Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images. First, to deal with color input sliding windows of different scales, a unified CNN architecture is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of robust multi-scale tampering detectors based on CNNs, complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse the maps and generate the final decision map. By exploiting the benefits of both the small-scale and large-scale analyses, the segmentation-based multi-scale analysis can lead to a performance leap in forgery localization of CNNs. Numerous experiments are conducted to demonstrate the effectiveness and efficiency of our method.Comment: 7 pages, 6 figure

The Impact of Platform Social Responsibility on Consumer Trust

The concept of social responsibility has been widely applied incorporates business philosophy to gain the trust of consumers. With the rise of two-side platforms, platforms have popped up the limelight along with the hot topic of the sharing economy. Despite this, we do not know much about the underlying mechanisms of consumer trust. A questionnaire survey was conducted with 263 consumers from China to explore the consequences of platform social responsibility on consumer trust. The results demonstrate that the implementation of social responsibility by platforms significantly increases consumer trust. Additionally, consumer confusion plays a mediating effect, and platform network externality plays a moderating role. Briefly, the platform does not play a dominant role in regulating supply and demand as we might think since the consciousness of consumer groups is rising. Their autonomy to collect information and make decisions after perception cannot be ignored. The study shows that sharing economy platforms should take their social responsibilities into consideration, rather than taking them as a subsidiary role. Platforms should see consumer trust as a key end rather than a means to promote profits

Exploring the feasibility of teaching Classical Chinese poetry: a learner-centred curriculum development for adult L2 learners of Chinese

This dissertation highlighted the importance of learning classical Chinese poetry in L2 Chinese learners’ journey. Not only because the living tradition of Classical Chinese poetry, but also due to the significant role of Classical Chinese poetry in today’s Chinese culture and society Then the two questions was answered why CCP is teachable, and what to teach when teaching CCP through analysing the characteristics of CCP from its visual effects, authority effects, grammatical aspects, as well as its metaphor and symbolic meaning. This research found the current CCP courses in the universities worldwideand compared the advantages and disadvantages of them, aiming to support syllabus design for my CCP-focused trial session. A CCP introduction session was designed, implemented and evaluated step by step in this research. Pre-course survey was adopted in needs analysis. Pro-course survey and follow-up interview was employed in evaluation. Different from other traditional CCP class, my CCP trial session was learner-centred and organised by two small groups. From the data analysis, research found that most of the students are interested in attending this CCP class. They found the class was enjoyable and generally approachable. Research also found that content which decided by involving participants’ opinions received most positive feedbacks in evaluation process. The findings should be highlighted that for some advanced participants, they are interested in tackling the problems which they viewed as most difficult aspects in pre-course survey. The findings also showed some conflicts between teacher’s expectation and students’ feedback. The aspects I viewed as most difficulties turned out to be students favourite part. The content I used to facilitate learners’ understanding turned out to confuse them. At last, the challenges for teachers to design a CCP course was highlighted

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application

Quasinormal modes of black holes in f(T) gravity

We calculate the quasinormal modes (QNM) frequencies of a test massless scalar field and an electromagnetic field around static black holes in $f(T)$ gravity. Focusing on quadratic $f(T)$ modifications, which is a good approximation for every realistic $f(T)$ theory, we first extract the spherically symmetric solutions using the perturbative method, imposing two ans$\ddot{\text{a}}$tze for the metric functions, which suitably quantify the deviation from the Schwarzschild solution. Moreover, we extract the effective potential, and then calculate the QNM frequency of the obtained solutions. Firstly, we numerically solve the Schr$\ddot{\text{o}}$dinger-like equation using the discretization method, and we extract the frequency and the time evolution of the dominant mode applying the function fit method. Secondly, we perform a semi-analytical calculation by applying the WKB method with the Pade approximation. We show that the results for $f(T)$ gravity are different compared to General Relativity, and in particular we obtain a different slope and period of the field decay behavior for different model parameter values. Hence, under the light of gravitational-wave observations of increasing accuracy from binary systems, the whole analysis could be used as an additional tool to test General Relativity and examine whether torsional gravitational modifications are possible.Comment: 22 pages, 7 figure