Quality of service (QoS) analysis frameworkn for text to speech (TTS) services

Quality of service (QoS) evaluations is significant and necessary for text to speech web service applications. Text to speech media conversion quality measurements has general and specific mechanisms for its functional and nonfunctional requirements. The main objective of this thesis is to introduce QoS framework which is able to evaluate and analyze the perceived quality of services (QoS) for text to speech (TTS) web services. To achieve this goal, the framework combines two main mechanisms for measuring the speech quality. General quality attributes measure the response time of TTS services, specific quality attributes measure intelligibility and naturalness through subjective quality measurements, which are mapped onto mean opinion score (MOS). Twenty individuals participated the experiment to test the speech quality by comparing three services fromtexttospeech.com, Natural Reader and Yakitome. Aggregate scores has been used to calculate the combination of general and specific nonfunctional QoS on TTS Web services. The result shown better scale for quality estimation, service1 (Fromtexttospeech) 47.84% is suitable TTS service provider where service2 and service3 (NaturalReader and Yakitome) are close 31.62 and 21.53% respectively and less preferred for listening tests to assess synthesized speech. It is essential to consider the user’s perspective when evaluating the quality of services for media conversion services such as text to speech (TTS) to enhance the user experience

Analysis of flow and heat transfer over louvered fins in compact geometries

The louvered fin is most widely used in automotive applications. Compared to other geometrical parameters ofthe fin, the louver angle has a stronger effect on heat transfer. This study is carried out by computational method to determine the louver inclination of a rectangular channel heat exchanger that has the greatest influence on flow and heat transfer and invariant with other geometrical parameters. The meshed CAD model is validated with an established correlation in literature. In the earlier years of study, the mean flow angle was defined in two dimensional flows. ANSYS-CFD is capable of defining a mean flow angle was defined in three dimensional flows. The validation agrees weB, with about 5.39% of error. Various graphs are plotted to determine the optimized louver inclination. From the plotted graphs ofNusselt number and pumping power against the Reynolds Number, it is observed that the louver angle has a strong influence on the heat transfer rate. Then, a ratio of heat transfer rate to pumping power is used in the graphs as the non�dimensional number representation to determine the optimum angle. In addition to this study, a general correlation is developed to represent the behavior oflouver angle at different ranges of pumping power. With a practical range of Reynolds numbers and louver angles, the optimum angle is found to be 20 degrees. This numerical result has a high confidence level where a good agreement between the meshed models with the established finding is obtained. The study has succeeded in obtaining the result that was set out as the objective

Automated IT Service Fault Diagnosis Based on Event Correlation Techniques

In the previous years a paradigm shift in the area of IT service management could be witnessed. IT management does not only deal with the network, end systems, or applications anymore, but is more and more concerned with IT services. This is caused by the need of organizations to monitor the efficiency of internal IT departments and to have the possibility to subscribe IT services from external providers. This trend has raised new challenges in the area of IT service management, especially with respect to service level agreements laying down the quality of service to be guaranteed by a service provider. Fault management is also facing new challenges which are related to ensuring the compliance to these service level agreements. For example, a high utilization of network links in the infrastructure can imply a delay increase in the delivery of services with respect to agreed time constraints. Such relationships have to be detected and treated in a service-oriented fault diagnosis which therefore does not deal with faults in a narrow sense, but with service quality degradations. This thesis aims at providing a concept for service fault diagnosis which is an important part of IT service fault management. At first, a motivation of the need of further examinations regarding this issue is given which is based on the analysis of services offered by a large IT service provider. A generalization of the scenario forms the basis for the specification of requirements which are used for a review of related research work and commercial products. Even though some solutions for particular challenges have already been provided, a general approach for service fault diagnosis is still missing. For addressing this issue, a framework is presented in the main part of this thesis using an event correlation component as its central part. Event correlation techniques which have been successfully applied to fault management in the area of network and systems management are adapted and extended accordingly. Guidelines for the application of the framework to a given scenario are provided afterwards. For showing their feasibility in a real world scenario, they are used for both example services referenced earlier

Abstractions for designing and evaluating communication bridges for people in developing regions

This paper describes two novel abstractions that help soft- ware engineers work in developing regions to align social and technical factors when building communication systems. The abstractions extend two concepts familiar to engineers of computer networks and applications: the Open Systems Interconnect stack for design, and Quality of Service for eval- uation. The novel nature of the abstractions lies in how they help cultivate awareness of socio-cultural and technical is- sues when designing and evaluating communication bridges in the eld. Advantages of the abstractions are that they can be understood easily by software engineers, they aid communication with bene ciaries, and can therefore facili- tate collaboration. The paper makes an argument for these socially aware abstractions, describes the abstractions in de- tail, provides examples of how we used the new abstractions in the eld and then gives practical guidelines for how to use them. The simple nature of the new abstractions can help software engineers and end-users to work together to produce useful information technology based communication systems for people in developing regions.Telkom, Cisco, THRIP, NRF, SANPADWeb of Scienc

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

LDA-Based Topic Strength Analysis

Topic strength is an important hotspot in topic research. The evolution of topic strength not only indicates emerging new topics, but also helps us to determine whether a topic will produce some fluctuation of topic strength over time. Thus, topic strength analysis can provide significant findings in public opinion monitoring and user personalization. In this paper, we present an LDA-based topic strength analysis approach. We take topic quality into our topic strength consideration by combining local LDA and global LDA. For empirical studies, we use three data sets in real applications: film critic data of "A Chinese Odyssey" in Douban Movies, corruption news data in Sina News, and public paper data. Compared to existing approaches, experimental results show that our proposed approach can obtain better results of topic strength analysis in detecting the time of event topic occurrences and distinguishing different types of topics, and it can be used to monitor the occurrences of public opinions and the changes of public concerns

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience