OpenRaiser/Intern-Atlas huggingface dataset:
Evolutionary Ensemble Learning for Multivariate Time Series Prediction
Venue: arXiv cs.NE 2021
Year: 2021

Multivariate time series (MTS) prediction plays a key role in many fields such as finance, energy and transport, where each individual time series corresponds to the data collected from a certain data source, so-called channel. A typical pipeline of building an MTS prediction model (PM) consists of selecting a subset of channels among all available ones, extracting features from the selected channels, and building a PM based on the extracted features, where each component involves certain optimization tasks, i.e., selection of channels, feature extraction (FE) methods, and PMs as well as configuration of the selected FE method and PM. Accordingly, pursuing the best prediction performance corresponds to optimizing the pipeline by solving all of its involved optimization problems. This is a non-trivial task due to the vastness of the solution space. Different from most of the existing works which target at optimizing certain components of the pipeline, we propose a novel evolutionary ensemble learning framework to optimize the entire pipeline in a holistic manner. In this framework, a specific pipeline is encoded as a candidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets (POSs). Finally, selective ensemble learning is designed to choose the optimal subset of solutions from the POSs and combine them to yield final prediction by using greedy sequential selection and least square methods. We implement the proposed framework and evaluate our implementation on two real-world applications, i.e., electricity consumption prediction and air quality prediction. The performance comparison with state-of-the-art techniques demonstrates the superiority of the proposed approach.
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation
Venue: arXiv cs.CV 2021
Year: 2021

3D detection plays an indispensable role in environment perception. Due to the high cost of commonly used LiDAR sensor, stereo vision based 3D detection, as an economical yet effective setting, attracts more attention recently. For these approaches based on 2D images, accurate depth information is the key to achieve 3D detection, and most existing methods resort to a preliminary stage for depth estimation. They mainly focus on the global depth and neglect the property of depth information in this specific task, namely, sparsity and locality, where exactly accurate depth is only needed for these 3D bounding boxes. Motivated by this finding, we propose a stereo-image based anchor-free 3D detection method, called structure-aware stereo 3D detector (termed as SIDE), where we explore the instance-level depth information via constructing the cost volume from RoIs of each object. Due to the information sparsity of local cost volume, we further introduce match reweighting and structure-aware attention, to make the depth information more concentrated. Experiments conducted on the KITTI dataset show that our method achieves the state-of-the-art performance compared to existing methods without depth map supervision.
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. State-of-the-art object Re-ID approaches adopt clustering algorithms to generate pseudo-labels for the unlabeled target domain. However, the inevitable label noise caused by the clustering procedure significantly degrades the discriminative power of Re-ID model. To address this problem, we propose an uncertainty-aware clustering framework (UCF) for UDA tasks. First, a novel hierarchical clustering scheme is proposed to promote clustering quality. Second, an uncertainty-aware collaborative instance selection method is introduced to select images with reliable labels for model training. Combining both techniques effectively reduces the impact of noisy labels. In addition, we introduce a strong baseline that features a compact contrastive loss. Our UCF method consistently achieves state-of-the-art performance in multiple UDA tasks for object Re-ID, and significantly reduces the gap between unsupervised and supervised Re-ID performance. In particular, the performance of our unsupervised UCF method in the MSMT17$\to$Market1501 task is better than that of the fully supervised setting on Market1501. The code of UCF is available at https://github.com/Wang-pengfei/UCF.
Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads
Venue: arXiv cs.CV 2021
Year: 2021

After DETR was proposed, this novel transformer-based detection paradigm which performs several cross-attentions between object queries and feature maps for predictions has subsequently derived a series of transformer-based detection heads. These models iterate object queries after each cross-attention. However, they don't renew the query position which indicates object queries' position information. Thus model needs extra learning to figure out the newest regions that query position should express and need more attention. To fix this issue, we propose the Guided Query Position (GQPos) method to embed the latest location information of object queries to query position iteratively. Another problem of such transformer-based detection heads is the high complexity to perform attention on multi-scale feature maps, which hinders them from improving detection performance at all scales. Therefore we propose a novel fusion scheme named Similar Attention (SiA): besides the feature maps is fused, SiA also fuse the attention weights maps to accelerate the learning of high-resolution attention weight map by well-learned low-resolution attention weight map. Our experiments show that the proposed GQPos improves the performance of a series of models, including DETR, SMCA, YoloS, and HoiTransformer and SiA consistently improve the performance of multi-scale transformer-based detection heads like DETR and HoiTransformer.
Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations
Venue: arXiv cs.CV 2021
Year: 2021

Upon the discovery of adversarial attacks, robust models have become obligatory for deep learning-based systems. Adversarial training with first-order attacks has been one of the most effective defenses against adversarial perturbations to this day. The majority of the adversarial training approaches focus on iteratively perturbing each pixel with the gradient of the loss function with respect to the input image. However, the adversarial training with gradient-based attacks lacks diversity and does not generalize well to natural images and various attacks. This study presents a robust training algorithm where the adversarial perturbations are automatically synthesized from a random vector using a generator network. The classifier is trained with cross-entropy loss regularized with the optimal transport distance between the representations of the natural and synthesized adversarial samples. Unlike prevailing generative defenses, the proposed one-step attack generation framework synthesizes diverse perturbations without utilizing gradient of the classifier's loss. Experimental results show that the proposed approach attains comparable robustness with various gradient-based and generative robust training techniques on CIFAR10, CIFAR100, and SVHN datasets. In addition, compared to the baselines, the proposed robust training framework generalizes well to the natural samples. Code and trained models will be made publicly available.
Digital Resilience to Covid-19: A Model for National Digital Health Systems to Bounce Forward From the Shock of a Global Pandemic
Venue: arXiv cs.CY 2021
Year: 2021

COVID-19 represented a major shock to global health systems, not the least to resource-challenged regions in the Global South. We report on a case of digital, information system resilience in the response to data needs from the COVID-19 pandemic in two countries in the Global South. In contrast to dominant perspectives where digital resilience enables bounce back or maintenance of a status quo, we identify five bounce forward resilience preconditions (i) distributed training, (ii) local expertise (iii) local autonomy and ownership (iv) local infrastructure and (v) platform design infrastructure. These preconditions enable an elevated degree of resilience that in the face of an external shock such as COVID-19 can deliver a bounce forward or strengthening of the information system beyond its pre-shock state.
Improving Data Use and Participatory Action and Design to Support Data Use: The Case of DHIS2 in Rwanda
Venue: arXiv cs.CY 2021
Year: 2021

This article reports from an ongoing 'evaluation for improvement' action research and participatory design project in Rwanda, where the aim is to improve data use practices and the capabilities of the District Health Information Software 2 (DHIS2), an open source health information management platform, to support data use. The study of data use at health facility and district level showed that while data was used routinely at, for example, monthly coordination meetings, the DHIS2 dashboards and other analytical tools were in limited use because users preferred to use Microsoft Excel for data analysis and use. Given such findings, a major focus of the project has been directed towards identifying shortcomings in data use practices and in the software platform and to suggest, design and eventually implement changes. While the practical work on implementing improvements have been slow due to the COVID-19 pandemic, the suggested design improvements involve many levels of system design and participation, from the global core DHIS2 software team, the country DHIS2 team and local app development, the Rwanda Ministry of Health, and health workers at local level.
EHealth Technologies Integration with Healthcare Work Activities in Public Hospitals: A Critical Realist Perspective
Venue: arXiv cs.CY 2021
Year: 2021

Integration of eHealth technologies with healthcare work activities has seen great advancement in many healthcare systems in developing countries. However, these efforts have been tainted by several challenges such as fragmentation, lack of standardization and co-ordination. Subsequently, the undertakings of eHealth articulated in health strategy/policy documents have not been fully realised. The implications of this has been that the majority of the population still access inadequate healthcare services. The aim of this paper is to explain why the current integration efforts do not adequately facilitate healthcare work activities in public hospitals in under-served contexts of South Africa. A critical realist perspective within a qualitative approach was adopted. A total of 21 participants were purposively sampled and interviewed because of their knowledge and experience in the healthcare service delivery process as well as their involvement in the integration of ehealth. The study applied the Activity Analysis and Development (ActAD) model as a theoretical analytical tool and draws on normalization process theory (NPT) as an explanatory framework. The findings highlight generative mechanisms such as the inadequate analysis of system's fit-for-purpose in healthcare workflows have inhibiting effects in the integration process of eHealth.
Graph2Pix: A Graph-Based Image to Image Translation Framework
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder (http://artbreeder.com), where users interpolate multiple GAN-generated images to create artworks. This unique approach of creating new images leads to a tree-like structure where one can track historical data about the creation of a particular image. Inspired by this structure, we propose a novel graph-to-image translation model called Graph2Pix, which takes a graph and corresponding images as input and generates a single image as output. Our experiments show that Graph2Pix is able to outperform several image-to-image translation frameworks on benchmark metrics, including LPIPS (with a 25% improvement) and human perception studies (n=60), where users preferred the images generated by our method 81.5% of the time. Our source code and dataset are publicly available at https://github.com/catlab-team/graph2pix.
Framework for Managing Cybercrime Risks in Nigerian Universities
Venue: arXiv cs.CY 2021
Year: 2021

