Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web

Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard

ACSIR: ANOVA Cosine Similarity Image Recommendation in vertical search

In today�s world, online shopping is very attractive and grown exponentially due to revolution in digitization. It is a crucial demand to provide recommendation for all the search engine to identify users� need. In this paper, we have proposed a ANOVA Cosine Similarity Image Recommendation (ACSIR) framework for vertical image search where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p value by considering image visual features on text-based search. Expanded queries are generated for user input query, and text-based search is performed to get the initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain-specific site. Experiment results show that the ACSIR outperforms iLike method by providing more relevant products to the user input query. © 2017, Springer-Verlag London

Climate Change Vulnerability in Agriculture Sector: Indexing and Mapping of Four Southern Indian States

Agriculture is the sector most vulnerable to climate change due to its high dependence on climate and weather conditions. Climate change is a main challenge for agriculture, food security and rural livelihoods for millions of people in India. Among India’s population of more than one billion people, about 68% are directly or indirectly involved in the agricultural sector. This sector is particularly vulnerable to present-day climate variability. In this paper an attempt is made to map and analyze the vulnerability to climate change in different districts of four south Indian states: Andhra Pradesh, Karnataka, Tamil Nadu and Kerala. We have taken five sources of vulnerability indicators: socio-demographic, climatic, agricultural, occupational and common property resources vulnerabilities to compute the composite vulnerability index. The composite vulnerability index suggests that, Adilabad, Chamarajanagar, Thiruvarur and Kasaragod are the most vulnerable districts of Andhra Pradesh, Karnataka, Tamil Nadu and Kerala respectively, whereas Hyderabad, Belgaum, Thoothukkudi, Kottayam are the least vulnerable districts

Enhancing Software-As-A-Service With Insufficient Domain Knowledge

This study addresses the question “How do Software-as-a-Service (SaaS) vendors enhance their software with insufficient domain knowledge?” Results were obtained by analyzing a dataset from a SaaS vendor that provides administrative software to small schools around the world. The dataset includes archived data (email messages, company documents, and Skype messages) and access to the company’s online repositories (sales pipeline, client online chats, and engineering repository). We identified three types of domain knowledge that are relevant to SaaS vendors – organization specific, industry-wide, and regional variation. We also generated six propositions explaining how industry-wide and regional variation knowledge influences the SaaS enhancement process, and at which points in the process these two types of domain knowledge come into play. This study refines our current knowledge by highlighting the unfolding stages between insufficient levels of domain knowledge and software enhancement outcomes

Security and Privacy Issues in Cloud Computing

Cloud computing transforming the way of information technology (IT) for consuming and managing, promising improving cost efficiencies, accelerate innovations, faster time-to-market and the ability to scale applications on demand (Leighton, 2009). According to Gartner, while the hype grew ex-ponentially during 2008 and continued since, it is clear that there is a major shift towards the cloud computing model and that the benefits may be substantial (Gartner Hype-Cycle, 2012). However, as the shape of the cloud computing is emerging and developing rapidly both conceptually and in reality, the legal/contractual, economic, service quality, interoperability, security and privacy issues still pose significant challenges. In this chapter, we describe various service and deployment models of cloud computing and identify major challenges. In particular, we discuss three critical challenges: regulatory, security and privacy issues in cloud computing. Some solutions to mitigate these challenges are also proposed along with a brief presentation on the future trends in cloud computing deployment

A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network

Wireless sensor network (WSN) consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST) routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST). The proposed algorithm computes the distance-based Minimum Spanning Tree (MST) of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm

On Evaluating Commercial Cloud Services: A Systematic Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons