OpenRaiser/Intern-Atlas huggingface dataset:
The ICT-Buen Vivir Paradox: Using Digital Tools to Defend Indigenous Cultures
Venue: arXiv cs.CY 2021
Year: 2021

Arguably shaped by political economy perspectives from the Global North, ICT4D aims to reduce socioeconomic disparities across countries and regions through ICT implementations, as well as to open up opportunities for empowerment and human development. Despite these aims, ICT4D has been criticized because 1) although ICT and internet have positive effects on societies across the Global North, their positive impact on people's lives in the Global South cannot be easily proved; 2) ICT4D's primary focus seems to be on ICT's series of artefacts rather than on ICT's positive transformative potential of living conditions in the world; 3) the type of development ICT4D aims for could mask global hegemonic interests and seek neoliberal restructuring within less socioeconomically favoured communities within the Global South. For these reasons, claim scholars, ICT4D should be revised. By presenting ICT appropriations among Wixarika peoples in Mexico to protect their sacred land, this paper aims to 1) shed a light on the need for postcolonial critical frameworks on what 'development' associated with ICT should be and 2) to foster discussions on whether ICT can enable alternative voices from the Global South to be heard, despite tensions between traditional views and contemporary technologies.
Impact of Culture on the Adoption of Diabetes Self-Management Applications: Cape Flats, South Africa
Venue: arXiv cs.CY 2021
Year: 2021

Diabetes is a global health problem with a high mortality rate. The research indicates low levels of technology use amongst diabetic patients in low socioeconomic environments and minority groups. We posit that the culture of patients is a potential reason for the low adoption and use of technology. However, research on the proliferation of culture at an individual level is limited. Therefore, this paper assessed the influence of culture on mobile application adoption and use amongst diabetic patients in the Cape Flats, South Africa. This study used key constructs from the Theory of Planned Behaviour (TPB) and Hofstede's cultural dimensions. It was analysed using survey data from 439 respondents using purposive sampling. It was found that the dimensions of Hofstede and the Theory of Planned Behaviour can identify how culture influences mobile application adoption of diabetic patients in the geographical Cape Flats area. However, this research indicates a stronger relationship between culture and diabetes self-management activities than culture and the adoption of mobile applications.
A Systematic Approach to Cleaning Routine Health Surveillance Datasets: An Illustration Using National Vector Borne Disease Control Programme Data of Punjab, India
Venue: arXiv cs.CY 2021
Year: 2021

Advances in ICT4D and data science facilitate systematic, reproducible, and scalable data cleaning for strengthening routine health information systems. A logic model for data cleaning was used and it included an algorithm for screening, diagnosis, and editing datasets in a rule-based, interactive, and semi-automated manner. Apriori computational workflows and operational definitions were prepared. Model performance was illustrated using the dengue line-list of the National Vector Borne Disease Control Programme, Punjab, India from 01 January 2015 to 31 December 2019. Cleaning and imputation for an estimated date were successful for 96.1% and 98.9% records for the year 2015 and 2016 respectively, and for all cases in the year 2017, 2018, and 2019. Information for age and sex was cleaned and extracted for more than 98.4% and 99.4% records. The logic model application resulted in the development of an analysis-ready dataset that can be used to understand spatiotemporal epidemiology and facilitate data-based public health decision making.
Exploring the Quality of GAN Generated Images for Person Re-Identification
Venue: arXiv cs.CV 2021
Year: 2021

Recently, GAN based method has demonstrated strong effectiveness in generating augmentation data for person re-identification (ReID), on account of its ability to bridge the gap between domains and enrich the data variety in feature space. However, most of the ReID works pick all the GAN generated data as additional training samples or evaluate the quality of GAN generation at the entire data set level, ignoring the image-level essential feature of data in ReID task. In this paper, we analyze the in-depth characteristics of ReID sample and solve the problem of "What makes a GAN-generated image good for ReID". Specifically, we propose to examine each data sample with id-consistency and diversity constraints by mapping image onto different spaces. With a metric-based sampling method, we demonstrate that not every GAN-generated data is beneficial for augmentation. Models trained with data filtered by our quality evaluation outperform those trained with the full augmentation set by a large margin. Extensive experiments show the effectiveness of our method on both supervised ReID task and unsupervised domain adaptation ReID task.
Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning
Venue: arXiv cs.RO 2021
Year: 2021

In this study, reinforcement learning was applied to learning two-dimensional path planning including obstacle avoidance by unmanned aerial vehicle (UAV) in an indoor environment. The task assigned to the UAV was to reach the goal position in the shortest amount of time without colliding with any obstacles. Reinforcement learning was performed in a virtual environment created using Gazebo, a virtual environment simulator, to reduce the learning time and cost. Curriculum learning, which consists of two stages was performed for more efficient learning. As a result of learning with two reward models, the maximum goal rates achieved were 71.2% and 88.0%.
Collation of Feasible Solutions for Domain Based Problems: An Analysis of Sentiments Based on Codeathon Activity
Venue: arXiv cs.SI 2021
Year: 2021

Codeathon activity is a practical approach for enduring the principles of Software Engineering and Object Oriented Modelling. Real world domain problem's solution was accomplished through team work. Analysing the problem and designing a feasible solution through a one day activity was achieved through virtual connection. There are three different sections in a semester, 13 teams were framed and assigned one problem statement. Individual team were supposed to prototype a solution which was further used to build one feasible solution. The feedback from students showed different sentiments associated with day long activity. Vivid emotions and expressions of students were analysed.
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents
Venue: arXiv cs.NE 2021
Year: 2021

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Strong AI. Reinforcement learning is similar to learning in biology. It is of great significance to study the combination of SNN and RL. We propose the reinforcement learning method of spike distillation network (SDN) with STBP. This method uses distillation to effectively avoid the weakness of STBP, which can achieve SOTA performance in classification, and can obtain a smaller, faster convergence and lower power consumption SNN reinforcement learning model. Experiments show that our method can converge faster than traditional SNN reinforcement learning and DNN reinforcement learning methods, about 1000 epochs faster, and obtain SNN 200 times smaller than DNN. We also deploy SDN to the PKU nc64c chip, which proves that SDN has lower power consumption than DNN, and the power consumption of SDN is more than 600 times lower than DNN on large-scale devices. SDN provides a new way of SNN reinforcement learning, and can achieve SOTA performance, which proves the possibility of further development of SNN reinforcement learning.
Cloud Computing Adoption: Opportunities and Challenges for Small, Medium and Micro Enterprises in South Africa
Venue: arXiv cs.CY 2021
Year: 2021

The purpose of the paper is to determine the opportunities and challenges that lead to cloud computing adoption by SMMEs in South Africa by looking at the factors that influence adoption. The TOE framework is used to contextualize the factors that influence cloud computing adoption and evaluate the opportunities and challenges that are presented by cloud computing to SMMEs in South Africa. An online survey questionnaire was used to collect data from leaders of SMMEs from all geographical regions and business industries in South Africa. A quantitative research approach was adopted to investigate the objectives, and descriptive analysis was used to evaluate the relationships and present the results. The findings of the study show that relative advantage is an important factor in the consideration of cloud computing adoption by SMMEs, while government and regulatory support is perceived as a barrier. Top management support, which has been previously found by other studies to be a significant factor has been found to be insignificant in this study. The study has revealed that cloud computing presents opportunities to SMMEs and improves their competitiveness.
On Kurtosis-limited Enumerative Sphere Shaping for Reach Increase in Single-span Systems
Venue: arXiv cs.IT 2021
Year: 2021

The effect of decreasing the kurtosis of channel inputs is investigated for the first time with an algorithmic shaping implementation. No significant gains in decoding performance are observed for multi-span systems, while an increase in reach is obtained for single-span transmission.
Access to Digital Platforms: Can 'Mobile' Network Coverage Reports be Relied Upon? Observations from Rural Zambia and Zimbabwe
Venue: arXiv cs.CY 2021
Year: 2021

As access to digital platforms in Africa is mostly through 'mobile' networks, this paper addresses the mismatch of universalised reports on 'mobile' access and the experiences from digital health practice and cases in Zambia and Zimbabwe. Further, the paper shines a critical light on the meaning of terms like access and the 4th industrial revolution from an African context. It argues for the need to invest in contextual research and development, also to gain a comprehensive understanding of how to access digital platforms in and from Africa.
Appropriation, coloniality, and digital technologies. Observations from within an African place
Venue: arXiv cs.CY 2021
Year: 2021

