OpenRaiser/Intern-Atlas huggingface dataset:
Boundary Spanning and the Support of Digital Entrepreneurs: A Case Study of Bahrain
Venue: arXiv cs.CY 2021
Year: 2021

This paper explores the role of public and private sector teams as they collaborate to form and manage a community and platform for digital entrepreneurs in Bahrain. The paper employed the theoretical concept of boundary spanners to explore the nature of interactions between the two teams as support digital entrepreneurship and the outcomes that emerged from these interactions. The findings present the nature of the inter-sectoral interactions as boundary spanning that contributed to the initiation and formalization of the community and platform.
Reflections, Learnings and Proposed Interventions on Data Validation and Data Use for Action in Health: A Case of Mozambique
Venue: arXiv cs.CY 2021
Year: 2021

The ideal of a country's health information system (HIS) is to develop processes that ensure easy collection of relevant data and enable their conversion to useful health indicators, which guide decision making and support health interventions. In many Low- and Middle-Income Countries (LMICs), actively engaged in health reform efforts, the role of HIS is crucial, particularly in terms of quality of data and its ability to inspire trust in decision makers to actively use routine HIS data. Recognizing digital platforms potential to support those efforts, several interventions have been implemented in many LMICs. In turn, while the transition from paper registers to digital platforms carries the promise of improving data quality processes, this promise has been notoriously complex to materialize in practice. The authors draw upon more than 15 years of experience implementing HIS in Mozambique to understand how the potential of digital platforms have been realized with respect to data quality, what are the gaps and required remedial steps.
The Quest for Development: When Social Media-Brokered Political Power Encounters Political 'Flak Jackets'
Venue: arXiv cs.CY 2021
Year: 2021

Social media provides an extended space for collective action, as netizens leverage it as a tool for claim-making and for demanding the dividends of governance. However, political regimes often greet expanding use of social media with censorship, which netizens often have to contend with, particularly in the quest for development outcomes. While existing studies having expansively explored multiple uses of social media, the specific features that signal their massive uptake and how this intersects with the quest for political power has not been substantially documented. This paper argues that social media is characterized by social buttons that expedite the multiplication of 'digital bullets' - in forms of tweets and perceived detestable comments - which compromise the defense lines of political regimes, hence, their uptake of censorship as metaphorical 'flak jackets'. This research is conducted on the basis of key informant interviews with voices against social media censorship in Nigeria since the inception of Nigeria's ruling government in 2015, particularly following the proposed 'Protection from Internet Falsehood and Manipulations Bill' in 2019.
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Venue: arXiv cs.LG 2021
Year: 2021

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID, imbalanced (statistical heterogeneity) and distribution shifted training data of FL is distributed in the federated network, which will increase the divergences between the local models and the global model, further degrading performance. In this paper, we propose a flexible clustered federated learning (CFL) framework named FlexCFL, in which we 1) group the training of clients based on the similarities between the clients' optimization directions for lower training divergence; 2) implement an efficient newcomer device cold start mechanism for framework scalability and practicality; 3) flexibly migrate clients to meet the challenge of client-level data distribution shift. FlexCFL can achieve improvements by dividing joint optimization into groups of sub-optimization and can strike a balance between accuracy and communication efficiency in the distribution shift environment. The convergence and complexity are analyzed to demonstrate the efficiency of FlexCFL. We also evaluate FlexCFL on several open datasets and made comparisons with related CFL frameworks. The results show that FlexCFL can significantly improve absolute test accuracy by +10.6% on FEMNIST compared to FedAvg, +3.5% on FashionMNIST compared to FedProx, +8.4% on MNIST compared to FeSEM. The experiment results show that FlexCFL is also communication efficient in the distribution shift environment.
Holistically Placing the ICT Artefact in Capability Approach
Venue: arXiv cs.CY 2021
Year: 2021

