Reviewerly/exHarmony huggingface dataset:
Steady-state entanglement and coherence of two coupled qubits in equilibrium and nonequilibrium environments

We investigate analytically and numerically the steady-state entanglement and coherence of two coupled qubits each interacting with a local boson or fermion reservoir, based on the Bloch-Redfield master equation beyond the secular approximation. We find that there is non-vanishing steady-state coherence in the nonequilibrium scenario, which grows monotonically with the nonequilibrium condition quantified by the temperature difference or chemical potential difference of the two baths. The steady-state entanglement in general is a non-monotonic function of the nonequilibrium condition as well as the bath parameters in the equilibrium setting. We also find that weak inter-qubit coupling and high base temperature or chemical potential of the baths can strongly suppress the steady-state entanglement and coherence, regardless of the strength of the nonequilibrium condition. On the other hand, the energy detuning of the two qubits, when used in a compensatory way with the nonequilibrium condition, can lead to significant enhancement of the steady-state entanglement in some parameter regimes. In addition, the qubits typically have a stronger steady-state entanglement when coupled to fermion baths exchanging particle with the system than boson baths exchanging energy with the system under similar conditions. We also discussed the possible experimental realization of measuring the steady state entanglement and coherence for coupled qubits systems in nonequilibrium environments. These results offer some general guidelines for optimizing the steady-state entanglement and coherence in the coupled qubit system and may find potential applications in quantum information technology.
QRCI: A new quantum representation model of color digital images

In this paper, a new quantum representation model of color digital images (QRCI) is investigated, in which the color information is encoded by the basis states of qubit sequences. QRCI model utilizes 2n+6 qubits to store a color digital image with size 2n×2n. Compared with the existing NCQI representation model, the storage capacity of QRCI improves 218 times. Moreover, some quantum color image processing operations concerning channels and bit-planes based on QRCI are discussed and their quantum circuits are designed. Comparison results of the quantum circuits indicate that these operations based on QRCI have lower quantum cost than NCQI. Therefore, the new proposed QRCI representation model can save more storage space and it is more convenient to conduct quantum color image processing operations concerning channels and bit-planes. This work will help the researchers to further investigate more complex quantum color image processing operations based on QRCI.
Product-Aware Helpfulness Prediction of Online Reviews

Helpful reviews are essential for e-commerce and review websites, as they can help customers make quick purchase decisions and merchants to increase profits. Due to a great number of online reviews with unknown helpfulness, it recently leads to promising research on building automatic mechanisms to assess review helpfulness. The mainstream methods generally extract various linguistic and embedding features solely from the text of a review as the evidence for helpfulness prediction. We, however, consider that the helpfulness of a review should be fully aware of the metadata (such as the title, the brand, the category, and the description) of its target product, besides the textual content of the review itself. Hence, in this paper we propose an end-to-end deep neural architecture directly fed by both the metadata of a product and the raw text of its reviews to acquire product-aware review representations for helpfulness prediction. The learned representations do not require tedious labor on feature engineering and are expected to be more informative as the target-aware evidence to assess the helpfulness of online reviews. We also construct two large-scale datasets which are a portion of the real-world web data in Amazon and Yelp, respectively, to train and test our approach. Experiments are conducted on two different tasks: helpfulness identification and regression of online reviews, and results demonstrate that our approach can achieve state-of-the-art performance with substantial improvements.
The status of function words in dependency grammar: A critique of Universal Dependencies (UD)

The article examines the Universal Dependencies (UD) annotation scheme. The UD project is an international initiative to produce treebanks of the world’s languages, whereby the treebanks have been annotated in a cross-linguistically consistent manner. A central aspect of the UD annotation scheme is its analysis of function words. The scheme advocates subordinating function words to content words. This article discusses linguistic and practical motivations behind the UD decision to subordinate function words to content words. It demonstrates that UD choices in this area are not supported linguistically. At the same time, the near convertibility of the UD treebanks to a more linguistically motivated annotation format means that the UD initiative remains of great value to linguistics in general.
JavaScript Template Attacks: Automatically Inferring Host Information for Targeted Exploits

