OpenRaiser/Intern-Atlas huggingface dataset:
Sustainable Development Through a Mobile Application for a Community Clinic
Venue: arXiv cs.CY 2021
Year: 2021

Implementing Information and Communication Technology (ICT) solutions can alleviate pressing problems in society and are a central component of sustainable development. Often, healthcare addresses the symptoms without approaching the socioeconomic limiters that can lead to reduced individual economic freedoms from receiving healthcare. This paper investigates the question: How can technology and training interventions enable clinicians to offer care that addresses the socioeconomic limitations of their patients? This paper observes the implementation of a mobile app designed to offer people who cannot access health resources in Omaha, Nebraska, a city in the Midwestern United States. This study follows the design science and action research approach, with clinicians participating in developing the mobile app. As a result of COVID, patients no longer have access to the free clinic because it was shut down. The app is available to the broader community needing basic resources to stay healthy. Through sets of application revisions and observations of usage, this paper arrives at insights into how such applications can support multi-ethnic and underserved communities. The contribution of this paper is to provide contextually specific and rich descriptions of how to implement sustainable ICT solutions to meet the information needs of patients in underserved communities.
Wind Power Projection using Weather Forecasts by Novel Deep Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

The transition from conventional methods of energy production to renewable energy production necessitates better prediction models of the upcoming supply of renewable energy. In wind power production, error in forecasting production is impossible to negate owing to the intermittence of wind. For successful power grid integration, it is crucial to understand the uncertainties that arise in predicting wind power production and use this information to build an accurate and reliable forecast. This can be achieved by observing the fluctuations in wind power production with changes in different parameters such as wind speed, temperature, and wind direction, and deriving functional dependencies for the same. Using optimized machine learning algorithms, it is possible to find obscured patterns in the observations and obtain meaningful data, which can then be used to accurately predict wind power requirements . Utilizing the required data provided by the Gamesa's wind farm at Bableshwar, the paper explores the use of both parametric and the non-parametric models for calculating wind power prediction using power curves. The obtained results are subject to comparison to better understand the accuracy of the utilized models and to determine the most suitable model for predicting wind power production based on the given data set.
Power Participation in Digital Citizen Engagement in South African Local Government: The Case of MOBISAM
Venue: arXiv cs.CY 2021
Year: 2021

A lack of service delivery and accountability are two characteristic challenges of numerous municipalities (local government) in South Africa. MobiSAM was introduced as a collaborative effort between a local university, civil society, residents, and local government to grow digital citizen engagement, facilitate two-way communication between local government and its citizenry, and contribute to the improved provision of basic services. Through the course of the project, it became clear that power, with respect to knowledge, was at play in the interactions or lack thereof between local government and citizens. This work-in-progress paper begins the journey of exploration of the power/knowledge dynamics at play in the MobiSAM project (using the SECI model and the Power Cube) by unpacking and understanding the nature of knowledge processed in the project and the associated knowledge creation processes that ensued between the different project stakeholders over time. The influence of power in determining the effective transfer of knowledge between key stakeholders of the project, for capacity building and organisational learning has emerged as an important issue in need of thorough investigation and critical analysis. This work in progress paper presents the preliminary framing of the research findings on the phases associated with the interplay of knowledge and power in the MobiSAM project, which are: 1) Realisation, 2) Navigating Responsiveness, and 3) Emergence.
APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback
Venue: arXiv cs.RO 2021
Year: 2021

Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to \emph{learn} how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce \textsc{apple}, Adaptive Planner Parameter Learning from \emph{Evaluative Feedback} (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show \textsc{apple} can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.
Factors Enhancing E-Government Service Gaps in a Developing Country Context
Venue: arXiv cs.CY 2021
Year: 2021

Globally, the discourse of e-government has gathered momentum in public service delivery. No country has been left untouched in the implementation of e-government. Several government departments and agencies are now using information and communication technology (ICTs) to deliver government services and information to citizens, other government departments, and businesses. However, most of the government departments have not provided all of their services electronically or at least the most important ones. Thus, this creates a phenomenon of e-government service gaps. The objective of this study was to investigate the contextual factors enhancing e-government service gaps in a developing country. To achieve this aim, the TOE framework was employed together with a qualitative case study to guide data collection and analysis. The data was collected through semi-structured interviews from government employees who are involved in the implementation of e-government services in Zimbabwe as well as from citizens and businesses. Eleven (11) factors were identified and grouped under the TOE framework. This research contributes significantly to the implementation and utilisation of e-government services in Zimbabwe. The study also contributes to providing a strong theoretical understanding of the factors that enhance e-government service gaps explored in the research model.
Moments in the Production of Space: Developing a Generic Adolescent Girls and Young Women Health Information Systems in Zimbabwe
Venue: arXiv cs.CY 2021
Year: 2021

With global targets to end AIDS by 2030 and to eliminate new HIV infections, Adolescent Girls and Young Women (AGYW) are seen to be particularly vulnerable, especially in Sub Saharan Africa. Numerous nations have therefore rolled out interventions to provide services to remove the determinants of vulnerability, such as limited education, early marriage, poverty, domestic violence, and exposure by male partners. Within this context, subpopulations such as sex workers increase the vulnerability amongst AGYW and are also supported through prevention programming. This study follows a project to develop a generic health information systems solution to provide a means to monitor and evaluate the successes of the AGYW initiative in reducing new infections. It borrows theoretical ideas from Henri Lefebvre's theory of moments to describe the process in which the space for the development of the solution is produced.
Burst Imaging for Light-Constrained Structure-From-Motion
Venue: arXiv cs.RO 2021
Year: 2021

Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this paper we develop an image processing technique for aiding 3D reconstruction from images acquired in low light conditions. Our technique, based on burst photography, uses direct methods for image registration within bursts of short exposure time images to improve the robustness and accuracy of feature-based structure-from-motion (SfM). We demonstrate improved SfM performance in challenging light-constrained scenes, including quantitative evaluations that show improved feature performance and camera pose estimates. Additionally, we show that our method converges more frequently to correct reconstructions than the state-of-the-art. Our method is a significant step towards allowing robots to operate in low light conditions, with potential applications to robots operating in environments such as underground mines and night time operation.
The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions
Venue: arXiv cs.SE 2021
Year: 2021

Code review is an important quality assurance activity for software development. Code review discussions among developers and maintainers can be heated and sometimes involve personal attacks and unnecessary disrespectful comments, demonstrating, therefore, incivility. Although incivility in public discussions has received increasing attention from researchers in different domains, the knowledge about the characteristics, causes, and consequences of uncivil communication is still very limited in the context of software development, and more specifically, code review. To address this gap in the literature, we leverage the mature social construct of incivility as a lens to understand confrontational conflicts in open source code review discussions. For that, we conducted a qualitative analysis on 1,545 emails from the Linux Kernel Mailing List (LKML) that were associated with rejected changes. We found that more than half 66.66% of the non-technical emails included uncivil features. Particularly, frustration, name calling, and impatience are the most frequent features in uncivil emails. We also found that there are civil alternatives to address arguments, while uncivil comments can potentially be made by any people when discussing any topic. Finally, we identified various causes and consequences of such uncivil communication. Our work serves as the first study about the phenomenon of in(civility) in open source software development, paving the road for a new field of research about collaboration and communication in the context of software engineering activities.
Analyzing the Granularity and Cost of Annotation in Clinical Sequence Labeling
Venue: arXiv cs.CL 2021
Year: 2021

Well-annotated datasets, as shown in recent top studies, are becoming more important for researchers than ever before in supervised machine learning (ML). However, the dataset annotation process and its related human labor costs remain overlooked. In this work, we analyze the relationship between the annotation granularity and ML performance in sequence labeling, using clinical records from nursing shift-change handover. We first study a model derived from textual language features alone, without additional information based on nursing knowledge. We find that this sequence tagger performs well in most categories under this granularity. Then, we further include the additional manual annotations by a nurse, and find the sequence tagging performance remaining nearly the same. Finally, we give a guideline and reference to the community arguing it is not necessary and even not recommended to annotate in detailed granularity because of a low Return on Investment. Therefore we recommend emphasizing other features, like textual knowledge, for researchers and practitioners as a cost-effective source for increasing the sequence labeling performance.
Towards a Formal Modelling, Analysis, and Verification of a Clone Node Attack Detection Scheme in the Internet of Things
Venue: arXiv cs.CR 2021
Year: 2021

