OpenRaiser/Intern-Atlas huggingface dataset:
Exploring Multi-Dimensional Events Characterizing Tech Start-Up Emergence in the Nigerian Entrepreneurial Ecosystem
Venue: arXiv cs.CY 2021
Year: 2021

Most countries across the globe identify technology-based start-ups as a driving force for job creation, economic growth and national development, and a critical tool for economic sustenance during pandemic crises like covid-19. However, its emergence are been argued to be problematic. Especially in a developing economy like Nigeria, where tech start-up founders are faced with diverse form of constraints and environmental uncertainties. Extant literature indicated that studies are been conducted to explain tech start-up emergence. However, such studies are fragmented with findings that are determinants to tech start-up emergence, with several determinants studied in isolation, and the emergence as linear and unidimensional events. Consequently, neglecting multi-dimensional perspective, which aggregate the dimensions of events characterizing tech start-up emergence. Given the iterative, event-based process, and interactive-dependent nature of tech start-up ventures to create activity-based products/services in an open, uncertain, nonlinear and dynamic environment, we argue that little are been known about tech start-up emergence. Thus, by drawing from synthesize literature review, activity theory, and exploratory case study design we identify opportunity discovery and selection; team formation and domain consensus; bootstrapping; minimum viable product development and market experimentation feedback as interdependent multi-dimensional events constituting tech start-up emergence in Nigerian tech start-up ecosystem.
The Decision Criteria Used by Large Enterprises in South Africa for the Adoption of Cloud Computing
Venue: arXiv cs.CY 2021
Year: 2021

Cloud computing is a technology that has become increasingly popular over the past decade within several enterprises. This popularity can be attributed to its benefits, including lower operating costs, improved computational capabilities, increased flexibility and on-demand storage space. As a result, many enterprises are already in various Cloud Computing (CC) adoption and implementation stages. This study investigates the decision criteria used by large enterprises in South Africa (SA) for the adoption of cloud technology. The majority of large enterprises have comprehensive resources, resulting in established Information Technology (IT) systems and infrastructure set up within their organizations. Though this is the case, the adoption of CC by large enterprises has been on the rise. This may not be a surprise as CC literature points out to benefits and influencers of CC adoption. However, the decision criteria used by large enterprises in SA in adopting CC are lacking in the literature reviewed. The study followed an inductive approach making use of qualitative methods. Findings revealed that large enterprises do not make use of a formalized or standardized decision criteria. However, operational cost, enterprise strategic intent and product efficiency formed key criteria for adopting CC. In addition, security, cloud service provider adoption frameworks and data sovereignty were the key criteria used to select a CC service provider. The research will contribute towards CC technology adoption literature, particularly for developing countries.
Relating CNNs with brain: Challenges and findings
Venue: arXiv cs.CV 2021
Year: 2021

Conventional neural network models (CNN), loosely inspired by the primate visual system, have been shown to predict neural responses in the visual cortex. However, the relationship between CNNs and the visual system is incomplete due to many reasons. On one hand state of the art CNN architecture is very complex, yet can be fooled by imperceptibly small, explicitly crafted perturbations which makes it hard difficult to map layers of the network with the visual system and to understand what they are doing. On the other hand, we don't know the exact mapping between feature space of the CNNs and the space domain of the visual cortex, which makes it hard to accurately predict neural responses. In this paper we review the challenges and the methods that have been used to predict neural responses in the visual cortex and whole brain as part of The Algonauts Project 2021 Challenge: "How the Human Brain Makes Sense of a World in Motion".
Empirical Study of Sustaining the Actualized Value Propositions of Implemented E-Government Projects in Sub-Saharan Africa
Venue: arXiv cs.CY 2021
Year: 2021

Governments in sub-Saharan Africa have implemented e-Government projects. Actualizing the value propositions and sustaining such values are becoming problematic. Some scanty studies on the value propositions of implemented e-Government projects did not consider actualization of the values. Besides, such studies lack theoretical underpinnings, the identification, and measure of what constitutes actualized values. Neither did they capture what mechanisms could sustain the actualized values nor the contextual conditions enabling its sustainability. Consequently, using a concept-centric systematic review, we identified the value proposition of such implemented projects. By drawing from theories of affordance actualization, realist evaluation (RE) theory, self-determination theory, and sustainability framework for e-Government success. We conducted a RE of the implemented e-Government projects in Rwanda using RE as a methodology in three phases. In phase one, we developed the initial program theory (IPT), in phase two, we used contingent valuation as a quantitative approach and realist interview as qualitative method to validate the IPT. Lastly, in the third phase, we synthesized the results of the two investigative case studies to develop the actualized values sustainability framework. Such framework encapsulates, the actualized value propositions, mechanisms and enabling conditions in interactions to sustain the value propositions discovered in the e-Government investigative contexts.
UltraBot: Autonomous Mobile Robot for Indoor UV-C Disinfection
Venue: arXiv cs.RO 2021
Year: 2021

