In fingerprint biometric systems, fingerprint recognition traditionally focuses on identifying individuals based on the distinct fingerprints of different fingers, which is finger-specific identity recognition (FsIR). However, real-world applications often require recognizing the same individual using fingerprints from different fingers, which is finger-agnostic identity recognition (FaIR). The FaIR task has proven challenging due to the prevailing assumption in the biometric field that there is no correlation between an individual’s different fingerprints. To address this issue, we propose a novel system, IP-Fing, which can learn the human-level similarity across the fingers. By using a pretrained localization encoder to capture fingerprint landmarks and the ArcFace marginal logit function, our IP-Fing recognition system can match a fingerprint query to all fingerprints of the same person while distinguishing them from others. We assess our method using comprehensive tests on three fingerprint datasets: our private fingerprint dataset, KO-RFing, which only has one sample per finger available, the public fingerprint dataset, CASIA-v5, which has a few missing fingerprint samples for the task of finger-agnostic identity recognition (FaIR), and NIST SD302b as an auxiliary. IP-Fing achieves the best AUC with an average of 94.0409 across the three datasets, showing that our method is more effective in applying FaIR than conventional methods. Furthermore, IP-Fing demonstrates superior AUC with an average of 97.7779 across the datasets in the task of traditional finger-specific identity recognition (FsIR).

As the focus of space exploration shifts from national agencies to private companies, the interest in space industry has been steadily increasing. With the increasing number of satellites, the risk of collisions between satellites and space debris has escalated, potentially leading to significant property and human losses. Therefore, accurately modeling the orbit is critical for satellite operations. In this work, we propose the Decomposed Attention Segment Recurrent Neural Network (DASR) model, adding two key components, Multi-Head Attention and Tensor Train Decomposition, to SegRNN for orbit prediction. The DASR model applies Multi-Head Attention before segmenting at input data and before the input of the GRU layers. In addition, Tensor Train (TT) Decomposition is applied to the weight matrices of the Multi-Head Attention in both the encoder and decoder. For evaluation, we use three real-world satellite datasets from the Korea Aerospace Research Institute (KARI), which are currently operating: KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites. Our proposed model demonstrates superior performance compared to other SOTA baseline models. We demonstrate that our approach has 94.13% higher predictive performance than the second-best model in the KOMPSAT-3 dataset, 89.79% higher in the KOMPSAT-3A dataset, and 76.71% higher in the KOMPSAT-5 dataset.

In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites.This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others.However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale.Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences.This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data.For more information, visit our website at https://sites.google.com/view/ansd23.

Although the collision of space objects not only incurs a high cost but also threatens human life, the risk of collision between satellites has increased, as the number of satellites has rapidly grown due to the significant interests in many space applications. However, it is not trivial to monitor the behavior of the satellite in real-time since the communication between the ground station and spacecraft is dynamic and sparse, and there is an increased latency due to the long distance. Accordingly, it is strongly required to predict the orbit of a satellite to prevent unexpected contingencies such as a collision. Therefore, the real-time monitoring and accurate orbit prediction are required. Furthermore, it is necessary to compress the prediction model, while achieving a high prediction performance in order to be deployable in the real systems. Although several machine learning and deep learning-based prediction approaches have been studied to address such issues, most of them have applied only basic machine learning models for orbit prediction without considering the size, running time, and complexity of the prediction model. In this research, we propose Selective Tensorized multi-layer LSTM (ST-LSTM) for orbit prediction, which not only improves the orbit prediction performance but also compresses the size of the model that can be applied in practical deployable scenarios. To evaluate our model, we use the real orbit dataset collected from the Korea Multi-Purpose Satellites (KOMPSAT-3 and KOMPSAT-3A) of the Korea Aerospace Research Institute (KARI) for 5 years. In addition, we compare our ST-LSTM to other machine learning-based regression models, LSTM, and basic tensorized LSTM models with regard to the prediction performance, model compression rate, and running time.

Reducing false positives while detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems. Undesired false positives can be costly for such systems, bringing the operation to a halt for human experts to determine if the anomalies are true anomalies that need to be mitigated. Although rule-based or machine learning-based anomaly detection approaches have been studied, a tensor-based decomposition method has not been extensively explored. In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system with the goal of minimizing false positives. We construct 3rd-order tensors with telemetry data collected from the Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and calculate the anomaly score using one of the component matrices obtained by applying CANDECOMP/PARAFAC decomposition to detect anomalies. Our result shows that our tensor-based approach outperforms existing methods, achieving higher accuracy and lower false positive rates. And we successfully deployed our anomaly detection system in real KOMPSAT-2 mission operation.

Detecting an anomaly is not only important for many terrestrial applications on Earth but also for space applications. Especially, satellite missions are highly risky because unexpected hardware and software failures can occur due to sudden or unforeseen space environment changes. Anomaly detection and spacecraft health monitoring systems have heavily relied on human expertise to investigate whether they are a true anomaly or not. Also, it is practically infeasible to produce labels on data due to the enormous amount of telemetries generated from a satellite. In this work, we propose a data-driven anomaly detection algorithm for Korea Multi-Purpose Satellite 2 (KOMPSAT-2). We develop a Multivariate Convolution LSTM with Mixtures of Probabilistic Principal Component Analyzers, where our approach uses both neural networks and probabilistic clustering to improve the anomaly detection performance. We evaluated our approach with a total of 22 million telemetry samples collected for 10 months from KOMPSAT-2. We also compare our approach with other state-of-the-art approaches. We show that our proposed approach is 35.8% better in precision, and 18.2% better in F-1 score than the best baseline approach. We plan to deploy our algorithm in the second half of 2019 to actually apply real operation of KOMPSAT-2.

In the past, there have been several studies in analyzing password strength and structures. However, there are still many unknown questions to understand what really makes passwords both memorable and strong. In this work, we aim to answer some of these questions by analyzing password dataset through the lenses of data science and machine learning perspectives. We use memorable 3,260 password dataset collected from prior IRB-approved user studies over 3 years and classify passwords into three strength groups using online and offline attack limits. Then, we apply a tensor decomposition to analyze password dataset by constructing a 3rd-order tensor with passwords' syntactic and semantic features. In particular, we used PARAFAC2 tensor decomposition to uncover the main characteristics and features that affect password strength. We quantitatively identified the underlying factors that are more frequently observed in strong and memorable passwords. We hope that our finding can validate widely accepted advice for creating strong passwords and provide useful insights to design a better password suggestion system.

For outdoor navigation, it is necessary to find the relevant features of outdoor road environments and detect drivable region for robot's motion. This paper presents a methodology for extracting the drivable road region by detecting the prominent road features and obstacles through a single laser range finder. The prominent features of roads are curbs and the road surface. The laser range finder is mounted on the mobile robot, looks down the road with a small tilt angle, and obtains two-dimensional range data. The proposed method is computationally more efficient in comparison with vision-based techniques and applicable for various road conditions in target environment. Experimental results confirm the reliability of the algorithm.