Reviewerly/exHarmony huggingface dataset:
Polynomial filters for camera-based structural intensity analysis on curved plates

Structural intensity can be used as a measure to detect energy sources, sinks, and transfer paths in solid structures. It provides a valuable design tool for vibro-acoustic problems next to modal analysis. However, it has been proven very challenging to determine structural intensity experimentally for the general case of curved plates. This is due to the requirement of an accurate measurement of the full 3D vibrational field and the computation of spatial gradients thereof. The purpose of this paper is to propose a mesh-free, inherently smoothing, polynomial filtering approach and investigate its application for structural intensity analysis on curved plates based on a stereo camera measurement. Numerical studies are conducted to determine reasonable algorithm parameters (discretization, fitting radius, and the number of iterations) and their performance for different levels of uncertainty and various displacement fields. The results show that, depending on the uncertainty in the measurement, optimal values of the algorithm parameters exist. Furthermore, it is essential to achieve a high signal-to-noise ratio in the camera measurement. The computation of structural intensity is validated experimentally on the case of a flat plate and a curved plate (mockup oil pan). For the flat plate, good agreement with a reference finite element simulation is obtained. While it is not possible to derive quantitative results for the mockup oil pan under realistic measurement uncertainty, qualitative conclusions can be still drawn.
N-ary relation prediction based on knowledge graphs with important entity detection

In recent years, the relation prediction has caught the attention of all walks of life, but previous work mainly focused on the binary relations. With the continuous expansion of real-world data, knowledge graphs have gradually expanded from binary relations to more complex higher-order relations. In particular, in n-ary facts, the influence of entities on different relations is diverse, and not all entities are indispensable. For this motivation, we propose a novel hypergraph and attention-based important entities graph convolutional network model for n-ary relation prediction. The hypergraph network considers the frequency of entities that appear in relations. Then, the relation-entity attention network assigns weights to the entities corresponding to each relation. Finally, graph convolutional network is to aggregate important higher-order information, and each layer emphasizes the influence of important entities from different perspectives. To verify the effectiveness of our algorithm, we conduct ablation analysis as well as parameters analysis on each module, and compare our algorithm with the state-of-the-art baselines. Experimental results on various datasets demonstrate the advantages of our algorithm.
The Three Terms Task - an open benchmark to compare human and artificial semantic representations

Word processing entails retrieval of a unitary yet multidimensional semantic representation (e.g., a lemon's colour, flavour, possible use) and has been investigated in both cognitive neuroscience and artificial intelligence. To enable the direct comparison of human and artificial semantic representations, and to support the use of natural language processing (NLP) for computational modelling of human understanding, a critical challenge is the development of benchmarks of appropriate size and complexity. Here we present a dataset probing semantic knowledge with a three-terms semantic associative task: which of two target words is more closely associated with a given anchor (e.g., is lemon closer to squeezer or sour?). The dataset includes both abstract and concrete nouns for a total of 10,107 triplets. For the 2,255 triplets with varying levels of agreement among NLP word embeddings, we additionally collected behavioural similarity judgments from 1,322 human raters. We hope that this openly available, large-scale dataset will be a useful benchmark for both computational and neuroscientific investigations of semantic knowledge.
Quantum technology’s role in cybersecurity

In the context of the development of quantum technology, most asymmetric encryption algorithms managed to be broken using Shor's algorithm, while symmetric encryption algorithms, such as AES-256, are believed to be unbroken for the moment, Grover's algorithm has been able only to reduce AES brute force attack time to its square root. This paper proposes to present how Quantum technology can affect current security algorithms and analyze how it can be used to improve our data protection by developing new algorithms. Quantum cryptography alongside Quantum key distribution could be a deal-breaker by using new encryption algorithms and providing new solutions to different cryptographic tasks that are known to be impossible by using normal computers. In the end, it will be discussed how the concept of quantum teleportation could provide new, faster, and much more secure solutions in telecommunication compared with the ones based on radio technologies.
COVID-19 approved mask detection using mathematical morphology

