Abstract

The availability of increasingly richer applications is providing surprisingly wide range of functionalities and new use cases on mobile devices. Even tough mobile devices are becoming increasingly more powerful, the resource utilization of richer application can overwhelm resources on these devices. At the same time, ubiquitous connectivity of mobile devices also opens up the possibility of leveraging cloud resources. Seamless and flexible path to mobile cloud computing requires recognizing opportunities where the application execution on cloud instead of mobile device. In this paper we propose a cloud aware scheduler for application offloading from mobile devices to clouds. We used learning based algorithm for predicting the gain attainable using performance monitoring and high level features. We evaluated prototype of our system on various workloads and under various conditions.

Abstract

Knowledge bases provide with the benefit of organizing knowledge in the relational form but suffer from incompleteness of new entities and relationships. Prior work on relation extraction has been focused on supervised learning techniques which are quite expensive. An alternative approach based on distant supervision has been of significant interest where one aligns database records with sentences of these records. A new line of work on embeddings of symbolic representations [2] has shown promise. We introduce a Matrix trifactorization model which can find missing information in knowledge bases. Experiments show that we are able to query and find missing information from text and shows improvement over existing methods.

Abstract

Wikipedia being a large, freely available, frequently updated and community maintained knowledge base, has been central to much recent research. However, quite often we find that the information extracted from it has extraneous content. This paper proposes a method to extract useful information from Wikipedia, using Semantic Features derived from Wikipedia categories. The proposed method provides good performance as a Wikipedia category based method. Experimental results on benchmark datasets show that the proposed method achieves a correlation coefficient of 0.66 with human judgments. The Semantic Features derived by this method gave good correlation with human rankings in a web search query completion application.

Abstract

Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.

Abstract

An ontology is defined using concepts and relationships between the concepts. In this paper, we focus on second problem: relation extraction from plain text. Generic Knowledge Bases like YAGO, Freebase, and DBPedia have made accessible huge collections of facts and their properties from various domains. But acquiring and maintaining various facts and their relations from domain specific corpus becomes very important and challenging task due to low availability of annotated data. Here, we proposed a label propagation based semi-supervised approach for relation extraction by choosing most informative instances for annotation. We also proposed weakly supervised approach for data annotation using generic ontologies like Freebase, which further reduces the cost of annotating data manually. We checked efficiency of our approach by performing experiments on various domain specific corpora.

Abstract

Cloud computing has enabled elastic and transparent access to infrastructure services without involving IT operating overhead. Virtualization has been a key enabler for cloud computing. While resource virtualization and service abstraction have been widely investigated, networking in cloud remains a difficult puzzle. Even though network has significant role in facilitating hybrid cloud scenarios, it hasn't received much attention in research community until recently. We propose Network as a Service (NaaS), which forms the basis of unifying public and private clouds. In this paper, we identify various challenges in adoption of hybrid cloud. We discuss the design and implementation of a cloud platform with NaaS. We discuss a platform and its implementation for hybrid clouds for providing NaaS along with other compute services, by exploiting the functionality provided by software defined networks. We also provide primary evaluation of our platform.

Abstract

Thermal comfort in building results from a complex set of physical conditions, occupant sensations and preferences as well as occupants behaviour therefore thermally comfortable environment leads to better energy efficiency in buildings. A field study was carried out at five star rated (Certified by Bureau of Energy Efficiency, BEE) air conditioned office building (day time occupancy) located at Jaipur, composite climate of India. The purpose of conducting this study was to evaluate the existing thermal environmental conditions and then to propose the required thermal comfort conditions and determine energy saving potential considering adaptive thermostat control strategy. Questionnaire was developed for conducting the field survey including objective and subjective information as per class˗ II protocol of field measurements. Statistical analysis of occupant responses and recorded environmental variables was performed to determine the required comfortable temperature in the building and this temperature was used as thermostat set point of the air conditioner. The office building was modelled using eQUEST simulation program and calibrated with measured annual energy consumption. Result revealed that majority of the occupants felt thermally comfortable at proposed room temperature of 27.5oC as against the existing practice of maintaining 20-22oC at which most of the occuapants were complaining overcooling and discomfort. Proposed set point temperature of air conditioner also resulted in building energy saving of about 7.86%.

Abstract

Field study of thermal comfort was conducted in six naturally ventilated hostel buildings of composite climate considering Class-II protocol of field measurement during summer 2011. Total 429 survey samples of same age group (average 19.6 years) were collected including objective and subjective measurements. Statistical analysis of student's responses and measured thermal environment variables was performed to determine existing indoor environmental conditions and priority of using behavioural controls. Thermal comfort indices were also calculated and compared to the student's perceptions. Neutral temperature was found to be 30.15 °C through regression analysis, with an average clothing of 0.41 Clo (min. 0.19 Clo, max 0.82 Clo). Results have shown a wide bandwidth of neutral temperatures (25.9–33.8 °C) for the hostel buildings which is higher than national/international standards of thermal comfort …

Abstract

BACKGROUND: Ankyrin repeat is one of the most frequently observed structural motif in proteins across all kingdoms of life. These proteins are involved in diverse set of cellular functions and act as transcriptional initiators, cell-cycle regulators, cytoskeletal, ion transporters and signal transducers, and consequently, defects in ankyrin repeat proteins have been found in a number of human diseases. Identification of these structural repeats at the sequence level is difficult due to low conservation between the repeat copies. Thus, analysis at the structure level is desirable. RESULTS: In this study, we propose a graph based approach in the identification and analysis of ankyrin repeats. The 3-dimensional topology of protein structures has been shown to be well captured by protein contact graphs. The connectivity information of these networks is represented in the adjacency matrix and here we propose the analysis of the eigen spectra of the adjacency matrix in the identification of structural repeats. A clear two-peak pattern corresponding to the helix-turn-helix region of the Ankyrin motif is observed in the principal eigenvector of the adjacency matrix. The length distribution of this repetitive pattern along with the organization of the secondary structure elements is used to design an algorithm to identify the Ankyrin structural motifs. The analysis has been carried out on a non-redundant set of 51 proteins annotated in the UniProt database and a very good agreement is observed. Analysis of all the proteins in the alpha class and alpha+beta class in SCOP database has been performed and a number of novel repeats, not annotated in the database have been identified. This approach is then applied on other structural repeats such as Tetraticopeptide repeat (TPR), Annexin, HEAT, ARM, etc. CONCLUSIONS: The graph based analysis of protein structures, along with domain information such as the organization of the secondary structure architecture provides a computationally efficient approach for the identification of structural repeats.

Abstract

In recent studies it has been shown that graph representation of protein structures is capable of capturing the 3-dimensional fold of the protein very well, thus providing a computationally efficient approach for protein structure analysis. Centrality measures are generally used to identify the relative importance of a node in the network. Here we demonstrate a novel application of centrality analysis: to identify tandemly repeated structural motifs in 3-d protein structures. This is done by analyzing the profile of various centrality measures in the repeat region. The comparative analysis of five centrality measures based on local connectivity, shortest paths, principal eigen spectra and feedback centrality is presented on proteins containing contiguous ankyrin structural motifs to identify which centrality measure best captures the repetitive pattern of ankyrin. We observe that principal eigen spectra of the adjacency matrix and Katz status index, both exhibit a distinct profile for the ankyrin motif capturing its characteristic anti-parallel helix-turn-helix fold. No such conserved pattern was observed in the repeat regions of equivalent random networks, suggesting that the conserved pattern arises from the 3d fold of the structural motif.