Reviewerly/exHarmony huggingface dataset:
Decomposition‐based mission planning for fixed‐wing UAVs surveying in wind

Abstract This paper presents a new method for planning fixed‐wing aerial survey paths that ensures efficient image coverage of a large complex agricultural field in the presence of wind. By decomposing any complex polygonal field into multiple convex polygons, the traditional back‐and‐forth boustrophedon paths can be used to ensure coverage of these decomposed regions. To decompose a complex field in an efficient and fast manner, a top‐down recursive greedy approach is used to traverse the search space to minimize the flight time of the survey. This optimization can be computed fast enough for use in the field. As wind can severely affect flight time, it is included in the flight time calculation in a systematic way using a verified cost function that offers greatly reduced survey times in the wind. Other improved cost functions have been developed to take into account real‐world problems, for example, No‐Fly Zones, in addition to flight time. A number of real surveys are performed to show the flight time in wind model is accurate, to make further comparisons to previous techniques and to show that the proposed method works in real‐world conditions providing total image coverage. A number of missions are generated and flown for real complex agricultural fields. In addition to this, the wind field around a survey area is measured from a multirotor carrying an ultrasonic wind speed sensor. This shows that the assumption of steady uniform wind holds true for the small areas and time scales of an unmanned aerial vehicle aerial survey.
Coarse-to-Fine Luminance Estimation for Low-Light Image Enhancement in Maritime Video Surveillance

Captured images in maritime video surveillance under non-uniform illumination conditions easily suffer from low contrast and details loss. The low-quality images may significantly result in negative effects in practical applications, e.g., target detection, recognition, classification and tracking, etc. Increasing attention has recently been paid to improve the quality of low-light images via computer vision techniques. In this paper, we propose to establish a two-step luminance estimation framework to enhance low-light images. In particular, the coarse luminance is firstly estimated using traditional Max-RGB which extracts the highest pixel values in each color channel. The refined luminance is obtained by introducing a weighted variational model which has the capacities of structure-preserving and texture-smoothing. Based on the estimated well-constructed luminance, the enhanced low-light images are obtained by combining Retinex model with its extended version. The image quality is further improved through a BM3D-based denoising approach. Experimental results on both synthetic and realistic low-light images have demonstrated the satisfactory imaging performance in termsP of quantitative and qualitative evaluations.
HybSMRP: a hybrid scheduling algorithm in Hadoop MapReduce framework

Abstract Due to the advent of new technologies, devices, and communication tools such as social networking sites, the amount of data produced by mankind is growing rapidly every year. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. MapReduce has been introduced to solve large-data computational problems. It is specifically designed to run on commodity hardware, and it depends on dividing and conquering principles. Nowadays, the focus of researchers has shifted towards Hadoop MapReduce. One of the most outstanding characteristics of MapReduce is data locality-aware scheduling. Data locality-aware scheduler is a further efficient solution to optimize one or a set of performance metrics such as data locality, energy consumption and job completion time. Similar to all situations, time and scheduling are the most important aspects of the MapReduce framework. Therefore, many scheduling algorithms have been proposed in the past decades. The main ideas of these algorithms are increasing data locality rate and decreasing the response and completion time. In this paper, a new hybrid scheduling algorithm has been proposed, which uses dynamic priority and localization ID techniques and focuses on increasing data locality rate and decreasing completion time. The proposed algorithm was evaluated and compared with Hadoop default schedulers (FIFO, Fair), by running concurrent workloads consisting of Wordcount and Terasort benchmarks. The experimental results show that the proposed algorithm is faster than FIFO and Fair scheduling, achieves higher data locality rate and avoids wasting resources.
Cross-Lingual Transfer Learning of Non-Native Acoustic Modeling for Pronunciation Error Detection and Diagnosis

In computer-assisted pronunciation training (CAPT), the scarcity of large-scale non-native corpora and human expert annotations are two fundamental challenges to non-native acoustic modeling.Most existing approaches of acoustic modeling in CAPT are based on non-native corpora while there are so many living languages in the world.It is impractical to collect and annotate every non-native speech corpus considering different language pairs.In this work, we address non-native acoustic modeling (both on phonetic and articulatory level) based on transfer learning.In order to effectively train acoustic models of non-native speech without using such data, we propose to exploit two large native speech corpora of learner's native language (L1) and target language (L2) to model cross-lingual phenomena.This kind of transfer learning can provide a better feature representation of non-native speech.Experimental evaluations are carried out for Japanese speakers learning English.We first demonstrate the proposed acoustic-phone model achieves a lower word error rate in non-native speech recognition.It also improves the pronunciation error detection based on goodness of pronunciation (GOP) score.For diagnosis of pronunciation errors, the proposed acoustic-articulatory modeling method is effective for providing detailed feedback at the articulation level.
Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images

Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information. The features of small targets may also be submerged by redundant top-level features, resulting in poor detection. To address these problems, we proposed a compact multi-scale dense convolutional neural network (MS-DenseNet) for aircraft detection in remote sensing images. Herein, DenseNet was utilized for feature extraction, which enhances the propagation and reuse of the bottom-level high-resolution features. Subsequently, we combined feature pyramid network (FPN) with DenseNet to form a MS-DenseNet for learning multi-scale features, especially features of small objects. Finally, by compressing some of the unnecessary convolution layers of each dense block, we designed three new compact architectures: MS-DenseNet-41, MS-DenseNet-65, and MS-DenseNet-77. Comparative experiments showed that the compact MS-DenseNet-65 obtained a noticeable improvement in detecting small aircrafts and achieved state-of-the-art performance with a recall of 94% and an F1-score of 92.7% and cost less computational time. Furthermore, the experimental results on robustness of UCAS-AOD and RSOD datasets also indicate the good transferability of our method.
Visual Security Evaluation of Perceptually Encrypted Images Based on Image Importance

Perceptual/selective encryption has been gaining widespread attention as an emerging technology for image privacy protection. However, few studies focus on the visual security evaluation of perceptually encrypted images, which has a significant impact on measuring the effectiveness and practicality of these encryption methods. In this paper, we propose an image importance-based visual security index (IIBVSI) by leveraging spatial contrast and texture features. Based on the characteristics of perceptually encrypted images, we present an averaged high-order gradient magnitude map to describe the spatial contrast feature and introduce a combined local amplitude map of multiple log-Gabor filters to represent the texture feature. Specifically, the multiresolution representation of an image is first created by downsampling to simulate the hierarchical property of the human visual system. Next, for each scale of image resolution, the spatial contrast and the texture feature maps are extracted from both plain and encrypted images. Similarity measurements are then conducted on these feature maps to generate the contrast and the texture similarity maps. An image importance-based pooling strategy is subsequently proposed to combine these measurements and generate a visual security score. The final IIBVSI score is computed by averaging the visual security scores of all scales of image resolution. Extensive experiments are conducted on several publicly available databases, and the results demonstrate the superiority and robustness of our proposed IIBVSI compared with existing state-of-the-art work in the low and moderate image quality ranges.
Advancing Meta-Analysis With Knowledge-Management Platforms: Using metaBUS in Psychology

In this article, we provide a review of research-curation and knowledge-management efforts that may be leveraged to advance research and education in psychological science. After reviewing the approaches and content of other efforts, we focus on the metaBUS project’s platform, the most comprehensive effort to date. The metaBUS platform uses standards-based protocols in combination with human judgment to organize and make readily accessible a database of research findings, currently numbering more than 1 million. It allows users to conduct rudimentary, instant meta-analyses, and capacities for visualization and communication of meta-analytic findings have recently been added. We conclude by discussing challenges, opportunities, and recommendations for expanding the project beyond applied psychology.
Exploring Discriminative Word-Level Domain Contexts for Multi-Domain Neural Machine Translation

Owing to its practical significance, multi-domain Neural Machine Translation (NMT) has attracted much attention recently. Recent studies mainly focus on constructing a unified NMT model with mixed-domain training corpora to switch translation between different domains. In these models, the words in the same sentence are not well distinguished, while intuitively, they are related to the sentence domain to varying degrees and thus should exert different effects on the multi-domain NMT model. In this article, we are committed to distinguishing and exploiting different word-level domain contexts for multi-domain NMT. For this purpose, we adopt multi-task learning to jointly model NMT and monolingual attention-based domain classification tasks, improving the NMT model in two ways: 1) One domain classifier and one adversarial domain classifier are introduced to conduct domain classifications of input sentences. During this process, two generated gating vectors are used to produce domain-specific and domain-shared annotations for decoder; 2) We equip decoder with an attentional domain classifier. Then, the derived attentional weights are utilized to refine the model training via word-level cost weighting, so that the impacts of target words can be discriminated by their relevance to sentence domain. Experimental results on several multi-domain translations demonstrate the effectiveness of our model.
Internet of Things based acquisition system of industrial intelligent bar code for smart city applications

