Reviewerly/exHarmony huggingface dataset:
Topological Navigation and Localization in Tunnels—Application to Autonomous Load-Haul-Dump Vehicles Operating in Underground Mines

Mobile robots are no longer used exclusively in research laboratories and indoor controlled environments, but are now also used in dynamic industrial environments, including outdoor sites. Mining is one industry where robots and autonomous vehicles are increasingly used to increase the safety of the workers, as well as to augment the productivity, efficiency, and predictability of the processes. Since autonomous vehicles navigate inside tunnels in underground mines, this kind of navigation has different precision requirements than navigating in an open environment. When driving inside tunnels, it is not relevant to have accurate self-localization, but it is necessary for autonomous vehicles to be able to move safely through the tunnel and to make appropriate decisions at its intersections and access points in the tunnel. To address these needs, a topological navigation system for mining vehicles operating in tunnels is proposed and validated in this paper. This system was specially designed to be used by Load-Haul-Dump (LHD) vehicles, also known as scoop trams, operating in underground mines. In addition, a localization system, specifically designed to be used with the topological navigation system and its associated topological map, is also proposed. The proposed topological navigation and localization systems were validated using a commercial LHD during several months at a copper sub-level stoping mine located in the Coquimbo Region in the northern part of Chile. An important aspect to be addressed when working with heavy-duty machinery, such as LHDs, is the way in which automation systems are developed and tested. For this reason, the development and testing methodology, which includes the use of simulators, scale-models of LHDs, validation, and testing using a commercial LHD in test-fields, and its final validation in a mine, are described.
Semantic framework for interdependent infrastructure resilience decision support

The increasing need for interdependent infrastructure systems to withstand natural disasters has called for the co-creation of resilience decisions to minimize the impact on society. However, issues related to information integration across different infrastructure systems hamper decision making from a system-to-systems perspective. To resolve this problem, the Semantic Web technologies are presented in this paper to serve four functions: (i) linking cross domains through ontology development to represent different domain knowledge; (ii) integrating multiple-source heterogeneous data by a common data format; (iii) retrieving useful information using semantic query language; and (iv) deriving machine automatic logical reasoning by rule languages and logic engines to provide informed resilience decision making support. The proposed framework is tested by a case scenario involving intertwined drainage-transport-building systems under the influence of urban flooding. The result indicates that the framework effectively facilitates information integration between diverse infrastructure systems and helps decision-makers by providing resilience decision-making support.
Logical Contradictions in the One-Way ANOVA and Tukey–Kramer Multiple Comparisons Tests with More Than Two Groups of Observations

We show that the one-way ANOVA and Tukey–Kramer (TK) tests agree on any sample with two groups. This result is based on a simple identity connecting the Fisher–Snedecor and studentized probabilistic distributions and is proven without any additional assumptions; in particular, the standard ANOVA assumptions (independence, normality, and homoscedasticity (INAH)) are not needed. In contrast, it is known that for a sample with k&gt;2 groups of observations, even under the INAH assumptions, with the same significance level α, the above two tests may give opposite results: (i) ANOVA rejects its null hypothesis H0A:μ1=…=μk, while the TK one, H0TK(i,j):μi=μj, is not rejected for any pair i,j∈{1,…,k}; (ii) the TK test rejects H0TK(i,j) for a pair (i,j) (with i≠j), while ANOVA does not reject H0A. We construct two large infinite pseudo-random families of samples of both types satisfying INAH: in case (i) for any k≥3 and in case (ii) for some larger k. Furthermore, case (ii) ANOVA, being restricted to the pair of groups (i,j), may reject equality μi=μj with the same α. This is an obvious contradiction, since μ1=…=μk implies μi=μj for all i,j∈{1,…,k}. Such contradictions appear already in the symmetric case for k=3, or in other words, for three groups of d,d, and c observations with sample means +1,−1, and 0, respectively. We outline conditions necessary and sufficient for this phenomenon. Similar contradictory examples are constructed for the multivariable linear regression (MLR). However, for these constructions, it seems difficult to verify the Gauss–Markov assumptions, which are standardly required for MLR. Mathematics Subject Classification: 62 Statistics.
An Integrated Mobile Augmented Reality Digital Twin Monitoring System

