Reviewerly/exHarmony huggingface dataset:
Single-shot high-precision 3D reconstruction with color fringe projection profilometry based BP neural network

In order to reduce the influence of nonlinear factors such as gamma nonlinearity and color crosstalk on the phase retrieval accuracy with color fringe projection profilometry (CFPP) in rapid measurement, a single-shot high-precision backpropagation (BP) neural network based CFPP is proposed. Firstly, neural networks are trained from samples through a training process. The neural network inputs are the RGB components of composite color fringe patterns. The numerator and denominator of the arctangent function are used as network outputs. The monochromatic fringe patterns are adopted. The numerator and denominator of the arctangent function are calculated by the 15-step phase-shift method, which serves as the network training target. Applying this neural network to the prediction of the standard phase can reduce the influence of nonlinear factors on the phase accuracy. Then, the absolute phase was unwrapped by using geometric constraints. Finally, the 3D reconstruction with high precision can be realized by using only a single-shot color fringe. The theoretical model of the proposed method and the process of neural network training are introduced. The results of 3D reconstruction are compared with other algorithms in the same condition. The experimental results demonstrate that the RMS of the proposed algorithm can reach 0.0107 mm, compared with 0.0121 mm of the traditional 3-step phase-shift method. The proposed method is applied to the online quality inspection of relay components, and the results verify the high accuracy and high efficiency of measurement.
Multi-attention mutual information distributed framework for few-shot learning

The purpose of few-shot learning is to learn a classifier, even if only a limited number of samples are used, a good generalization effect can be achieved. Recently, many methods based on meta-learning learn a large number of multi-classification tasks to train a general classifier to solve this problem. Methods based on metric learning use the distance relationship between labeled samples and unlabeled samples for classification. These methods all have good performance. However, these methods rarely pay attention to the problems of insufficient feature extraction and low training efficiency. To this end, we propose multi-attention mutual information distributed framework for few-shot learning (MAMD). Specifically, we use the attention mechanism to help the feature embedding module extract more representative features. We use multiple attention mechanisms because different attention mechanisms can focus on different features. After multiple attention modules extract features, we use the mutual learning to aggregate the extracted features. The mutual learning method is similar to knowledge distillation. The difference between knowledge distillation and mutual learning is that mutual learning does not require a large network to guide a small network, but two networks learn from each other and progress together. In addition, we use distributed learning to improve the training speed and shorten the consumption of time. We combine distributed learning with few-shot learning for the first time and propose the concept of distributed few-shot learning. It provides a new direction for few-shot learning. We evaluate MAMD on two few-shot learning benchmark datasets and achieve the expected results.
Optimization of photovoltaic provision in a three-dimensional city using real-time electricity demand

Promoting the use of solar photovoltaic (PV) systems in global cities can be an effective way to cope with severe environmental problems caused by the consuming of fossil fuels. However, a complex urban environment challenges the effective use of PV systems for practical applications. Essentially, this is a spatial optimization problem, where the goal is maximizing the harvesting of solar energy while minimizing occupied urban surfaces. To address this problem, this paper proposes three hierarchical optimizations. First, computational optimization provides a parallel architecture for an established 3D solar estimation model to achieve spatially scalable computation with high spatio-temporal resolution. Second, priority optimization determines the use of different urban partitions considering various constraints. Third, capacity optimization analyzes the spatial and quantitative distribution of solar potential, constrained by the smallest solar irradiation and the minimum surface area to be used. The overall optimization framework is then set to obtain the minimum PV installation capacity required to meet the real demand with the identification of urban surfaces to be equipped with PV modules. By using smart meter data with high temporal resolution in the city of Bologna, Italy, our analysis not only provides executable plans to meet the real demand but also reveals that rebalance and storage capacity are needed to achieve a real-time self-supportive architecture. The proposed analytic and optimization framework can promote distributed PV systems in urban areas and facilitate energy transition adapted to a variety of applications.
Exploring Presence, Avatar Embodiment, and Body Perception with a Holographic Augmented Reality Mirror

The embodiment of avatars in virtual reality (VR) is a promising tool for enhancing the user&#x2019;s mental health. A great example is the treatment of body image disturbances, where eliciting a full-body illusion can help identify, visualize, and modulate persisting misperceptions. Augmented reality (AR) could complement recent advances in the field by incorporating real elements, such as the therapist or the user&#x2019;s real body, into therapeutic scenarios. However, research on the use of AR in this context is very sparse. Therefore, we present a holographic AR mirror system based on an optical see-through (OST) device and markerless body tracking, collect valuable qualitative feedback regarding its user experience, and compare quantitative results regarding presence, embodiment, and body weight perception to similar systems using video see-through (VST) AR and VR. For our OST AR system, a total of 27 normal-weight female participants provided predominantly positive feedback on display properties (field of view, luminosity, and transparency of virtual objects), body tracking, and the perception of the avatar&#x2019;s appearance and movements. In the quantitative comparison to the VST AR and VR systems, participants reported significantly lower feelings of presence, while they estimated the body weight of the generic avatar significantly higher when using our OST AR system. For virtual body ownership and agency, we found only partially significant differences. In summary, our study shows the general applicability of OST AR in the given context offering huge potential in future therapeutic scenarios. However, the comparative evaluation between OST AR, VST AR, and VR also revealed significant differences in relevant measures. Future work is mandatory to corroborate our findings and to classify the significance in a therapeutic context.
Motion Planning of the Citrus-Picking Manipulator Based on the TO-RRT Algorithm

