Reviewerly/exHarmony huggingface dataset:
Application of Structural Deformation Monitoring Based on Close-Range Photogrammetry Technology

The problem of structural deformation monitoring has always been a research hotspot in engineering architecture. Research on structural deformation monitoring is the main way to solve the risk of engineering construction. Based on using close-range photogrammetry technology to monitor structural deformation, combining with image recognition technology to collect and process image data, and using the conditions of direct linear transformation and collinear equation to calculate based on the extracted coordinates of the measuring point, finally, according to the coordinates of the measuring point and the deflection value of the bridge structure, the study is carried out. Experiment: first, five sets of data are used to calibrate the close-range photogrammetry equipment of measurement point accuracy are analyzed, then the bridge piers and the measurement point coordinates, the lasso, under different load on the accuracy of close-range photogrammetry technology is analyzed, according to the relevant data of the error of the close-range photogrammetry technology are analyzed, and through the calculation of different measurement methods to analyze the direct linear transformation manifested in the article. The final results show that the X-axis coordinate range of the measured points in the auxiliary spatial coordinate system is 25∼40, Y-axis coordinate range is 8∼36, and Z-axis coordinate range is 10∼35. The coordinates of measuring points of bridge cables are distributed in the fourth quadrant of the spatial coordinate system. The accuracy values of all coordinates of the measuring points under the close-range photogrammetry equipment are less than 0.1 mm, indicating that the close-range photogrammetry equipment has a good effect on the calibration of measuring points, and the error generated when the load action of each measuring point of the bridge cable is 10 N is greater than that generated when the load action is 20 N. In different calculation and measurement methods, the results obtained by total station measurement and calculation method are reduced by about 0.04∼0.07 m compared with the actual value; the results obtained by direct linear transformation method are increased by about 0.02∼0.04 m compared with the actual value; and the results obtained by other measurement and calculation methods are increased by about 0.04∼0.06 m compared with the actual value.
WDRN: A Wavelet Decomposed RelightNet for Image Relighting

The task of recalibrating the illumination settings in an image to a target configuration is known as relighting. Relighting techniques have potential applications in digital photography, gaming industry and in augmented reality. In this paper, we address the one-to-one relighting problem where an image at a target illumination settings is predicted given an input image with specific illumination conditions. To this end, we propose a wavelet decomposed RelightNet called WDRN which is a novel encoder-decoder network employing wavelet based decomposition followed by convolution layers under a muti-resolution framework. We also propose a novel loss function called gray loss that ensures efficient learning of gradient in illumination along different directions of the ground truth image giving rise to visually superior relit images. The proposed solution won the first position in the relighting challenge event in advances in image manipulation (AIM) 2020 workshop which proves its effectiveness measured in terms of a Mean Perceptual Score which in turn is measured using SSIM and a Learned Perceptual Image Patch Similarity score.
Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

Background : Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method : Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results : The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion : Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.
Leadership Effects on Employee Perception about CSR in Kurdistan Region of Iraq

Corporate social responsibility has been an emerging issue all around the world after 90's.On the other hand, effect of leadership on feeling and behaving responsibly toward society is incontrovertible.Thus, the question asks which type of leadership would be more effective on the corporate social responsibility practices toward employees of a firm.Hence this research aims to investigate the leadership style influence on the level of CSR practices toward employees.We have collected data from 197 employees of various private corporations in Kurdistan Region of Iraq.Further, we have proposed structural equations modeling to understand the level of impact.The results have shown us that ethical leadership plays much more significant role in CSR practices toward employees than transformational leadership does.
Cross-lingual Visual Pre-training for Multimodal Machine Translation

Pre-trained language models have been shown to improve performance in many natural language tasks substantially.Although the early focus of such models was single language pre-training, recent advances have resulted in cross-lingual and visual pre-training methods.In this paper, we combine these two approaches to learn visually-grounded crosslingual representations.Specifically, we extend the translation language modelling (Lample and Conneau, 2019) with masked region classification and perform pre-training with three-way parallel vision & language corpora.We show that when fine-tuned for multimodal machine translation, these models obtain stateof-the-art performance.We also provide qualitative insights into the usefulness of the learned grounded representations.
The Protection of User Preference Privacy in Personalized Information Retrieval: Challenges and Overviews

Abstract This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users’ privacy protection, i.e., it is required to comprehensively improve the security of users’ preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.
Digital Object Identifier (DOI) and DOI Services: An Overview

Abstract In the establishing anniversary of the two biggest Digital Object Identifier (DOI) registration agencies all over the world, Crossref and DataCite, the paper intends to provide an overview of the development and approaches and of DOI and DOI services, from which scholarly communication has benefited greatly. At first, the author explores the initiation of DOI and differences of DOI from other persistent identifiers. After that, DOIs for different kinds of objects and DOIs’ value in enhancing scholarly communication is discussed; then, in the second part, DOI services at different levels in a pyramid and those particularly in Germany are described. The active involvement of the library world are also introduced here; finally, the current situation and prospects as well as some issues dealing with DOIs and DOI services are investigated in the last part of the paper.
VRFromX: From Scanned Reality to Interactive Virtual Experience with Human-in-the-Loop

