Reviewerly/exHarmony huggingface dataset:
Continual learning with hypernetworks

Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. To overcome this problem, we present a novel approach based on task-conditioned hypernetworks, i.e., networks that generate the weights of a target model based on task identity. Continual learning (CL) is less difficult for this class of models thanks to a simple key feature: instead of recalling the input-output relations of all previously seen data, task-conditioned hypernetworks only require rehearsing task-specific weight realizations, which can be maintained in memory using a simple regularizer. Besides achieving state-of-the-art performance on standard CL benchmarks, additional experiments on long task sequences reveal that task-conditioned hypernetworks display a very large capacity to retain previous memories. Notably, such long memory lifetimes are achieved in a compressive regime, when the number of trainable weights is comparable or smaller than target network size. We provide insight into the structure of low-dimensional task embedding spaces (the input space of the hypernetwork) and show that task-conditioned hypernetworks demonstrate transfer learning. Finally, forward information transfer is further supported by empirical results on a challenging CL benchmark based on the CIFAR-10/100 image datasets.
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores residual and illumination with Generative Adversarial Networks for shadow removal. Combined with the coarse shadow-removal image, the estimated negative residual images and inverse illumination maps can be used to generate indirect shadow-removal images to refine the coarse shadow-removal result to the fine shadow-free image in a coarse-to-fine fashion. Three discriminators are designed to distinguish whether the predicted negative residual images, shadow-removal images, and the inverse illumination maps are real or fake jointly compared with the corresponding ground-truth information. To our best knowledge, we are the first one to explore residual and illumination for shadow removal. We evaluate our proposed method on two benchmark datasets, i.e., SRD and ISTD, and the extensive experiments demonstrate that our proposed method achieves the superior performance to state-of-the-arts, although we have no particular shadow-aware components designed in our generators.
A Knowledge-Aware Attentional Reasoning Network for Recommendation

Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently. Many existing knowledge-aware recommendation methods have achieved better performance, which usually perform recommendation by reasoning on the paths between users and items in knowledge graphs. However, they ignore the users' personal clicked history sequences that can better reflect users' preferences within a period of time for recommendation. In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users' clicked history sequences and path connectivity between users and items for recommendation. The proposed KARN not only develops an attention-based RNN to capture the user's history interests from the user's clicked history sequences, but also a hierarchical attentional neural network to reason on paths between users and items for inferring the potential user intents on items. Based on both user's history interest and potential intent, KARN can predict the clicking probability of the user with respective to a candidate item. We conduct experiment on Amazon review dataset, and the experimental results demonstrate the superiority and effectiveness of our proposed KARN model.
An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.
Enhancing governance for environmental sustainability in sub-Saharan Africa

This study assesses whether improving governance standards affects environmental quality in 44 countries in sub-Saharan Africa for the period 2000–2012. The empirical evidence is based on generalized method of moments. Bundled and unbundled governance dynamics are used, notably: (i) political governance (consisting of political stability and “voice and accountability”); (ii) economic governance (entailing government effectiveness and regulation quality), (iii) institutional governance (represented by the rule of law and corruption-control); and (iv) general governance (encompassing political, economic, and institutional governance dynamics). The following hypotheses are tested: (i) Hypothesis 1 ( improving political governance is negatively related to carbon dioxide (CO 2 ) emissions); (ii) Hypothesis 2 ( increasing economic governance is negatively related to CO 2 emissions); and (iii) Hypothesis 3 ( enhancing institutional governance is negatively related to CO 2 emissions). Results of the tested hypotheses show that the validity of Hypothesis 3 cannot be determined based on the results; Hypothesis 2 is not valid, while Hypothesis 1 is partially not valid. The main policy implication is that governance standards need to be further improved in order for government quality to generate the expected unfavorable effects on CO 2 emissions.
Generation of Dynamical S-Boxes for Block Ciphers via Extended Logistic Map

In this work, we present a simple algorithm to design n × n -bits substitution boxes (S-boxes) based on chaotic time series of the logistic map for different carrying capacities. The use of different carrying capacities in the chaotic map leads to low computational complexity, which is desirable to get high-speed communication systems. We generate a main sequence by means of two auxiliary sequences with uniform distribution via the logistic map for different carrying capacities. The elements of the main sequence are useful for generating the elements of an S-box. The auxiliary sequences are generated by considering lag time chaotic series; this helps to hide the chaotic map used. The U-shape distribution of logistic chaotic map is also avoided, in contrast with common chaos-based schemes without considering lag time chaotic series, and uncorrelated S-box elements are obtained. The proposed algorithm guarantees the generation of strong S-boxes that fulfill the following criteria: bijection, nonlinearity, strict avalanche criterion, output bits independence criterion, criterion of equiprobable input/output XOR distribution, and maximum expected linear probability. Finally, an application premised on polyalphabetic ciphers principle is developed to obtain a uniform distribution of the plaintext via dynamical S-boxes.
Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies

Abstract The use of primary care electronic health records for research is abundant. The benefits gained from utilising such records lies in their size, longitudinal data collection and data quality. However, the use of such data to undertake high quality epidemiological studies, can lead to significant challenges particularly in dealing with misclassification, variation in coding and the significant effort required to pre-process the data in a meaningful format for statistical analysis. In this paper, we describe a methodology to aid with the extraction and processing of such databases, delivered by a novel software programme; the “Data extraction for epidemiological research” (DExtER). The basis of DExtER relies on principles of extract, transform and load processes. The tool initially provides the ability for the healthcare dataset to be extracted, then transformed in a format whereby data is normalised, converted and reformatted. DExtER has a user interface designed to obtain data extracts specific to each research question and observational study design. There are facilities to input the requirements for; eligible study period, definition of exposed and unexposed groups, outcome measures and important baseline covariates. To date the tool has been utilised and validated in a multitude of settings. There have been over 35 peer-reviewed publications using the tool, and DExtER has been implemented as a validated public health surveillance tool for obtaining accurate statistics on epidemiology of key morbidities. Future direction of this work will be the application of the framework to linked as well as international datasets and the development of standardised methods for conducting electronic pre-processing and extraction from datasets for research purposes.
Digital advantage in the COVID-19 response: perspective from Canada’s largest integrated digitalized healthcare system

The SARS-CoV-2 pandemic has challenged healthcare systems worldwide. Uncertainty of transmission, limitations of physical healthcare system infrastructure and supplies as well as workforce shortages require dynamic adaption of resource deployment to manage rapidly evolving care demands, ideally based on real time data for the entire population. Moreover, shut down of traditional face-to-face care infrastructure requires rapid deployment of virtual health care options to avoid collapse of health organizations. The Alberta Electronic Health Record Information System is one of the largest population based comprehensive electronic medical record (EMR) installations. Alberta's long standing solid telehealth hardware-, training-, provider remuneration- and legislation infrastructure has enabled quick transition to virtual healthcare. Virtual health services including asynchronous secure clinical communications, real-time virtual care via messaging, telephony or video conferencing (telehealth) and ancillary functions like triage, scheduling, documentation and reporting, the previously established virtual hospital program with home monitoring, virtual health assessments, medication review, education and support for patients and families and coordination between family doctors, specialists and other health team members help to control viral transmission, protect healthcare personnel and save supplies. Moreover, rapid launch of online screening and triage tools to guide testing and isolation, online result sharing, infected patient and contact tracing including a smartphone exposure tracking application (ABTraceTogether), electronic best practice alerts and decision support tools, test and treatment order sets for standardized COVID-19 management, continuous access to population level real-time data to inform healthcare provider, public health and government decisions have become key factors in the management of a global crisis in Alberta.
Line Segment Detection Using Transformers without Edges

In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LETR), takes advantages of having integrated tokenized queries, a self-attention mechanism, and encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. In our experiments, we show state-of-the-art results on Wireframe and YorkUrban benchmarks.
A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality solution from one task to other tasks to enhance the optimization ability, which, however, may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this article proposes a meta-knowledge transfer (MKT)-based differential evolution (MKTDE) algorithm by using a more general MKT method to solve MTOPs more efficiently. The meta-knowledge defined in this article refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">knowledge of knowledge</i> ,” which denotes the knowledge of “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how to solve problem via evolution</i> ” and “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the feature/way/method of evolving high-quality solution</i> .” The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multisource data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is multiple populations for the multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms.
MeMOT: Multi-Object Tracking with Memory

We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span. This is realized by preserving a large spatio-temporal memory to store the identity embeddings of the tracked objects, and by adaptively referencing and aggregating useful information from the memory as needed. Our model, called MeMOT, consists of three main modules that are all Transformer-based: 1) Hypothesis Generation that produce object proposals in the current video frame; 2) Memory Encoding that extracts the core information from the memory for each tracked object; and 3) Memory Decoding that solves the object detection and data association tasks simultaneously for multi-object tracking. When evaluated on widely adopted MOT benchmark datasets, MeMOT observes very competitive performance.
Artificial intelligence in education

The article is an excerpt from Wayne Holmes/ Maya Bialik/ Charles Fadel, Artificial Intelligence in Education : Promises and Implications for Teaching and Learning, The Center for Curriculum Redesign, Boston, 2019, 151-180 (ISBN-13: 978-1-794-29370-0). With permission of the publisher. Abstract available from: https://discovery.ucl.ac.uk/id/eprint/10139722/).
Can information improve rural governance and service delivery?

