Reviewerly/exHarmony huggingface dataset:
Analysis of the cloud enhancement phenomenon and its effects on photovoltaic generators based on cloud speed sensor measurements

The irradiance incident on photovoltaic (PV) generators can considerably exceed the expected clear sky irradiance. Due to this phenomenon, called cloud enhancement (CE), the maximum power of the PV generator can exceed the rated power of the inverter connecting the generator to the grid. CE event characteristics and the effects of CE on the electrical operation of PV generators were studied based on measured irradiances and cloud edge velocities. Over eleven months in San Diego, California, the highest measured peak irradiance was 1466 W/m2. In addition, the highest simulated average irradiances for up to 1 MW generators were over 1400 W/m2. The largest lengths for CE events exceeding 1000 W/m2 were multiple kilometers. These results indicate that even large utility-scale PV power plants can be affected significantly by CE events. Moreover, the operation of three PV plants was simulated during around 2400 measured CE events with a detailed spatiotemporal model. The effects of inverter sizing on the operation of the plants were also studied, and the negative impacts of CE on the operation of PV systems were shown to increase with the increasing DC/AC ratio. During the CE events, the energy losses due to power curtailment were from 5% to 50% of the available energy production. While CE affects the operation of the PV plants, these effects were small in terms of aggregate energy since CE events that most strongly impact PV system operation are very rare, meaning that CE does not cause major problems for the operation of PV systems.
Warehouse Vis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts

Abstract Selecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision‐making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely‐used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within‐subject study with baseline systems to assess the efficacy of our system. Experts ‘feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.
Adaptive Nonconvex Sparsity Based Background Subtraction for Intelligent Video Surveillance

Intelligent video surveillance is a vital technique in smart city construction, where detection of surveillance objects is generally achieved by subtracting estimated background from the raw video. Common wisdom of background estimation focuses on introducing meaningful structure or discriminative hypothesis to sparsity-based objectives. However, relaxation optimization, which is always considered a most effective solution, definitely leads to information loss. So, in this article, as to preserve more information, a new nonconvex sparsity model that can be solved directly by explicit solution is proposed for the stationary component of video. The solution, called generalized shrinkage thresholding operator, is designed by integrating the advantages of three common shrinkage operators. Then, for the regularly changing patterns, a purified dictionary learning operation is designed to find self-repeating texture patches. Eventually, foreground objects are detected by combining background subtraction with a spatiotemporal continuity constraint. Besides, built on optimizations of both models, we then show the way to refine the joint estimates using alternative optimization of all the subproblems. Experimental results have shown that, as to foreground detection task, when compared against current state-of-the-art techniques, the proposed model achieves comparable and often superior performance in terms of F-measure scores in most cases.
Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.
Unconditionally secured classical cryptography using quantum superposition and unitary transformation

Abstract Over decades quantum cryptography has been intensively studied for unconditionally secured key distribution in a quantum regime. Due to the quantum loopholes caused by imperfect single photon detectors and/or lossy quantum channels, however, the quantum cryptography is practically inefficient and even vulnerable to eavesdropping. Here, a method of unconditionally secured key distribution potentially compatible with current fiber-optic communications networks is proposed in a classical regime for high-speed optical backbone networks. The unconditional security is due to the quantum superposition-caused measurement indistinguishability between paired transmission channels and its unitary transformation resulting in deterministic randomness corresponding to the no-cloning theorem in a quantum key distribution protocol.
DYNAMIC HYPERINTENSIONAL BELIEF REVISION

Abstract We propose a dynamic hyperintensional logic of belief revision for non-omniscient agents, reducing the logical omniscience phenomena affecting standard doxastic/epistemic logic as well as AGM belief revision theory. Our agents don’t know all a priori truths; their belief states are not closed under classical logical consequence; and their belief update policies are such that logically or necessarily equivalent contents can lead to different revisions. We model both plain and conditional belief, then focus on dynamic belief revision. The key idea we exploit to achieve non-omniscience focuses on topic- or subject matter-sensitivity : a feature of belief states which is gaining growing attention in the recent literature.
The effectiveness of a marketing virtual reality learning simulation: A quantitative survey with psychophysiological measures

Abstract Virtual reality (VR) technology has been shown to be a promising teaching method in STEM subjects. Extending these findings, the current study is the first to develop and examine the feasibility of using a VR simulation in marketing (ie, a non‐STEM subject). Specifically, the levels of immersion and three learning outcomes (learning attitude, enjoyment and performance) were compared between the marketing students who learned the same content through a VR simulation prototype versus a traditional static video presentation. Learning enjoyment was measured using facial electromyography, which served as a more objective measures for the feeling of enjoyment. Compared to the video condition, the results showed that VR resulted in a higher experience of immersion, learning attitude and learning enjoyment. Furthermore, immersion was found to fully mediate the positive effects of the VR simulation on learning attitude, but not enjoyment. Surprisingly, students in the video condition performed better on the knowledge‐based test than those in the VR condition. The current study suggests that the current prototype of the VR simulations should be used as supplementary resource to increase the learning attitude and enjoyment, but not as a main teaching material to enhance knowledge‐based performance in the marketing discipline.
General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution

