Reviewerly/exHarmony huggingface dataset:
Formal verification of neural network controlled autonomous systems

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial states such that a robot trajectory starting from these initial states is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of safe initial states. To mathematically model the imaging function, that maps the robot position to the LiDAR image, we introduce the notion of imaging-adapted partitions of the workspace in which the imaging function is guaranteed to be affine. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) non-linearity, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible assignments of different ReLUs. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space of feasible ReLU assignments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with the increasing complexity of the neural network controller.
Bi-Directional Spatial-Semantic Attention Networks for Image-Text Matching

Image-text matching by deep models has recently made remarkable achievements in many tasks, such as image caption and image search. A major challenge of matching the image and text lies in that they usually have complicated underlying relations between them and simply modeling the relations may lead to suboptimal performance. In this paper, we develop a novel approach bi-directional spatial-semantic attention network, which leverages both the word to regions (W2R) relation and visual object to words (O2W) relation in a holistic deep framework for more effectively matching. Specifically, to effectively encode the W2R relation, we adopt LSTM with bilinear attention function to infer the image regions which are more related to the particular words, which is referred as the W2R attention networks. On the other side, the O2W attention networks are proposed to discover the semantically close words for each visual object in the image, i.e., the visual O2W relation. Then, a deep model unifying both of the two directional attention networks into a holistic learning framework is proposed to learn the matching scores of image and text pairs. Compared to the existing image-text matching methods, our approach achieves state-of-the-art performance on the datasets of Flickr30K and MSCOCO.
Health Care and Cybersecurity: Bibliometric Analysis of the Literature

Over the past decade, clinical care has become globally dependent on information technology. The cybersecurity of health care information systems is now an essential component of safe, reliable, and effective health care delivery.The objective of this study was to provide an overview of the literature at the intersection of cybersecurity and health care delivery.A comprehensive search was conducted using PubMed and Web of Science for English-language peer-reviewed articles. We carried out chronological analysis, domain clustering analysis, and text analysis of the included articles to generate a high-level concept map composed of specific words and the connections between them.Our final sample included 472 English-language journal articles. Our review results revealed that majority of the articles were focused on technology: Technology-focused articles made up more than half of all the clusters, whereas managerial articles accounted for only 32% of all clusters. This finding suggests that nontechnological variables (human-based and organizational aspects, strategy, and management) may be understudied. In addition, Software Development Security, Business Continuity, and Disaster Recovery Planning each accounted for 3% of the studied articles. Our results also showed that publications on Physical Security account for only 1% of the literature, and research in this area is lacking. Cyber vulnerabilities are not all digital; many physical threats contribute to breaches and potentially affect the physical safety of patients.Our results revealed an overall increase in research on cybersecurity and identified major gaps and opportunities for future work.
Designated-server identity-based authenticated encryption with keyword search for encrypted emails

In encrypted email system, how to search over encrypted cloud emails without decryption is an important and practical problem. Public key encryption with keyword search (PEKS) is an efficient solution to it. However, PEKS suffers from the complex key management problem in the public key infrastructure. Its variant in the identity-based setting addresses the drawback, however, almost all the schemes does not resist against offline keyword guessing attacks (KGA) by inside adversaries. In this work we introduce the notion of designated-server identity-based authenticated encryption with keyword search (dIBAEKS), in which the email sender authenticates the message while encrypting so that no adversary including the server can launch offline KGA. Furthermore, we strengthen the security requirement so that only the designated server has the capability to search over encrypted emails for receivers. We formally define dIBAEKS and its security models, and propose two dIBAEKS constructions using Type-I and Type-III bilinear pairing, respectively. We compare our schemes with some related IBEKS schemes in the literature, and do experiments to demonstrate its efficiency. Although they are slightly less computationally efficient than but still comparable with the related schemes, our schemes provide stronger security guarantee and better protect users’ privacy.
Deep learning for seismic phase detection and picking in the aftershock zone of 2008 M7.9 Wenchuan Earthquake