The working environment of a picking robot is complex, and the motion-planning algorithm of the picking manipulator will directly affect the obstacle avoidance effect and picking efficiency of the manipulator. In this study, a time-optimal rapidly-exploring random tree (TO-RRT) algorithm is proposed. First, this algorithm controls the target offset probability of the random tree through the potential field and introduces a node-first search strategy to make the random tree quickly escape from the repulsive potential field. Second, an attractive step size and a “step-size dichotomy” are proposed to improve the directional search ability of the random tree outside the repulsive potential field and solve the problem of an excessively large step size in extreme cases. Finally, a regression superposition algorithm is used to enhance the ability of the random tree to explore unknown space in the repulsive potential field. In this paper, independent experiments were carried out in MATLAB, MoveIt!, and real environments. The path-planning speed was increased by 99.73%, the path length was decreased by 17.88%, and the number of collision detections was reduced by 99.08%. The TO-RRT algorithm can be used to provide key technical support for the subsequent design of picking robots.
Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems

Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight.
Philosophy of science at sea: Clarifying the interpretability of machine learning

Abstract In computer science, there are efforts to make machine learning more interpretable or explainable, and thus to better understand the underlying models, algorithms, and their behavior. But what exactly is interpretability, and how can it be achieved? Such questions lead into philosophical waters because their answers depend on what explanation and understanding are—and thus on issues that have been central to the philosophy of science. In this paper, we review the recent philosophical literature on interpretability. We propose a systematization in terms of four tasks for philosophers: (i) clarify the notion of interpretability, (ii) explain the value of interpretability, (iii) provide frameworks to think about interpretability, and (iv) explore important features of it to adjust our expectations about it.
Time Series Forecasting of Solar Power Generation Using Facebook Prophet and XG Boost

In recent years solar energy penetration in local grids is increasing, resulting in a reduction in reliability, so smart grid planning is required to improve grid reliability and leverage the grid's capabilities. Due to the increasing, no of solar power plants, day by day in the energy sector, and reduction of dependency on fossil fuels, prediction of solar power generation is necessary for future planning for smart grid integration. Forecasting techniques are employed to tackle a wide range of problems, such as renewable energy generation, electric load and price, demand-side management, the financial & banking sector, healthcare, stock market, and cyberspace security prediction, etc. In the time-series dataset, there is information related to the time that can be used to predict and analyze data. Machine Learning algorithms such as Facebook (FB) Prophet and Extreme Gradient Boost (XGB) are used for predicting solar energy generation on a monthly and weekly basis. From this proposed research, it concluded that the XGB model is efficient to forecast in terms of better prediction and better fitting than the FB prophet model. RMSE, MAPE, and MAE parameters are calculated to check the performance of the time series model.
Three-dimensional affinity learning based multi-branch ensemble network for breast tumor segmentation in MRI

Accurate and automatic breast tumor segmentation based on dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) plays an important role in breast cancer analysis. However, the background parenchymal enhancement and large variations in tumor size, shape or appearance make the task very challenging, and also the segmentation performance is still not satisfactory, especially for non-mass enhancement (NME) and small size tumors (≤2 cm). To address these challenges, we propose a novel 3D affinity learning based multi-branch ensemble network for accurate breast tumor segmentation. Specifically, two different types of subnetworks are built to form a multi-branch network. The two subnetworks are equipped with effective operation components, i.e., residual connection and channel-wise attention or making use of dense connectivity patterns, which can process the input images in parallel. Second, we propose an end-to-end trainable 3D affinity learning based refinement module by calculating the similarities between features of voxels, which is useful in discovering more pixels belonging to breast tumors. Third, two local affinity matrices are constructed by 3D affinity learning, which are used to refine the segmentation outputs of two subnetworks, respectively. Finally, a novel ensemble module is proposed to combine the information derived from the subnetworks, which can hierarchically merge the local and global affinity matrices derived from subnetworks. A large-scale breast DCE-MR images dataset with 420 subjects are built for evaluation, and comprehensive experiments have been conducted to demonstrate that our proposed method achieves superior performance over state-of-the-art medical image segmentation methods.
What Can 5G Do for Public Safety? Structural Health Monitoring and Earthquake Early Warning Scenarios

