A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.

Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.