This paper provides an assessment of experiences and understanding of digital technologies from within an African place. It provides philosophical reflections upon the introduction and existence - appropriation - of digital technologies. Digital technologies are inherently linked to a colonialising power and, in general, unaligned with local, African ways of knowing. Imported technologies are set in modern, universalised doing and unsensitive to the importance of aligned being in African contexts. Sensitivities, it is argued, can be fostered by a decolonial turn, where focus shifts from the individual to the community.
Gender Data 4 Girls?: A Postcolonial Feminist Participatory Study in Bangladesh
Venue: arXiv cs.CY 2021
Year: 2021

Premised on the logic that more, high-quality information on majority world women's lives will improve the effectiveness of interventions addressing gender inequality, mainstream development institutions have invested heavily in gender data initiatives of late. However, critical empirical and theoretical investigations into gender data for development policy and practice are lacking. Postcolonial feminist theory has long provided a critical lens through which to analyse international development projects that target women in the majority world. However, postcolonial feminism remains underutilised for critically investigating data for development projects. This paper addresses these gaps through presenting the findings from a participatory action research project with young women involved in a gender data for development project in Bangladesh. Echoing postcolonial feminist concerns with development, the 'DataGirls' had some concerns that data was being extracted from their communities, representing the priorities of external NGOs to a greater extent than their own. However, through collaborating to develop and deliver community events on child marriage with the 'DataGirls', this research demonstrates that participatory approaches can address some postcolonial feminist criticisms of (data for) development, by ensuring that gender data is enacted by and for majority world women rather than Western development institutions.
Artificial Intelligence in the Global South (AI4D): Potential and Risks
Venue: arXiv cs.CY 2021
Year: 2021

Artificial intelligence is becoming more widely available in all parts of the world. This has created many previously unforeseen possibilities for addressing the challenges outlined in the Sustainable Development Goals in the Global South. However, the use of AI in such contexts brings with it a unique set of risks and challenges. Among these are the potential for Governments to use such technologies to suppress their own people, and the ethical questions arising from implementing AI primarily designed and developed in the Global North into vastly different social, cultural, and political environments in the Global South. This paper examines the key issues and questions arising in the emerging sub-field of AI for global development (AI4D) and the potential and risks associated with using such technologies in the Global South. We propose that although there are many risks associated with the use of AI, the potential benefits are enough to warrant detailed research and investigation of the most appropriate and effective ways to design, develop, implement, and use such technologies in the Global South. We conclude by calling for the wider ICT4D community to continue to conduct detailed research and investigation of all aspects of AI4D.
Rényi entropy and variance comparison for symmetric log-concave random variables
Venue: arXiv cs.IT 2021
Year: 2021

We show that for any $α>0$ the Rényi entropy of order $α$ is minimized, among all symmetric log-concave random variables with fixed variance, either for a uniform distribution or for a two sided exponential distribution. The first case occurs for $α\in (0,α^*]$ and the second case for $α\in [α^*,\infty)$, where $α^*$ satisfies the equation $\frac{1}{α^*-1}\log α^*= \frac12 \log 6$, that is $α^* \approx 1.241$. Using those results, we prove that one-sided exponential distribution minimizes Rényi entropy of order $α\geq 2$ among all log-concave random variables with fixed variance.
Deep neural networks approach to microbial colony detection -- a comparative analysis
Venue: arXiv cs.CV 2021
Year: 2021

Counting microbial colonies is a fundamental task in microbiology and has many applications in numerous industry branches. Despite this, current studies towards automatic microbial counting using artificial intelligence are hardly comparable due to the lack of unified methodology and the availability of large datasets. The recently introduced AGAR dataset is the answer to the second need, but the research carried out is still not exhaustive. To tackle this problem, we compared the performance of three well-known deep learning approaches for object detection on the AGAR dataset, namely two-stage, one-stage and transformer based neural networks. The achieved results may serve as a benchmark for future experiments.
Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry
Venue: arXiv cs.CV 2021
Year: 2021

Background: Thyroid volumetry is crucial in diagnosis, treatment and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D ultrasound and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time and accuracy. Volume reference was MRI. Methods: 28 healthy volunteers were scanned with 2D and 3D ultrasound as well as by MRI. Three physicians (MD 1, 2, 3) with different levels of experience (6, 4 and 1 a) performed three 2D ultrasound and three tracked 3D ultrasound scans on each volunteer. In the 2D scans the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) segmented the 3D thyroid lobes automatically. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced medical doctor. Results: The CNN was trained to obtain a dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 ml to 0.52 ml (MD1 vs. 2), -1.33 ml to -0.17 ml (MD1 vs. 3) and -1.89 ml to -0.70 ml (MD2 vs. 3). Paired samples t-tests showed significant differences in two comparisons for 2D and none for 3D. Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes by paired samples t-tests showed a significant difference for the 2D volumetry of all MDs, and no significant difference for 3D ultrasound. Acquisition time was significantly shorter for 3D ultrasound. Conclusion: Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times.
Legal Search in Case Law and Statute Law
Venue: arXiv cs.CL 2021
Year: 2021

