Image Tamper Detection and Multi-Scale Self-Recovery Using Reference Embedding with Multi-Rate Data Protection

This paper proposes a multi-scale self-recovery(MSSR)approach to protect images against content forgery.The main idea is to provide more resistance against image tampering while enabling the recovery process in a multi-scale quality manner.In the proposed approach,the reference data composed of several parts and each part is protected by a channel coding rate according to its importance.The first part,which is used to reconstruct a rough approximation of the original image,is highly protected in order to resist against higher tampering rates.Other parts are protected with lower rates according to their importance leading to lower tolerable tampering rate(TTR),but the higher quality of the recovered images.The proposed MSSR approach is an efficient solution for the main disadvantage of the current methods,which either recover a tampered image in low tampering rates or fails when tampering rate is above the TTR value.The simulation results on 10000 test images represent the efficiency of the multi-scale self-recovery feature of the proposed approach in comparison with the existing methods.

校园欺凌现象中家长角色的负面影响及其矫正——基于自我心像(self-image)分析的视角

校园欺凌事件的发生与家长角色的负面影响有着深刻的关联。造成负面影响的家长角色,在亲子关系中主要表现为专制者、臣服者、冷漠者三种,在亲朋关系中主要表现为征服者与被征服者、妥协者和旁观者。这些角色属于潜在的“精神暗能”,会在无形中影响孩子美好的自我心像的塑造,如果不断“层累”,则会导致孩子损己害人的攻击性、破坏性、自伤性事件发生。因此,家长应不断反躬自省、慎独自律、明德积学,学会民主有效地沟通,完善自我心像,给孩子以情感的关怀、行为的示范、思想的引领,与学校教育和社会教育形成真正意义上的合力,从心像塑造的源头上防范校园欺凌现象的发生,或将已发生的校园欺凌的负面影响降到最低。

Quito Municipal Schools—Cohort Study: Self-Perception of Body Image and Factors Related with It

Self-perception of body image is one factor to be considered when planning and performing preventive initiatives and interventions in childhood and adolescence. The aim of this study was to analyse self-perception of body image in relation to body-mass index, physical inactivity, not eating breakfast, dieting, smoking and alcohol consumption or toxic habits. Materials and Methods: using data from the “Quito municipal schools” cohort study 6964 students aged from 9 to 17 years were described using a multivariate log linear model of the multidimensional table generated by the variables. A logistic regression model was then fitted to assess associations via OR. Results: 8.2% perceived themselves as overweight. Of those subjects overweight according to their BMI, 21.8% perceived themselves as such, while among those not overweight according to BMI, 96.8% considered themselves as slim or of normal weight. Among students who were dieting, 15.1% perceived themselves as overweight. Among those pupils who perceived themselves has having excess weight, the most common reasons for dieting were: lose weight (56.8%), be healthier (22.6%), and maintain current weight (8.4%). Self-perception of excess weight interacts with excess weight (ORadjusted 8.42;CI95% 6.92-10.25), no breakfast (ORadjusted 2.83;CI95% 2.13-3.77), diet (ORadjusted 2.38;CI95% 1.95-2.89), and with all the variables except toxic habits (ORadjusted 1.01;CI95% 0.78 a 1.29). Conclusion: Interventions to prevent obesity in childhood and adolescence ought to take account of specific determinants within the personal, behavioural and socio-environmental factors, such as the promotion of a positive body image.

Watermarking Images in the Frequency Domain by Exploiting Self-Inverting Permutations

In this work we propose efficient codec algorithms for watermarking images that are intended for uploading on the web under intellectual property protection. Headed to this direction, we recently suggested a way in which an integer number w which being transformed into a self-inverting permutation, can be represented in a two dimensional (2D) object and thus, since images are 2D structures, we have proposed a watermarking algorithm that embeds marks on them using the 2D representation of w in the spatial domain. Based on the idea behind this technique, we now expand the usage of this concept by marking the image in the frequency domain. In particular, we propose a watermarking technique that also uses the 2D representation of self-inverting permutations and utilizes marking at specific areas thanks to partial modifications of the image’s Discrete Fourier Transform (DFT). Those modifications are made on the magnitude of specific frequency bands and they are the least possible additive information ensuring robustness and imperceptiveness. We have experimentally evaluated our algorithms using various images of different characteristics under JPEG compression. The experimental results show an improvement in comparison to the previously obtained results and they also depict the validity of our proposed codec algorithms.