In order to improve the acquisition efficiency of bar code acquisition technology and the management efficiency of enterprises in the Internet of things, and bring more benefits to related enterprises, the barcode technology of perception layer in the Internet of things was added into the enterprise resource planning system, thus getting an effective and convenient barcode acquisition system. The research results show that the circuit board of the automatic barcode acquisition system studied here is based on the built-in barcode program and joined the enterprise resource planning system. It realizes the connection and integration of the two, which can not only run independently, but also simultaneously, to a large extent, playing its role. It can be seen that the integration of the barcode technology of the perception layer in Internet of things and the enterprise resource planning system can obtain a new industrial automatic barcode acquisition system. It can not only meet the requirements of barcode acquisition, but also improve the efficiency of barcode acquisition, which can be used for reference for manufacturing enterprises in terms of projects that produce according to the order.
CoFiGAN: Collaborative filtering by generative and discriminative training for one-class recommendation

In this paper, we study an important collaborative filtering problem with users’ one-class feedback such as purchases and likes that are pervasive in various recommendation scenarios. In particular, we make a significant extension of IRGAN by introducing rich interactions between a generator and a discriminator, and then design a novel collaborative filtering algorithm termed as CoFiGAN. In our CoFiGAN, the complementarity of the generative training and the discriminative training is exploited more completely, which enhances the accuracy of modeling users’ behaviors. Similar to other GAN-based algorithms, our CoFiGAN can also be interpreted as playing a minimax game, i.e., the generator generates samples close to the true ones aiming to confuse the discriminator, while the latter focuses on distinguishing between the true and generated samples. Different from others, the generator in our CoFiGAN generates items from a more direct and effective way under the guidance of the discriminator in order to accelerate convergence in adversarial training and increase the diversity of the generated samples to avoid mode collapse to some extent. Extensive empirical studies on four public and real-world datasets show that our CoFiGAN performs better than IRGAN and other very strong recommendation algorithms in terms of the commonly used ranking-oriented evaluation metrics.
Classification With the Matrix-Variate-<i>t</i> Distribution

Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal, or repeated measures. This article develops an expectation-maximization algorithm for discriminant analysis and classification with matrix-variate t-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or to the classification of functional magnetic resonance, satellite, or hand gestures images. Supplementary materials for this article are available online.
An Enhanced Facial Expression Recognition Model Using Local Feature Fusion of Gabor Wavelets and Local Directionality Patterns

Facial expression analysis and recognition has gained popularity in the last few years for its challenging nature and broad area of applications like HCI, pain detection, operator fatigue detection, surveillance, etc. The key of real-time FER system is exploiting its variety of features extracted from the source image. In this article, three different features viz. local binary pattern, Gabor, and local directionality pattern were exploited to perform feature fusion and two classification algorithms viz. support vector machines and artificial neural networks were used to validate the proposed model on benchmark datasets. The classification accuracy has been improved in the proposed feature fusion of Gabor and LDP features with SVM classifier, recorded an average accuracy of 93.83% on JAFFE, 95.83% on CK and 96.50% on MMI. The recognition rates were compared with the existing studies in the literature and found that the proposed feature fusion model has improved the performance.
Optimal Bounded-Collusion Secure Functional Encryption

We construct private-key and public-key functional encryption schemes in the bounded-key setting; that is, secure against adversaries that obtain an a-priori bounded number of functional keys (also known as the collusion bound). An important metric considered in the literature on bounded-key functional encryption schemes is the dependence of the running time of the encryption algorithm on the collusion bound $$Q=Q(\lambda )$$ (where $$\lambda $$ is the security parameter). It is known that bounded-key functional encryption schemes with encryption complexity growing with $$Q^{1-\varepsilon }$$ , for any constant $$\varepsilon > 0$$ , implies indistinguishability obfuscation. On the other hand, in the public-key setting, it was previously unknown whether we could achieve encryption complexity growing linear with Q, also known as optimal bounded-key FE, based on well-studied assumptions. In this work, we give the first construction of an optimal bounded-key public-key functional encryption scheme under the minimal assumption of the existence of any public-key encryption scheme. Moreover, our scheme supports the class of all polynomial-size circuits. Our techniques also extend to the private-key setting. We achieve a construction of an optimal bounded-key functional encryption in the private-key setting based on the minimal assumption of one-way functions, instead of learning with errors as achieved in prior works.
Useful Equations About Bell States and Their Applications to Quantum Secret Sharing