This paper proposes a framework that holistically places the Information and Communication Technology (ICT) Artefact in Capability Approach (CA). The framework harmonises the different conceptualisations of technology within CA-based frameworks in ICT4D, in order to address the inconsistencies. To illustrate the framework, while simultaneously addressing the highest thematic research gap among post-2015 ICT4D research priorities, the study collected primary data from users of Pay-As-You-Go (PAYGO) Solar Home Systems who reside in rural Kenya. Using the framework, the study revealed that the ICT-artefact can holistically be conceptualised within three of CA's concepts: under material resources as a capability input; as a new category of conversion factors (technological conversion factors); and as a component within the structural context. The study further demonstrated how the same ICT artefact could play out in the three different conceptualisations, resulting in different development outcomes for individuals. The study finally presents the implications for policy and practice.
Global Transfers: M-Pesa, Intellectual Property Rights and Digital Innovation
Venue: arXiv cs.CY 2021
Year: 2021

In July 2020, in the midst of the COVID crisis, the Kenyan mobile operator Safaricom announced that the intellectual property rights (IPR) for mobile money service M-Pesa were "moving back into African control". This paper tracks how the IPR originally came to be held outside Kenya, and the implications for understanding M-Pesa as an inclusive innovation. Through reflection of this analysis of IPR and innovation, the paper contributes to discussions on structural aspects of digital innovation in the global south. By focussing on IPR, it unpacks some of the processes by which global intellectual property regimes and cross-border IPR practices shape uneven outcomes and power.
Network Weaving to Foster Resilience and Sustainability in ICT4D
Venue: arXiv cs.CY 2021
Year: 2021

A number of studies in Information and Communication Technologies for Development (ICT4D) focus on projects' sustainability and resilience. Over the years, scholars have identified many elements to enable achievement of these goals. Nevertheless, barriers to achieving them are still a common reality in the field. In this paper, we propose that special attention should be paid to communities' relationships, self-organizing, and social capital - and the people's networks that enable them - within ICT4D scholarship and practice, as a way to achieve sustainability and resilience. Building on Green's work (2016) on social change as a force that cannot be understood without focusing on systems and power, we claim that ICT4D would benefit from intentionally growing social capital and fostering networks within its systems. We propose "network weaving" (Holley, 2013) as a practical approach, and we explore its potential to complement and advance existing ICT4D frameworks and practices, including the sense of community of the researchers themselves.
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
Venue: arXiv cs.CG 2021
Year: 2021

This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contributions to the fields of computational geometry and topology through the open-source repositories Geomstats and Giotto-TDA. The challenge attracted 16 teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings.
Embodied AI-Driven Operation of Smart Cities: A Concise Review
Venue: arXiv cs.AI 2021
Year: 2021

A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient. Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. Autonomous driving cars and personal companions are some of the examples that benefit from Embodied AI nowadays. In this paper, we attempt to do a concise review of this field. We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions that are being used in different components of it (e.g. Vision, NLP, RL). We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible. Finally, we will address its challenges and identify its potentials for future research.
Explainable Machine Learning using Real, Synthetic and Augmented Fire Tests to Predict Fire Resistance and Spalling of RC Columns
Venue: arXiv cs.LG 2021
Year: 2021

This paper presents the development of systematic machine learning (ML) approach to enable explainable and rapid assessment of fire resistance and fire-induced spalling of reinforced concrete (RC) columns. The developed approach comprises of an ensemble of three novel ML algorithms namely; random forest (RF), extreme gradient boosted trees (ExGBT), and deep learning (DL). These algorithms are trained to account for a wide collection of geometric characteristics and material properties, as well as loading conditions to examine fire performance of normal and high strength RC columns by analyzing a comprehensive database of fire tests comprising of over 494 observations. The developed ensemble is also capable of presenting quantifiable insights to ML predictions; thus, breaking free from the notion of 'blackbox' ML and establishing a solid step towards transparent and explainable ML. Most importantly, this work tackles the scarcity of available fire tests by proposing new techniques to leverage the use of real, synthetic and augmented fire test observations. The developed ML ensemble has been calibrated and validated for standard and design fire exposures and for one, two, three and four-sided fire exposures thus; covering a wide range of practical scenarios present during fire incidents. When fully deployed, the developed ensemble can analyze over 5,000 RC columns in under 60 seconds thus, providing an attractive solution for researchers and practitioners. The presented approach can also be easily extended for evaluating fire resistance and spalling of other structural members and under varying fire scenarios and loading conditions and hence paves the way to modernize the state of this research area and practice.
A Unified Transformer-based Framework for Duplex Text Normalization
Venue: arXiv cs.CL 2021
Year: 2021

Text normalization (TN) and inverse text normalization (ITN) are essential preprocessing and postprocessing steps for text-to-speech synthesis and automatic speech recognition, respectively. Many methods have been proposed for either TN or ITN, ranging from weighted finite-state transducers to neural networks. Despite their impressive performance, these methods aim to tackle only one of the two tasks but not both. As a result, in a complete spoken dialog system, two separate models for TN and ITN need to be built. This heterogeneity increases the technical complexity of the system, which in turn increases the cost of maintenance in a production setting. Motivated by this observation, we propose a unified framework for building a single neural duplex system that can simultaneously handle TN and ITN. Combined with a simple but effective data augmentation method, our systems achieve state-of-the-art results on the Google TN dataset for English and Russian. They can also reach over 95% sentence-level accuracy on an internal English TN dataset without any additional fine-tuning. In addition, we also create a cleaned dataset from the Spoken Wikipedia Corpora for German and report the performance of our systems on the dataset. Overall, experimental results demonstrate the proposed duplex text normalization framework is highly effective and applicable to a range of domains and languages
Rate distortion comparison of a few gradient quantizers
Venue: arXiv cs.DC 2021
Year: 2021

This article is in the context of gradient compression. Gradient compression is a popular technique for mitigating the communication bottleneck observed when training large machine learning models in a distributed manner using gradient-based methods such as stochastic gradient descent. In this article, assuming a Gaussian distribution for the components in gradient, we find the rate distortion trade-off of gradient quantization schemes such as Scaled-sign and Top-K, and compare with the Shannon rate distortion limit. A similar comparison with vector quantizers also is presented.
Fluent: An AI Augmented Writing Tool for People who Stutter
Venue: arXiv cs.AI 2021
Year: 2021

Stuttering is a speech disorder which impacts the personal and professional lives of millions of people worldwide. To save themselves from stigma and discrimination, people who stutter (PWS) may adopt different strategies to conceal their stuttering. One of the common strategies is word substitution where an individual avoids saying a word they might stutter on and use an alternative instead. This process itself can cause stress and add more burden. In this work, we present Fluent, an AI augmented writing tool which assists PWS in writing scripts which they can speak more fluently. Fluent embodies a novel active learning based method of identifying words an individual might struggle pronouncing. Such words are highlighted in the interface. On hovering over any such word, Fluent presents a set of alternative words which have similar meaning but are easier to speak. The user is free to accept or ignore these suggestions. Based on such user interaction (feedback), Fluent continuously evolves its classifier to better suit the personalized needs of each user. We evaluated our tool by measuring its ability to identify difficult words for 10 simulated users. We found that our tool can identify difficult words with a mean accuracy of over 80% in under 20 interactions and it keeps improving with more feedback. Our tool can be beneficial for certain important life situations like giving a talk, presentation, etc. The source code for this tool has been made publicly accessible at github.com/bhavyaghai/Fluent.
Pulse-Width Modulation Neuron Implemented by Single Positive-Feedback Device
Venue: arXiv cs.ET 2021
Year: 2021

Positive-feedback (PF) device and its operation scheme to implement pulse width modulation (PWM) function was proposed and demonstrated, and the device operation mechanism for implementing PWM function was analyzed. By adjusting the amount of the charge stored in the n- floating body (Qn), the potential of the floating body linearly changes with time. When Qn reaches to a threshold value (Qth), the PF device turns on abruptly. From the linear time-varying property of Qn and the gate bias dependency of Qth, fully functionable PWM neuron properties including voltage to pulse width conversion and hard-sigmoid activation function were successfully obtained from a single PF device. A PWM neuron can be implemented by using a single PF device, thus it is beneficial to extremely reduce the area of a PWM neuron circuit than the previously reported one.
Artificial Intelligence Ethics: An Inclusive Global Discourse?
Venue: arXiv cs.CY 2021
Year: 2021

