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  • 1
    Online Resource
    Online Resource
    Fault diagnosis of wireless sensor network based on optimized probabilistic neural network
    International Information and Engineering Technology Association ; 2018
    In:  Journal Européen des Systèmes Automatisés Vol. 51, No. 4-6 ( 2018-12-28), p. 295-308
    Details
    In: Journal Européen des Systèmes Automatisés, International Information and Engineering Technology Association, Vol. 51, No. 4-6 ( 2018-12-28), p. 295-308
    Type of Medium: Online Resource
    ISSN: 1269-6935
    URL: Article
    DOI: 10.3166/jesa.51.295-308
    RVK:
    QA 10000
    Language: Unknown
    Publisher: International Information and Engineering Technology Association
    Publication Date: 2018
    detail.hit.zdb_id: 2390481-1
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  • 2
    Online Resource
    Online Resource
    The Impact of Social Nudges on User-Generated Content for Social Network Platforms
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  Management Science Vol. 69, No. 9 ( 2023-09), p. 5189-5208
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 69, No. 9 ( 2023-09), p. 5189-5208
    Abstract: Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N [Formula: see text]) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: H. Dai thanks the University of California, Los Angeles (UCLA) [Hellman Fellowship and Faculty Development Award] for funding support. R. Zhang is grateful for financial support from the Hong Kong Research Grants Council [Grant 16505418] . Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4622 .
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2022.4622
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2023
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 3
    Online Resource
    Online Resource
    Cold Start to Improve Market Thickness on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  Management Science Vol. 69, No. 7 ( 2023-07), p. 3838-3860
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 69, No. 7 ( 2023-07), p. 3838-3860
    Abstract: Cold start describes a commonly recognized challenge for online advertising platforms: with limited data, the machine learning system cannot accurately estimate the click-through rates (CTR) of new ads and, in turn, cannot efficiently price these new ads or match them with platform users. Traditional cold start algorithms often focus on improving the learning rates of CTR for new ads to improve short-term revenue, but unsuccessful cold start can prompt advertisers to leave the platform, decreasing the thickness of the ad marketplace. To address these issues, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform. Based on duality theory and bandit algorithms, we develop the shadow bidding with learning (SBL) algorithms with a provable regret upper bound of [Formula: see text], where K is the number of ads and d captures the error magnitude of the underlying machine learning oracle for predicting CTR. Our proposed algorithms can be implemented in a real online advertising system with minimal adjustments. To demonstrate this practicality, we have collaborated with a large-scale video-sharing platform, conducting a novel, two-sided randomized field experiment to examine the effectiveness of our SBL algorithm. Our results show that the algorithm increased the cold start success rate by 61.62% while compromising short-term revenue by only 0.717%. Our algorithm has also boosted the platform’s overall market thickness by 3.13% and its long-term advertising revenue by (at least) 5.35%. Our study bridges the gap between the theory of bandit algorithms and the practice of cold start in online advertising, highlighting the value of well-designed cold start algorithms for online advertising platforms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4550 .
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2022.4550
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2023
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 4
    Online Resource
    Online Resource
    Online Demand Fulfillment Under Limited Flexibility
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2020
    In:  Management Science Vol. 66, No. 10 ( 2020-10), p. 4667-4685
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 66, No. 10 ( 2020-10), p. 4667-4685
    Abstract: We study online demand fulfillment in a class of networks with limited flexibility and arbitrary numbers of resources and request types. We show analytically that such a network is both necessary and sufficient to guarantee a performance gap independent of the market size compared with networks with full flexibility, extending the previous literature from the long chains to more general sparse networks. Inspired by the performance bound, we develop simple inventory allocation rules and guidelines for designing such network structures. Numerical experiments including one using some real data from Amazon China are conducted to confirm our findings as well as some of the flexibility principles conjectured in the literature. This paper was accepted by Chung Piaw Teo, optimization.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2019.3449
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2020
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 5
    Online Resource
    Online Resource
    A Comprehensive Survey on Sharding in Blockchains
    Hindawi Limited ; 2021
    In:  Mobile Information Systems Vol. 2021 ( 2021-12-18), p. 1-22
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2021 ( 2021-12-18), p. 1-22
    Abstract: Blockchain technology has been widely used in many fields, such as smart cities, smart health care, and smart manufacturing, due to its anonymity, decentralization, and tamper resistance in peer-to-peer (P2P) networks. However, poor scalability has severely affected the widespread adoption of traditional blockchain technology in high-throughput and low-latency applications. Therefore, based on the three-layer architecture, this study presents a variety of solutions to improve the scalability of the blockchain. As the scale of the network expands, one of the most practical ways to achieve horizontal scalability is sharding, where the network is divided into multiple subnetworks to avoid repeated communication overhead, storage, and calculations. This study provides a systematic and comprehensive introduction to blockchain sharding, along with a detailed comparison and evaluation for primarily considered sharding mechanisms. We also provide the detailed calculations and then analyze the characteristics of existing solutions along with our insights.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2021/5483243
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2187808-0
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  • 6
    Online Resource
    Online Resource
    The Effect of Previous Working Relationship between Rotating Partners on Mandatory Audit Partner Rotation
    World Scientific Pub Co Pte Ltd ; 2017
    In:  The International Journal of Accounting Vol. 52, No. 2 ( 2017-06), p. 101-121
    Details
    In: The International Journal of Accounting, World Scientific Pub Co Pte Ltd, Vol. 52, No. 2 ( 2017-06), p. 101-121
    Type of Medium: Online Resource
    ISSN: 0020-7063
    URL: Article
    DOI: 10.1016/j.intacc.2017.04.003
    RVK:
    QA 10000
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2017
    detail.hit.zdb_id: 2002289-X
