Reviewerly/exHarmony huggingface dataset:
Why are ontologies not reused across the same domain?

Even though one of the main characteristics of ontologies has always been claimed to be their reusability, throughout this paper it will be shown that ontology reuse across a given domain is not a consolidated practice. We have carried out a statistical study on ontology reuse in the ontologies collected in Linked Open Vocabularies (LOV), in addition to a particular analysis of a use case. The results of the present work show that, when building an ontology, the heterogeneity between the needed conceptualization and that of available ontologies, as well as the deficiencies in some of such ontologies (concerning documentation, licensing, etc.) are important obstacles for reusing ontologies of the same domain of the ontology under development. A possible approach to lessen these problems could be the creation of communities similar to open software ones in charge of developing and maintaining ontologies.
Prediction of diffuse solar radiation based on multiple variables in China

The accurate knowledge of diffuse solar radiation is of vital importance for climatology, sustainable energy, agriculture and biological activities. However, the spatial coverage of diffuse solar radiation measurements is limited in many regions, due to the lack of measuring devices, high operation and maintenance costs. Therefore, numerous empirical models have been proposed in different regions and climates for predicting diffuse solar radiation. The aim of this study was to establish, test and compare various models for predicting diffuse solar radiation in China. The performances of newly proposed models were compared with empirical models in this study. Using daily observations at 17 stations during 1993-2015, 97 models with 11 independent variables were established at each station. Meanwhile, the performances of newly-established models were compared with empirical models. The results showed: (1) larger model errors were found at Ejinaqi, Wulumuqi and Kashi stations, due to the dusty air conditions. Relatively poor model performances were also observed at Sanya station, owing to the rainy weather characteristics. (2) The comparisons for the five categories of models showed that the fourth category models with four input parameters generally had higher accuracies, except the case at Wulumuqi. (3) Comparisons of Kd-based with KD-based models showed that kd-based models generally had higher accuracies, the mean MBE, MAE, MARE, RMSE, MPE, t-stat, RRMSE, R and centered RMS for Kd-based models at all 17 stations were −0.43 MJ m−2 day−1, 1.5453 MJ m−2 day−1, 0.2583 MJ m−2 day−1, 2.1422 MJ m−2 day−1, 1.7611%, 15.2127, 0.3134, 0.8111 and 1.9969 MJ m−2 day−1, respectively. (4) By comparing with the models in literature, the newly-established models were better than the models in terms of model performances. The models proposed in this study were valuable for some areas without diffuse radiation record, which also supported the development and utilization of solar energy in China and other regions around the world.
Exploiting the generative adversarial framework for one-class multi-dimensional fault detection

Fault detection is an important and demanding problem in industry. However, in many applications, abnormal data are difficult to be collected and are not available during training. In this paper, we propose a one class fault detection scheme for multi-dimensional problems, based on the unsupervised training of a Generative Adversarial Network (GAN). The generator tries to learn the manifold of normal behavior of the process, while the final decision of fault occurrence is taken from the discriminator. The network architectures of the discriminator and the generator and the hyper-parameters, that are used during training, are crucial for the stability of the GAN model. To ensure system convergence during training and to enhance the accuracy of the unsupervised classifier, a model selection algorithm is used. The latter one evaluates each trained model on a validation set of the normal training dataset, based on a proposed performance metric. Also, a method for evaluating the generating ability of each trained generative model is introduced, based on the reconstruction cost of an Autoencoder (AE). The proposed evaluation method of generative models, is used on the search algorithm, which selects the final classifier model. Finally, it is shown by experiments that the proposed system outperforms One Class SVM and Isolation Forest algorithm, two state of the art anomaly detection methods, on two testing cases.
An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions

Energy related costs and environmental sustainability present a significant challenge for cloud computing practitioners and the development of next generation data centers. Virtual Machine (VM) placement provides a promising technique to save energy and improve resource management which is one of the greatest causes of high energy consumption in the operation of data centers today. A key challenge for VM placement algorithms is the ability to accurately forecast future resource demands due to the dynamic nature of cloud applications. Furthermore, the literature rarely considers placement strategies based on co-located resource consumption which has the potential to improve allocation decisions. Using real workload traces this work presents a comparative study of the most widely used prediction models and introduces our novel Predictive Anti-Correlated Placement Algorithm (PACPA) which considers both CPU and bandwidth resource consumption. Our empirical results demonstrate how the proposed approach reduces energy by 18% while also reducing service violations by over 34% compared to some of the most commonly used placement algorithms.
Outsourced dynamic provable data possession with batch update for secure cloud storage

