Reviewerly/exHarmony huggingface dataset:
Deformable attention-oriented feature pyramid network for semantic segmentation

In the field of computer vision, the use of pyramid features can significantly improve network performance. However, the misalignment of semantic information and the scale limitation of small-scale features lead to an imbalance of feature contributions, which severely limits the performance of the feature pyramid network. In order to solve the problem of model efficiency decline caused by feature contribution imbalance, in this paper, we propose a deformable attention-oriented feature pyramid network (DAFPN). Unlike previous models, which focus solely on the semantic information between features, DAFPN uses the deformable attention mechanism to model the relationship between multiple features and then merges them in the pyramid feature fusion process. Based on DAFPN, we further propose a fully transformer-based semantic segmentation head, which achieves high performance and good scalability. Comparisons on multiple backbones reveal that our proposed model outperforms the baseline model. Under the same conditions, our method can improve the mIoU by 1∼4%, which is higher than the baseline semantic segmentation model.
Graph and dynamics interpretation in robotic reinforcement learning task

Robot control tasks are typically solved by reinforcement learning approaches in a circular way of trial and learn. A recent trend of the research on robotic reinforcement learning is the employment of the deep learning methods. Existing deep learning methods achieve the control by training the approximation models of the dynamic function, value function or the policy function in the control algorithms. However, these methods usually handle the modeling from a statistical perspective without considering the physics characteristics of the robot’s motion. One of the typical problems is the force transmission through different parts of a robot and the calculations of the robotic dynamics quantities are prone to be ignored. To this end, we propose to use the force transmission graph to interpret the force transmission mechanism obeyed by the motion of the robot and estimate the dynamics quantities of the robot’s motion with a quadratic model. Following this concern, we propose a model-based reinforcement learning framework for robotic control in which the dynamic model comprises two components, i.e. the Graph Convolution Network (GCN) and the Two-Layer Perception (TLP) network. The GCN serves as a parameter estimator of the force transmission graph and a structural feature extractor. The TLP network approximates the quadratic model that should be able to estimate the dynamics quantities of the robot’s motion. For this reason, the proposed framework is named as GCN of Dynamics estimation in Reinforcement Learning method (GDRL for short). The deployed method interprets the intrinsic mechanism of robotic force transmissions through robot limbs, therefore the model is highly interpretable. Experimental results show that GDRL can predict the gesture and location of the robot for the next move well such that the performance of our method surpasses that of the previous methods in robot control task in our task setting of multiple types of robots. We also designed to compare with the previous model-free methods in our task setting, and the results are outstanding, which is owing to the interpretation of the physical characteristics.
Unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering

Large texts are not always entirely meaningful: they might include repetitions and useless details, and might not be easy to interpret by humans. Automatic text summarization aims to simplify text by making it shorter and (possibly) more informative. This paper describes a new solution for extractive text summarization, designed to efficiently process flat (unstructured) text. It performs unsupervised frequency-based document processing to identify the candidate sentences having the highest potential to represent informative content in the document. It introduces a dedicated feature vector representation for sentences to evaluate the relative impact of different sentence terms. The sentence feature vectors are run through a partitional k-means clustering process, to build the extractive summary based on the cluster representatives. Experimental results highlight the quality and efficiency of our approach.
Foreign Object Detection in Railway Images Based on an Efficient Two-Stage Convolutional Neural Network

Foreign object intrusion is one of the main causes of train accidents that threaten human life and public property. Thus, the real-time detection of foreign objects intruding on the railway is important to prevent the train from colliding with foreign objects. Currently, the detection of railway foreign objects is mainly performed manually, which is prone to negligence and inefficient. In this study, an efficient two-stage framework is proposed for foreign object detection in railway images. In the first stage, a lightweight railway image classification network is established to classify any input railway images into one of two classes: normal or intruded. To enable real-time and accurate classification, we propose an improved inverted residual unit by introducing two improvements to the original inverted residual unit. First, the selective kernel convolution is used to dynamically select kernel size and learn multiscale features from railway images. Second, we employ a lightweight attention mechanism, called the convolutional block attention module, to exploit both spatial and channel-wise relationships between feature maps. In the second stage of our framework, the intruded image is fed to the foreign object detection network to further detect the location and class of the objects in the image. Experimental results confirm that the performance of our classification network is comparable to the widely used baselines, and it obtains outperforming efficiency. Moreover, the performances of the second-stage object detection are satisfying.
Quantum algorithms for attacking hardness assumptions in classical and post‐quantum cryptography

