Reviewerly/exHarmony huggingface dataset:
Inner Shifting-Phase Method for High-Speed High-Resolution 3-D Measurement

Fringe projection profilometry (FPP) is widely used in the 3-D noncontact measurement. In FPP, one of the most important steps is obtaining the absolute phase through fringe orders and wrapped phase. The existing methods usually acquire the fringe orders and wrapped phase independently. Therefore, more patterns are needed to project onto the object, which decreases the measuring speed a lot. We propose an inner shifting-phase method that codes the fringe orders and the wrapped phase into the same four patterns. With the unwrapping algorithm, we obtain the fringe orders and the wrapped phase simultaneously, which decreases the number of required patterns a lot. The proposed method calculates the absolute phase pixel-by-pixel independently without any preacquired information or geometric constraints. Experiment results show that the proposed method achieves almost the same accuracy with only a third of the patterns required by the conventional three-frequency four-step method. Experiment results also verify the validity of the proposed method in measuring both complex and separated objects.
irradpy: Python package for MERRA-2 download, extraction and usage for clear-sky irradiance modelling

Clear-sky irradiance modelling requires many input variables to access the best available methodologies in literature. It is the authors’ experience that there are unnecessary computational barriers to obtaining and using the data needed for worldwide clear-sky modelling. For this reason, we present a data article introducing a new Python package called irradpy that, in this first release, (i) downloads the specific variables required for worldwide clear-sky irradiance modelling from the MERRA-2 reanalysis database and reorganises them for optimised usage, (ii) provides two functions for the world leading global horizontal irradiance clear-sky models (MAC2 and REST2v5) to make use of the downloaded MERRA-2 reanalysis database, (iii) provides an example script showing how it works and can be used. The package is available through the Python Package Index (PyPi) and can also be found in the GitHub repository at https://github.com/BXYMartin/Python-irradpy.
Sand-Dust Image Restoration Based on Reversing the Blue Channel Prior

Images acquired under sand-dust weather conditions are severely degraded, with low contrast and severe color shift. The reason is that, due to the influence of sand-dust particles, light is scattered and absorbed, resulting in a blurred image and low contrast; the color shift is caused by the rapid attenuation of blue light. Therefore, to solve the problem of color shift and poor visibility in sand-dust images, this paper proposes a sand-dust image restoration method based on reversing the blue channel prior (RBCP). Under the influence of the blue channel, the dark channel prior (DCP) method will fail. Therefore, the method first reverses the blue channel of the sand-dust image and uses the dark channel prior method, which we call RBCP, and then, RBCP is used to estimate the atmospheric light and transmission map and recover the sand-dust image. The restored image shows significantly improved visibility. When estimating the transmission map, a guiding filter is used to improve the coarse transmission map, and a tolerance mechanism is introduced to modify the transmission map of bright areas in the sky to solve the problem of distortion in the sky. Finally, combined with the gray world, an adaptive color adjustment factor is introduced into the restoration model to remove the color shift. Experimental results via qualitative and quantitative evaluation demonstrate that the proposed method can effectively recover clear sand-dust images and produce results superior to those of other state-of-the-art methods.
Lightweight Cloud Storage Auditing With Deduplication Supporting Strong Privacy Protection

The cloud storage auditing with deduplication is able to verify the integrity of data stored in the cloud while the cloud needs to keep only a single copy of duplicated file.To the best of our knowledge, all of the existing cloud storage auditing schemes with deduplication are vulnerable to brute-force dictionary attacks, which incurs the leakage of user privacy.In this paper, we focus on a new aspect of being against brute-force dictionary attacks on cloud storage auditing.We propose a cloud storage auditing scheme with deduplication supporting strong privacy protection, in which the privacy of user's file would not be disclosed to the cloud and other parties when this user's file is predictable or from a small space.In the proposed scheme, we design a novel method to generate the file index for duplicate check, and use a new strategy to generate the key for file encryption.In addition, the user only needs to perform lightweight computation to generate data authenticators, verify cloud data integrity, and retrieve the file from the cloud.The security proof and the performance evaluation demonstrate that the proposed scheme achieves desirable security and efficiency.
Prioritization of Information Security Controls through Fuzzy AHP for Cloud Computing Networks and Wireless Sensor Networks

With the advent of cloud computing and wireless sensor networks, the number of cyberattacks has rapidly increased. Therefore, the proportionate security of networks has become a challenge for organizations. Information security advisors of organizations face difficult and complex decisions in the evaluation and selection of information security controls that permit the defense of their resources and assets. Information security controls must be selected based on an appropriate level of security. However, their selection needs intensive investigation regarding vulnerabilities, risks, and threats prevailing in the organization as well as consideration of the implementation, mitigation, and budgetary constraints of the organization. The goal of this paper was to improve the information security control analysis method by proposing a formalized approach, i.e., fuzzy Analytical Hierarchy Process (AHP). This approach was used to prioritize and select the most relevant set of information security controls to satisfy the information security requirements of an organization. We argue that the prioritization of the information security controls using fuzzy AHP leads to an efficient and cost-effective assessment and evaluation of information security controls for an organization in order to select the most appropriate ones. The proposed formalized approach and prioritization processes are based on International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001:2013. But in practice, organizations may apply this approach to any information security baseline manual.
Variational Single Image Dehazing for Enhanced Visualization

