Reviewerly/exHarmony huggingface dataset:
Semantics analysis model based on deep learning for vessel traffic service application

Abstract Vessel Traffic Service (VTS) significantly improves the navigation efficiency of ports. This paper proposes a model called Joint Extraction of Triples from the VHF Speech (JER‐VHF) to ensure the efficiency of the VTS. Numerous texts are extracted from the Very High Frequency (VHF) speech communication contents and these texts are organized into a dataset named VHFDT. The proposed model's transforming task transforms the voice communication contents of this dataset into a triple representation. VHFDT has a large number of overlapping triples. Therefore, this paper proposes a combined model with three categories to model the entity relations in VHF sentences, including pre‐training Chinese language model for initializing embedding from VHFDT, BiLSTM for rich features, and Multi‐head Attention for focusing on triples. In experimental part, this study uses Precision(P), Recall(R), and F 1 to evaluate the accuracy and effectiveness of the proposed method and baseline models. According to experimental results, the proposed model efficiently extracts the key information from complex language environment and achieves better work on relational triple extraction than other baseline models. The model achieved an F 1 ‐score of 83.2% on the overlapping testing data, which is an improvement of 1.8% compared to the second‐best baseline model.
Exploiting statistical effective fault attack in a blind setting

Abstract In order to obtain the secret key, the majority of physical attacks require knowledge of the plaintext or ciphertext, which may be unavailable or cannot be exploited. Blind attacks are introduced to do key recovery in circumstances where the adversary has no direct access to plaintext and ciphertext. A combination of fault and power attacks can circumvent typical countermeasures in this setting, for example, Fault Template Attack (FTA). However, FTA relies on bit fault injection, which is difficult to implement in practice. The SIFA‐blind, a framework for executing the Statistical Ineffective Fault Attack, is more flexible, but sensitivity to setup noise and missed faults is its main drawback. To address this deficiency, we suggest two ways to use Statistical Effective Fault Attack in a blind setting that are much less affected by missed faults and noise when measuring power traces, even though they do not use fault injection at the bit level. In order to demonstrate the viability and adaptability of our proposed attacks, we injected a fault via glitch frequency onto the ChipWhisperer board. While SEFA‐blind does not need a bit‐level fault, our results demonstrate that it is better than SIFA‐blind when the number of missed faults increases.
Attribute‐guided transformer for robust person re‐identification

Abstract Recent studies reveal the crucial role of local features in learning robust and discriminative representations for person re‐identification (Re‐ID). Existing approaches typically rely on external tasks, for example, semantic segmentation, or pose estimation, to locate identifiable parts of given images. However, they heuristically utilise the predictions from off‐the‐shelf models, which may be sub‐optimal in terms of both local partition and computational efficiency. They also ignore the mutual information with other inputs, which weakens the representation capabilities of local features. In this study, the authors put forward a novel Attribute‐guided Transformer (AiT), which explicitly exploits pedestrian attributes as semantic priors for discriminative representation learning. Specifically, the authors first introduce an attribute learning process, which generates a set of attention maps highlighting the informative parts of pedestrian images. Then, the authors design a Feature Diffusion Module (FDM) to iteratively inject attribute information into global feature maps, aiming at suppressing unnecessary noise and inferring attribute‐aware representations. Last, the authors propose a Feature Aggregation Module (FAM) to exploit mutual information for aggregating attribute characteristics from different images, enhancing the representation capabilities of feature embedding. Extensive experiments demonstrate the superiority of our AiT in learning robust and discriminative representations. As a result, the authors achieve competitive performance with state‐of‐the‐art methods on several challenging benchmarks without any bells and whistles.
Testing for the Markov property in time series via deep conditional generative learning

