A Single-Sized Metasurface for Image Steganography and Multi-Key Information Encryption

With the escalating flow of information and digital communication,information security has become an increasingly important issue.Traditional cryptographic methods are being threatened by advancing progress in computing,while physical encryption methods are favored as a viable and compelling avenue.Metasurfaces,which are known for their extraordinary ability to manipulate optical parameters at the nanoscale,exhibit significant potential for the revolution of optical devices,making them a highly promising candidate for optical encryption applications.Here,a single-sized metasurface with four independent channels is proposed for conducting steganography and multi-key information encryption.More specifically,plaintext is transformed into a ciphertext image,which is encoded into a metasurface,while the decryption key is discretely integrated into another channel within the same metasurface.Two different keys for steganographic image unveiling are also encoded into the metasurface and can be retrieved with different channels and spatial positions.This distributed multi-key encryption approach can enhance security,while strategically distributing images across distinct spatial zones serves as an additional measure to reduce the risk of information leakage.This minimalist designed metasurface,with its advantages of high information density and robust security,holds promise across applications including portable encryption,high-camouflaged image display,and high-density optical storage.

Multiplex network infomax:Multiplex network embedding via information fusion

For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes.However,numerous real-world networks are naturally composed of multiple layers with different relation types;such a network is called a multiplex network.The majority of existing multiplex network embedding methods either overlook node attributes,resort to node labels for training,or underutilize underlying information shared across multiple layers.In this paper,we propose Multiplex Network Infomax(MNI),an unsupervised embedding framework to represent information of multiple layers into a unified embedding space.To be more specific,we aim to maximize the mutual information between the unified embedding and node embeddings of each layer.On the basis of this framework,we present an unsupervised network embedding method for attributed multiplex networks.Experimental results show that our method achieves competitive performance on not only node-related tasks,such as node classification,clustering,and similarity search,but also a typical edge-related task,i.e.,link prediction,at times even outperforming relevant supervised methods,despite that MNI is fully unsupervised.

Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

Can ChatGPT evaluate research quality?

Purpose:Assess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task.Design/methodology/approach:Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework(REF)2021 to create a research evaluation ChatGPT.This was applied to 51 of my own articles and compared against my own quality judgements.Findings:ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria.Its overall scores have weak correlations with my self-evaluation scores of the same documents(averaging r=0.281 over 15 iterations,with 8 being statistically significantly different from 0).In contrast,the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509.Thus,averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores.The positive correlation may be due to ChatGPT being able to extract the author’s significance,rigour,and originality claims from inside each paper.If my weakest articles are removed,then the correlation with average scores(r=0.200)falls below statistical significance,suggesting that ChatGPT struggles to make fine-grained evaluations.Research limitations:The data is self-evaluations of a convenience sample of articles from one academic in one field.Practical implications:Overall,ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks.Research evaluators,including journal editors,should therefore take steps to control its use.Originality/value:This is the first published attempt at post-publication expert review accuracy testing for ChatGPT.

Semiannual oscillation,annual oscillation,quasibiennial oscillation,and solar cycle variation of the OH airglow emission in the mesopause region

The vertically integrated emission rate,centroid altitude,peak emission rate,and peak height of the hydroxyl(OH)airglow were calculated from Thermosphere Ionosphere Mesosphere Energetics and Dynamics(TIMED)/Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)observations to study the seasonal and interannual variations in the intensity and location of the OH emission.The emission rate is inversely proportional to the height of the emission,with the semiannual oscillation dominating at low latitudes and the annual oscillation dominating at higher latitudes.The OH emission is modulated by the quasibiennial oscillation at the equator,and the quasibiennial oscillation signal is weak at other latitudes.We represented the vertical transport of atomic oxygen by using atomic oxygen concentrations obtained from a global atmospheric model,the Specified Dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension simulations.Compared with the amplitudes of the migrating diurnal tide(DW1)calculated from temperature data observed by TIMED/SABER,we found that both the vertical transport of atomic oxygen and DW1 amplitudes in the equatorial region exhibit semiannual oscillation and quasibiennial oscillation,which have a strong correlation with the variations in the amplitude and phase of semiannual oscillation and quasibiennial oscillation in OH emission.It is likely that the DW1 affects the vertical transport of atomic oxygen that is involved in the reaction to produce O3,thus affecting the OH emission.We analyzed the relationship between OH emission and solar activity by using the solar radio flux at 10.7 cm as a proxy for solar activity.The results showed that the OH emission is well correlated with solar activity,and the modulation of OH emission by solar activity has a significant latitudinal variation.The small correlation between emission height and solar activity indicates that solar activity modulates OH emission mainly through chemical rather than dynamic processes.

