The Legal Framework of Electronic Data Crimes

In order to determine the legal framework of these crimes, we should distinguish between two types of crimes or attack electronic data, the first type when the technology of electronic data process and telecommunication have used in remote to commit crimes. In other words these crimes are committed through computer and the criminal description of these businesses belongs to the known of types of traditional crimes like theft, fraud and other crimes. This type of crimes call un informatics in the global information network “ internet” also this field includes the crimes of usage of “internet” and electronic data processes tools to show the pornographic images or diffusion a messages which are inciting to racism, racial, religious, discrimination or exposure to the personal liberty or intellectual property. The second types of electronic data process crimes when the technology of the electronic data and telecommunications are on remote and make it as a means of this crimes and their purpose too. And now we are in front of anew criminal acts which associated mostly to the exposure of security and integrity of electronic data systems and the confidentiality of the data and information that consist on. And this type of criminal information network called “internet”, this is done in the case of illegal entry in to these systems and exposure to it or to the information that contain it. So this research will base on the electronic data crimes which are connected to the internet, when it becomes a direct target and a goal in their contents, and regardless on the impulsive of behind of committing

A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery

This paper presents a pansharpening technique based on the non-subsampled contourlet transform (NSCT) and convolutional autoencoder (CAE). NSCT is exceptionally proficient at presenting orientation information and capturing the internal geometry of objects. First, it’s used to decompose the multispectral (MS) and panchromatic (PAN) images into high-frequency and low-frequency components using the same number of decomposition levels. Second, a CAE network is trained to generate original low-frequency PAN images from their spatially degraded versions. Low-resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high-resolution multispectral images. Third, another CAE network is trained to generate original high-frequency PAN images from their spatially degraded versions. The result of low-pass CAE is fed to the trained high-pass CAE to generate estimated high-resolution multispectral images. The final pan-sharpened image is accomplished by injecting the detailed map of the spectral bands into the corresponding estimated high-resolution multispectral bands. The proposed method is tested on QuickBird datasets and compared with some existing pan-sharpening techniques. Objective and subjective results demonstrate the efficiency of the proposed method

Smart COVID-3D-SCNN: A novel method to classify x-ray images of COVID-19

The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. For extracting the discriminative features through these modalities, deep convolutional neural networks (CNNs) are used. A siamese convolutional neural network model (COVID-3D-SCNN) is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans. To extract the useful features, we used three consecutive models working in parallel in the proposed approach. We acquired 575 COVID-19, 1200 non-COVID, and 1400 pneumonia images, which are publicly available. In our framework, augmentation is used to enlarge the dataset. The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%, specificity 95.55%, and sensitivity 96.62% over (COVID-19 vs. non-COVID19 vs. Pneumonia)

Deposition of stainless steel thin films: an electron beam physical vapour deposition approach

This study demonstrates an electron beam physical vapour deposition approach as an alternative stainless steel thin films fabrication method with controlled layer thickness and uniform particles distribution capability. The films were fabricated at a range of starting electron beam power percentages of 3–10%, and thickness of 50–150 nm. Surface topography and wettability analysis of the samples were investigated to observe the changes in surface microstructure and the contact angle behaviour of 20 °C to 60 °C deionised waters, of pH 4, pH 7, and pH 9, with the as-prepared surfaces. The results indicated that films fabricated at low controlled deposition rates provided uniform particles distribution and had the closest elemental percentages to stainless steel 316L and that increasing the deposition thickness caused the surface roughness to reduce by 38%. Surface wettability behaviour, in general, showed that the surface hydrophobic nature tends to weaken with the increase in temperature of the three examined fluids

A pyoderma gangrenous‑like cutaneous leishmaniasis in a Libyan woman with rheumatoid arthritis: a case report

