Reviewerly/exHarmony huggingface dataset:
Mediating effect of organisational innovation in the prediction of change-oriented leadership on organisational performance

Purpose To address this question, this study aims to drew from resource-based view to examine the mediating role organisational innovation in the relationship between change-oriented leadership and organisational performance. Design/methodology/approach Given that the unit of analysis is an organisation, 219 principal officers, including Presidents/Rectors, Vice Presidents/Vice-Rectors, Registrars and Deans, were invited to complete the survey on behalf of their universities. Findings Results of the partial least squares structural equation modelling confirmed the positive relationship between change-oriented leadership and university performance. The results provide evidence that organisational innovation directly facilitates university performance. As expected, the relationship between change-oriented leadership and university performance was found to be mediated by organisational innovation. Practical implications Overall, this study successfully modelled organisational innovation as an underlying mechanism behind the change-oriented leadership – university performance relationship, thereby by testing resource-based theory in Saudi Arabian context. From a practical perspective, results suggest that to improve their performance is imperative for executive leaders in Saudi universities to focus on several specific work design strategies, obtaining feedback about their change-oriented leadership style from followers. Originality/value Whilst there has been a considerable amount of research emphasising the importance of change-oriented leadership, there has been little research linking this fundamental aspect of leadership to organisational performance. Yet an understanding of the possible mechanisms behind this linkage has not received attention.
A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

In real life, images are inevitably interfered by various noises during acquisition and transmission, resulting in a significant reduction in image quality. The process of solving this kind of problem is called image denoising. Image denoising is a basic problem in the field of computer vision and image processing, which is essential for subsequent image processing and applications. It can ensure that people can obtain more effective information of images more accurately. This paper mainly studies a new method of crop image denoising with improved SVD in wavelet domain. The algorithm used in this study firstly carried out a 3-layer wavelet transform on the crop noise image, leaving the low-frequency subimage unchanged; then, for the high-frequency subimages distributed in the horizontal, vertical, and diagonal directions, the improved adaptive SVD algorithm was used to filter the noise; finally perform wavelet coefficient reconstruction. To effectively test the performance of the algorithm, field crop images were taken as test images, and the denoising performance of the algorithm, SVD algorithm, and the improved SVD algorithm used in this study were compared, and the peak signal-to--to-noise ratio (PSNR) was introduced. Quantitative evaluation of the denoising results of several types of algorithms. The experimental data in this paper show that when the noise standard deviation is greater than 20, the enhanced experimental results clearly achieve higher PSNR and SSIM values than WNNM. The average peak signal-to-noise ratio (PSNR) is about 0.1 dB higher, and the average SSIM is larger about 0.01. The results show that the algorithm used in this study is superior to the other two algorithms, which provides a more effective method for crop noise image processing.
Deep Snapshot Hdr Reconstruction Based On The Polarization Camera

The recent development of the on-chip micro-polarizer technology has made it possible to acquire four spatially aligned and temporally synchronized polarization images with the same ease of operation as a conventional camera. In this paper, we investigate the use of this sensor technology in high-dynamic-range (HDR) imaging. Specifically, observing that natural light can be attenuated differently by varying the orientation of the polarization filter, we treat the multiple images captured by the polarization camera as a set captured under different exposure times. In our approach, we first study the relationship among polarizer orientation, degree and angle of polarization of light to the exposure time of a pixel in the polarization image. Subsequently, we propose a deep snapshot HDR reconstruction framework to recover an HDR image using the polarization images. A polarized HDR dataset is created to train and evaluate our approach. We demonstrate that our approach performs favorably against state-of-the-art HDR reconstruction algorithms.
Adaptive Kernel Kalman Filter

This paper presents a novel model-based Bayesian filter called the adaptive kernel Kalman filter (AKKF). The proposed filter approximates the arbitrary probability distribution functions (PDFs) of hidden states as empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). Specifically, particles are generated and updated in the data space to capture the properties of the dynamical system model, while the corresponding kernel weight vector and matrix associated with the particles’ feature mappings are predicted and updated in the RKHS based on the kernel Kalman rule (KKR). We illustrate and confirm the advantages of our approach through simulation, offering detailed comparison with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF) algorithms.
Deep Weibull hashing with maximum mean discrepancy quantization for image retrieval