There is an increasing trend of Virtual-Reality (VR) applications found in education, entertainment, and industry. Many of them utilize real world tools, environments, and interactions as bases for creation. However, creating such applications is tedious, fragmented, and involves expertise in authoring VR using programming and 3D-modelling softwares. This hinders VR adoption by decoupling subject matter experts from the actual process of authoring while increasing cost and time. We present VRFromX, an in-situ Do-It-Yourself (DIY) platform for content creation in VR that allows users to create interactive virtual experiences. Using our system, users can select region(s) of interest (ROI) in scanned point cloud or sketch in mid-air using a brush tool to retrieve virtual models and then attach behavioral properties to them. We ran an exploratory study to evaluate usability of VRFromX and the results demonstrate feasibility of the framework as an authoring tool. Finally, we implemented three possible use-cases to showcase potential applications.
An efficient post-processing adaptive filtering technique to rectifying the flickering effects

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7–1.9db) while (35.83–47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.
Immutability of Distributed Hash Model on Blockchain Node Storage

The blockchain system uses hash functions. Hash is used in the blockchain to mark each block of data. Hash function algorithms map a string that is usually hexadecimal of any size to a sequence of fixed-size bits. This journal will discuss the distributed hash model for immutable blockchain node storageThe methodology used in the preparation of this research is mind mapping and literature review, namely the collection of scientific journals, articles and ebooks. Storage of nodes on the blockchain using a distributed hash model. The distributed hash model only stores a portion of the block data at each node, and the block data is taken as a resource. A hash connects each block with the previous on the blockchain, so the entire blockchain transaction cannot be changed or deleted. For further research will expand the research topic regarding storage immutability on the blockchain so that it becomes more complete and detailed.
Upper Bounds on Device-Independent Quantum Key Distribution Rates and a Revised Peres Conjecture

Device-independent quantum key distribution (DIQKD) is one of the most challenging tasks in quantum cryptography. The protocols and their security are based on the existence of Bell inequalities and the ability to violate them by measuring entangled states. We study the entanglement needed for DIQKD protocols in two different ways. Our first contribution is the derivation of upper bounds on the key rates of CHSH-based DIQKD protocols in terms of the violation of the inequality; this sets an upper limit on the possible DI key extraction rate from states with a given violation. Our upper bound improves on the previously known bound of Kaur et al. Our second contribution is the initiation of the study of the role of bound entangled states in DIQKD. We present a revised Peres conjecture stating that such states cannot be used as a resource for DIQKD. We give a first piece of evidence for the conjecture by showing that the bound entangled state found by Vertesi and Brunner, even though it can certify DI randomness, cannot be used to produce a key using protocols analogous to the well-studied CHSH-based DIQKD protocol.
Single-shot digital multiplexed holography for the measurement of deep shapes

This work develops a single-shot holographic profilometer that enables shape characterization of discontinuous deep surfaces. This is achieved by combining hologram frequency multiplexing and an illumination technique of complex amplitude in multi-incidence angle profilometer. Object illumination is carried out from seven directions simultaneously, where the radial angular coordinates of illumination plane waves obey the geometric series. It is shown that: (i) the illumination pattern provides the required frequency separation of all object wavefronts in transverse frequency space, which is necessary for hologram demultiplexing, and (ii) numerical generation of longitudinal scanning function (LSF) is possible, which has large measurement range, high axial resolution, and small side lobes. Low side lobes of LSF and the developed multiplexed field dependent aberration compensation method are essential to minimize the negative influence of speckle noise of single-shot capture on the measurement result. The utility of the proposed method is demonstrated with experimental measurement of heights of two step-like objects.
CMPCC: Corridor-Based Model Predictive Contouring Control for Aggressive Drone Flight

Among the criteria of designing autonomous quadrotors, generating optimized trajectories and tracking the flight paths precisely are two critical components in the action aspect.
A universal protocol for bidirectional controlled teleportation with network coding

We investigate bidirectional teleportation that works in a fair and efficient manner. Two explicit protocols are proposed to realize bidirectional teleportation with a controller. One is a symmetric protocol for two-qubit states. The other is an asymmetric protocol for single- and two-qubit states. We then devise a universal protocol for arbitrary n1- and n2-qubit states via a (2n1 + 2n2 + 1)-qubit entangled state, where n1 ≤ n2. The receiver only needs to perform the single-qubit recovery operation, which is derived by a general expression. Moreover, a (2n1 + 1)-bit classical communication cost can be saved within the controller's broadcast channel by the use of network coding technology.
Business English Translation Model Based on BP Neural Network Optimized by Genetic Algorithm

