Reviewerly/exHarmony huggingface dataset:
Blockchain-Powered Smart Healthcare System

The advent of blockchain derives its roots from the cryptocurrency called bitcoin that emerged during the financial slowdown around 2008. The premise of blockchain is based on a unique technological offering of trust and transparency being offered in a decentralized environment. Since then, researchers and practitioners are thoughtful about multiple applications of blockchain around areas like human resource, marketing, finance, supply chain, and so on. Healthcare is no exception to this situation where a decentralized technology like blockchain acts as a positive catalyst to integrated healthcare by providing data integrity, actionable insights, and mitigating many challenges in the realm of healthcare. Blockchain is considered as a promising solution to resolve data safety and integrity issues while restoring stakeholder consensus. Another blockchain-based solution on similar lines is BlocHIE. Blockchain-based framework is also presented to gather insightful information through predictive techniques embedded in the framework. Ranging from Proof of Disease to Disease Surveillance are some applications presented around healthcare domain through utilization of blockchain technology. Monitoring of drug supply chain through blockchain technology has also been a key interest area especially from the patient point of view. An interesting perspective on cognitive healthcare blockchain demonstrates the usage of automated and intelligent blockchain for healthcare. However, these discrete applications around different arenas of healthcare need to be looked from a holistic point of view. This will help policy makers to shape a smart healthcare system especially in the context of smart cities. The capabilities of blockchain aided with technologies like analytics, Internet of things, artificial intelligence, and so on can help mitigate many challenges and issues with healthcare systems around the world. In this chapter, we present a conceptual model along with research-based implementation instances to define and illustrate a smart healthcare system powered with blockchain. This work provides a holistic perspective in terms of applicability and understanding of blockchain to healthcare and helps in planning and strategizing the usage of blockchain technology.
Differential privacy for eye tracking with temporal correlations

New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.
Joint image deblurring and matching with feature-based sparse representation prior

Image matching aims to find a similar area of the small image in the large image, which is one of the key steps in image fusion and vision-based navigation; however, most matching methods perform poorly when the images to be matched are blurred. Traditional approaches for blurred image matching usually follow a two-stage framework - first resorting to image deblurring and then performing image matching with the recovered image. However, the matching accuracy of these methods often suffers greatly from the deficiency of image deblurring. Recently, a joint image deblurring and matching method that utilizes the sparse representation prior to exploit the correlation between deblurring and matching was proposed to address this problem and found to obtain a higher matching accuracy. Yet, that technique is not efficient when the image is seriously blurred, and the method's time complexity is excessive. In this paper, we propose a joint image deblurring and matching approach with a feature-based sparse representation prior. Our approach utilizes two-directional two-dimensional (2D)2PCA to extract feature vectors from images and obtains a sparse representation prior in a robust feature space rather than the original pixel space, thus mitigating the influence of image blur. Moreover, the reduction in the feature dimension can also increase the computational efficiency. Extensive experiments show that our approach significantly outperforms state-of-the-art approaches in terms of both accuracy and speed.
Industrial design and implementation of a large-scale dual-axis sun tracker with a vertical-axis-rotating-platform and multiple-row-elevation structures

A dual-axis tracked Photovoltaic (PV) system can produce up to 30% more electrical energy in a year as compared to a fixed-tilt PV system. To be economically feasible, a sun-tracker must also be accountable for the cost effectiveness, durability and long-term reliability despite the gain in electrical yield. To achieve this purpose, in this paper, we are reporting a practical industrial design of a large-scale dual-axis sun-tracker consisting of a vertical-axis-rotating-platform and multiple-row-elevation structures (VRP-MRE). The advantages of the tracker design include lower wind-load moment, high tracking precision, high repeatability and high durability. All these favourable features are attributed to the use of a large perimeter rotating platform coupled with a low-cost electro-mechanical encoder, and a torque tube mechanism that can simultaneously drive multi-row elevation movement through a shared push-pull type of linear movement. Several important characteristics of the new tracker have been studied in detail, such as the range of motion, slew rate, control system, electrical wiring, special tracking behaviour and special requirements when it is used in the equatorial region. A prototype of industrial-scale sun-tracker with a 35-m of diameter has been designed to achieve tracking resolutions of 0.111° in the azimuthal direction and 0.03° in the elevation direction. The overall measured tracking accuracy was 0.28°, and the achieved precision was 0.0111°. A total of 60 kWdc PV modules were installed on the sun-tracker. The energy consumption by the driving systems of the sun-tracker was calculated, which on average ranges from 0.40% to 0.50% of the total energy generation by the PV system.
Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams

Learning from data streams is one of the most promising and challenging domains in modern machine learning. Proliferating online data sources provide us access to real-time knowledge we have never had before. At the same time, new obstacles emerge and we have to overcome them in order to fully and effectively utilize the potential of the data. Prohibitive time and memory constraints or non-stationary distributions are only some of the problems. When dealing with classification tasks, one has to remember that effective adaptation has to be achieved on weak foundations of partially labeled and often imbalanced data. In our work, we propose an online framework for binary classification, that aims to handle the complex problem of working with dynamic, sparsely labeled and imbalanced streams. The main part of it is a novel active learning strategy (MD-OAL) that is able to prioritize labeling of minority instances and, as a result, improve the balance of the learning process. We combine the strategy with a dynamic ensemble of base learners that can abstain from making decisions, if they are very uncertain. We adjust the abstaining mechanism in favor of minority instances, providing an effective method for handling remaining imbalance and a concept drift simultaneously. The conducted evaluation shows that in the challenging and realistic scenarios our framework outperforms state-of-the-art algorithms, providing higher resilience to the combined effect of limited labeling and imbalance.
HanGrawler: Large-Payload and High-Speed Ceiling Mobile Robot Using Crawler

In this article, we present a ceiling-mounted mobile robot, HanGrawler, with high-speed mobility and the ability to freely select and adjust its route under a ceiling plate. HanGrawler hangs from the holes of a perforated metal ceiling plate using newly developed mechanically constrained hanging mechanisms mounted on crawler-type traveling equipment. These mechanisms continuously grasp the ceiling holes while moving in a straight line. Specially designed turntables are installed at certain ceiling points to change the HanGrawler movement direction to facilitate two-dimensional motion. A performance evaluation experiment confirms that HanGrawler can travel linearly at 0.1 m/s, turn at an 8.5°/s rotational speed, and carry a maximum load of 60 kg. The experiment also reveals that HanGrawler can carry a heavy load only when the timings of the hanging mechanism hooking motions and turntable rotational speed are tuned. This transport system can be utilized not only for part transportation on production lines but also for logistics in general.
G-Finder: Approximate Attributed Subgraph Matching

Subgraph matching is a core primitive across a number of disciplines, ranging from data mining, databases, information retrieval, computer vision to natural language processing. Despite decades of efforts, it is still highly challenging to balance between the matching accuracy and the computational efficiency, especially when the query graph and/or the data graph are large. In this paper, we propose an index-based algorithm (G-FINDER) to find the top-k approximate matching subgraphs. At the heart of the proposed algorithm are two techniques, including (1) a novel auxiliary data structure (LOOKUP-TABLE) in conjunction with a neighborhood expansion method to effectively and efficiently index candidate vertices, and (2) a dynamic filtering and refinement strategy to prune the false candidates at an early stage. The proposed G-FINDER bears some distinctive features, including (1) generality, being able to handle different types of inexact matching (e.g., missing nodes, missing edges, intermediate vertices) on node attributed and/or edge attributed graphs or multigraphs; (2) effectiveness, achieving up to 30% Fl-Score improvement over the best known competitor; and (3) efficiency, scaling near-linearly w.r.t. the size of the data graph as well as the query graph.
Heuristic and random search algorithm in optimization of route planning for Robot’s geomagnetic navigation

To improve the efficiency and accuracy, a new combination algorithm for route planning is proposed, by considering underwater geomagnetic matching navigation area and distribution of environmental constraints. Firstly, with geomagnetic navigation matching regions, Dijkstra algorithm can obtain the primary route points. Secondly, the environmental constraints models are built and normalized, and the route planning environment constrained cost model is established. Thirdly, with the relationships between time, function relation, constraint condition and variable in the environment constrained cost model, the particle swarm optimization algorithm is introduced. With the primary route pints, the route planning is transformed into route optimization. Finally, the primary route points are used as the initial input of the particle swarm optimization algorithm, then the methods of selecting the inertia weight of the particle swarm and the particle coding are improved. The optimal route planning of Dijkstra algorithm and particle swarm optimization is realized. Simulation results demonstrate that the particle size of the search space can get a minimized evaluation, more narrowed search range and higher efficient search. The combination algorithm guarantees the global optimal while ensures the local optimal, then, the non-matching navigation areas can be effectively avoided, and efficient route planning functions can be achieved.
Controlling of an Under-Actuated Quadrotor UAV Equipped With a Manipulator