In this letter, we systematically analyze the parity properties about Bell states, Pauli operators and Bell measurements, which are described as four interesting and flexible equations. Furthermore, we investigate their applications in fields of secure multiparty computations, and especially present a novel secret sharing protocol with anonymous authentication based on these equations, which can guarantee the unconditional security of the proposed protocol.
Why PV Modules Should Preferably No Longer Be Oriented to the South in the Near Future

PV modules tilted and oriented toward east and west directions gain gradually more importance as an alternative to the presently-preferred south (north in the Southern Hemisphere) orientation and it is shown to become economically superior even under the reimbursement of feed-in tariff (FIT). This is a consequence of the increasing spread between the decreasing costs of self-consumed solar power and the costs for power from the grid. One-minute values of irradiance were measured by silicon sensors at different orientations and tilt angles in Hannover (Germany) over three years. We show that south-oriented collectors give the highest electrical power during the day, whereas combinations of east and west orientations (E-W) result in the highest self-consumption rate (SC), and combinations of southeast and southwest (SE-SW) orientations result in the highest degree of autarky (AD), although they reduce the yearly PV Power by 5–6%. Moreover, the economic analysis of PV systems without FIT shows that the SE-SW and E-W combinations have the lowest electricity cost and they are more beneficial in terms of internal rate of return (IRR), compared to the S orientation at the same tilt. For PV systems with FIT, the S orientation presently provides the highest transfer of money from the supplier. However, as a consequence of the continuing decline of FIT, the economic advantage of S orientation is decreasing. E-W and SE-SW orientations are more beneficial for the owner as soon as FIT decreases to 7 Ct/kWh. East and west orientations of PV modules do not only have benefits for the individual owner but avoid high costs for storing energy—regardless who would own the storage facilities—and by avoiding high noon peaks of solar energy production during sunny periods, which would become an increasing problem for the grid if more solar power is installed. Furthermore, two types of commonly used PV software (PVSOL and PVsyst) were used to simulate the system performance. The comparison with measurements showed that both PV software underestimate SC and AD for all studied orientations, leading to the conclusion that improvements are necessary in modelling.
4-Round Luby-Rackoff Construction is a qPRP

The Luby-Rackoff construction, or the Feistel construction, is one of the most important approaches to construct secure block ciphers from secure pseudorandom functions. The 3- and 4-round Luby-Rackoff constructions are proven to be secure against chosen-plaintext attacks (CPAs) and chosen-ciphertext attacks (CCAs), respectively, in the classical setting. However, Kuwakado and Morii showed that a quantum superposed chosen-plaintext attack (qCPA) can distinguish the 3-round Luby-Rackoff construction from a random permutation in polynomial time. In addition, Ito et al. recently showed a quantum superposed chosen-ciphertext attack (qCCA) that distinguishes the 4-round Luby-Rackoff construction. Since Kuwakado and Morii showed the result, a problem of much interest has been how many rounds are sufficient to achieve provable security against quantum query attacks. This paper answers to this fundamental question by showing that 4-rounds suffice against qCPAs. Concretely, we prove that the 4-round Luby-Rackoff construction is secure up to $$O(2^{n/12})$$ quantum queries. We also give a query upper bound for the problem of distinguishing the 4-round Luby-Rackoff construction from a random permutation by showing a distinguishing qCPA with $$O(2^{n/6})$$ quantum queries. Our result is the first to demonstrate the security of a typical block-cipher construction against quantum query attacks, without any algebraic assumptions. To give security proofs, we use an alternative formalization of Zhandry's compressed oracle technique.
Augmented reality as a form of digital technology in early childhood education

The incorporation of digital technologies is explicitly addressed in the Early Years Learning Framework for Australia. The use of augmented reality (AR) technology, as one form of digital technology, is increasingly embedded in digital applications because it allows individuals to interact with real and virtual objects. A significant body of research has reported the benefits afforded by the use of AR technology in schools and higher education settings. However, little is known about the contribution of AR technology to teaching practice and child learning outcomes in the preschool years. Here, we present a summary of the limited research that has explored the use of AR in preschool curricula and argue for the need for further research to explore the contribution of AR to high-quality pedagogical practice.