Face detection has multiple applications including recognition, people identification and detection of facial expressions. With the current pandemic crisis and due to the measures imposed to prevent Covid-19 spreading, the wearing of a protection mask became mandatory. The object of interest of this paper is to detect the wearing of an approved mask face using Viola Jones algorithm, aggregate channel features (ACF) and mathematical morphology. COVID-19 virus spread through the air, so it is necessary that all the materials used for manufacturing of the face masks to filter properly the air, and only the approved face masks to be used in order to control the spread infection. The algorithm used for face detection is Viola Jones with notable success in real time face detection and real time impression speed in face detection. Identification of the approved face mask against Covid-19 virus is made with a trained ACF detector. Eye detection, necessary to check if the face mask is properly placed, is based on mathematical morphology operators. These operations used together are robust with high results on the image processing.
A Mandarin Tone Recognition Algorithm Based on Random Forest and Features Fusion

The tone recognition is a necessary function in human-computer interaction (HCI) systems of Mandarin training, especially for foreign learner whose mother tongue is non-tonal. This paper presents a novel tone recognition algorithm which is based on random forest (RF) model and features fusion. First, a variety of voiced source features related to Mandarin syllable tone were selected and three fusion feature sets (FFSs) were obtained by different fusion processing. Then the three FFSs were used to construct CART decision trees respectively and the corresponding RF tone classifiers were modeled and optimized. The RF algorithms were experimented with multi-speakers Syllable Corpus of Standard Chinese (SCSC) and compared with other classifiers which are back propagation neural network (BPNN), support vector machine (SVM), Naive Bayes model (NBM) and AdaBoost. And for the small sample training sets, the effects were also evaluated. The results showed that the three optimized RF classifiers all achieved high tone recognition rate (>97.50%), model generalization ability (>98.35%) and ability to classify unbalanced data (>97.50%), and obtained the highest recognition rates on each FFS respectively, even for small training sets, the recognition rates remained above 93.57%. This indicates that the proposed algorithm is high efficient, robustness and suitable for portable HCI of Mandarin training.
A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

Abstract This article proposes a multimodal sentiment analysis system for recognizing a person’s aggressiveness in pain. The implementation has been divided into five components. The first three steps are related to a text-based sentiment analysis system to perform classification tasks such as predicting the classes into non-aggressive, covertly aggressive, and overtly aggressive classes. The remaining two components are related to an image-based sentiment analysis system. A deep learning-based approach has been employed to do feature learning and predict the three types of pain classes. An aggression dataset for the text-based system and the UNBC-McMaster database for an image-based system has been employed, respectively. Experimental results have been compared with the state-of-the-art methods, showing the superiority of the proposed approach. Finally, the scores due to text-based and image-based sentiment analysis systems are fused to obtain the performance for the proposed multimodal sentiment analysis system.
Dynamic Successor Features for transfer learning and guided exploration

The Successor Feature framework for Reinforcement Learning algorithms improves task transfer by decomposing the learned state–action value function. The decomposition involves two components, one that captures future-expected state features and the other that models the task-related reward structure. However, successful transfer between tasks depends heavily on how the reward function changes, possibly leading to failure of the original Successor Feature formulation. This paper proposes the Dynamic Successor Feature framework, DynSF, by extending the mathematical formulation of the original Successor Feature framework to center around a learned state-transition model. Under this formulation, the state-transition model dynamically induces the acting policy. The flexibility of DynSF also extends to the architecture, requiring only a state-transition model and a small vector of parameters. This architecture provides immense flexibility in the choice of the model used to learn the state-transition model. The DynSF framework is evaluated and compared to other baseline algorithms through several experiments in a continuous grid world environment, a robotic Reacher, and pixels in the Doom environment.
An Interpretable Channelwise Attention Mechanism based on Asymmetric and Skewed Gaussian Distribution

Channelwise attention mechanisms have recently been demonstrated to boost the performance of deep convolutional neural networks (CNNs). The hypothesis on the negative correlation between channelwise responses and their importance levels has been verified. Therefore, according to the shifted means and unbalanced responses that are observed in attention distribution, two empirical hypotheses are proposed in this paper. Then, as an interpretable attention module, bilateral asymmetric skewed Gaussian attention (bi-SGA) is utilized to combine skewed and asymmetric properties into the Gaussian context transformer (GCT), which is the state-of-the-art. Finally, extensive experiments on the different benchmarks validate the rationality of the hypotheses. The proposed bi-SGA improves the overall performance of GCT attention mechanisms with only two extra parameters to be learned. Moreover, our attention mechanism also provides a perspective on analyzing the channelwise importance levels in deep neural networks in an interpretable and logical manner.
Deep domain-invariant learning for facial age estimation

