Reviewerly/exHarmony huggingface dataset:
Factors Influencing the Adoption of Blockchain in the Construction Industry: A Hybrid Approach Using PLS-SEM and fsQCA

Blockchain is considered a breakthrough technology in the construction industry, with the potential to improve the trust environment and workflow of construction stakeholders. Although recent research offers hints regarding possible contributing elements to blockchain adoption in the construction industry, no specific study has addressed this topic. This knowledge gap hinders the adoption and promotion of blockchain in construction organizations. This study aimed to identify the determinants of blockchain adoption in the construction industry and verify the influence of the combination of various factors on adoption intention. Based on the technology–organization–environment framework, a conceptual model of blockchain adoption in the construction industry was constructed. Data were collected through the distribution of questionnaires, and 244 professionals in the construction field participated in this study. To evaluate the model hypotheses, we used a two-stage partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) combination. The PLS-SEM revealed that factors such as compatibility, top management support, relative advantage, regulatory support, cost, competitive pressure, organizational readiness, and firm size significantly influence blockchain adoption. The fsQCA indicated that six causal conditions achieve high adoption intention. This is one of the first empirical studies on blockchain adoption in the construction industry, which can aid organizations, policymakers, and project participants in making informed decisions regarding the adoption of blockchain.
Usage-Centric Benchmarking of RDF Triple Stores

A central component in many applications is the underlying data management layer. In Data-Web applications, the central component of this layer is the triple store. It is thus evident that finding the most adequate store for the application to develop is of crucial importance for individual projects as well as for data integration on the Data Web in general. In this paper, we propose a generic benchmark creation procedure for SPARQL, which we apply to the DBpedia knowledge base. In contrast to previous approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. In addition, our approach does not only take the query string but also the features of the queries into consideration during the benchmark generation process. Our generic procedure for benchmark creation is based on query-log mining, SPARQL feature analysis and clustering. After presenting the method underlying our benchmark generation algorithm, we use the generated benchmark to compare the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM.
Empirical Bayesian Light-Field Stereo Matching by Robust Pseudo Random Field Modeling

Light-field stereo matching problems are commonly modeled by Markov Random Fields (MRFs) for statistical inference of depth maps. Nevertheless, most previous approaches did not adapt to image statistics but instead adopted fixed model parameters. They explored explicit vision cues, such as depth consistency and occlusion, to provide local adaptability and enhance depth quality. However, such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense view sampling are not suitable for sparse one. In this paper, we get back to MRF fundamentals and develop an empirical Bayesian framework-Robust Pseudo Random Field-to explore intrinsic statistical cues for broad applicability. Based on pseudo-likelihoods with hidden soft-decision priors, we apply soft expectation-maximization (EM) for good model fitting and perform hard EM for robust depth estimation. We introduce novel pixel difference models to enable such adaptability and robustness simultaneously. Accordingly, we devise a stereo matching algorithm to employ this framework on dense, sparse, and even denoised light fields. It can be applied to both true-color and grey-scale pixels. Experimental results show that it estimates scene-dependent parameters robustly and converges quickly. In terms of depth accuracy and computation speed, it also outperforms state-of-the-art algorithms constantly.
Generalized quantum Rabi model with both one- and two-photon terms: A concise analytical study

A generalized quantum Rabi Hamiltonian with both one- and two-photon terms has emerged in the circuit quantum electrodynamics system for a decade. The usual parity symmetry is broken naturally in the simultaneous presence of both couplings, which complicates analytical treatments, even in the rotating wave approximations. In this paper, we propose an adiabatic approximation to this generic model by using Bogoliubov operators, and obtain a very concise analytical solution for both eigenvalues and eigenstates. Although the adiabatic approximation is only exact in the vanishing limit of the qubit frequency, the results for some physical observables nevertheless agree well with the numerical ones in a wide parameter regime. In the rotating-wave approximations, we also derive an analytical eigensolution. Two dominant Rabi frequencies are found in the Rabi oscillations of this generalized model. We also apply the present analytical theory to the vacuum Rabi splitting. It is found that some new phenomena emerge just because of the presence of the additional two-photon coupling term.
GPdoemd: A Python package for design of experiments for model discrimination

Model discrimination identifies a mathematical model that usefully explains and predicts a given system’s behaviour. Researchers will often have several models, i.e. hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. discard inaccurate models. Given rival mathematical models and an initial experimental data set, optimal design of experiments suggests maximally informative experimental observations that maximise a design criterion weighted by prediction uncertainty. The model uncertainty requires gradients, which may not be readily available for black-box models. This paper (i) proposes a new design criterion using the Jensen-Rényi divergence, and (ii) develops a novel method replacing black-box models with Gaussian process surrogates. Using the surrogates, we marginalise out the model parameters with approximate inference. Results show these contributions working well for both classical and new test instances. We also (iii) introduce and discuss GPdoemd, the open-source implementation of the Gaussian process surrogate method.
Controlling error orientation to improve quantum algorithm success rates