The Markov property is widely imposed in analysis of time series data. Correspondingly, testing the Markov property, and relatedly, inferring the order of a Markov model, are of paramount importance. In this article, we propose a nonparametric test for the Markov property in high-dimensional time series via deep conditional generative learning. We also apply the test sequentially to determine the order of the Markov model. We show that the test controls the type-I error asymptotically, and has the power approaching one. Our proposal makes novel contributions in several ways. We utilise and extend state-of-the-art deep generative learning to estimate the conditional density functions, and establish a sharp upper bound on the approximation error of the estimators. We derive a doubly robust test statistic, which employs a nonparametric estimation but achieves a parametric convergence rate. We further adopt sample splitting and cross-fitting to minimise the conditions required to ensure the consistency of the test. We demonstrate the efficacy of the test through both simulations and the three data applications.
Digital image watermarking using discrete cosine transformation based linear modulation

Abstract The proportion of multimedia traffic in data networks has grown substantially as a result of advancements in IT. As a result, it's become necessary to address the following challenges in protecting multimedia data: prevention of unauthorized disclosure of sensitive data, in addition to tracking down the leak's origin, making sure no alterations may be made without permission, and safeguarding intellectual property for digital assets. watermarking is a technique developed to combat this issue, which transfer secure data over the network. The main goal of invisible watermarking is a hidden exchange of data and a message from being discovered by a third party. The objective of this work is to develop a digital image watermarking using discrete cosine transformation based linear modulation. This paper proposed an invisible watermarking method for embedding information into the transformation domain for the grey scale images. This method used the embedding of a stego-text into the least significant bit (LSB) of the Discrete Cosine Transformation (DCT) coefficient by using a linear modulation algorithm. Also, a stego-text is embedded with different sizes ten times within images after embedding the stego-image immune to different kinds of attack, such as salt and pepper, rotation, cropping, and JPEG compression with different criteria. The proposed method is tested using four benchmark images. Also, to evaluate the embedding effect, PSNR, NC and BER are calculated. The outcomes show that the proposed approach is practical and robust, where the obtained results are promising and do not raise any suspicion. In addition, it has a large capacity, and its results are imperceptible, especially when 1bit/block is embedded.
Weak-PMLC: A large-scale framework for multi-label policy classification based on extremely weak supervision

With the development of e-government, multiple local governments in China are developing Internet-based open policy platforms, and these online platforms need to automatically classify policies. Current policy classification methods are usually supervised models that require massive amounts of annotated policies, which can be expensive and difficult to obtain in practice. To alleviate the burden of human experts annotating a large number of policies, we propose a large-scale framework (Weak-PMLC) for multi-label policy classification based on extremely weak supervision, which does not rely on any labeled documents and uses only the label names of each category. Specifically, we first pre-train a language model (LM) on a given dataset to extend the LM from general to domain-specific. We then utilize the domain-specific LM to generate seed words semantically related to label names. Finally, following the category-related seed words, we generate massive pseudo-labeled policies as training data for high-performance supervised models. To verify the effectiveness of our proposed method, we created two new human-labeled datasets containing about 56k and 37k policies, respectively. We also define 59 label names, which are several key feature words used to summarize all collected policies. We show that Weak-PMLC achieves around 90% F1-scores on these two datasets and improves performance by 4% over state-of-the-art weakly supervised methods. Further experiments show that the proposed Weak-PMLC is even comparable to some supervised models.
Automatic first break picking with structured random forests

Abstract In the seismic wave exploration field, first break picking is an important underlying task. With the emergence of massive seismic data, it is urgent to develop automatic and accurate picking algorithms to relieve the huge workload of manual picking. Although many traditional and machine learning based techniques have been developed, it is still a challenging task to obtain satisfactory picking results in practice because of complex subsurface structures and low signal‐to‐noise ratio. Ensemble learning has been demonstrated to be a powerful tool to produce good prediction results in many fields. Its core idea is to create multiple models with some special techniques and then to combine the prediction results of each model with a fusion strategy. Structured random forests , a type of ensemble learning method, have been proven to be quite effective in implementing structured learning tasks. Based on the observation that adjacent traces can provide useful information to pick the first arrival time for a specific trace, we propose in this paper a novel structured random forest based first break picking framework. In particular, a multi‐scale normalization technique is presented to make full use of the amplitude information at different scales. To capture local features of first breaks, an enhanced feature map based on the short‐term/long‐term average ratio method is also computed. By extracting patches from two channel feature maps, we construct a structured random forest to predict the locations of the first breaks. On the basis of the probability score map produced by the structured random forest, an effective post‐processing strategy is proposed to further deal with the detected results. By conducting experiments with synthetic and field data, the proposed method is shown to be effective in identifying first breaks. It is significantly superior to the short‐term/long‐term average ratio method and support vector machine in terms of picking accuracy. With the advantage of parallel computing, the computational cost of structured random forest is acceptable, that is, it is much lower than the support vector machine while being higher than the short‐term/long‐term average ratio method. In addition, the experiments also confirm that the multi‐scale normalization plays an important role in improving the picking performance of the structured random forest.
Normalised latent measure factor models