In recent years, most of the communication places are using business English manual simultaneous interpretation or electronic equipment translation. In the context of diverse cultures, the way English is used and its grammar vary from country to country. In the face of this situation, how to optimize business English translation technology and improve the accuracy of business communication content is one of the research contents of scholars all over the world. This paper first introduces the purpose of business English translation and the gap between business English translation and general English translation. Secondly, a genetic algorithm is used to optimize the structure of the BP neural network, and the combination of the two improves the ability of translation search. This paper compares the influence of the traditional BP algorithm and the BP algorithm optimized by genetic algorithm on the construction of a business English translation model. The results show that BP neural network optimized by the genetic algorithm can improve the speed of business English text translation, reduce the impact of semantic errors on the accuracy of the translation model, and improve the efficiency of translation.
Weakly-Supervised Domain Adaptation With Adversarial Entropy for Building Segmentation in Cross-Domain Aerial Imagery

Building segmentation is a classical and challenging task in high-resolution remote sensing imagery.This approach has achieved remarkable performance based on a fully convolutional network (FCN) with adequate pixel-wise annotations.However, due to differences in sensor technology as well as appearance in different regions, datasets gathered from these various sources are quite distinct, and dense annotations for a particular area are not always available.Thus, directly applying a segmentation model trained on one dataset (source domain) to another unseen dataset (target domain) usually results in a drop in performance, called the domain gap.In this paper, we propose a weakly-supervised domain adaptation method using adversarial entropy for building segmentation to address this problem.First, we use an adversarial entropy strategy to decrease the entropy and improve the prediction certainty for target images, causing the distributions between the source and target domains to become closer to each other.Second, we propose a simple and effective self-training strategy for the target domain that produces high-confidence predictions using pseudo-labels.We use a series of thresholds to generate the pseudo-labels without introducing extra parameters.This strategy effectively enhances the discriminability of the target domain and further minimizes the distribution discrepancy between the two domains.Experiments on cross-domain aerial datasets have demonstrated the effectiveness and superiority of our proposed method when compared to other state-of-the-art methods.
Variational preparation of finite-temperature states on a quantum computer

Abstract The preparation of thermal equilibrium states is important for the simulation of condensed matter and cosmology systems using a quantum computer. We present a method to prepare such mixed states with unitary operators and demonstrate this technique experimentally using a gate-based quantum processor. Our method targets the generation of thermofield double states using a hybrid quantum-classical variational approach motivated by quantum-approximate optimization algorithms, without prior calculation of optimal variational parameters by numerical simulation. The fidelity of generated states to the thermal-equilibrium state smoothly varies from 99 to 75% between infinite and near-zero simulated temperature, in quantitative agreement with numerical simulations of the noisy quantum processor with error parameters drawn from experiment.
Movement Modulation in Virtual Rehabilitation: Its Influence on Agency and Motor Performance

Virtual reality has attracted lots of attention recently as a promising tool integrated with motor rehabilitation. Movement modulation in virtual rehabilitation can purposefully influence motor learning by visually changing the virtual representation. Moreover, the effect of movement modulation on the sense of agency and motor performance deserves further in-depth investigations. This paper proposes the design of a virtual rehabilitation system for reaching movements with modulation strategy. The experiment study explored the effect of movement modulation in five modes on agency and motor performance by analyzing data from both subjective responses and objective measures. The results reveal that the sense of agency is significantly influenced by the implemented modulation mode, and there is also a noticeable impact of movement modulation on motor performance in movement trajectory and velocity. The virtual rehabilitation system and the experiment study demonstrated can help support the development of more effective applications for virtual rehabilitation with movement modulation settings.
Does the EU COVID Digital Certificate Strike a Reasonable Balance between Mobility Needs and Public Health?

The need to fight a highly aggressive virus such as SARS-CoV-2 has compelled governments to put in place measures, which, in the name of health protection, have constrained many freedoms we all enjoy, including freedom of movement, both nationally and within the European Union. In order to encourage and facilitate the return to free movement, the European Parliament has launched a "COVID-19 digital certificate". A spirited debate centered around the use of this certificate is still ongoing among scholars, many of whom have pointed out the uncertainties relative to COVID-19 immunity, privacy issues and the risk of discriminatory effects. The authors, while highlighting some critical aspects, argue that the COVID digital certificate in its current approved version can effectively help prevent the spread of the infection and promote free movement, while upholding the right to health as much as possible. However, they also stress the need for a thorough information campaign to illustrate the advantages and limitations of this document in order to avoid creating a false sense of security in the public opinion, who may wrongly assume that the emergency has been overcome for good.
Non-Markovian memory strength bounds quantum process recoverability

Generic non-Markovian quantum processes have infinitely long memory, implying an exact description that grows exponentially in complexity with observation time. Here, we present a finite memory ansatz that approximates (or recovers) the true process with errors bounded by the strength of the non-Markovian memory. The introduced memory strength is an operational quantity and depends on the way the process is probed. Remarkably, the recovery error is bounded by the smallest memory strength over all possible probing methods. This allows for an unambiguous and efficient description of non-Markovian phenomena, enabling compression and recovery techniques pivotal to near-term technologies. We highlight the implications of our results by analyzing an exactly solvable model to show that memory truncation is possible even in a highly non-Markovian regime.