Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.

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Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic analysis, and others are based on static analysis, while some are completely dependent on both. In this paper, we studied static, dynamic, and hybrid analyses to identify malicious applications. We leverage machine learning classifiers to detect malware activities as we explain the effectiveness of these classifiers in the classification process. Our results prove the efficiency of permissions and the action repetition feature set and their influential roles in detecting malware in Android applications. Our results show empirically very close accuracy results when using static, dynamic, and hybrid analyses. Thus, we use static analyses due to their lower cost compared to dynamic and hybrid analyses. In other words, we found the best results in terms of accuracy and cost (the trade-off) make us select static analysis over other techniques.

<p>This work investigates the problems of extending the sensors network lifetime in smart cities. The limited capacity of the sensors’ batteries, and the difficulty of replacing the sensors’ batteries in hard-to-reach areas are some of the main challenges that contribute in reducing the lifetimes of the networks. The direction of this study is to use renewable energy as an energy source for collecting data from various infrastructures that are distributed throughout these cities. We present a model for data collection based on combining energy harvesting (EH) with the cluster head rotation feature, which results in flexible and sustainable networks that can be used in smart cities. Simulation results depict the performance of the proposed model with and without EH technology. The metrics used to compare the performance of the proposed model with and without EH technology include the consumed energy by sensors, number of live and dead sensors, and energy variance. The results show that the network lifetime increases when EH technology is used.</p>

PurposeOcular manifestations were reported in many recent observations that studied either the effect of COVID-19 directly on eyes or of face mask use. Hence, this study aimed to investigate the effect of COVID-19 on the eyes and make a clear comparison of its direct and indirect effect from face mask-wearing.MethodsThis was a cross-sectional study of both written and web-based questionnaires, distributed among a group of COVID-19 patients and a matched control group, the questionnaire consisted of common demographic data, COVID-19 infection history and its symptoms, focusing on ocular symptoms and the presence of conditions related to or cause eye symptoms. As well as the use of face masks that were assessed in terms of the complained ocular manifestationResultsOf 618 participants, 252 had COVID-19 and 366 never had COVID-19. Ocular manifestation among COVID-19 incidence was 44%, significantly higher than non-infected participants’ incidence (35.8%), adjusted odds ratio, 95% confidence interval (AOR, 95%CI); 1.45 (1.02-2.06)). Eye discharges (p-value = 0.033) and photosensitivity (p-value = 0.003) were noted more commonly among COVID-19 participants compared to healthy control. When comparing long periods of face mask use with each ocular symptom; dry eye based on OSDI, forging body sensation, eye pain and eye discharges, were found significantly common among extended periods of face mask use.ConclusionCOVID-19 pandemic affected eyes, both directly from the virus or from its preventive measure of face mask use.