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 paper discusses the way context is used for Web services personalization. A Web service is an accessible application that other applications and humans can discover and trigger to satisfy various needs such as car rental. Context is the information that characterizes the interactions between humans, applications, and the surrounding environment. Web services are personalized so that users' preferences can be accommodated. Preferences are of different types varying from when the execution of a Web service should start to where the outcome of this execution should be delivered. Besides users' preferences, this paper highlights that the resources on which the Web services are performed have an impact on Web services personalization.

/pdf/context-for-personalized-web-services-4s8nroujcz.pdf

Context for Personalized Web Services

This paper addresses issues relating to the efficient access and discovery of Web services across multiple UDDI business registries (UBRs). The ability to explore Web services across multiple UBRs is becoming a challenge particularly as size and magnitude of these registries increase. As Web services proliferate, finding an appropriate Web service across one or more service registries using existing registry APIs (i.e. UDDI APIs) raises a number of concerns such as performance, efficiency, end-to-end reliability, and most importantly quality of returned results. Clients do not have to endlessly search accessible UBRs for finding appropriate Web services particularly when operating via mobile devices. Finding relevant Web services should be time effective and highly productive. In an attempt to enhance the efficiency of searching for businesses and Web services across multiple UBRs, we propose a novel exploration engine, the Web service crawler engine (WSCE). WSCE is capable of crawling multiple UBRs, and enables for the establishment of a centralized Web services' repository which can be used for large-scale discovery of Web services. The paper presents experimental validation, results, and analysis of the presented ideas.

WSCE: A Crawler Engine for Large-Scale Discovery of Web Services

The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to identify a malicious activity correctly and mitigate the impact of such activity promptly. In this paper, we propose a two-level anomalous activity detection model for intrusion detection system in IoT networks. The level-1 model categorizes the network flow as normal flow or abnormal flow, while the level-2 model classifies the category or subcategory of detected malicious activity. When the network flow classified as an anomaly by the level-1 model, then the level-1 model forwards the stream to the level-2 model for further investigation to find the category or subcategory of the detected anomaly. Our proposed model constructed on flow-based features of the IoT network. Flow-based detection methodologies only inspect packet headers to classify the network traffic. Flow-based features extracted from the IoT Botnet dataset and various machine learning algorithms were investigated and tested via different cross-fold validation tests to select the best algorithm. The decision tree classifier yielded the highest predictive results for level-1, and the random forest classifier produced the highest predictive results for level-2. Our proposed model Accuracy, Precision, Recall, and F score for level-1 were measured as 99.99% and 99.90% for level-2. A two-level anomalous activity detection system for IoT networks we proposed will provide a robust framework for the development of malicious activity detection system for IoT networks. It would be of interest to researchers in academia and industry.

A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions, and is the underlying technology for the Bitcoin cryptocurrency. However, its applications are far beyond the financial sector. The transaction verification process for cryptocurrencies is much slower than traditional digital transaction systems. One approach to increase transaction speed and scalability is to identify a solution that offers faster Proof of Work. In this paper, we propose a method for accelerating the process of Proof of Work based on parallel mining rather than solo mining. The goal is to ensure that no more than two or more miners put the same effort into solving a specific block. The proposed method includes a process for selection of a manager, distribution of work and a reward system. This method has been implemented in a test environment that contains all the characteristics needed to perform Proof of Work for Bitcoin and has been tested, using a variety of case scenarios, by varying the difficulty level and number of validators. Preliminary results show improvement in the scalability of Proof of Work up to 34% compared to the current system.

Qusay H. Mahmoud

Papers

Context for Personalized Web Services

WSCE: A Crawler Engine for Large-Scale Discovery of Web Services

A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks

A Parallel Proof of Work to Improve Transaction Speed and Scalability in Blockchain Systems

Middleware for communications