Reviewerly/exHarmony huggingface dataset:
HABFD: Houma Alliance Book Facsimiles Database

The Houma Alliance Book contains official characters from the Jin State (approximately 550 BC) and is also the earliest discovered calligraphy using writing brushes in the archaeological community. As an ancient handwriting system, HAB stores a wealth of information that helps us understand the world, the history of the Jin State in the Spring and Autumn Period, the evolution of calligraphy, and more. However, HAB was only discovered approximately sixty years ago, and there is little research on it. Due to environmental corrosion, the HAB has become less legible, making the technology for automatic detection and recognition of characters an important solution. This paper aims to make an Houma Alliance Book facsimiles database namely FADB, and to carry out detection and recognition. The database contains 655 facsimiles with 327 classes of Chinese characters. More specifically, it has the individual characters from the 655 facsimiles. Then, this paper uses a deep learning network to recognize these samples. Experiments are performed on the new database. The experimental results provide new baseline results of detection and recognition for this new database and demonstrate the feasibility of our dataset.
Optimization strategies in WAHTOR algorithm for quantum computing empirical ansatz: a comparative study

Abstract By exploiting the invariance of the molecular Hamiltonian by a unitary transformation of the orbitals it is possible to significantly shorter the depth of the variational circuit in the variational quantum eigensolver (VQE) algorithm by using the wavefunction adapted Hamiltonian through orbital rotation (WAHTOR) algorithm. This work introduces a non-adiabatic version of the WAHTOR algorithm and compares its efficiency with three implementations by estimating quantum processing unit (QPU) resources in prototypical benchmarking systems. Calculating first and second-order derivatives of the Hamiltonian at fixed VQE parameters does not introduce a significant QPU overload, leading to results on small molecules that indicate the non-adiabatic Newton–Raphson method as the more convenient choice. On the contrary, we find out that in the case of Hubbard model systems the trust region non-adiabatic optimization is more efficient. The preset work therefore clearly indicates the best optimization strategies for empirical variational ansatzes, facilitating the optimization of larger variational wavefunctions for quantum computing.
A Lightweight Deep Neural Network-Based Optical Image Detection Method for Underwater Submerged Rocket Wrecks

Maritime recycling plays a significant role in reusing components from spacecraft. However, the current methods for detecting and recovering sunken spacecraft wrecks suffer from inaccurate detection and low efficiency. To improve the detection effectiveness of rocket wrecks, this paper proposes a method based on underwater optical images which leverage the high-resolution features of images. To address the issue of low accuracy and efficiency in detecting rocket wrecks, a lightweight deep neural network detection model is introduced to enhance accuracy while reducing the computational load and increasing efficiency. Finally, through quantitative and qualitative analyses, our proposed method achieves a detection accuracy of 96.0% with low computational power, which lays a solid foundation for spacecraft recycling.
A Dataset and System for Service Robot Action Interaction Based on Skeleton Action Recognition

Service robots have been widely used in many indoor scenes, but their interaction ability based on action recognition has been developed slowly. In this work, we focus on human action recognition for service robots. We find that the existing action recognition datasets are rarely based on robot perspective, and do not focus on human-robot interaction. Therefore, we propose a multi-modal visual dataset named THU-HRIA dataset on the perspective of service robot, including total of eight human daily actions and interactive actions. Based on this dataset, we generated dataset for model training through data division and expansion, then selected several advanced GCN networks and improved their accuracy through transfer learning and finally compared the impact of various factors on recognition performance. According to the experimental results, we design a prototype of action recognition system, which can carry out end-to-end real-time action recognition on a laptop. We propose a time-frames constraint sampling strategy for this system and prove the feasibility of the system through experiments.
Learning Class-Aware Prototypical Representations for Few-Shot Learning

Few-shot learning in visual classification aims to emulate human capacity to recognize unseen classes using only a few samples. Prototype-based methods have shown promising classification outcomes, but most of them focus only on exploiting the intra-class distribution information while neglecting the importance of inter-class information. In this paper, we propose a class-aware approach, Boosted Prototypical Networks (BPN), which comprehensively evaluates the intra- and cross-class performance of samples by using their silhouette coefficients, and weights them accordingly to construct more representative prototypes. Furthermore, we design a discriminant loss function to guide the model to learn hard features adaptively, resulting in an embedding space that is more discriminative for hard classes. Ultimately, our comprehensive experimental results on benchmark datasets demonstrate that the proposed BPN is more competitive than existing algorithms.
Method of Reducing the Path Bypassing a Difficult Obstacles by an Autonomous Underwater Vehicle

