Reviewerly/exHarmony huggingface dataset:
Unsupervised Monocular Depth Estimation for Night-Time Images Using Adversarial Domain Feature Adaptation

In this paper, we look into the problem of estimating per-pixel depth maps from unconstrained RGB monocular night-time images which is a difficult task that has not been addressed adequately in the literature. The state-of-the-art day-time depth estimation methods fail miserably when tested with night-time images due to a large domain shift between them. The usual photometric losses used for training these networks may not work for night-time images due to the absence of uniform lighting which is commonly present in day-time images, making it a difficult problem to solve. We propose to solve this problem by posing it as a domain adaptation problem where a network trained with day-time images is adapted to work for night-time images. Specifically, an encoder is trained to generate features from night-time images that are indistinguishable from those obtained from day-time images by using a PatchGAN-based adversarial discriminative learning method. Unlike the existing methods that directly adapt depth prediction (network output), we propose to adapt feature maps obtained from the encoder network so that a pre-trained day-time depth decoder can be directly used for predicting depth from these adapted features. Hence, the resulting method is termed as “Adversarial Domain Feature Adaptation (ADFA)” and its efficacy is demonstrated through experimentation on the challenging Oxford night driving dataset. To the best of our knowledge, this work is a first of its kind to estimate depth from unconstrained night-time monocular RGB images that uses a completely unsupervised learning process. The modular encoder-decoder architecture for the proposed ADFA method allows us to use the encoder module as a feature extractor which can be used in many other applications. One such application is demonstrated where the features obtained from our adapted encoder network are shown to outperform other state-of-the-art methods in a visual place recognition problem, thereby, further establishing the usefulness and effectiveness of the proposed approach.
Endpoint prediction of heart failure using electronic health records

Heart failure (HF) is a serious condition associated with high morbidity and mortality rates. Effective endpoint prediction in patient treatment trajectories provides preventative information about HF prognosis, guides decision making about the type and intensity of care, and enables better understanding of provider performance. We explored the potential of a large volume of electronic health records (EHRs) for endpoint prediction of HF. Specifically, a suite of patient features observed at the prediction time point were utilized as the auxiliary information during the training of the prediction model. We extract the latent representation of patient treatment trajectory by equipping a recurrent neural network (RNN) with two learning strategies, namely adversarial learning and multi-task learning. As for the adversarial learning strategy, an adversarial learning scheme is used to differentiate the generated feature vector from the real one, while in the multi-task learning strategy, we consider the prediction of patient feature vector as an auxiliary task other than endpoint prediction. With such learning strategies, the extracted representation of patient treatment trajectory is particularly optimized for predicting HF endpoint, including HF-readmission, all-cause mortality and their combination (i.e., composite endpoint). We evaluate the proposed approach on a real clinical dataset collected from a Chinese hospital. The experimental dataset contains 2102 HF patient treatment trajectories with 13,545 visits on the hospital. The area under the ROC curve (AUC) achieved by our best model in predicting composite endpoint is 0.744, which is better than that of state-of-the-art models, including the standard Long Short Term Memory (0.727), Gated Recurrent Unit (0.732), RETAIN(0.730). With respect to the prediction of HF-readmission and all-cause mortality, our method also shows better performance than benchmark models. The experimental results show that the proposed model can achieve competitive performance over state-of-the-art models in terms of endpoint prediction for HF, and reveal some suggestive hypotheses that could be validated by further investigations in the medical domain.
Towards Security and Privacy for Edge AI in IoT/IoE based Digital Marketing Environments

Edge Artificial Intelligence (Edge AI) is a crucial aspect of the current and futuristic digital marketing Internet of Things (IoT) / Internet of Everything (IoE) environment. Consumers often provide data to marketers which is used to enhance services and provide a personalized customer experience (CX). However, use, storage and processing of data has been a key concern. Edge computing can enhance security and privacy which has been said to raise the current state of the art in these areas. For example, when certain processing of data can be done local to where requested, security and privacy can be enhanced. However, Edge AI in such an environment can be prone to its own security and privacy considerations, especially in the digital marketing context where personal data is involved. An ongoing challenge is maintaining security in such context and meeting various legal privacy requirements as they themselves continue to evolve, and many of which are not entirely clear from the technical perspective. This paper navigates some key security and privacy issues for Edge AI in IoT/IoE digital marketing environments along with some possible mitigations.
Unique ethical challenges for the 21st century: Online technology and virtue education

