Abstract

Domain specific search engines (DSSE) are gaining popularity because of better search relevance and domain specificity. The growth of IT and internet led to increase of cyber attacks, however, lack of DSSE for Security is making users refer multiple sites for security information. We demonstrate SIREN, a search engine for ’Information and Cyber Security’ with subdomain coverage, classification and site credibility for ranking search results. As part of our demonstration, we also automated identification of seed URLs (34,007) and related child URLs (400,726) of the security domain using Artificial Bee Colony algorithm. We also evaluated functional and non-functional parameters of available open source software stack that can be used for building other DSSEs.

Abstract

The increasing usage of smart-phones has resulted in mobile applications replacing or supplementing traditional web-based applications. Given the limitations of the form factor in smartphones, usability can be considered as one of the important attributes that determine the success of a mobile application. The measures available for assessing the usability of mobile applications tend to focus more on human aspects and less on the functional aspects of usability. As part of this paper, we propose a usability evaluation framework to identify functional usability issues specific to mobile applications. This framework use usability guidelines and code analysis to improve the usability of a mobile application. As a proof of concept, we have built an end-to-end system using the framework to validate and verify usability issues in Android mobile applications. We also generate code recommendations to implement failed usability guidelines.

Abstract

Software systems often serve as the agents of operation for both enterprise systems and embedded systems. Engineering such systems is a knowledge-centric activity. A clear understanding of the relationship between knowledge, systems and engineering can help us to establish firm theoretical foundations for software and systems engineering.

Abstract

he paradigm of connected vehicles is fast gaining lot of attraction in the automotive industry. Recently, a lot of technological innovation has been pushed through to realize this paradigm using vehicle to cloud (V2C), infrastructure (V2I) and vehicle (V2V) communications. This has also opened the doors for efficient delivery of data/service to the vehicles via edge devices that are closer to the vehicles. In this work, we propose an optimization framework that can be used to deliver data/service to the connected vehicles such that a bandwidth cost objective is optimized. For the first time, we also integrate a vehicle flow model in the optimization framework to model the traffic flow in the coverage area of the edges. Using the optimization framework, we study the variation of the optimal bandwidth cost for varying problem sizes and vehicle flow model parameter values for both data and service delivery.

Abstract

With more than a billion web sites, volume and variety of content available for consumption is huge. However, credibility, an important quality characteristic of web pages is questionable in many cases and tends to be non-uniform. Credibility can increase or reduce the importance of web page leading to potential gain or loss of user base. Credibility without factoring genre of content (for example, Help, Article, Discussion, etc.) can lead to incorrect assessment. Depending on the genre, the importance of features such as web page date time modified, grammar, image to text ratio, in and out links, and other web page features differ. We propose a genre credibility assessment based on web page surface features and their importance in a genre. Further, we built a W EBCred framework to assess GCS (Genre based Credibility Score) with flexibility to add/modify genres, its features and their importance. We validated our approach on 10,429 ’Information Security’ related web pages; the assessed score correlated 35% with crowdsourced Web Of Trust (WOT) score and 39% with Alexa ranking.

Abstract

Systems engineering is widely perceived as an empirical discipline, with a need for theoretical foundations that can facilitate reasoning about practice. This is an attempt to help build such foundations by systems‐theoretic inquiry into the nature of the relationship between knowledge and engineering. We conceptualize this relationship in terms of four worlds: the real world, the world of systems models, a world of aspect knowledge, and a world of wholes knowledge: knowledge that indicates how aspects come together and also how wholes relate to each other. This leads us to a generative understanding of systems engineering: synthesizing aspects to develop blocks; and generating the network of blocks that form a system, through recursive performance of three activities: decomposition, dependency closure and refinement. The problem of systems engineering practice involves augmenting this core with the …

Abstract

In many application scenarios, data consumed by real-time tasks are required to meet a maximum age, or freshness, guarantee. In this paper, we consider the end-to-end freshness constraint of data that is passed along a chain of tasks in a uniprocessor setting. We do so with few assumptions regarding the scheduling algorithm used. We present a method for selecting the periods of tasks in chains of length two and three such that the end-to-end freshness requirement is satisfied, and then extend our method to arbitrary chains. We perform evaluations of both methods using parameters from an embedded benchmark suite (E3S) and several schedulers to support our

Abstract

Executing multiple applications on a single MPSoC brings the major challenge of satisfying multiple quality requirements regarding real-time, energy, and so on. Hybrid application mapping denotes the combination of design-time analysis with run-time application mapping. In this article, we present such a methodology, which comprises a design space exploration coupled with a formal performance analysis. This results in several resource reservation configurations, optimized for multiple objectives, with verified real-time guarantees for each individual application. The Pareto-optimal configurations are handed over to run-time management, which searches for a suitable mapping according to this information. To provide any real-time guarantees, the performance analysis needs to be composable and the influence of the applications on each other has to be bounded. We achieve this either by spatial or a novel temporal isolation for tasks and by exploiting composable networks-on-chip (NoCs). With the proposed temporal isolation, tasks of different applications can be mapped to the same resource, while, with spatial isolation, one computing resource can be exclusively used by only one application. The experiments reveal that the success rate in finding feasible application mappings can be increased by the proposed temporal isolation by up to 30% and energy consumption can be reduced compared to spatial isolation.

Abstract

Recent advancements in big data processing and machine learning, among others, increase the resource demand for running applications with larger problem sizes. Elastic cloud computing resources with pay-per-use pricing offers new opportunities where large application execution is constrained only by the cost budget. Given a cost budget and a time deadline, this paper introduces a measurement-driven analytical modeling approach to determine the largest Pareto-optimal problem size and its corresponding cloud configuration for execution. We evaluate our approach with a set of representative applications that exhibit a range of resource demand growth patterns on Amazon AWS cloud. We show the existence of cost-time-size Pareto-frontier with multiple sweet spots meeting user constraints. To characterize the cost-performance of cloud resources, we use Performance Cost Ratio (PCR) metric. We extend Gustafson's fixed-time scaling in the context of cloud, and, investigate fixed-cost-time scaling of applications and show that using resources with higher PCR yields better cost-time performance. We discuss a number of useful insights on the trade-off between the execution time and the largest Pareto-optimal problem size, and, show that time deadline could be tightened for a proportionately much smaller reduction of problem size.

Abstract

Increased internet bandwidth at low cost is leading to the creation of large volumes of unstructured data. This data explosion opens up opportunities for the creation of a variety of data-driven intelligent systems, such as the Semantic Web. Ontologies form one of the most crucial layers of semantic web, and the extraction and enrichment of ontologies given this data explosion becomes an inevitable research problem. In this paper, we survey the literature on semi-automatic and automatic ontology extraction and enrichment and classify them into four broad categories based on the approach. Then, we proceed to narrow down four algorithms from each of these categories, implement and analytically compare them based on parameters like context relevance, efficiency and precision. Lastly, we propose a Long Short Term Memory Networks (LSTM) based deep learning approach to try and overcome the gaps identified in these approaches.