The paper focuses on the development of the autonomous robot UltraBot to reduce COVID-19 transmission and other harmful bacteria and viruses. The motivation behind the research is to develop such a robot that is capable of performing disinfection tasks without the use of harmful sprays and chemicals that can leave residues, require airing the room afterward for a long time, and can cause the corrosion of the metal structures. UltraBot technology has the potential to offer the most optimal autonomous disinfection performance along with taking care of people, keeping them from getting under UV-C radiation. The paper highlights UltraBot's mechanical and electrical structures as well as low-level and high-level control systems. The conducted experiments demonstrate the effectiveness of the robot localization module and optimal trajectories for UV-C disinfection. The results of UV-C disinfection performance revealed a decrease of the total bacterial count (TBC) by 94% on the distance of 2.8 meters from the robot after 10 minutes of UV-C irradiation.
Understanding the Interplay between Boundary Resources and Governance Practices in Influencing Ecosystem Value Co-creation for Digital Platforms: A Case From the Global South
Venue: arXiv cs.CY 2021
Year: 2021

Despite their potentially transformative impact, few studies have investigated how commercially-driven digital platforms such as mobile money facilitate ecosystem value co-creation in the global South. Consequently, using a boundary resources model and platform governance approaches, this paper aims to examine how a payment platform facilitates ecosystem value co-creation with third party actors. An in-depth qualitative inquiry was adopted for the study using an embedded single-case design. The results show that although ecosystem value co-creation was enabled by the introduction of boundary resources tools, some platform governance practices hindered some complementors from harnessing the affordances of the platforms. These constraints include lack of visibility of the boundary resources and transparency challenges in the acceptance criteria. We thus argue that platform generativity on its own is not sufficient to support ecosystem value co-creation, but requires appropriate platform governance to deal with behavioural complexity of ecosystem actors by using optimal control mechanisms.
UltraBot: Autonomous Mobile Robot for Indoor UV-C Disinfection with Non-trivial Shape of Disinfection Zone
Venue: arXiv cs.RO 2021
Year: 2021

The paper focuses on the development of an autonomous disinfection robot UltraBot to reduce COVID-19 transmission along with other harmful bacteria and viruses. The motivation behind the research is to develop such a robot that is capable of performing disinfection tasks without the use of harmful sprays and chemicals that can leave residues and require airing the room afterward for a long time. UltraBot technology has the potential to offer the most optimal autonomous disinfection performance along with taking care of people, keeping them from getting under the UV-C radiation. The paper highlights UltraBot's mechanical and electrical design as well as disinfection performance. The conducted experiments demonstrate the effectiveness of robot disinfection ability and actual disinfection area per each side with UV-C lamp array. The disinfection effectiveness results show actual performance for the multi-pass technique that provides 1-log reduction with combined direct UV-C exposure and ozone-based air purification after two robot passes at a speed of 0.14 m/s. This technique has the same performance as ten minutes static disinfection. Finally, we have calculated the non-trivial form of the robot disinfection zone by two consecutive experiment to produce optimal path planning and to provide full disinfection in selected areas.
Market Digitalization and Resilience in Crisis Times
Venue: arXiv cs.CY 2021
Year: 2021

Based on a conceptual framework that integrates three dimensions of digital transformation (DT), namely, the nature of the product, client interaction, and the level of coordination with industry players, this paper aims to explain the level of influence that contextual crisis factors may have played in organizational digitalization choices in search for resilience as part of adaptation strategies. In particular, this investigation would analyze digitalization choices as survival strategies for COVID-19 crisis in the case of Mexican enterprises. The selected country is of particular interest as research target in the Global South, in that public policy has offered little support to keep business organizations up and running, leaving entrepreneurs with no other option but to implement bottom-up resilience strategies, including digitalization. Qualitative Comparative Analysis (QCA) has been proposed to identify combinations of conditions to explain the role played by COVID crisis-related contextual factors that may have led to particular forms of digitalization. Semi-structured interviews with industry associations are also proposed to gain knowledge about group responses to the crisis.
ICT Use and Livelihoods of Women Microenterprises in Malawi
Venue: arXiv cs.CY 2021
Year: 2021