This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community.
A Waypoint Tracking Controller for Autonomous Road Vehicles Using ROS Framework

Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles provide as output a sequence of waypoints to be followed. This paper proposes a modular and scalable waypoint tracking controller for Robot Operating System (ROS)-based autonomous guided vehicles. The proposed controller performs a smooth interpolation of the waypoints and uses optimal control techniques to ensure robust trajectory tracking even at high speeds in urban environments (up to 50 km/h). The delays in the localization system and actuators are compensated in the control loop to stabilize the system. Forward velocity is adapted to path characteristics using a velocity profiler. The controller has been implemented as an ROS package providing scalability and exportability to the system in order to be used with a wide variety of simulators and real vehicles. We show the results of this controller using the novel and hyper realistic CARLA Simulator and carrying out a comparison with other standard and state-of-art trajectory tracking controllers.
CTMC: Cell Tracking with Mitosis Detection Dataset Challenge

While significant developments have been made in cell tracking algorithms, current datasets are still limited in size and diversity, especially for data-hungry generalized deep learning models. We introduce a new larger and more diverse cell tracking dataset in terms of number of sequences, length of sequences, and cell lines, accompanied with a public evaluation server and leaderboard to accelerate progress on this new challenging dataset. Our benchmarking of four top performing tracking algorithms highlights new challenges and opportunities to improve the state-of-the-art in cell tracking.
Novel hybrid pair recommendations based on a large-scale comparative study of concept drift detection

During the classification of streaming data, changes in the underlying distribution make formerly learned models insecure and imprecise, which is known as the concept drift phenomenon. Online learning derives information from a vast volume of stream data, which are usually affected by these changes in unforeseen ways and are currently generated primarily by the Internet of Things, social media applications, and the stock market. There is abundant literature focused on addressing concept drift using detectors, which essentially attempt to forecast the position of the change to improve the overall accuracy by altering the base learner. This paper presents novel hybrid pairs (classifier and detector) collected from a large-scale comparison of 15 drift detectors; drift detection method (DDM), early drift detection method (EDDM), EWMA for concept drift detection (ECDD), adaptive sliding window (ADWIN), geometrical moving average (GMA), drift detection methods based on Hoeffding’s bound (HDDMA and HDDMW), Fisher exact test drift detector (FTDD), fast Hoeffding drift detection method (FHDDM), Page–Hinkley test (PH), reactive drift detection method (RDDM), SEED, statistical test of equal proportions (STEPD), SeqDrift2, and Wilcoxon rank-sum test drift detector (WSTD) and six classifiers; Naïve Bayes (NB), Hoeffding tree (HT), Hoeffding option tree (HOT), Perceptron (P), decision stump (DS), and k-nearest neighbour (KNN), to determine and recommend the best pair in accordance with the properties of the dataset. The objective of this study is to assess the contribution of a detector to a classifier and obtain the most efficient matched pairs. Through these pairwise comparison experiments, the accuracy rates and evaluation times of the pairs, as well as their false positives, true negatives, false negatives, true positives, drift detection delay, and the MCC. Additionally, the Nemenyi test is employed to compare the pairs against other methods to identify the method(s) for which there is a statistical difference. The results of the experiments indicate that the most efficient pairs—which differed for each dataset type and size—primarily include the HDDMA, RDDM, WSTD, and FHDDM detectors.
SVD++ Recommendation Algorithm Based on Backtracking

Collaborative filtering (CF) has successfully achieved application in personalized recommendation systems. The singular value decomposition (SVD)++ algorithm is employed as an optimized SVD algorithm to enhance the accuracy of prediction by generating implicit feedback. However, the SVD++ algorithm is limited primarily by its low efficiency of calculation in the recommendation. To address this limitation of the algorithm, this study proposes a novel method to accelerate the computation of the SVD++ algorithm, which can help achieve more accurate recommendation results. The core of the proposed method is to conduct a backtracking line search in the SVD++ algorithm, optimize the recommendation algorithm, and find the optimal solution via the backtracking line search on the local gradient of the objective function. The algorithm is compared with the conventional CF algorithm in the FilmTrust, MovieLens 1 M and 10 M public datasets. The effectiveness of the proposed method is demonstrated by comparing the root mean square error, absolute mean error and recall rate simulation results.
Automated fabrication of hybrid printed electronic circuits