Abstract We propose a methodology for modelling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalised random measures, we consider a prior distribution for a collection of discrete random measures where each measure is a linear combination of a set of latent measures, interpretable as characteristic traits shared by different distributions, with positive random weights. The model is nonidentified and a method for postprocessing posterior samples to achieve identified inference is developed. This uses Riemannian optimisation to solve a nontrivial optimisation problem over a Lie group of matrices. The effectiveness of our approach is validated on simulated data and in two applications to two real-world data sets: school student test scores and personal incomes in California. Our approach leads to interesting insights for populations and easily interpretable posterior inference.
Trusted edge and cross-domain privacy enhancement model under multi-blockchain

With its promising security and distributed qualities, blockchain offers a significant opportunity to break through the privacy protection issues in the edge computing paradigm. However, when edge participants submit security tasks to a distributed blockchain network across domains, they may expose their data privacy and location privacy. These privacy will be maliciously attacked and exploited by attackers such as external attackers and untrusted third parties, increasing the difficulty of access control. In addition, the performance bottleneck of blockchain systems cannot meet the demand for efficient data processing. To address these problems, we propose a cross-domain privacy enhancement scheme based on multi-blockchain. The scheme first integrates edge computing based on master-slave multi-blockchain and designs an identity authentication mechanism and trust assessment mechanism to deploy a three-layer trusted network architecture, which ensures the security stability and operational efficiency at the edge side. Secondly, role mapping rules are developed and trust degree is evaluated for domain nodes, and a cross-domain access control model based on trust degree and role is proposed. Data in this model cannot be exchanged across domains until all nodes jointly verify whether the access control policy is effective, which ensures the secure sharing of data in heterogeneous domains. In addition, to ensure the privacy and authenticity of data in cross-domain sharing, a hybrid searchable encryption method based on symmetric encryption and public key encryption is proposed. Finally, the security theoretical analysis proves that the model successfully ensures the non-repudiation of access control policy and the controllability of cross-domain data. Experiments show that the proposed model improves access dynamics by 74.8% and reduces CPU usage by 24.6% on average compared to traditional RBAC. The scheme is scalable, and its sending rate to throughput ratio reaches 1:1, gaining a 2X throughput advantage over existing schemes.
Quantum-inspired optimization algorithm with adaptive correction of energy position: Methodology and a case study

Efficient and stable global optimizers constitute a noteworthy arena of academic study and real-world applications. Since Multi-scale Quantum Harmonic Oscillator Algorithm inspired by the quantum motion for solving optimization problems was proposed, considerable contributions regarding this algorithm have been achieved in recent years. Nevertheless, issues such as the aggregation effect during sampling as well as recurrence and blindness in random searches hinder the performance of the algorithm. Motivated by this situation, a variant of Multi-scale Quantum Harmonic Oscillator Algorithm is put forward to improve the efficiency of the system convergence while maintaining the solution diversity. The measurement of the solution position through the collapse of the quantum state to the classical state is realized by means of quantum Monte Carlo simulations, and the energy position is established as a metric for energy observation. Then, the adaptive correction of the energy position is explored to improve algorithm performance. The core idea of our mechanism is to adaptively guide the candidate solutions toward convergence to the ground state by means of attractive factors based on the relationship among the energy positions of several reference points. Experimental results obtained on the CEC2013 benchmark functions and a real-world application indicate that the performance of our scheme is competitive and that it achieves prominence among the compared algorithms as the dimensionality increases.
An Intelligent Facial Expression Recognizer Using Modified ResNet-110 Using Edge Computing