The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more reliable phase detection and picking. However, many less studied regions lack a significant amount of labeled events needed for traditional CNN approaches. In this paper, we present a CNN-based Phase- Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets. When trained on 30,146 labeled phases and applied to one-month of continuous recordings during the aftershock sequences of the 2008 MW 7.9 Wenchuan Earthquake in Sichuan, China, CPIC detects 97.5% of the manually picked phases in the standard catalog and predicts their arrival times with a five-times improvement over the ObsPy AR picker. In addition, unlike other CNN-based approaches that require millions of training samples, when the off-line training set size of CPIC is reduced to only a few thousand training samples the accuracy stays above 95%. The online implementation of CPIC takes less than 12 hours to pick arrivals in 31-day recordings on 14 stations. In addition to the catalog phases manually picked by analysts, CPIC finds more phases for existing events and new events missed in the catalog. Among those additional detections, some are confirmed by a matched filter method while others require further investigation. Finally, when tested on a small dataset from a different region (Oklahoma, US), CPIC achieves 97% accuracy after fine tuning only the fully connected layer of the model. This result suggests that the CPIC developed in this study can be used to identify and pick P/S arrivals in other regions with no or minimum labeled phases.
Height estimation from single aerial images using a deep convolutional encoder-decoder network

Extracting 3D information from aerial images is an important and still challenging topic in photogrammetry and remote sensing. Height estimation from only a single aerial image is an ambiguous and ill-posed problem. To address this challenging problem, in this paper, an architecture based on a deep convolutional neural network (CNN) is proposed in order to estimate the height values from a single aerial image. Methodologies for data preprocessing, selection of training data as well as data augmentation are presented. Subsequently, a deep CNN architecture is proposed consisting of encoding and decoding steps. In the encoding part, a deep residual learning is employed for extracting the local and global features. An up-sampling approach is proposed in the decoding part for increasing the output resolution and skip connections are employed in each scale to modify the estimated height values at the object boundaries. Finally, a post-processing approach is proposed to merge the predicted height image patches and generate a seamless continuous height map. The quantitative evaluation of the proposed approaches on the ISPRS datasets indicates relative and root mean square errors of approximately 0.9 m and 3.2 m, respectively.
Simultaneous feature selection and discretization based on mutual information

Recently mutual information based feature selection criteria have gained popularity for their superior performances in different applications of pattern recognition and machine learning areas. However, these methods do not consider the correction while computing mutual information for finite samples. Again, finding appropriate discretization of features is often a necessary step prior to feature selection. However, existing researches rarely discuss both discretization and feature selection simultaneously. To solve these issues, Joint Bias corrected Mutual Information (JBMI) is firstly proposed in this paper for feature selection. Secondly, a framework namely modified discretization and feature selection based on mutual information is proposed that incorporates JBMI based feature selection and dynamic discretization, both of which use a χ2 based searching method. Experimental results on thirty benchmark datasets show that in most of the cases, the proposed methods outperform the state-of-the-art methods.
The effects of augmented reality on students' academic achievement and motivation in a biology course

Abstract This study aimed to investigate the effects of augmented reality (AR) activities on students' academic achievement and motivation in a biology course. For this purpose, a mixed study was conducted, and a pretest and posttest control group model was used. In addition, the opinions of the experimental group students and the teacher about the AR activities were taken, and classroom observations were made during the study. The study group consisted of 40 (22 female and 18 male) ninth‐grade biology course students. The control group followed the biology course programme, whereas the experimental group students conducted AR activities in addition to the course programme using tablets. Consequently, it was found that the motivation of the students in the experimental group increased more than that of the students in the control group. However, no significant difference was found between the academic achievement scores of the groups. The teacher and the students stated that AR activities might be effective in increasing course success and motivation.
Prediction Model for PV Performance With Correlation Analysis of Environmental Variables