Living well in the 21 st century will present human beings with a unique set of demands and ethical challenges, many of which will require a rapid response to developments in the online space.Online activities increasingly permeate our practical lives.Although there is every indication that this activity will intensify, even experts on digital technology recognise that the precise effects of future emergent technology will be uncertain and remains unknown.We argue that education directed at the cultivation of cyber-wisdom and other cyber-virtues provides our best chance of creating a moral vocabulary that can guide us towards living well in the 21 st century.The aim of this article is to offer the first outline an educational model, founded on neo-Aristotelian theory, that illustrates how these qualities could be cultivated through moral education.
Joint Image and Depth Estimation With Mask-Based Lensless Cameras

Mask-based lensless cameras replace the lens of a conventional camera with a custom mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume. Both these approaches fail to recover the scenes with large depth variations. In this paper, we propose a new approach for depth estimation based on an alternating gradient descent algorithm that jointly estimates a continuous depth map and light distribution of the unknown scene from its lensless measurements. We present simulation results on image and depth reconstruction for a variety of 3D test scenes. A comparison between the proposed algorithm and other method shows that our algorithm is more robust for natural scenes with a large range of depths. We built a prototype lensless camera and present experimental results for reconstruction of intensity and depth maps of different real objects.
Dynamic Modeling, Energy Analysis, and Path Planning of Spherical Robots on Uneven Terrains

Spherical robots are generally comprised of a spherical shell and an internal actuation unit. These robots have a variety of applications ranging from search and rescue to agriculture. Although one of the main advantages of spherical robots is their capability to operate on uneven surfaces, energy analysis and path planning of such systems have been studied only for flat terrains. This work introduces a novel approach to evaluate the dynamic equations, energy consumption, and separation analysis of these robots rolling on uneven terrains. The presented dynamics modeling, separation analysis, and energy analysis allow us to implement path planning algorithms to find an optimal path. One of the advantages of this work is that these algorithms can be used when either the analytical or the empirical information about the terrain is available.
An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis

Considering the multi-scale and occlusion problem of pedestrian detection in natural scenes, we propose an improved Faster R-CNN pedestrian detection algorithm based on feature fusion and context analysis (FCF R-CNN). We design a feature fusion method of progressive cascade on VGG16 network, and add LRN to speed up the convergence of the network. The improved feature extraction network enables our model to generate high-resolution feature maps containing rich, detailed and semantic information. We also adjust the RPN parameters to improve the proposal efficiency. In addition, we add a multi-layer iterative LSTM module to the detection model, which uses LSTM's memory ability to extract the global context information of the candidate boxes. This method only needs the feature map of the image itself as input, which highlights useful context information and enables the model to generate more accurate candidate boxes containing potential pedestrians. Our method performs better than existing methods in detecting small-size and occluded pedestrians, and has strong robustness in challenging scenes. Our method achieves competitive results in both accuracy and speed on Caltech pedestrian dataset, achieving a LAMR value of 36.75% and a runtime of 0.20 seconds per image. The validity of the algorithm has been proved.
Understanding Cryptocurrencies

Cryptocurrency refers to a type of digital asset that uses distributed ledger, or blockchain, technology to enable a secure transaction. Although the technology is widely misunderstood, many central banks are considering launching their own national cryptocurrency. In contrast to most data in financial economics, detailed data on the history of every transaction in the cryptocurrency complex are freely available. Furthermore, empirically-oriented research is only now beginning, presenting an extraordinary research opportunity for academia. We provide some insights into the mechanics of cryptocurrencies, describing summary statistics and focusing on potential future research avenues in financial economics.
Do Users Behave Similarly in VR? Investigation of the User Influence on the System Design

With the overarching goal of developing user-centric Virtual Reality (VR) systems, a new wave of studies focused on understanding how users interact in VR environments has recently emerged. Despite the intense efforts, however, current literature still does not provide the right framework to fully interpret and predict users’ trajectories while navigating in VR scenes. This work advances the state-of-the-art on both the study of users’ behaviour in VR and the user-centric system design. In more detail, we complement current datasets by presenting a publicly available dataset that provides navigation trajectories acquired for heterogeneous omnidirectional videos and different viewing platforms—namely, head-mounted display, tablet, and laptop. We then present an exhaustive analysis on the collected data to better understand navigation in VR across users, content, and, for the first time, across viewing platforms. The novelty lies in the user-affinity metric, proposed in this work to investigate users’ similarities when navigating within the content. The analysis reveals useful insights on the effect of device and content on the navigation, which could be precious considerations from the system design perspective. As a case study of the importance of studying users’ behaviour when designing VR systems, we finally propose a user-centric server optimisation. We formulate an integer linear program that seeks the best stored set of omnidirectional content that minimises encoding and storage cost while maximising the user’s experience. This is posed while taking into account network dynamics, type of video content, and also user population interactivity. Experimental results prove that our solution outperforms common company recommendations in terms of experienced quality but also in terms of encoding and storage, achieving a savings up to 70%. More importantly, we highlight a strong correlation between the storage cost and the user-affinity metric, showing the impact of the latter in the system architecture design.
Multilevel minimum cross entropy thresholding: A comparative study

Multilevel thresholding method is one of the most popular techniques in image segmentation. However, the multilevel thresholding method is time-consuming, its time complexity increases exponentially with the number of threshold levels. In this paper, in order to improve the computation efficiency of the multilevel minimum cross entropy thresholding, the iterative formula of the multilevel cross entropy thresholding algorithm is proposed and compared with the modern meta-heuristic optimization algorithms. The iterative multilevel cross entropy thresholding algorithm can find the thresholds close to the global optimums with less time. The computation cost of the iterative multilevel cross entropy thresholding algorithm is linear in the number of the threshold levels. We prove the convergence of the iterative algorithm and compare the iterative multilevel cross entropy thresholding algorithm with multilevel cross entropy thresholding methods combined with the state-of-the-art meta-heuristic optimization techniques including particle swarm optimization (PSO), cuckoo search algorithm (CS), differential evolution (DE), crow search algorithm (CSA) and genetic algorithm (GA). Experimental results show that the iterative multilevel cross entropy thresholding algorithm is efficient and effective. Therefore, iterative algorithm for the multilevel cross entropy thresholding is an effective method to improve the computation efficiency.
Lifelong Incremental Reinforcement Learning With Online Bayesian Inference

A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios. In this article, we propose lifelong incremental reinforcement learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments. We develop and maintain a library that contains an infinite mixture of parameterized environment models, which is equivalent to clustering environment parameters in a latent space. The prior distribution over the mixture is formulated as a Chinese restaurant process (CRP), which incrementally instantiates new environment models without any external information to signal environmental changes in advance. During lifelong learning, we employ the expectation-maximization (EM) algorithm with online Bayesian inference to update the mixture in a fully incremental manner. In EM, the E-step involves estimating the posterior expectation of environment-to-cluster assignments, whereas the M-step updates the environment parameters for future learning. This method allows for all environment models to be adapted as necessary, with new models instantiated for environmental changes and old models retrieved when previously seen environments are encountered again. Simulation experiments demonstrate that LLIRL outperforms relevant existing methods and enables effective incremental adaptation to various dynamic environments for lifelong learning.
Optical character recognition (OCR) using partial least square (PLS) based feature reduction: an application to artificial intelligence for biometric identification

Purpose In artificial intelligence, the optical character recognition (OCR) is an active research area based on famous applications such as automation and transformation of printed documents into machine-readable text document. The major purpose of OCR in academia and banks is to achieve a significant performance to save storage space. Design/methodology/approach A novel technique is proposed for automated OCR based on multi-properties features fusion and selection. The features are fused using serially formulation and output passed to partial least square (PLS) based selection method. The selection is done based on the entropy fitness function. The final features are classified by an ensemble classifier. Findings The presented method was extensively tested on two datasets such as the authors proposed and Chars74k benchmark and achieved an accuracy of 91.2 and 99.9%. Comparing the results with existing techniques, it is found that the proposed method gives improved performance. Originality/value The technique presented in this work will help for license plate recognition and text conversion from a printed document to machine-readable.
Meta Learning for Few-Shot Joint Intent Detection and Slot-Filling