The 5th generation of mobile networks has come to the market bringing the promise of disruptive performances as low latency, availability and reliability, imposing the development of the so-called "killer applications". This contribution presents a 5G use case in the context of Structural Health Monitoring which guarantees an unprecedented level of reliability when exploited for public safety purposes as Earthquake Early Warning. The interest on this topic is at first justified through a deep market analysis, and subsequently declined in terms of public safety benefits. A specific sensor board, guaranteeing real-time processing and 5G connectivity, is presented as the foundation on which the architecture of the network is designed and developed. Advantages of 5G-enabled urban safety are then discussed and proven in the experimentation results, showing that the proposed architecture guarantees lower latency delays and overcome the impairments of cloud solutions especially in terms of delays variability.
Introducing Immersive Virtual Reality in the Initial Phases of the Design Process—Case Study: Freshmen Designing Ephemeral Architecture

Immersive Virtual Reality (IVR) has proven to be an important tool for the exploration and communication of architectural projects prior to their real construction; however, there have been few scientific advances of its use in the understanding, exploration, and definition of architectural space by architecture students in their initial design processes. The purpose of this research is to determine how the use of IVR incorporated in the initial phases of the architectural design process improves, among students, the achievement of three specifics design competencies, and to know the evaluation that professors make of the advantages and disadvantages of the use of this tool in the design process. A mixed methodology was applied, considering participatory observations and surveys of students and teachers concerning the initial architecture workshop on architectural careers. It was found that the three analyzed competencies are better achieved with the use of IVR due to its high utility in the perception of space on a real scale and in its interior experimentation, both referred to as important advantages by students and teachers. It is concluded that the application of the interactive and immersive VR is a pedagogical tool that allows students to get feedback from their own spatial experience to correct and improve their designs, while teachers find the tool useful in the initial phases of architectural design.
Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review

With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care.The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care.Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes.Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases.Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.
Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems

In this paper, a hybrid gradient simulated annealing algorithm is guided to solve the constrained optimization problem. In trying to solve constrained optimization problems using deterministic, stochastic optimization methods or hybridization between them, penalty function methods are the most popular approach due to their simplicity and ease of implementation. There are many approaches to handling the existence of the constraints in the constrained problem. The simulated-annealing algorithm (SA) is one of the most successful meta-heuristic strategies. On the other hand, the gradient method is the most inexpensive method among the deterministic methods. In previous literature, the hybrid gradient simulated annealing algorithm (GLMSA) has demonstrated efficiency and effectiveness to solve unconstrained optimization problems. In this paper, therefore, the GLMSA algorithm is generalized to solve the constrained optimization problems. Hence, a new approach penalty function is proposed to handle the existence of the constraints. The proposed approach penalty function is used to guide the hybrid gradient simulated annealing algorithm (GLMSA) to obtain a new algorithm (GHMSA) that finds the constrained optimization problem. The performance of the proposed algorithm is tested on several benchmark optimization test problems and some well-known engineering design problems with varying dimensions. Comprehensive comparisons against other methods in the literature are also presented. The results indicate that the proposed method is promising and competitive. The comparison results between the GHMSA and the other four state-Meta-heuristic algorithms indicate that the proposed GHMSA algorithm is competitive with, and in some cases superior to, other existing algorithms in terms of the quality, efficiency, convergence rate, and robustness of the final result.
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback

Ranking algorithms in recommender systems influence people to make decisions. Conventional ranking algorithms based on implicit feedback data aim to maximize the utility to users by capturing users’ preferences over items. However, these utility-focused algorithms tend to cause fairness issues that require careful consideration in online platforms. Existing fairness-focused studies does not explicitly consider the problem of lacking negative feedback in implicit feedback data, while previous utility-focused methods ignore the importance of fairness in recommendations. To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data. FairGAN does not explicitly treat unobserved interactions as negative, but instead, adopts a novel fairness-aware learning strategy to dynamically generate fairness signals. This optimizes the search direction to make FairGAN capable of searching the space of the optimal ranking that can fairly allocate exposure to individual items while preserving users’ utilities as high as possible.
Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation

Recent works have shown the effectiveness of incorporating textual and visual information to tackle the sparsity problem in recommendation scenarios. To fuse these useful heterogeneous modality information, an essential prerequisite is to align these information for modality-robust features learning and semantic understanding. Unfortunately, existing works mainly focus on tackling the learning of common knowledge across modalities, while the specific characteristics of each modality is discarded, which may inevitably degrade the recommendation performance.
Quantum regression in dephasing phenomena

We investigate the validity of quantum regression for a family of quantum Hamiltonians on a multipartite system leading to phase-damping reduced dynamics. After finding necessary and sufficient conditions for the CP-divisibility of the corresponding channel, we evaluate a hierarchy of equations equivalent to the validity of quantum regression under arbitrary interventions; in particular, we find necessary conditions for a nontrivial dephasing to be compatible with quantum regression. In this framework, we study a class of dephasing-type generalized spin-boson (GSB) models, investigating the existence of qubit-environment coupling functions that ensure the exact validity of quantum regression.