In this intelligent society, facial expression recognition is an important way to make assumptions about people mood and mentation. It can be applied in security and risk analysis. Many edge computing applications need facial expression recognition as the basic support. Some shortcomings have been observed in the narrative probe, such as dirty universalization ability of network architecture and low firmness of recognition system. This chapter proposes an intelligent computing method based on ResNet-110. We entered this model into the experiment and achieved a 96.29% accuracy. The laboratory findings imply that the proposed is outperform the main current recognition models. In order to express our thoughts clearly, the method can be applied to the edge computing environment.
Multi-target landmark detection with incomplete images via reinforcement learning and shape prior embedding

Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential to tackle this challenge in an efficient manner. Inspired by this, in this work we propose a multi-agent RL framework for simultaneous multi-target landmark detection. This framework is aimed to learn from incomplete or (and) complete images to form an implicit knowledge of global structure, which is consolidated during the training stage for the detection of targets from either complete or incomplete test images. To further explicitly exploit the global structural information from incomplete images, we propose to embed a shape model into the RL process. With this prior knowledge, the proposed RL model can not only localize dozens of targets simultaneously, but also work effectively and robustly in the presence of incomplete images. We validated the applicability and efficacy of the proposed method on various multi-target detection tasks with incomplete images from practical clinics, using body dual-energy X-ray absorptiometry (DXA), cardiac MRI and head CT datasets. Results showed that our method could predict whole set of landmarks with incomplete training images up to 80% missing proportion (average distance error 2.29 cm on body DXA), and could detect unseen landmarks in regions with missing image information outside FOV of target images (average distance error 6.84 mm on 3D half-head CT). Our code will be released via https://zmiclab.github.io/projects.html.
Moving Object Detection for Complex Scenes by Merging BG Modeling and Deep Learning Method

Abstract In recent years, many studies have attempted to use deep learning for moving object detection. Some research also combines object detection methods with traditional background modeling. However, this approach may run into some problems with parameter settings and weight imbalances. In order to solve the aforementioned problems, this paper proposes a new way to combine ViBe and Faster-RCNN for moving object detection. To be more specific, our approach is to confine the candidate boxes to only retain the area containing moving objects through traditional background modeling. Furthermore, in order to make the detection able to more accurately filter out the static object, the probability of each region proposal then being retained. In this paper, we compare four famous methods, namely GMM and ViBe for the traditional methods, and DeepBS and SFEN for the deep learning-based methods. The result of the experiment shows that the proposed method has the best overall performance score among all methods. The proposed method is also robust to the dynamic background and environmental changes and is able to separate stationary objects from moving objects. Especially the overall F -measure with the CDNET 2014 dataset (like in the dynamic background and intermittent object motion cases) was 0,8572.
Agent behavior modeling method based on reinforcement learning and human in the loop

Computer generated force (CGF) is one of the increasingly important research topics in the field of simulation. However, low modeling efficiency and lack of adaptability are acute problems of traditional CGF modeling. In this study, a method for modeling the agent behavior based on reinforcement learning and human in the loop is proposed to improve the ability and efficiency of agent behavior modeling. First, an overall framework for modeling the behavior of intelligent agents is constructed based on the deep reinforcement learning algorithm Soft Actor Critic (SAC) framework. Second, in order to overcome the slow convergence speed of the SAC framework, a method for human interaction and value evaluation in the loop is introduced, and the specific algorithm flow is designed. Third, in order to verify the performance of the proposed method, experiments are conducted and compared with algorithms using a pure SAC framework based on an example of agent completing specific tasks. Result shows that after 100 episodes of training, the task completion rate of the agent can approach 100% while a pure SAC framework require at least 500 episodes of training to gradually improve the completion rate. Finally, the results demonstrate that the proposed method can significantly improve the efficiency of agent behavior modeling and the task completion rate increases with the number of human interventions in the loop.