Unmanned aerial vehicles (UAV) equipped with a manipulator offer an additional flexibility and smart way to grasp the desired objects from inaccessible locations where the access of ground vehicles (GV) are not possible. In this research, we design an adaptive control based regulation, pole-placement and tracking (RST) control scheme for controlling the nonlinear behavior of an under-actuated quad rotor aerial vehicle. The overall performance of the system dealt by MIT rules. The aerial vehicle is equipped with a camera and a gripper that helps us to locate the wanted object from inaccessible location. The model of quad rotor UAV has six degrees of freedom (6-DOF) and the equipped gripper is about (2-DOF). For a successful flight operation UAV requires a reliable controller to stabilize the aerodynamic effects, disturbances that produced by the gripper. Due to aforementioned issue, design an adaptive RST controller, to control the dynamic behavior of the highly nonlinear complex system. Moreover, the effectiveness of the designed controller proven by applying computer-based simulation and it will verify experimentally. Lastly, it observes that the designed controller shows better robustness and good steady state performance to accomplish the given task.
Target Tracking of Moving and Rotating Object by High-Speed Monocular Active Vision

In recent years, the importance of measuring the three-dimensional position and orientation of manipulation targets in robot systems has been increasing. There is great demand to integrate robots with high-frame-rate vision systems to improve the working efficiency. A number of high-speed vision systems have been developed for high-frame-rate visual feedback. In this study, we focus on high-speed target tracking of both 3D position and orientation using only a monocular camera. This is a lightweight, low-cost solution for many fields, such as surgical navigation and drone avoidance. We modified the pixel-wise posterior 3D (PWP3D) framework and proposed a fast-PWP3D algorithm for high-speed target tracking and pose estimation. Unlike the original PWP3D method, our method improved the tracking speed (our current implementation runs at 400 Hz on a GPU board), and we showed that the tracking accuracy against changes in the environment (e.g., partial occlusion) was improved compared with the original PWP3D algorithm. By combining the fast-PWP3D algorithm with a visual servoing controller, we realized 500 Hz target tracking of both 3D position and orientation.
Deep Learning-Based Vulnerable Function Detection: A Benchmark

The application of Deep Learning (DL) technique for code analysis enables the rich and latent patterns within software code to be revealed, facilitating various downstream tasks such as the software defect and vulnerability detection. Many DL architectures have been applied for identifying vulnerable code segments in recent literature. However, the proposed studies were evaluated on self-constructed/-collected datasets. There is a lack of unified performance criteria, acting as a baseline for measuring the effectiveness of the proposed DL-based approaches. This paper proposes a benchmarking framework for building and testing DL-based vulnerability detectors, providing six built-in mainstream neural network models with three embedding solutions available for selection. The framework also offers easy-to-use APIs for integration of new network models and embedding methods. In addition, we constructed a real-world vulnerability ground truth dataset containing manually labelled 1,471 vulnerable functions and 1,320 vulnerable files from nine open-source software projects. With the proposed framework and the ground truth dataset, researchers can conveniently establish a vulnerability detection baseline system for comparison and evaluation. This paper also includes usage examples of the proposed framework, aiming to investigate the performance behaviours of mainstream neural network models and providing a reference for DL-based vulnerability detection at function-level.
SOLAR-GP: Sparse Online Locally Adaptive Regression Using Gaussian Processes for Bayesian Robot Model Learning and Control

Machine learning methods have been widely used in robot control to learn inverse mappings. These methods are used to capture the entire non-linearities and non-idealities of a system that make geometric or phenomenological modeling difficult. Most methods employ some form of off-line or batch learning where training may be performed prior to a task, or in an intermittent manner, respectively. These strategies are generally unsuitable for teleoperation, where commands and sensor data are received in sequential streams and models must be learned on-the-fly. We combine sparse, local, and streaming methods to form Sparse Online Locally Adaptive Regression using Gaussian Processes (SOLAR-GP), which trains streaming data on localized sparse Gaussian Process models and infers a weighted local function mapping of the robot sensor states to joint states. The resultant prediction of the teleoperation command is used for joint control. The algorithm was adapted to perform on arbitrary link manipulators including the Baxter robot, where modifications to the algorithm are made to run training and prediction in parallel so as to keep consistent, high-frequency control loop rates. This framework allows for a user-defined cap on complexity of generated local models while retaining information on older regions of the explored state-space.
Robust dimensionality reduction for data visualization with deep neural networks

We elaborate on the robustness assessment of a deep neural network (DNN) approach to dimensionality reduction for data visualization. The proposed DNN seeks to improve the class separability and compactness in a low-dimensional feature space, which is a natural strategy to obtain well-clustered visualizations. It consists of a DNN-based nonlinear generalization of Fisher's linear discriminant analysis and a DNN-based regularizer. Regarding data visualization, a well-regularized DNN guarantees to learn sufficiently similar data visualizations for different sets of samples that represent the data approximately equally well. Such a robustness against fluctuations in the data is essential for many real-world applications. Our results show that the combined DNN is considerably more robust than the generalized discriminant analysis alone. We further support this conclusion by examining feature representations from four comparative approaches. As a means of measuring the structural dissimilarity between different feature representations, we propose a hierarchical cluster analysis.
Hyperparameter Optimisation for Improving Classification under Class Imbalance

Although the class-imbalance classification problem has caught a huge amount of attention, hyperparameter optimisation has not been studied in detail in this field. Both classification algorithms and resampling techniques involve some hyperparameters that can be tuned. This paper sets up several experiments and draws the conclusion that, compared to using default hyperparameters, applying hyperparameter optimisation for both classification algorithms and resampling approaches can produce the best results for classifying the imbalanced datasets. Moreover, this paper shows that data complexity, especially the overlap between classes, has a big impact on the potential improvement that can be achieved through hyperparameter optimisation. Results of our experiments also indicate that using resampling techniques cannot improve the performance for some complex datasets, which further emphasizes the importance of analyzing data complexity before dealing with imbalanced datasets.
Impact of Body Size Match to an Avatar on the Body Ownership Illusion and User's Subjective Experience

The current study sought to investigate the effects of matching body movement and body size between an avatar and a participant on the body ownership illusion (BOI), the body size illusion, the sense of presence, simulator sickness, and emotional responses within an immersive virtual reality (IVR). Forty participants experienced their life-sized virtual avatars from a first-person perspective using a full-body motion-capture system. The experiment used a 2 (Motion: Synchrony and Asynchrony) × 2 (Size: Matched and Unmatched) within-subject design. Each participant completed a self-reported questionnaire that evaluated BOI, body size illusion, presence, simulator sickness, and emotional valence. Results showed that matching participant's motion to that of an avatar increased the BOI and sense of presence, while reducing simulator sickness. Furthermore, participants reported more positive emotions when motion was synchronized to the virtual avatar. Most notably, synchronizing body movement between participants and avatars resulted in strong body size illusion even when the body size of an avatar was larger than the one of a participant. To the best of our knowledge, this is the first study to directly link synchronization of user motion and body size to a virtual avatar with user's subjective experience (BOI, body size illusion, sense of presence, simulator sickness, and valence) in IVR. Our findings suggest beneficial effects of synchronized body motion and matched body size between a user and avatar on user's subjective experience in IVR, which can possibly boost the effects of virtual reality applications in the fields of entertainment, psychotherapy, and education.
On the Convergence of Mirror Descent beyond Stochastic Convex Programming

In this paper, we examine the convergence of mirror descent in a class of stochastic optimization problems that are not necessarily convex (or even quasi-convex), and which we call variationally coherent.Since the standard technique of "ergodic averaging" offers no tangible benefits beyond convex programming, we focus directly on the algorithm's last generated sample (its "last iterate"), and we show that it converges with probabiility 1 if the underlying problem is coherent.We further consider a localized version of variational coherence which ensures local convergence of SMD with high probability.These results contribute to the landscape of nonconvex stochastic optimization by showing that (quasi-)convexity is not essential for convergence to a global minimum: rather, variational coherence, a much weaker requirement, suffices.Finally, building on the above, we reveal an interesting insight regarding the convergence speed of SMD: in problems with sharp minima (such as generic linear programs or concave minimization problems), SMD reaches a minimum point in a finite number of steps (a.s.), even in the presence of persistent gradient noise.This result is to be contrasted with existing black-box convergence rate estimates that are only asymptotic.