Users of interactive search dialogue systems specify their preferences with natural language utterances.However, a schema-driven system is limited to handling the preferences that correspond to the predefined database content.In this work, we present a methodology for extending a schema-driven interactive search dialogue system with the ability to handle unconstrained user preferences.Using unsupervised semantic similarity metrics and text snippets associated with the search items, the system identifies suitable items for the user's unconstrained natural language query.In a crowd-sourced evaluation, the users were asked to chat with our extended restaurant search system.Based on objective metrics and subjective user ratings, we demonstrate the feasibility of using this unsupervised low latency approach to extend a schema-driven search dialogue system to handle unconstrained user preferences.

In this article, we address the problem of data scarcity for the sequence classification tasks. We propose AugmentGAN, a simple-yet-effective generative adversarial network-based text augmentation model, which ensures syntactic coherency in the newly generated samples. Given an input with a label, AugmentGAN aims to generate a semantically similar sequence that follows the syntactic structure of the original sample. Exhaustive task-based evaluation is conducted to show the efficacy of AugmentGAN—we employ 12 different datasets across five classification tasks, i.e., sentiment analysis, emotion recognition, sarcasm detection, intent classification, and spam detection. We observe that, compared to the existing text augmentation techniques, AugmentGAN yields an improved performance across datasets for all the tasks. AugmentGAN also turns out to be effective for multiple languages, i.e., English, Hindi, and Bengali.

It is crucial for the police department to automatically determine if suspects are present in the criminal database, sometimes based on the informant's visual memory alone. FaceFetch [15] is a state-of-the-art face retrieval system capable of retrieving an envisioned face from a large-scale database. Although FaceFetch can retrieve images effectively, it lacks sophisticated techniques to produce results efficiently. To this end, we propose SeekSuspect, a faster interactive suspect retrieval framework, which introduces several optimization algorithms to FaceFetch's framework. We train and test our system on a real-world dataset curated in collaboration with a metropolitan police department in India. Results reveal that SeekSuspect beats FaceFetch and can be employed by law enforcement agencies to retrieve suspects.

Cloud computing has been widely adopted by enterprises in their business application in the last decade. Although Cloud computing was incepted with business computing needs in mind, it is increasingly being considered by scientific communities. In recent times, a number of science Clouds have been built for specific research communities. Many research communities use workflow environments and tools such as Taverna and Galaxy to model the execution of tasks to support and expedite the re-enactment of complex processes, and ultimately support the repeatability of science. As a result, a number of systems that integrate Clouds with these workflow systems have emerged including CloudMap, CloudMan, Galaxy Cloud, BioBlend. Whilst there has been many concerns about the capability of virtualized Cloud resources to meet the needs of scientific computation, recent results have shown that the performance of public Clouds are comparable with HPC systems for notable applications. In recent times, a combination of science Cloud and public Cloud has also been used for scientific applications. Given that a large number of scientific applications and research communities use automated process controls, usually in the form of workflows, there is a need to facilitate workflow execution on scalable platforms, such as public Clouds. Along with generic Cloud-based workflow systems, many specific Cloud-based workflow systems have been developed for science applications such as life sciences, and astronomy.

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.

Business process simulation is an effective approach to many organisations to understand the progress of processes over a period of time and predict future activities, which can then be given as a feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to study the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete. This paper proposes the architecture for business process simulation and describes its prototype implementation. It lists several prediction techniques that have been implemented as part of the prototype system. The paper also describes an artificial neural network model that could be used for predicting the completion time of business processes that are constrained by the availability of resources.

Many scientific experiments are carried out in collaboration with researchers around the world to use existing infrastructures and conduct experiments at massive scale. Data produced by such experiments are thus replicated and cached at multiple geographic locations. This gives rise to new challenges when selecting distributed data and compute resources so that the execution of applications is time- and cost-efficient. Existing heuristic techniques select ‘best’ data source for retrieving data to a compute resource and subsequently process task-resource assignment. However, this approach of scheduling, which is based only on single source data retrieval, may not give time-efficient schedules when: (i) tasks are interdependent on data, (ii) the average size of data processed by most tasks is large and (iii) data transfer time exceeds task computation time by at least one order of magnitude. In order to address these characteristics of data-intensive applications, we propose to leverage the presence of replicated data sources, retrieve data in parallel from multiple locations and thus achieve time-efficient schedules. In this article, we propose two multi-source data-retrieval-based scheduling heuristic that assigns interdependent tasks to compute resources based on both data retrieval time and task-computation time. We carry out experiments using real applications and deploy them on emulated as well as real environments. With a combination of data retrieval and task-resource mapping technique, we show that our heuristic produces time-efficient schedules that are better than existing heuristic-based techniques for scheduling application workflows.

Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be fo

Cloud computing is an emerging technology that allows users to utilize on-demand computation, storage, data and services from around the world. However, Cloud service providers charge users for these services. Specifically, to access data from their globally distributed storage edge servers, providers charge users depending on the user's location and the amount of data transferred. When deploying data-intensive applications in a Cloud computing environment, optimizing the cost of transferring data to and from these edge servers is a priority, as data play the dominant role in the application's execution. In this paper, we formulate a non-linear programming model to minimize the data retrieval and execution cost of data-intensive workflows in Clouds. Our model retrieves data from Cloud storage resources such that the amount of data transferred is inversely proportional to the communication cost. We take an example of an `intrusion detection' application workflow, where the data logs are made available from globally distributed Cloud storage servers. We construct the application as a workflow and experiment with Cloud based storage and compute resources. We compare the cost of multiple executions of the workflow given by a solution of our non-linear program against that given by Amazon CloudFront's `nearest' single data source selection. Our results show a savings of three-quarters of total cost using our model.

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.

Scientific applications like neuroscience data analysis are usually compute and data-intensive. With the use of globally distributed resources and suitable middlewares, we can achieve much shorter execution time, distribute compute and storage load, and add greater flexibility to the execution of these scientific applications than we could ever achieve in a single compute resource.In this paper, we present the processing of Image Registration (IR) for Functional Magnetic Resonance Imaging(fMRI) studies on global Grids. We characterize the application, list its requirements and transform it to a workflow. We then execute the application on Grid’5000 platform and present extensive performance results. We show that the IR application can have 1) significantly improved makespan, 2) distribution of compute and storage load among resources used, and 3) flexibility when executing multiple times on global Grids.

Abstract Scientific applications like neuroscience data analysis are usually compute and data‐intensive. With the use of the additional capacity offered by distributed resources and suitable middlewares, we can achieve much shorter execution time, distribute compute and storage load, and add greater flexibility to the execution of these scientific applications than we could ever achieve in a single compute resource. In this paper, we present the processing of image registration (IR) for functional magnetic resonance imaging studies on Global Grids. We characterize the application, list its requirements and then transform it to a workflow. We use Gridbus Broker and Gridbus Workflow Engine technologies for executing the neuroscience application on the Grid. We developed a complete web‐based portal integrating GUI‐based workflow editor, execution management, monitoring and visualization of tasks and resources. We describe each component of the system in detail. We then execute the application on Grid'5000 platform and present extensive performance results. We show that the IR application can have (1) significantly improved makespan, (2) distribution of compute and storage load among resources used, and (3) flexibility when executing multiple times on Grid resources. Copyright © 2009 John Wiley & Sons, Ltd.

Selection of resources for execution of scientific workflows in data grids becomes challenging with the exponential growth of files as a result of the distribution of scientific experiments around the world. With more runs of these experiments, huge number of data-files produced can be made available from numerous resources. There is lack of work in optimal selection of data-hosts and compute resources in the presence of replicated files for scientific workflows. Foreseeing this, the thesis work aims at proposing novel workflow scheduling algorithms on data grids with large number of replicated files that incorporates practical constraints in heterogeneous environments such as grids. In this paper, we define the workflow scheduling problem statement in the context of data grids, supported by motivating applications; list research issues arising from practical constraints; propose two algorithms for experimenting with the problem; report simulation results obtained as a result of preliminary studies. The results are promising enough to motivate us to research on the problem stated.

The Gridbus workflow management system (GWMS) is designed to execute scientific applications, expressed in the form of workflows, onto Grid and Cloud resources. With the help of this system, we demonstrate a computational and data-intensive Image Registration (IR) workflow for functional Magnetic Resonance Imaging (fMRI) applications on Clouds and global Grids. We also present a demonstration of the Aneka System. Aneka is a .NET based Cloud software platform that provides: (i) a configurable service container hosting pluggable services for discovering, scheduling various types of workloads and (ii) a flexible and extensible framework supporting various programming models. We use distributed Grid and Cloud resources from Australia, Austria, France, Japan, and USA for the executions of image registration workflow, and resources from Melbourne University for executing map-reduce applications in Aneka cloud environment.