Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

/pdf/introduction-to-recommender-systems-handbook-4qtj503mvw.pdf

Introduction to Recommender Systems Handbook

5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

https://cyberleninka.org/article/n/1480197.pdf

Big Data in Smart Farming – A review

Hawaii International Conference on System Sciences

The rapid increase of Internet of Things (IoT) devices will have a significant impact on people's daily life, providing a wide range of services, such as smart lights, door locks, and cameras remotely controlled by smartphones. Remote access within the same environment or via the Internet requires effective authentication and authorization mechanisms beyond those provided by static authentication methods. We believe a dynamic authentication mechanism for mobile users is needed to protect against unauthorized access. This paper proposes a context-aware authentication service for mobile users in Smart Home environments. The service we have designed and implemented provides secure and flexible access to local as well as remote users, as demonstrated by the evaluation results.

A context-aware authentication service for smart homes

Most public and private cloud providers have experienced failure in one of their services that may affect numerous applications and websites. Thus, in order to understand the causes of different types of failures and remediate the issue, failure analysis is one of the most critical steps. Failure analysis has been developed based on monitoring the most significant metrics of the system in order to study the behavior and frequency changes in the systems. Then, the monitored data will be stored in log files to be utilized for analysis and prediction tasks. In this paper, we primarily focus on analyzing and interpreting the characteristic behavior of finished/failed jobs in association with physically available resources using a publicly available dataset, Google cluster trace. The primary objective of our work is to enhance the understanding of job failure in cloud computing environments. Our results show a clear correlation between failed jobs and requested resources including memory, CPU, and disk space. Based on our results, we find that many techniques can be applied to increase the reliability and availability of cloud applications, such as developing scheduling algorithms, predicting job failure, limiting task resubmission or changing the priority policies.

Failure Analysis and Characterization of Scheduling Jobs in Google Cluster Trace

Web service interfaces can potentially be discovered through service registries, search engines, service portals, peer-to-peer networks, among others. To discover pertinent Web services, clients have to devote excessive amount of time to browse through accessible service resources and be able to differentiate between services that share similar characteristics. Discovering Web services throughout heterogeneous environments is becoming a challenging task and raises several concerns such as performance, reliability, and robustness. In this paper, we introduce the Web Service Broker (WSB) framework that provides a universal access point for discovering Web services. WSB uses a crawler to collect the plurality of Web services disseminated throughout the Web, continuously monitor the behaviour of Web services in delivering the expected functionality, and enable clients to articulate service queries tailored to their needs. WSB can seamlessly be integrated into the existing service-oriented architecture without any alterations to existing environments.

https://www.researchgate.net/profile/Eyhab_Al-Masri/publication/221365357_A_Broker_for_Universal_Access_to_Web_Services/links/5599622008ae5d8f39363005.pdf

A Broker for Universal Access to Web Services

When users on the net visit a homepage that has an embedded applet, the mobile code is downloaded to the user's machine and executed there. In other words, the applet's code migrates from the host's machine to the user's machine, and it will run on the user's machine. In such an environment, we want to make sure that the code being downloaded does not do any h arm to the system on which it will be executed. Also, when network computers (devices with not much local storage) get deployed on the net, they would have to use the network as a source for all sorts of full-fledged applications. In such an environment, it is difficult to predict what a downloaded application will need to do. In such distributed environments, security is a major concern. This paper presents the Security Policy pattern, a design pattern that has been used in many contexts, and proved to be useful, to develop applications capable of securely loading classes off the network and executing them locally. The Security Policy pattern can be used either on the client- or server-side. For example, in the case of a W eb b rowser, the pattern is used on the client-side, and in the case of a global compute e ngine the pattern is used on the server-side. While the pattern may sound Java-centric, it can however be implemented in other languages.

/pdf/security-policy-a-design-pattern-for-mobile-java-code-24q3gguxm8.pdf

Security Policy: A Design Pattern for Mobile Java Code

In recent times, the number of Internet of Things (IoT) devices and the applications developed for these devices has increased; as a result, these IoT devices are targeted by many malicious activities that cause potential damage in many smart infrastructures. A technique is required to appropriately classify anomalous activities to minimize the impact of these activities. The IoT networks are difficult to analyze and test because of the lack of sufficient well-structured IoT datasets for anomaly-based intrusion detection. In this paper, we present a technique we have used to generate a new Botnet dataset, from an existing one, for anomalous activity detection in IoT networks. The new IoT botnet dataset has a wider network and flow-based features. A flow-based Intrusion Detection System (IDS) can be analyzed and tested using flow-based features. Finally, we use different machine learning methods to test the accuracy of our proposed dataset. We also test the accuracy of our proposed dataset through various feature correlation and the methodology for recursive feature elimination. Our proposed IoT botnet dataset provides a ground to analyze and evaluate anomalous activity detection model for IoT networks. We have shared the newly generated Botnet dataset publicly, and a link is provided in this paper.

Qusay H. Mahmoud

Papers

A context-aware authentication service for smart homes

Failure Analysis and Characterization of Scheduling Jobs in Google Cluster Trace

A Broker for Universal Access to Web Services

Security Policy: A Design Pattern for Mobile Java Code

A Technique for Generating a Botnet Dataset for Anomalous Activity Detection in IoT Networks