Universities in developing countries, including those in Nigeria, experience cybercrime risks due to poor management of their cyber spaces and resources. The outcome of these cybercrimes are threats and breaches of universities' cyber security. The threats and breaches have resulted in substantial financial, social, and intellectual property losses. In the recent past, Nigerian universities have started to respond to these cyber-attacks. Many of them now invest in anti-cybercrime tools and programs to mitigate cyber security threats and breaches. Despite this, the number of times Nigerian universities suffer from cyber-attacks and the losses that result from them keeps increasing. Our observation, however, indicates that most Nigerian universities run their cyber security without using scientifically derived frameworks that spell out how to manage threats and breaches that emanate from within and outside them. We consider this a problem to ongoing efforts made by Nigerian universities to mitigate cyber security threats and breaches. The study reported in this paper was therefore, carried out to explicate how Nigerian universities can develop actionable frameworks that can help them to mitigate cyber security threats and breaches. The study is based on literature review and propose how an actionable framework that Nigerian Universities can adopt to setoff cybersecurity programs can be developed. The process comprises of problem identification, description of objectives, designing and developing the artefact, testing, and evaluating the artefact, and communicating the result. We conclude that the framework provides a lucrative starting point for Nigerian universities to setoff efficient and effective cyber security program.
Smart Cities: Potentialities and Challenges in a Context of Sharing Economy
Venue: arXiv cs.CY 2021
Year: 2021

The purpose of the present paper is to show how blockchain and IoT technologies can benefit smart city projects, which tend to spread in the context of the sharing economy. The article also aims to describe the challenges and potentialities of smart city projects. It was found that technology platforms can serve as a strategy to build the basis for product development (goods and services) and technology-based innovation.
A Climate Change Vulnerability Assessment Framework: A Spatial Approach
Venue: arXiv cs.CY 2021
Year: 2021

Climate change is affecting every known society, especially for small farmers in Low-Income Countries because they depend heavily on rain, seasonality patterns, and known temperature ranges. To build climate change resilient communities among rural farmers, the first step is to understand the impact of climate change on the population. This paper proposes a Climate Change Vulnerability Assessment Framework (CCVAF) to assess climate change vulnerabilities among rural farmers. The CCVAF framework uses information and communication technology (ICT) to assess climate change vulnerabilities among rural farmers by integrating both community level and individual household level indicators. The CCVAF was instantiated into a GIS-based web application named THRIVE for different decision-makers to better assess how climate change is affecting rural farmers in Western Honduras. Qualitative evaluation of the THRIVE showed that it is an innovative and useful tool. The CCVAF contributes to not only the knowledge base of the climate change vulnerability assessment but also the design science literature by providing guidelines to design a class of climate change vulnerability assessment solutions.
Describing Emergency Remote Teaching using a Learning Management System: A South African COVID-19 Study of Resilience through ICT
Venue: arXiv cs.CY 2021
Year: 2021

In an effort to counter the spread of COVID-19 many schools were forced to shut down. Primary schools in South Africa were forced to shift to emergency remote teaching abruptly relying on using a Learning Management System (LMS) to aid their teaching. LMSs helped primary schools build resilience to cope with unexpected events. An opportunity rose to study the affordances and constraints faced when using a LMS for remote teaching, specifically for primary school learners ('Gen Z') - a largely ignored area of research. Through a case study of 6 schools, this research describes the affordances and constraints of the LMS supported teaching system in use in primary schools. Affordances related to schools, learners and teachers while constraints were classified from a financial, technological, school, learner and teacher perspective. Noteworthy affordances included using LMSs as notice boards and satisfying parents, the key stakeholders. In terms of constraints, the digital divide was a recurring theme while device and data costs were consistently a limitation. There were many cases of schools not realizing the full potential of LMS usage. This research should be useful for primary schools wanting to use a LMS for their teaching.
Role of Information and ICTs as Determinants of Farmer's Adaptive Capacity to Climate Risk: An Empirical Study From Haryana, India
Venue: arXiv cs.CY 2021
Year: 2021

