Modelling Confidence for Quality of Service Assessment in Cloud Computing

The ability to assess the quality of a service (QoS) is important to the emerging cloud computing paradigm. When many cloud service providers exist offering many functionally identical services, the prospective users of these services will wish to use one that offers the best quality. Many techniques and tools have been proposed to assess QoS, and the ability to deal with uncertainty surrounding the QoS verdicts given by any such techniques or tools is essential. In this paper, we present a probabilistic model to quantify confidence in QoS assessment. More specifically, we take the number of QoS data items used in assessment and the variation of data in the dataset into account in our measure of assessment reliability. Our experiments show that our confidence model can help consumers to select services based on their requirements effectively

Quality of service assessment over multiple attributes

The development of the Internet and World Wide Web have led to many services being offered electronically. When there is sufficient demand from consumers for a certain service, multiple providers may exist, each offering identical service functionality but with varying qualities. It is desirable therefore that we are able to assess the quality of a service (QoS), so that service consumers can be given additional guidance in se lecting their preferred services. Various methods have been proposed to assess QoS using the data collected by monitoring tools, but they do not deal with multiple QoS attributes adequately. Typically these methods assume that the quality of a service may be assessed by first assessing the quality level delivered by each of its attributes individ ually, and then aggregating these in some way to give an overall verdict for the service. These methods, however, do not consider interaction among the multiple attributes of a service when some packaging of qualities exist (i.e. multiple levels of quality over multiple attributes for the same service). In this thesis, we propose a method that can give a better prediction in assessing QoS over multiple attributes, especially when the qualities of these attributes are monitored asynchronously. We do so by assessing QoS attributes collectively rather than indi vidually and employ a k nearest neighbour based technique to deal with asynchronous data. To quantify the confidence of a QoS assessment, we present a probabilistic model that integrates two reliability measures: the number of QoS data items used in the as sessment and the variation of data in this dataset. Our empirical evaluation shows that the new method is able to give a better prediction over multiple attributes, and thus provides better guidance for consumers in selecting their preferred services than the existing methods do

Ontology for Task and Quality Management in Crowdsourcing

This paper suggests an ontology for task and quality control mechanisms representation in crowdsourcing systems. The ontology is built to provide reasoning about tasks and quality control mechanisms to improve tasks and quality management in crowdsourcing. The ontology is formalized in OWL (Web Ontology Language) and implemented using Protégé. The developed ontology consists of 19 classes, 7 object properties, and 32 data properties. The development methodology of the ontology involves three phases including Specification (identifying scope, purpose and competency questions), Conceptualization (data dictionary, UML, and instance creation), and finally Implementation and Evaluation

A Lexicon-Based Approach to Build Reputation from Social Media

Nowadays, many social media platforms are widely used to express people’s opinions about their daily experiences and interests. These platforms encourage people to exchange and share information about a particular brand, company or even a political point of view. Consequently, huge amount of data which can be extracted and analyzed to obtain some useful knowledge are available. In this paper, we propose to build a reputation of a given service provider (i.e. brand, product or service) from the collected social media data. To do so, we have developed a lexicon as a basic component for sentiment polarity in Arabic idioms. That is, the lexicon is used to classify words extracted from “Tweets†into either a positive or negative word. We use beta probability density functions to combine feedback from the lexicon to derive reputation scores. The experimental results show that our proposed approach is consistent with sentiment analysis approach results

Recognizing Physical Activities for Spinal Cord Injury Rehabilitation Using Wearable Sensors

The research area of activity recognition is fast growing with diverse applications. However, advances in this field have not yet been used to monitor the rehabilitation of individuals with spinal cord injury. Noteworthily, relying on patient surveys to assess adherence can undermine the outcomes of rehabilitation. Therefore, this paper presents and implements a systematic activity recognition method to recognize physical activities applied by subjects during rehabilitation for spinal cord injury. In the method, raw sensor data are divided into fragments using a dynamic segmentation technique, providing higher recognition performance compared to the sliding window, which is a commonly used approach. To develop the method and build a predictive model, a machine learning approach was adopted. The proposed method was evaluated on a dataset obtained from a single wrist-worn accelerometer. The results demonstrated the effectiveness of the proposed method in recognizing all of the activities that were examined, and it achieved an overall accuracy of 96.86%

Identifying Users and Developers of Mobile Apps in Social Network Crowd

In the last fifteen years, an immense expansion has been witnessed in mobile app usage and production. The intense competition in the tech sector and also the rapidly and constantly evolving user requirements have led to increased burden on mobile app creators. Nowadays, fulfilling users’ expectations cannot be readily achieved and new and unconventional approaches are needed to permit an interested crowd of users to contribute in the introduction of creative mobile apps. Indeed, users and developers of mobile apps are the most influential candidates to engage in any of the requirements engineering activities. The place where both can best be found is on Twitter, one of the most widely used social media platforms. More interestingly, Twitter is considered as a fertile ground for textual content generated by the crowd that can assist in building robust predictive classification models using machine learning (ML) and natural language processing (NLP) techniques. Therefore, in this study, we have built two classification models that can identify mobile apps users and developers using tweets. A thorough empirical comparison of different feature extraction techniques and machine learning classification algorithms were experimented with to find the best-performing mobile app user and developer classifiers. The results revealed that for mobile app user classification, the highest accuracy achieved was ≈0.86, produced via logistic regression (LR) using Term Frequency Inverse Document Frequency (TF-IDF) with N-gram (unigram, bigram and trigram), and the highest precision was ≈0.86, produced via LR using Bag-of-Words (BOW) with N-gram (unigram and bigram). On the other hand, for mobile app developer classification, the highest accuracy achieved was ≈0.87, produced by random forest (RF) using BOW with N-gram (unigram and bigram), and the highest precision was ≈0.88, produced by multi-layer perception neural network (MLP NN) using BERTweet for feature extraction. According to the results, we believe that the developed classification models are efficient and can assist in identifying mobile app users and developers from tweets. Moreover, we envision that our models can be harnessed as a crowd selection approach for crowdsourcing requirements engineering activities to enhance and design inventive and satisfying mobile apps

Handling asynchronous data in assessing QoS over multiple attributes

The ability to assess the quality of a service (QoS) is important to the emerging SOC paradigm. When multiple providers offer functionally identical services in a SOC environment, it is only natural that consumers should ask how their qualities would compare. While various methods have been proposed to help assess QoS using monitored quality data, they do not handle multiple QoS attributes adequately, especially when the qualities of these attributes are monitored asynchronously. In this paper, we proposed a method that takes both accuracy and confidence into account when assessing QoS over multiple attributes, and employs a kNN based technique to deal with asynchronous data. Our experiments show that the new method can give a more accurate QoS assessment over multiple attributes than existing methods do

QoS Assessment over Multiple Attributes

In an open service oriented computing environment, multiple providers may offer functionally identical services but with varying qualities. It is desirable therefore that we are able to assess the quality of a service (QoS), so that service consumers can be given additional guidance in selecting their preferred services. Various methods have been proposed to assess QoS using the data collected from monitoring tools, but they do not deal with multiple QoS attributes adequately. Typically these methods assume that the quality of a service may be assessed by first assessing the quality level delivered by each QoS attribute individually, and then aggregating them in some way to give an overall verdict for the service. In this paper, we show that this may lead to incorrect assessment, and suggest how existing methods may be improved to deal with multiple attributes more effectivel