Hashing has been a promising technology for fast nearest neighbor retrieval in large-scale datasets due to the low storage cost and fast retrieval speed. Most existing deep hashing approaches learn compact hash codes through pair-based deep metric learning such as the triplet loss. However, these methods often consider that the intra-class and inter-class similarity make the same contribution, and consequently it is difficult to assign larger weights for informative samples during the training procedure. Furthermore, only imposing relative distance constraint increases the possibility of being clustered with larger average intra-class distance for similar pairs, which is harmful to learning a high separability Hamming space. To tackle the issues, we put forward deep Weibull hashing with maximum mean discrepancy quantization (DWH), which jointly performs neighborhood structure optimization and error-minimizing quantization to learn high-quality hash codes in a unified framework. Specifically, DWH learns the desired neighborhood structure in conjunction with a flexible pair similarity optimization strategy and a Weibull distribution-based constraint between anchors and their neighbors in Hamming space. More importantly, we design a maximum mean discrepancy quantization objective function to preserve the pairwise similarity when performing binary quantization. Besides, a class-level loss is introduced to mine the semantic structural information of images by using supervision information. The encouraging experimental results on various benchmark datasets demonstrate the efficacy of the proposed DWH.
FL-QKD based on optical-THz biphotons generated by spontaneous parametric downconversion in inter-satellite wireless communication

Floodlight quantum key distribution (FL-QKD) is a new QKD protocol that can achieve a 2 Gbps secret key rate (SKR) in a 50 km fiber link without multiplexing technology [Q. Zhuang et al. , Phys. Rev. A 94 , 012322 ( 2016 ) PLRAAN 1050-2947 10.1103/PhysRevA.94.012322 ]. In this paper, we propose a wireless FL-QKD at terahertz bands (THz-FL-QKD) in inter-satellite links. THz-FL-QKD is the two-way protocol that sends quantum signals in the forward channel, modulates and amplifies the received signals at the receiver, and then returns to the transmitter through the backward channel for homodyne detection and decoding. We analyze the security of THz-FL-QKD against individual attacks and optimum collective attacks. Numerical simulations show that THz-FL-QKD is capable of a 50 Mbps SKR at 10 THz frequency in a 200 km inter-satellite wireless link. We expect this work will provide an efficient path to build a high-speed global quantum communication network.
Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients’ narratives

Owing to the quick spread of minute health-related experiences, the distillation of knowledge from such unstructured narratives is an extremely challenging task. In spite of the success of neural networks methods in improving learning structural reliability, they result in inadequate accuracy of the bio-medical sentiment classification when employing less useful features sets. Therefore, they lack discriminatory potential. In this study, we propose to add a deep associative memory into neural networks for an effective sentiment decomposition, which emphasizes correctly on bio-medical entities related to the extraction of different data-object properties, and contextual-semantics dependencies for a given aspect. The underlying trust of these measures is behind the ability to compute the completion of unseen medical patterns, where comprehensive bio-medical distributed representations are used for representing the formal medical connections from PubMed databases. Experiments on a biomedical sentiment analysis task show that the model provides comprehensive embeddings with meaningful medical patterns. It achieved an average performance of 87% on varied large online datasets. It also outperforms baselines in discovering and identifying medical natural concepts. We provide meaningful support to bio-medical sentiment analysis applications in social networks. Indeed, the facets of this study might be used in many health concerns such as analyzing change in health status or unexpected situations.
TGAN: A simple model update strategy for visual tracking via template-guidance attention network

Visual attention has been widely used in various fields of visual tasks in recent years. Recently, visual trackers based on probabilistic discriminative model prediction (PrDiMP) and Siamese box adaptive network (SiamBAN) have attracted much attention due to their excellent performance and high efficiency. However, the target template of the model in both the PrDiMP and SiamBAN is not updated online, and feature vectors of the template image and the search image are independent of each other in the IoU-Net and Siamese frameworks. In this research, we proposed a template-guidance attention network in both the IoU-Net (denoted as TGAN-I) and Siamese (denoted as TGAN-S) frameworks for visual tracking. TGAN-I and TGAN-S can comprehensively utilize the feature information of the template image and search image, and provide an implicit way to update the template. By utilizing a simple template update strategy, the TGAN-I and TGAN-S trackers can be more robust under certain challenging conditions such as occlusion and deformation. Besides, we introduce a channel and spatial attention module in feature maps of the template image and search image for adaptive feature refinement. Deformable convolutional networks are further used to enhance the model generalization capability in various transformations aspect ratios and scales of tracking targets. To verify the effectiveness of the proposed method, we evaluate the TGAN-I and TGAN-S trackers on six benchmarks and achieve state-of-the-art results. In particular, the TGAN-I method outperforms the strong baseline, PrDiMP, by 0.323 → 0.355 and 0.471 → 0.501 of EAO score on VOT2019 and VOT2016, respectively.
Random Forest-Based Diffusion Information Geometry for Supervised Visualization and Data Exploration

