Open Source Software (OSS) has become ubiquitous nowadays. It is crucial for the OSS project managers as well as developers to understand users' perception of quality to remain consistent with producing good quality software. To understand users' point of view, like many studies in the commercial product/service sectors which rely upon customer reviews to understand customer behaviour, the authors' main focus is to analyse user ratings and reviews of OSS projects that may represent user satisfaction for that particular software application. We have analysed 41,428 customer reviews (obtained from SourceForge.net) of 886 most popular OSS projects belonging to a specific domain and programming language. The results indicate that overall user ratings and reviews of the popular OSS projects contain a very positive sentiment and more frequent occurrence of emotions like joy, anticipation, and trust as compared to disgust, fear, and surprise. Further, we have examined that the affectiveness of customer reviews with respect to OSS popularity and quality aspects along with their programming languages and problem domains. The results show a stronger association of review affectiveness with the number of reviews than with the number of downloads of the OSS projects, and more downloads do not mean more reviews.

Social media and other online websites have rich traffic information. How to extract and mine useful traffic information from online web data to address transportation problems has become a valuable and interesting research topic in current data-explosive era. In this chapter, we introduce a traffic analytic system with online web data. The proposed system can collect online data, use machine learning and natural language processing methods to extract traffic events, analyze traffic sentiment, and reason traffic scenarios. We also present some results based on the proposed system and techniques in practice.

Industry 4.0 refers to automation and data exchange in manufacturing technologies. From innovative research, challenges, solutions and strategies to real-world case studies, the aim of this edited book is to focus on the nine pillars of technology that are supporting the transition to Industry 4.0 and smart manufacturing. The nine pillars include the internet of things, cloud computing, autonomous and robotics systems, big data analytics, augmented reality, cyber security, simulation, system integration, and additive manufacturing. A key role is played by the industrial IoTs and state-of-the-art technologies such as fog and edge computing, advanced data analytics, innovative data exchange models, artificial intelligence, machine learning, mobile and network technologies, robotics and sensors. This book is a useful resource for an audience of academic and industry researchers and engineers, as well as advanced students in the fields of information and communication technologies, robotics and automation, big data analytics and data mining, machine learning, artificial intelligence, AR/VR/ER, cybersecurity, cyber physical systems, sensing and robotics with a focus on Industry 4.0, and smart manufacturing.

The advent of high-speed internet connections, advanced video coding algorithms, and consumer-grade computers with high computational capabilities has led videostreaming-over-the-internet to make up the majority of network traffic. This effect has led to a continuously expanding video streaming industry that seeks to offer enhanced quality-of-experience (QoE) to its users at the lowest cost possible. Video streaming services are now able to adapt to the hardware and network restrictions that each user faces and thus provide the best experience possible under those restrictions. The most common way to adapt to network bandwidth restrictions is to offer a video stream at the highest possible visual quality, for the maximum achievable bitrate under the network connection in use. This is achieved by storing various preencoded versions of the video content with different bitrate and visual quality settings. Visual quality is measured by means of objective quality metrics, such as the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, visual information fidelity (VIF), and others, which can be easily computed analytically. Nevertheless, it is widely accepted that although these metrics provide an accurate estimate of the statistical quality degradation, they do not reflect the viewer's perception of visual quality accurately. As a result, the acquisition of user ratings in the form of mean opinion scores (MOSs) remains the most accurate depiction of human-perceived video quality, albeit very costly and time consuming, and thus cannot be practically employed by video streaming providers that have hundreds or thousands of videos in their catalogues. A recent very promising approach for addressing this limitation is the use of machine learning techniques in order to train models that represent human video quality perception more accurately. To this end, regression techniques are used in order to map objective quality metrics to human video quality ratings, acquired for a large number of diverse video sequences. Results have been very promising, with approaches like the Video Multimethod Assessment Fusion (VMAF) metric achieving higher correlations to user-acquired MOS ratings compared to traditional widely used objective quality metrics. In this chapter, we examine the performance of VMAF and its potential as a replacement for common objective video quality metrics.

Blockchain in healthcare plays a vital role in the form of encryption-based technology, which has been used to keep the patients' data in a secured and more distributed way. Blockchain technology in E-healthcare is also being used for digital payment systems that focus on cybersecurity, EHRs (electronic health records), patients data management, and so on. The framework has been designed to describe the major obstacles to the adaptation of blockchain technology in E -healthcare, such as data security, interoperability, data integrity, identity validation, and scalability. In addition, here comes the Ethereum as new technology based on blockchain technology. Ethereum is used for developing distributed applications (DApps) and replacing the Internet of third parties which store data, transactions and keep track. Using Ethereum, there is a chance to recreate the Internet without the involvement of third parties. The process of elimination of the third party could be implemented using smart contracts by creating trust, and Ethereum uses consensus proof -of -work (PoW), and Ethereum provides a peer -to -peer network to eliminate the involvement of third parties. The proposed system is a proof -of -concept implementation of a blockchain for research in clinical trials.

