This letter proposes a time-domain method to improve speech intelligibility in noisy scenarios. In the proposed approach, a series of Gammatone filters are adopted to detect the harmonic components of speech. The filters outputs are amplified to emphasize the first harmonics, reducing the masking effects of acoustic noises. The proposed GTFF0solution and two baseline techniques are examined considering four background noises with different non-stationarity degrees. Three intelligibility measures (ESTOI, ESII and ASIIST) are adopted for objective evaluation. The experiments results show that the proposed scheme leads to expressive speech intelligibility gain when compared to the competing approaches. Furthermore, the PESQ and OQCM objective scores demonstrate that the proposed technique also provides interesting quality improvement.

Managing large systems in order to guarantee certain behavior is a difficult problem due to their dynamic behavior and complex interactions. Policies have been shown to provide a very expressive and easy way to define such desired behaviors, mainly because they separate the definition of desired behavior from the enforcement mechanism, allowing either one to be changed fairly easily. Unfortunately, it is often difficult to define policies in terms of attributes that can be measured and/or directly controlled, or to set adaptable (i.e. non-static) parameters in order to account for rapidly changing system behavior. Dynamic policies are meant to solve these problems by allowing system administrators to define higher level parameters, which are more closely related to the business goals, while providing an automated mechanism to adapt them at a lower level, where attributes can be measured and/or controlled. Here, we present a way to define such policies, and a machine learning model that is able to dynamically apply lower level static policies by learning a hidden relationship between the high level business attribute space, and the low level monitoring space. We show that this relationship exists, and that we can learn it producing an error of at most 8.78% at least 96% of the time.

Integrating heterogeneous data sets has been a significant barrier to many analytics tasks, due to the variety in structure and level of cleanliness of raw data sets requiring one-off ETL code. We propose HiperFuse, which significantly automates the data integration process by providing a declarative interface, robust type inference, extensible domain-specific data models, and a data integration planner which optimizes for plan completion time. The proposed tool is designed for schema-less data querying, code reuse within specific domains, and robustness in the face of messy unstructured data. To demonstrate the tool and its reference implementation, we show the requirements and execution steps for a use case in which IP addresses from a web clickstream log are joined with census data to obtain average income for particular site visitors (IPs), and offer preliminary performance results and qualitative comparisons to existing data integration and ETL tools.

Integrating heterogeneous datasets has been a significant barrier to many analytics tasks, due to the variety in structure and level of cleanliness of raw datasets requiring one-off ETL code. We propose HiperFuse, which significantly automates the data integration process by providing a declarative interface, robust type inference, extensible domain-specific data models, and a data integration planner which optimizes for plan completion time.

Ensuring high reliability of large-scale clusters is becoming more critical as the size of these machines continues to grow, since this increases the complexity and amount of interactions between different nodes and thus results in a high failure frequency. For this reason, predicting node failures in order to prevent errors from happening in the first place has become extremely valuable. A common approach for failure prediction is to analyze traces of system events to find correlations between event types or anomalous event patterns and node failures, and to use the types or patterns identified as failure predictors at run-time. However, typical centralized solutions for failure prediction in this manner suffer from high transmission and processing overheads at very large scales. We present a solution to the problem of predicting compute node soft-lockups in large scale clusters by using a decentralized online clustering algorithm (DOC) to detect anomalies in resource usage logs, which have been shown to correlate to particular types of node failures in supercomputer clusters. We demonstrate the effectiveness of this system by using the monitoring logs from the Ranger supercomputer at Texas Advanced Computing Center. Experiments shows that this approach can achieve similar accuracy as other related approaches, while maintaining low RAM and bandwidth usage, with a runtime impact to current running applications of less than 2%.

Business Process Management (BPM) software provides visibility into business processes in organizations of all sizes and helps increase process efficiency continuously. However, the time and effort involved in modeling, deploying and executing a business process is tremendous and as a result organizations struggle to agilely adapt business processes to dynamic business requirements. On the other hand, the growing popularity of cloud computing poses opportunities and challenges on how business processes can leverage resource outsourcing and elasticity. In light of the above, this paper presents a business process management platform that assists business analysts lacking necessary programming expertise by automating manual steps and providing guidance and recommendations to quickly and efficiently design, implement, deploy and execute business processes in a hybrid cloud environment.

The proliferation of web services in both number and variety implies the co-existence of a wide number of service options, input/output data types, and encapsulations. Consequently, composing services into usable workflows has become increasingly development intensive. In order to leverage the design of a workflow and facilitate its reusability and maintenance, many research efforts have advocated to compose services at the type level instead of the instance level while using customized glue code to map service types to service instances. Another challenge then appears: Service types, either manually defined by domain experts or automatically generated from an ontology, cannot be automatically instantiated into concrete services due to the coarse granularity of service types and the complexity of input/output parameter mapping. This paper proposes a platform to automatically extract instantiable abstract operations from registered services and that enables the automatic generation of glue code that links concrete services to service types in order to produce reusable executable workflows.

Ensuring the efficient and robust operation of distributed computational infrastructures is critical, given that their scale and overall complexity is growing at an alarming rate and that their management is rapidly exceeding human capability. Clustering analysis can be used to find patterns and trends in system operational data, as well as highlight deviations from these patterns. Such analysis can be essential for verifying the correctness and efficiency of the operation of the system, as well as for discovering specific situations of interest, such as anomalies or faults, that require appropriate management actions. This work analyzes the automated application of clustering for online system management, from the point of view of the suitability of different clustering approaches for the online analysis of system data in a distributed environment, with minimal prior knowledge and within a timeframe that allows the timely interpretation of and response to clustering results. For this purpose, we evaluate DOC (Decentralized Online Clustering), a clustering algorithm designed to support data analysis for autonomic management, and compare it to existing and widely used clustering algorithms. The comparative evaluations will show that DOC achieves a good balance in the trade-offs inherent in the challenges for this type of online management.

As energy efficiency and associated costs become key concerns, consolidated and virtualized data centers and clouds are attractive computing platforms for data- and compute-intensive applications. Recently, these platforms are also being considered for more traditional high-performance computing (HPC) applications. However, maximizing energy efficiency, cost-effectiveness, and utilization for these applications while ensuring performance and other Quality of Service (QoS) guarantees, requires leveraging important and extremely challenging tradeoffs. These include, for example, the tradeoff between the need to efficiently create and provision Virtual Machines (VMs) on data center resources and the need to accommodate the heterogeneous resource demands and runtimes of the applications that run on them. In this paper we propose an energy-aware online provisioning approach for HPC applications on consolidated and virtualized computing platforms. Energy efficiency is achieved using a workload-aware, just-right dynamic provisioning mechanism and the ability to power down subsystems of a host system that are not required by the VMs mapped to it. Our preliminary evaluations show that our approach can improve energy efficiency with an acceptable QoS penalty.

Abstract Emerging pervasive information and computational environments require a content‐based middleware infrastructure that is scalable, self‐managing, and asynchronous. In this paper, we propose associative rendezvous (AR) as a paradigm for content‐based decoupled interactions for pervasive grid applications. We also present Meteor, a content‐based middleware infrastructure to support AR interactions. The design, implementation, and experimental evaluation of Meteor are presented. Evaluations include experiments using deployments on a local area network, the wireless ORBIT testbed at Rutgers University, and the PlanetLab wide‐area testbed, as well as simulations. Evaluation results demonstrate the scalability, effectiveness, and performance of Meteor to support pervasive grid applications. Copyright © 2007 John Wiley & Sons, Ltd.