With increasing installations of photovoltaic (PV) systems, interest in power forecasting has also increased. Inaccurate forecasts would result in substantial economic losses and system reliability issues. The correlation between weather variables and PV power is critical to ensure the efficient use of energy in PV systems. A key step toward accurate power forecasting is estimating the output from a PV system based on known environmental input data. In this research, all available weather data are used to predict the PV power. Meteorological and power data are then analyzed using a statistical approach to identify the order of significance of the input variables. Then, a predictive model is suggested as a function of irradiance, ambient temperature, wind speed, and relative humidity. The model produces a root mean square error of 4.957% and a mean absolute percentage error of 5.468% during the measurement period and over the entire range of irradiation.
Graph-theoretic Simplification of Quantum Circuits with the ZX-calculus

We present a completely new approach to quantum circuit optimisation, based on the ZX-calculus. We first interpret quantum circuits as ZX-diagrams, which provide a flexible, lower-level language for describing quantum computations graphically. Then, using the rules of the ZX-calculus, we give a simplification strategy for ZX-diagrams based on the two graph transformations of local complementation and pivoting and show that the resulting reduced diagram can be transformed back into a quantum circuit. While little is known about extracting circuits from arbitrary ZX-diagrams, we show that the underlying graph of our simplified ZX-diagram always has a graph-theoretic property called generalised flow, which in turn yields a deterministic circuit extraction procedure. For Clifford circuits, this extraction procedure yields a new normal form that is both asymptotically optimal in size and gives a new, smaller upper bound on gate depth for nearest-neighbour architectures. For Clifford+T and more general circuits, our technique enables us to to `see around' gates that obstruct the Clifford structure and produce smaller circuits than naïve `cut-and-resynthesise' methods.
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features that explain three related tasks - fine-grained action recognition, commentary generation, and estimating the AQA score. A new multitask-AQA dataset, the largest to date, comprising of 1412 diving samples was collected to evaluate our approach (http://rtis.oit.unlv.edu/datasets.html). We show that our MTL approach outperforms STL approach using two different kinds of architectures: C3D-AVG and MSCADC. The C3D-AVG-MTL approach achieves the new state-of-the-art performance with a rank correlation of 90.44%. Detailed experiments were performed to show that MTL offers better generalization than STL, and representations from action recognition models are not sufficient for the AQA task and instead should be learned.
Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network

Owing to a shortage of fossil fuels, environmental pollution and the greenhouse effect, renewable energy generation has become important in a modern smart grid. However, the characteristics of renewable power generation are volatile and uncertain. This work proposes a new day-ahead spatiotemporal forecasting method for solar irradiation to ensure the efficient operation of power systems. First, the frequency features of a solar irradiation time-series are extracted by discrete Fourier transform (DFT). Principal component analysis (PCA) is then used to identify the crucial frequency features, which are input to an Elman-based neural network to carry out subsequent 24-hour (day ahead) solar irradiation forecasting. Historical weather data from five weather stations that are near the target location are used. Comparative studies of traditional Autoregressive Integrated Moving Average Model (ARIMA), PCA-Back-Propagation (BP)-based neural network, the persistence method, DFT-PCA-BP and the proposed DFT-PCA-Elman method reveal that the proposed method is the most accurate in day-ahead forecasting using spatiotemporal data.
Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement

In this work, the authors develop a working software-based approach named ‘linearly quantile separated histogram equalisation-grey relational analysis’ for mammogram image (MI). This approach improves overall contrast (local and global) of given MI and segments breast-region with a specific end goal to acquire better visual elucidation, examination, and grouping of mammogram masses to help radiologists in settling on more precise choices. The fundamental commitment of this work is to demonstrate that results of good quality of breast-region segmentation can be accomplished from basic breast-region segmentation if the input image has good contrast and a better interpretation of hidden details. They have evaluated the proposed strategy for MIAS-MIs. Experimental results have shown that the proposed approach works better than state-of-the-art.
Managing Messes in Computational Notebooks

