Reviewerly/exHarmony huggingface dataset:
Assessment of the cybersecurity vulnerability of construction networks

Purpose The aim of this study is o examine the advantages and disadvantages of different existing scoring systems in the cybersecurity domain and their applicability to the AEC industry and to systematically apply a scoring system to determine scores for some of the most significant construction participants. Design/methodology/approach This study proposes a methodology that uses the Common Vulnerability Scoring System (CVSS) to calculate scores and the likelihood of occurrence based on communication frequencies to ultimately determine risk categories for different paths in a construction network. As a proof of concept, the proposed methodology is implemented in a construction network from a real project found in the literature. Findings Results show that the proposed methodology could provide valuable information to assist project participants to assess the overall cybersecurity vulnerability of construction and assist during the vulnerability-management processes. For example, a project owner can use this information to get a better understanding of what to do to limit its vulnerability, which will lead to the overall improvement of the security of the construction network. Research limitations/implications It has to be noted that the scoring systems, the scores and categories adopted in the study need not necessarily be an exact representation of all the construction participants or networks. Therefore, caution should be exercised to avoid generalizing the results of this study. Practical implications The proposed methodology can provide valuable information and assist project participants to assess the overall cyber-vulnerability of construction projects and support the vulnerability-management processes. For example, a project owner can use this approach to get a better understanding of what to do to limit its cyber-vulnerability exposure, which will ultimately lead to the overall improvement of the construction network's security. This study will also help raise more awareness about the cybersecurity implications of the digitalization and automation of the AEC industry among practitioners and construction researchers. Social implications Given the amount of digitized services and tools used in the AEC industry, cybersecurity is increasingly becoming critical for society in general. In some cases, (e.g. critical infrastructure) incidents could have significant economic and societal or public safety implications. Therefore, proper consideration and action from the AEC research community and industry are needed. Originality/value To the authors' knowledge, this is the first attempt to measure and assess the cybersecurity of individual participants and the construction network as a whole by using the Common Vulnerability Scoring System.
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation

Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution. This work investigates a novel multi-reference based super-resolution problem by proposing a Content Independent Multi-Reference Super-Resolution (CIMR-SR) model, which is able to adaptively match the visual pattern between references and target image in the low resolution and enhance the feature representation of the target image in the higher resolution. CIMR-SR significantly improves the flexibility of the recently proposed reference-based super-resolution (RefSR), which needs to select the specific high-resolution reference (e.g., content similarity, camera view and relative scale) for each target image. In practice, a universal reference pool (RP) is built up for recovering all LR targets by searching the local matched patterns. By exploiting feature-based patch searching and attentive reference feature aggregation, the proposed CIMR-SR generates realistic images with much better perceptual quality and richer fine-details. Extensive experiments demonstrate the proposed CIMR-SR outperforms state-of-the-art methods in both qualitative and quantitative reconstructions.
Object Detection for Autonomous Vehicle with LiDAR Using Deep Learning

This paper presents an object detection for Autonomous Vehicle (AV) using deep learning algorithm. Currently, most AVs use the camera for visualization to detect surrounding objects. However, the performance of a sensor, such as a camera with visual perception, is diminished in dim light, for instance at night-time due to the less light environment. Thus, the study attempts to employ the Light Detection and Ranging (LiDAR) sensor that uses light in the form of a pulsed laser to calculate ranges and ultimately detect objects. The use of LiDAR with the recent deep learning algorithm, namely You Only Look Once (YOLO) v2, was simulated on the Robot Operating System (ROS) in the Linux environment. The collected data has undergone several filtering processes, which includes noise removal, downsampling, and transformation. The study then applies the model on real-time data from the LiDAR sensor to perform object detection. The results show that YOLOv2 can identify the objects better compared to Single Shot Detection (SSD) algorithm.
Do you have space for dessert? a verified space cost semantics for CakeML programs