Abstract In this survey, the authors review the main quantum algorithms for solving the computational problems that serve as hardness assumptions for cryptosystem. To this end, the authors consider both the currently most widely used classically secure cryptosystems, and the most promising candidates for post‐quantum secure cryptosystems. The authors provide details on the cost of the quantum algorithms presented in this survey. The authors furthermore discuss ongoing research directions that can impact quantum cryptanalysis in the future.
Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application

Machine learning (ML) is a key technology in smart manufacturing as it provides insights into complex processes without requiring deep domain expertise. This work deals with deep learning algorithms to determine a 3D reconstruction from a single 2D grayscale image. The potential of 3D reconstruction can be used for quality control because the height values contain relevant information that is not visible in 2D data. Instead of 3D scans, estimated depth maps based on a 2D input image can be used with the advantage of a simple setup and a short recording time. Determining a 3D reconstruction from a single input image is a difficult task for which many algorithms and methods have been proposed in the past decades. In this work, three deep learning methods, namely stacked autoencoder (SAE), generative adversarial networks (GANs) and U-Nets are investigated, evaluated and compared for 3D reconstruction from a 2D grayscale image of laser-welded components. In this work, different variants of GANs are tested, with the conclusion that Wasserstein GANs (WGANs) are the most robust approach among them. To the best of our knowledge, the present paper considers for the first time the U-Net, which achieves outstanding results in semantic segmentation, in the context of 3D reconstruction tasks. Unlike the U-Net, which uses standard convolutions, the stacked dilated U-Net (SDU-Net) applies stacked dilated convolutions. Of all the 3D reconstruction approaches considered in this work, the SDU-Net shows the best performance, not only in terms of evaluation metrics but also in terms of computation time. Due to the comparably small number of trainable parameters and the suitability of the architecture for strong data augmentation, a robust model can be generated with only a few training data.
Analysis of Insider Threats in the Healthcare Industry: A Text Mining Approach

To address rapidly growing data breach incidents effectively, healthcare providers need to identify various insider and outsider threats, analyze the vulnerabilities of their internal security systems, and develop more appropriate data security measures against the threats. While there have been studies on trends of data breach incidents, there is a lack of research on the analysis of descriptive contents posted on the data breach reporting website of the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR). Hence, this study develops a novel approach to the analysis of descriptive data breach information with the use of text mining and visualization. Insider threats, vulnerabilities, breach incidents, impacts, and responses to the breaches are analyzed for three data breach types.
Evaluation of College English Teaching Quality Based on Improved BT-SVM Algorithm

With the development of teaching evaluation program, colleges and universities have reformed according to the actual situation of the school. With the development of evaluation activities, many universities are eager to establish their own teaching quality evaluation system, so as to pre-evaluate the teaching quality of schools. SVM is one of the most widely used machine learning algorithms that enables efficient statistical learning with a very limited number of samples. Considering the excellent learning performance of SVM, it is very suitable for the teaching quality evaluation system. In this paper, we optimize the existing multiple classification algorithm for binary trees and propose a new method. Learning the popular teaching quality evaluation system in colleges and universities, the binary tree support vector machine classification algorithm, and design comparison experiment, the experimental results show that the evaluation model proposed in this paper has strong generalization ability and higher classification accuracy and better classification efficiency.
Blockchain-based Access Control Model with Privacy preservation in a Fog Computing Environment

Fog computing is a novel distributed computing architecture that brings the cloud to the edge of the network. Fog computing aims to improve service quality, data access, networking, computing, and storage. Even though numerous solutions have been presented, security and privacy concerns remain in fog computing because of its unique characteristics, including mobility, heterogeneity, and geo-distribution. Blockchain is a new idea that is bringing efficiency to a variety of professions. This research presents a novel fog computing access control method based on blockchain technology that ensures data privacy. Blockchain technology is utilized to create a secure management authentication and data access method. To protect privacy, proxy re-encryption and other cryptographic methods are used. The proposed model outperforms the existing technique in the literature, according to the results of the performance analysis.
Parts of Speech Tagging for Kannada and Hindi Languages using ML and DL models

Part-of-speech (POS) tagging is one of the vital Natural Language Processing (NLP) tasks that entails categorising words in a text (corpus) in accordance with a specific part of the speech, based on the word’s context. POS tagging for Indian Languages is not widely explored. Kannada is extremely inflectional and contains one of the most complex and richest collections of linguistic traits. Hence, developing a POS tagger for a resource-poor language such as Kannada is difficult The morphological complexity of Hindi becomes a challenge despite there having been numerous attempts of building a POS tagger for the language. The proposed work deals with the development of a POS tagger for both Kannada and Hindi by employing Machine Learning (ML) and Deep Learning (DL) algorithms. The results obtained are based on experiments conducted on a corpus consisting of around 3 lakh unique words for Kannada and Hindi combined. The 17 POS tags have been taken from the BIS tag set.
AITP - AI Thermal Pedestrians Dataset

