Abstract

Understanding synergistic effect by calculating electronic structure is essential to fine-tune the catalytic properties of bimetallic nanoalloy clusters which might be used for design of novel efficient catalysts. Density functional theory PBE0 calculations were performed to investigate the structure and energetics of various intermediates involved in the CO oxidation reaction catalyzed by Au3-xYx (x= 0–3 and Y denotes Ag or, Pt or, Pd) trimeric clusters through two possible pathways: Eley-Rideal (ER) and Langmuir-Hinshelwood (LH). The results of this investigation show that the catalytic behavior of the nanocluster highly depends on its composition and the reaction site taken into consideration. The most active reaction centres of goldsilver, gold-palladium, gold-platinum clusters are gold, palladium and platinum atoms respectively. The gold-silver clusters and AuPt2 prefer ER mechanism whereas, gold-palladium and Au2Pt selectively favour LH mechanism in comparison to the other. Bimetallic clusters, in general, are more efficient in comparison to their pristine mono-metallic counterparts, in activating the OO bond for the reaction and have relatively easy CO2 dissociation. Overall results indicate that the alloyed clusters could potentially have a better catalytic activity as compared to pure gold clusters for CO oxidation at low temperatures.

Abstract

We are interested in the problem of enabling transformation of existing, already published web pages. We call this Renarration of web content. In our earlier work, we had already established the role and importance of renarration for improving Web Accessibility. There are nearly a billion websites on the web, making transformation of pages a domain on its own. In this paper, we present the development of a Domain-Specific Language (DSL) for the purpose of web page transformation. We show how the design and implementation of our DSL is driven by our problem domain, its terminology and its unique requirements. We take up an existing online video-course delivery system, which has accessibility challenges, as a specific case to demonstrate our DSL. We end with insights and reflections for future work in both DSL and web page transformations.

Abstract

While edge computing is gaining traction, organizations operating in geographically distributed locations are still using cloud computing to collect and post-process data. In this context, it is useful to analyze the performance trade-offs of cloud-only, edge-only and hybrid edge-cloud processing. To facilitate this analysis, we provide an analytic model validated by measurements on representative edge and cloud platforms. Our model is easy to apply even without performing measurements on the target edge hardware, as long as useful performance specifications are available. Our measurement-driven analysis reveals a diverse performance landscape where there is no clear winner among cloud-only, edge-only and hybrid processing. However, application characteristics and edge-cloud transfer bandwidth are the key factors affecting performance.

Abstract

Software Engineering Researchers in India from both academia and industry are widely contributing to various research problems. In this report, we briefly summarize the key insights from 1st Software Engineering Research in India Update Meeting (SERI 2019), which provides a platform for all the researchers to present and discuss their research work. The essence of this research meeting is to examine the current\ research areas within the software engineering research community and discover the scope of collaboration. Specifically, the first SERI update meeting had a series of invited research talks presented by researchers from academia and industry representing various software engineering labs across India. These talks unveiled notable views through presentations and group discussions, and cogitation of topics that can lead to fruitful collaborations between software engineering researchers and industry practitioners. The main objective of this update meeting is the exchange of research areas that promotes successful short-term and long-term collaborations.

Abstract

Big-data application processing is increasingly geo-distributed, a paradigm shift from the traditional cluster-based processing frameworks. As the communication time for data movement across geo-distributed data centers is not a design criterion for traditional cluster-based processing approaches, there are research gaps in the algorithms used for staging and scheduling big-data applications for geo-distributed clusters. We address these gaps by proposing Harmony, an approach consisting of both staging and scheduling strategies to minimize an application's total execution time. The staging strategy of Harmony exploits the intra-stage parallelism by having concurrent operators within a stage in contrast to the traditional Apache spark which uses fine-grained stages, thus reducing the computation time within each stage. Secondly, the scheduling strategy of Harmony reduces data transfers between geo-distributed data centers by exploiting data locality and thus reducing communication time and total execution time. The proposed approach Harmony achieves a speedup of two times with respect to geo-distributed Apache Spark. In addition, Harmony achieves a speedup of 1.6 times and 2.1 times when compared with the state-of-the-art framework Iridium for geo-distributed analytics over five locations with uniform and non-uniform network link bandwidths respectively.

Abstract

The focus of the ongoing Digital and Industry 4.0 revolution is on re-engineering business operations to take advantage of various technologies, including robotics. Conceptualizing, say, a robotics solution to automate aspects of warehouse operations involves multiple activities: understanding the problem space in sufficient detail to identify the right automation opportunities; working through the space of possible solution options and developing the solution design; and building a prototype solution with sufficient functional detail to enable customer experts to assess its suitability with respect to multiple concerns, and its impact on the business processes and environment. Only after that can a project be initiated to engineer and deploy the production solution, while making the necessary changes to the business system.

Abstract

Domain Specific Modeling Languages (DSML) significantly improve productivity in designing Computer Based System (CBS), by enabling them to be modeled at higher levels of abstraction. It is common for large and complex systems with distributed teams, to use DSMLs, to express and communicate designs of such systems uniformly, using a common language. DSMLs enable domain experts, with no or minimal software development background, to model solutions, using the language and terminologies used in their respective domains. Although, there are already a number of DSMLs available for modeling CBSs, their need is felt strongly across multiple domains, which still are not well supported with DSMLs. Developing a new DSML, however, is non trivial, as it requires (a) significant knowledge about the domain for which the DSML needs to be developed, as well as (b) skills to create new languages. In the …

Abstract

With the technology scaling down to sub-50 nm regime, the necessity of process variation aware estimation of Leakage Power is emphasized for robust digital circuit design. Variations in Leakage power results in a large increase in the variation of total power dissipation. This paper presents a Regression based estimation of leakage powers and total power dissipation in nanoscale standard cell-based designs that show an impressive speed-up advantage with respect to analog SPICE-level simulation. We propose a statistical variation aware estimation model through a Multivariate Linear Regression (MLR) and Multivariate Polynomial Regression (MPR) techniques. Exhaustive tests report shows MPR technique outperforms MLR technique in estimating the leakage and total power for the targeted 16 nm CMOS technology with negligible error (<1%). The proposed methodology works as black box i.e. equally valid for 16 nm, 22 nm and 45 nm technology nodes.

Abstract

Student internships are necessary for every student to learn and apply the learning to deliver tangible and relevant outcomes. In-person internship opportunities are less in number and have major challenges in scaling such as lesser number of available projects in relevant technologies, inadequate mentorship during the internship, varying college calendars and others. This paper presents SRIP (Student Remote Internship Program) approach which focuses on the domain of programming in open source technologies and projects for the internships that are relevant to 2nd and 3rd year engineering college students. It aims to overcome the stated challenges and simultaneously make a contribution to the open source community. Virtual Labs is the open source repository that we leveraged to implement SRIP pilot study for interns to contribute and develop programming skills.

Abstract

Due to the advent of deep sub-micron technologies, statistical (in addition to temperature and supply voltage) variations aware estimation of leakages power has become prominent. Also, estimation of leakage currents at SPICE level guarantees the most accurate results, however not feasible means in high complexity ICs. This performs adversely for Monte-Carlo iterations for statistical analysis. In this paper we introduced an accurate machine learning technique to model statistical and operating variation aware estimation from Artificial Neural Network and regression based Multivariate Polynomial Regression which exhibits innately faster computation and attained error less than 1% for the targeted 16nm FinFET technology node although model is black box for any technology. The accuracy of the proposed technique has been tested over several basic cells and estimation of the complex circuits have been carried out utilizing the pre-modelled basic cells.