority over end-users is an avenue for conflict. The paper concludes that a HIS organizational structure where decision-making is centralized is a ground for conflict and a major roadblock of building local capacity and providing infrastructural support at the peripheral.
Building Resilient Information Systems for Child Nutrition in Post-conflict Sri Lanka during COVID-19 Pandemic
Venue: arXiv cs.CY 2021
Year: 2021

Post-conflict, low-resource settings are menaced with challenges related to low-resources, economic and social instability. The objective of the study is to understand the socio-technical determinants of resilience of resilience of routine information systems a backdrop of an implementation of a mobile-based nutrition information system in a post-conflict district in Sri Lanka. The longitudinal events in the study spans across several years into the period of COVID-19 pandemic and tries to understand the process of developing resilience of in a vulnerable district. The qualitative study deploys interviews, observations and document analysis for collection of empirical data. The case study reveals the long-standing capacity building, leadership and local governance, multisector collaboration, platform resilience and empowering of field health staff contribute in building resilience in everyday context. The empirical insights include the mechanisms in building resilience in routine system in low resource settings while promoting data quality and data use at field level.
Building Agility in COVID-19 Information Systems Response in Sri Lanka: Recommendations for Practice
Venue: arXiv cs.CY 2021
Year: 2021

COVID-19 pandemic tested the capacity of information systems in countries on the ability to rapidly respond to requirements which were not anticipated. This article analyzes the socio-technical determinants of agility in building the IS response to the COVID-19 pandemic in Sri Lanka. We deploy qualitative research methods to explore the case study of implementation of COVID-19 surveillance system in Sri Lanka. Three key recommendations are developed for practice relating to high-level multisectoral governance, use of lightweight digital platforms and leveraging on existing capacities and infrastructure.
Joint Link Rate Selection and Channel State Change Detection in Block-Fading Channels
Venue: arXiv cs.NI 2021
Year: 2021

In this work, we consider the problem of transmission rate selection for a discrete time point-to-point block fading wireless communication link. The wireless channel remains constant within the channel coherence time but can change rapidly across blocks. The goal is to design a link rate selection strategy that can identify the best transmission rate quickly and adaptively in quasi-static channels. This problem can be cast into the stochastic bandit framework, and the unawareness of time-stamps where channel changes necessitates running change-point detection simultaneously with stochastic bandit algorithms to improve adaptivity. We present a joint channel change-point detection and link rate selection algorithm based on Thompson Sampling (CD-TS) and show it can achieve a sublinear regret with respect to the number of time steps $T$ when the channel coherence time is larger than a threshold. We then improve the CD-TS algorithm by considering the fact that higher transmission rate has higher packet-loss probability. Finally, we validate the performance of the proposed algorithms through numerical simulations.
Data Justice in Digital Social Welfare: A Study of the Rythu Bharosa Scheme
Venue: arXiv cs.CY 2021
Year: 2021

While digital social protection systems have been claimed to bring efficacy in user identification and entitlement assignation, their data justice implications have been questioned. In particular, the delivery of subsidies based on biometric identification has been found to magnify exclusions, imply informational asymmetries, and reproduce policy structures that negatively affect recipients. In this paper, we use a data justice lens to study Rythu Bharosa, a social welfare scheme targeting farmers in the Andhra Pradesh state of India. While coverage of the scheme in terms of number of recipients is reportedly high, our fieldwork revealed three forms of data justice to be monitored for intended recipients. A first form is design-related, as mismatches of recipients with their registered biometric credentials and bank account details are associated to denial of subsidies. A second form is informational, as users who do not receive subsidies are often not informed of the reason why it is so, or of the grievance redressal processes available to them. To these dimensions our data add a structural one, centred on the conditionality of subsidy to approval by landowners, which forces tenant farmers to request a type of landowner consent that reproduces existing patterns of class and caste subordination. Identifying such data justice issues, the paper adds to problematisations of digital social welfare systems, contributing a structural dimension to studies of data justice in digital social protection.
A Resilient ICT4D Approach to ECO Countries' Education Response during COVID-19 Pandemic
Venue: arXiv cs.CY 2021
Year: 2021