In this work we describe a method to identify document pairwise relevance in the context of a typical legal document collection: limited resources, long queries and long documents. We review the usage of generalized language models, including supervised and unsupervised learning. We observe how our method, while using text summaries, overperforms existing baselines based on full text, and motivate potential improvement directions for future work.
Improving the trustworthiness of image classification models by utilizing bounding-box annotations
Venue: arXiv cs.CV 2021
Year: 2021

We study utilizing auxiliary information in training data to improve the trustworthiness of machine learning models. Specifically, in the context of image classification, we propose to optimize a training objective that incorporates bounding box information, which is available in many image classification datasets. Preliminary experimental results show that the proposed algorithm achieves better performance in accuracy, robustness, and interpretability compared with baselines.
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning has become an integral part of various computer vision systems in recent years due to its outstanding achievements for object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to be fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in the physical world applications. To address this phenomenon, we present, what to our knowledge, is the first ever image set based adversarial defence approach. Image set classification has shown an exceptional performance for object and face recognition, owing to its intrinsic property of handling appearance variability. We propose a robust deep Bayesian image set classification as a defence framework against a broad range of adversarial attacks. We extensively experiment the performance of the proposed technique with several voting strategies. We further analyse the effects of image size, perturbation magnitude, along with the ratio of perturbed images in each image set. We also evaluate our technique with the recent state-of-the-art defence methods, and single-shot recognition task. The empirical results demonstrate superior performance on CIFAR-10, MNIST, ETH-80, and Tiny ImageNet datasets.
Pattern Inversion as a Pattern Recognition Method for Machine Learning
Venue: arXiv cs.CV 2021
Year: 2021

Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome sequencing, etc. The paper discusses the use of indexing-based methods for pattern recognition. It is shown that for pattern recognition applications such indexing methods replace with inverse patterns the fully inverted files, which are typically employed in search engines. Not only such inversion provide automatic feature extraction, which is a distinguishing mark of deep learning, but, unlike deep learning, pattern inversion supports almost instantaneous learning, which is a consequence of absence of coefficients. The paper discusses a pattern inversion formalism that makes use on a novel pattern transform and its application for unsupervised instant learning. Examples demonstrate a view-angle independent recognition of three-dimensional objects, such as cars, against arbitrary background, prediction of remaining useful life of aircraft engines, and other applications. In conclusion, it is noted that, in neurophysiology, the function of the neocortical mini-column has been widely debated since 1957. This paper hypothesize that, mathematically, the cortical mini-column can be described as an inverse pattern, which physically serves as a connection multiplier expanding associations of inputs with relevant pattern classes.
The Multiverse: Logical Modularity for Proof Assistants
Venue: arXiv cs.LO 2021
Year: 2021

Proof assistants play a dual role as programming languages and logical systems. As programming languages, proof assistants offer standard modularity mechanisms such as first-class functions, type polymorphism and modules. As logical systems, however, modularity is lacking, and understandably so: incompatible reasoning principles -- such as univalence and uniqueness of identity proofs -- can indirectly lead to logical inconsistency when used in a given development, even when they appear to be confined to different modules. The lack of logical modularity in proof assistants also hinders the adoption of richer programming constructs, such as effects. We propose the multiverse, a general type-theoretic approach to endow proof assistants with logical modularity. The multiverse consists of multiple universe hierarchies that statically describe the reasoning principles and effects available to define a term at a given type. We identify sufficient conditions for this structuring to modularly ensure that incompatible principles do not interfere, and to locally restrict the power of dependent elimination when necessary. This extensible approach generalizes the ad-hoc treatment of the sort of propositions in the Coq proof assistant. We illustrate the power of the multiverse by describing the inclusion of Coq-style propositions, the strict propositions of Gilbert et al., the exceptional type theory of Pédrot and Tabareau, and general axiomatic extensions of the logic.