Using the primary data collected for 463 farmers in six districts of Haryana, India, the present study attempts to understand the constituents of farmer's adaptive capacity at local level and how it can be enhanced. We use path analysis technique using the lavaan package in RStudio to empirically test the role of information. We find that information is a direct and significant contributor to enhancing farmers' adaptive capacity. However, even with exponential growth in use of technology, particularly information and communication technologies (ICTs), small farmers still lack access to information which hinders their capacity to respond to weather and climate risks. Thus, understanding the mechanism that can facilitate exchange and use of information by the farming community more effectively is important. We take an ensemble view of ICTs operationalized using ICT ecosystem and find significant interlinkages between information, technology and the ICT ecosystem that facilitate learning and information exchange and therefore contribute to enhancing farmers' adaptive capacity and building resilience to climate shocks. We find that ICT ecosystem does facilitate access to information and also mediate the effect of farmer's capability and willingness to use ICTs for agricultural purposes. Development of sound ICT ecosystem is likely to help farmers to better respond to changing climate in the future.
From Digital Divide to Digital Justice in the Global South: Conceptualising Adverse Digital Incorporation
Venue: arXiv cs.CY 2021
Year: 2021

The connection between digital and inequality has traditionally been understood in terms of the digital divide or of forms of digital inequality whose core conceptualisation is exclusion. This paper argues that, as the global South moves into a digital development paradigm of growing breadth and depth of digital engagement, an exclusion worldview is no longer sufficient. Drawing from ideas in the development studies literature on chronic poverty, the paper argues the need for a new concept: "adverse digital incorporation", meaning inclusion in a digital system that enables a more-advantaged group to extract disproportionate value from the work or resources of another, less-advantaged group. This explains why inequality persists - even grows - in a digital development paradigm. To help ground future research and practice on this issue, the paper inductively builds a conceptual model of adverse digital incorporation with three main component sets: the processes, the drivers, and the causes of adverse digital incorporation. The paper concludes with thoughts on a future research and practice agenda that seeks to deliver digital justice in the global South: a necessary reconfiguration of the broader components of power that currently shape the inclusionary connection between digital and inequality.
Towards an Integrated Knowledge Management and Information and Communication Technology Framework for Improving Disaster Response in a Developing Country Context
Venue: arXiv cs.CY 2021
Year: 2021

This paper is part of an ongoing project that seeks to address a gap in disaster information coordination and collaboration in Zimbabwe. There is lack of coordinated information and knowledge in natural disaster and emergency situations in Zimbabwe. This results in weak collaboration links among the various organizations that respond to emergencies, leading to slow decision making processes and long response times. This negatively affects the affected communities, exacerbating poverty in Zimbabwe. This has been evidenced in the recent catastrophic cyclone Idai where many people were left dead, infrastructure destroyed and some people marooned. To address this, the research seeks to develop an integrated Knowledge Management and ICT framework that aid in coordination and collaboration among the various crisis responders. This will be achieved through a case study approach using Zimbabwe's Civil Protection Unit. PAR within DSRM will be used to gather data from CPU as well as with NGO respondents, traditional leaders and disaster response experts. Findings will be compared and contrasted with secondary data gathered in literature, this, with collected data will be used in developing a home grown coordination and collaboration solution. Qualitative approach to data collection will be adopted using interviews, visioning workshops and document analysis.
UzBERT: pretraining a BERT model for Uzbek
Venue: arXiv cs.CL 2021
Year: 2021

Pretrained language models based on the Transformer architecture have achieved state-of-the-art results in various natural language processing tasks such as part-of-speech tagging, named entity recognition, and question answering. However, no such monolingual model for the Uzbek language is publicly available. In this paper, we introduce UzBERT, a pretrained Uzbek language model based on the BERT architecture. Our model greatly outperforms multilingual BERT on masked language model accuracy. We make the model publicly available under the MIT open-source license.
Probabilistic computing with p-bits
Venue: arXiv cs.ET 2021
Year: 2021

Digital computers store information in the form of bits that can take on one of two values 0 and 1, while quantum computers are based on qubits that are described by a complex wavefunction, whose squared magnitude gives the probability of measuring either 0 or 1. Here, we make the case for a probabilistic computer based on p-bits, which take on values 0 and 1 with controlled probabilities and can be implemented with specialized compact energy-efficient hardware. We propose a generic architecture for such p-computers and emulate systems with thousands of p-bits to show that they can significantly accelerate randomized algorithms used in a wide variety of applications including but not limited to Bayesian networks, optimization, Ising models, and quantum Monte Carlo.