Considered the movement of an autonomous underwater vehicle in conditions difficult obstacles. In order to shorten the path of traversing the obstacle, it is proposed to switch to the movement "by snake" and "by the circle" at the expense of turning the stern handlebars. Such movement increases the amount of information about the boundary of the obstacle and increases the likelihood of selecting the shortest path bypassing the obstacle, reducing time and energy resources. Variants of movement "by snake" at various stages are considered: at the first detection of obstacles, at the circumvention of obstacles in a horizontal plane, when examining obstacles in a vertical plane by moving in a circle. Mathematical dependencies are given of calculation of parameters of "by snake", estimation of increase of the viewing band, time and energy consumption on movement "by snake", increase of probability of selection of the shortest path. The simulation and the results of the numerical experiment showed that under the considered conditions, the approach of the apparatus with the obstacle "by snake" resulted in a reduction of the traveled path while bypassing obstacles, saving time and energy resources by 20%.
Task-Based PolyLoss Improves Low-Resource Speech Recognition

Multilingual meta learning has emerged as a promising paradigm for transferring the knowledge from source languages to facilitate the learning of low-resource target languages. Loss functions are a type of meta-knowledge that is crucial to effective training of neural networks, however, the misalignment between the loss functions and learning paradigms of meta learning degrade the network performance. To address this challenge, we propose a new method called Task-based Meta PolyLoss (TMPL) for meta learning. By regarding speech recognition tasks as normal samples and apply PolyLoss to meta loss function, TMPL can be denoted as a linear combination of polynomial functions based on task query loss. We conduct extensive experiments on four low-resource languages from the IARPA BABEL dataset. The results show a significant improvement in performance when applying our TMPL to meta learning.
Cloud Computing Task Scheduling Based on Multi-strategy Improved Harris Hawks Optimization

Aiming at the problem of multi-objective optimization conflict between makespan and average resource utilization in cloud computing task scheduling, this paper proposed a cloud computing task scheduling strategy based on multi-strategy improved harris hawk optimization algorithm. Firstly, the fusion strategy of Tent chaotic sequence and opposition-based learning is introduced to initialize the population to enhance the diversity of the population and the quality of the initial solution, thereby improving the optimization efficiency of the population; Secondly, an energy adaptive exponential decay factor is employed as a control parameter for global exploration and local exploitation to improve the accuracy and convergence speed of the algorithm; Finally, apply the elite representative exploration mechanism of the grey wolf optimization (GWO) algorithm to the global search stage of the population to improve the global search ability of the algorithm and avoid falling into a local optimal state. The simulation results show that for task scheduling with different distributions and scales, the proposed algorithm is superior to the comparison algorithm in terms of reducing makespan and increasing the average resource utilization rate, and has obvious advantages in algorithm convergence and convergence accuracy.
A High-Performance Universal FPGA Implementation for PM and DPM Based on Differential Addition Chain

Point multiplication (PM) and double point multiplication (DPM) are fundamental essential operations in elliptic curve cryptography (ECC). This paper presented a novel and versatile architecture for PM and DPM based on the differential addition chain. The execution pattern of PM and DPM is uniform to resist side-channel attacks. The datapath and modular operation units are shared by PM and DPM to optimize the utilization of hardware resources. The pipeline stages are carefully designed, leading to a compact timing schedule for PM and DPM. The architecture has been implemented on Virtex-7, supporting ${GF}(2^{163})$, ${GF}(2^{283})$ and ${GF}(2^{571})$. The implementation results indicate that the proposed design achieves 5.7%, 8.7% and 23.6% performance improvements over existing works.
A Deep Learning Approach to Single Image Dehazing Inspired by Euler Numerical Schemes

Haze concentration variation challenges traditional dehazing methods in accurately estimating atmospheric haze. Conventional techniques can result in over-smoothed images and visible artifacts. Recently, deep neural network-based methods have gained attention, with network module performance being crucial. This paper introduces ProgNet, a numerical scheme-inspired module enhancing interpretability by connecting network structure with the forward Euler progressive multi-step numerical scheme. ProgNet is integrated into PFF-Net and AOD-Net dehazing models, addressing limitations of the classical atmospheric scattering model using PFF-Net's extended dehazing model. Experiments on public datasets show the module's effectiveness in dehazing various scenes and improving PFF-Net and AOD-Net's performance.
QoS to QoE Mapping Based on Optimised BP Neural Networks

With the commercialization of 5G technology, network video operators are paying more attention to the end-user experience due to higher network speeds and larger network coverage areas. In this paper, a user-centric Quality of Service (QoS) to Quality of Experience (QoE) mapping model is proposed based on BP neural networks (BPNN), incorporating network parameters and Mean Opinion Score (MOS). A combination of algorithms for network optimization is applied, and improvements are made to the optimization algorithm based on the internal characteristics of the QoS/QoE mapping. The WRprop algorithm is proposed as a suitable solution to the problem. Experimental results indicate that the combination of WRprop and PSO has better performance.
Zernike Moments-based Robust and Reversible Watermarking Scheme