Printed electronics offer great potential for new applications such as Internet of Things devices and wearables. However, to date, only a limited number of electronic functions and integration densities can be realised by printing processes. Hence, hybrid printed electronic circuits are actually created by mounting silicon electronic components. Since both printed materials and processes are continuously evolving, an accompanying structured development methodology is required. This paper highlights a digital workflow from design to automated fabrication using the example of a demonstrator circuit. A multi-layer vector ink-jet printing process to print electronic devices onto foil substrates with three functional inks is presented. This printing process is improved using a newly set-up printing system: Integrating a piezo print head into the path planning of the printing system and its control as a virtual stepper axis enable highly precise vector printing. This leads to printed resistors with low tolerances. Adaptations of surface mount technology for assembling silicon electronic components onto printed foil substrates are discussed. Finally, image processing methods to cope with deformations of the flexible foil substrates in the fabrication process are introduced.
Recovery of Time-Varying Graph Signals via Distributed Algorithms on Regularized Problems

The recovery of missing samples from available noisy measurements is a fundamental problem in signal processing. This process is also sometimes known as graph signal inpainting, reconstruction, forecasting or inference. Many of the existing algorithms do not scale well with the size of the graph and/or they cannot be implemented efficiently in a distributed manner. In this paper, we develop efficient distributed algorithms for the recovery of time-varying graph signals. The a priori assumptions are that the signal is smooth with respect to the graph topology and correlative across time. These assumptions can be incorporated in an optimization formulation of the algorithm via Tikhonov regularization terms. Our formulation is tailored to yield algorithms that can be efficiently implemented in a distributed manner. Two different distributed algorithms, arising from two different formulations, are proposed to solve the optimization problems. The first involves the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm, and a distributed least squared recovery algorithm (DLSRA) is proposed that leverages the graph topology and sparsity of the corresponding Hessian matrix. Updates of the Hessian inverse are not required here. The second involves the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm and the philosophy of the alternating direction method of multipliers (ADMM) is utilized to develop the algorithm. An inexact Newton method is incorporated into the conventional ADMM to give a distributed ADMM recovery algorithm (DAMRA). The two distributed algorithms require only data exchanges between vertices in localized neighbourhood subgraphs. Experiments on a variety of synthetic and real-world datasets demonstrate that the proposed algorithms are superior to the existing methods in terms of the computational complexity and convergence rate.
Capped $\ell _p$-Norm LDA for Outliers Robust Dimension Reduction

Linear discriminant analysis technique is an effective strategy to solve the long-standing issue, i.e., the “curse of dimensionality” that brings many obstacles on high-dimensional data storage and analysis. However, the projections are prone to be affected, especially when the training set contains outlier samples whose distribution deviates from the globality. In many real-world applications, the outlier samples contaminated by noisy signal or spottiness have negative effects on the classification and clustering performance. To address this issue, we propose to develop a novel capped ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm LDA model for robust dimension reduction against to outliers specifically. Proposed method integrates the capped ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm based loss into the objective, which not only suppresses the light outliers but also works well even though the training set is contaminated seriously. Furthermore, we derive an alternative iterative re-weighted optimization algorithm to minimize the proposed objective based on capped ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm with rigorous convergence proofs. Extensive experiments conducted on synthetic and real-world datasets demonstrate the robustness against to outliers of proposed method.
Customer Acceptance of Use of Artificial Intelligence in Hospitality Services: An Indian Hospitality Sector Perspective

This research endeavour tested and validated the artificially intelligent device use acceptance (AIDUA) three-stage AI acceptance framework in the context of the Indian hospitality sector. For this purpose, data on the constructs that captured primary appraisal (i.e., social influence, hedonic motivation and anthropomorphism), secondary appraisal (i.e., performance and effort expectancy), emotion, willingness to use AI devices and objection to use AI devices were captured from 210 guests/customers from 14 luxury hotels spread across the union territory of New Delhi and the state of Chandigarh in India. Findings that emerge from this study validate the fact that customers do indeed go through three stages of decision-making process before they demonstrate their proclivity to use AI devices or exhibit objection to use AI devices. In particular, the study found that both performance and effort expectancy influenced customer emotion which, in its turn, exercised its effect on the construct of willingness to use AI devices and objection to use AI devices among hotel customers. Accordingly, drawing from the findings of this study, implications for practitioners, decision-makers, and academic researchers are discussed in the article.
Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

Recognition of activities, such as preparing meal or watching TV, performed by a smart home resident, can promote the independent living of elderly in a safe and comfortable environment of their own homes, for an extended period of time. Different activities performed at the same location have commonalities resulting in less inter-class variations; while the same activity performed multiple times, or by multiple residents, varies in its execution resulting in high intra-class variations. We propose a Local Feature Weighting approach (LFW) that assigns weights based on both inter-class and intra-class importance of a feature in an activity. Multiple sensors are deployed at different locations in a smart home to gather information. We exploit the obtained information, such as frequency and duration of activation of sensors, and the total sensors in an activity for feature weighting. The weights for the same features vary among activities, since a feature may have more importance for one activity but less for the other. For the classification, we exploit the two variants of K-Nearest Neighbors (KNN): Evidence Theoretic KNN (ETKNN) and Fuzzy KNN (FKNN). The evaluation of the proposed approach on three datasets, from CASAS smart home project, demonstrates its ability in the correct recognition of activities compared to the existing approaches.