In this paper, we investigate the challenging task of removing haze from a single natural image. The analysis on the haze formation model shows that the atmospheric veil has much less relevance to chrominance than luminance, which motivates us to neglect the haze in the chrominance channel and concentrate on the luminance channel in the dehazing process. Besides, the experimental study illustrates that the YUV color space is most suitable for image dehazing. Accordingly, a variational model is proposed in the Y channel of the YUV color space by combining the reformulation of the haze model and the two effective priors. As we mainly focus on the Y channel, most of the chrominance information of the image is preserved after dehazing. The numerical procedure based on the alternating direction method of multipliers (ADMM) scheme is presented to obtain the optimal solution. Extensive experimental results on real-world hazy images and synthetic dataset demonstrate clearly that our method can unveil the details and recover vivid color information, which is competitive among many existing dehazing algorithms. Further experiments show that our model also can be applied for image enhancement.
Hyper-Parameter Selection in Convolutional Neural Networks Using Microcanonical Optimization Algorithm

The success of Convolutional Neural Networks is highly dependent on the selected architecture and the hyper-parameters.The need for the automatic design of the networks is especially important for complex architectures where the parameter space is so large that trying all possible combinations is computationally infeasible.In this study, Microcanonical Optimization algorithm which is a variant of Simulated Annealing method is used for hyper-parameter optimization and architecture selection for Convolutional Neural Networks.To the best of our knowledge, our study provides a first attempt at applying Microcanonical Optimization for this task.The networks generated by the proposed method is compared to the networks generated by Simulated Annealing method in terms of both accuracy and size using six widely-used image recognition datasets.Moreover, a performance comparison using Tree Parzen Estimator which is a Bayesion optimization-based approach is also presented.It is shown that the proposed method is able to achieve competitive classification results with the state-of-the-art architectures.When the size of the networks is also taken into account, one can see that the networks generated by Microcanonical Optimization method contain far less parameters than the state-of-the-art architectures.Therefore, the proposed method can be preferred for automatically tuning the networks especially in situations where fast training is as important as the accuracy.
ENTMOOT: A framework for optimization over ensemble tree models

Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications. These tree models (i) offer insight into important prediction features, (ii) effectively manage sparse data, and (iii) have excellent prediction capabilities. Despite their advantages, they are generally unpopular for decision-making tasks and black-box optimization, which is due to their difficult-to-optimize structure and the lack of a reliable uncertainty measure. ENTMOOT is our new framework for integrating (already trained) tree models into larger optimization problems. The contributions of ENTMOOT include: (i) explicitly introducing a reliable uncertainty measure that is compatible with tree models, (ii) solving the larger optimization problems that incorporate these uncertainty aware tree models, (iii) proving that the solutions are globally optimal, i.e. no better solution exists. In particular, we show how the ENTMOOT approach allows a simple integration of tree models into decision-making and black-box optimization, where it proves as a strong competitor to commonly-used frameworks.
Robust Multi-Agent Path Finding and Executing

Multi-agent path-finding (MAPF) is the problem of finding a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. In this work, we propose a holistic solution for MAPF that is robust to such unexpected delays. First, we introduce the notion of a k-robust MAPF plan, which is a plan that can be executed even if a limited number (k) of delays occur. We propose sufficient and required conditions for finding a k-robust plan, and show how to convert several MAPF solvers to find such plans. Then, we propose several robust execution policies. An execution policy is a policy for agents executing a MAPF plan. An execution policy is robust if following it guarantees that the agents reach their goals even if they encounter unexpected delays. Several classes of such robust execution policies are proposed and evaluated experimentally. Finally, we present robust execution policies for cases where communication between the agents may also be delayed. We performed an extensive experimental evaluation in which we compared different algorithms for finding robust MAPF plans, compared different ro- bust execution policies, and studied the interplay between having a robust plan and the performance when using a robust execution policy.
Nighttime Data Augmentation Using GAN for Improving Blind-Spot Detection

Camera-based blind-spot detection systems improve the shortcomings of radar-based systems for accurately detecting the position of a vehicle. However, as with many camera-based applications, the detection performance is insufficient in a low-illumination environment such as at night. This problem can be solved with augmented nighttime images in the training data but acquiring them and annotating the additional images are cumbersome tasks. Therefore, we propose a framework that converts daytime images into synthetic nighttime images using a generative adversarial network and that augments the synthetic images for the training process of the vehicle detector. A public dataset comprising different viewpoints of target images was used to easily obtain the images required for training the generative adversarial network. Experiments on a real nighttime dataset demonstrate that the proposed framework improved the detection performance considerably in comparison with using daytime images only.
Does Augmented Reality Affect Sociability, Entertainment, and Learning? A Field Experiment

