Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction.However,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized assessment.In this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton features.Furthermore,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in monkeys.Using the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual detection.Mon Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

Side-Channel Leakage Analysis of Inner Product Masking

The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security.Some previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour.In this paper,we investigate the security characteristics of IPM under different conditions.In adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate attacks.In implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear function.We observe that the security of IPMis affected by the degree and the linear component in the leakage function.In addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.

A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis:A Cross-Sectional Multicenter Study

In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise diagnosis.To address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into consideration.Using the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion enhancement.Pathological consistency loss is also applied to maintain fundus feature integrity,significantly improving image quality.Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after enhancement.In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease classification.The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

Empowerment of Precise Ideological and Political Education in Higher Education with Educational Digitalization

The study explores how educational digitalization enables the precise development of ideological and political education in colleges and universities.Digital transformation enables colleges and universities to accurately define educational objectives,content strategies,effect evaluation,and process management,and realize the precision and intelligence of ideological and political education.The application of big data technology enhances the data-oriented thinking of teachers and students,promotes the accurate application of data,and improves the efficiency of ideological and political education.The research also prospected a new vision of the digital construction of ideological and political courses and clarified the theoretical and practical path of the implementation and evaluation mode of ideological and political courses under digital empowerment.Education digitalization enables precise ideological and political education,which is a key way to promote the innovative development of ideological and political education in colleges and universities and will strongly support the improvement of the overall quality of higher education and the training of excellent talents.

Performance analysis and comparison of PoW,PoS and DAG based blockchains

In the blockchain,the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks.Various blockchain consensus mechanisms have been proposed.However,there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/application.To this end,this work investigates three mainstream consensus mechanisms in the blockchain,namely,Proof of Work(PoW),Proof of Stake(PoS),and Direct Acyclic Graph(DAG),and identifies their performances in terms of the average time to generate a new block,the confirmation delay,the Transaction Per Second(TPS)and the confirmation failure probability.The results show that the consensus process is affected by both network resource(computation power/coin age,buffer size)and network load conditions.In addition,it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions.

Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction

Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video.However,how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge.In this paper,we propose a novel model of structured sparse-codingbased key frame extraction,wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error.To automatically extract key frames,a decomposition scheme is designed to separate the sparse coefficient matrix by rows.The rows enforced by the nonconvex group log-regularizer become zero or nonzero,leading to the learning of the structured sparse coefficient matrix.To solve the nonconvex problems due to the log-regularizer,the difference of convex algorithm(DCA)is employed to decompose the log-regularizer into the difference of two convex functions related to the l1 norm,which can be directly obtained through the proximal operator.Therefore,an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed,which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error.Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most Sum Me videos compared to the stateof-the-art methods.Furthermore,the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection(SMRS)and an 8% increase compared to SC-det on the VSUMM dataset.

Exploiting structural similarity of log files in fault diagnosis for Web service composition

With increasing deployment of Web services, the research on the dependability and availability of Web service composition becomes more and more active. Since unexpected faults of Web service composition may occur in different levels at runtime, log analysis as a typical data- driven approach for fault diagnosis is more applicable and scalable in various architectures. Considering the trend that more and more service logs are represented using XML or JSON format which has good flexibility and interoperability, fault classification problem of semi-structured logs is considered as a challenging issue in this area. However, most existing approaches focus on the log content analysis but ignore the structural information and lead to poor performance. To improve the accuracy of fault classification, we exploit structural similarity of log files and propose a similarity based Bayesian learning approach for semi-structured logs in this paper. Our solution estimates degrees of similarity among structural elements from heterogeneous log data, constructs combined Bayesian network （CBN）, uses similarity based learning algorithm to compute probabilities in CBN, and classifies test log data into most probable fault categories based on the generated CBN. Experimental results show that our approach outperforms other learning approaches on structural log datasets.

Cloud detection from visual band of satellite image based on variance of fractal dimension

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

More relaxed condition for dynamics of discrete time delayed Hopfield neural networks

The dynamics of discrete time delayed Hopfield neural networks is investigated. By using a difference inequality combining with the linear matrix inequality, a sufficient condition ensuring global exponential stability of the unique equilibrium point of the networks is found. The result obtained holds not only for constant delay but also for time-varying delays.

Superposed Transparency Effect and Entanglement Generation with Hybrid System of Photonic Molecule and Dipole Emitter

We theoretically investigate the transparency effect with a hybrid system composed of a photonic molecule and dipole emitter. It is shown that the transparency effect incorporates both the coupled resonator-induced transparency(CRIT) effect and the dipole-induced transparency(DIT) effect. It is found that the superposed transparency windows are consistently narrower than the CRIT and DIT transparency windows. Benefiting from the superposed transparency effect, the photonic Faraday rotation effect could be realized in the photonic molecule system, which is useful for entanglement generation and quantum information processing.

