Reviewerly/exHarmony huggingface dataset:
A virtual fields method for identifying anisotropic elastic constants of fiber reinforced composites using a single tension test: Theory and validation

A novel inverse identification method using only a single tensile specimen for obtaining four anisotropic elastic constants of fiber reinforced composites sheet is proposed in this study. The method is based on virtual fields method (VFM) for solving weak form equilibrium equation with an optimized tension-type non-standard specimen specially designed with an elliptic hole at the center of the specimen (though not limited to this special shape). Full-field strain measured by a digital image correlation (DIC) technique is applied as experimental strain fields which are used as inputs of the VFM. For optimizing the tension-type VFM specimen, finite element (FE) analysis is conducted as a virtual experiment to generate ideal strain fields. The determined tension-type VFM specimen has an elliptic hole with 45° inclined to the loading direction, which is optimized from the error analysis between the FE input and VFM calculated material constants. For the validation of the proposed approach, experiments are performed with unidirectional carbon fiber reinforced plastic (UD-CFRP) sheets. The comparison of VFM identified anisotropic constants to those determined from the standard tensile tests with three different orientated samples shows satisfactory accuracy ranging between −5.94 and 0.71%, errors which confirm the validity of the new inverse method for the characterization of anisotropic elasticity of composites sheets.
Cryptanalysis and improvement of a reversible data-hiding scheme in encrypted images by redundant space transfer

Recently, a reversible data hiding scheme by redundant space transfer has been proposed by Liu et al. In this scheme, the encryption process consists of bit plane disordering, sub-block position disordering and different-time Arnold transform in each sub-block. Then, the additional data is embedded into the LSB planes of the encrypted image by compressing the redundant space. However, to transfer the redundancy of the plain image into encrypted image, this scheme essentially just performs three types of disorder operations, which is vulnerable to security analysis. In this paper, we firstly demonstrate that the encryption method of this scheme can be broken by chosen-plaintext attack (CPA). Furthermore, in order to achieve the resistance against CPA, we introduce 2-D Logistic-adjusted-sine map (2D-LASM) to produce the encryption sequences with the control parameters generated from the plain image. Apart from that, we also improve the sparse matrix compression to achieve a superior embedding capacity. Extensive experiments and analyses demonstrate that the proposed scheme outperforms the original one in terms of both security and embedding capacity.
LOW: Training deep neural networks by learning optimal sample weights

The performance of deep learning (DL) models is highly dependent on the quality and size of the training data, whose annotations are often expensive and hard to obtain. This work proposes a new strategy to train DL models by Learning Optimal samples Weights (LOW), making better use of the available data. LOW determines how much each sample in a batch should contribute to the training process, by automatically estimating its weight in the loss function. This effectively forces the model to focus on more relevant samples. Consequently, the models exhibit a faster convergence and better generalization, specially on imbalanced data sets where class distribution is long-tailed. LOW can be easily integrated to train any DL model and can be combined with any loss function, while adding marginal computational burden to the training process. Additionally, the analysis of how sample weights change during training provides insights on what the model is learning and which samples or classes are more challenging. Results on popular computer vision benchmarks and on medical data sets show that DL models trained with LOW perform better than with other state-of-the-art strategies.1
In-air signature verification system using Leap Motion

Signature verification is a widely explored field due to its high acceptance and its compromise between security and comfort. Recently, different techniques have appeared to improve the capture, processing, and classification of signatures. In this work, authors present a novel and robust in-air signature verification system, which applies the use of Leap Motion controller to characterize in-air strokes, due to its stability and good performance for this task, as it will be demonstrated. Therefore, a database has been built for developing the experiments, which is composed of 100 users, with 10 genuine and 10 forgery samples per user. The implemented system is tested against two tests of impostor samples, zero effort attacks and active impostors. The second type of attacks are developed by different users, who showed very good abilities with the sensor. The classification is done by a Least Square Support Vector Machine. The equal error rate was 0.25% and 1.20%, respectively. The proposed system achieves very good results in comparison with the state-of-the-art one, which suggests that in-air signature processing gives an opportunity to increase systems’ security.
Automatic edge detection of ply cracks in glass fiber composite laminates under quasi-static and fatigue loading using multi-scale Digital Image Correlation

