Reviewerly/exHarmony huggingface dataset:
A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare

Abstract Background There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, approaches to explaining complex machine learning (ML) models are rarely informed by end-user needs and user evaluations of model interpretability are lacking in the healthcare domain. We used extended revisions of previously-published theoretical frameworks to propose a framework for the design of user-centered displays of explanations. This new framework served as the basis for qualitative inquiries and design review sessions with critical care nurses and physicians that informed the design of a user-centered explanation display for an ML-based prediction tool. Methods We used our framework to propose explanation displays for predictions from a p ediatric i ntensive c are u nit (PICU) in-hospital mortality risk model. Proposed displays were based on a model-agnostic, instance-level explanation approach based on feature influence, as determined by Shapley values. Focus group sessions solicited critical care provider feedback on the proposed displays, which were then revised accordingly. Results The proposed displays were perceived as useful tools in assessing model predictions. However, specific explanation goals and information needs varied by clinical role and level of predictive modeling knowledge. Providers preferred explanation displays that required less information processing effort and could support the information needs of a variety of users. Providing supporting information to assist in interpretation was seen as critical for fostering provider understanding and acceptance of the predictions and explanations. The user-centered explanation display for the PICU in-hospital mortality risk model incorporated elements from the initial displays along with enhancements suggested by providers. Conclusions We proposed a framework for the design of user-centered displays of explanations for ML models. We used the proposed framework to motivate the design of a user-centered display of an explanation for predictions from a PICU in-hospital mortality risk model. Positive feedback from focus group participants provides preliminary support for the use of model-agnostic, instance-level explanations of feature influence as an approach to understand ML model predictions in healthcare and advances the discussion on how to effectively communicate ML model information to healthcare providers.
Private blockchain-envisioned security framework for AI-enabled IoT-based drone-aided healthcare services

Internet of Drones (IoD) architecture is designed to support a co-ordinated access for the airspace using the unmanned aerial vehicles (UAVs) known as drones. Recently, IoD communication environment is extremely useful for various applications in our daily activities. Artificial intelligence (AI)-enabled Internet of Things (IoT)-based drone-aided healthcare service is a specialized environment which can be used for different types of tasks, for instance, blood and urine samples collections, medicine delivery and for the delivery of other medical needs including the current pandemic of COVID-19. Due to wireless nature of communication among the deployed drones and their ground station server, several attacks (for example, replay, man-in-the-middle, impersonation and privileged-insider attacks) can be easily mounted by malicious attackers. To protect such attacks, the deployment of effective authentication, access control and key management schemes are extremely important in the IoD environment. Furthermore, combining the blockchain mechanism with deployed authentication make it more robust against various types of attacks. To mitigate such issues, we propose a private-blockchain based framework for secure communication in an IoT-enabled drone-aided healthcare environment. The blockchain-based simulation of the proposed framework has been carried out to measure its impact on various performance parameters.
INCLUDE

Indian Sign Language (ISL) is a complete language with its own grammar, syntax, vocabulary and several unique linguistic attributes. It is used by over 5 million deaf people in India. Currently, there is no publicly available dataset on ISL to evaluate Sign Language Recognition (SLR) approaches. In this work, we present the Indian Lexicon Sign Language Dataset - INCLUDE - an ISL dataset that contains 0.27 million frames across 4,287 videos over 263 word signs from 15 different word categories. INCLUDE is recorded with the help of experienced signers to provide close resemblance to natural conditions. A subset of 50 word signs is chosen across word categories to define INCLUDE-50 for rapid evaluation of SLR meth- ods with hyperparameter tuning. As the first large scale study of SLR on ISL, we evaluate several deep neural networks combining different methods for augmentation, feature extraction, encoding and decoding. The best performing model achieves an accuracy of 94.5% on the INCLUDE-50 dataset and 85.6% on the INCLUDE dataset. This model uses a pre-trained feature extractor and encoder and only trains a decoder. We further explore generalisation by fine-tuning the decoder for an American Sign Language dataset. On the ASLLVD with 48 classes, our model has an accuracy of 92.1%; improving on existing results and providing an efficient method to support SLR for multiple languages.
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that RGB-RGB person re-identification mainly overcomes, VI-ReID suffers from additional inter-modality variance caused by the inherent heterogeneous gap. To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively. Disentangling cross-modality identity-discriminable features leads to more robust retrieval for VI-ReID. To achieve efficient optimization like conventional VAE, we theoretically derive two variational inference terms for the MoG prior under the supervised setting, which not only restricts the identity-discriminable subspace so that the model explicitly handles the cross-modality intra-identity variance, but also enables the MoG distribution to avoid posterior collapse. Furthermore, we propose a triplet swap reconstruction (TSR) strategy to promote the above disentangling process. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two VI-ReID datasets. Codes will be available at https://github.com/TPCD/DG-VAE.
PRIMAL$_2$: Pathfinding Via Reinforcement and Imitation Multi-Agent Learning - Lifelong

Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation. In particular, this work addresses lifelong MAPF (LMAPF) - an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one - in dense and highly structured environments, typical of real-world warehouse operations. Effectively solving LMAPF in such environments requires expensive coordination between agents as well as frequent replanning abilities, a daunting task for existing coupled and decoupled approaches alike. With the purpose of achieving considerable agent coordination without any compromise on reactivity and scalability, we introduce PRIMAL2, a distributed reinforcement learning framework for LMAPF where agents learn fully decentralized policies to reactively plan paths online in a partially observable world. We extend our previous work, which was effective in low-density sparsely occupied worlds, to highly structured and constrained worlds by identifying behaviors and conventions which improve implicit agent coordination, and enable their learning through the construction of a novel local agent observation and various training aids. We present extensive results of PRIMAL2 in both MAPF and LMAPF environments and compare its performance to state-of-the-art planners in terms of makespan and throughput. We show that PRIMAL2 significantly surpasses our previous work and performs comparably to these baselines, while allowing real-time re-planning and scaling up to 2048 agents.
Uplift: A Tangible and Immersive Tabletop System for Casual Collaborative Visual Analytics

Collaborative visual analytics leverages social interaction to support data exploration and sensemaking. These processes are typically imagined as formalised, extended activities, between groups of dedicated experts, requiring expertise with sophisticated data analysis tools. However, there are many professional domains that benefit from support for short 'bursts' of data exploration between a subset of stakeholders with a diverse breadth of knowledge. Such 'casual collaborative' scenarios will require engaging features to draw users' attention, with intuitive, 'walk-up and use' interfaces. This paper presents Uplift, a novel prototype system to support 'casual collaborative visual analytics' for a campus microgrid, co-designed with local stakeholders. An elicitation workshop with key members of the building management team revealed relevant knowledge is distributed among multiple experts in their team, each using bespoke analysis tools. Uplift combines an engaging 3D model on a central tabletop display with intuitive tangible interaction, as well as augmented-reality, mid-air data visualisation, in order to support casual collaborative visual analytics for this complex domain. Evaluations with expert stakeholders from the building management and energy domains were conducted during and following our prototype development and indicate that Uplift is successful as an engaging backdrop for casual collaboration. Experts see high potential in such a system to bring together diverse knowledge holders and reveal complex interactions between structural, operational, and financial aspects of their domain. Such systems have further potential in other domains that require collaborative discussion or demonstration of models, forecasts, or cost-benefit analyses to high-level stakeholders.
DefeXtiles

We present DefeXtiles, a rapid and low-cost technique to produce tulle-like fabrics on unmodified fused deposition modeling (FDM) printers. The under-extrusion of filament is a common cause of print failure, resulting in objects with periodic gap defects. In this paper, we demonstrate that these defects can be finely controlled to quickly print thinner, more flexible textiles than previous approaches allow. Our approach allows hierarchical control from micrometer structure to decameter form and is compatible with all common 3D printing materials.
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications

Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficiencies in mapping the computation to hardware. We present a novel method for designing neural network topologies that directly map to a highly efficient FPGA implementation. By exploiting the equivalence of artificial neurons with quantized inputs/outputs and truth tables, we can train quantized neural networks that can be directly converted to a netlist of truth tables, and subsequently deployed as a highly pipelinable, massively parallel FPGA circuit. However, the neural network topology requires careful consideration since the hardware cost of truth tables grows exponentially with neuron fan-in. To obtain smaller networks where the whole netlist can be placed-and-routed onto a single FPGA, we derive a fan-in driven hardware cost model to guide topology design, and combine high sparsity with few-bit activation quantization to limit the neuron fan-in. We evaluate our approach on two tasks with very high intrinsic throughput requirements in high-energy physics and network intrusion detection. We show that the combination of sparsity and few-bit activation quantization results in high-speed circuits with small logic depth and low LUT cost, demonstrating competitive accuracy with less than 15 ns of inference latency and throughput in the hundreds of millions of inferences per second.
Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning

Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain. The key issue of domain adaptation is how to reduce the distribution discrepancy between two domains in a proper way such that they can be treated indifferently for learning. In this paper, we propose a novel domain adaptation approach, which can thoroughly explore the data distribution structure of target domain. Specifically, we regard the samples within the same cluster in target domain as a whole rather than individuals and assigns pseudo-labels to the target cluster by class centroid matching. Besides, to exploit the manifold structure information of target data more thoroughly, we further introduce a local manifold self-learning strategy into our proposal to adaptively capture the inherent local connectivity of target samples. An efficient iterative optimization algorithm is designed to solve the objective function of our proposal with theoretical convergence guarantee. In addition to unsupervised domain adaptation, we further extend our method to the semi-supervised scenario including both homogeneous and heterogeneous settings in a direct but elegant way. Extensive experiments on seven benchmark datasets validate the significant superiority of our proposal in both unsupervised and semi-supervised manners.
ARTIFICIAL INTELLIGENCE IN BUSINESS AND ECONOMICS RESEARCH: TRENDS AND FUTURE

Artificial Intelligence is a disruptive technology developed during the 20th century, which has undergone an accelerated evolution, underpinning solutions to complex problems in the business world. Neural Networks, Machine Learning, or Deep Learning are concepts currently associated with terms such as digital marketing, decision making, industry 4.0 and business digital transformation. Interest in this technology will increase as the competitive advantages of the use of Artificial Intelligence by economic entities is realised. The aim of this research is to analyse the state-of-the-art research of Artificial Intelligence in business. To this end, a bibliometric analysis has been implement using the Web of Science and Scopus online databases. By using a fractional counting method, this paper identifies 11 clusters and the most frequent terms used in Artificial Intelligence research. The present study identifies the main trends in research on Artificial Intelligence in business and proposes future lines of inquiry.
Forecasting Hourly Global Horizontal Solar Irradiance in South Africa Using Machine Learning Models

Solar irradiance forecasting is essential in renewable energy grids amongst others for back-up programming, operational planning, and short-term power purchases.This study focuses on forecasting hourly solar irradiance using data obtained from the Southern African Universities Radiometric Network at the University of Pretoria radiometric station.The study compares the predictive performance of long short-term memory (LSTM) networks, support vector regression and feed forward neural networks (FFNN) models for forecasting short-term solar irradiance.While all the models outperform principal component regression model, a benchmark model in this study, the FFNN yields the lowest mean absolute error and root mean square error on the testing set.Empirical results show that the FFNN model produces the most accurate forecasts based on mean absolute error and root mean square error.Forecast combination of machine learning models' forecasts is done using convex combination and quantile regression averaging (QRA).The predictive performance we found is statistically significant on the Diebold Mariano and Giacomini-White tests.Based on all the forecast accuracy measures used in this study including the statistical tests, QRA is found to be the best forecast combination method.QRA was also the best forecasting model compared with the stand-alone machine learning models.The median method for combining interval limits gives the best results on prediction interval widths analysis.This is the first application of LSTM on South African and African solar irradiance data to the best of our knowledge.This study has shown that providing adequate and detailed evaluation metrics, including statistical tests in forecasting gives more insight into the developed forecasting models.
SLIM: A Lightweight Block Cipher for Internet of Health Things

Nowadays, there is a strong demand for increasing the protection of resource-constrained devices such as Radio frequency identification (RFID) systems. Current cryptographic algorithms are sufficient for high-resource desktop computers. RFID systems are commonly used in high-security applications such as access control systems, transaction banking systems, and payment systems. The attacker attempts to mislead RFIDs for unauthorized access to services without payment or to circumvent security mechanisms by detecting a secret password. The biggest challenge in RFID systems is how to ensure successful protection against such infringements. Lightweight cryptography can provide security assurance for protecting RFID systems. This article presents a new ultra-lightweight cryptography algorithm for RFID systems called SLIM. SLIM is a 32-bit block cipher based on the Feistel structure since block ciphers are the most used cryptographic and provide very tight protection for IoT devices. The key challenge in designing a lightweight block cipher is to cope with performance, cost, and security. SLIM, like all symmetric block cipher, uses the same key for encryption and decryption. The proposed algorithm has an excellent performance in both hardware and software environments, with a limited implementation area, an acceptable cost/security for RFID systems, and an energy-efficient behaviour. SLIM has demonstrated high immunity against the most effective linear and differential cryptanalysis attacks and has a sufficient margin of defence against these attacks.
HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing

In this paper, we present a Haze-Aware Representation Distillation Generative Adversarial Network (HardGAN) for single-image dehazing. Unlike previous studies that intend to model the transmission map and global atmospheric light jointly to restore a clear image, we approach this restoration problem by using a multi-scale structure neural network composed of our proposed haze-aware representation distillation layers. Moreover, we re-introduce to utilize the normalization layer skillfully instead of stacking with the convolutional layers directly as before to avoid useful information wash away, as claimed in many image quality enhancement studies. Extensive experiments on several synthetic benchmark datasets as well as the NTIRE 2020 real-world images show our proposed HardGAN performs favorably against the state-of-the-art methods in terms of PSNR, SSIM, LPIPS, and individual subjective evaluation.
IoTFinder: Efficient Large-Scale Identification of IoT Devices via Passive DNS Traffic Analysis

Being able to enumerate potentially vulnerable IoT devices across the Internet is important, because it allows for assessing global Internet risks and enables network operators to check the hygiene of their own networks. To this end, in this paper we propose IoTFinder, a system for efficient, large-scale <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive</inf> identification of IoT devices. Specifically, we leverage distributed passive DNS data collection, and develop a machine learning-based system that aims to accurately identify a large variety of IoT devices based solely on their <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNS fingerprints</inf> . Our system is independent of whether the devices reside behind a NAT or other middleboxes, or whether they are assigned an IPv4 or IPv6 address. We design IoTFinder as a multi-label classifier, and evaluate its accuracy in several different settings, including computing detection results over a third-party IoT traffic dataset and DNS traffic collected at a US-based ISP hosting more than 40 million clients. The experimental results show that our approach allows for accurately detecting many diverse IoT devices, even when they are hosted behind a NAT and their traffic is “mixed” with traffic generated by other IoT and non-IoT devices hosted in the same local network.
Probabilistic Solar Irradiation Forecasting Based on Variational Bayesian Inference With Secure Federated Learning

The irradiation forecasting technology is important for the effective utilization of solar power. Existing irradiation forecasting methods have achieved excellent performance with a massive amount of data in a centralized way. However, concerns about privacy protection and data security, which may arise in the process of data collection and transmission from distributed points to the centralized server, pose challenges to current forecasting methods. In this article, a novel federated probabilistic forecasting scheme of solar irradiation is proposed based on deep learning, variational Bayesian inference, and federated learning (FL). In this scheme, the training data are stored and computed in local Internet of Things devices, only forecasting models are shared. Two real-world datasets from SolarGIS and National Solar Radiation Database, and one benchmark dataset of Folsom are used to verify the feasibility and performance of the federated-based scheme. Comprehensive case studies are conducted to analyze the performance of the proposed scheme in multihorizon. And the effects of using meteorological features and variational Bayesian inference are evaluated. Compared with other state-of-the-art probabilistic centralized models, when data can be shared, the proposed scheme achieves competitive forecasting performance on the basis of data privacy protection. When data sharing is unavailable, due to the cooperative nature inherent (model-sharing) of FL, the performance advantage of the proposed scheme is more obvious.
RobustFusion: Human Volumetric Capture with Data-Driven Visual Cues Using a RGBD Camera

High-quality and complete 4D reconstruction of human activities is critical for immersive VR/AR experience, but it suffers from inherent self-scanning constraint and consequent fragile tracking under the monocular setting. In this paper, inspired by the huge potential of learning-based human modeling, we propose RobustFusion, a robust human performance capture system combined with various data-driven visual cues using a single RGBD camera. To break the orchestrated self-scanning constraint, we propose a data-driven model completion scheme to generate a complete and fine-detailed initial model using only the front-view input. To enable robust tracking, we embrace both the initial model and the various visual cues into a novel performance capture scheme with hybrid motion optimization and semantic volumetric fusion, which can successfully capture challenging human motions under the monocular setting without pre-scanned detailed template and owns the reinitialization ability to recover from tracking failures and the disappear-reoccur scenarios. Extensive experiments demonstrate the robustness of our approach to achieve high-quality 4D reconstruction for challenging human motions, liberating the cumbersome self-scanning constraint.