Today, more and more web browsers and extensions provide anonymity features to hide user details.Primarily used to evade tracking by websites and advertisements, these features are also used by criminals to prevent identification.Thus, not only tracking companies but also law-enforcement agencies have an interest in finding flaws which break these anonymity features.For instance, for targeted exploitation using zero days, it is essential to have as much information about the target as possible.A failed exploitation attempt, e.g., due to a wrongly guessed operating system, can burn the zero-day, effectively costing the attacker money.Also for side-channel attacks, it is of the utmost importance to know certain aspects of the victim's hardware configuration, e.g., the instruction-set architecture.Moreover, knowledge about specific environmental properties, such as the operating system, allows crafting more plausible dialogues for phishing attacks.In this paper, we present a fully automated approach to find subtle differences in browser engines caused by the environment.Furthermore, we present two new side-channel attacks on browser engines to detect the instruction-set architecture and the used memory allocator.Using these differences, we can deduce information about the system, both about the software as well as the hardware.As a result, we cannot only ease the creation of fingerprints, but we gain the advantage of having a more precise picture for targeted exploitation.Our approach allows automating the cumbersome manual search for such differences.We collect all data available to the JavaScript engine and build templates from these properties.If a property of such a template stays the same on one system but differs on a different system, we found an environment-dependent property.We found environment-dependent properties in Firefox, Chrome, Edge, and mobile Tor, allowing us to reveal the underlying operating system, CPU architecture, used privacy-enhancing plugins, as well as exact browser version.We stress that our method should be used in the development of browsers and privacy extensions to automatically find flaws in the implementation.
Network motifs for translator stylometry identification

Despite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguistics. In a two stage process, this paper first evaluates the use of existing lexical measures for the translator stylometry problem. Similar to previous research we found that using vocabulary richness in its traditional form as it has been used in the literature could not identify translator stylometry. This encouraged us to design an approach with the aim of identifying the distinctive patterns of a translator by employing network-motifs. Networks motifs are small sub-graphs which aim at capturing the local structure of a complex network. The proposed approach achieved an average accuracy of 83% in three-way classification. These results demonstrate that classic tools based on lexical features can be used for identifying translator stylometry if they get augmented with appropriate non-parametric scaling. Moreover, the use of complex network analysis and network motifs mining provided made it possible to design features that can solve translator stylometry analysis problems.
3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition

Human activity recognition is an active field of research in computer vision with numerous applications. Recently, deep convolutional networks and recurrent neural networks (RNN) have received increasing attention in multimedia studies, and have yielded state-of-the-art results. In this research work, we propose a new framework which intelligently combines 3D-CNN and LSTM networks. First, we integrate discriminative information from a video into a map called a ‘motion map’ by using a deep 3-dimensional convolutional network (C3D). A motion map and the next video frame can be integrated into a new motion map, and this technique can be trained by increasing the training video length iteratively; then, the final acquired network can be used for generating the motion map of the whole video. Next, a linear weighted fusion scheme is used to fuse the network feature maps into spatio-temporal features. Finally, we use a Long-Short-Term-Memory (LSTM) encoder-decoder for final predictions. This method is simple to implement and retains discriminative and dynamic information. The improved results on benchmark public datasets prove the effectiveness and practicability of the proposed method.
A Unified Variational Model for Single Image Dehazing

Haze is a common weather phenomenon, which hinders many outdoor computer vision applications such as outdoor surveillance, navigation control, vehicle driving, and so on.In this paper, a simple but effective unified variational model for single image dehazing is presented based on the total variation regularization.From the perspective of the relationship between image dehazing and Retinex, the dehazing problem can be formulated as the minimization of a variational Retinex model.The proposed variational model incorporates two 1 -norm regularization terms to constrain the scene transmission and the inverted scene radiance respectively, which can be better applied into image dehazing field.Different from the conventional two-step framework, our proposed model can simultaneously obtain the accurate transmission map and the recovered scene radiance by integrating the transmission estimation stage and the image recovery stage into the unified variational model.The entire optimization of the proposed unified variational model can be solved by an alternating direction minimization scheme.The experiments on various simulated and real-world hazy images indicate that the proposed algorithm can yield considerably promising results comparative to several state-of-the-art dehazing and enhancement techniques.
Measurement of Economic and Banking Stability in Emerging Markets by Considering Income Inequality and Nonperforming Loans

Economic and banking instability are the factors that can affect each other significantly. This chapter aims to measure the relationship between income inequality and nonperforming loans ratio. For this purpose, 20 different emerging economies are evaluated by using Pedroni panel cointegration and Dumitrescu Hurlin panel causality analysis. In addition to this aspect, annual data between the years 2000 and 2015 is considered in the analysis process. It is concluded that there is a long-term relationship between these variables. Hence, it can be said that these countries should take some actions to improve banking system. In other words, nonperforming loans ratio in banking sector can be decreased when banks in these countries can choose customers more effectively. Therefore, income inequality problem can be minimized in emerging economies.
Assembly‐Induced Emission of Cellulose Nanocrystals for Hiding Information