With the advent of data outsourcing, how to efficiently verify the integrity of data stored at an untrusted cloud service provider (CSP) has become a significant problem in cloud storage. Provable data possession (PDP) is a model that allows clients or a trusted auditor to verify whether CSP possesses the outsourced data without downloading it. However, this model requires clients to tolerate non-negligible computation burden incurred by frequent verifications in private schemes, and does not provide any security assurances when client or/and auditor are dishonest. Therefore, it cannot be directly transformed into a secure outsourced auditing scheme, where any one of three participants (i.e., CSP, client and auditor) may be dishonest and any two participants may be colluded with each other. In this paper, we propose an outsourced dynamic provable data possession (ODPDP) scheme, which migrates frequent auditing task to an external auditor to reduce clients' verification overhead, and simultaneously provides log audit mechanism with lower computation burden for clients to prevent from dishonest auditor. In addition, we propose a batch update algorithm that can perform and verify multiple update operations at once, avoiding repetitive calculations and transmissions. Security analysis proves that our scheme is secure in the enhanced threat model, and experimental results show that our scheme achieves high efficiency in terms of computation time and communication cost compared with existing outsourced auditing schemes.
Submodular Function Optimization for Motion Clustering and Image Segmentation

In this paper, we propose a framework of maximizing quadratic submodular energy with a knapsack constraint approximately, to solve certain computer vision problems. The proposed submodular maximization problem can be viewed as a generalization of the classic 0/1 knapsack problem. Importantly, maximization of our knapsack constrained submodular energy function can be solved via dynamic programing. We further introduce a range-reduction step prior to dynamic programing as a two-stage procedure for more efficient maximization. In order to demonstrate the effectiveness of the proposed energy function and its maximization algorithm, we apply it to two representative computer vision tasks: image segmentation and motion trajectory clustering. Experimental results of image segmentation demonstrate that our method outperforms the classic segmentation algorithms of graph cuts and random walks. Moreover, our framework achieves better performance than state-of-the-art methods on the motion trajectory clustering task.
Image Encryption with Double Spiral Scans and Chaotic Maps

Image encryption is a useful technique of image content protection. In this paper, we propose a novel image encryption algorithm by jointly exploiting random overlapping block partition, double spiral scans, Henon chaotic map, and Lü chaotic map. Specifically, the input image is first divided into overlapping blocks and pixels of every block are scrambled via double spiral scans. During spiral scans, the start-point is randomly selected under the control of Henon chaotic map. Next, image content based secret keys are generated and used to control the Lü chaotic map for calculating a secret matrix with the same size of input image. Finally, the encrypted image is obtained by calculating XOR operation between the corresponding elements of the scrambled image and the secret matrix. Experimental result shows that the proposed algorithm has good encrypted results and outperforms some popular encryption algorithms.
Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

The development of cognitive radio and radar electronic reconnaissance has put forward an important demand for improving the recognition ability of modulated signals in complex electromagnetic environment.In this paper, we propose a valid radar signal modulation recognition technology under low signal-to-noise ratio (SNR).The recognition technology can recognize 12 different modulation signals, including Costas, LFM, NLFM, BPSK, P1-P4, and T1-T4 codes.First, we propose the image fusion algorithm of non-multi-scale decomposition to fuse images of a single signal with different time-frequency (T-F) methods.Specifically, weights are designed by the principal component analysis, which could combine significative details of T-F images.Second, we adopt transfer learning-based convolutional neural networks and self-training-based stacked autoencoder, which extract the effective information on fusion image, furthermore guarantee the recognition performance.Moreover, multi-feature fusion algorithm is used to fuse features, which reduces redundant information on features and enhances computing efficiency.Finally, the classifier is performed by a classical algorithm called support vector machine.Simulation results show that the average recognition success rate is 95.5% at SNR of -6dB.It is testified that proposed recognition technology possesses good robustness and superiority in RSR with a wide range of SNR.
Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data

The aim of this work is to investigate the possibility of using machine learning (ML) methods in order to generate novel, safety-relevant knowledge from existing flight data. Airlines routinely generate vast amounts of flight data from routine monitoring, but the concept of extracting safety knowledge from this data is still based on detecting exceedances of expert-defined thresholds. This system is conceptually unable to detect novel occurrences for which no such filters exist. ML techniques are able to close this gap. This paper first reviews the literature to select an appropriate ML method. A form of unsupervised learning called “Local Outlier Probability” is selected. Next, an appropriate feature space is developed and implemented in the flight data monitoring system of a supporting airline to generate the dataset. This dataset is cleaned and the outlier calculation performed. The results are statistically analysed. Furthermore, the top outliers are reviewed by the airline’s review pilots in the same way as the traditional exceedance events. Last, the severities and safety relevance of both types of events are compared. This work successfully shows that the chosen approach is able to reduce the number of undetected safety-relevant occurrences by finding novel occurrence types which were undetected by a contemporary and mature flight data monitoring system. This research builds on recent literature by developing a novel method which can be scaled to work in an airline production environment with large datasets, as demonstrated by the efficient analysis of 1.2 million flights.
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks

This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution or image enhancement to provide accurate yet visually pleasing images on mobile devices by addressing the following three main issues. First, the considered FEQE performs majority of its computation in a low-resolution space. Second, the number of channels used in the convolutional layers is small which allows FEQE to be very deep. Third, the FEQE performs downsampling referred to as desubpixel that does not lead to loss of information. Experimental results on a number of standard benchmark datasets show significant improvements in image fidelity and reduction in processing time of the proposed FEQE compared to the recent state-of-the-art methods. In the PIRM 2018 challenge, the proposed FEQE placed first on the image super-resolution task for mobile devices. The code is available at https://github.com/thangvubk/FEQE.git .
Addressing Trust Bias for Unbiased Learning-to-Rank

Existing unbiased learning-to-rank models use counterfactual inference, notably Inverse Propensity Scoring (IPS), to learn a ranking function from biased click data. They handle the click incompleteness bias, but usually assume that the clicks are noise-free, i.e., a clicked document is always assumed to be relevant. In this paper, we relax this unrealistic assumption and study click noise explicitly in the unbiased learning-to-rank setting. Specifically, we model the noise as the position-dependent trust bias and propose a noise-aware Position-Based Model, named TrustPBM, to better capture user click behavior. We propose an Expectation-Maximization algorithm to estimate both examination and trust bias from click data in TrustPBM. Furthermore, we show that it is difficult to use a pure IPS method to incorporate click noise and thus propose a novel method that combines a Bayes rule application with IPS for unbiased learning-to-rank. We evaluate our proposed methods on three personal search data sets and demonstrate that our proposed model can significantly outperform the existing unbiased learning-to-rank methods.
Multi-task least squares twin support vector machine for classification

With the bloom of machine learning, pattern recognition plays an important role in many aspects. However, traditional pattern recognition mainly focuses on single task learning (STL), and the multi-task learning (MTL) has largely been ignored. Compared to STL, MTL can improve the performance of learning methods through the shared information among all tasks. Inspired by the recently proposed directed multi-task twin support vector machine (DMTSVM) and the least squares twin support vector machine (LSTWSVM), we put forward a novel multi-task least squares twin support vector machine (MTLS-TWSVM). Instead of two dual quadratic programming problems (QPPs) solved in DMTSVM, our algorithm only needs to deal with two smaller linear equations. This leads to simple solutions, and the calculation can be effectively accelerated. Thus, our proposed model can be applied to the large scale datasets. In addition, it can deal with linear inseparable samples by using kernel trick. Experiments on three popular multi-task datasets show the effectiveness of our proposed methods. Finally, we apply it to two popular image datasets, and the experimental results also demonstrate the validity of our proposed algorithm.
Abstractive Meeting Summarization via Hierarchical Adaptive Segmental Network Learning

Abstractive meeting summarization is a challenging problem in natural language understanding, which automatically generates the condensed summary covering the important points in the meeting conversation. However, the existing abstractive summarization works mainly focus on the structured text documents, which may be ineffectively applied to the meeting summarization task due to the lack of modeling the unstructured long-form conversational contents. In this paper, we consider the problem of abstractive meeting summarization from the viewpoint of hierarchical adaptive segmental encoder-decoder network learning. We propose the hierarchical neural encoder based on adaptive recurrent networks to learn the semantic representation of meeting conversation with adaptive conversation segmentation. We then develop the reinforced decoder network to generate the high-quality summaries for abstractive meeting summarization. We conduct the extensive experiments on the well-known AMI meeting conversation dataset to validate the effectiveness of our proposed method.
The Sound of Being There: Audiovisual Cartography with Immersive Virtual Environments

