In this paper, we propose a novel copy-move forgery detection scheme which can accurately localize duplicated regions with a reasonable computational cost. To this end, a new interest point detector is proposed utilizing the advantages of both block-based and traditional keypoint-based methods. The detected keypoints adaptively cover the entire image, even low contrast regions, based on a uniqueness metric. Moreover, a new filtering algorithm is employed which can effectively prune the falsely matched regions. Considering the new interest point detector, an iterative improvement strategy is proposed. The whole procedure is iterated along with adjusting the keypoints density based on the achieved information. The experimental results demonstrate that the proposed scheme outperforms the state-of-the-art methods using two public benchmark databases.

Background: Stress is an important part of a college student's life. Psycho-technology has greatly helped students cope more effectively with stress. Objectives:The aim of the present study was to compare the effectiveness of three methods of intervention for stress management in students based on mindfulness-based stress reduction, including blended therapy, smartphone mobile application, and face-toface therapy. Methods: A quasi-experimental study was designed with pretests, posttests, and follow-ups for a month on randomly selected students. The first group was treated by the Aramgar application only, the second group by both the face-to-face therapy and Aramgar application, and the third group by the face-to-face therapy only. The DASS-21 was utilized for both pretest and posttest administration. The ANOVA and post hoc tests were applied to analyze the data. Results:The mean age of the participants was 24.29 ± 3.21 years. There was a significant difference in the mean score reduction of depression, stress, and anxiety between the three groups (P < 0.001). The post hoc test showed that the blended therapy group had the greatest mean score reduction on stress, depression, and anxiety among the three groups. Conclusions:The blended approach could improve the mental health of students more than the two other approaches. Therefore, the use of mobile platforms of new technologies is highly suggested along with face-to-face interventions in clinics to support people within their daily routine.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) -including Machine Learning (ML) and Big Data analyticsas well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common

Wireless capsule endoscopy (WCE) is relatively a new device which investigates the entire gastrointestinal (GI). About 55000 frames are captured during an examination (two frames per second). Thus, it is bene icial to ind an automatic method to detect diseases frames or regions of a frame. The WCE videos have lots of uninformative regions (such as intestinal juice, bubbled, and dark regions); therefore, preprocessing is useful and necessary in diseases detection. In this paper, three practical methods are introduced to detect the informative and uninformative regions in a frame. In order to achieve this goal, morphological operations, fuzzy k-means, sigmoid function, statistic features, Gabor ilters, Fisher test, neural network, and discriminators in the HSV color space are used to detect uninformative regions (do not carry clinical information) in a frame. Our experimental results indicate that precision, sensitivity, accuracy, and speci icity are respectively 97.76%, 97.80%, 98.15%, and 98.40% in the irst method, 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the second one, and 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the third method.

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