PAPS: Progressive Attention-Based Pan-sharpening

Pan-sharpening aims to seek high-resolution multispectral(HRMS) images from paired multispectral images of low resolution(LRMS) and panchromatic(PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS images.Having a better MS base(enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of constructing the desired HRMS. Extensive experiments together with ablation studies on widely-used datasets are provided to verify the efficacy of our design, and demonstrate its superiority over other state-of-the-art methods both quantitatively and qualitatively. Our code has been released at https://github.com/JiaYN1/PAPS.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

MAUN:Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction

Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D measurements.The algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging process.Early approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging priors.While some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative stages.In this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI reconstruction.Specifically,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information loss.Moreover,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration stages.Extensive experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and metrically.Our code is publicly available at https://github.com/HuQ1an/MAUN.

Low-Complexity Reconstruction of Covariance Matrix in Hybrid Uniform Circular Array

Spatial covariance matrix(SCM) is essential in many multi-antenna systems such as massive multiple-input multiple-output(MIMO). For multi-antenna systems operating at millimeter-wave bands, hybrid analog-digital structure has been widely adopted to reduce the cost of radio frequency chains.In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid multi-antenna systems. To address this issue, beam sweeping algorithm(BSA) which can reconstruct the SCM effectively for a hybrid uniform linear array, has been proposed in our previous works. However, direct extension of BSA to a hybrid uniform circular array(UCA)will result in a huge computational burden. To this end, a low-complexity approach is proposed in this paper. By exploiting the symmetry features of SCM for the UCA, the number of unknowns can be reduced significantly and thus the complexity of reconstruction can be saved accordingly. Furthermore, an insightful analysis is also presented in this paper, showing that the reduction of the number of unknowns can also improve the accuracy of the reconstructed SCM. Simulation results are also shown to demonstrate the proposed approach.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

Chlorine-Substituent Regulation in Dopant-Free Small-Molecule Hole-Transport Materials Improves the Effi ciency and Stability of Inverted Perovskite Solar Cells

Although doped hole-transport materials(HTMs)off er an effi ciency benefi t for perovskite solar cells(PSCs),they inevi-tably diminish the stability.Here,we describe the use of various chlorinated small molecules,specifi cally fl uorenone-triphenylamine(FO-TPA)-x-Cl[x=para,meta,and ortho(p,m,and o)],with diff erent chlorine-substituent positions,as dopant-free HTMs for PSCs.These chlorinated molecules feature a symmetrical donor-acceptor-donor structure and ideal intramolecular charge transfer properties,allowing for self-doping and the establishment of built-in potentials for improving charge extraction.Highly effi cient hole-transfer interfaces are constructed between perovskites and these HTMs by strategi-cally modifying the chlorine substitution.Thus,the chlorinated HTM-derived inverted PSCs exhibited superior effi ciencies and air stabilities.Importantly,the dopant-free HTM FO-TPA-o-Cl not only attains a power conversion effi ciency of 20.82% but also demonstrates exceptional stability,retaining 93.8%of its initial effi ciency even after a 30-day aging test conducted under ambient air conditions in PSCs without encapsulation.These fi ndings underscore the critical role of chlorine-substituent regulation in HTMs in ensuring the formation and maintenance of effi cient and stable PSCs.

Student Academic Performance Predictive Model Based on Dual-stream Deep Network

Blended teaching is one of the essential teaching methods with the development of information technology.Constructing a learning effect evaluation model is helpful to improve students’academic performance and helps teachers to better implement course teaching.However,a lack of evaluation models for the fusion of temporal and non-temporal behavioral data leads to an unsatisfactory evaluation effect.To meet the demand for predicting students’academic performance through learning behavior data,this study proposes a learning effect evaluation method that integrates expert perspective indicators to predict academic performance by constructing a dual-stream network that combines temporal behavior data and non-temporal behavior data in the learning process.In this paper,firstly,the Delphi method is used to analyze and process the course learning behavior data of students and establish an effective evaluation index system of learning behavior with universality;secondly,the Mann-Whitney U-test and the complex correlation analysis are used to analyze further and validate the evaluation indexes;and lastly,a dual-stream information fusion model,which combines temporal and non-temporal features,is established.The learning effect evaluation model is built,and the results of the mean absolute error(MAE)and root mean square error(RMSE)indexes are 4.16 and 5.29,respectively.This study indicates that combining expert perspectives for evaluation index selection and further fusing temporal and non-temporal behavioral features that for learning effect evaluation and prediction is rationality,accuracy,and effectiveness,which provides a powerful help for the practical application of learning effect evaluation and prediction.

The Conflict between Individuals and Society in Seize the Day

This paper explores the existential problem in Seize the Day-the conflict between individuals and society in modern society.With a brief introduction of Saul Bellow and the novel,the paper emphasizes the analysis of the conflict and the sources of the conflict.Besides,Saul Bellow uses many rhetorical devices to intensify the conflict.The analysis of these rhetorical devices is beneficial for us to interpret and understand the true meaning of the novel.Through the study of Bellow’s vision and the novel’s metaphorical meaning,we may find an effective solution to the conflict that our human being is involved in,and realize the social values of this novel.