Digital image correlation is utilized to detect automatically crack density in glass/epoxy laminates with different lay-ups under quasi-static and fatigue loading conditions. The DIC setups used in the present work are 2D-DIC and 3D-DIC which are considered to be at meso-scale and macro-scale, respectively. The main objective of this work is to study whether the DIC technique is reliable in automatic crack detection and functional in calculation of crack density. Therefore, an optimization analysis is implemented to evaluate the resolution and the standard uncertainty in strain and displacement measurements. An optimized compromise is attained between the resolution and the uncertainty of both measurements to decide about the final configuration of the DIC setup. The setup is executed by acquiring in-situ images under quasi-static and fatigue experiments so that the images are synchronized with the data acquired from the testing machine. The crack detection analysis is attained by DIC analysis using strain and displacement fields in order to obtain optimum results. Then a semi-automatic program is created to evaluate the crack density and weighted crack density from the edge and top of the specimen, respectively. Moreover, a comparison is established between both strain and displacement fields in crack characterization. Consequently, the displacement field has proved to deliver better and more accurate results than strain fields in crack density calculation and individual crack characterization. Accordingly, the results obtained from DIC analysis are compared and validated by microscopic images which are acquired after performing each test.
Dynamics of spatially indistinguishable particles and quantum entanglement protection

We provide a general framework which allows one to obtain the dynamics of $N$ noninteracting spatially indistinguishable particles locally coupled to separated environments. The approach is universal, being valid for both bosons and fermions and for any type of system-environment interaction. It is then applied to study the dynamics of two identical qubits under paradigmatic Markovian noises, such as phase damping, depolarizing and amplitude damping. We find that spatial indistinguishability of identical qubits is a controllable intrinsic property of the system which protects quantum entanglement against detrimental noise.
Implementation of Smart-Contract, Based on Hyperledger Fabric Blockchain

Nowadays smart contracts apply in management, bank system, insurance, estate, IoT and others, because they perform credible and trackable irreversible transactions without third parties. The paper presents a solution for creation of a smart contract based on Permission Blockchain, in particular Hyperledger Fabric. The tests are presented with Hyperledger Explorer for Fabric 1.4.x under Linux Ubuntu.
Privacy-Preserving Continuous Data Collection for Predictive Maintenance in Vehicular Fog-Cloud

With the advances of Internet of Things (IoT) solutions in intelligent transportation systems, collected vehicle data can produce insights on emerging vehicular phenomenon, and further contribute to the further improvement of innovative and efficient vehicular systems. Particularly, by leveraging data collected from vehicle sensors and maintenance models constructed from operation and repair history, predictive maintenance aims to detect the anomalies of vehicles and provide early warnings before the occurrence of failure. However, privacy preservation still remains as one of the top concerns for vehicle owners in predictive maintenance, as the sensory data could potentially violate their location and identity privacy. To address this challenge, in this article, we propose a privacy-preserving and verifiable continuous data collection scheme with the intent of predictive maintenance in vehicular fog, which gathers and organizes the sensor data of each individual vehicle on a sliding window basis. Specifically, our proposed scheme exploits the homomorphic Paillier cryptosystem and truncated α-geometric technique to protect the content of each individual piece of sensory data. Meanwhile, our proposed scheme also aggregates and authenticates the collected sensory data reports on a time-series sliding window basis, which achieves the continuous observation of the recently collected vehicular sensory data. Detailed security analysis is carried out to demonstrate the security properties of our proposed scheme, including confidentiality, authentication and privacy preservation. In performance evaluations, we also compare our proposed scheme with a traditional scheme, and our scheme shows great improvement in terms of communication and computation overheads. Furthermore, to show the feasibility of our proposed scheme, we also compare and discuss the expected squared error introduced by the differential privacy mechanism.
Empirical Evaluation of Activation Functions in Deep Convolution Neural Network for Facial Expression Recognition

Deep Convolutional Neural Network (DCNN) is widely used as state-of-the art models for image classification. There is a variety of applications related to image classification such as object classification, facial expression classification, and scene classification. In this paper, different activation functions are evaluated for facial expression recognition (FER). The activation functions used for evaluation are Rectified Linear Unit (ReLU), Leaky Rectified Linear Unit (Leaky ReLU), Hyperbolic Tangent Tanh, and Sigmoid. Experiments are conducted on a benchmark dataset known as Fer2013 which is publicly available on Kaggle. Our experiments show that Tanh achieved better performance compared to other activation functions.
Detection of replay spoof speech using teager energy feature cues