Cover lossless robust watermarking technology is one of the frontier hotspots in the field of information hiding. It can fully restore the original image without external attacks, while when there is an attack, it can extract robust watermarks for secure verification. Most of the countermeasures proposed in the literature are robust to geometric attacks, but most schemes are less imperceptible, mainly due to the fixed location of the embeddings. In this paper, we propose a Zernike moments-based robust and reversible watermarking scheme. It embeds robust watermarks in low-order Zernike moments to keep their rotational invariance, thus it can be used for information authentication and is not easy to detect. Then the reversible watermark is embedded outside the robust watermark region as compensation information to fully restore the original image. The proposed scheme enables the image has the ability to restore the image and secure verification at the same time. Theoretical and simulation results show that it has better bit error rate and imperceptibility.
Single Image Super-Resolution Reconstruction Technique with Lightweight Dual-Branch Network

With the development of convolutional neural networks, the performance of Single Image Super-Resolution (SISR) has been significantly improved. However, as these methods are inevitably accompanied by complex operation and performance consumption issues, it is difficult to apply in practice. This paper proposes a lightweight Dual-Branch network (DBNet) for SISR, which can achieve a good balance between model complexity and performance. DBNet adopts a new intermediate module, Feature Extraction Dual Attention Block (FERDB) for feature extraction. DBNet combines Backward Fusion Module (BFM) and recursive Transformer. A new Attention mechanism, Multipath Attention (MA) is used to obtain key feature information. In addition, DBNet uses a Dual-Branch structure to capture more characteristic information. The experimental results show that DBNet is superior to other methods in SISR performance, with relatively lower complex operation and performance consumption.
TAR: A Dataset of Teacher-Teaching Action Recognition

With the rise of artificial intelligence in education, the utilization of video understanding technology to identify teachers' teaching actions is becoming increasingly important for improving the quality of teaching and accelerating the process of educational informatization. However, the lack of a dedicated dataset for teacher action recognition has been a hindrance to the development of intelligent teaching. In this paper, we construct a dataset of teacher-teaching action recognition (TAR). The dataset collected classroom videos of 25 teachers in eight classrooms of vocational and technical schools, from which a total of 13,288 valid video samples were obtained after processing. The dataset contains 8 behavior categories, including 5 categories of normative teaching behaviors and 3 categories of misbehaviors. To ensure the authenticity and representativeness of the data, all actions were collected from actual classroom videos. We then tested mainstream methods on the newly constructed dataset and proposed a Cross-channel Non-local (CNL) module based on SlowFast to capture long-range spatiotemporal dependencies. The experimental results demonstrated that the proposed method significantly outperformed other existing methods.
RGB-D Camera-Based Scanner for Hand Measurement

The application of additive manufacturing for production efficiency in industry is needed to produce a minimum cost burden, especially in the health sector. 3D scanning measurement using Intel RealSense camera with RGB-D camera type is used to measure the arm dimension of the patient to carry out the concept of additive manufacturing. This study was conducted based on anthropometric measurement data on a group of respondents who are samples of part of a population with certain conditions. In the upper limbs of humans, there are several points that become references in anthropometric measurements. The digital measurement tools used in this research are the Intel RealSense D435i RGB-D camera and the Intel RealSense SR300 coded light-based 3D system sense scanner. The results of the 3D scan are reconstructed with the stages of feature extraction, feature matching, registration, loop closure detection, pose graph optimization, and meshing. The results of 3D data processing conclude the camera that has an advantage in terms of good scanning results is the System Sense 3D Scanner with smaller error results compared to the Intel RealSense D435i depth camera.
AgeTrans: Estimating the Age of Faces Using Global Features

To meet many different needs in practice, the estimation of the age via face images is of great interest and this is also one of the important tasks of the machine vision field. Therefore, there are currently many methods that can solve this task, typically methods using Convolution Neural Network (CNN). However, the structures using CNN method can only extract local features of the face and not showing much of the relationship between facial parts. Therefore, we propose a solution called AgeTrans that combines Transformer method and CNN method to create an “end to end” model with better efficiency. Specifically, taking advantage of the Attention mechanism of Transformer method to extract the global feature of the face and using CNN method to extract the local feature, then logically combining the two types of features and giving the result of the task. From the experimental results, our model is more efficient and competitive enough on the UTKFace dataset and at the same time, confirms that it is possible to use global features to estimate the age of the face.
Research of the Information Fusion Technique for Millimeter Wave Radar and Camera

