Multiobjective Gate Assignment Based on Passenger Walking Distance and Fairness

Passenger walking distance is an important index of the airport service quality. How to shorten the walking distance and balance the airlines' service quality is the focus of much research on airport gate assignment problems. According to the problems of airport passenger service quality, an optimization gate assignment model is established. The gate assignment model is based on minimizing the total walking distance of all passengers and balancing the average walking distance of passengers among different airlines. Lingo is used in the simulation of a large airport gate assignment. Test results show that the optimization model can reduce the average walking distance of passenger effectively, improve the number of flights assigned to gate, balance airline service quality, and enhance the overall service level of airports and airlines. The model provides reference for the airport gate preassignment

Why I Chose the Product I Didn’t Want? The Undesired Impact of Recommendation Badge

This paper investigates the influence of recommendation badges in consumer decision-making. Online recommendation badge is a type of digital nudge that affects customer’s behavior to a desired direction while preserving all the available options and maintaining the same economic incentives. With the advances of Artificial Intelligence/Machine Learning (AI/ML), algorithmically driven nudges have been widely employed to influence individuals and collective behaviors that include undesired consequences for both end-users and firms. Drawing on nudge literature and cognitive theory, this study focuses on two types of recommendation badges in the world’s largest e-commerce platform and proposed the concept of ambiguous badge (e.g., Amazon’s Choice) and specific badge (e.g., Best Seller) based on the accountability of the recommendation generation and badge placement. More specifically, we hypothesize that: (1) consumers’ preference will be modified by the badge when the recommendation doesn’t not match their preference, (2) ambiguous badge will modify consumer’s preference more than specific badge, (3) recommendation badge from a large and well-known platform is more likely to affect user decision and modify consumer’s preference than from a small and unfamous platform, (4) consumers have higher choice confidence with specific badge than that with ambiguous badge. This study will contribute to the literature on nudge, biased recommendation agent/systems research, and the dark side of IS by unveiling the impact of accountability on user preference manipulation in online recommendation

Real-Time Gate Reassignment Based on Flight Delay Feature in Hub Airport

Appropriate gate reassignment is crucially important in efficiency improvement on airport sources and service quality of travelers. The paper divides delay flight into certain delay time flight and uncertain delay time flight based on flight delay feature. The main objective functions of model are to minimize the disturbance led by gate reassignment in the case of certain delay time flight and uncertain delay time flight, respectively. Another objective function of model is to build penalty function when the gate reassignment of certain delay time flight influences uncertain delay time flight. Ant colony algorithm (ACO) is presented to simulate and verify the effectiveness of the model. The comparison between simulation result and artificial assignment shows that the result coming from ACO is obvious prior to the result coming from artificial assignment. The maximum disturbance of gate assignment is decreased by 13.64%, and the operation time of ACO is 118 s. The results show that the strategy of gate reassignment is feasible and effective

Bending behavior test and assessment for full-scale PC box girder reinforced by prestressed CFRP plate

This paper focuses on behavior of full scale prestressed concrete (PC) box girder with difference degrees of damage derived from service stage. According to typical structural damage, strengthening measures are proposed, including gluing steel plate, gluing prestressed CFRP plate and so on. In order to testify the effectiveness of reinforced method, bending behavior test are conducted for full scale PC box girder both before and after strengthening. After the test, the bending behaviors of test girder are comparatively analyzed, and the failure mechanism of test girder reinforced by prestressed CFRP plate is studied. What’s more, the strengthening method of gluing prestressed CFRP plate is applied in in-situ prestressed concrete box girder bridge with obvious damage. The static and dynamic testing of this reinforced bridge shows the feasibility and effectiveness of gluing prestressed CFRP plate strengthening method. Studies in this paper provide reliable guidance for engineering application

Mining the Relationship between Emoji Usage Patterns and Personality

Emojis have been widely used in textual communications as a new way to convey nonverbal cues. An interesting observation is the various emoji usage patterns among different users. In this paper, we investigate the correlation between user personality traits and their emoji usage patterns, particularly on overall amounts and specific preferences. To achieve this goal, we build a large Twitter dataset which includes 352,245 users and over 1.13 billion tweets associated with calculated personality traits and emoji usage patterns. Our correlation and emoji prediction results provide insights into the power of diverse personalities that lead to varies emoji usage patterns as well as its potential in emoji recommendation tasks.Comment: To appear at The International AAAI Conference on Web and Social Media (ICWSM) 201

When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?

In recent years, large language models (LLMs) have demonstrated substantial potential in addressing automatic program repair (APR) tasks. However, the current evaluation of these models for APR tasks focuses solely on the limited context of the single function or file where the bug is located, overlooking the valuable information in the repository-level context. This paper investigates the performance of popular LLMs in handling repository-level repair tasks. We introduce RepoBugs, a new benchmark comprising 124 typical repository-level bugs from open-source repositories. Preliminary experiments using GPT3.5 based on the function where the error is located, reveal that the repair rate on RepoBugs is only 22.58%, significantly diverging from the performance of GPT3.5 on function-level bugs in related studies. This underscores the importance of providing repository-level context when addressing bugs at this level. However, the repository-level context offered by the preliminary method often proves redundant and imprecise and easily exceeds the prompt length limit of LLMs. To solve the problem, we propose a simple and universal repository-level context extraction method (RLCE) designed to provide more precise context for repository-level code repair tasks. Evaluations of three mainstream LLMs show that RLCE significantly enhances the ability to repair repository-level bugs. The improvement reaches a maximum of 160% compared to the preliminary method. Additionally, we conduct a comprehensive analysis of the effectiveness and limitations of RLCE, along with the capacity of LLMs to address repository-level bugs, offering valuable insights for future research.Comment: Accepted by ICSE 2024 Industry Challenge Trac

More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching

Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to its capability of learning fine-grained relevance across different modalities. However, the cross-modal attention models of existing methods could be sub-optimal and inaccurate because there is no direct supervision provided during the training process. In this work, we propose two novel training strategies, namely Contrastive Content Re-sourcing (CCR) and Contrastive Content Swapping (CCS) constraints, to address such limitations. These constraints supervise the training of cross-modal attention models in a contrastive learning manner without requiring explicit attention annotations. They are plug-in training strategies and can be easily integrated into existing cross-modal attention models. Additionally, we introduce three metrics including Attention Precision, Recall, and F1-Score to quantitatively measure the quality of learned attention models. We evaluate the proposed constraints by incorporating them into four state-of-the-art cross-modal attention-based image-text matching models. Experimental results on both Flickr30k and MS-COCO datasets demonstrate that integrating these constraints improves the model performance in terms of both retrieval performance and attention metrics.Comment: Accepted to WACV 202

Digital imaging and qPCR analysis and comparison of short-term plaque removal effects of tooth brushing

PurposeDigital image technology and a real-time fluorescent quantitative polymerase chain reaction (RQ-PCR) were used to determine the changes in dental plaque caused by different toothbrushing tools.MethodsA total of 120 subjects were selected and divided into four groups: a manual toothbrush group, a manual toothbrush combined with an oral irrigator group, an electric toothbrush combined with an oral irrigator group, and an electric toothbrush group. We compared the changes in plaque count, plaque area, and colony colonization of the four groups after different cleaning tools had been used for a period of time.ResultsDental plaque count and plaque area decreased in all four groups. The decreases in plaque count and Streptococcus mutans in the electric toothbrush combined with an oral irrigator group were significantly higher than those in other groups.ConclusionElectric toothbrush combined with an oral irrigator shows a good result for plaque removal effect. Digital image analysis combined with biological methods can be used to evaluate dental plaque