Reviewerly/exHarmony huggingface dataset:
Anonymous security authentication protocol based on identity encryption for cyber–physical power system

With the rapid development of wireless technology, the deep coupling of cyber–physical power system (CPPS) is accelerated, and the openness of communication network makes the importance of secure information transmission increasingly prominent. As the communication module of CPPS, smart devices play an important role in data transmission, to ensure reliable and confident communication between the two layers. This paper presents a design for a secure and efficient smart devices utilizing anonymous security authentication and negotiation protocol supported by the identity-based cryptograph, elliptic curve cryptography, and digital signature algorithm are mainly used for that purpose. The security of the protocol is proved and validated under the random oracle model. The analysis show that the protocol has security performance such as bidirectional authentication, privacy protection, and replay attacks resistance; moreover, it has less computation and communication cost.
DAAR: Drift Adaption and Alternatives Ranking approach for interpretable clinical decision support systems

Diagnosing allergy comorbidities is challenging because the comprehensive studies on the prevalence of each allergy and the relation with other diseases are scarce. Moreover, there is a chance for uncertainty in the intradermal skin test results due to poor administration, manual observation and interpretation. Hence this work aims to develop a Decision Support System (DSS) for enhancing the diagnosis of allergy comorbidities. The DSS framework consists of a knowledge engineering phase and decision-making phase. The former focuses on detecting the concept drift, pre-processing the data, and extracting the knowledge, whereas, the latter focuses on the DAAR approach for adapting the concept drift and ranking the alternatives. The intradermal skin test data collected from allergy testing centre in India during 2012 and 2019 is used for developing and evaluating the DSS. The three techniques of concept drift adaption, namely, replacement, ensemble, and incremental are experimented on both balanced and unbalanced datasets. It can be observed from results that the incremental adaption technique has achieved an accuracy of 70.28% and 54.94% with complete decision, and 91.42% and 76.37% with partial decision on query data of 2012 and 2019 respectively. Moreover, the accuracy of the incremental technique is above the average of the accuracies of state-of-art machine learning algorithms. This implies that the framework is capable of supporting clinicians in choosing an optimal decision from the preference order of alternatives. Moreover, the interpretable knowledge provided by the system about all possible combinations of alternatives aids clinicians in understanding the reasoning behind their choice.
Names and Quantifiers: Bringing Them Together in Classical Logic

Abstract Putting individual constants and quantifiers into the same syntactic category within first-order language promises to have far-reaching consequences: a syntax of this kind can reveal the potential of any such language, allowing us to realize that a vast class of noun phrases, including non-denoting terms, can be accommodated in the new syntax as expressions suited to being subjects of sentences. In the light of this, a formal system that is an extension of classical first-order logic is developed here, and is equipped with an appropriate semantics. An ontological interpretation of the new logic is then also provided, with several categories of object and notions of existence being distinguished. Last but not least, a modal version of the logic, with some interesting formal features, is proposed.
Predicting the eyebrow from the orbit using three-dimensional CT imaging in the application of forensic facial reconstruction and identification

Eyebrows are the most important facial feature in facial recognition with its shape rated to be more helpful than color or density for facial reconstruction or approximation. However, little extant research has estimated the position and morphological territory of the eyebrow from the orbit. Three-dimensional craniofacial models, produced from CT scans of 180 Koreans autopsied at the National Forensic Service Seoul Institute, were used to conduct metric analyses of subjects (125 males and 55 females) between 19 and 49 (mean 35.1) years. We employed 18 craniofacial landmarks to examine the morphometry of the eyebrow and orbit with 35 pairs of distances per subject measured between landmark and reference planes. Additionally, we used linear regression analyses to predict eyebrow shape from the orbit for every possible combination of variables. The morphology of the orbit has more influence on the position of the superior margin of the eyebrow. In addition, the middle part of the eyebrow was more predictable. The highest point of the eyebrow in female was located more medially than the male. Based on our findings, the equations for estimating the position of the eyebrow from the shape of the orbit is useful information for face reconstruction or approximation.
Markov properties of partially open quantum random walks

In this paper, we first construct (nonhomogeneous) quantum Markov chains (QMCs, for short) associated with partially open quantum random walks (POQRWs, for short). We then focus on the study of the analogs of irreducibility, period, and ergodic behavior of POQRWs by QMCs. Moreover, we get the characterization of invariant states of POQRWs via QMCs.
Improved neural distinguishers with multi-round and multi-splicing construction