Background: Several case reports describe diseases presenting with skin ulcerations, which resemble pyoderma gangrenosum especially in immune-compromised patients, often proven on further workup, to have an infective or malignant etiology. However, treatment of pyoderma gangrenosum by systemic steroids or other immunosuppressive agents may worsen the condition. Case presentation: We report here, a 45 year-old Libyan woman with rheumatoid arthritis on low dose steroids with pyoderma gangrenosum-like skin lesions and positive pathergy. Slit–smear was positive for Leishmania amastigotes and histopathological examination confirmed the diagnosis of cutaneous leishmaniasis. The lesions healed completely by parenteral sodium stibogluconate (Pentostam) 600 mg daily. Conclusion: We report for the first time, a rare and unusual presentation of pyoderma gangrenosum like-cutaneous leishmaniasis in a patient with rheumatoid arthritis. Atypical cutaneous leishmaniasis should not be ruled out in the differential diagnosis of unresponsive skin diseases, with slit/smear and a skin biopsy is required.Acknowledgements Not applicable. Funding The authors declare that no funding support was obtained for this study

IIoT based trustworthy demographic dynamics tracking with advanced Bayesian learning

Tracking demographic dynamics for the built environment is important for a smart city. As a kind of ubiquitous Industrial Internet of Things (IIoT) device, portable devices (e.g., mobile phones) afford a great potential to achieve this goal. Tracking the demographic dynamics illuminates two things: populations mobility (where do people go) and the related demographics (who are they). Many past studies have investigated the tracking of population dynamics; however, few of them tried tracking the demographic dynamics. In this context, our study proposed a ubiquitous IIoT based trustworthy approach for built environment demographic dynamics tracking. First, we employed a meta-graph-based data structure to represent users life patterns and projected them into a low-dimension space as uniform features. Then, based on the life-pattern features, we derived a variation-inference-based advanced Bayesian model to infer the demographics. Finally, taking a region in Tokyo as a case study, we compared our methods with baseline methods (heuristic algorithm, deep learning), and the result proved a superior accuracy (the MAPE improved by 0.07 to 0.28) as well as reliability (0.78 Pearson correlation coefficient with survey data)

International Journal of Video & Image Processing and Network Security Vol:10 No: 02 8 A Hybrid Method for Extracting Key Terms of Text Documents

Abstract: key terms are important terms in the document, which can give high-level description of contents for the reader. Extracting key terms is a basic step for many problems in natural language processing, such as document classification, clustering documents, text summarization and output the general subject of the document. This article proposed a new method for extracting key terms from text documents. As an important feature of this method, we note the fact that the result of its work is a group of key terms, with terms from each group are semantically related by one of the main subjects of the document. Our proposed method is based on a combination of the following two techniques: a measure of semantic proximity of terms, calculated based on the knowledge base of Wikipedia and an algorithm for detecting communities in networks. One of the advantages of our proposed method is no need for preliminary learning, because the method works with the knowledge base of Wikipedia. Experimental evaluation of the method showed that it extracts key terms with high accuracy and completeness

Genotype – environment interaction study in sugar beet (Beta vulgaris L.)

The research was carried out to study the response of 16 cultivars of sugar beet in 3 seasons at one major sugar beet producing location, Hama, in Syria in autumn time, and assess genotype by environment interaction, and to estimate the stability of the varieties performance, according to the yield stability statistics (Ysi), for the studied traits of these varieties. A randomized complete block design with four replications was used. Data collected from each experiment were subjected to simple analysis of variance and after homogenization of error variance, combined analysis for four traits including Sucrose content (SC %), Purity (P %), Root yield (RY ton.ha-1), and Sugar yield (SY ton.ha-1) were carried out. Combined analysis of variance over years, exhibited significant differences (P≤0.05) among the varieties. Results of yield stability statistics (Ysi) revealed that five of the monogerm sugar beet varieties (Vico, Dita, Al Ceste, Chimene, and SR305) were stable for all of the studied traits, during three seasons, which is recommended to be planted in autumn time.International Journal of Environment Vol.5(3) 2016, pp.74-86</p