According to the United Nations, schools' closures have impacted up to 99 per cent of the student population in low and lower-middle-income countries. This research-in-progress report introduces a project on Emergency Remote Teaching (ERT) measures in the ten member states of the Economic Cooperation Organization (ECO) with a focus on the application of Information and Communication Technologies (ICTs) in primary and secondary education levels. The project takes a comparative approach within a resilient ICT-for-Development (ICT4D) framework, where the coping, endurance, and return to pre-crisis functionalities in education systems are studied. The preliminary research demonstrates the impacts of the country's general COVID-19 strategy, the education system in place, and digital infrastructure's level of development on instigating distance-learning platforms. The paper further shows that in addition to access to stable internet connections and digital devices, other infrastructural factors such as access to food, electricity and health services play a significant role in education response planning and implementation. Human factors in the education system, such as teacher training for the usage of ICTs, digital literacy of students and parents, and already existing vulnerabilities in the education system pose challenges to crisis management in the education sector. Other socio-political factors such as attitudes towards girls' education, level of corruption, institutional capacity, and international sanctions or available funds also make the education system less resilient.
Towards a Resilient Information System for Agriculture Extension Information Service: An Exploratory Study
Venue: arXiv cs.CY 2021
Year: 2021

Although digital technologies are contributing to human development, several information systems (IS) interventions for development especially in developing countries do not perform as expected nor deliver anticipated outcomes at scale. This raises questions about how to develop and enhance resilient IS for development, an area that requires more research attention. A sound and systematic understanding of the mechanisms local communities apply to maintain resilience and the key transformation areas for a resilient IS development will help to improve the situation. This study addresses how stakeholders can ensure resilient information provision within the Agricultural Extension Information Service (AEIS) and identifies the challenges in designing resilient IS. Conceptually, the study draws from the IS resilience framework. Empirically, it draws from interview data collected from the AEIS provision practice in Ethiopia. The findings show the robustness, self-organization, learning, redundancy, rapidity, scale, diversity and equality mechanisms, the challenges and the key transformations required to advance the resilience of IS for AEIS. The study contributes to the conversation on the application of the IS resilience framework in analyzing local information provision practices as well as to practice highlighting the key transformation areas to improve the effectiveness and impact of AEIS.
Custom-Tailored Clone Detection for IEC 61131-3 Programming Languages
Venue: arXiv cs.SE 2021
Year: 2021

Automated production systems (aPS) are highly customized systems that consist of hardware and software. Such aPS are controlled by a programmable logic controller (PLC), often in accordance with the IEC 61131-3 standard that divides system implementation into so-called program organization units (POUs) as the smallest software unit and is comprised of multiple textual and graphical programming languages that can be arbitrarily nested. A common practice during the development of such systems is reusing implementation artifacts by copying, pasting, and then modifying code. This approach is referred to as code cloning. It is used on a fine-granular level where a POU is cloned within a system variant. It is also applied on the coarse-granular system level, where the entire system is cloned and adapted to create a system variant, for example for another customer. This ad hoc practice for the development of variants is commonly referred to as clone-and-own. It allows the fast development of variants to meet varying customer requirements or altered regulatory guidelines. However, clone-and-own is a non-sustainable approach and does not scale with an increasing number of variants. It has a detrimental effect on the overall quality of a software system, such as the propagation of bugs to other variants, which harms maintenance. In order to support the effective development and maintenance of such systems, a detailed code clone analysis is required. On the one hand, an analysis of code clones within a variant (i.e., clone detection in the classical sense) supports experts in refactoring respective code into library components. On the other hand, an analysis of commonalities and differences between cloned variants (i.e., variability analysis) supports the maintenance and further reuse and facilitates the migration of variants into a software product line (SPL).
A Proposed Framework for the Comprehensive Scalability Assessment of ICTD Projects
Venue: arXiv cs.CY 2021
Year: 2021

The scalability of ICTD projects is an imperative topic that has been neglected in the field. Little has been written or investigated about the assessment of the scalability of ICTD projects due to factors, such as the lack of proven business models for success, the high failure rate of projects, undefined aspects of assessment, and the small number of projects that have scaled. Therefore, there are various factors that should be taken into consideration to alleviate the challenges experienced in the process of scaling up. This research study is guided by an investigation into how can the scalability of an ICTD project be assessed using a comprehensive evaluation approach that considers the impact and potential sustainability of the project. This research study proposes a Comprehensive Scalability Assessment Framework (CSAF), using systems theory and amplification theory to guide the theoretical analysis and empirical investigation. A theorizing approach is used to develop the framework, which is structured around three components: assessment guidelines and proceeding domains of evaluation; four scalability themes (stakeholder composition, models feasibility, resources sustainability and resilience) and judge scalability.
The Liberalities and Tyrannies of ICTs for Vulnerable Migrants: The Status Quo, Gaps and Directions
Venue: arXiv cs.CY 2021
Year: 2021

Information and communication technologies (ICTs) have increasingly become vital for people on the move including the nearly 80 million displaced due to conflict, violence, and human right violations globally. However, existing research on ICTs and migrants, which almost entirely focused on migrants' ICT use 'en route' or within developed economies principally in the perspectives of researchers from these regions, is very fragmented posing a difficulty in understanding the key objects of research. Moreover, ICTs are often celebrated as liberating and exploitable at migrants' rational discretion even though they are 'double-edged swords' with significant risks, burdens, pressures and inequality challenges particularly for vulnerable migrants including those forcefully displaced and trafficked. Towards addressing these limitations and illuminating future directions, this paper, first, scrutinises the existing research vis-a-vis ICTs' liberating and authoritarian role particularly for vulnerable migrants whereby explicating key issues in the research domain. Second, it identifies key gaps and opportunities for future research. Using a tailored methodology, broad literature relating to ICTs and migration/development published in the period 1990-2020 was surveyed resulting in 157 selected publications which were critically appraised vis-a-vis the key themes, major technologies dealt with, and methodologies and theories/concepts adopted. Furthermore, key insights, trends, gaps, and future research opportunities pertaining to both the existing and missing objects of research in ICTs and migration/development are spotlighted.
Big Data Analytics in Humanitarian and Disaster Operations: A Systematic Review
Venue: arXiv cs.CY 2021
Year: 2021

By the outset of this review, 168 million people needed humanitarian aid, and the number grew to 235 million by the end of the completion of this review. There is no time to lose, definitely no data to lose. Humanitarian relief is crucial not just to contend with a pandemic once a century but also to provide help during civil conflicts, ever-increasing natural disasters, and other forms of crisis. Reliance on technology has never been so relevant and critical than now. The creation of more data and advancements in data analytics provides an opportunity to the humanitarian field. This review aimed at providing a holistic understanding of big data analytics in a humanitarian and disaster setting. A systematic literature review method is used to examine the field and the results of this review explain research gaps, and opportunities available for future research. This study has shown a significant research imbalance in the disaster phase, highlighting how the emphasis is on responsive measures than preventive measures. Such reactionary measures would only exacerbate the disaster, as is the case in many nations with COVID-19. Overall this research details the current state of big data analytics in a humanitarian and disaster setting.
Curiosity Notebook: The Design of a Research Platform for Learning by Teaching
Venue: arXiv cs.HC 2021
Year: 2021

While learning by teaching is a popular pedagogical technique, it is a learning phenomenon that is difficult to study due to variability in the tutor-tutee pairings and learning environments. In this paper, we introduce the Curiosity Notebook, a web-based research infrastructure for studying learning by teaching via the use of a teachable agent. We describe and provide rationale for the set of features that are essential for such a research infrastructure, outline how these features have evolved over two design iterations of the Curiosity Notebook and through two studies -- a 4-week field study with 12 elementary school students interacting with a NAO robot and an hour-long online observational study with 41 university students interacting with an agent -- demonstrate the utility of our platform for making observations of learning-by-teaching phenomena in diverse learning environments. Based on these findings, we conclude the paper by reflecting on our design evolution and envisioning future iterations of the Curiosity Notebook.
Angular Velocity Estimation using Non-coplanar Accelerometer Array
Venue: arXiv cs.RO 2021
Year: 2021

Over the last few decades, Gyro-Free IMUs have been extensively researched to overcome the limitations of gyroscopes. This research presents a Non-coplanar Accelerometer Array (NAA) for estimating angular velocity with non-specific geometric arrangement of four or more triaxial accelerometers with non-coplanarity constraint. The presented proof of non-coplanar spacial arrangement also provides insights into propagation of the sensor noise and construction of the noise covariance matrices. The system noise depends on the singular values of the relative displacement matrix (between the sensors). A dynamical system model with uncorrelated process and measurement noise is proposed where the accelerometer readings are used simultaneously as process and measurement inputs. The angular velocity is estimated using an EKF that discretizes and linearizes the continuous-discrete time dynamical system. The simulations are performed on a Cube-NAA (Cu-NAA) comprising four accelerometers placed at different vertices of a cube. They analyze the estimation error for static and dynamic movement as the distance between the accelerometers is varied. Here, the system noise is observed to decrease inversely with the length of the cube edge as the arrangement is kept identical. Consequently, the simulation results indicate asymptotic decrease in the standard error of estimation with edge length. The experiments are conducted on a Cu-NAA with five reflective optical markers. The reflective markers are visually tracked using VICON to construct the ground truth. This unique experimental setup, apart from providing three degrees of rotational freedom of movement, also allows for three degrees of spacial translation (linear acceleration of the Cu-NAA in space). The simulation and experimental results indicate better performance of the proposed EKF as compared to one with correlated process and measurement noises.