Reviewerly/exHarmony huggingface dataset:
Discontinuity in the production rate due to the solar resource intermittency

The economic risk of solar plant projects due to the inter-annual solar resource variability is usually quantified by the exceedance probabilities. The estimation of these probabilities depends on the datasets used for the study; actually, the uncertainty associated to these datasets affects the viability study of the project. Due to this dependence, in this work two different types of datasets are used in the estimation of the exceedance probabilities and their uncertainties: synthetic datasets and satellite-derived datasets. Besides, the intra-annual variability of the irradiation variations (intermittency) can affect the production rate of the solar plant. Thus, the knowledge of this variability allows us to foresee the discontinuities in the production rate of the solar plants and to plan the needs for high or low storage capacity according to different temporal periods. In order to quantify the effect of the intra-annual variability in the viability study, a Viability Index based on Discontinuity in the production rate, VID, is proposed, which estimates viability from the exceedance probabilities of the variations. The inclusion of this analysis can provide us with important information about the viability assessment of the projects.
Using Digital Open Source and Crowdsourced Data in Studies of Deviance and Crime

As the internet has become cheaper, faster, and more widely used, the amount of data generated by people has increased exponentially. Much of the data is provided by users’ activities, often mundane tasks, like making purchases, engaging in exercise routines, and consuming streaming content. In some cases, these tasks are leveraged by criminal actors; in others, these tasks include criminal activities. Using data to explore patterns of offending and victimization is critical to understanding crime trends in the digital age. This chapter explores how researchers have used open source data collection techniques and have solicited data using crowdsourcing to develop viable datasets to explore social scientific enquiries. These techniques illustrate that it is increasingly possible to gather and solicit information and analytical help to explore deviance, victimization, social taboos, and behaviors that are often kept out of public view but nonetheless impact society.
An Algebraic Fuzzy Pole Placement Approach to Stabilize Nonlinear Mechanical Systems

Based on the general structure of mechanical systems described by their state-space representation, the Takagi–Sugeno fuzzy modeling, and the controllability property of fuzzy systems, an algebraic and practical approach to computing the fuzzy gain capable of ensuring the stability property of the Takagi–Sugeno fuzzy model is proposed in this article. The main idea consists of finding a continuous fuzzy gain such that any linear behavior, defined by the adequate selection of eigenvalues, is induced in the closed-loop fuzzy system. Therefore, by continuity, if the fuzzy model is an approximation sufficiently close to the mechanical system, then such a nonlinear system is also stabilized by the fuzzy controller. A notable advantage of the proposed method, when compared with similar approaches, is the simplicity of the resulting gain. The validity of the approach is illustrated through the numerical simulation of a sufficiently complex nonlinear system.
Quantum computing in renewable energy exploration: status, opportunities, and challenges

There is a potential disruptive tendency in renewable energy that could be birthed by the enormous computing power of quantum computers. It is expected that the approach to renewable energy exploration would be significantly transformed, given the computational efficiency and optimization capacity of quantum computing associated with highly efficient algorithms that can provide exponential speedup for renewable energy process modeling. Quantum computing integrates several functional elements which produced distinguish characteristics from the classic computing system. This chapter discusses the application of quantum computing to renewable energy exploration and associated optimization problems. The current status and challenges associated with quantum computing are discussed while the opportunities are outlined. Also, the basic underlying concepts of quantum computation are discussed while their peculiar attributes compared to classical computing are highlighted. Some existing quantum optimization algorithms were briefly discussed. Given the enormous amount of data that are currently generated across renewable energy exploration, the energy company will be able to harness new opportunities which have hitherto not been possible and by so doing the world will further progress toward achieving climate change mitigation obligation.
A Multimodal Classification of Noisy Hate Speech using Character Level Embedding and Attention

Hate speech has become a critical problem in all social media, leading to many hate crimes alongside affecting the mental and emotional well-being of affected individuals. This calls out for methods to detect online hate speech more than ever. While numerous architectures exist for hate speech detection in unimodal setup (i.e., either textual or visual) we have targeted the problem in the context of both text and images inspired by the real-world raw data which involves several modalities. We propose a multimodal hate speech classifier, called as Character Text Image Classifier (CTIC), which builds upon Bidirectional Encoder Representations from Transformers (BERT), Capsule Network, and EfficientNet involving four modalities, namely word embeddings, character embeddings, sentence embeddings, and images. We report the experiments performed upon our proposed model and several other models which have been tested in the process. We train our model with different sampling techniques, and selective training, upon a Twitter dataset, called MMHS150K, consisting of both texts and associated images. Our proposed multimodal approach attains better performance than the previous models constructed upon the dataset.
Estimation of Human Sensitivity for Curvature Gain of Redirected Walking Technology