Technologies for traffic and travel information (TTI) have been evolving rapidly in recent years. This book offers an overview of three generations of TTI technology, beginning with the first generation of human to human information transmission. The second generation incorporated machine to human information exchange, with the development of classification and transmission protocols such as RDS-TMC and TPEG. A third generation of machine to machine communication is now emerging, disrupting TTI technology and creating the need for this new reference work, which covers gathering information from automated sensors, its digital processing and transmission, and its use by increasingly intelligent agents and vehicles. The book provides coverage of the following topics, written by a range of experts in their respective fields: road traffic data collection; road traffic news from the air; location referencing; digital coding, collation and exchange of traffic information; European developments in traffic and travel information; the role of the commercial sector in developing road traffic information in the UK and Europe; dynamic road traffic signage; smart motorway information development; contribution of cooperative ITS to road traffic and travel information; multi modal traffic and travel information; social media and traffic and travel information; social media services and business models; and finally traffic and travel information into the future. Providing essential information to researchers and industry professionals involved with or interested in technologies and organisations that provide or deliver travel information, this book could also be of interest to policymakers needing an overview of the evolution and recent developments of this field, and advanced students of subjects related to telecommunications with an interest in traffic information.

Delegation is a technique that allows a subject receiving a delegation (the delegatee) to act on behalf of the delegating subject (the delegator). Although the existing Key Aggregate Searchable Encryption (KASE) schemes support delegation of search rights over any set of ciphertexts using a key of constant-size, two critical issues still should be considered. Firstly, an adversary can intercept the aggregate key or query trapdoor from the insecure communication channels involving the cloud server and impersonate as an authorized user to the server for accessing the data. Secondly, the existing KASE schemes only discuss the delegation of rights from the data owner to other users. However, if a subject receiving a delegation cannot perform the time-critical task on the shared data because of the unavailability, it becomes necessary for the delegatee to further delegate his received rights to another user. In this paper, we propose a novel KASE scheme that allows a fine-grained multi-delegation, i.e., if the attributes of the delegatee satisfy the hidden access policy (defined by the data owner), the delegatee can delegate his received rights to another user, without compromising data privacy. The proposed scheme provides security against the impersonation attack by verifying the user's authentication.

Security vulnerabilities in web traffic can directly lead to data leak. Preventing these data leaks to a large extent has become an important problem to solve. Besides, the accurate detection and prevention of abnormal changes in web traffic is of great importance. In this study, a hybrid approach, called C-NSA, based on the negative selection algorithm (NSA) and clonal selection algorithm (CSA) of artificial immune systems for the detection of abnormal web traffic on the network is proposed and a user-friendly application software is developed. The real and synthetic data in the Yahoo Webscope S5 dataset are used for web traffic and the data are split into windows using the window sliding. In the experimental studies, the abnormal web traffic data is detected by monitoring the changes in the number of activated detectors in the C-NSA. It is observed that the average accuracy performance of finding anomalies in real web traffic data is 94.30% and the overall classification accuracy is 98.22% based on proposed approach. In addition, false positive rate of the proposed approach using C-NSA is obtained as 0.029. In addition, the results in synthetic web traffic data using C-NSA are achieved as average 98.57% classification accuracy.

With the development of network services and location-based systems, many mobile applications begin to use users’ geographical location to provide better services. In terms of social networks, geographical location is actively shared by users. In some applications with recommendation services, before the geographical location recommendation is provided, the authors have to obtain user's permission. This kind of social network integrated with geographical location information is called location-based social networks (abbreviate for LBSNs). In the LBSN, each user has location information when he or she checked in hotels or feature spots. Based on this information, they can identify user's trajectory of movement behaviour and activity patterns. In general, if there is friendship between two users, their trajectories in reality are likely to be similar. In this study, according to user's geographical location information over a period of time, they explore whether there exists friendly relationship between two users based on trajectory similarity and the structure theory of graphs. In particular, they propose a new factor function and a factor graph model based on user's geographical location to predict the friendship between two users in the real LBSN.

This edited book explores the use of mobile technologies such as phones, drones, robots, apps, and wearable monitoring devices for improving access to healthcare for socially disadvantaged populations in remote, rural or developing regions. This book brings together examples of large scale, international projects from developing regions of China and Belt and Road countries from researchers in Australia, Bangladesh, Denmark, Norway, Japan, Spain, Thailand and China. The chapters discuss the challenges presented to those seeking to deploy emerging mobile technologies (e.g., smartphones, IoT, drones, robots etc.) for healthcare (mHealth) in developing countries and discuss the solutions undertaken in these case study projects. This book brings together joint work in mHealth projects across multiple disciplines (software, healthcare, mobile communications, entrepreneurship and business and social development). Bringing together research from different institutions and disciplines, the editors illustrate the technical and entrepreneurial aspects of using mobile technologies for healthcare development in remote regions. Chapters are grouped into five key themes: the global challenge, portable health clinics, sustainable and resilient mHealth services, mHealth for the elderly, and mHealth for chronic illnesses. The book will be of particular interest to engineers, entrepreneurs, NGOs and researchers working in healthcare in sustainable development settings.