The Interpretations of “Mother Images” and Their “Self-Trauma-Healing Mechanism” in Kipling's War Poetry

Rudyard Kipling is the first British writer to win the Nobel Prize for literature in 1907.Due to the influence of the First World War and the huge blow of his own beloved son John's death on the battlefield,Kipling's later writing pays more attention to the pain and trauma which the war brings,and the noble qualities of love,belief,and tenacity in human nature after the baptisms of pain and trauma.This paper selects two poems—"My Boy Jack"and"A Nativity"in Kipling's later poetry creation career.Freud's "trauma" and treatment theories are employed to study the two mothers' "self- trauma- healing mechanism"in face of pain and trauma,and to interpret the two mother images that experience the war-torn pain and trauma,and achieve their spiritual salvation at last.Through the transformation of the two mothers,Kipling wants to mourn his beloved son's death and expresses his determination and hope of faith and commitment in order to heal his inner pain and trauma,which unwittingly realizes Freud's trauma-healing theory.Key words:

Reconstructing the landing trajectory of the CE-3 lunar probe by using images from the landing camera

An accurate determination of the landing trajectory of Chang＇e-3 （CE-3） is significant for verifying orbital control strategy, optimizing orbital planning, accu- rately determining the landing site of CE-3 and analyzing the geological background of the landing site. Due to complexities involved in the landing process, there are some differences between the planned trajectory and the actual trajectory of CE-3. The land- ing camera on CE-3 recorded a sequence of the landing process with a frequency of 10 frames per second. These images recorded by the landing camera and high-resolution images of the lunar surface are utilized to calculate the position of the probe, so as to reconstruct its precise trajectory. This paper proposes using the method of trajectory reconstruction by Single Image Space Resection to make a detailed study of the hov- ering stage at a height of 100 m above the lunar surface. Analysis of the data shows that the closer CE-3 came to the lunar surface, the higher the spatial resolution of im- ages that were acquired became, and the more accurately the horizontal and vertical position of CE-3 could be determined. The horizontal and vertical accuracies were 7.09 m and 4.27 m respectively during the hovering stage at a height of 100.02 m. The reconstructed trajectory can reflect the change in CE-3＇s position during the powered descent process. A slight movement in CE-3 during the hovering stage is also clearly demonstrated. These results will provide a basis for analysis of orbit control strategy, and it will be conducive to adjustment and optimization of orbit control strategy in follow-up missions.

Population Spatial Distribution Based on Luojia 1-01 Nighttime Light Image:A Case Study of Beijing

With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.

Simulation analysis of evolution of hot-images induced by coplanar multi-scatterers

Based on the diffraction theory model for hot-image formation, the evolution of hot-images induced by multiscatterers located in the same plane perpendicular to the propagation axis is numerically simulated. The simulation results show that hot-images induced by coplanar multi-scatterers are also coplanar no matter whether they exist simultaneously or severally. However, if they exist simultaneously the peak intensity of the primary hot-images is weaker than if they exist severally. The unequal competition for energy between the scattered beams from the scatterers leads to the fact that part of the corresponding hot-images are relatively enhanced and the others are restrained. The results show that the hot-images of certain scatterers become stronger when any of these parameters, i.e. amplitude modulation coefficient, phase modulation coefficient and size of the surrounding scatterer, decrease.

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.

BACKGROUND RECTIFICATION AND FEATURE EXTRACTION OF IMAGE IN A SPOT WELD OF AL ALLOY X-RAY DETECTION

A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defect features was presented.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

Discrepancy between the Image Held by Nursing Students of Themselves as Employed Nurses during the Pre-Employment Period and the Post-Employment Reality Faced by Novice Nurses

The difference between the expectations of work an individual forms before joining an organization and their perception of work after becoming an organization member is called reality shock and has a significant impact on the reasons why new nurses leave their jobs. To reduce reality shock, it is necessary to clarify the discrepancy between the reality and the image of oneself after employment, which is the source of expectations. However, while there is a large body of literature on reality shock among nurses, no studies have specifically investigated student nurses’ image of post-employment confidence. We conducted semi-structured interviews with eight nursing students who had received job offers and 10 professional nurses up to two years after graduation to clarify the differences between the image that nursing students hold of themselves as employed nurses during the period immediately before employment and the reality that they find after employment. As a result, four core categories (with 25 categories) related to participants’ images of themselves as employed nurses were extracted: life after employment, adaptation to work, adaptation to the workplace, and career foundation-building. Fourteen categories were extracted regarding the reality that nurses found after employment;upon comparing and classifying these categories according to the four core categories, a lack of concreteness in the nursing students’ image of themselves as employed nurses was identified. The results suggest that support measures, both in the basic nursing education program and in clinical hospital practice, to improve the concreteness of the image that nursing students hold of themselves as employed nurses and of nursing work can reduce reality shock and prevent job turnover upon their entry into the workforce.