Research on the Teaching Mode Innovation of Computer Majors Under the Background of Industry and Education Integration

With the rapid development of the times,the continuous innovation of teaching concepts,and the deficiency of the traditional computer teaching mode,the rapid development of computer technology has put forward higher requirements of the teaching mode,so it is imperative to carry on the innovation of teaching mode for computer majors.Combined with the development needs of computer technology in today’s society,this paper combs the shortcomings of the existing teaching mode.Under the background of the integration of industry and education,this paper puts forward the tripartite cooperation mode with students as the core,schools as the bridge,and enterprises as the support,and establishes a new mechanism of the teaching mode of computer majors under the background of the integration of industry and education.This way of learning theory on campus and practicing in society will supplement and strengthen students’professional ability needs in the environment of the rapid development of computers in the future.

The Bright Single Pulse Emission from PSR B1133+16

We have conducted a comprehensive investigation into the bright single pulse emission from PSR B1133+16using the Giant Metrewave Radio Telescope.High time resolution data(61μs)were obtained at a center frequency of 322 MHz with a bandwidth of 32 MHz over a continuous observation period of 7.45 hr.A total of 1082 bright pulses were sporadically detected with peak flux densities ranging from 10 to 23 times stronger than the average pulse profile.However,no giant pulse-like emission with a relative pulse energy larger than 10 and extremely short duration was detected,indicating that these bright pulses cannot be categorized as giant pulse emission.The majority of these bright pulses are concentrated in pulse phases at both the leading and trailing windows of the average pulse profile,with an occurrence ratio of approximately 2.74.The pulse energy distribution for all individual pulses can be described by a combination of two Gaussian components and a cutoff power-law with an index of α=-3.2.An updated nulling fraction of 15.35%±0.45% was determined from the energy distribution.The emission of individual pulses follows a log-normal distribution in peak flux density ratio.It is imperative that regular phase drifting in bright pulse sequence is identified in both the leading and trailing components for the first time.Possible physical mechanisms are discussed in detail to provide insights into these observations.

A Localization Method of High Energy Transients for All-sky Gamma-ray Monitor

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up observations.Localization based on the relative counts of different detectors has been widely used for all-sky gamma-ray monitors.There are two major methods for this count distribution localization:χ^(2)minimization method and the Bayesian method.Here we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the χ^(2)method.With comprehensive simulations,we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than the χ^(2)method,especially for weak bursts.We further proposed a location-spectrum iteration approach based on the Bayesian inference,which could alleviate the problems caused by the spectral difference between the burst and location templates.Our method is very suitable for scenarios with limited computation resources or timesensitive applications,such as in-flight localization software,and low-latency localization for rapidly follow-up observations.

Component-Based Software Developing Method for Group Companies Management Information System

Considering the special requirements of group enterprise information management software, a new method of massive software development was put forward basing on the component technology and facing the ＂assemblage＂. It generalizes five key parts of the method system including the overall strategy, the application project method basing on the operation ＂assemblage＂, the field project method basing on component, the development method facing the component on the basis of the visible component platform, and the software development process model basing on the component ＂assemblage＂. Besides, this paper describes the process and principle of this method application with an example of the construction of a Communications Investment Group enterprise＇s management information system. The method provides a new idea for breaking away from the predicament appeared in the construction of present enterprise informatization and exploring the way of software industrialization.

Extraction of seismic precursory information from gravity tide at Kunming station based on HHT

Hilbert-Huang Transformation （HHT） is a recently-developed method for non-linear signal processing （Huang et al, 1998）. By performing empirical mode decomposition （EMD） with HHT, various frequency components can be effectively separated as intrinsic mode function （IMF）. After Hilbert transformation to the IMF se- quence, the 3-D discrete time-frequency spectrum containing time, frequency and amplitude can be obtained, which provides very clear time-frequency characteristics with local details. The method is suitable for describing siznals with non-linear and non-stable variations.

Development of Web Mapping Application for Spatial Ecotourism Information Using QGIS Plugin and Freely Available Web Platforms in North West Highlands of Ethiopia a Case Study: Chokie Mountain Watersheds

Ecotourism is a sustainable, green and smokeless industry of the society by providing alternative source to the livelihood and local community, in order to re-naturalize the environment to bring the man closer to the natural environment. The use of web application has changed the way tourists gather information about tourist attraction spots of the research area. The aim of the study is to assess and identify ecotourism attraction sites of Chokie Mountain watersheds for touristic activities to develop a web-based GIS mapping portal for the improvement of nature-based ecotourism activities. The web-based GIS portal for Web Mapping Application is available for the user with the skills necessary to create standard web mapping services. We have implemented web mapping services based on formal cartographic visualization rules in the open source QGIS software and threejs JavaScript plugin. Threejs plugin is used for 3D visualization, interaction and export terrain data, map canvas image and vector data to HTML page and JS. The result of web-based GIS portal supports spatial and non-spatial database of tourist attraction and tourist service data with attractive user interface.