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.

Abstract

BACKGROUND: Repetition of super secondary structure is a common phenomenon, especially in higher eukaryotic organism. The copy number and assembly of these repeating units are responsible for diverse protein-protein interactions, and consequently, defects in repeat proteins are linked to many diseases. The variation within the repeat units makes their identification difficult at the sequence level and structure based approaches are desired. Since most structure based methods employ computationally intensive structure-structure alignment, we propose a computationally efficient structure-based approach for the identification of repeats using concepts from graph theory. RESULTS: The three-dimensional topology of protein structures is known to be well captured by protein contact graphs. The connectivity information in a graph is represented in the adjacency matrix and the eigenspectra of the adjacency matrix depicts the topological importance of each node to the connectivity of the graph. In our earlier work, we observed that the principal eigenspectra of the adjacency matrix well captures the tandemly repeated structural motifs. Here we propose an algorithm for the identification of tandemly repeated structural motif using graph properties and secondary structure information from STRIDE database. The algorithm begins by first identifying the length of repeat motif by analyzing the periodicity of peaks in eigenvector centrality. The repeat boundaries are then identified by superposing the contiguous repeats and extending on either side of the peak regions to the start/end of the secondary structure elements and checking for periodicity of the secondary structure architecture in the identified repeat regions. Thus, using the secondary structure annotation helps in refining the boundaries of the repeat regions and to discard false positives. We have tested the algorithm for identifying various structural repeats such as HEAT, WD, Ankyrin (ANK), Tetratricopeptide repeat (TPR), Leucine rich repeat (LRR), etc. with different super secondary structure motif, ranging from all alpha, to all beta to a mixed topology such alpha-turn-alpha, beta-alpha, etc. The predictions are in agreement with annotation in UniProt database. CONCLUSIONS: The graph based analysis of protein structures, along with domain information such as the organization of the secondary structure elements provides a computationally efficient approach for the identification of structural repeats.

Abstract

Reliable message transmission is a fundamental problem in distributed communication networks. Of late, several interesting results have been obtained by modelling the network as a directed graph. An important result among them was due to Bhavani et al. [Unconditionally reliable message transmission in directed networks, 18 in: SODA’08: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, Philadelphia, PA, USA, 2008, pp. 1048–1055], where it was shown that if a negligible probability of error is allowed, the connectivity required in a synchronous network for unconditionally reliable message transmission (URMT) is significantly reduced. We refer to the variant of URMT studied by them as Monte Carlo URMT; here the receiver is allowed to output an incorrect message with small probability. Another interesting variant which has received little attention so far in the literature is the Las Vegas variant, where the receiver is allowed to abort with a small probability, but never to output an incorrect message. We show that the minimum connectivity requirements for the existence of Las Vegas URMT protocols over synchronous networks are the same as that of Monte Carlo URMT protocols over asynchronous networks—a surprising equivalence between two very different models. Furthermore, we show that the higher connectivity requirements of Las Vegas URMT over asynchronous networks match exactly with that of zero-error (perfect) variant over (a)synchronous networks. We also show that there exists a family of graphs where in the number of critical edges for the ‘easier’ randomized variants are asymptotically higher than that for the perfect variants. Hence, our results demonstrate an interesting interplay between (im)perfectness, synchrony and connectivity for the case of reliable message transmission.