In a clone node attack, an attacker attempted to physically capture the devices to gather sensitive information to conduct various insider attacks. Several solutions for detecting clone node attacks on IoT networks have been presented in the viewpoints above. These solutions are focused on specific system designs, processes, and feature sets and act as a high-level abstraction of underlying system architectures based on a few performance requirements. However, critical features like formal analysis, modelling, and verification are frequently overlooked in existing proposed solutions aimed at verifying the correctness and robustness of systems in order to ensure that no problematic scenarios or anomalies exist. This paper presents a formal analysis, modelling, and verification of our existing proposed clone node attack detection scheme in IoT. Firstly, we modelled the architectural components of the proposed scheme using High-Level Petri Nets (HLPNs) and then mapped them using their specified functionalities. Secondly, we defined and analysed the behavioural properties of the proposed scheme using Z specification language. Furthermore, we used the Satisfiability Modulo Theories Library (SMT-Lib) and the Z3 Solver to validate and demonstrate the overall functionality of the proposed scheme. Finally, in addition to modelling and analysis, this work employs Coloured Petri Nets (CPNs), which combine Petri Nets with a high-level programming language, making them more suitable for large-scale system modelling. To perform the simulations in CPN, we used both timed and untimed models, where timed models are used to evaluate performance, and untimed models are used to validate logical validity.
An Interactive Dashboard for Real-Time Analytics and Monitoring of COVID-19 Outbreak in India: A proof of Concept
Venue: arXiv cs.CY 2021
Year: 2021

Data analysis and visualization are essential for exploring and communicating findings in medical research, especially in epidemiological surveillance. Data on COVID-19 diagnosed cases and mortality, from crowdsourced website COVID-19 India Tracker, Census 2011, and Google Mobility reports have been used to develop a real-time analytics and monitoring system for the COVID-19 outbreak in India. We have developed a dashboard application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using data science techniques. A district-level tool for basic epidemiological surveillance, in an interactive and user-friendly manner which includes time trends, epidemic curves, key epidemiological parameters such as growth rate, doubling time, and effective reproduction number have been estimated. This demonstrates the application of data science methods and epidemiological techniques in public health decision-making while addressing the gap of timely and reliable decision aiding tools.
Transcending Old Boundaries: Digital Afterlife in the Age of COVID-19
Venue: arXiv cs.CY 2021
Year: 2021

The primary objective of our exploratory research is to contribute to the ongoing conversation on Digital Afterlife from the lenses of Global South during the COVID-19 period. Digital Afterlife is fast becoming a challenge for our increasingly connected society. Moreover, the situation got worse with the COVID-19 pandemic. The on-going research is to address the disparity in the Global South, specifically in countries like Indonesia, India and The Philippines compared to the Global North for Digital Afterlife services such as policies and digital mourning services. By addressing the research question, 'What services and policy frameworks are available for Digital Afterlife in the Global South during COVID-19?', we aim to find the multitude of ways people in the Global South are managing their digital footprints. Our preliminary findings show that some considerable research and death related digital services and innovation have taken place during the pandemic. However, overwhelming majority of these works are western-centric and mainly dealing with post-mortem personal asset management. Cultural nuances, socio-economic perspectives, religion, political climate, regional infrastructures are mostly sidelined. We found significant disparity in Digital Afterlife product and service designs, which got worse during the global pandemic. Our goal is to collect further in-depth data within the three big ICT powerhouses of global south (Indonesia, India and The Philippines), identify the challenges as well as the innovations around Digital Afterlife.We envision proposing a set of recommendations, based on our findings, for developing a more inclusive and equitable digital space in this pandemic-stricken world.
Role of Digital Platforms in Entrepreneurial Processes: A Resource Enabling Perspective of Startups in Pakistan
Venue: arXiv cs.CY 2021
Year: 2021

This article aims to explore the role of digital platforms as external enablers in entrepreneurial processes. The recent infusion of digital platforms into different aspects of innovation and entrepreneurship has supported digital entrepreneurship; however, the altered entrepreneurial processes are yet to be explored. This study focuses on digital platform-based startups of Pakistan and draws on entrepreneurial bricolage theory to understand the enabling external resources. We followed multiple qualitative case studies approach and collected data through semi-structured interviews from two startups operating solely on digital platforms, 1) XYLEXA and 2) Toycycle. The findings show that entrepreneurial process is a continuous process. Digital platforms have made entrepreneurial processes less bounded i.e. the products and services keep on evolving even after they have been endorsed to the end user. Moreover, platform-based startups having limited resources can move through the entire entrepreneurial process by combining available resources efficiently and effectively.
Sequence-to-Sequence Learning on Keywords for Efficient FAQ Retrieval
Venue: arXiv cs.IR 2021
Year: 2021

