Today’s typical computing environment has changed from mainframe systems to small computing systems that often cooperate via communication networks. Distributed Operating Systems: Concepts and Design addresses the organization and principles of distributed computing systems. Although it does not concentrate on any particular operating system or hardware, it introduces the major concepts of distributed operating systems without requiring that readers know all the theoretical or mathematical fundamentals. Each chapter covers a special issue of distributed computing systems and concludes with a summary, exercises, and a bibliography (with pointers to other bibliographies on the Internet). Pradeep Sinha encourages readers to use the exercises to test their understanding of the text and stimulate further investigations. In Chapter 1, Sinha focuses on hardware, declaring that distributed computing systems based on networks of loosely coupled processing nodes are different from “true distributed systems”-distributed computing systems using distributed operating systems. After a short historical review, he presents major distributed computing system models and reviews issues to consider when designing a distributed operating system. Finally, he introduces OSF DCE (the Open Systems Foundation’s Distributed Computing Environement), which contains almost half the key technologies presented in this book. Chapter 2 briefly introduces computer networks and the state of the art in networking technology. It emphasizes models and protocols of communication in distributed networks and discusses internetworking and ATM technologies. The next three chapters cover the elements of interprocess communication used in distributed environments. Chapter 3 introduces message passing, the basic mechanism of information sharing between two or more processes on different computers. A case study of 4.3 BSD Unix interprocess communication mechanisms illustrates the relevant aspects of message passing. Chapter 4 discusses remote procedure calls, a special implementation of the general message-passing model and concludes with a case study of RPC implementations-SUN RPC and DCE W C . Chapter 5 describes distributed shared memory. It clearly presents how shared memory interprocess communication works differently on tightly or loosely coupled multiprocessor systems, and it introduces a distributed shared-virtual-memory model. Sinha discusses important issues concerning data management such as consistency and granularity, thrashing, and replacement strategies. Chapter 6 covers synchronization, a major problem of concurrent computing created when distributed processes cooperatively or competitively share distributed resources. Issues discussed include deadlock, mutual exclusion, clock synchronization, and event ordering. The next two chapters address in more detail the management of computing resources, from different points of view. Chapter 7 deals with the allocation of processing nodes to processes-that is, task assignment, load balancing and load sharing. Sinha describes each of these three approaches in detail and compares it to the others. Chapter 8 adopts a different viewpoint; it discusses how to tile processes and distribute them efficiently on a network of processing nodes. Topics of interest are threading and process migration. Chapter 9 deals with file systems in distributed environments. It defines terms, describes 10 desirable features of an ideal distributed file system, and discusses file models, file-accessing, file-sharing, file-caching, file-replication, and transactions. The chapter concludes with a case study of the DCE Distributed File Service model. Chapter 10 describes the problems of naming and locating objects in a distributed environment. This is the first chapter that explicitly associates processes, files, devices, and processing nodes with an abstract object model. Sinha introduces terminologies, naming concepts, and desirable features for naming systems. H e explains system-oriented and human-oriented naming conventions, derived name spaces, and how these are related, as well as name resolution and name cache. Chapter 10 concludes with a case study of the DCE Directory Service. Chapter 11 illustrates several aspects and problems of internal security-for example,

Distributed Operating Systems: Concepts And Design

Countries the world over have focused on protecting human health and combatting the COVID-19 outbreak. It has had a destructive effect on human health and daily life. Many people have been infected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. Although laboratory tests have been widely applied as diagnostic tools, findings suggest that the application of X-ray and computed tomography images and pretrained deep convolutional neural network (CNN) models can help in the accurate detection of this disease. In this study, we propose a model for COVID-19 diagnosis, applying a deep CNN technique based on raw chest X-ray images of COVID-19 patients, which can be accessed publicly on GitHub. Fifty positive and 50 negative COVID-19 X-ray images for training and 20 positive and 20 negative images for testing phases are included. Because the classification of X-ray images needs a deep architecture to cope with the complicated structure of images, we apply five different architectures of well-known pretrained deep CNN models: VGG16, VGG19, ResNet, DenseNet, and InceptionV3. The pretrained VGG16 model can detect COVID-19 from non-COVID-19 cases with the highest classification performance of 80% accuracy among the other four proposed models, and it can be used as a helpful tool in the department of radiology. In the proposed model, a limited dataset of COVID-19 X-ray images is used that can provide more accurate performance when the number of instances in the dataset increases.

Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images

Although blockchain-based digital services promise trust, accountability, and transparency, multiple paradoxes between blockchains and GDPR have been highlighted in the recent literature. Some of the recent literature also proposed possible solutions to these paradoxes. This article aims to conduct a systematic literature review on GDPR compliant blockchains and synthesize the findings. In particular, the goal was to identify 1) the GDPR articles that have been explored in prior literature; 2) the relevant research domains that have been explored, and 3) the research gaps. Our findings synthesized that the blockchains relevant GDPR articles can be categorized into six major groups, namely data deletion and modification (Article 16, 17, and 18), protection by design by default (Article 25), responsibilities of controllers and processors (Article 24, 26, and 28), consent management (Article 7), data processing principles and lawfulness (Article 5,6 and 12), and territorial scope (Article 3). We also found seven research domains where GDPR compliant blockchains have been discussed, which include IoT, financial data, healthcare, personal identity, online data, information governance, and smart city. From our analysis, we have identified a few key research gaps and present a future research direction.

GDPR Compliant Blockchains–A Systematic Literature Review

The main aim of this paper is to propose a conceptual model of technology acceptance that explains how individual, social, organizational factors affect the users’ behavioural intention to use technology by academics for teaching and learning activities. More specifically, the proposed model extends the Technology Acceptance Model (TAM) to include nine constructs namely, peer influence, superior influence, resource facilitation, technology facilitations; self-efficacy, academic tasks, nonacademic tasks, government support, and finally, institute support. In addition, seven demographic and situational moderators (age, education level, organisation type, academic position, voluntariness and usage experience) are hypothesized to have a moderating effect on individuals’ acceptance behaviour. This model provides valuable insights into the factors that influence the acceptance or resistance of Internet and technology by intended users and offers opportunities for future research in understanding the acceptance of technology.

http://eujournal.org/index.php/esj/article/download/5279/5151

Social, organizational, demography and individuals’ technology acceptance behaviour: a conceptual model

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

/pdf/human-all-too-human-an-all-around-appraisal-of-the-58sb421vry.pdf

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection. This study is focused on metamorphic malware, which is the most advanced member of the malware family. It is quite impossible for anti-virus applications using traditional signature-based methods to detect metamorphic malware, which makes it difficult to classify this type of malware accordingly. Recent research literature about malware detection and classification discusses this issue related to malware behavior. The main goal of this paper is to develop a classification method according to malware types by taking into consideration the behavior of malware. We started this research by developing a new dataset containing API calls made on the windows operating system, which represents the behavior of malicious software. The types of malicious malware included in the dataset are Adware, Backdoor, Downloader, Dropper, spyware, Trojan, Virus, and Worm. The classification method used in this study is LSTM (Long Short-Term Memory), which is a widely used classification method in sequential data. The results obtained by the classifier demonstrate accuracy up to 95% with 0.83 $F_1$-score, which is quite satisfactory. We also run our experiments with binary and multi-class malware datasets to show the classification performance of the LSTM model. Another significant contribution of this research paper is the development of a new dataset for Windows operating systems based on API calls. To the best of our knowledge, there is no such dataset available before our research. The availability of our dataset on GitHub facilitates the research community in the domain of malware detection to benefit and make a further contribution to this domain.

Deep learning based Sequential model for malware analysis using Windows exe API Calls.

Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world. Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Some of the most prominent examples of ransomware attacks in history are WannaCry and Petya, which impacted companies' finances throughout the globe. Both WannaCry and Petya caused operational processes inoperable by targeting critical infrastructure. It is quite impossible for anti-virus applications using traditional signature-based methods to detect this type of malware because they have different characteristics on each contaminated computer. The most important feature of this type of malware is that they change their contents using their mutation engines to create another hash representation of the executable file as they propagate from one computer to another. To overcome this method that attackers use to camouflage malware, we have created three-channel image files of malicious software. Attackers make different variants of the same software because they modify the contents of the malware. In the solution to this problem, we created variants of the images by applying data augmentation methods. This article aims to provide an image augmentation enhanced deep convolutional neural network (CNN) models for detecting malware families in a metamorphic malware environment. The main contributions of the article consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a CNN model. In the first component, the collected malware samples are converted into binary file to 3-channel images using the windowing technique. The second component of the system create the augmented version of the images, and the last part builds a classification model. This study uses five different deep CNN model for malware family detection. The results obtained by the classifier demonstrate accuracy up to 98%, which is quite satisfactory.

Data augmentation based malware detection using convolutional neural networks.

Recently, cyber-attacks have been extensively seen due to the everlasting increase of malware in the cyber world. These attacks cause irreversible damage not only to end-users but also to corporate computer systems. Ransomware attacks such as WannaCry and Petya specifically targets to make critical infrastructures such as airports and rendered operational processes inoperable. Hence, it has attracted increasing attention in terms of volume, versatility, and intricacy. The most important feature of this type of malware is that they change shape as they propagate from one computer to another. Since standard signature-based detection software fails to identify this type of malware because they have different characteristics on each contaminated computer. This paper aims at providing an image augmentation enhanced deep convolutional neural network (CNN) models for the detection of malware families in a metamorphic malware environment. The main contributions of the paper's model structure consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a convolutional neural network model. In the first component, the collected malware samples are converted binary representation to 3-channel images using windowing technique. The second component of the system create the augmented version of the images, and the last component builds a classification model. In this study, five different deep convolutional neural network model for malware family detection is used.

Data Augmentation Based Malware Detection using Convolutional Neural Networks

The enforcement of the General Data Protection Regulation (GDPR) represents a great challenge for online social networks (OSNs). Several OSNs are making significant changes to their systems to achieve compliance with GDPR. OSNs are required to obtain meaningful consent from users to achieve GDPR compliance. GDPR recognizes user's consent as a legitimate ground for personal data processing in the context of online social networks. This article presents a comparative study about the criteria for valid consent under GDPR and existing consent seeking practices of OSNs. In order to simplify the comparative process, Facebook is taken as a case study for online social networks. In conclusion of the comparative study, we argue that existing consent mechanisms in OSNs are not GDPR compliant. To achieve GDPR compliance in online social networks, we advocate a blockchain-based approach for consent management. This paper paves the way for designing a blockchain-based GDPR compliant consent management model for personal data processing in online social networks.

GDPR Compliant Consent Driven Data Protection in Online Social Networks: A Blockchain-Based Approach

Most of the privacy research in online social networks has focused on protecting profile information of users from other users of online social networks. Another equally important research area is protection of users profile information from social applications. With the introduction of the Google's OpenSocial and Facebook's Developer Platform millions of third party developers are building thousands of social applications for existing massive user base. This fact poses serious privacy risk because current social networking platforms don't provide any mechanism to control disclosure of user's personal information to social applications and their developers. In this paper, we investigate two very popular social networking platform s, i.e. Facebook Developers Platform and OpenSocial. We demonstrate inherit flaws in these social networking platforms. We also point out limitations in existing solutions to the problem suggested by researcher community in the area. We recognise the need for extension of current APIs available for these social networking platforms so that extended version of APIs provide fine grain access control to the user of online social networks.

Javed Ahmed

Papers

Deep learning based Sequential model for malware analysis using Windows exe API Calls.

Data augmentation based malware detection using convolutional neural networks.

Data Augmentation Based Malware Detection using Convolutional Neural Networks

GDPR Compliant Consent Driven Data Protection in Online Social Networks: A Blockchain-Based Approach

Privacy issues in social networking platforms: comparative study of facebook developers platform and opensocial