This study aimed to assess the impacts of ICTs on livelihoods of women microenterprises in Malawi. The study was an interpretive qualitative approach in which semi-structured interviews, observation and field notes were used to collect data. About 25 women involved in various microenterprises in three rural areas of Karonga district in Malawi were purposively selected to participate in the study. The framework for the study was based on Serrat's sustainable livelihood approach. The study noted that the use of ICTs potentially enabled women microenterprises to build their financial, human, social, and informational capital assets. The study found that ICTs to some extent contributed to the livelihoods of women microenterprises such as improved access to information; diversification of business opportunities, improved communication, improved marketing, and reduced transport costs. As a result, this led to sustainable use of resources, improved well-being, and empowerment for women. However, unreliable electricity; lack of affordable ICT devices; lack of awareness to utilise ICTs in businesses and lack of ICT literacy skills were major concerns that affected women microenterprises. The study offers insights to research practioners, policy makers and other stakeholders on the role of ICTs in fostering women microentrepreneurship in rural communities of Malawi.
On universal covers for carpenter's rule folding
Venue: arXiv cs.CG 2021
Year: 2021

We present improved universal covers for carpenter's rule folding in the plane.
Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we propose a convex formulation for learning logistic regression model (logit) with latent heterogeneous effect on sub-population. In transportation, logistic regression and its variants are often interpreted as discrete choice models under utility theory (McFadden, 2001). Two prominent applications of logit models in the transportation domain are traffic accident analysis and choice modeling. In these applications, researchers often want to understand and capture the individual variation under the same accident or choice scenario. The mixed effect logistic regression (mixed logit) is a popular model employed by transportation researchers. To estimate the distribution of mixed logit parameters, a non-convex optimization problem with nested high-dimensional integrals needs to be solved. Simulation-based optimization is typically applied to solve the mixed logit parameter estimation problem. Despite its popularity, the mixed logit approach for learning individual heterogeneity has several downsides. First, the parametric form of the distribution requires domain knowledge and assumptions imposed by users, although this issue can be addressed to some extent by using a non-parametric approach. Second, the optimization problems arise from parameter estimation for mixed logit and the non-parametric extensions are non-convex, which leads to unstable model interpretation. Third, the simulation size in simulation-assisted estimation lacks finite-sample theoretical guarantees and is chosen somewhat arbitrarily in practice. To address these issues, we are motivated to develop a formulation that models the latent individual heterogeneity while preserving convexity, and avoids the need for simulation-based approximation. Our setup is based on decomposing the parameters into a sparse homogeneous component in the population and low-rank heterogeneous parts for each individual.
A Round-Robin Packet Scheduler for Hierarchical Max-Min Fairness
Venue: arXiv cs.NI 2021
Year: 2021

Hierarchical link sharing addresses the demand for fine-grain traffic control at multiple levels of aggregation. At present, packet schedulers that can support hierarchical link sharing are not suitable for an implementation at line rates, and deployed schedulers perform poorly when distributing excess capacity to classes that need additional bandwidth. We present HLS, a packet scheduler that ensures a hierarchical max-min fair allocation of the link bandwidth. HLS supports minimum rate guarantees and isolation between classes. Since it is realized as a non-hierarchical round robin scheduler, it is suitable to operate at high rates. We implement HLS in the Linux kernel and evaluate it with respect to achieved rate allocations and overhead. We compare the results with those obtained for CBQ and HTB, the existing scheduling algorithms in Linux for hierarchical link sharing. We show that the overhead of HLS is comparable to that of other classful packet schedulers.
AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation
Venue: arXiv cs.CR 2021
Year: 2021

Entity resolution is the task of identifying records in different datasets that refer to the same entity in the real world. In sensitive domains (e.g. financial accounts, hospital health records), entity resolution must meet privacy requirements to avoid revealing sensitive information such as personal identifiable information to untrusted parties. Existing solutions are either too algorithmically-specific or come with an implicit trade-off between accuracy of the computation, privacy, and run-time efficiency. We propose AMMPERE, an abstract computation model for performing universal privacy-preserving entity resolution. AMPPERE offers abstractions that encapsulate multiple algorithmic and platform-agnostic approaches using variants of Jaccard similarity to perform private data matching and entity resolution. Specifically, we show that two parties can perform entity resolution over their data, without leaking sensitive information. We rigorously compare and analyze the feasibility, performance overhead and privacy-preserving properties of these approaches on the Sharemind multi-party computation (MPC) platform as well as on PALISADE, a lattice-based homomorphic encryption library. The AMPPERE system demonstrates the efficacy of privacy-preserving entity resolution for real-world data while providing a precise characterization of the induced cost of preventing information leakage.
Teaching and learning mathematics with Prolog
Venue: arXiv cs.CY 2021
Year: 2021