Augmented reality (AR) applications have recently emerged for entertainment and educational purposes and have been proposed to have positive effects on social interaction. In this study, we investigated the impact of a mobile, indoor AR feature on sociability, entertainment, and learning. We conducted a field experiment using a quiz game in a Finnish science center exhibition. We divided participants (N = 372) into an experimental group (AR app users) and two control groups (non-AR app users; pen-and-paper participants), including 28 AR users of follow-up interviews. We used Kruskal–Wallis rank test to compare the experimental groups and the content analysis method to explore AR users’ experiences. Although interviewed AR participants recognized the entertainment value and learning opportunities for AR, we did not detect an increase in perceived sociability, social behavior, positive affect, or learning performance when comparing the experimental groups. Instead, AR interviewees experienced a strong conflict between the two different realities. Despite the engaging novelty value of new technology, performance and other improvements do not automatically emerge. We also discuss potential conditional factors. Future research and development of AR and related technologies should note the possible negative effects of dividing attention to both realities.
Adaptive visual servoing with an uncalibrated camera using extreme learning machine and Q-leaning

In this paper, a novel image-based visual servoing (IBVS) method using Extreme Learning Machine (ELM) and Q-learning is proposed to solve the problems of complex modeling and selection of the servo gain. First, the pseudoinverse of the interaction matrix is approached by ELM which avoids the singularity of the interaction matrix effectively and is robust to interferences such as feature noises and camera calibration errors. Second, a reinforcement learning method, Q-learning, is adopted to adaptively adjust the servo gain in order to improve the convergence speed and stability. Compared with other methods, ELM has better generalization performance, faster operation speed and a unique optimal solution. Also, Q-learning has self-learning ability without experience in advance. The effectiveness of the proposed method is validated by simulations and experiment on a 6-DOF robot with eye-in-hand configuration.
CORE: Automating Review Recommendation for Code Changes

Code review is a common process that is used by developers, in which a reviewer provides useful comments or points out defects in the submitted source code changes via pull request. Code review has been widely used for both industry and open-source projects due to its capacity in early defect identification, project maintenance, and code improvement. With rapid updates on project developments, code review becomes a non-trivial and labor-intensive task for reviewers. Thus, an automated code review engine can be beneficial and useful for project development in practice. Although there exist prior studies on automating the code review process by adopting static analysis tools or deep learning techniques, they often require external sources such as partial or full source code for accurate review suggestion. In this paper, we aim at automating the code review process only based on code changes and the corresponding reviews but with better performance. The hinge of accurate code review suggestion is to learn good representations for both code changes and reviews. To achieve this with limited source, we design a multi-level embedding (i.e., word embedding and character embedding) approachto represent the semantics provided by code changes and reviews. The embeddings are then well trained through a proposed attentional deep learning model, as a whole named CORE. We evaluate the effectiveness of CORE on code changes and reviews collected from 19 popular Java projects hosted on Github. Experimental results show that our model CORE can achieve significantly better performance than the state-of-the-art model (DeepMem), with an increase of 131.03% in terms of Recall@10 and 150.69% in terms of Mean Reciprocal Rank. Qualitative general word analysis among project developers also demonstrates the performance of CORE in automating code review.
Multi-view projected clustering with graph learning

Graph based multi-view learning is well known due to its effectiveness and good clustering performance. However, most existing methods directly construct graph from original high-dimensional data which always contain redundancy, noise and outlying entries in real applications, resulting in unreliable and inaccurate graph. Moreover, they do not effectively select some useful features which are important for graph learning and clustering. To solve these limits, we propose a novel model that combines dimensionality reduction, manifold structure learning and feature selection into a framework. We map high-dimensional data into low-dimensional space to reduce the complexity of the algorithm and reduce the effect of noise and redundance. Therefore, we can adaptively learn a more accurate graph. Further more, ℓ21-norm regularization is adopted to adaptively select some important features which help improve clustering performance. Finally, an efficiently algorithm is proposed to solve the optimal solution. Extensive experimental results on some benchmark datasets demonstrate the superiority of the proposed method.
Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting

A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy.
Communication Lower Bound in Convolution Accelerators

In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communication for CNN accelerators. For the off-chip communication, we derive the theoretical lower bound for any convolutional layer and propose a dataflow to reach the lower bound. This fundamental problem has never been solved by prior studies. The on-chip communication is minimized based on an elaborate workload and storage mapping scheme. We in addition design a communication-optimal CNN accelerator architecture. Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a three-level memory hierarchy and it is computation dominant. The gap between the energy efficiency of our accelerator and the theoretical best value is only 37-87%.
Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.
Setting Up Experimental Bell Tests with Reinforcement Learning

Finding optical setups producing measurement results with a targeted probability distribution is hard, as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices. To tackle this complexity, we introduce a method combining reinforcement learning and simulated annealing enabling the automated design of optical experiments producing results with the desired probability distributions. We illustrate the relevance of our method by applying it to a probability distribution favouring high violations of the Bell-Clauser-Horne-Shimony-Holt (CHSH) inequality. As a result, we propose new unintuitive experiments leading to higher Bell-CHSH inequality violations than the best currently known setups. Our method might positively impact the usefulness of photonic experiments for device-independent quantum information processing.