The vulnerability of Automatic Speaker Verification (ASV) systems to spoofing or presentation attacks is still an open security issue. In this context, replay spoofing attacks pose a great threat to an ASV system since they can be easily performed (using a playback device, and without needing any technical skill). In this paper, we analyze replay speech signals in terms of reverberation that may occur during recording of the speech signal. Such reverberation introduces delay and changes in amplitude, producing close copies of speech signals, which significantly influences the replay components. To that effect, we propose to exploit the capabilities of the Teager Energy Operator (TEO) to compute a running estimate of subband energies for replay vs. genuine signals. We have used a linearly-spaced Gabor filterbank to obtain a narrowband filtered signal. The TEO has the ability to track the instantaneous changes of a signal. Experiments are performed on the ASVspoof 2017 Challenge version 2.0 database using a Gaussian Mixture Model (GMM) as pattern classifier. Furthermore, we compared our results with state-of-the-art feature sets, namely, Constant Q Cepstral Coefficients (CQCC), Linear Frequency Cepstral Coefficients (LFCC), Mel Frequency Cepstral Coefficients (MFCC), and used their score-level fusion with the proposed feature sets, i.e., Teager Energy Cepstral Coefficients (TECC), in order to obtain possible complementary information that further reduces the Equal Error Rate (EER). Relatively low EERs are obtained with score-level fusion of CQCC, MFCC, LFCC, and TECC feature sets, resulted in 6.68% and 10.45% on development and evaluation sets, respectively. Moreover, for the evaluation dataset, we also studied the performance of the TECC feature set on different Replay Configurations (RC), namely, for acoustic environments, playback, and recording devices. For all the levels of threat conditions (i.e., low, medium, and high-level) to an ASV system, the proposed feature set performed better compared to existing state-of-the-art feature sets. In addition to the ASVspoof 2017 Challenge database, we also performed experiments on other spoofing databases, namely, the ASVspoof 2015 Challenge, BTAS 2016, and ASVspoof 2019 Challenge databases. For all the spoofing databases used in this study, the proposed TECC feature set perform significantly better than the other feature sets.
The Status of Quantum-Key-Distribution-Based Long-Term Secure Internet Communication

A large amount of sensitive data must remain accessible for decades or even centuries (e.g, electronic health records, governmental documents). Communicating such data over the Internet requires long-term secure communication channels, which, in turn, require robust key distribution protocols. Currently used key distribution protocols, however, are not designed for long-term security. Their security is either threatened by quantum computers, or, in principle, due to their reliance on computational problems. Quantum key distribution (QKD) protocols are information-theoretically secure and thereby offer long-term security against computational attacks. However, significant obstacles to their real-world use remain. This position paper, which is a multidisciplinary effort of computer scientists and physicists, systematizes knowledge about challenges of and strategies for realizing long-term secure Internet communication from QKD. We first analyze the performance and security of existing point-to-point QKD technology. Then, we discuss several approaches to enabling QKD in large-scale multi-user networks. Finally, we list important challenges that need to be addressed in order to make QKD-based long-term secure communication on the Internet practical.
Risks in neural machine translation

Abstract The new paradigm of neural machine translation is leading to profound changes in the translation industry. Surprisingly good results have led to high expectations; however, there are substantial risks that have not yet been sufficiently taken into account. Risks exist on three levels: first, what kind of damage can clients and end users incur in safety-critical domains if the NMT result contains errors; second, who is liable for damage caused by the use of NMT; third, what cyber risks can the use of NMT entail, especially when free online engines are used. When establishing sustainable measures to reduce such risks, we also need to consider general principles of human behaviour if we want to make sure that all agents comply with provisions and requirements.
Ensemble solar forecasting using data-driven models with probabilistic post-processing through GAMLSS

Forecast performance of data-driven models depends on the local weather and climate regime, which makes model selection a tedious task for forecast practitioners. Ensemble forecasting, or forecast combination, is beneficial in such cases, in that, forecasts from multiple models are combined to form a final forecast. In ensemble forecasting, additional to the final deterministic-style forecasts, predictive distributions are also available, which can be used by grid operators for better decision-making. Such empirical predictive distributions are useful to represent the uncertainty associated with the forecasts. However, raw ensemble forecasts are often not calibrated, e.g., due to the lack of diversity in the ensemble members. The lack of ensemble spread is known as underdispersion, and it can be ameliorated through post-processing. This study aims to calibrate hourly ensemble clear-sky index forecasts, generated by 20 data-driven models, using both parametric and nonparametric post-processing techniques. Four years of data collected at 7 research-grade sites are used in the empirical part of the paper. Quantitative and qualitative methods are used to evaluate the performance of post-processing techniques in terms of calibration and sharpness. Post-processed ensemble forecasts outperform raw ensemble forecasts under all verification metrics. The proposed parametric post-processing technique, namely, generalized additive models for location, scale and shape, substantially reduces the continuous ranked probability score (CRPS) of the raw ensemble forecasts from 32–59 W/m2 to 25–45 W/m2 and quantile score from 16–30 W/m2 to 13–23 W/m2. In terms of CRPS skill score, the proposed method achieved 38–58% improvements over a climatology reference.
Unsupervised text-to-image synthesis

