4,571 research outputs found
Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule
In this paper, a likelihood based evidence acquisition approach is proposed
to acquire evidence from experts'assessments as recorded in historical
datasets. Then a data-driven evidential reasoning rule based model is
introduced to R&D project selection process by combining multiple pieces of
evidence with different weights and reliabilities. As a result, the total
belief degrees and the overall performance can be generated for ranking and
selecting projects. Finally, a case study on the R&D project selection for the
National Science Foundation of China is conducted to show the effectiveness of
the proposed model. The data-driven evidential reasoning rule based model for
project evaluation and selection (1) utilizes experimental data to represent
experts' assessments by using belief distributions over the set of final
funding outcomes, and through this historic statistics it helps experts and
applicants to understand the funding probability to a given assessment grade,
(2) implies the mapping relationships between the evaluation grades and the
final funding outcomes by using historical data, and (3) provides a way to make
fair decisions by taking experts' reliabilities into account. In the
data-driven evidential reasoning rule based model, experts play different roles
in accordance with their reliabilities which are determined by their previous
review track records, and the selection process is made interpretable and
fairer. The newly proposed model reduces the time-consuming panel review work
for both managers and experts, and significantly improves the efficiency and
quality of project selection process. Although the model is demonstrated for
project selection in the NSFC, it can be generalized to other funding agencies
or industries.Comment: 20 pages, forthcoming in International Journal of Project Management
(2019
A TIME SERIOUS ANALYSIS OF THE DYNAMIC INFLUENCE OF FEMALE'S MENSTRUAL CYCLE TO SPORT PERFORMANCE
This research uses Cross Correlation Function, C.C.F., as a dynamic relationship evaluation model to study the dynamic influences of the menstrual cycle on sport performances. This research takes females with a regular menstrual cycle to be the test subjects. Their basic body temperatures were recorded every day. A Kistler Quattro Jump force plate was used to record continuously for sixty days the parameters of muscular strength, jump performance, and fatigue index during the subjects performance of a counter-movement Jump (CMJ), squat Jump (SJ), and thirty-second continuous bent leg jumps (CJB). The late stage of the follicular phase and the early stage of the luteal phase have a positive influence on sport performance. This also illustrates that sport performance for female athletes will be varied dynamically in accordance with the time of menstrual cycle
Service Alliance in Competition: A Game Theory Perspective
This research focuses on services based on alliance and devises a novel concept called service alliance. Service alliance uses service as the unit that companies can exchange, furnish, and share services in the alliance. Service alliance emphasizes on the representation and exchange for value of single service from an individual company. This research utilizes game theory to discuss the best action of service alliance in competition. We use airline industry as the example and expect to contribute to any service industry. The results show that four strategies are similar to the process from competition to cooperation. The best case is that the members in the alliance co-fund a new service team to serve all members. We hope the proposed concept can be applied to all service industry and create a new operation model for alliance
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in
sentiment analysis research, aiming to extract triplets of the aspect term, its
corresponding opinion term, and its associated sentiment polarity from a given
sentence. Recently, many neural networks based models with different tagging
schemes have been proposed, but almost all of them have their limitations:
heavily relying on 1) prior assumption that each word is only associated with a
single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level
interactions and treating each opinion/aspect as a set of independent words.
Hence, they perform poorly on the complex ASTE task, such as a word associated
with multiple roles or an aspect/opinion term with multiple words. Hence, we
propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract
sentiment triplets in span-level, where each span may consist of multiple words
and play different roles simultaneously. To this end, this paper formulates the
ASTE task as a multi-class span classification problem. Specifically, STAGE
generates more accurate aspect sentiment triplet extractions via exploring
span-level information and constraints, which consists of two components,
namely, span tagging scheme and greedy inference strategy. The former tag all
possible candidate spans based on a newly-defined tagging set. The latter
retrieves the aspect/opinion term with the maximum length from the candidate
sentiment snippet to output sentiment triplets. Furthermore, we propose a
simple but effective model based on the STAGE, which outperforms the
state-of-the-arts by a large margin on four widely-used datasets. Moreover, our
STAGE can be easily generalized to other pair/triplet extraction tasks, which
also demonstrates the superiority of the proposed scheme STAGE.Comment: Accepted by AAAI 202
Modeling Paying Behavior in Game Social Networks
Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy
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