Smart In Situ Fibers And Their Applications

This study describes an innovative fiber technology with various applications, such as fiber-reinforced concrete (FRC), fiber-reinforced plastic (FRP), and plastic foaming. Unlike incumbent passive fiber reinforcing technology, in situ shrinking fibers that respond to an external stimulus such as heat, pH, or moisture variations can induce pre-compression to the matrix and create additional resistance from external loads, creating stronger composite structures. This new technology includes the design and fabrication of in situ shrinking fibers to improve performances in each application. Shrinking ratios and tensile strengths of fibers used in each application were measured. Specimens with active shrinking fibers, passive non shrinking fibers, as well as control samples have been made, and their performances have been compared. The first part describes the application of shrinking fibers in cementitious composite structures to provide supplemental strength-enhancing compressive stresses. Mechanical properties of the samples are compared with compression and three-point bending tests, using heat activated shrinking (HAS) fibers pH activated shrinking (pHAS) fibers. The second part describes the applications of through-thickness fiber reinforcement technology for polymeric laminate to provide supplemental strength-enhancing interlaminar stresses. To prove this concept, peel strengths of epoxy/glass fiber composite layers are measured. Also, in-plane tensile tests are conducted to investigate whether the through-thickness shrinking fibers affect in-plane properties. The third part demonstrates the application of shrinking fiber in improving foaming ability of linear polymers. The smart fiber blending technology would be able to tune the optimum degree of strain hardening behavior cost efficiently. The modification of the rheological properties by the fiber shrinkage is discussed. The extensional viscosity measurements are described in terms of strain-hardening behaviors in polymer composites containing shrinking fibers. Final foam properties resulting from these structures are also presented

Customs Import Declaration Datasets

Given the huge volume of cross-border flows, effective and efficient control of trade becomes more crucial in protecting people and society from illicit trade. However, limited accessibility of the transaction-level trade datasets hinders the progress of open research, and lots of customs administrations have not benefited from the recent progress in data-based risk management. In this paper, we introduce an import declaration dataset to facilitate the collaboration between domain experts in customs administrations and researchers from diverse domains, such as data science and machine learning. The dataset contains 54,000 artificially generated trades with 22 key attributes, and it is synthesized with conditional tabular GAN while maintaining correlated features. Synthetic data has several advantages. First, releasing the dataset is free from restrictions that do not allow disclosing the original import data. The fabrication step minimizes the possible identity risk which may exist in trade statistics. Second, the published data follow a similar distribution to the source data so that it can be used in various downstream tasks. Hence, our dataset can be used as a benchmark for testing the performance of any classification algorithm. With the provision of data and its generation process, we open baseline codes for fraud detection tasks, as we empirically show that more advanced algorithms can better detect fraud.Comment: Datasets: https://github.com/Seondong/Customs-Declaration-Dataset

Explainable Product Classification for Customs

The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for experienced officers. Together with the Korea Customs Service (KCS), we propose a first-ever explainable decision supporting model that suggests the most likely subheadings (i.e., the first six digits) of the HS code. The model also provides reasoning for its suggestion in the form of a document that is interpretable by customs officers. We evaluated the model using 5,000 cases that recently received a classification request. The results showed that the top-3 suggestions made by our model had an accuracy of 93.9\% when classifying 925 challenging subheadings. A user study with 32 customs experts further confirmed that our algorithmic suggestions accompanied by explainable reasonings, can substantially reduce the time and effort taken by customs officers for classification reviews.Comment: 24 pages, Accepted to ACM Transactions on Intelligent Systems and Technolog

FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-sided knowledge distillation with contrastive learning as a core component, allowing the federated system to function without requiring clients to share any data features. Furthermore, its adaptable architecture can be used as an add-on module for existing unsupervised algorithms in federated settings. Experiments show that our model improves performance significantly (1.58--5.52pp) on five unsupervised algorithms.Comment: Accepted and will be published at ECCV202

Towards Attack-tolerant Federated Learning via Critical Parameter Analysis

Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. Existing defense strategies are ineffective under non-IID data settings. This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Parameter Analysis). Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not. Experiments with different attack scenarios on multiple datasets demonstrate that our model outperforms existing defense strategies in defending against poisoning attacks.Comment: ICCV'23 Accepte