Human Interactive Proofs (HIPs) are automatic inverse Turing tests, which are intended to differentiate between people and malicious computer programs. The mission of making good HIP system is a challenging issue, since the resultant HIP must be secure against attacks and in the same time it must be practical for humans. Text-based HIPs is one of the most popular HIPs types. It exploits the capability of humans to recite text images more than Optical Character Recognition (OCR), but the current text-based HIPs are not well-matched with rapid development of computer vision techniques, since they are either vey simply passed or very hard to resolve, thus this motivate that continuous efforts are required to improve the development of HIPs base text. In this paper, a new proposed scheme is designed for animated text-based HIP; this scheme exploits the gap between the usual perception of human and the ability of computer to mimic this perception and to achieve more secured and more human usable HIP. This scheme could prevent attacks since it's hard for the machine to distinguish characters with animation environment displayed by digital video, but it's certainly still easy and practical to be used by humans because humans are attuned to perceiving motion easily. The proposed scheme has been tested by many Optical Character Recognition applications, and it overtakes all these tests successfully and it achieves a high usability rate of 95%.
Carbon nanotubes were prepared by an arc-discharge method,
under different values of pressure of oxygen gas. The structure of
multi-walled carbon nanotubes powders has been characterized by
low-angle X-ray diffraction .The morphology of carbon nanotube
powder was examined by transmission electron microscope. The
capacitance-voltage and current- voltage (dark and illumination
current) characterization were measured under different values of
pressure (10-3, 10-4, 10-5) mbar of oxygen gas
Objective(s): assessment of the health follow up and weight control for women with osteoporosis and find out the relationship between their health follow up and weight control and their socio-demographic characteristics.
Methodology: A descriptive study was conducted on women with osteoporosis for the period of September, 26th 2020 to Jun, 20th 2021. Non- probability (convenient) sample of (70) women with osteoporosis selected from (5) Private Clinics for Joints and Fractures in Baqubah City. A questionnaire was designed though extensive review of literatures and it consists of three parts: the first part includes women’s socio demographic characteristics, the second part inclu
... Show MoreThe present work focuses on the experimental implementation of one of the fiber optical sensors, the optical glass fiber built on surface Plasmon resonance. A type of optical glass fiber was used in this work, single-mode no-core fiber with pre-tapering diameter: (125.1 μm) and (125.3 μm), respectively. The taper method can be tested by measuring the output power of the optical fiber before and after chemical etching to show the difference in cladding diameter due to the effect of hydrofluoric acid with increasing time for the taper process. The optical glass fiber sensor can be fabricated using the taper method to reduce the cladding diameter of the fibers to (83.12 µm, 64.37 µm, and 52.45 µm) for single-mode fibers using Hydrofluoric
... Show MoreThis paper reports a comprehensive study on the behavior of concavely curved soffit reinforced concrete (RC) beams strengthened in flexure with carbon fiber-reinforced polymer (CFRP) composites under static loading. The main objective of this paper is to explore the effect of surface concavity on the bond performance of externally bonded wet layup CFRP sheets and laminates. An experimental program consisting of flexural strengthening of 24 RC beams with concavely curved soffits was carried out. All specimens were simply supported RC beams tested under three-point bending. Of the 24 beams, 6 beams were flat soffit RC beams, and the remainder were fabricated with concavely curved soffits with a degree of curvature that is ranging from 5 mm/m
... Show MoreVideo streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified in
... Show MoreThe prostheses sockets use normally composite materials which means that their applications may be related with the human body. Therefore, it was very necessary to improve the mechanical properties of these materials. The prosthetic sockets are subjected to varying stresses in gait cycle scenario which may cause a fatigue damage. Therefore, it is necessary or this work to modify the fatigue behavior of the materials used for manufacturing the prostheses sockets. In this work, different Nano particle materials are used to modify the mechanical properties of the composite materials, and increase the fatigue strength. By using an experimental technique, the effect of using different volu
Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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