Recently, text-to-image synthesis has achieved great progresses with the advancement of the Generative Adversarial Network (GAN). However, training the GAN models requires a large amount of pairwise image-text data, which is extremely labor-intensive to collect. In this paper, we make the first attempt to train a text-to-image synthesis model in an unsupervised manner, which does not require any human labeled image-text pair data. Specifically, we first rely on the visual concepts to bridge two independent image and sentence sets and thereby yield the pseudo image-text pair data, based on which one GAN model can thereby be initialized. One novel visual concept discrimination loss is proposed to train both generator and discriminator, which not only encourages the image expressing the true local visual concepts but also ensures the noisy visual concepts contained in the pseudo sentence being suppressed. Afterwards, one global semantic consistency regarding to the real sentence is used to adapt the pretrained GAN model to real sentences. Experimental results demonstrate that our proposed unsupervised training strategy is able to generate favorable images for given sentences, which even outperforms some existing models trained in the supervised manner. The code of this paper is available at https://github.com/dylls/Unsupervised_Text-to-Image_Synthesis.
What to Expect from Code Review Bots on GitHub?

Software bots are used by Open Source Software (OSS) projects to streamline the code review process. Interfacing between developers and automated services, code review bots report continuous integration failures, code quality checks, and code coverage. However, the impact of such bots on maintenance tasks is still neglected. In this paper, we study how project maintainers experience code review bots. We surveyed 127 maintainers and asked about their expectations and perception of changes incurred by code review bots. Our findings reveal that the most frequent expectations include enhancing the feedback bots provide to developers, reducing the maintenance burden for developers, and enforcing code coverage. While maintainers report that bots satisfied their expectations, they also perceived unexpected effects, such as communication noise and newcomers' dropout. Based on these results, we provide a series of implications for bot developers, as well as insights for future research.
Cold-start Point-of-interest Recommendation through Crowdsourcing

Recommender system is a popular tool that aims to provide personalized suggestions to user about items, products, services, and so on. Recommender system has effectively been used in online social networks, especially the location-based social networks for providing suggestions for interesting places known as POIs (points-of-interest). Popular recommender systems explore historical data to learn users’ preferences and, subsequently, they recommend locations to an active user. This strategy faces a major problem when a new POI or business evolves in a city. New business has no historical user experience data. Thus, a recommender system fails to gather enough knowledge about the new businesses, resulting in ignoring them during recommendations. This scenario is popularly known as a cold-start POI problem. Users never get recommendations of the new businesses in a city even though they can be relevant to a user. Also, from a business owner’s perspective, such a recommendation strategy does not help its reachability among users. Therefore, it is important for a recommender system to remain updated with new businesses in a city and ensure that all relevant POIs are recommended to a user irrespective of their lifetime. A POI recommendation approach is proposed in this work that can effectively handle the new businesses, or the cold-start POI problem, in a city. We crowdsource descriptions of cold-start POIs from various online social networks. The reviews of users are exploited here to learn the inherent features at the existing POIs and the new crowdsourced POIs. Finally, the proposed approach recommends top- K POIs consisting of the existing and new POIs. We perform experiments on the real-world Yelp dataset, which is one of the largest available data resources containing details on a wide range of businesses, users, and reviews. The proposed approach is compared with four existing POI recommendation approaches. The obtained results show that our approach outperforms others in handling cold-start POIs.
Learning stacking regression for no-reference super-resolution image quality assessment

No-reference super-resolution (SR) image quality assessment (NR-SRIQA) aims to evaluate the quality of SR images without relying on any reference images. Currently, most previous methods usually utilize a certain handcrafted perceptual statistical features to quantify the degradation of SR images and a simple regression model to learn the mapping relationship from the features to the perceptual quality. Although these methods achieved promising performance, they still have some limitations: 1) the handcrafted features cannot accurately quantify the degradation of SR images; 2) the complex mapping relationship between the features and the quality scores cannot be well approximated by a simple regression model. To alleviate the above problems, we propose a novel stacking regression framework for NR-SRIQA. In the proposed method, we use a pre-trained VGGNet to extract the deep features for measuring the degradation of SR images, and then develop a stacking regression framework to establish the relationship between the learned deep features and the quality scores to achieve the NR-SRIQA. The stacking regression integrates two base regressors, namely Support Vector Regression (SVR) and K-Nearest Neighbor (K-NN) regression, and a simple linear regression as a meta-regressor. Thanks to the feature representation capability of deep neural networks (DNNs) and the complementary features of the two base regressors, the experimental results indicate that the proposed stacking regression framework is capable of yielding higher consistency with human visual judgments on the quality of SR images than other state-of-the-art SRIQA methods.