Efficient pedestrian detection is a very important task in ensuring safety within road conditions, especially after sunset. One way to achieve this goal is to use thermal imaging in conjunction with deep learning methods and an annotated dataset for models training. In this work, such a dataset has been created by capturing thermal images of pedestrians in different weather and traffic conditions. All images were manually annotated with bounding boxes. As a result, the created dataset consists of 9178 annotated, thermal images that can be used in many applications including nighttime pedestrian detection.
Bridging the quantum divides: a chance to repair classic(al) mistakes?

Abstract Classical computing, which has transformed the world in unprecedented ways, has not always been deployed in ways that prioritise ethical values such as fairness, justice, and equity. The Western-focused, Silicon-Valley-centric 21st-century-computing model creates digital ‘haves’ and ‘have-nots’. Quantum computers promise to be exponentially more powerful than classical computers for some classically hard problems, potentially transforming application areas such as chemistry, drug discovery, and machine learning. However, if access and control over quantum computing is not shared equitably, then this may serve to amplify existing inequalities and create even deeper divides. Here we consider some of the possible implications for responsible quantum computing, looking ahead to ways in which the rollout of quantum computing could centre ethical principles such as fairness and equity, in order to prevent the mistakes of the ‘classical-only’ past. The issues raised in this paper will be of interest to those engaged in quantum computing research and to those concerned with the societal implications of this major new technology.
Vertical coordination in agri‐food supply chain and blockchain: A proposed framework solution for Vietnamese cashew nut business

Abstract The agri‐food supply chain has been a popular research topic in recent years. The combination of a demand for products of high quality and safety standards and the increasing number of stakeholders in supply chain networks has required the agri‐food business to transform from vertically integrated to vertically operated supply chain networks. Nevertheless, this traditional agri‐food supply chain form has exposure to some drawbacks. Meanwhile, blockchain technology as a disruptive technology today has the potential to provide value‐added contributions towards multiple‐disciplinary applications. Blockchain technology gains success and proves its functionality in improving the current supply chain by ensuring traceability, transparency and efficiency across the network from farmer to consumer. Therefore, the purpose of this paper is to review the existing literature on vertical coordination in the supply chain and its mechanism and to develop a framework that explores how blockchain technology can address the challenges in coordination mechanisms. Furthermore, taking the current Vietnamese vertical coordination cashew supply chain as a case study, this research is based on the proposed framework to suggest a solution combining blockchain and other relative technologies for the Vietnamese cashew business. This paper will close a research gap, as the blockchain‐based supply chain for Vietnam cashew nuts has potential benefits for each supply chain actor in terms of effectiveness in operations, costs and time‐saving, and human resources.
Image Captioning with Novel Topics Guidance and Retrieval-based Topics Re-weighting

Topic modelling (TM) has shown significant progress in boosting the effectiveness of image captioning in the last few years. Although important improvements have been shown in previous topic-guided image captioning models, some challenges remain unsolved, such as the independence of the topic predictors and the sentence generators, resulting in ineffective exploitation of semantic information. Also, all the predicted topics or the top-one topic are used throughout the whole captioning task without considering the current time step&#x0027;s linguistic context, which deviates the captioning network to focus on inaccurate image objects. To tackle these challenges, we propose a novel image captioning method consisting of four modules: enhanced topic predictor (ETP), retrieval-based topics re-weighting module (RTR), subsequent topic predictor (STP), and caption generation module. The prediction and generation modules are trained in an end-to-end manner to promote the efficient use of topics by predicting suitable topics at each time step. ETP predicts the topics using the image features, and is enhanced with topic embedding (TE). The RTR is only applied in the testing stage for re-weighting the topics predicted by ETP. In each time step, the STP automatically predicts concise topics subsets to alleviate the diversity of the image topics. Compared with the existing topic-based models, our model can automatically generate more accurate and diverse captions, boosting the explainability of how the topics influence the generated word in each time step. Extensive experiments on the MS-COCO and Flickr30K benchmark datasets show that our method enhances the overall image captioning&#x0027;s performance and the topic prediction task, and outperforms many recent image captioning approaches in terms of the evaluation metrics.
USV Parameter Estimation: Adaptive Unscented Kalman Filter-Based Approach