Intelligent assisted driving heavily relies on accurate environment perception, a vital component for ensuring road safety. In real-world scenarios, relying solely on a single sensor or multiple sensors can be insufficient to effectively perceive the surrounding environment. Therefore, there is a growing need to harness the power of multi-source sensor modalities and integrate their data. In this context, the combination of millimeter-wave radar and camera sensors in vehicles has emerged as a prominent choice for perceiving upfront information, allowing for comprehensive analysis and comparison of different sensor options. Prior to aligning the multi-source data in space and time, it is essential to calculate the precise coordinate relationship between these sensors. Subsequently, the signals are processed using the advanced global nearest neighbor matching algorithm, synchronizing the two signals and fusing the matched targets using a weighted fusion method. To further enhance accuracy, the adaptive extended Kalman filter method efficiently tracks both matched and unmatched targets, ultimately determining the final state. Experimental outcomes strongly support the effectiveness of this fusion method in overcoming the limitations of individual sensors, thereby significantly improving target identification rates in intelligent assisted driving systems.
Review of Quality Assessment Algorithms on the Realistic Blurred Image Database (BID2011)

Accurate realistic blurred image quality assessment (RB-IQA) is challenging due to potential occurrence of blurring in image acquistion, compression and transmission. Derived from the Blurred Image Database (BID2011), a technical review is executed by screening literatures that cited the database for fully understanding the current achievement on realistic image sharpness estimation. By removing review and non-English-written papers and other irrelevant publications, 43 technical papers remain. Generally, the technical algorithms can grouped into shallow- and deep-learning categories. Notably, deep-learning-based RB-IQA algorithms via advanced learning strategies (transfer learning, rank learning, self-supvised learning, continual learning, meta-learning and domain adaptation) are predominantly developed, and remarkable progress has been made. RB-IQA is crucial for evaluating digital imaging devices and benchmarking restoration algorithms, and in future work, efforts should be made to improve the RB-IQA performance closer to human visual perception.
Model-driven Simulation of Eye Gaze Dynamics in Standard Visual Cognitive Assessments

The increasing popularity of wearable eye-tracking systems has led to growing interest in monitoring and predicting mental disorders and dementia in mobile health (m-health) applications. However, collecting eye gaze data is a challenging and time-consuming process due to the heterogeneity of human visual behavior and privacy concerns. This scarcity of data limits the performance of classifiers. Even with data augmentation and deep generative models, the newly generated datasets may still suffer from the problem of domain shift, where the training and test data distributions do not match. To address this issue, this paper proposes a model-driven simulation approach for synthesizing eye gaze dynamics data based on clinical statistics during standard visual cognitive assessments. The synthesized dataset provides a more balanced and annotated set of signals that can enhance the performance gaze activity recognition.
Scene Cartoonization Algorithm Based on Generative Adversarial Network

Currently cartoons are very popular in education, film, social, etc. However, there are still problems such as the degree of cartoonization is not high, or the number of parameters of the model is large. This paper proposes a scene cartoonization algorithm. Our method is to use the current popular cartooning framework White-Box, combined with the latest research results of neural network and cartooning method to improve the original network, to achieve better cartooning effect and fewer parameters. First, link low-level details from feature maps of various scales with high-level meanings by using full-scale skip connections, and use full-scale feature maps to the greatest extent; secondly, ACmix, which combines convolution and self-attention, is introduced as residual blocks to improve the effect while reducing the amount of parameters; finally, the cartoon texture saliency sampler CTSS module proposed by Gao et al. and the algorithm for improving brightness are introduced, and the cartoon texture salient blocks are dynamically sampled from the training data to improve the cartoon effect. Experimental results show that the algorithm generates high-quality cartoon pictures with extremely low parameters.
Segmentation for Images of a Single Stem Cell Using Morphological Operations and Statistical Region Merging

For some images of a single stem cell, the boundary delineating the cell is discontinuous or blurry at some locations. If the statistical region merging (SRM) method is applied directly on such images, the image segmentation results may not be ideal. In this paper, for each such image, we add some gradient information into the image; then apply a discontinuous filter on the image so that the image is smoothed a bit and the edges of the image are kept well. Next, closing operations of morphology are applied on the filtered image; and the processed image is segmented using SRM. Finally, apply a threshold on the segmented image to obtain a binary image; apply a hole-filling function to the binary image; extract the biggest connected component in the hole-filled image; and apply a linear transform on the image of the biggest component to match the input image as well as possible in terms of the least squares fitting. This transformed image is the segmentation result. We have applied SRM using connectivity of 4 and 8 as well as a hexagonal lattice. The corresponding segmentation results are tabulated for convenient comparisons; and the results can show that the proposed method may be helpful for the segmentation of such images.