0000000000237762
AUTHOR
Vladimir Zadorozhny
showing 8 related works from this author
Automatic evaluation of information provider reliability and expertise
2013
Published version of an article in the journal: World Wide Web. Also available from the publisher at: http://dx.doi.org/10.1007/s11280-013-0249-x Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes (reliability and expertise) significantly affect the quality o…
SLFTD: A Subjective Logic Based Framework for Truth Discovery
2019
Finding truth from various conflicting candidate values provided by different data sources is called truth discovery, which is of vital importance in data integration. Several algorithms have been proposed in this area, which usually have similar procedure: iteratively inferring the truth and provider’s reliability on providing truth until converge. Therefore, an accurate provider’s reliability evaluation is essential. However, no work pays attention to “how reliable this provider continuously providing truth”. Therefore, we introduce subjective logic, which can record both (1) the provider’s reliability of generating truth, and (2) reliability of provider continuously doing so. Our propose…
Two novel subjective logic-based in-network data processing schemes in wireless sensor networks
2016
Wireless sensor networks (WSNs) consist of connected low-cost and small-size sensor nodes. The sensor nodes are characterized by various limitations, such as energy availability, processing power, and storage capacity. Typically, nodes collect data from an environment and transmit the raw or processed data to a sink. However, the collected data contains often redundant information. An in-network processing scheme attempts to eliminate or reduce such redundancy in sensed data. In this paper, we propose two in-network data processing schemes for WSNs, which are built based on a lightweight algebra for data processing. The schemes bring also benefits like decreased network traffic load and inc…
A Cognitive-based scheme for user reliability and expertise assessment in Q&A social networks
2011
Q&A social media has gained a great deal of attention during recent years. People rely on these sites to obtain information due to the number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradictory answers, causing ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. In this work, we propose a novel approach for estimating the reliability and expertise of a user based on human cognitive traits. Every user can individually estimate these values based on local pairwise interactions. We examine…
An Efficient, Robust, and Scalable Trust Management Scheme for Unattended Wireless Sensor Networks
2012
Unattended Wireless Sensor Networks (UWSNs) are characterized by long periods of disconnected operation and fixed or irregular intervals between visits by the sink. The absence of an online trusted third party, i.e., an on-site sink, makes existing trust management schemes used in legacy wireless sensor networks not applicable to UWSNs directly. In this paper, we propose a trust management scheme for UWSNs to provide efficient, robust and scalable trust data storage. For trust data storage, we employ geographic hash table to efficiently identify data storage nodes and to significantly reduce storage cost. We demonstrate, through detailed analyses and extensive simulations, that the proposed…
Subjective Logic-Based In-Network Data Processing for Trust Management in Collocated and Distributed Wireless Sensor Networks
2018
While analyzing an explosive amount of data collected in today’s wireless sensor networks (WSNs), the redundant information in the sensed data needs to be handled. In-network data processing is a technique which can eliminate or reduce such redundancy, leading to minimized resource consumption. On the other hand, trust management techniques establish trust relationships among nodes and detect unreliable nodes. In this paper, we propose two novel in-network data processing schemes for trust management in static WSNs. The first scheme targets at networks, where sensor nodes are closely collocated to report the same event. Considering both spatial and temporal correlations, this scheme generat…
Towards Open Domain Chatbots—A GRU Architecture for Data Driven Conversations
2018
Understanding of textual content, such as topic and intent recognition, is a critical part of chatbots, allowing the chatbot to provide relevant responses. Although successful in several narrow domains, the potential diversity of content in broader and more open domains renders traditional pattern recognition techniques inaccurate. In this paper, we propose a novel deep learning architecture for content recognition that consists of multiple levels of gated recurrent units (GRUs). The architecture is designed to capture complex sentence structure at multiple levels of abstraction, seeking content recognition for very wide domains, through a distributed scalable representation of content. To …
Collaborative Assessment of Information Provider's Reliability and Expertise Using Subjective Logic
2011
QA each user can individually estimate the expertise and the reliability of her peers using her direct interactions with them and our framework. The online SN (OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assessment in order to reach a consensus. We emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter's behavior change, provided that the cognitive traits hold in practice.