In recent years, immersion has become a frequently emphasized factor in the geovisualization research agenda. A principal reason for this growing interest is the assumption that immersive virtual environments (IVE) facilitate the formation of spatial presence, generally understood as the sense of "being there". In a virtually mediated environment, the feeling of being there is of particular concern for cartographic ambitions, in terms of generating insights through geospatial representation. Current literature indicates that immersive VR systems stimulate the experience of spatial presence; however, this assumption is mainly based upon user studies in the visual communication channel. Moreover, research on IVE for geovisualization matters has to date been focused on visual-graphical rather than on auditive or even multisensory representations in virtual space. In this context, the present paper aims to evaluate the potential of audiovisual cartography with immersive virtual environments. Following a brief discussion of basic concepts, such as immersion, spatial presence and embodiment, we will integrate these aspects into a geovisualization immersion pipeline (GIP), as a framework with which to systematically link the technical and cognitive aspects of IVE. In the subsequent sections, we will examine this framework in the audio channel by analyzing how sound is implemented and perceived in GeoIVE. As we shall see, the positive effect of a combined audio-visual vs. exclusively visual presentation is supported by a series of user studies of sound effects, making audiovisual cartography with IVE a rich and worthwhile field of research.
Differential Cryptanalysis of Round-Reduced SPECK Suitable for Internet of Things Devices

In this paper, we focus on differential cryptanalysis of a lightweight ARX cipher. These ciphers use three simple arithmetic operations, namely, modular addition, bitwise rotation, and exclusive-OR, and therefore, are designed very well to perform over the Internet-of-Things (IoT) devices. We choose a very well-known ARX cipher designed by the National Security Agency (NSA) of the United States of America in June 2013, named SPECK. SPECK was subjected to several years of detailed cryptanalytic analysis within NSA and has been subjected to academic analysis by researchers worldwide. SPECK is specially optimized for low-cost processors like those used in the IoT devices. We first find the differential paths for all the variants of SPECK, and based on that differential path, we attack the round-reduced variant of the cipher. Finding differential paths in ARX is one of the most difficult and time-consuming problems due to the huge state space. We use a nested-based heuristic technique to find a differential path which is inspired by the nested Monte Carlo search (NMCS) algorithm. NMCS was successfully applied before for different games: Morpion Solitaire, SameGame, and 16 × 16 Sudoku, but the use of such heuristic techniques in cryptography is entirely new and time-saving.
Adaptive comprehensive learning particle swarm optimization with cooperative archive

Comprehensive learning particle swarm optimization (CLPSO) enhances its exploration capability by exploiting all other particles’ historical information to update each particle’s velocity. However, CLPSO adopts a set of fixed comprehensive learning (CL) probabilities to learn from other particles, which may impair its performance on complex optimization problems. To improve the performance and adaptability of CLPSO, an adaptive mechanism for adjusting CL probability and a cooperative archive (CA) are combined with CLPSO, and the resultant algorithm is referred to as adaptive comprehensive learning particle swarm optimization with cooperative archive (ACLPSO-CA). The adaptive mechanism dividing the CL probability into three levels and adjusting the individual particle’s CL probability level dynamically according to the performance of the particles during the optimization process. The cooperative archive is employed to provide additional promising information for ACLPO-CA and itself is updated by the cooperative operation of the current swarm and archive. To evaluate the performance of ACLPSO-CA, ACLPSO-CA is tested on CEC2013 test suite and CEC2017 test suite and compared with seven popular PSO variants. The test results show that ACLPSO-CA outperforms other comparative PSO variants on the two CEC test suites. ACLPSO-CA achieves high performance on different types of benchmark functions and exhibits high adaptability as well. In the end, ACLPSO-CA is further applied to a radar system design problem to demonstrate its potential in real-life optimization.
Hybrid reference-based Video Source Identification

Millions of users share images and videos generated by mobile devices with different profiles on social media platforms. When publishing illegal content, they prefer to use anonymous profiles. Multimedia Forensics allows us to determine whether videos or images have been captured with the same device, and thus, possibly, by the same person. Currently, the most promising technology to achieve this task exploits unique traces left by the camera sensor into the visual content. However, image and video source identification are still treated separately from one another. This approach is limited and anachronistic, if we consider that most of the visual media are today acquired using smartphones that capture both images and videos. In this paper we overcome this limitation by exploring a new approach that synergistically exploits images and videos to study the device from which they both come. Indeed, we prove it is possible to identify the source of a digital video by exploiting a reference sensor pattern noise generated from still images taken by the same device. The proposed method provides performance comparable with or even better than the state-of-the-art, where a reference pattern is estimated from video frames. Finally, we show that this strategy is effective even in the case of in-camera digitally stabilized videos, where a non-stabilized reference is not available, thus solving the limitations of the current state-of-the-art. We also show how this approach allows us to link social media profiles containing images and videos captured by the same sensor.