In the context of an exponential rise in access to information in the last two decades, this special issue explores when and how information might be harnessed to improve governance and public service delivery in rural areas. Information is a critical component of government and citizens’ decision-making; therefore, improvements in its availability and reliability stand to benefit many dimensions of governance, including service delivery. Service delivery is especially difficult in rural areas which contain the majority of the world’s poor but face unique logistical challenges due to their remoteness. We review the features of the recent information revolution, including increased access to information due to both technological and institutional innovations. We then raise the question of why information often fails to support the goals of improved governance and service delivery. We argue that information alone is insufficient. To be impactful, the information must be deemed relevant, in the sense of being salient and having a high perceived signal-to-noise ratio, and individuals must have both the power and incentives to act on it. Bringing all three of these factors together in any setting is challenging, particularly for rural areas, where capacity to receive, understand, and act on information is relatively low. Research failing to find significant effects of greater access to information on rural governance and service delivery has largely failed due to one of these three factors not being in place. This interpretation is broadly supported by our review of 48 empirical studies on the impacts of information on governance and service delivery. We conclude by discussing broader lessons for both development research, including randomized control trials, and the development process itself. The goals of interventions to provide information may need to be more modest, and their design may merit more scrutiny.
Characterization of dynamical measurement's uncertainty in a two-qubit system coupled with bosonic reservoirs

We study the dynamical characteristics of the entropy-based uncertainty with regard to a pair of incompatible measurements under a bipartite qubit-system suffering from quantum decoherence induced by hierarchical environments. How non-Markovian and Markovian environments affect the dynamical behaviors of the measurement's uncertainty is revealed. We prove that the measured uncertainty of interest demonstrates a non-monotonic behavior, viz., the amount will increase initially and subsequently oscillate periodically with the growth of time in a non-Markovian regime; On the contrary, the uncertainty will inflate firstly and monotonically decrease in a Markovian regime. Noteworthily, we put forward a simple and feasible strategy to suppress the damping of the system and hence be good for decreasing the magnitude of the uncertainty, by virtue of optimal combination of pre-weak measurements and post-filtering operations. Furthermore, we explore the applications of the uncertainty relation investigated on entanglement witness and channel capacity.
Applicability and Appropriateness of Distributed Ledgers Consensus Protocols in Public and Private Sectors: A Systematic Review

Advancement of consensus protocols in recent years has enabled distributed ledger technologies (DLTs) to find its application and value in sectors beyond cryptocurrencies.Here we reviewed 66 known consensus protocols and classified them into philosophical and architectural categories, also providing a visual representation.As a case study, we focus on the public sector and highlighted potential protocols.We have also listed these protocols against basic features and sector preference in a tabular format to facilitate selection.We argue that no protocol is a silver bullet, therefore should be selected carefully, considering the sector requirements and environment.
Blockchain technology and BIM process: review and potential applications

A Blockchain Technology (BCT) is an emergent digital technology that in recent years has gained widespread traction in various industrial, public, and business sectors; primarily in financial and banking realms owing to the rapid increase of cryptocurrency valuation in recent years. A blockchain Technology (BCT) is, in essence, a decentralized ledger that records every transaction made in the network, known as a ‘block’, the body of which comprises of encrypted data of the entire transaction history. It was introduced as the working mechanism that formed the operational basis of Bitcoin, the first digital cryptocurrency to gain extensive mainstream appeal. The introduction of decentralized technology in any industry would require strengthened security, enforce accountability, and could potentially accelerate a shift in workflow dynamics from the current centralized architectures to a decentralized, cooperative chain of command and affect a cultural and societal change by encouraging trust and transparency. It was evident that the underlying principles characteristic of BCT functional attributes and secure digital infrastructure could find application in nearly any conceivable industry. BCT aims at creating a system that would be a robust self-regulating, self-monitoring and cyber-resilient system that assures the facilitation and protection of truly efficient data exchange system.This research depicts a survey of blockchain technology and its applications in the Architecture, Engineering, and Construction (AEC) industry and examines the potential incorporation within the Building Information Modeling (BIM) process. Furthermore, the paper investigates how employing distributed ledger technology (DLT) could be advantageous in the BIM workflow by emphasizing network security, providing more reliable data storage and management of permissions, ensuring change tracing and data ownership. The focus on collaboration and the distributed nature of both technologies imply the suitability for implementing an integrative framework, and in extension, posit that effectively leveraging DLT’s robust, reliable, and secure network infrastructure, and BIM workflow processes can be significantly enhanced. The paper examines the fundamentals of distributed ledgers, their prospective future applications and current advances, and their categorization based on intrinsic characteristics of consensus reaching and permissions. Moreover, the study investigates the potential application of BCT in improving the framework for automating the design review process such as using Smart Contract (SC) technologies and Hyperledger Fabric (HLF), as well as discussing future research areas. HLF is the underlying BCT infrastructure that can serve to highlight key postulations to elucidate the idealized BIM workflow aptly.