Garbage collectors relieve the programmer from manual memory management, but lead to compiler-generated machine code that can behave differently (e.g. out-of-memory errors) from the source code. To ensure that the generated code behaves exactly like the source code, programmers need a way to answer questions of the form: what is a sufficient amount of memory for my program to never reach an out-of-memory error? This paper develops a cost semantics that can answer such questions for CakeML programs. The work described in this paper is the first to be able to answer such questions with proofs in the context of a language that depends on garbage collection. We demonstrate that positive answers can be used to transfer liveness results proved for the source code to liveness guarantees about the generated machine code. Without guarantees about space usage, only safety results can be transferred from source to machine code. Our cost semantics is phrased in terms of an abstract intermediate language of the CakeML compiler, but results proved at that level map directly to the space cost of the compiler-generated machine code. All of the work described in this paper has been developed in the HOL4 theorem prover.
Modality independent adversarial network for generalized zero shot image classification

Zero Shot Learning (ZSL) aims to classify images of unseen target classes by transferring knowledge from source classes through semantic embeddings. The core of ZSL research is to embed both visual representation of object instance and semantic description of object class into a joint latent space and learn cross-modal (visual and semantic) latent representations. However, the learned representations by existing efforts often fail to fully capture the underlying cross-modal semantic consistency, and some of the representations are very similar and less discriminative. To circumvent these issues, in this paper, we propose a novel deep framework, called Modality Independent Adversarial Network (MIANet) for Generalized Zero Shot Learning (GZSL), which is an end-to-end deep architecture with three submodules. First, both visual feature and semantic description are embedded into a latent hyper-spherical space, where two orthogonal constraints are employed to ensure the learned latent representations discriminative. Second, a modality adversarial submodule is employed to make the latent representations independent of modalities to make the shared representations grab more cross-modal high-level semantic information during training. Third, a cross reconstruction submodule is proposed to reconstruct latent representations into the counterparts instead of themselves to make them capture more modality irrelevant information. Comprehensive experiments on five widely used benchmark datasets are conducted on both GZSL and standard ZSL settings, and the results show the effectiveness of our proposed method.
FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

Abstract HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.
Real-time vision-based tracking of a moving terrain target from Light Weight Fixed Wing UAV using gimbal control

In recent years, the use of unmanned aerial vehicles (UAVs) is widespread in urban traffic monitoring. However, the challenges faced while practical implementation from a fixed-wing UAV's involves compensating for the device vibration, real-time video transmission lag, small object sizes tracking while sending a command from Ground Control Station (GCS) to UAV and vice versa. The constantly changing position and orientation of UAV during different flight maneuvers lead to the loss of tracked objects from the field of view. The paper's primary objective is to develop a gimbal control algorithm for real-time object tracking and object location from a small-unmanned fixed-wing aircraft. The proposed algorithm deals with two scenarios, the first scenario offers gimbal pointing at a given coordinate, while second incorporates gimbal pointing such as to align the optical axis with a certain point in the image plane. The solution proposed is to turn the camera head in azimuth and elevation angles towards the object. The alternative suggested includes rotating the camera head in azimuth and elevation angles towards the object, and the suitable angles were calculated based on the camera's distance from the object, Global Positioning System (GPS) and Inertial Measurement Unit (IMU) (camera positioning and orientation) information. The algorithm was first simulated using MATLAB/Simulink using the same parameter of the actual aircraft UAV and further implemented in fixed-wing UAV. The result advocates the applicability of the algorithm and the future work to be done for robust object tracking.
SCNN: A Secure Convolutional Neural Network using Blockchain

Real-time applications like object detection, fire detection, face recognition and cancer detection are solely or partially relying on deep learning algorithms. Any tempering in these models can cause huge damages in many ways, therefore an utter need to secure these deep learning models is critically required. Blockchain technology has gained a wide popularity in tractability and security. In this article, the properties of blockchain are applied on the CNN models to produce secure CNN models. Each layer of a CNN model relates to a block, which contains the hash keys, public and private keys of their neighbors, while there exists a ledger block, which contains the detailed information about each layer of the model. The proposed SCNN model is tested using SVGG19 and SInceptionV3 models on publicly available datasets, which provides satisfactory results.
Layer-Wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs

Conditioning analysis uncovers the landscape of an optimization objective by exploring the spectrum of its curvature matrix. This has been well explored theoretically for linear models. We extend this analysis to deep neural networks (DNNs) in order to investigate their learning dynamics. To this end, we propose layer-wise conditioning analysis, which explores the optimization landscape with respect to each layer independently. Such an analysis is theoretically supported under mild assumptions that approximately hold in practice. Based on our analysis, we show that batch normalization (BN) can stabilize the training, but sometimes result in the false impression of a local minimum, which has detrimental effects on the learning. Besides, we experimentally observe that BN can improve the layer-wise conditioning of the optimization problem. Finally, we find that the last linear layer of a very deep residual network displays ill-conditioned behavior. We solve this problem by only adding one BN layer before the last linear layer, which achieves improved performance over the original and pre-activation residual networks.
Sentiment analysis for news and social media in COVID-19

During the COVID-19 epidemic, the news is overwhelming in people's daily life. So, we aim to extract key information from a large amount of public news. This paper focus on the daily sentiment distribution of news and public opinion on Weibo that refers to the key word COVID-19. First, we refining the key news from all the news in a day to deal with long and large news data. Second, we transformer the headline into a high-dimensional vector. And then, divided them into k categories on the strength of k-means clustering algorithm. Finally, choose the closet news to the mean vector as the key news of the day. Moreover, we conduct sentiment analysis on all key news and Weibo data. By comparing the sentiment trend of news and Weibo, this study provides a new channel to analyze social public opinion.
Cross Coupling of a Solid-State Qubit to an Input Signal due to Multiplexed Dispersive Readout

The integration, scale-up, and multiplexing arrays of superconducting qubits in quantum circuits are the main challenges of superconducting quantum technology. Here we experimentally investigate the solid-state qubit multiplexing readout scheme, containing coplanar quarter-wavelength resonators coupled with a planar Xmon-type qubit, connected to a common coplanar transmission line. We find that the qubit energy spectrum is modified in the presence of an additional exciting signal at the fundamental frequency of the neighboring resonators. We attribute the origin of this effect to the electromagnetic field propagating through the common ground plane, which changes the qubit's characteristics. Our finding may be useful for the development of scalable superconducting quantum integrated circuits with arrays of multiplexed or coupled qubits for applications in superconducting quantum processing and computing.
Observation of second-order interference beyond the coherence time with true thermal photons

It has recently been shown that counter-intuitive Franson-like second-order interference can be observed with a pair of classically correlated pseudo thermal light beams and two separate unbalanced interferometers (UIs): the second-order interference visibility remains fixed at 1/3 even though the path length difference in each UI is increased significantly beyond the coherence length of the pseudo thermal light [ Phys. Rev. Lett. 119 , 223603 ( 2017 ) PRLTAO 0031-9007 10.1103/PhysRevLett.119.223603 ]. However, as the pseudo thermal beam itself originated from a long-coherence laser (and by using a rotating ground disk), there exists the possibility of a classical theoretical model to account for second-order interference beyond the coherence time on the long coherence time of the original laser beam. In this work, we experimentally explore this counter-intuitive phenomenon with a true thermal photon source generated via quantum thermalization, i.e., obtaining a mixed state from a pure two-photon entangled state. This experiment not only demonstrates the unique second-order coherence properties of thermal light clearly but may also open up remote sensing applications based on such effects.
Computing the Bidirectional Scattering of a Microstructure Using Scalar Diffraction Theory and Path Tracing