In CRYPTO 2019, Gohr successfully applied deep learning to differential cryptanalysis against the NSA block cipher Speck32/64, achieving higher accuracy than traditional differential distinguishers. Until now, the improvement of neural differential distinguishers is a mainstream research direction in neural-aided cryptanalysis. But the current development of training data formats for neural distinguishers forms barriers: (1) The source of data features is limited to linear combinations of ciphertexts, which does not provide more learnable features to the training samples for improving the neural distinguishers. (2) Lacking breakthroughs in constructing data format for network training from the deep learning perspective. In this paper, considering both the domain knowledge about deep learning and information on differential cryptanalysis, we use the output features of the penultimate round to proposing a two-dimensional and non-realistic input data generation method of neural differential distinguishers. Then, we validate that the proposed new input data format has excellent features through experiments and theoretical analysis. Moreover, combining the idea of multiple ciphertext pairs, we generate two specific models for data input construction: MRMSP(Multiple Rounds Multiple Splicing Pairs) and MRMSD(Multiple Rounds Multiple Splicing Differences) and then build new neural distinguishers against Speck and Simon family, which effectively improve the performance compared with the previous works. To the best of our knowledge, our neural distinguishers achieve the longest rounds and the higher accuracy for NSA block ciphers Speck and Simon.
Efficient Criteria Based Method for Selection of Relevant Ontologies in Transport Domain

The smart city is one of the most appropriate solutions to the challenges posed by uncontrolled urbanization, rapid population growth, waste of energy re-sources, and environmental pollution.As a result, many cities have undergone real transformation in the economic, civic, governmental, housing, environmental and especially transport domain.However, given the difficulties of representing the transport domain, several ontologies have been designed to overcome this difficulty.These ontologies enable requirements planning and decision making.Despite this fact, many of the proposed ontologies are difficult to reuse for various reasons such as their obsolescence or their specificity to a given city.Thus, in this study, we propose methods using criteria to determine the relevant ontologies among several others.Simulations confirmed that one these methods is very efficient.
FSRSI: New Deep Learning-Based Approach for Super-Resolution of Multispectral Satellite Images

Open access in space remote sensing has allowed easy access to satellite imagery; however, access to high-resolution imagery is not given to everyone, but only to those who master space technology.Thus, this paper presents a new approach for improving the quality of Sentinel-2 satellite images by super-resolution exploiting deep learning techniques.In this context, this work proposes a generic solution that improves the spatial resolution from 10m to 2.5m (scaling factor 4) taking into account the constraints of volumetry and dependence between spectral bands imposed by the specificities of satellite images.This study proposes the FSRSI model which exploits the potential of deep convolutional networks (CNN) and integrates new state-of-the-art concepts including Network in Network, end-to-end learning, multi-scale fusion, neural network optimization, acceleration, and filter transfer.This model has also been improved by an efficient mosaicking technique for the Super-Resolution of satellite images in addition to the consideration of inter-spectral dependence combined with the efficient choice of training data.This approach shows better performance than what has been proven in the field of spatial imagery.The experimental results showed that the adopted algorithm restores the details of satellite images quickly and efficiently; outperforming several state-of-the-art methods.These performances were observed following a benchmark with several neural networks and experimentation of applications to a carefully constructed dataset.The proposed solution showed promising results in terms of visual and perceptual quality with a better inference speed.
Selection of features and hidden Markov model parameters for English word recognition from Leap Motion air‐writing trajectories

Abstract Air‐writing recognition is relevant in areas such as natural human–computer interaction, augmented reality, and virtual reality. A trajectory is the most natural way to represent air writing. We analyze the recognition accuracy of words written in air considering five features, namely, writing direction, curvature, trajectory, orthocenter, and ellipsoid, as well as different parameters of a hidden Markov model classifier. Experiments were performed on two representative datasets, whose sample trajectories were collected using a Leap Motion Controller from a fingertip performing air writing. Dataset contains 840 English words from 21 classes, and dataset contains 1600 English words from 40 classes. A genetic algorithm was combined with a hidden Markov model classifier to obtain the best subset of features. Combination trajectory, orthocenter, writing direction, curvature provided the best feature set, achieving recognition accuracies on datasets and of 98.81 % and 83.58 % , respectively.
Study on the design of composite beams under complex environment in mountainous areas

