Robust blind image watermarking based on interest points

Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.

Comparison of clinical efficacy of neddle knife at acupoints selected from recent regions along meridians and according to anatomical structures in treating early and middle stage knee osteoarthritis

Objective:To stimulate tenderness points around knee joint in two metholds-corresponding acupoints selected from recent regions along meridians and anatomical structures,to compare the clinical efficacy of neddle knife of to cure early and middle stage knee osteoarthritis.Method:70 patients were randomly(Random Number Tables)divided into test group(acupoints selected from recent regions along meridians,n=35)and control group(anatomical structures,n=35),who were diagnosed as knee osteoarthritis.Observe the VAS(visual analogue scale),Lysholm,WOMAC(the Western Ontario and McMaster Unive rsities Osteoarthritis Index)and ROM(rang of motion)between two groups in first week,first month,third month after treatment.Recording the degree of improvement of knee joint’s pain,dysfunction and symptoms of osteoarthritis and rang of motion.Results:Within groups,the VAS,Lysholm,WOMAC and ROM were obviously different from pre-therapy scores in the third,sixth and twelfth week post-therapy(P<0.05).Between groups no significant difference were observed in the third week post-therapy about VAS,Lysholm,WOMAC scores(P>0.05).However,there were differences in the sixth,twelfth weeks post-therapy(P<0.05);compared with control group,the ROM of test group were difference in the third,sixth,twelfth weeks post-therapy(P<0.05).Conclusion:The clinical efficacy of stimulating corresponding acupoints tenderness points selected from recent regions along meridians to treat early and middle stage knee osteoarthritis was superior to anatomical structures,which can effectively relieve pain,dysfunction,symptoms of osteoarthritis of knee joint and rang of motion.

Influential Factors in Employment Location Selection Based on “Push-Pull” Migration Theory—A Case Study in Three Gorges Reservoir Area in China

In China, farmers employed in non-farm work have become important socio-economic actors, but few studies have examined the farmers' perspective in making their work location choices. Based on "push-pull" migration theory, this paper utilizes sectional data from a 2013 survey of farmers in China's Three Gorges Reservoir area to empirically analyze the factors influencing migrant workers' choice of employment location. The results indicate that 60.46% of laborers have migrated from their home province, whereas 39.54% have remained in their home province. Focusing on personal, household, and community characteristics—in addition to the economic characteristics of the sample counties—multinomial logistic regression models reveal that farmer-laborers' employment location decisions are influenced by their personal capital endowment(age, years of education and social networks), family structure(the number of laborers, elders, children and students), home village characteristics(location, economic development level and the degree of relief of the land) and home county economic development level. Notably, male and female laborers' location decisions reveal a converging trend, and their differences are not pronounced. Per capita arable land area has little influence on location decisions, whereas the educational level of laborers has a significant impact. The results differ significantly from those found in previous studies.

Image Region Selection and Ensemble for Face Recognition

In this paper, a novel framework for face recognition, namely Selective Ensemble of Image Regions （SEIR）, is proposed. In this framework, all possible regions in the face image are regarded as a certain kind of features. There are two main steps in SEIR： the first step is to automatically select several regions from all possible candidates; the second step is to construct classifier ensemble from the selected regions. An implementation of SEIR based on multiple eigenspaces, namely SEME, is also proposed in this paper. SEME is analyzed and compared with eigenface, PCA ＋ LDA, eigenfeature, and eigenface ＋ eigenfeature through experiments. The experimental results show that SEME achieves the best performance.

Computing a ground appropriateness index for route selection in permafrost regions

The reasonable calculation of ground appropriateness index in permafrost region is the precondition of highway route design in permafrost region. The theory of knowledge base and fuzzy mathematics are applied, and the damage effect of permafrost is considered in the paper. Based on the idea of protecting permafrost the calculation method of ground appro- priateness index is put forward. Firstly, based on the actual environment conditions, the paper determines the factors affecting the road layout in permafrost areas by qualitative and quantitative analysis, including the annual slope, the average annual ground temperature of permafrost, the amount of ice in frozen soil, and the interference engineering. Secondly, based on the knowledge base theory and the use of Delphi method, the paper establishes the knowledge base, the rule base of the permafrost region and inference mechanism. The method of selecting the road in permafrost region is completed and realized by using the software platform. Thirdly, taking the Tuotuo River to Kaixin Mountain section of permafrost region as an example, the application of the method is studied by using an ArcGIS platform. Results show that the route plan determined by the method of selecting the road in perma-frost region can avoid the high temperature and high ice content area, conform the terrain changes and evade the heat disturbance among the existing projects. A reasonable route plan can be achieved, and it can provide the basis for the next engineering construction.

A Plan to Save the Species China hopes to draw more attention to endangered species by selecting regional representatives

Always plainly dressed and amiable, Ji Xianlin wouldn’t be given a second glance in a crowd. Yet looks can be deceiving. He was the country’s leading scholar of