Most dimensionality reduction techniques to date are unsupervised; they do not take class labels into account (e.g., PCA, MDS, t-SNE, Isomap). Such methods require large amounts of data and are often sensitive to noise that may obfuscate important patterns in the data. Various attempts at supervised dimensionality reduction methods that take into account auxiliary annotations (e.g., class labels) have been successfully implemented with goals of increased classification accuracy or improved data visualization. In this paper, we describe a novel supervised visualization technique based on random forest proximities and a diffusion-based information geometry. We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structure in data, while demonstrating the spatial relevance of features important for the supervised task. Importantly, our approach is robust to noise and parameter tuning, thus making it simple to use while producing reliable visualizations for data exploration.
Dynamic Privacy-Preserving Recommendations on Academic Graph Data

In the age of digital information, where the internet and social networks, as well as personalised systems, have become an integral part of everyone’s life, it is often challenging to be aware of the amount of data produced daily and, unfortunately, of the potential risks caused by the indiscriminate sharing of personal data. Recently, attention to privacy has grown thanks to the introduction of specific regulations such as the European GDPR. In some fields, including recommender systems, this has inevitably led to a decrease in the amount of usable data, and, occasionally, to significant degradation in performance mainly due to information no longer being attributable to specific individuals. In this article, we present a dynamic privacy-preserving approach for recommendations in an academic context. We aim to implement a personalised system capable of protecting personal data while at the same time allowing sensible and meaningful use of the available data. The proposed approach introduces several pseudonymisation procedures based on the design goals described by the European Union Agency for Cybersecurity in their guidelines, in order to dynamically transform entities (e.g., persons) and attributes (e.g., authored papers and research interests) in such a way that any user processing the data are not able to identify individuals. We present a case study using data from researchers of the Georg Eckert Institute for International Textbook Research (Brunswick, Germany). Building a knowledge graph and exploiting a Neo4j database for data management, we first generate several pseudoN-graphs, being graphs with different rates of pseudonymised persons. Then, we evaluate our approach by leveraging the graph embedding algorithm node2vec to produce recommendations through node relatedness. The recommendations provided by the graphs in different privacy-preserving scenarios are compared with those provided by the fully non-pseudonymised graph, considered as the baseline of our evaluation. The experimental results show that, despite the structural modifications to the knowledge graph structure due to the de-identification processes, applying the approach proposed in this article allows for preserving significant performance values in terms of precision.
Model-Agnostic Adversarial Example Detection Through Logit Distribution Learning

Recent research on vision-based tasks has achieved great improvement due to the development of deep learning solutions. However, deep models have been found vulnerable to adversarial attacks where the original inputs are maliciously manipulated and cause dramatic shifts to the outputs. In this paper, we focus on adversarial attacks in image classifiers built with deep neural networks and propose a model-agnostic approach to detect adversarial inputs. We argue that the logit semantics of adversarial inputs follow a different evolution with respect to original inputs, and construct a logits-based embedding of features for effective representation learning. We train an LSTM network to further analyze the sequence of logits-based features to detect adversarial examples. Experimental results on the MNIST, CFAR-10, and CFAR-100 datasets show that our method achieves state-of-the-art accuracy for detecting adversarial examples and has strong generalizability.
Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation

This letter considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">potential gap</i> , for hierarchical navigation systems. Central to the construction of the local planner is the use of sensory-derived local free-space models that detect gaps and use them for the synthesis of the APF. Trajectories derived from the APF are provably collision-free for idealized robot models. The provable property is lost when applied to more realistic models. A set of algorithm modifications correct for these errors and enhance robustness to non-ideal models, in particular a nonholonomic robot model. Integration of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">potential gap</i> local planner into a hierarchical navigation system provides the local goals and trajectories needed for collision-free navigation through unknown environments. Monte Carlo experiments in benchmark worlds confirm the asserted safety and robustness properties.
Algorithm for Multi-drone Path Planning and Coverage of Agricultural Fields