In this article, a new kind of adaptive unscented Kalman filter is proposed to deal with the parameter estimation problem for a class of nonlinear unmanned surface vessel (USV) models with unknown statistical characteristics of process noises. Specifically, the considered parameter estimation problem is first transformed into the state estimation problem by extending 18 parameters and 3 unknown inputs into augmented states for the USVs. With the help of such a transformation, the unknown inputs including disturbances and modeling errors are estimated effectively, and employed to construct the estimators such that the effect of these unknown inputs on parameter estimation can be significantly suppressed. Under the condition that the structure of the covariance matrix of the process noise is available, an adaptive law is further designed to estimate such a high-dimensional covariance matrix where the covariance estimation errors can be reduced. Finally, the proposed estimation approach is verified via performing the practical experiment as well as numerical simulations.
Robust Blind Deblurring Under Stripe Noise for Remote Sensing Images

The blind image deblurring methods have achieved great progress for Gaussian random noise. Few works have paid attention to the image deblurring under the structural noise, which is a very common degradation in multi-detectors imaging systems. This paper considers the practical yet challenging problem of blind deblurring in presence of the line-pattern stripe noise for remote sensing images. To overcome this issue, we explicitly formulate the structural noise into a novel and robust blind image deblurring framework. We observe that the structural line-pattern stripe noise would deteriorate both the kernel estimation and non-blind deblurring, and propose a three-stage restoration framework to progressively estimate the blur kernel and clean image. Specifically, we first estimate an intermediate blur kernel by getting rid of the negative influence of the stripe noise in the unidirectional gradient domain. Next, a learning-based kernel refinement network is introduced to rectify the missing details of the inaccurate kernel. Finally, a low-rank decomposition-based non-blind deblurring model is proposed to simultaneously estimate the clean image and stripe noise. Experimental results on real and synthetic datasets demonstrate that the proposed RBDS method outperforms the state-of-the-art blind deblurring methods.
Anisotropic Spatial–Spectral Total Variation Regularized Double Low-Rank Approximation for HSI Denoising and Destriping

Hyperspectral images (HSIs) can finely discriminate distinct objects with a high spectral resolution, and they are widely employed in various applications. However, mixed noise severely degrades the quality of HSI and restricts the performance of subsequent tasks. As one of the critical pre-processing steps, HSI denoising has been developed rapidly, among which low-rank (LR) prior-based methods have achieved superior performance. Nevertheless, the existing approaches frequently fail to completely remove noise and reconstruct high-quality HSIs when tackling complicated mixed noise with multiple types of high-intensity stripe noise. To solve this problem, we propose an HSI denoising and destriping method based on anisotropic spatial and spectral total variation regularized double low-rank approximation (ATVDLR). The double low-rank approximation framework is devoted to separating the clean image from the mixed noise by exploiting both the global correlations of HSI tensor and the LR structure of stripe noise. Furthermore, the anisotropic spatial and spectral total variation regularization is introduced to preserve the spatial&#x2013;spectral smoothness of HSI and the directional feature of stripes, thereby further suppressing high-level stripes and Gaussian noise. Finally, the alternating direction method of multipliers (ADMM) technique is designed to solve the proposed ATVDLR model. Extensive experimental results indicate that the proposed method outperforms other state-of-the-art techniques in multi-type high-intensity mixed noise reduction and image structural information protection, and has superior performance in complex mixed noise removal of real Gaofen-5 HSIs.
Facial Expression Recognition Based on LDA Feature Space Optimization

With the development of artificial intelligence, facial expression recognition has become an important part of the current research due to its wide application potential. However, the qualities of the face features will directly affect the accuracy of the model. Based on the KDEF face public dataset, the author conducts a comprehensive analysis of the effect of linear discriminant analysis (LDA) dimensionality reduction on facial expression recognition. First, the features of face images are extracted respectively by manual method and deep learning method, which constitute 35-dimensional artificial features, 128-dimensional deep features, and the hybrid features. Second, LDA is used to reduce the dimensionality of the three feature sets. Then, machine learning models, such as Naive Bayes and decision tree, are used to analyze the results of facial expression recognition before and after LDA feature dimensionality reduction. Finally, the effects of several classical feature reduction methods on the effectiveness of facial expression recognition are evaluated. The results show that after the LDA feature dimensionality reduction being used, the facial expression recognition based on these three feature sets is improved to a certain extent, which indicates the good effect of LDA in reducing feature redundancy.
Building a Human-Intelligent Metaverse

This article describes research into computational human perception’s potential to be the core of a human-intelligent metaverse, a sensed world that interacts with our physical, emotional, and mental states.