A Co-Citation Bibliometric Analysis of Crowdsourcing Research

Crowdsourcing has gained increasing attention from scholars in a broad range of fields such as computer science, engineering, information system, business, and economics. However, few crowdsourcing studies are conducted from the bibliometric perspective. This paper conducts document co-citation, author co-citation, journal co-citation, and keyword co-word analysis in the crowdsourcing research field by using CiteSpace and Web of Science TM Core Collection database, aiming to identify highly cited articles and journals and influential authors in the crowdsourcing research field during the time span from 2008 to 2016 and to find out current hot research topics and future directions in the crowdsourcing research field

KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

AstonCAT-plus:an efficient specialist for the TAC market design tournament

This paper describes the strategies used by AstonCAT-Plus, the post-tournament version of the specialist designed for the TAC Market Design Tournament 2010. It details how AstonCATPlus accepts shouts, clears market, sets transaction prices and charges fees. Through empirical evaluation, we show that AstonCAT-Plus not only outperforms AstonCAT (tournament version) significantly but also achieves the second best overall score against some top entrants of the competition. In particular, it achieves the highest allocative efficiency, transaction success rate and average trader profit among all the specialists in our controlled experiments

Designing a successful adaptive agent for TAC Ad auction

This paper describes the design and evaluation of Aston-TAC, the runner-up in the Ad Auction Game of 2009 International Trading Agent Competition. In particular, we focus on how Aston-TAC generates adaptive bid prices according to the Market-based Value Per Click and how it selects a set of keyword queries to bid on to maximise the expected profit under limited conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments

Structural decomposition of general singular linear systems and its applications

Ph.DDOCTOR OF PHILOSOPH

A multi-demand negotiation model based on fuzzy rules elicited via psychological experiments

This paper proposes a multi-demand negotiation model that takes the effect of human users’ psychological characteristics into consideration. Specifically, in our model each negotiating agent's preference over its demands can be changed, according to human users’ attitudes to risk, patience and regret, during the course of a negotiation. And the change of preference structures is determined by fuzzy logic rules, which are elicited through our psychological experiments. The applicability of our model is illustrated by using our model to solve a problem of political negotiation between two countries. Moreover, we do lots of theoretical and empirical analyses to reveal some insights into our model. In addition, to compare our model with existing ones, we make a survey on fuzzy logic based negotiation, and discuss the similarities and differences between our negotiation model and various consensus models

Casing Pipe Damage Detection with Optical Fiber Sensors: A Case Study in Oil Well Constructions

Casing pipes in oil well constructions may suddenly buckle inward as their inside and outside hydrostatic pressure difference increases. For the safety of construction workers and the steady development of oil industries, it is critically important to measure the stress state of a casing pipe. This study develops a rugged, real-time monitoring, and warning system that combines the distributed Brillouin Scattering Time Domain Reflectometry (BOTDR) and the discrete fiber Bragg grating (FBG) measurement. The BOTDR optical fiber sensors were embedded with no optical fiber splice joints in a fiber-reinforced polymer (FRP) rebar and the FBG sensors were wrapped in epoxy resins and glass clothes, both installed during the segmental construction of casing pipes. In situ tests indicate that the proposed sensing system and installation technique can survive the downhole driving process of casing pipes, withstand a harsh service environment, and remain intact with the casing pipes for compatible strain measurements. The relative error of the measured strains between the distributed and discrete sensors is less than 12%. The FBG sensors successfully measured the maximum horizontal principal stress with a relative error of 6.7% in comparison with a cross multipole array acoustic instrument

An intelligent broker agent for energy trading:an MDP approach

This paper details the development and evaluation of AstonTAC, an energy broker that successfully participated in the 2012 Power Trading Agent Competition (Power TAC). AstonTAC buys electrical energy from the wholesale market and sells it in the retail market. The main focus of the paper is on the broker’s bidding strategy in the wholesale market. In particular, it employs Markov Decision Processes (MDP) to purchase energy at low prices in a day-ahead power wholesale market, and keeps energy supply and demand balanced. Moreover, we explain how the agent uses Non-Homogeneous Hidden Markov Model (NHHMM) to forecast energy demand and price. An evaluation and analysis of the 2012 Power TAC finals show that AstonTAC is the only agent that can buy energy at low price in the wholesale market and keep energy imbalance low

Tibetan Word Segmentation as Syllable Tagging Using Conditional Random Field

In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He.In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He