    detail.hit.zdb_id: 411149-7
    SSG: 3,2
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  • 7
    Online Resource
    Online Resource
    Adaptive Clustering Algorithm of Complex Network Based on Fuzzy Neural Networks
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-6-24), p. 1-10
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-6-24), p. 1-10
    Abstract: Clustering analysis, as one of the important methods in data mining technology, merely provides a method for the research and analysis of large amounts of data. Starting with the most important nodes, this paper divides clustering based on data field characteristics. The sensitivity pruning algorithm is then used to further adjust and optimize the structure of the fuzzy neural network, allowing the network to automatically learn the structure and parameters of the system in different environments and obtain the optimal control rules. Finally, the clustering function brings the algorithm’s output result to a close. The experimental results show that the adaptive clustering algorithm of complex networks presented in this paper can effectively improve network cluster division, reduce algorithm time complexity, and avoid the problem of providing the number of cluster structures in advance. This method’s cluster structure efficiency can reach 97.6 percent, and its highest clustering accuracy can reach 96.8 percent. The adaptive clustering algorithm proposed in this paper not only overcomes the traditional algorithm’s flaws, such as the need to predetermine the number of clusters and the clustering result being dependent on the initial clustering center selection, but also has ideal clustering accuracy. This study introduces a novel and more effective method for addressing the difficult problems of practical complex control systems.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2022/9220581
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
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  • 8
    Online Resource
    Online Resource
    A Multi-Level Fuzzy Comprehensive Evaluation Method for Knowledge Transfer Efficiency in Innovation Cluster
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-7-31), p. 1-12
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-7-31), p. 1-12
    Abstract: Knowledge transfer is the essential requirement for innovation clusters to carry out collaborative innovation, and it is a necessary process for innovation clusters to realize the knowledge value enhancement. The evaluation of knowledge transfer efficiency in innovation cluster can effectively reflect the knowledge gap, environment, and whether it is effectively coordinated among members of the innovation cluster. In order to evaluate the knowledge transfer efficiency in innovation clusters more scientifically and accurately, this paper analyzes the main factors affecting the efficiency of knowledge transfer based on the characteristics of innovation clusters and establishes a multi-level comprehensive evaluation system including knowledge transfer subject features, knowledge content features, knowledge transfer environment, and knowledge transfer coordination behavior. Furthermore, a set of AHP-Entropy index weight determination method and multi-level fuzzy comprehensive evaluation method are proposed to evaluate the knowledge transfer efficiency in innovation cluster. The results of the case study show that the evaluation system and method of knowledge transfer efficiency established in this paper are effective, and they can provide valuable reference for the management of knowledge transfer activities in innovation clusters.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2022/3949597
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
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  • 9
    Online Resource
    Online Resource
    A two‐period pricing model with intertemporal and horizontal reference price effects
    Wiley ; 2021
    In:  International Transactions in Operational Research Vol. 28, No. 3 ( 2021-05), p. 1417-1440
    Details
    In: International Transactions in Operational Research, Wiley, Vol. 28, No. 3 ( 2021-05), p. 1417-1440
    Abstract: Consumers tend to compare the current price with historical prices of the same brand and selling prices of other brands, when they make purchase decisions. The intertemporal and horizontal reference price effect (RPE), formed by the historical price and the competitor's price, respectively, should be taken into account when developing optimal pricing strategies over several periods and in a competitive environment. This paper considers a two‐period pricing problem with two competing sellers, incorporating both types of RPE. We first develop a duopoly game to study the impacts and interactions of RPE of different types. Then we study a practice of price commitment when one firm gives up dynamic pricing. We find different types of RPEs have distinct impacts. The intertemporal RPE (IRPE) leads to a Hi‐Lo pricing strategy, while the horizontal RPE (HRPE) drives the selling prices of both periods downward. The IRPE is weakened by the HRPE, while the HRPE is weakened (intensified) in the first (second) period as the IRPE becomes stronger. Also we find price commitment is not beneficial. On the contrary, both firms may be worse off if one firm makes price commitment. Furthermore, if one firm decides to make price commitment he should announce the selling price well in advance.
    Type of Medium: Online Resource
    ISSN: 0969-6016 , 1475-3995
    URL: Issue
    URL: Article
    DOI: 10.1111/itor.v28.3
    DOI: 10.1111/itor.12613
    RVK:
    QA 10000
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2019815-2
    SSG: 3,2
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  • 10
    Online Resource
    Online Resource
    A Multitask Sign Language Recognition System Using Commodity Wi-Fi
    Hindawi Limited ; 2023
    In:  Mobile Information Systems Vol. 2023 ( 2023-2-10), p. 1-11
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2023 ( 2023-2-10), p. 1-11
    Abstract: Wi-Fi sensing for gesture recognition systems is a fascinating and challenging research topic. We propose a multitask sign language recognition framework called Wi-SignFi, which accounts for gestures in the real world associated with various objects, actions, or scenes. The proposed framework comprises a convolutional neural network (CNN) and K-nearest neighbor (KNN) module. It is evaluated on the public SignFi dataset and achieves 98.91%, 86.67%, and 99.99% average gesture recognition accuracies on 276/150 activities, five users, and two environments, respectively. The experimental results show that the proposed gesture recognition method outperforms previous methods. Instead of converting the channel state information (CSI) data of multiple antennas into three-dimensional matrices (i.e., color images) as in the existing literature, we found that the CSI data can be converted into matrices (i.e., grayscale images) by concatenating different channels, allowing the Wi-SignFi model to balance between speed and accuracy. This finding facilitates deploying Wi-SignFi on Nvidia’s Jetson Nano edge embedded devices. We expect this work to promote the integration of Wi-Fi sensing and the Internet of Things (IoT) and improve the quality of life of the deaf community.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2023/7959916
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2187808-0
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