Frequently-Asked-Question (FAQ) retrieval provides an effective procedure for responding to user's natural language based queries. Such platforms are becoming common in enterprise chatbots, product question answering, and preliminary technical support for customers. However, the challenge in such scenarios lies in bridging the lexical and semantic gap between varied query formulations and the corresponding answers, both of which typically have a very short span. This paper proposes TI-S2S, a novel learning framework combining TF-IDF based keyword extraction and Word2Vec embeddings for training a Sequence-to-Sequence (Seq2Seq) architecture. It achieves high precision for FAQ retrieval by better understanding the underlying intent of a user question captured via the representative keywords. We further propose a variant with an additional neural network module for guiding retrieval via relevant candidate identification based on similarity features. Experiments on publicly available dataset depict our approaches to provide around 92% precision-at-rank-5, exhibiting nearly 13% improvement over existing approaches.
QDEF and Its Approximations in OBDM
Venue: arXiv cs.AI 2021
Year: 2021

Given an input dataset (i.e., a set of tuples), query definability in Ontology-based Data Management (OBDM) amounts to find a query over the ontology whose certain answers coincide with the tuples in the given dataset. We refer to such a query as a characterization of the dataset with respect to the OBDM system. Our first contribution is to propose approximations of perfect characterizations in terms of recall (complete characterizations) and precision (sound characterizations). A second contribution is to present a thorough complexity analysis of three computational problems, namely verification (check whether a given query is a perfect, or an approximated characterization of a given dataset), existence (check whether a perfect, or a best approximated characterization of a given dataset exists), and computation (compute a perfect, or best approximated characterization of a given dataset).
Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19
Venue: arXiv cs.LG 2021
Year: 2021

In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6% and 7%. From the food crop production side, the travel bans and border closures, the late reception and the use of agricultural inputs such as imported seeds, fertilizers, and pesticides could lead to poor food crop production performances. Another layer of disruption introduced by the mobility restriction measures is the scarcity of agricultural workers, mainly seasonal workers. The lockdown measures and border closures limit seasonal workers' availability to get to the farm on time for planting and harvesting activities. Moreover, most of the imported agricultural inputs travel by air, which the pandemic has heavily impacted. Such transportation disruptions can also negatively affect the food crop production system. This chapter assesses food crop production levels in 2020 -- before the harvesting period -- in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique. The remote sensing products are used as input variables and the ANNs as the predictive modeling framework. The input remote sensing products are the Normalized Difference Vegetation Index (NDVI), the daytime Land Surface Temperature (LST), rainfall data, and agricultural lands' Evapotranspiration (ET). The output maps and data are made publicly available on a web-based platform, AAgWa (Africa Agriculture Watch, www.aagwa.org), to facilitate access to such information to policymakers, deciders, and other stakeholders.
EEG-based Classification of Drivers Attention using Convolutional Neural Network
Venue: arXiv cs.HC 2021
Year: 2021

Accurate detection of a drivers attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several attention classifiers trained on participants brain activity. Participants performed a driving task in an immersive simulator where the car randomly deviated from the cruising lane. They had to correct the deviation and their response time was considered as an indicator of attention level. Participants repeated the task in two sessions; in one session they received kinesthetic feedback and in another session no feedback. Using their EEG signals, we trained three attention classifiers; a support vector machine (SVM) using EEG spectral band powers, and a Convolutional Neural Network (CNN) using either spectral features or the raw EEG data. Our results indicated that the CNN model trained on raw EEG data obtained under kinesthetic feedback achieved the highest accuracy (89%). While using a participants own brain activity to train the model resulted in the best performances, inter-subject transfer learning still performed high (75%), showing promise for calibration-free Brain-Computer Interface (BCI) systems. Our findings show that CNN and raw EEG signals can be employed for effective training of a passive BCI for real-time attention classification.