The increasing digitalization and advancement in information communication technologies has greatly changed how humans interact with digital information. Nowadays, it is not sufficient to only display relevant data in production activities, as the enormous amount of data generated from smart devices can overwhelm operators without being fully utilized. Operators often require extensive knowledge of the machines in use to make informed decisions during processes such as maintenance and production. To enable novice operators to access such knowledge, it is important to reinvent the way of interacting with digitally enhanced smart devices. In this research, a mobile augmented reality remote monitoring system is proposed to help operators with low knowledge and experience level comprehend digital twin data of a device and interact with the device. It analyses both historic logs as well as real-time data through a cloud server and enriches 2D data with 3D models and animations in the 3D physical space. A cloud-based machine learning algorithm is applied to transform learned knowledge into live presentations on a mobile device for users to interact with. A scaled-down case study is conducted using a tower crane model to demonstrate the potential benefits as well as implications when the system is deployed in industrial environments. This user study verifies that the proposed solution yields consistent measurable improvements for novice users in human-device interaction that is statistically significant.
Promoting Vaccination from a Human Rights and Equity Perspective: Lessons from the Israeli “Green Pass”

Israel’s vaccination percentage was among the highest recorded worldwide. The Israeli government opted for a model using a proof-of-vaccine document (“Green Pass”). However, the “Green Pass” policy raises practical, legal and ethical concerns. While immunisation passports could be utilised to protect one’s health, significant legal difficulties arise from their usage as a vaccination encouragement scheme. Protecting health is a proper purpose, particularly as minimising the pandemic ameliorates the human rights violations that stem from the COVID-19 response, enables individuals to return to their daily lives and enhances economic activity. However, any privileges or restrictions guided by one’s COVID-19 immunisation status must be designed with the utmost attention to prevent a disproportionate violation of the human rights of the non-vaccinated and the public at large. Furthermore, as “Green Pass” policies might entrench existing discriminatory structures, ensuring equality is vital in moving forward. By exploring two case studies – labour rights and the right to privacy – we demonstrate the legal and public implications of the “Green Pass” regime. Despite the removal of the “Green Pass” in Israel, discussions continue regarding its modified reimplementation. The wider implications of the model might extend beyond its specific legal arrangements and limited temporal phase, requiring us to bring long-term public health into consideration.
Quantum Chaotic Butterfly Optimization Algorithm With Ranking Strategy for Constrained Optimization Problems

Nature-inspired metaheuristic optimization algorithms, e.g., the butterfly optimization algorithm (BOA), have become increasingly popular. The BOA, which adapts the food foraging and social behaviors of butterflies, involves randomly defined, algorithmic-dependent parameters that affect the exploration and exploitation strategies, which negatively influences the overall performance of the algorithm. To address this issue and improve performance, this paper proposes a modified BOA, i.e., the quantum chaos BOA (QCBOA), that relies on chaos theory and quantum computing techniques. Chaos mapping of unpredictable and divergent behavior helps tune critical parameters, and the quantum wave concept helps the representative butterflies in the algorithm explore the search space more effectively. The proposed QCBOA also implements a ranking strategy to maintain balance between the exploration and exploitation phases, which is lacking in conventional BOAs. To evaluate reliability and efficiency, the proposed QCBOA is tested against a well-utilized set of 20 benchmark functions and travelling salesman problem which belongs to the class of combinatorial optimization problems. Besides, the proposed method is also adopted to photovoltaic system parameter extraction to demonstrate its application to real-word problems. An extensive comparative study was also conducted to compare the performance of QCBOA with that of the conventional BOAs, fine-tuned particle swarm optimization (PSO) algorithm, differential evolution (DE), and genetic algorithm (GA). The results demonstrate that, chaos functions with the quantum wave concept yield better performance for most tested cases and comparative results in the rest of the cases. The speed of convergence also increased compared to the conventional BOAs. The proposed QCBOA is expected to provide better results in other real-word optimization problems and benchmark functions.
Concentrated Differentially Private Federated Learning With Performance Analysis

Federated learning engages a set of edge devices to collaboratively train a common model without sharing their local data and has advantage in user privacy over traditional cloud-based learning approaches. However, recent model inversion attacks and membership inference attacks have demonstrated that shared model updates during the interactive training process could still leak sensitive user information. Thus, it is desirable to provide rigorous differential privacy (DP) guarantee in federated learning. The main challenge to providing DP is to maintain high utility of federated learning model with repeatedly introduced randomness of DP mechanisms, especially when the server is not fully trusted. In this paper, we investigate how to provide DP to the most widely adopted federated learning scheme, federated averaging. Our approach combines local gradient perturbation, secure aggregation, and zero-concentrated differential privacy (zCDP) for better utility and privacy protection without a trusted server. We jointly consider the performance impacts of randomnesses introduced by the DP mechanism, client sampling and data subsampling in our approach, and theoretically analyze the convergence rate and end-to-end DP guarantee with non-convex loss functions. We also demonstrate that our proposed method has good utility-privacy trade-off through extensive numerical experiments on the real-world dataset.
Augmented Reality for Subsurface Utility Engineering, Revisited

Civil engineering is a primary domain for new augmented reality technologies. In this work, the area of subsurface utility engineering is revisited, and new methods tackling well-known, yet unsolved problems are presented. We describe our solution to the outdoor localization problem, which is deemed one of the most critical issues in outdoor augmented reality, proposing a novel, lightweight hardware platform to generate highly accurate position and orientation estimates in a global context. Furthermore, we present new approaches to drastically improve realism of outdoor data visualizations. First, a novel method to replace physical spray markings by indistinguishable virtual counterparts is described. Second, the visualization of 3D reconstructions of real excavations is presented, fusing seamlessly with the view onto the real environment. We demonstrate the power of these new methods on a set of different outdoor scenarios.
DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders

Figure 1: [Better seen in color].Overview of the proposed solution.Two different views of the same subject are shown for each image: (a) 2D/3D Human Pose Estimation (HPE) and (b) 3D Human Mesh Recovery (HMR) methods achieve good accuracy on the front-view (second row).Changing the viewpoint turns into performance degradation (first row).Our method (c) promotes viewpoint equivariance, showing good results in both the RGB and depth domains.
A segment enhanced span-based model for nested named entity recognition

Named entity recognition (NER) is a fundamental problem in natural language processing. In particular, nested entities are commonly existed in real-life textual data for the NER task. However, the current span-based methods for nested NER are computationally expensive, lacking of explicit boundary supervision and generating many negative samples for span classification, which affect their overall performance. In this paper, we propose a Segment Enhanced Span-based model for nested NER (SESNER). The proposed model treats the nested NER task as a segment covering problem. First, it models entities as segments, detects the segment endpoints and identifies the positional relationship between neighboring endpoints. Then, it detects the outermost segments to generate candidate entity spans nested in it for span classification. Our proposed model has the advantages of enhancing boundary supervision in learning span representations by detecting segment endpoints, reducing the number of negative samples without losing long entities that are ignored by most span-based methods, and improving runtime performance. Moreover, a novel augmented training mechanism is also proposed to further improve the model performance by extending the training dataset with data that were wrongly predicted before. Experimental results show that our proposed SESNER model has achieved promising performance with near linear time complexity on the benchmark datasets.
Multi-scale attention network for image super-resolution

The power of convolutional neural networks (CNN) has demonstrated irreplaceable advantages in super-resolution. However, many CNN-based methods need large model sizes to achieve superior performance, making them difficult to apply in the practical world with limited memory footprints. To efficiently balance model complexity and performance, we propose a multi-scale attention network (MSAN) by cascading multiple multi-scale attention blocks (MSAB), each of which integrates a multi-scale cross block (MSCB) and a multi-path wide-activated attention block (MWAB). Specifically, MSCB initially connects three parallel convolutions with different dilation rates hierarchically to aggregate the knowledge of features at different levels and scales. Then, MWAB split the channel features from MSCB into three portions to further improve performance. Rather than being treated equally and independently, each portion is responsible for a specific function, enabling internal communication among channels. Experimental results show that our MSAN outperforms most state-of-the-art methods with relatively few parameters and Mult-Adds.
Deep Learning Methods for 3D Human Pose Estimation under Different Supervision Paradigms: A Survey

The rise of deep learning technology has broadly promoted the practical application of artificial intelligence in production and daily life. In computer vision, many human-centered applications, such as video surveillance, human-computer interaction, digital entertainment, etc., rely heavily on accurate and efficient human pose estimation techniques. Inspired by the remarkable achievements in learning-based 2D human pose estimation, numerous research studies are devoted to the topic of 3D human pose estimation via deep learning methods. Against this backdrop, this paper provides an extensive literature survey of recent literature about deep learning methods for 3D human pose estimation to display the development process of these research studies, track the latest research trends, and analyze the characteristics of devised types of methods. The literature is reviewed, along with the general pipeline of 3D human pose estimation, which consists of human body modeling, learning-based pose estimation, and regularization for refinement. Different from existing reviews of the same topic, this paper focus on deep learning-based methods. The learning-based pose estimation is discussed from two categories: single-person and multi-person. Each one is further categorized by data type to the image-based methods and the video-based methods. Moreover, due to the significance of data for learning-based methods, this paper surveys the 3D human pose estimation methods according to the taxonomy of supervision form. At last, this paper also enlists the current and widely used datasets and compares performances of reviewed methods. Based on this literature survey, it can be concluded that each branch of 3D human pose estimation starts with fully-supervised methods, and there is still much room for multi-person pose estimation based on other supervision methods from both image and video. Besides the significant development of 3D human pose estimation via deep learning, the inherent ambiguity and occlusion problems remain challenging issues that need to be better addressed.
Transfer Learning for Event Detection From PMU Measurements With Scarce Labels

Event detection in electrical grids is a challenging problem for machine learning methods due to spatiotemporally nonstationary systems and the inability to automate event labeling in high-volume data such as PMU measurements. As a result, the existing historical event logs created manually do not correlate well with the corresponding PMU measurements due to scarce and temporally imprecise labels. Trying to overcome this problem by extending event logs to a complete set of labeled events is very costly and often infeasible. We focused on utilizing a transfer learning model to reduce the need for additional data labeling by leveraging some labeled data instances available from a small number of well-defined event detection task. To demonstrate the feasibility, we tested our approach on a large dataset collected by 38 PMUs from the Western Interconnection of the U.S.A. over two years. The model evaluation performed based on varying percentages of labeled source data corresponding to ~20-700 characteristic events on different sizes of time windows ranging from 2-seconds to 1-minute demonstrates that the developed method can significantly improve automated event detection based on PMU measurements when extensive labeling is costly or impossible to obtain. When compared to the state-of-the-art machine learning algorithms (unsupervised, semi-supervised, and supervised), the results show that the transfer learning method has significantly better performances when detecting events by learning from as low as 20 representative labeled data instances.
A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.
LGD: Label-Guided Self-Distillation for Object Detection

In this paper, we propose the first self-distillation framework for general object detection, termed LGD (Label-Guided self-Distillation). Previous studies rely on a strong pretrained teacher to provide instructive knowledge that could be unavailable in real-world scenarios. Instead, we generate an instructive knowledge by inter-and-intra relation modeling among objects, requiring only student representations and regular labels. Concretely, our framework involves sparse label-appearance encoding, inter-object relation adaptation and intra-object knowledge mapping to obtain the instructive knowledge. They jointly form an implicit teacher at training phase, dynamically dependent on labels and evolving student representations. Modules in LGD are trained end-to-end with student detector and are discarded in inference. Experimentally, LGD obtains decent results on various detectors, datasets, and extensive tasks like instance segmentation. For example in MS-COCO dataset, LGD improves RetinaNet with ResNet-50 under 2x single-scale training from 36.2% to 39.0% mAP (+ 2.8%). It boosts much stronger detectors like FCOS with ResNeXt-101 DCN v2 under 2x multi-scale training from 46.1% to 47.9% (+ 1.8%). Compared with a classical teacher-based method FGFI, LGD not only performs better without requiring pretrained teacher but also reduces 51% training cost beyond inherent student learning.
Joint estimation of state, parameter, and unknown input for nonlinear systems: A composite estimation scheme

Abstract This study is concerned with the joint estimation problem for a class of nonlinear systems with the simultaneous unknown of the system state, the parameter, as well as the input signal. A composite estimation scheme is proposed where the estimator consists of both linear and nonlinear components, under which the estimation performance is improved. The analysis and synthesis issues of the developed estimation algorithm are addressed for both the continuous‐time case and the discrete‐time case. By utilizing the Lyapunov stability theory combined with the singular value decomposition technique, sufficient conditions are established for both continuous‐time and discrete‐time cases to guarantee the convergence of the estimation error, rather than the boundedness in most of the existing literature. To facilitate the algorithm implementation in practical engineering, the Newton–Raphson method is adopted to deal with the feasibility issue for the discrete‐time case. Numerical simulations are provided for both the continuous‐ and discrete‐time cases to demonstrate the effectiveness of the proposed joint estimation strategies.