The curvature gain of redirected walking method enables users to explore virtual spaces that are larger than real spaces. The estimation of human sensitivity for curvature gain is important for redirected walking. Herein, we conduct two experiments under different path conditions. By adopting the psychophysical “method of limits” to be a new approach, the first experiment re-estimates the sensitivity for a curved path and further proves that people are more sensitive for right-curved paths than left-curved paths. The second experiment investigates the characteristics of preorder paths on the sensitivity to postorder paths, and finds that the existing of preorder paths significantly increases the sensitivity for the postorder path. Moreover, we find an interesting moderation effect on the direction consistency of preorder and postorder paths: the larger curved preorder path can significantly decrease the sensitivity for postorder path when they have the same direction. If their directions diverge, the effect of preorder path is not significant.
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine

Detecting out-of-distribution (OOD) samples is an essential requirement for the deployment of machine learning systems in the real world.Until now, research on energy-based OOD detectors has focused on the softmax confidence score from a pre-trained neural network classifier with access to class labels.In contrast, we propose an unsupervised energy-based OOD detector leveraging the Stiefel-Restricted Kernel Machine (St-RKM).Training requires minimizing an objective function with an autoencoder loss term and the RKM energy where the interconnection matrix lies on the Stiefel manifold.Further, we outline multiple energy function definitions based on the RKM framework and discuss their utility.In the experiments on standard datasets, the proposed method improves over the existing energy-based OOD detectors and deep generative models.Through several ablation studies, we further illustrate the merit of each proposed energy function on the OOD detection performance.
Cloud Data Breach Disclosures: the Consumer and their Personally Identifiable Information (PII)?

The incidence of cloud computing data breaches across the world is increasing as retailers, financial institutions, health providers and businesses of all sizes rely on infrastructure, platform, software, and anything as a service (IaaS, PaaS, SaaS and XaaS respectively). As cloud provisioning requires the Internet for the realization of services, security has become paramount. Companies have adopted cloud services to allow for agility in response to growth, particularly of digital services, while reducing their overall costs and maintenance issues. At the same time, the personally identifiable information (PII) of hundreds of millions of end-users is now at risk of being hacked, through insecure cloud computing practices that lead to data breaches. In this article, we explore three cases of significant cloud computing data breaches: 2011 Sony PlayStation Network (PSN); 2014 eBay, Inc.; and 2014 Yahoo!. This paper employs a qualitative methodology, using mini case studies to demonstrate the significance of data breaches. While class action lawsuits by citizens have brought attention to potential matters of disclosure, the misuse of data, and identity fraud in the context of cloud computing, US courts continue to rule that the direct costs to the individual end-user are merely speculative and not provable. The role of consumer class action for data breaches in the cloud are presented in this article.
BOOMER — An algorithm for learning gradient boosted multi-label classification rules

Multi-label classification is concerned with the assignment of sets of labels to individual data points. Due to its diverse real-world applications, e.g., the annotation of text documents with topics, it has become a well-established field of machine learning research. Compared to traditional classification, where classes are mutually exclusive, multi-label classification comes with interesting challenges, most prominently the requirement to take dependencies between labels into account. In this work, we present a modular and customizable implementation of BOOMER – an algorithm for learning gradient boosted multi-label classification rules – that can flexibly be adjusted to different use cases and requirements.
Intermittent evolution routes to the periodic or the chaotic orbits in Rulkov map

This paper concerns the intermittent evolution routes to the asymptotic regimes in the Rulkov map. That is, the windows with transient approximate periodic and transient chaotic behaviors occur alternatively before the system reaches the periodic or the chaotic orbits. Meanwhile, the evolution routes to chaotic orbits can be classified into different types according to the windows before reaching asymptotic chaotic states. In addition, the initial values can be regarded as a key factor affecting the asymptotic behaviors and the evolution routes. The effects of the initial values are given by parameter planes, bifurcation diagrams, and waveforms. In order to investigate whether the intermittent evolution routes can be learned by machine learning, some experiments are given to understanding the differences between the trajectories of the Rulkov map generated by the numerical simulations and predicted by the neural networks. These results show that there is about 60% accuracy rate of successfully predicting both the evolution routes and the asymptotic period-3 orbits using a three-layer feedforward neural network, while the bifurcation diagrams can be reconstructed using reservoir computing except a few parameter conditions.
The use of VR tilt brush in art and psychomotor therapy: An innovative perspective