The mountain area is faced with complex conditions such as vertical and horizontal valleys, complex geological topography, many earthquake fault zones, high seismic intensity, fragile ecology, and changeable climate. The traditional concrete bridges have a heavy weight, high consumption of sand and gravel materials, large structural dimensions of steel box composite bridges, high steel consumption, and poor overall horizontal performance of steel plate composite beams, which are not suitable for mountain construction conditions. Therefore, based on the construction environment and application requirements of highway bridges in mountainous areas, the structural scheme of steel box-steel plate composite beam is put forward in this paper, and the design of a 25 m–50 m standard-span steel box-steel plate composite beam is studied. The results show that the steel box-steel plate composite beam structure scheme is adopted, which has excellent mechanical characteristics of steel box girder and simple structure of steel plate girder. It has the mechanical characteristics of “balanced transverse force and excellent longitudinal force”, so it is a reasonable choice for small radius curved beam bridge in a mountainous area, so it is a reasonable choice for small radius curved beam bridge in a mountainous area. The steel box-steel plate composite beam adopts the integrated technology of manufacture, transportation and installation, which can better meet the road transport conditions in the mountain area. The longitudinal assembly of the manufacturing unit as the installation unit can ensure the rapid and high-precision installation of the construction site, and the assembly construction of the bridge in the mountain area has been realized. The technical and economic comparison shows that the mechanical performance of steel box-steel plate composite beam is similar to that of steel box beam and obviously better than that of steel plate girder. The steel consumption and construction period of steel box-steel plate composite beam are similar to that of steel plate girder, and are significantly less than that of the steel box girder. Therefore, the steel box-steel plate composite beam is a good choice for mountain bridge construction.
AWEncoder: Adversarial Watermarking Pre-Trained Encoders in Contrastive Learning

As a self-supervised learning paradigm, contrastive learning has been widely used to pre-train a powerful encoder as an effective feature extractor for various downstream tasks. This process requires numerous unlabeled training data and computational resources, which makes the pre-trained encoder become the valuable intellectual property of the owner. However, the lack of a priori knowledge of downstream tasks makes it non-trivial to protect the intellectual property of the pre-trained encoder by applying conventional watermarking methods. To deal with this problem, in this paper, we introduce AWEncoder, an adversarial method for watermarking the pre-trained encoder in contrastive learning. First, as an adversarial perturbation, the watermark is generated by enforcing the training samples to be marked to deviate respective location and surround a randomly selected key image in the embedding space. Then, the watermark is embedded into the pre-trained encoder by further optimizing a joint loss function. As a result, the watermarked encoder not only performs very well for downstream tasks, but also enables us to verify its ownership by analyzing the discrepancy of output provided using the encoder as the backbone under both white-box and black-box conditions. Extensive experiments demonstrate that the proposed work enjoys quite good effectiveness and robustness on different contrastive learning algorithms and downstream tasks, which has verified the superiority and applicability of the proposed work.
Joint Syntax-Enhanced and Topic-Driven Graph Networks for Emotion Recognition in Multi-Speaker Conversations

Daily conversations contain rich emotional information, and identifying this emotional information has become a hot task in the field of natural language processing. The traditional dialogue sentiment analysis method studies one-to-one dialogues and cannot be effectively applied to multi-speaker dialogues. This paper focuses on the relationship between participants in a multi-speaker conversation and analyzes the influence of each speaker on the emotion of the whole conversation. We summarize the challenges of emotion recognition work in multi-speaker dialogue, focusing on the context-topic switching problem caused by multi-speaker dialogue due to its free flow of topics. For this challenge, this paper proposes a graph network that combines syntactic structure and topic information. A syntax module is designed to convert sentences into graphs, using edges to represent dependencies between words, solving the colloquial problem of daily conversations. We use graph convolutional networks to extract the implicit meaning of discourse. In addition, we focus on the impact of topic information on sentiment, so we design a topic module to optimize the topic extraction and classification of sentences by VAE. Then, we use the combination of attention mechanism and syntactic structure to strengthen the model’s ability to analyze sentences. In addition, the topic segmentation technology is adopted to solve the long-term dependencies problem, and a heterogeneous graph is used to model the dialogue. The nodes of the graph combine speaker information and utterance information. Aiming at the interaction relationship between the subject and the object of the dialogue, different edge types are used to represent different interaction relationships, and different weights are assigned to them. The experimental results of our work on multiple public datasets show that the new model outperforms several other alternative methods in sentiment label classification results. In the multi-person dialogue dataset, the classification accuracy is increased by more than 4%, which verifies the effectiveness of constructing heterogeneous dialogue graphs.
The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.
EffShuffNet: An Efficient Neural Architecture for Adopting a Multi-Model