This article addresses the problem of optimal flight path planning when surveying the field using several unmanned aerial vehicles (UAVs). The trajectories of the drones are configured in order to minimize the time of the sub-areas coverage (assigned to each drone in the group), provided that the whole field is completely covered. The algorithm consists of four stages: converting the coordinates of a given field into Cartesian (metric system); splitting the field into sub-areas assigned to be covered by each drone in the group; forming the flight trajectory (plan) for each drone; and iterative minimization of the maximum time needed to cover the assigned sub-areas. The algorithm for optimal flight path planning is more time-effective than the ones where the drone moves straight from one end of the field to the other with shifts (moving along the boundary of the field at a certain distance). To implement the developed, an additional software module of the virtual simulator has been developed. The workability and the adequacy of the proposed algorithm were experimentally confirmed using a three-dimensional model of a real agriculture field in the virtual simulator.
Gradient temporal-difference learning for off-policy evaluation using emphatic weightings

The problem of off-policy evaluation (OPE) has long been advocated as one of the foremost challenges in reinforcement learning. Gradient-based and emphasis-based temporal-difference (TD) learning algorithms comprise the major part of off-policy TD learning methods. In this work, we investigate the derivation of efficient OPE algorithms from a novel perspective based on the advantages of these two categories. The gradient-based framework is adopted, and the emphatic approach is used to improve convergence performance. We begin by proposing a new analogue of the on-policy objective, called the distribution-correction-based mean square projected Bellman error (DC-MSPBE). The key to the construction of DC-MSPBE is the use of emphatic weightings on the representable subspace of the original MSPBE. Based on this objective function, the emphatic TD with lower-variance gradient correction (ETD-LVC) algorithm is proposed. Under standard off-policy and stochastic approximation conditions, we provide the convergence analysis of the ETD-LVC algorithm in the case of linear function approximation. Further, we generalize the algorithm to nonlinear smooth function approximation. Finally, we empirically demonstrate the improved performance of our ETD-LVC algorithm on off-policy benchmarks. Taken together, we hope that our work can guide the future discovery of a better alternative in the off-policy TD learning algorithm family.
All You Need to Know to Build a Product Knowledge Graph

Knowledge graphs have been pivotal in supporting downstream applications like search, recommendation, and question answering, among others. Therefore, knowledge graphs have naturally become key enabling technologies in e-Commerce platforms. Developing a high coverage product knowledge graph is more challenging than generic knowledge graphs. The highly specific and complex domain, the sparsity of training data, along with the dynamic taxonomies and product types, can constrain the resulting knowledge graphs. In this tutorial we present best practices and ML innovations in industry towards building a scalable product knowledge graph. Contributions in this domain benefit from the general literature in areas including information extraction and data mining, tailored to address the specific characteristics of e-Commerce platforms.
APReL: A Library for Active Preference-based Reward Learning Algorithms

Reward learning is a fundamental problem in human-robot interaction to have robots that operate in alignment with what their human user wants. Many preference-based learning algorithms and active querying techniques have been proposed as a solution to this problem. In this paper, we present APReL, a library for active preference-based reward learning algorithms, which enable researchers and practitioners to experiment with the existing techniques and easily develop their own algorithms for various modules of the problem. APReL is available at https://github.com/Stanford-ILIAD/APReL.
Effectiveness Error: Measuring and Improving RadViz Visual Effectiveness

RadViz contributes to multidimensional analysis by using 2D points for encoding data elements and interpreting them along the original data dimensions. For these characteristics it is used in different application domains, like clustering, anomaly detection, and software visualization. However, it is likely that using the dimension arrangement that comes with the data will produce a plot that leads users to make inaccurate conclusions about points values and data distribution. This article attacks this problem without altering the original RadViz design: It defines, for both a single point and a set of points, the metric of effectiveness error, and uses it to define the objective function of a dimension arrangement strategy, arguing that minimizing it increases the overall RadViz visual quality. This article investigated the intuition that reducing the effectiveness error is beneficial for other well-known RadViz problems, like points clumping toward the center, many-to-one plotting of non-proportional points, and cluster separation. It presents an algorithm that reduces to zero the effectiveness error for a single point and a heuristic that approximates the dimension arrangement minimizing the effectiveness error for an arbitrary set of points. A set of experiments based on 21 real datasets has been performed, with the goals of analyzing the advantages of reducing the effectiveness error, comparing the proposed dimension arrangement strategy with other related proposals, and investigating the heuristic accuracy. The Effectiveness Error metric, the algorithm, and the heuristic presented in this article have been made available in a d3.js plugin at https://aware-diag-sapienza.github.io/d3-radviz.