It is widely accepted that technology is ubiquitous across the planet and has the potential to solve many of the problems existing in the Global South. Moreover, the rapid advancement of artificial intelligence (AI) brings with it the potential to address many of the challenges outlined in the Sustainable Development Goals (SDGs) in ways which were never before possible. However, there are many questions about how such advanced technologies should be managed and governed, and whether or not the emerging ethical frameworks and standards for AI are dominated by the Global North. This research examines the growing body of documentation on AI ethics to examine whether or not there is equality of participation in the ongoing global discourse. Specifically, it seeks to discover if both countries in the Global South and women are underrepresented in this discourse. Findings indicate a dearth of references to both of these themes in the AI ethics documents, suggesting that the associated ethical implications and risks are being neglected. Without adequate input from both countries in the Global South and from women, such ethical frameworks and standards may be discriminatory with the potential to reinforce marginalisation.
Relative Entropy-Regularized Optimal Transport on a Graph: a new algorithm and an experimental comparison
Venue: arXiv cs.LG 2021
Year: 2021

Following [21, 23], the present work investigates a new relative entropy-regularized algorithm for solving the optimal transport on a graph problem within the randomized shortest paths formalism. More precisely, a unit flow is injected into a set of input nodes and collected from a set of output nodes while minimizing the expected transportation cost together with a paths relative entropy regularization term, providing a randomized routing policy. The main advantage of this new formulation is the fact that it can easily accommodate edge flow capacity constraints which commonly occur in real-world problems. The resulting optimal routing policy, i.e., the probability distribution of following an edge in each node, is Markovian and is computed by constraining the input and output flows to the prescribed marginal probabilities thanks to a variant of the algorithm developed in [8]. In addition, experimental comparisons with other recently developed techniques show that the distance measure between nodes derived from the introduced model provides competitive results on semi-supervised classification tasks.
Effective and Privacy preserving Tabular Data Synthesizing
Venue: arXiv cs.LG 2021
Year: 2021

While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limits its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GAN). In this thesis, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types with complex distributions. CTAB-GAN is extensively evaluated with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results in higher accuracy for five machine learning algorithms, by up to 17%. Additionally, to ensure greater security for training tabular GANs against malicious privacy attacks, differential privacy (DP) is studied and used to train CTAB-GAN with strict privacy guarantees. DP-CTAB-GAN is rigorously evaluated using state-of-the-art DP-tabular GANs in terms of data utility and privacy robustness against membership and attribute inference attacks. Our results on three datasets indicate that strict theoretical differential privacy guarantees come only after severely affecting data utility. However, it is shown empirically that these guarantees help provide a stronger defence against privacy attacks. Overall, it is found that DP-CTABGAN is capable of being robust to privacy attacks while maintaining the highest data utility as compared to prior work, by up to 18% in terms of the average precision score.
Dynamic Neural Network Architectural and Topological Adaptation and Related Methods -- A Survey
Venue: arXiv cs.LG 2021
Year: 2021

Training and inference in deep neural networks (DNNs) has, due to a steady increase in architectural complexity and data set size, lead to the development of strategies for reducing time and space requirements of DNN training and inference, which is of particular importance in scenarios where training takes place in resource constrained computation environments or inference is part of a time critical application. In this survey, we aim to provide a general overview and categorization of state-of-the-art (SOTA) of techniques to reduced DNN training and inference time and space complexities with a particular focus on architectural adaptions.
A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
Venue: arXiv cs.NI 2021
Year: 2021

The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine type communication (mMTC) applications. To this end, 3GPP introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart internet-of-things (IoT) applications with strict QoS constraints. We propose a novel FUG allocation based on support vector machine (SVM), First, MTC devices are prioritized using SVM classifier. Second, LSTM architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta Anomaly Benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 $\%$ when serving the target massive and critical MTC applications with a limited number of resources.