Abstract Most models for bidirectional surface scattering by arbitrary explicitly defined microgeometry are either based on geometric optics and include multiple scattering but no diffraction effects or based on wave optics and include diffraction but no multiple scattering effects. The few exceptions to this tendency are based on rigorous solution of Maxwell's equations and are computationally intractable for surface microgeometries that are tens or hundreds of microns wide. We set up a measurement equation for combining results from single scattering scalar diffraction theory with multiple scattering geometric optics using Monte Carlo integration. Since we consider an arbitrary surface microgeometry, our method enables us to compute expected bidirectional scattering of the metasurfaces with increasingly smaller details seen more and more often in production. In addition, we can take a measured microstructure as input and, for example, compute the difference in bidirectional scattering between a desired surface and a produced surface. In effect, our model can account for both diffraction colors due to wavelength‐sized features in the microgeometry and brightening due to multiple scattering. We include scalar diffraction for refraction, and we verify that our model is reasonable by comparing with the rigorous solution for a microsurface with half ellipsoids.
Mobile-Assisted Language Learning (MALL): Students’ Perception and Problems towards Mobile Learning in English Language

Abstract Mobile-Assisted Language Learning (MALL) is the latest learning way in the language education where applications or websites are used to facilitate students learning activities. Mobile phone has been studied years by researchers in its connection with education-related activities. This research will focus on its main purposes, they are: 1) students’ perceptions in using mobile phone in English language learning classroom activities; 2). the problem of using mobile phone to support classroom activities, especially in English language learning. The method of the research was quantitative method which used 70 students as research object. The data were collected through a 5-point Likert Scale questionnaire. The research found; first, the students’ majority had positive perception on the usage of MALL to support classroom activities, especially in learning English language. Second, the problem in internet connectivity is the biggest problems that students faced in terms of using MALL in English language classroom. But, It is hoped that MALL will be used as one of the teaching aids that could assist students in learning English as a Foreign Language (EFL) more effectively.
A Unified Formulation and Nonconvex Optimization Method for Mixed-Type Decision-Making of Robotic Systems

Mixed-type decision-making is ubiquitously required in robotic systems and has attracted significant research interests. Examples include, but not limited to, the integrated task and motion planning and optimal control of hybrid systems involving both continuous and discrete dynamic behaviors. For decision-making of robotic systems to improve operational efficiency, safety, and/or mission success rate, they involve both discrete variables representing task allocation or transitions between discrete modes and continuous variables representing trajectories of the planned motion or states governed by differential equations. This paper formulates a class of mixed-type decision-making problems with polynomial objective and constraints as quadratically constrained quadratic programming (QCQP) problems and a nonconvex optimization method based on alternating direction method of multipliers is proposed to solve the QCQP. The proposed optimization method consists of three sequential subproblems, all of which admit closed-form solutions. Moreover, convergence proof of the optimization algorithm is provided. Two representative problems, traveling salesman with obstacle avoidance and rendezvous and docking of a charging station with distinct phase constraints, are described and solved via the proposed method. Numerical simulations as well as experimental verification of both problems are presented and compared with a state-of-art method to validate the effectiveness, efficacy and robustness of the nonconvex optimization method.
LiTag

The development of Internet of Things calls for ubiquitous and low-cost localization and posture estimation. We present LiTag, a visible light based localization and posture estimation solution with COTS cameras. The core of LiTag is based on the design of a chip-less and battery-less optical tag which can show different color patterns from different observation directions. After capturing a photo containing the tag, LiTag can calculate the tag position and posture by combining the color pattern and the geometry relation between the camera image plane and the real world. Unlike existing marker-based visible localization and posture estimation approaches, LiTag can work with a single camera without calibration, which significantly reduces the calibration overhead and deployment costs. We implement LiTag and evaluate its performance extensively. Results show that LiTag can provide the tag position with a median error of 1.6 cm in the 2D plane, a median error of 12 cm in the 3D space, and posture estimation with a median error of 1°. We believe that LiTag has a high potential to provide a low-cost and easy-to-use solution for ubiquitous localization and posture estimation with existing widely deployed cameras.