Electrostatic force of dust deposition originating from contact between particles and photovoltaic glass

Charged photovoltaic glass produces an electrostatic field.The electrostatic field exerts an electrostatic force on dust particles,thus making more dust particles deposited on the glass.In this paper,the contact electrification between the deposited dust particles and the photovoltaic glass is studied.Meanwhile,the surface charge density model of the photovoltaic glass and the electrostatic force of charged particles are analyzed.The results show that with the increasing of the particle impact speed and the inclination angle of the photovoltaic panel,the charges on particles increase to different degrees.Under a given condition,the electrostatic forces acting on the charged particles at different positions above the glass plate form a bell-shaped distribution at a macro level,and present a maximum value in the center of the plate.As the distance between the particle and the charged glass decreases,the electrostatic force exerted on the particle increases significantly and fluctuates greatly.However,its mean value is still higher than the force caused by gravity and the adhesion force,reported by some studies.Therefore,we suggest that photovoltaic glass panels used in the severe wind-sand environment should be made of an anti-static transparent material,which can lessen the dust particles accumulated on the panels.

A General Framework of Reversible Data Hiding with Controlled Contrast Enhancement

This paper proposes a two-step general framework for reversible data hiding(RDH)schemes with controllable contrast enhancement.The first step aims at preserving visual perception as much as possible on the basis of achieving high embedding capacity(EC),while the second step is used for increasing image contrast.In the second step,some peak-pairs are utilized so that the histogram of pixel values is modified to perform histogram equalization(HE),which would lead to the image contrast enhancement.However,for HE,the utilization of some peak-pairs easily leads to over-enhanced image contrast when a large number of bits are embedded.Therefore,in our proposed framework,contrast over-enhancement is avoided by controlling the degree of contrast enhancement.Since the second step can only provide a small amount of data due to controlled contrast enhancement,the first one helps to achieve a large amount of data without degrading visual quality.Any RDH method which can achieve high EC while preserve good visual quality,can be selected for the first step.In fact,Gao et al.’s method is a special case of our proposed framework.In addition,two simple and commonly-used RDH methods are also introduced to further demonstrate the generalization of our framework.

Support vector machine with mixture of kernels for automatic image annotation

Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is proposed.On one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible.On the other hand,SVM-MK is constructed to shoot for better annotating performance.Experimental results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.

Hybrid Designing of a Neural System by Combining Fuzzy Logical Framework and PSVM for Visual Haze-Free Task

Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 106 neurons) to simulate complex process of our brain. But such kind of task is not easy to achieve only based on the analysis of partial differential equations, especially for those complex neural models, e.g. Rose-Hindmarsh (RH) model. So in this paper, we develop a novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifiers (PSVM) learning in the designing of large scale neural networks. Particularly, our approach can effectively simplify the designing process, which is crucial for both cognition science and neural science. At last, we conduct our approach on an artificial neural system with more than 108 neurons for haze-free task, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.

OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance problem.Some studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class imbalance.However,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local optimum.To achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this study.Specifically,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper bound.Then,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each sample.Finally,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP optimisation.Extensive experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

An Effective Method of SNR Estimation for LDPC-CPM

The technique of SNR estimation is one of the key technologies in adaptive frequency hopping system. The methods of channel quality estimation for non-linear continuous phase modulation (CPM) signals have some limitations. Therefore, the algorithm of channel quality estimation for CPM signals is worthy of further study. Some similar phase characteristics between sampling CPM and MPSK motivate us to propose a channel estimation algorithm with applications to nonlinear CPM using linear modulation signal processing. A comprehensive analysis of LDPC-CPM schemes using proposed algorithm is presented, and simulation results indicate that the proposed method can not only estimate channel quality well but also make the normalized MSE (NMSE) of SNR estimate close to/less than 0.1 dB at SNR of 4 dB using short block codes. It shows that the algorithm in this paper is effective enough to estimate the signal to noise ratio (SNR). Meanwhile, the algorithm in this paper reduces the complexity of computation compared with other traditional algorithms.

Application of Adaptive Coded Modulation Technology in UAV Data Link

UAV data link has been considered as an important part of UAV communication system, through which the UAV could communicate with warships. However, constant coding and modulation scheme that UAV adopts does not make full use of the channel capacity when UAV communicates with warships in a good channel environment. In order to improve channel capacity and spectral efficiency, adaptive coded modulation technology is studied. Based on maritime channel model, SNR estimation technology and adaptive threshold determination technology, the simulation of UAV data link communication is carried out in this paper. Theoretic analysis and simulation results show that according to changes in maritime channel state, UAV can dynamically adjust the adaptive coded modulation scheme on the condition of meeting target Bit-Error-Rate (BER), the maximum amount of data transfer is non-adaptive systems three times.

Teaching Innovation and Practice of Basic Computer Courses in Local Colleges and Universities

The rapid advancement of information technology poses significant pedagogical challenges for basic computer courses in local colleges and universities.This study proposes a comprehensive framework for instructional innovation,encompassing the restructuring of course content,enrichment of teaching resources,reformulation of instructional methods,and the establishment of feedback channels,as well as a comprehensive framework for optimizing assessment mechanisms.The implementation of this framework has yielded remarkable outcomes,including heightened classroom engagement and academic rigor as well as an overall enhancement in students’critical thinking and practical skills.These transformative changes have propelled basic computer courses in local higher education institutions to a new stage while offering valuable insights for future pedagogical reforms.