Procedural computer languages have long been used in many aspects of mathematics pedagogy. In this work, we examine the use of Prolog, a declarative language for the same purpose. We find the facts+rules aspect of Prolog to be a novel platform for developing coding lessons to supplement the learning of mathematics. Specific examples are presented.
Black-Box Test-Time Shape REFINEment for Single View 3D Reconstruction
Venue: arXiv cs.CV 2021
Year: 2021

Much recent progress has been made in reconstructing the 3D shape of an object from an image of it, i.e. single view 3D reconstruction. However, it has been suggested that current methods simply adopt a "nearest-neighbor" strategy, instead of genuinely understanding the shape behind the input image. In this paper, we rigorously show that for many state of the art methods, this issue manifests as (1) inconsistencies between coarse reconstructions and input images, and (2) inability to generalize across domains. We thus propose REFINE, a postprocessing mesh refinement step that can be easily integrated into the pipeline of any black-box method in the literature. At test time, REFINE optimizes a network per mesh instance, to encourage consistency between the mesh and the given object view. This, along with a novel combination of regularizing losses, reduces the domain gap and achieves state of the art performance. We believe that this novel paradigm is an important step towards robust, accurate reconstructions, remaining relevant as new reconstruction networks are introduced.
The Commodification of Open Educational Resources for Teaching and Learning by Academics in an Open Distance e-Learning Institution
Venue: arXiv cs.CY 2021
Year: 2021

The use of open educational resources (OER) is gaining momentum in higher education institutions. This study sought to establish academics' perceptions and knowledge of OER for teaching and learning in an open distance e-learning (ODeL) university. The study also sought to establish how perceptions are formed. The inductive approach followed the lens of commodification to answer the research questions. The commodification phase allowed for a better understanding of the academics' prior knowledge, informers, academics behaviour about OER, and how they perceived OER to be useful for teaching and learning. The study employed a qualitative method, with semi-structured interviews to collect data. The study found that academics with prior experience and knowledge of OER are more successful in the use of these resources for teaching, learning, and research. OER is also perceived as a useful tool to promote African knowledge, showcase the contributions of African academics, improve academic research capabilities, improve student's success rate, particularly for financially vulnerable students. Based on the acquired perceptions, the study able to propose a new guideline to formulate user perceptions. However, this can only be achieved through a solid OER policy with the support of government and tertiary institution top management. The findings may inform higher education institutions when they consider the development of OER strategies and policies, especially in response to the Covid-19 emergency online learning transition.
Legitimization of Data Quality Practices in Health Management Information Systems Using DHIS2. Case of Malawi
Venue: arXiv cs.CY 2021
Year: 2021

Medical doctors consider data quality management a secondary priority when delivering health care. Medical practitioners find data quality management practices intrusive to their operations. Using Health Management Information System (HMIS) that uses DHIS2 platform, our qualitative case study establishes that isomorphism leads to legitimization of data quality management practices among health practitioners and subsequently data quality. This case study employed the methods of observation, semi structured interviews and review of artefacts to explore how through isomorphic processes data quality management practices are legitimized among the stakeholders. Data was collected from Ministry of Health's (Malawi) HMIS Technical Working Group members in Lilongwe and from medical practitioners and data clerks in Thyolo district. From the findings we noted that mimetic isomorphism led to moral and pragmatic legitimacy while and normative isomorphism led to cognitive legitimacy within the HMIS structure and helped to attain correctness and timeliness of the data and reports respectively. Through this understanding we firstly contribute to literature on organizational issues in IS research. Secondly, we contribute to practice as we motivate health service managers to capitalize on isomorphic forces to help legitimization of data quality management practices among health practitioners.
Investigating Personalisation-Privacy Paradox Among Young Irish Consumers: A Case of Smart Speakers
Venue: arXiv cs.CY 2021
Year: 2021

Personalisation refers to the catering of online services to match consumer's interests. In order to provide personalised service, companies gather data on the consumer. In this situation, consumers must navigate a trade-off when they want the benefits of personalised information and services while simultaneously wish to protect themselves from privacy risks. However, despite many individuals claiming that privacy is an essential right to them, they behave contradictorily in online environments by not engaging in privacy-preserving behaviours. This paradox is known as the personalisation-privacy Paradox. The personalisation-privacy paradox has been studied in many different scenarios, ranging from location-based advertising to online shopping. The objective of this study is to investigate the personalisation-privacy paradox in the context of smart speakers. Based on an exploratory study with young Irish consumers, this study suggests a difference between the users and non-users of smart speakers in terms of their perception of privacy risks and corresponding privacy-preserving behaviours. In so doing, it also explains the existence of the personalisation-privacy paradox and offers insights for further research.