Neuroimaging Phenomenology of the Central Self-Regulation Mechanisms

Functional MRI was used to map the brains of subjects on-line during the process of media training for the acquisition and improvement of self-regulation mechanisms. The temporal and spatial dynamics of the new neural network formation were studied in real and simulated (false) biofeedback game, and their qualitative characteristics were discussed. It has been shown that immersion into a virtual competitive game, controlled by physiological responses, causes a wide involvement of the cortices, characterized by a high volume of activation in the mid-temporal, occipital and frontal areas, the cuneus and the precuneus. In both forms of media training, high values of activation volume were identified in the cerebellar structures.

Eurovet Unveils the New Image of Hong Kong Mode Lingerie 2010 March 30-31, at the Hong Kong Convention and Exhibition Centre

Organized by Eurovet, the world’s leading lingerie and beachwear trade show organizer and supported by Hong

基于Depth-wise卷积和视觉Transformer的图像分类模型

图像分类作为一种常见的视觉识别任务,有着广阔的应用场景。在处理图像分类问题时,传统的方法通常使用卷积神经网络,然而,卷积网络的感受野有限,难以建模图像的全局关系表示,导致分类精度低,难以处理复杂多样的图像数据。为了对全局关系进行建模,一些研究者将Transformer应用于图像分类任务,但为了满足Transformer的序列化和并行化要求,需要将图像分割成大小相等、互不重叠的图像块,破坏了相邻图像数据块之间的局部信息。此外,由于Transformer具有较少的先验知识,模型往往需要在大规模数据集上进行预训练,因此计算复杂度较高。为了同时建模图像相邻块之间的局部信息并充分利用图像的全局信息,提出了一种基于Depth-wise卷积的视觉Transformer(Efficient Pyramid Vision Transformer,EPVT)模型。EPVT模型可以实现以较低的计算成本提取相邻图像块之间的局部和全局信息。EPVT模型主要包含3个关键组件:局部感知模块(Local Perceptron Module,LPM)、空间信息融合模块(Spatial Information Fusion,SIF)和“+卷积前馈神经网络(Convolution Feed-forward Network,CFFN)。LPM模块用于捕获图像的局部相关性;SIF模块用于融合相邻图像块之间的局部信息,并利用不同图像块之间的远距离依赖关系,提升模型的特征表达能力,使模型学习到输出特征在不同维度下的语义信息;CFFN模块用于编码位置信息和重塑张量。在图像分类数据集ImageNet-1K上,所提模型优于现有的同等规模的视觉Transformer分类模型,取得了82.6%的分类准确度,证明了该模型在大规模数据集上具有竞争力。

基于选择性自校正卷积U-Net的肺部X射线图像肺实质分割

针对U-Net分割算法无法提取多尺度特征、易受到伪影和噪声干扰而导致在肺部X射线图像中肺实质分割不精确的问题,提出一种基于选择性自校正卷积的U-Net改进算法。改进后的U-Net算法将普通卷积模块替换为选择性自校正卷积模块,该模块采用多分支结构提取多尺度特征信息,使用Sigmoid函数和Softmax函数对多尺度特征信息进行选择性校正,使校正后的特征信息聚焦于肺实质区域,输出特征更加具有针对性。实验表明,该方法对骰子系数、交并比、F_(1)评分结果以及对肺实质分割结果都有一定程度的提升。

局部-全局特征引导的图文多级关系分析与挖掘方法

具有语义相关性的文本、图像数据往往具有互补性,可以从不同角度增强语义理解,因此,图文语义关系挖掘是图文数据得以充分利用的关键。为解决图文数据深层语义关系挖掘不充分、检索阶段预测不精准的问题,本文提出了一种局部-全局特征引导的多级关系分析与挖掘方法。采用多头自注意力机制的Transformer建模图像关系,构建图像引导的文本注意力模块,挖掘图像区域和全局文本间的细粒度关系,融合局部-全局特征有效增强图文数据的语义关系。为验证本文方法,在Flickr30K、MSCOCO-1K和MSCOCO-3K数据集上进行实验,并与VSM、SGRAF等13种方法进行对比分析,本文方法中以文索图的召回率平均提升了0.62%,以图索文的召回率平均提高了0.5%,实验结果验证了本文方法的有效性。

基于Sparse R-CNN的遥感目标检测研究

遥感图像目标检测任务在天气预报、环境监测及军事应用等领域均有应用,但其小目标众多、类间相似度大、尺度多样等问题导致提取特征困难。基于深度学习的方法在目标检测领域已经流行起来,Sparse R-CNN是一种结构简单且效果较好的模型,但将其直接应用到遥感图像上结果较差,针对遥感图像特点引入了自监督学习框架跟选择性查询收集提高了遥感图像目标检测的效果,在mAP指标上提高3.8个百分点。