This work discusses the challenges of multi-label image classification and presents a novel Efficient Shuffle Net (EffShuffNet) based on a convolutional neural network (CNN) architecture to address these challenges. Multi-label classification is difficult as the complexity of prediction increases with the number of labels and classes, and current multi-model approaches require optimized deep learning models which increase computational costs. The EffShuff block divides the input feature map into two parts and processes them differently, with one half undergoing a lightweight convolution and the other half undergoing average pooling. The EffShuff transition component shuffles the feature maps after lightweight convolution, resulting in a 57.9% reduction in computational cost compared to ShuffleNetv2. Furthermore, we propose EffShuff-Dense architecture, which incorporates Dense connection to further emphasize low-level features. In experiments, the EffShuffNet achieved 96.975% accuracy in age and gender classification, which is 5.83% higher than the state-of-the-art, while EffShuffDenseNet was even better with 97.63% accuracy. Additionally, the proposed models were found to have better classification performance with smaller model sizes in fine-grained image classification experiments.
Incentive Public Auditing Scheme with Identity-Based Designated Verifier in Cloud

With the rapid development of cloud storage and cloud computing technology, users can upload data to the cloud and share it with other users. However, the integrity and the privacy of data files at the cloud service provider suffer from challenges. In order to protect the data of user in the cloud, particularly for user groups without honest managers and semitrusted third-party auditors, we construct a new public auditing scheme that integrates a (t,n) threshold signature, an incentive mechanism, an identity-based designated verifier group, blinding technology, and a multiblock signature technique. In our scheme, the (t,n) threshold signature can eliminate the single power of the user group’s managers, to ensure that members of the user group take part in data sharing fairly and equally. The generation of a user’s key pairs and the signatures of data can be encouraged by an incentive mechanism based on a blockchain. Moreover, we introduce an identity-based designated verifier group and blinding technology to preserve data privacy during the data integrity auditing process. Furthermore, the multiblock signature technique reduces the costs to sign data blocks and verify. Finally, the security analysis and performance analysis demonstrate that our scheme is provably reliable and efficient.
Improving the Performance of Open-Set Recognition with Generated Fake Data

Open-set recognition models, in addition to generalizing to unseen instances of known categories, have to identify samples of unknown classes during the training phase. The main reason the latter is much more complicated is that there is very little or no information about the properties of these unknown classes. There are methodologies available to handle the unknowns. One possible method is to construct models for them by using generated inputs labeled as unknown. Generative adversarial networks are frequently deployed to generate synthetic samples representing unknown classes to create better models for known classes. In this paper, we introduce a novel approach to improve the accuracy of recognition methods while reducing the time complexity. Instead of generating synthetic input data to train neural networks, feature vectors are generated using the output of a hidden layer. This approach results in a less complex structure for the neural network representation of the classes. A distance-based classifier implemented by a convolutional neural network is used in our implementation. Our solution’s open-set detection performance reaches an AUC value of 0.839 on the CIFAR-10 dataset, while the closed-set accuracy is 91.4%, the highest among the open-set recognition methods. The generator and discriminator networks are much smaller when generating synthetic inner features. There is no need to run these samples through the first part of the classifier with the convolutional layers. Hence, this solution not only gives better performance than generating samples in the input space but also makes it less expensive in terms of computational complexity.
The Relationship between the Rational Management of Resources and Performance: Examining the Case of the Government Sector in the Al-Jouf Region

The study aimed to identify the relationship between rational resource management and the performance of the government sector in the Al-Jouf region. It is divided into 5 dimensions: strategy, discipline, justice, transparency and accountability. The second dimension (the dependent variable) represents performance, which consists of three dimensions: the financial dimension, the customer dimension, and the learning and growth dimension. A questionnaire was distributed to a sample of 245 public sector employees of the Al-Jouf region. The study sample was comprehensive, non-probabilistic and intentional. It was analyzed using the Smart PLS program. The results showed a positive relationship between the dimensions of the rational management of resources) and performance in the government sector institutions in the Al-Jouf region.