Digitisation requires mental healthcare workers to be competent in using technology. Virtual Reality offers unique treatment possibilities because of its presence, its immersiveness, and the affect responses it produces. Although VR has been used in psychotherapy, it is fairly new in the field of art therapy. Painting applications such as Google Tilt Brush provide opportunities for practice, but art therapists have reported that they do not feel competent enough in the use of digital tools and online therapy. On another front, there have been no studies in clinical practice using Tilt Brush. In this study participants (N = 7, five art therapists, two psychomotor therapists) were given the opportunity to experiment with the Tilt Brush application in their clinical practice, using the Lean Startup Method. Participants filled out preparatory questionnaires, and a semi-structured interview was held at the end of the three-month trial to gain insight into experiences. The results showed that art and psychomotor therapists view Tilt Brush as an application with much potential in therapy practice. Participants said they gained competence and insight into how to use this application in therapy. This paper discusses qualities of Tilt Brush as well as its advantages and disadvantages in practice.
Weighted distances on the truncated hexagonal grid

Recently chamfer distances have been developed not only on the usual integer grids, but also on some non traditional grids including grids which are not lattices. In this paper the truncated hexagonal grid is considered: its pixels are dodecagons and two shaped (oriented) triangles. Two types of 'natural' neighborhood relations are considered on the grid, consequently two weights are used to describe the chamfer distances. Formulae to compute the minimal weights of a connecting path, i.e., the distance of any two pixels, are provided to cases depending on the relative ratio of the weights. Some properties of these distances, including metricity are also analysed. Digital disks based on the weighted distances are also investigated. In some cases, these disks may not be convex, moreover they may contain holes. The conditions of holes are characterised.
Hybrid Riemannian Graph-Embedding Metric Learning for Image Set Classification

With the continuously increasing amount of video data, image set classification has recently received widespread attention in the CV&PR community. However, the intra-class diversity and inter-class ambiguity of representations remain an open challenge. To tackle this issue, several methods have been put forward to perform multiple geometry-aware image set modelling and learning. Although the extracted complementary geometric information is beneficial for decision making, the sophisticated computational paradigm (e.g., scatter matrices computation and iterative optimisation) of such algorithms is counterproductive. As a countermeasure, we propose an effective hybrid Riemannian metric learning framework in this paper. Specifically, we design a multiple graph embedding-guided metric learning framework for the sake of fusing these complementary kernel features, obtained via the explicit RKHS embeddings of the Grassmannian manifold, SPD manifold, and Gaussian embedded Riemannian manifold, into a unified subspace for classification. Furthermore, the involved optimisation problem of the developed model can be solved in terms of a series of sub-problems, achieving improved efficiency theoretically and experimentally. Substantial experiments are carried out to evaluate the efficacy of our approach. The experimental results suggest the superiority of it over the state-of-the-art methods.
Multi-Level Query Interaction for Temporal Language Grounding

Understanding what is happening in the surveillance video is important for human-machine interface in transportation systems, where temporal language grounding is one of the key tasks, targeting at localizing the desired moment in an untrimmed video with a given sentence query that is relevant to the moment. This task is challenging due to the following reasons: 1) the requirement of understanding the video contents and query semantics comprehensively, and 2) building the bridge between the cross-modal semantics. To tackle these problems, early methods first sample video clips and then match them with the sentence to find the most relevant one. To reduce the computational complexity associated with video clip sampling, recent methods directly predict the temporal boundaries of the desired moment on the fused features of the sentence and the video frames. However, all the previous methods often learn the word-level or phrase-level features of the sentence, or directly generates the global sentence representation by attention mechanisms or graph network. However, we argue that applying only word-level or phrase-level semantic information and cross-modal interactions is not enough to fully capture the correspondence between the video and the query. To this end, we proposed a novel Multi-level Query Exploration and Interaction (MQEI) model, which explores the semantics in both the word- and phrase-level and captures the multi-level interactions between the video and the query through an attention module. Extensive experiments on two public benchmark datasets ActivityNet Captions and Charades-STA demonstrate that the proposed model can outperform all the state-of-the-art methods consistently.
Anchor-guided online meta adaptation for fast one-Shot instrument segmentation from robotic surgical videos