The success probability of a quantum algorithm constructed from noisy quantum gates cannot be accurately predicted from single parameter metrics that compare noisy and ideal gates. We illustrate this concept by examining a system with coherent errors and comparing algorithm success rates for different choices of two-qubit gates that are constructed from composite pulse sequences, where the residual gate errors are related by a unitary transformation. As a result, all of the sequences have the same error relative to the ideal gate under any distance measure that is invariant under unitary transformations. However, the circuit success can vary dramatically by choosing error orientations that do not affect the final outcome and error orientations that cancel between conjugate controlled-nots, as demonstrated here with Clifford circuits, compiled Toffoli gates, and quantum simulation algorithms. The results point to the utility of both minimizing the error and optimizing the error direction and also to the advantages of using multiple control sequences for the same gate type within a single algorithm.
Deep Visual Saliency on Stereoscopic Images

Visual saliency on stereoscopic 3D (S3D) images has been shown to be heavily influenced by image quality. Hence, this dependency is an important factor in image quality prediction, image restoration and discomfort reduction, but it is still very difficult to predict such a nonlinear relation in images. In addition, most algorithms specialized in detecting visual saliency on pristine images may unsurprisingly fail when facing distorted images. In this paper, we investigate a deep learning scheme named Deep Visual Saliency (DeepVS) to achieve a more accurate and reliable saliency predictor even in the presence of distortions. Since visual saliency is influenced by low-level features (contrast, luminance, and depth information) from a psychophysical point of view, we propose seven low-level features derived from S3D image pairs and utilize them in the context of deep learning to detect visual attention adaptively to human perception. During analysis, it turns out that the low-level features play a role to extract distortion and saliency information. To construct saliency predictors, we weight and model the human visual saliency through two different network architectures, a regression and a fully convolutional neural networks. Our results from thorough experiments confirm that the predicted saliency maps are up to 70% correlated with human gaze patterns, which emphasize the need for the hand-crafted features as input to deep neural networks in S3D saliency detection.
Human health monitoring using wearable sensor

Purpose The purpose of this paper is to design a human health monitoring system (HHMS) which helps in improving diagnostics at an earlier stage and monitoring after recoup. Design/methodology/approach The methodology involves a combination of three subsystems which monitors the human parameters such as temperature, heart rate, SpO2, fall and location of the person. Various sensors are used to extract the human parameters, and the data are analysed in a computer subsystem, through Global System for Mobile Communications (GSM) and Internet of Things (IoT) subsystem; the parameters measured are communicated to the caregiver and doctor. Findings Results have successfully demonstrated monitoring human temperature human temperature, heart rate, SpO2 and fall and location continuously using the HHMS prototype. Reliability of the technique used for monitoring these parameters is assessed by Proteus Professional 8 and LabVIEW simulators. Practical implications The HHMS enables long-term monitoring without any sort of interference from regular activities and allows daily health monitoring, elderly monitoring and so on. Originality/value First, the proposed HHMS simultaneously monitors five human parameters. Second, unlike most monitoring systems which uses older communication module, the proposed system is made smart using IoT. The proposed method has been made into a prototype system as detailed in this paper. The proposed HHMS can achieve high detection accuracy. Therefore, this system can be reliably deployed into a consumer product for use as monitoring device with high accuracy.
Towards Labour Market Intelligence through Topic Modelling

Nowadays, the number of people and companies using the Web to search for and advertise job opportunities is growing apace, making data related to the Web labor market a rich source of information for understanding labor market dynamics and trends. In this paper, the emerging term labor market intelligence (LMI) refers to the definition of AI algorithms and frameworks that derive useful knowledge for labor market-related activities, by putting AI into the labor market. At the same time, another branch of AI is developing known as Explainable AI (XAI), whose goal is to obtain interpretable models from current (and future) AI algorithms, given that most of them actually act like black boxes, providing no interpretable explanations of their behavior, as in the case of machine learning. In this paper we connect these two approaches, using a graph model obtained through an NLP-based (Natural Language Processing) methodology for classifying job vacancies. We compare the results obtained with those from a European Project in LMI that employs machine learning for the classification task, to show that our approach is effective and promising.
VR Exploration Assistance through Automatic Occlusion Removal

Virtual Reality (VR) applications allow a user to explore a scene intuitively through a tracked head-mounted display (HMD). However, in complex scenes, occlusions make scene exploration inefficient, as the user has to navigate around occluders to gain line of sight to potential regions of interest. When a scene region proves to be of no interest, the user has to retrace their path, and such a sequential scene exploration implies significant amounts of wasted navigation. Furthermore, as the virtual world is typically much larger than the tracked physical space hosting the VR application, the intuitive one-to-one mapping between the virtual and real space has to be temporarily suspended for the user to teleport or redirect in order to conform to the physical space constraints. In this paper we introduce a method for improving VR exploration efficiency by automatically constructing a multiperspective visualization that removes occlusions. For each frame, the scene is first rendered conventionally, the z-buffer is analyzed to detect horizontal and vertical depth discontinuities, the discontinuities are used to define disocclusion portals which are 3D scene rectangles for routing rays around occluders, and the disocclusion portals are used to render a multiperpsective image that alleviates occlusions. The user controls the multiperspective disocclusion effect, deploying and retracting it with small head translations. We have quantified the VR exploration efficiency brought by our occlusion removal method in a study where participants searched for a stationary target, and chased a dynamic target. Our method showed an advantage over conventional VR exploration in terms of reducing the navigation distance, the view direction rotation, the number of redirections, and the task completion time. These advantages did not come at the cost of a reduction in depth perception or situational awareness, or of an increase in simulator sickness.
BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.
Cuckoo search-based method for trajectory planning of quadrotor in an urban environment

Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.
Impact of Privacy Issues on Smart City Services in a Model Smart City

With the recent technological development, there is prevalent trend for smart infrastructure deployment with intention to provide smart services for inhabitants.City governments of current era are under huge pressure to facilitate their residents by offering state of the art services equipped with modern technology gadgets.To achieve this goal they have been forced for massive investment in IT infrastructure deployment, eventually they are collecting huge amount of data from users with intention of providing them better or improved services.These services are very exciting but on the other side they also pose a big threat to the privacy of individuals.This paper designed and simulated a smart city model.This model is connected with some mandatory communication devices which also produce data for different sensors, Based on simulation results and possible threats for alteration of this data, it suggests solution for privacy issues which are to be considered at top priority to ensure secrecy and privacy of smart city residents.
Can GCNs Go as Deep as CNNs

Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to be reliably trained. Despite their merits, CNNs fail to properly address problems with non-Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent non-Euclidean data, borrow concepts from CNNs, and apply them in training. GCNs show promising results, but they are usually limited to very shallow models due to the vanishing gradient problem. As a result, most state-of-the-art GCN models are no deeper than 3 or 4 layers. In this work, we present new ways to successfully train very deep GCNs. We do this by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures. Extensive experiments show the positive effect of these deep GCN frameworks. Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3.7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation. We believe that the community can greatly benefit from this work, as it opens up many opportunities for advancing GCN-based research.
Neural Text Generation from Rich Semantic Representations

Valerie Hajdik, Jan Buys, Michael Wayne Goodman, Emily M. Bender. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Faster-than-light signaling in the rotating-wave approximation

We present new results on the causality violations introduced by the rotating wave approximation commonly used in quantum optics and high-energy physics. We find that the causality violations and faster-than-light signaling induced by the approximation have ``fat tails,'' i.e., they are polynomially decaying with the distance from the light-cone of the emitter. Furthermore, we also show that the fundamental problems with the incompatibility between the approximation and relativity are not cured even in the long interaction time regime (where the approximation is often taken). This renders the approximation unsuitable for any regime where we are concerned about relativistic causality and information transmission via the electromagnetic field.
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment

Arijit Ghosh Chowdhury, Ramit Sawhney, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. 2019.
A Hybrid Information Reconciliation Method for Physical Layer Key Generation

Physical layer key generation (PKG) has become a research focus as it solves the key distribution problem, which is difficult in traditional cryptographic mechanisms. Information reconciliation is a critical process in PKG to obtain symmetric keys. Various reconciliation schemes have been proposed, including the error detection protocol-based approach (EDPA) and error correction code-based approach (ECCA). Both EDPA and ECCA have advantages and drawbacks, regarding information leakage, interaction delay, and computation complexity. In this paper, we choose the BBBSS protocol from EDPA and BCH code from ECCA as a case study, analyzing their comprehensive efficiency performance versus pass number and bit disagreement ratio (BDR), respectively. Next, we integrate the strength of the two to design a new hybrid information reconciliation protocol (HIRP). The design of HIRP consists of three main phases, i.e., training, table lookup, and testing. To comprehensively evaluate the reconciliation schemes, we propose a novel efficiency metric to achieve a balance of corrected bits, information leakage, time delay, and computation time, which represents the effectively corrected bits per unit time. The simulation results show that our proposed method outperforms other reconciliation schemes to improve the comprehensive reconciliation efficiency. The average improvement in efficiency is 2.48 and 22.36 times over the BBBSS and BCH code, respectively, when the range of the BDR is from 0.5% to 11.5%. Compared to the BBBSS protocol and the BCH code, HIRP lies at a mid-level in terms of information leakage and computation time cost. Besides, with the lowest time delay cost, HIRP reaches the highest reconciliation efficiency.
Direct structural analysis of domains defined by point clouds

This contribution presents a method that aims at the numerical analysis of solids represented by oriented point clouds. The proposed approach is based on the Finite Cell Method, a high-order immersed boundary technique that computes on a regular background grid of finite elements and requires only inside-outside information from the geometric model. It is shown that oriented point clouds provide sufficient information for these point-membership classifications. Further, we address a tessellation-free formulation of contour integrals that allows to apply Neumann boundary conditions on point clouds without having to recover the underlying surface. Two-dimensional linear elastic benchmark examples demonstrate that the method is able to provide the same accuracy as those computed with conventional, continuous surface descriptions, because the associated error can be controlled by the density of the cloud. Three-dimensional examples computed on point clouds of historical structures show how the method can be employed to establish seamless connections between digital shape measurement techniques and numerical analyses.