ABSTRACT
The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.
ABSTRACT
The analysis undertaken in the article focuses on a group of memes selected from the database which drew on culture-specific references. Specifically, they embrace the memories of socialist times and call on references to comic films and easily recognized characters in order to bring out the re-discovered absurdity of the current COVID-19 situation. This material seems ideal to revisit Raskin's early notion of sophistication, which was broadly argued to derive from intertextuality as well complexity of references that function as sources of humor. In all the examples discussed we can observe the intertextual and metatextual elements, multiple levels and shifts in points of view and attitudes as well as the mutual relations of verbal to visual within the meme cycles. In order to identify specific mechanisms of sophisticated humor, we attempt to identify the visual or verbal triggers of overlap of the two worlds in question, and discuss comic mechanisms of sophistication, including attributions of desire, belief and intention (purpose) to characters or the narrator as commentators on events or situations. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Dr. Eliot Young of Southwest Research institute (SwRI) is the PI of a proposal entitled: Testbed for High-Acuity Imaging and Stable Photometry and Image-Motion Compensation (THAI-SPICE). The main goal of this larger project is to build and demonstrate a fine-pointing system for stratospheric payloads to enable diffraction-limited performance by balloon-borne telescopes. This proposal from MIT Lincoln Laboratory (LL) was as a Co-Investigation of this Sub-Orbital Mission, within the Astronomy and Physics Research and Analysis Program (APRA) and was to furnish to NASA an electronic camera using orthogonal-transfer charge-coupled devices (OTCCD), with a high charge-transfer efficiency (CTE), to stabilize the images acquired by a balloon-borne telescope flying at approximately 120,000 ft. The camera, which has high sensitivity and low noise, will be integrated with the SwRI telescope as government-furnished equipment and will be able to integrate photons arriving from dim stars and other objects over long periods of time. This will enable the acquisition of images with jitter of less than 1 arcsecond. It will be a significant advance for both NASA and astronomy, in general, to have balloon-borne, diffraction-limited telescopes that are free of atmospheric aberrations operating at a much lower cost than similar-aperture satellite-borne telescopes. MIT LL provided the CCID77 and built the prototype camera, in which it was housed, to SwRI through NASA, in the summer of 2020. The balloon launch was to take place in the fall of 2020, but was delayed until the fall of 2021, due to the COVID-19 pandemic and its effect on the NASA balloon launch schedule. The camera has worked well on the ground and has been integrated into the balloon payload. This has completed the obligations of MIT LL to the program.
ABSTRACT
Many countries around the world have been influenced by Covid-19 which is a serious virus as it gets transmitted by human communication. Although, its syndrome is quite similar to the ordinary flu. The critical step involved in Covid-19 is the initial screening or testing of the infected patients. As there are no special detection tools, the demand for such diagnostic tools has been increasing continuously. So, it is eminently admissible to find out positive cases of this disease at the earliest so that the spreading of this dangerous virus can be controlled. Although, some methods for the detection of Covid-19 patients are available, which are performed upon respiratory based samples and among them, a critical approach for treatment is radiologic imaging or X-ray imaging. The latest conclusions obtained from X-ray digital imaging based algorithms and techniques recommend that such type of digital images may consist of significant facts regarding the SARS-CoV-2 virus. The utilization of Deep Neural Networks based methodologies clubbed with digital radiological imaging has been proved useful for accurately identifying this disease. This could also be adjuvant in conquering the problem of dearth of competent physicians in far-flung areas. In this paper, a CheXImageNet model has been introduced for detecting Covid-19 disease by using digital images of Chest X-ray with the help of an openly accessible dataset. Experiments for both binary class and multi-class have been performed in this work for benchmarking the effectiveness of the proposed work. An accuracy of 100 % is reported for both binary classification (having cases of Covid-19 and Normal X-Ray) and classification for three classes (including cases of Covid-19, Normal X-Ray and, cases of Pneumonia disease) respectively.
ABSTRACT
Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system.