The scarcity of annotated surgical data in robot-assisted surgery (RAS) motivates prior works to borrow related domain knowledge to achieve promising segmentation results in surgical images by adaptation. For dense instrument tracking in a robotic surgical video, collecting one initial scene to specify target instruments (or parts of tools) is desirable and feasible during the preoperative preparation. In this paper, we study the challenging one-shot instrument segmentation for robotic surgical videos, in which only the first frame mask of each video is provided at test time, such that the pre-trained model (learned from easily accessible source) can adapt to the target instruments. Straightforward methods transfer the domain knowledge by fine-tuning the model on each given mask. Such one-shot optimization takes hundred of iterations and the test runtime is unfeasible. We present anchor-guided online meta adaptation (AOMA) for this problem. We achieve fast one-shot test time optimization by meta-learning a good model initialization and learning rates from source videos to avoid the laborious and handcrafted fine-tuning. The trainable two components are optimized in a video-specific task space with a matching-aware loss. Furthermore, we design an anchor-guided online adaptation to tackle the performance drop throughout a robotic surgical sequence. The model is continuously adapted on motion-insensitive pseudo-masks supported by anchor matching. AOMA achieves state-of-the-art results on two practical scenarios: (1) general videos to surgical videos, (2) public surgical videos to in-house surgical videos, while reducing the test runtime substantially.
Nonlinear Mixed Effects Modeling of Deterministic and Stochastic Dynamical Systems in Wolfram Mathematica

Nonlinear mixed effects (NLME) modeling is a powerful tool to analyze time-series data from several individual entities in an experiment. In this paper, we give a brief overview of a package for NLME modeling in Wolfram Mathematica entitled NLMEModeling, implementing the first-order conditional estimation method with sensitivity equation-based gradients for parameter estimation. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with observation equations with flexible observation error models. Moreover, NLMEModeling is a user-friendly package with functionality for parameter estimation, model diagnostics (such as goodness-of-fit analysis and visual predictive checks), and model simulation. The package is freely available and provides an extensible add-on to Wolfram Mathematica.
Learning Video Actions in Two Stream Recurrent Neural Network

The paper investigates the Long short term memory (LSTM) networks for human action recognition in videos. In spite of significant progress in the field, recognizing actions in real-world videos is a challenging task due to the spatial and temporal variations within and across video clips. We propose a novel two-stream deep network for action recognition by applying the LSTM for learning the fusion of spatial and temporal feature streams. The LSTM type of Recurrent neural network by design possess unique capability to preserve long range context in temporal streams. The proposed method capitalizes on LSTMs memory attribute to fuse the input streams in high-dimensional space exploring the spatial and temporal correlations. The temporal stream input is defined on the LSTM learned deep features summarizing the input frame sequence. Our approach of combining the convolutional features based spatial stream and the deep features based temporal stream in LSTM network efficiently captures the long range temporal dependencies in video streams. We perform primary evaluation of the proposed approach on UCF101, HMBD51 and Kinetics400 datasets achieving competitive recognition accuracy of 93.1%, 71.3% and 74.6% respectively.
Quantum Rabi model with dissipation and qubit driving

We consider the quantum Rabi model as an open system that is subject to dissipation, dephasing, and sinusoidal qubit driving. One can change to an interaction picture where the qubit-driving term disappears at the expense of changing the free energy of the qubit which becomes time dependent. If the driving frequency is large with respect to the rest of the parameters with the exception of the driving strength, then one can obtain an effective Hamiltonian that accurately describes the dynamics of the system. The driving has two effects: the qubit-transition frequency is changed and the qubit has reduced dephasing. The driving strength can be chosen so that the qubit-transition frequency is reduced, made equal to zero, or even made negative so that the excited and ground states of the qubit are interchanged. Therefore, sinusoidal qubit driving offers another method to control the qubit-transition frequency and to reduce qubit dephasing. Adjusting the driving strength allows one to consider a qubit with degenerate energy levels. Not taking dissipation into account, the evolution operator of the qubit-harmonic oscillator system is given by a linear combination of the orthogonal projectors onto the eigenstates of ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{\ensuremath{\sigma}}}_{x}$ followed by the evolution operator of a forced harmonic oscillator, the harmonic oscillator can be prepared in such a way that it is always found in a Schr\"odinger cat state, and the transition probability of the qubit can exhibit a collapse-revival behavior. In addition, the Born-Markov-secular master equation is deduced and the effects of dissipation are presented. In particular, smaller ultrastrong-coupling values are preferable over larger ultrastrong-coupling values and deep strong-coupling values in order to have long-lived, easily distinguishable Schr\"odinger cat states because the decoherence rate is inversely proportional to the square of the coupling. Finally, the qubit-harmonic oscillator system can be prepared in highly entangled states that are stable under dissipation.