Data analysts use computational notebooks to write code for analyzing and visualizing data. Notebooks help analysts iteratively write analysis code by letting them interleave code with output, and selectively execute cells. However, as analysis progresses, analysts leave behind old code and outputs, and overwrite important code, producing cluttered and inconsistent notebooks. This paper introduces code gathering tools, extensions to computational notebooks that help analysts find, clean, recover, and compare versions of code in cluttered, inconsistent notebooks. The tools archive all versions of code outputs, allowing analysts to review these versions and recover the subsets of code that produced them. These subsets can serve as succinct summaries of analysis activity or starting points for new analyses. In a qualitative usability study, 12 professional analysts found the tools useful for cleaning notebooks and writing analysis code, and discovered new ways to use them, like generating personal documentation and lightweight versioning.
Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems

Machine learning-based channel estimation and equalization methods may improve the robustness and bit error rate (BER) performance of communication systems. However, the implementation of these methods has been blocked by some limitations, mainly including channel model-based offline training and high-computational complexity for training deep neural network (DNN). To overcome those limitations, we propose an online fully complex extreme learning machine (C-ELM)-based channel estimation and equalization scheme with a single hidden layer feedforward network (SLFN) for orthogonal frequency-division multiplexing (OFDM) systems against fading channels and the nonlinear distortion resulting from an high-power amplifier (HPA). Computer simulations show that the proposed scheme can acquire the information of channels accurately and has the ability to resist nonlinear distortion and fading without pre-training and feedback link between receiver and transmitter. Furthermore, the robustness of the proposed scheme is well investigated by extensive simulations in various fading channels, and its excellent generalization ability is also discussed and compared with the DNN.
Exploring the implications of wearable virtual reality technology for museum visitors' experience: A cluster analysis

Abstract Despite the spreading of virtual reality (VR) in tourism, little is known about the characteristics of different segments of tourists experiencing cultural tourism attractions through VR applications. This paper proposes to profile museum visitors based on their perceptions and attitudes towards VR applications and investigates whether they are significantly different in terms of socio‐demographic characteristics and emotional responses toward s the VR‐mediated experience. With the use of exploratory factor‐cluster analysis of 287 visitors of a cultural attraction, three clusters were identified with significant differences in terms of attitudes and perceptions towards the VR‐mediated experience and emotional feelings. Implications for research and practice are discussed.
Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks

This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with existing geostatistics-based modeling methods, our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks (GANs). GANs couple a generator with a discriminator, and each uses a deep convolutional neural network. The networks are trained in an adversarial manner until the generator can create “fake” images that the discriminator cannot distinguish from “real” images. We extend the original GAN approach to 3D geological modeling at the reservoir scale. The GANs are trained using a library of 3D facies models. Once the GANs have been trained, they can generate a variety of geologically realistic facies models constrained by well data interpretations. This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends. The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods, which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.
Strategically-motivated advanced persistent threat: Definition, process, tactics and a disinformation model of counterattack

Advanced persistent threat (APT) is widely acknowledged to be the most sophisticated and potent class of security threat. APT refers to knowledgeable human attackers that are organized, highly sophisticated and motivated to achieve their objectives against a targeted organization(s) over a prolonged period. Strategically-motivated APTs or S-APTs are distinct in that they draw their objectives from the broader strategic agenda of third parties such as criminal syndicates, nation-states, and rival corporations. In this paper we review the use of the term “advanced persistent threat,” and present a formal definition. We then draw on military science, the science of organized conflict, for a theoretical basis to develop a rigorous and holistic model of the stages of an APT operation which we subsequently use to explain how S-APTs execute their strategically motivated operations using tactics, techniques and procedures. Finally, we present a general disinformation model, derived from situation awareness theory, and explain how disinformation can be used to attack the situation awareness and decision making of not only S-APT operators, but also the entities that back them.