Abstract Fluorescent‐labeled cellulose nanocrystal (CNC) films have been used to record and protect information in paper materials, whereas the fluorescent materials usually suffer photobleaching. Herein, a strategy of solid‐state emission induced by the vertical assembly of CNCs was established. The assembly‐induced emission starts from the structural diffraction of CNC, whose wavelength is adjusted into the ultraviolet (UV) region for hiding information under natural light. The small diameter (≈10 nm) of CNCs then promotes the resonance between the vertically assembled CNCs and the UV light, leading to a strong blue emission with an emitting quantum efficiency as high as 13.90%. By introducing the vertical‐assembly film with a specific pattern into paper materials, an anti‐counterfeiting image is obtained under a UV radiation. Since CNCs are a kind of cellulose with high crystallinity, this material can be a wear‐resistant anti‐counterfeiting material for banknotes or other paper applications.
Self-calibration of in situ monoscopic deflectometric measurement in precision optical manufacturing

Accurate in situ measurement of complex optical surfaces is desired for precision manufacturing, and deflectometry is a promising measuring method. However, deflectometry's measurement accuracy depends heavily on the geometric calibration's reliability. Existing calibration methods suffer from the limitations of low efficiency, complicated operations, and expensive equipment. Therefore, a self-calibration method is proposed for the monoscopic deflectometry in order to determine the the camera and screen positions. A flat mirror without markers, rather than high-precision metrological instruments, is employed to assist with the self-calibration work, and a re-projection model is adopted in order to trace the rays from the screen to the camera. Numerical optimization is performed on the global parameters-including the camera intrinsic parameters, the screen intrinsic parameters, and the geometric transformations-in order to improve the geometric calibration's reliability. This self-calibration method only requires that four images be captured, hereby reducing the operating complexity and improving the measuring efficiency. Experiments demonstrate that the re-projection error can be significantly decreased, and also that the measuring accuracy of specular surfaces is comparable to high-precision interferometry.
Scalable Zero-Shot Learning via Binary Visual-Semantic Embeddings

Zero-shot learning aims to classify the visual instances from unseen classes in the absence of training examples. This is typically achieved by directly mapping visual features to a semantic embedding space of classes (e.g., attributes or word vectors), where the similarity between the two modalities can be readily measured. However, the semantic space may not be reliable for recognition due to the noisy class embeddings or visual bias problem. In this paper, we propose a novel binary embedding-based zero-shot learning (BZSL) method, which recognizes the visual instances from unseen classes through an intermediate discriminative Hamming space. Specifically, BZSL jointly learns two binary coding functions to encode both visual instances and class embeddings into the Hamming space, which well alleviates the visual-semantic bias problem. As a desiring property, classifying an unseen instance thereby can be efficiently done by retrieving its nearest class codes with minimal Hamming distance. During training, by introducing two auxiliary variables for the coding functions, we formulate an equivalent correlation maximization problem, which admits an analytical solution. The resulting algorithm thus enjoys both highly efficient training and scalable novel class inferring. Extensive experiments on four benchmark datasets, including the full ImageNet Fall 2011 dataset with over 20k unseen classes, demonstrate the superiority of our method on the zero-shot learning task. Particularly, we show that increasing the binary embedding dimension can inevitably improve the recognition accuracy.
Insights Into Multiple/Single Lower Bound Approximation for Extended Variational Inference in Non-Gaussian Structured Data Modeling

For most of the non-Gaussian statistical models, the data being modeled represent strongly structured properties, such as scalar data with bounded support (e.g., beta distribution), vector data with unit length (e.g., Dirichlet distribution), and vector data with positive elements (e.g., generalized inverted Dirichlet distribution). In practical implementations of non-Gaussian statistical models, it is infeasible to find an analytically tractable solution to estimating the posterior distributions of the parameters. Variational inference (VI) is a widely used framework in Bayesian estimation. Recently, an improved framework, namely, the extended VI (EVI), has been introduced and applied successfully to a number of non-Gaussian statistical models. EVI derives analytically tractable solutions by introducing lower bound approximations to the variational objective function. In this paper, we compare two approximation strategies, namely, the multiple lower bounds (MLBs) approximation and the single lower bound (SLB) approximation, which can be applied to carry out the EVI. For implementation, two different conditions, the weak and the strong conditions, are discussed. Convergence of the EVI depends on the selection of the lower bound, regardless of the choice of weak or strong condition. We also discuss the convergence properties to clarify the differences between MLB and SLB. Extensive comparisons are made based on some EVI-based non-Gaussian statistical models. Theoretical analysis is conducted to demonstrate the differences between the weak and strong conditions. Experimental results based on real data show advantages of the SLB approximation over the MLB approximation.
Information Geometrical Characterization of Quantum Statistical Models in Quantum Estimation Theory

In this paper, we characterize quantum parametric models into different classes based on the estimation error bound, known as the Holevo bound. These classes are given by the classical, quasi-classical, D-invariant, and asymptotically classical models. We first explore the relationships among these four models and show that: i) The classical model having the diagonal elements only is characterized by the intersection of the D-invariant and asymptotically classical models. ii) There exists a gap between the classical model and the quasi-classical model, where all logarithmic derivative operators commute with each other. Further, we characterize each class with several equivalent conditions. This result then reveals the geometrical understanding of quantum statistical models.
Geostatistical Distribution and Contamination Status of Heavy Metals in the Sediment of Perak River, Malaysia

Heavy metal pollution is one of the major environmental issues in recent decades owing to the rapid increase in urbanisation and industrialisation. Sediments usually act as sinks for heavy metals due to their complex physical and chemical adsorption mechanisms. In this study, heavy metals like lead (Pb), Zinc (Zn), Cadmium (Cd), Copper (Cu) and Iron (Fe) in the surface sediment from 15 location (upstream and downstream) on the Perak River, Malaysia were investigated by means of inductively coupled plasma optical emission spectroscopy (ICP-OES). The geostatistical prediction map showed the range of Pb, Zn, Cd, Cu and Fe concentration in upstream area was 14.56–27.0 µg/g, 20–51.27 µg/g, 1.51–3.0 µg/g, 6.6–19.12 µg/g and 20.24–56.58%, respectively, and in downstream areas was 27.6–60.76 µg/g, 49.04–160.5 µg/g, 2.77–4.02 µg/g, 9.82–59.99 µg/g and 31.34–39.5%, respectively. Based on the enrichment factor and geoaccumulation index, Cd was found to be the most dominant pollutant in the study area. Pollution load index, sediment quality guidelines and sediment environmental toxicity quotient data showed that the downstream sediment was more polluted than the upstream sediment in the Perak River. The multivariate analysis showed that Pb, Zn and Cu mainly originated from natural sources with minor contribution from human activities, whereas Fe and Cd originated from various industrial and agricultural activities along the studied area.
Characterizing industry-academia collaborations in software engineering: evidence from 101 projects

Research collaboration between industry and academia supports improvement and innovation in industry and helps ensure the industrial relevance of academic research. However, many researchers and practitioners in the community believe that the level of joint industry-academia collaboration (IAC) projects in Software Engineering (SE) research is relatively low, creating a barrier between research and practice. The goal of the empirical study reported in this paper is to explore and characterize the state of IAC with respect to industrial needs, developed solutions, impacts of the projects and also a set of challenges, patterns and anti-patterns identified by a recent Systematic Literature Review (SLR) study. To address the above goal, we conducted an opinion survey among researchers and practitioners with respect to their experience in IAC. Our dataset includes 101 data points from IAC projects conducted in 21 different countries. Our findings include: (1) the most popular topics of the IAC projects, in the dataset, are: software testing, quality, process, and project managements; (2) over 90% of IAC projects result in at least one publication; (3) almost 50% of IACs are initiated by industry, busting the myth that industry tends to avoid IACs; and (4) 61% of the IAC projects report having a positive impact on their industrial context, while 31% report no noticeable impacts or were "not sure". To improve this situation, we present evidence-based recommendations to increase the success of IAC projects, such as the importance of testing pilot solutions before using them in industry. This study aims to contribute to the body of evidence in the area of IAC, and benefit researchers and practitioners. Using the data and evidence presented in this paper, they can conduct more successful IAC projects in SE by being aware of the challenges and how to overcome them, by applying best practices (patterns), and by preventing anti-patterns.
Geollery

We present Geollery, an interactive mixed reality social media platform for creating, sharing, and exploring geotagged information. Geollery introduces a real-time pipeline to progressively render an interactive mirrored world with three-dimensional (3D) buildings, internal user-generated content, and external geotagged social media. This mirrored world allows users to see, chat, and collaborate with remote participants with the same spatial context in an immersive virtual environment. We describe the system architecture of Geollery, its key interactive capabilities, and our design decisions. Finally, we conduct a user study with 20 participants to qualitatively compare Geollery with another social media system, Social Street View. Based on the participants' responses, we discuss the benefits and drawbacks of each system and derive key insights for designing an interactive mirrored world with geotagged social media. User feedback from our study reveals several use cases for Geollery including travel planning, virtual meetings, and family gathering.