2020
Authors
Kurunathan, H; Severino, R; Koubaa, A; Tovar, E;
Publication
ACM SIGBED Review
Abstract
2020
Authors
Kurunathan, H; Severino, R; Koubaa, A; Tovar, E;
Publication
ACM SIGBED Review
Abstract
2020
Authors
Sarkar, S; Malta, MC; Dutta, A;
Publication
2020 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2020)
Abstract
Over the years, workers have joined in producer organizations to face the difficulties that the capitalist market poses to them. Together they can gain efficiency and equity compared to big companies, and they can gain bargaining power over the product market. In our case, we target smallholder farmers who face many difficulties in increasing their welfare. To overcome them, they group together in producer organizations such as cooperatives. With the development of technology, it became possible for these cooperatives of workers to use the Web to operate - such type of organization and operation is called a Platform Cooperative (PC). This paper presents a multi-agent based modeling of Farmers' Coalition Formation (FCF) for smallholder farmers so that they can operate by means of a Platform cooperative. We present the design of a characteristic function that calculates the coalition values in this context, finds the best way of partitioning the farmers into smaller groups and divides the payoff in a stable manner. We empirically analyze the model using value distributions. The results show that forming coalitions is profitable for farmers. We also proved that the model ensures a fair distribution of the payoff among the farmers.
2020
Authors
Tyagi, P; Malta, MC; Dutta, A;
Publication
2020 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2020)
Abstract
There are many information retrieval tasks over the Web, which cannot be attended with a simple keyword-based lookup search. Such an important exploratory search problem is the comparison of two Web resources. To manually compare two data resources by looking for information from one Web page to another without any software support is inefficient and time-consuming. This paper discusses a solution to automatize the comparison of two data resources present in a RDF graph. In our work, we provide an improvement over the current state-of-the-art method, by reverse engineering SPARQL queries using a hashing based recursive procedure. We empirically verify how hashing could largely benefit in reducing the size of the returned query and hence making it practically comprehensible for users or agents to understand the similarity concepts returned.
2020
Authors
Sen, S; Malta, MC; Dutta, B; Dutta, A;
Publication
IETE TECHNICAL REVIEW
Abstract
The integration of meta-knowledge on the Web of data is essential to support trustworthiness. This is in fact an issue because of the enormous amount of data that exists on the Web of Data. Meta-knowledge describes how the data is generated, manipulated, and disseminated. In the last few years, several approaches have been proposed for tracing and representing meta-knowledge efficiently on a statement or on a set of statements in the Semantic Web. The approaches differ significantly; for instance, in terms of modelling patterns, the number of statements generation, redundancy of the resources, query length, or query response time. This article reports a systematic review of the various approaches of the four dimensions (namely time, trust, fuzzy, and provenance) to provide an overview of the meta-knowledge assertion techniques in the field of the Semantic Web. Some experiments are conducted to analyze the actual performance of the approaches of meta-knowledge assertion considering the provenance dimension. These experiments are based on specific parameters such as graph size, number of statements generation, redundancy, query length, and query response time. All the experiments are done with real-world datasets. The semantics of the different approaches are compared to analyze the methodology of the approaches. Our study and experiments highlight the advantages and limitations of the approaches in terms of the parameters mentioned above.
2020
Authors
Maji, G; Namtirtha, A; Dutta, A; Malta, MC;
Publication
EXPERT SYSTEMS WITH APPLICATIONS
Abstract
Identifying influential spreaders in a complex network has practical and theoretical significance. In applications such as disease spreading, virus infection in computer networks, viral marketing, immunization, rumor containment, among others, the main strategy is to identify the influential nodes in the network. Hence many different centrality measures evolved to identify central nodes in a complex network. The degree centrality is the most simple and easy to compute whereas closeness and betweenness centrality are complex and more time-consuming. The k-shell centrality has the problem of placing too many nodes in a single shell. Over the time many improvements over k-shell have been proposed with pros and cons. The k-shell hybrid (ksh) method has been recently proposed with promising results but with a free parameter that is set empirically which may cause some constraints to the performance of the method. This paper presents an improvement of the ksh method by providing a mathematical model for the free parameter based on standard network parameters. Experiments on real and artificially generated networks show that the proposed method outperforms the ksh method and most of the state-of-the-art node indexing methods. It has a better performance in terms of ranking performance as measured by the Kendall's rank correlation, and in terms of ranking efficiency as measured by the monotonicity value. Due to the absence of any empirically set free parameter, no time-consuming preprocessing is required for optimal parameter value selection prior to actual ranking of nodes in a large network.
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