Intent detection and slot filling are the two main tasks in natural language understanding module in goal oriented conversational agents. Models which optimize these two objectives simultaneously within a single network (joint models) have proven themselves to be superior to mono-objective networks. However, these data-intensive deep learning approaches have not been successful in catering the demand of the industry for adaptable, multilingual dialogue systems. To this end, we cast joint intent detection as an n-way k-shot classification problem and establish it within meta learning setup. Our approach is motivated by the success of meta learning on few-shot image classification tasks. We empirically demonstrate that, our approach can meta-learn a prior from similar tasks under highly resource constrained settings which enable rapid inference on target tasks. First, we show the adaptability of proposed approach by meta learning n-way k-shot joint intent detection using set of intents and evaluating on a completely new set of intents. Second, we exemplify the cross-lingual adaptability by learning a prior, utilizing English utterances and evaluating on Spanish and Thai utterances. Compared to random initialization, our method significantly improves the accuracy in both intent detection and slot-filling.
Exchange-free computation on an unknown qubit at a distance

We present a way of directly manipulating an arbitrary qubit, without the exchange of any particles. This includes as an application the exchange-free preparation of an arbitrary quantum state at Alice by a remote classical Bob. As a result, we are able to propose a protocol that allows one party to directly enact, by means of a suitable program, any computation exchange-free on a remote second party's unknown qubit. Further, we show how to use this for the exchange-free control of a universal two-qubit gate, thus opening the possibility of directly enacting any desired algorithm remotely on a programmable quantum circuit.
Semantic assessment of smart healthcare ontology

Purpose Health-care ontologies and their terminologies play a vital role in knowledge representation and data integration for health information. In health-care systems, Internet of Technology (IoT) technologies provide data exchange among various entities and ontologies offer a formal description to present the knowledge of health-care domains. These ontologies are advised to assure the quality of their adoption and applicability in the real world. Design/methodology/approach Ontology assessment is an integral part of ontology construction and maintenance. It is always performed to identify inconsistencies and modeling errors by the experts during the ontology development. A smart health-care ontology (SHCO) has been designed to deal with health-care information and IoT devices. In this paper, an integrated approach has been proposed to assess the SHCO on different assessment tools such as Themis, Test-Driven Development (TDD)onto, Protégé and OOPs! Several test cases are framed to assess the ontology on these tools, in this research, Themis and TDDonto tools provide the verification for the test cases while Protégé and OOPs! provides validation of modeled knowledge in the ontology. Findings As of the best knowledge, no other study has been presented earlier to conduct the integrated assessment on different tools. All test cases are successfully analyzed on these tools and results are drawn and compared with other ontologies. Originality/value The developed ontology is analyzed on different verification and validation tools to assure the quality of ontologies.
Mobile augmented reality to support fuselage assembly

One major manufacturing challenge of the fourth industrial revolution is the quality of product development information used in the assembly line. One of the most studied information visualization technologies is Augmented Reality (AR), whose deployment via mobile devices is currently being promoted. However, the industrial sector still has doubts regarding the technology’s maturity and the costs incurred by such production processes, restricting its deployment in production environments. Thus, in the present study, we statistically analyze a markerless AR application, using a hand-held mobile device, that addresses the requirements of the structural assemblies used in aeronautical industries comparing them to those of traditional assembly methods. The main technological benefits and limitations of such applications are identified. To perform this analysis, we conducted a field evaluation where qualified professionals marked the positions of small brackets in an aeronautical structure by using AR. After collecting the time and measurement data for each bracket’s position, a statistical analysis was performed to compare these positionings against those achieved using a traditional, metric scale-based method. The results indicate that AR offers a faster solution for highly complex assemblies, despite several limitations regarding the positioning tolerance when using the technology. As a future study, several application modifications are suggested to reduce markup errors and overall usage time.
Efficient Identity-Based Provable Multi-Copy Data Possession in Multi-Cloud Storage, Revisited

Storing multi-copy of data file on multi-cloud may be useful in some practical contexts. How to ensure the integrity of a user's multi-copy becomes very important in this case. In recent work, Li et al. proposed an efficient “identity-based” auditing protocol (LYZ-protocol, for short) to achieve this goal. The “identity-based” property guarantees that the resulting primitive does not rely on PKI to manage certificates. However, in this letter, we note that the performances of this protocol can be further improved. More precisely, by introducing a new hash function modeled as a random oracle, the communication costs of original LYZ-protocol can be greatly reduced. In addition, the building block of a traditional signature scheme used in this protocol results in the entire protocol not being completely identity-based, and thus it may still bear the burden of managing certificates. Therefore, by replacing this signature with an identity-based scheme, we can obtain a completely identity-based protocol. Finally, the performance analysis shows that the modified LYZ-protocol outperforms the original one.