Previous studies in facial age estimation can achieve promising performance when the training and test sets have a similar condition. However, these methods often fail to maintain performance and show significant degradation when encountering unseen domains. Therefore, we propose a novel method named Deep Domain-Invariant Learning (DDIL) to solve the Out-of-Distribution (OOD) generalization problem for facial age estimation. The proposed DDIL consists of the domain-invariant and style-invariant modules. The former extracts domain-specific features and trains a domain-invariant feature extractor by reducing the covariance discrepancy among features from different domains, while the latter leverages style randomization to overcome CNN’s induction bias towards styles. Consolidating these two modules, our DDIL can effectively decrease the influence of domain discrepancy. Extensive experiments on multiple age benchmark datasets under the Leave-One-Domain-Out Cross-Validation setting indicate superior performance in tackling age estimation generalization.
Amplitude Ratios and Neural Network Quantum States

Neural Network Quantum States (NQS) represent quantum wavefunctions by artificial neural networks. Here we study the wavefunction access provided by NQS defined in [Science, 355, 6325, pp. 602-606 (2017)] and relate it to results from distribution testing. This leads to improved distribution testing algorithms for such NQS. It also motivates an independent definition of a wavefunction access model: the amplitude ratio access. We compare it to sample and sample and query access models, previously considered in the study of dequantization of quantum algorithms. First, we show that the amplitude ratio access is strictly stronger than sample access. Second, we argue that the amplitude ratio access is strictly weaker than sample and query access, but also show that it retains many of its simulation capabilities. Interestingly, we only show such separation under computational assumptions. Lastly, we use the connection to distribution testing algorithms to produce an NQS with just three nodes that does not encode a valid wavefunction and cannot be sampled from.
BERTCWS: unsupervised multi-granular Chinese word segmentation based on a BERT method for the geoscience domain

Unlike alphabet-based languages such as English, the Chinese language has no specifying word boundaries. Segmentation, particularly for the Chinese language, is a fundamental step towards Chinese text processing, information retrieval, and knowledge discovery. In the geoscience domain, most existing Chinese word segmentation tools/models require a prespecified dictionary and a large amount of relevant training corpus, and the segmentation accuracies drop significantly when processing out-domain situations using these same methods. To address this issue, a purely unsupervised and generic two-stage architecture (named BERTCWS) for domain-specific Chinese word segmentation is proposed. We first design an incidence matrix termed the ‘character combination tightness’ to calculate the closeness between characters. Then, BERTCWS recognizes geoscience terms based on a Bidirectional Encoder Representations from Transformers(BERT)-based segmenter, and multi-granular segmentation is generated by setting different thresholds. Finally, the discriminator is constructed to validate the correctness of the segmented words. Our numerical study demonstrates that BERTCWS can identify both general-domain terms and geoscience-domain terms. Additionally, multi-granular segmentation could be applied to offer a set of potential geoscience terms of various lengths.
Mirror-assisted multi-view high-speed digital image correlation for dual-surface dynamic deformation measurement

Based on the recently proposed mirror-assisted multi-view digital image correlation (MV-DIC), we establish a cost-effective and easy-to-implement mirror-assisted multi-view high-speed digital image correlation (MVHS-DIC) method and explore its applications for dual-surface full-field dynamic deformation measurement. In contrast to the general requirement of four expensive high-speed cameras for dual-surface dynamic deformation field measurement, the established mirror-assisted MVHS-DIC halves the cost by involving only two synchronized high-speed cameras and two planar mirrors. The two synchronized high-speed cameras can dynamically measure the front and rear surfaces of a sheet sample simultaneously through the reflection of the two mirrors. The results on the two surfaces are then transformed into the same coordinate system, leading to the required dual-surface 3D dynamical deformation fields. The effectiveness and accuracy of the established system are validated through modal tests of a cantilever aluminum sheet. The vibration measurement of a drum and dual-surface transient deformation measurement of a smartphone in the drop-collision process further prove its practicability. Benefiting from the attractive advantages of multi-view dynamic deformation measurement in a cost-efficient way, the established mirror-assisted MVHS-DIC is expected to encourage more comprehensive dynamic mechanical behavior characterization of regular-sized materials and structures in vibration and impact engineering fields.
Blockchain‐based reliable image copyright protection

Abstract The indiscriminate distribution of a large number of unlicensed images through the Internet seriously harms the benefits of image owners, which prompts the study of image copyright protection solutions. Existing blockchain‐free solutions are limited to using centralized servers to store copyright information, leaving data vulnerable to being lost or tampered with by external attacks. Although the blockchain‐based image copyright protection solutions are capable of addressing the above problem, improvements are needed in choosing a suitable blockchain platform and supporting integrated copyright life cycle management. To this end, this paper designs a blockchain‐based reliable image copyright protection system named BB‐RICP using Hyperledger Fabric. For one thing, the adoption of Fabric mitigates the limitations of other blockchain platforms represented by Ethereum in terms of efficiency, economy, and availability. For another, BB‐RICP successes in achieving integrated copyright lifecycle management. In addition, BB‐RICP achieves enhanced applicability performance by firstly introducing GM algorithms recommended by Chinese national standard and adapting the more consortium blockchain‐oriented practical byzantine fault tolerance algorithm. Moreover, the adoption of spread spectrum watermarking brings convenience to users. Finally, Kubernetes is used to assist in the simulation of BB‐RICP to verify that users can operate reliably in the blockchain network.
An Access Control Model Based on System Security Risk for Dynamic Sensitive Data Storage in the Cloud

In cloud computing, dynamic storage of data generated by users, applications, tasks, workflows, etc. requires frequent access operations, so traditional encryption cannot be applied in this case. Considering the vulnerability of dynamic data, its protection needs to consider an efficient and dynamic security protection scheme. In data-oriented access control, the traditional approach is generally static policy matching, which cannot deal with emergencies and has the problem of privileged users. To solve this problem, this paper proposes a data-oriented risk-based access control model, which adds risk assessment to the traditional attribute-based access control and aims at the source of risk from three aspects: subject attribute, resource attribute, and environment attribute. A set of risk assessment indexes is proposed, and the calculation method of risk assessment is quantitatively analyzed by combining fuzzy consistency AHP analysis method, and finally, the realization of XACML is given. The validity of the proposed model is analyzed, and the carried out experimental analysis verifies its effectiveness. The proposed model benefits cloud data storage applications that require dynamic data storage, for example, medical/patient data storage.
Enhanced Embedding Capacity for Data Hiding Approach Based on Pixel Value Differencing and Pixel Shifting Technology

Data hiding algorithms can achieve the issue of information security when secret data are transmitted via the public network. This paper proposes a novel data-hiding scheme based on pixel value differencing and pixel shifting to increase embedding capacity and visual quality. In the proposed method, the cover image is first divided into nonoverlapping blocks, and eight groups of different values are generated with the center pixel as the reference pixel to embed the secret message. The pixel shifting strategy is applied to adjust the stego image to improve its quality. Experimental results show that the proposed method has an embedding capacity of 740,000 bits with a peak signal-to-noise ratio value greater than 35 dB. Therefore, it is undetected by the human eye. Other compared state-of-the-art schemes have embedding capacities of 51,219 bits, 70,217 bits, and 104,055 bits, which are lower than the proposed methods’ 740,000 bits. The RS, chi-square, and rotation attack analyses prove that the proposed method can withstand security attacks. Thus, the proposed method performs better than other state-of-the-art methods with respect to embedding capacity and ability to withstand attacks.
An Efficient Data Augmentation Method for Automatic Modulation Recognition from Low-Data Imbalanced-Class Regime

The application of deep neural networks to address automatic modulation recognition (AMR) challenges has gained increasing popularity. Despite the outstanding capability of deep learning in automatic feature extraction, predictions based on low-data regimes with imbalanced classes of modulation signals generally result in low accuracy due to an insufficient number of training examples, which hinders the wide adoption of deep learning methods in practical applications of AMR. The identification of the minority class of samples can be crucial, as they tend to be of higher value. However, in AMR tasks, there is a lack of attention and effective solutions to the problem of Imbalanced-class in a low-data regime. In this work, we present a practical automatic data augmentation method for radio signals, called SigAugment, which incorporates eight individual transformations and effectively improves the performance of AMR tasks without additional searches. It surpasses existing data augmentation methods and mainstream methods for solving low-data and imbalanced-class problems on multiple datasets. By simply embedding SigAugment into the training pipeline of an existing model, it can achieve state-of-the-art performance on benchmark datasets and dramatically improve the classification accuracy of minority classes in the low-data imbalanced-class regime. SigAugment can be trained for uniform use on different types of models and datasets and works right out of the box.