Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring
Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands（数学与计算机科学系，埃因霍温理工大学，荷兰）；Department of Mathematics and Computer Science, University of Padua, Italy（数学与计算机科学系，帕多瓦大学，意大利）
Sjoerd van Straten, Alessandro Padella, Marwan Hassani
Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches are often limited by the low variability and small size of real-world event logs. To address this, we introduce SiamSA-PPM, a novel self-supervised learning framework that combines Siamese learning with Statistical Augmentation for Predictive Process Monitoring. It employs three novel statistically grounded transformation methods that leverage control-flow semantics and frequent behavioral patterns to generate realistic, semantically valid new trace variants. These augmented views are used within a Siamese learning setup to learn generalizable representations of process prefixes without the need for labeled supervision. Extensive experiments on real-life event logs demonstrate that SiamSA-PPM achieves competitive or superior performance compared to the SOTA in both next activity and final outcome prediction tasks. Our results further show that statistical augmentation significantly outperforms random transformations and improves variability in the data, highlighting SiamSA-PPM as a promising direction for training data enrichment in process prediction.
Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operat…
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Amazon AWS AI（亚马逊云科技人工智能）
Training speaker-discriminative and robust speaker verification systems without speaker labels is still challenging and worthwhile to explore. In this study, we propose an effective self-supervised learning framework and a novel regularization strategy to facilitate self-supervised speaker representation learning. Different from contrastive learning-based self-supervised learning methods, the proposed self-supervised regularization (SSReg) focuses exclusively on the similarity between the latent representations of positive data pairs. We also explore the effectiveness of alternative online dat…
Self-supervised learning has shown superior performances over supervised methods on various vision benchmarks. The siamese network, which encourages embeddings to be invariant to distortions, is one of the most successful self-supervised visual representation learning approaches. Among all the augmentation methods, masking is the most general and straightforward method that has the potential to be applied to all kinds of input and requires the least amount of domain knowledge. However, masked siamese networks require particular inductive bias and practically only work well with Vision Transfor…
Siamese-network-based self-supervised learning (SSL) suffers from slow convergence and instability in training. To alleviate this, we propose a framework to exploit intermediate self-supervisions in each stage of deep nets, called the Ladder Siamese Network. Our self-supervised losses encourage the intermediate layers to be consistent with different data augmentations to single samples, which facilitates training progress and enhances the discriminative ability of the intermediate layers themselves. While some existing work has already utilized multi-level self supervisions in SSL, ours is dif…
ETH Zürich（苏黎世联邦理工学院）；Amazon（亚马逊）；MPI-IS（马克斯·普朗克智能系统研究所）
Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with …
In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this paper, we focus on condensing large training sets into significantly smaller synthetic sets which can be used to train deep neural networks from scratch with minimum drop in performance. Inspired from the recent training set synthesis methods, we propose Differentiable Siamese Augmentation that enables effective use of data augmentation to synthesize more informative synthetic images and thus achieves better performanc…
Tracking tasks based on deep neural networks have greatly improved with the emergence of Siamese trackers. However, the appearance of targets often changes during tracking, which can reduce the robustness of the tracker when facing challenges such as aspect ratio change, occlusion, and scale variation. In addition, cluttered backgrounds can lead to multiple high response points in the response map, leading to incorrect target positioning. In this paper, we introduce two transformer-based modules to improve Siamese tracking called DASTSiam: the spatio-temporal (ST) fusion module and the Discrim…
Access to labeled time series data is often limited in the real world, which constrains the performance of deep learning models in the field of time series analysis. Data augmentation is an effective way to solve the problem of small sample size and imbalance in time series datasets. The two key factors of data augmentation are the distance metric and the choice of interpolation method. SMOTE does not perform well on time series data because it uses a Euclidean distance metric and interpolates directly on the object. Therefore, we propose a DTW-based synthetic minority oversampling technique u…
Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The Beijing Key Laboratory of Mobile Computing and Pervasive Device（北京移动计算与普适设备重点实验室）；Shandong Academy of Intelligent Computing Technology（山东省智能计算技术研究院）；Peng Cheng Laboratory（鹏城实验室）；Institute of Robotics and Autonomous Systems（机器人与自主系统研究所）
Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for selfsupervised identification of atypical scenes or objects. State-of-the-art augmentation methods arbitrarily embed "structural" peculiarity on typical images so that classifying these artefacts can provide guidance for learning representations for the detection of anomalous visual signals. In this paper, however, we argue that learning such structure-sensitive representations can be a suboptimal approach to some classes of anomaly (e.g., unhealthy fruits) which could be better recogn…
SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks
KIOS Research and Innovation Center of Excellence, University of Cyprus（塞浦路斯大学KIOS卓越研究与创新中心）；Department of Electrical and Computer Engineering, University of Cyprus（塞浦路斯大学电气与计算机工程系）
Nowadays, many cities are equipped with surveillance systems and traffic control centers to monitor vehicular traffic for road safety and efficiency. The monitoring process is mostly done manually which is inefficient and expensive. In recent years, several data-driven solutions have been proposed in the literature to automatically analyze traffic flow data using machine learning techniques. However, existing solutions require large and comprehensive datasets for training which are not readily available, thus limiting their application. In this paper, we develop a traffic anomaly detection sys…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Technical University of Munich（慕尼黑工业大学）
Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs. A Siamese network, which relies on data augmentation, is the popular architecture used in these methods. However, these methods rely on heuristically crafted data augmentation techniques. Furthermore, they use either contrastive terms or other tricks (e.g., asymmetry) to avoid trivial solutions that can occur in Siamese networks. In this study, we prop…
Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox
School of Biomedical Engineering（生物医学工程学院）；Guangzhou Medical University（广州医科大学）；Foshan Sanshui District People’s Hospital（佛山市三水区人民医院）；School of Mathematics and Systems Science（数学与系统科学学院）；Guangdong Polytechnic Normal University（广东技术师范大学）
Oak Ridge National Laboratory（橡树岭国家实验室）；Thomas Jefferson National Accelerator Facility（托马斯·杰斐逊国家加速器设施）
High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications. As such, we describe the applicationof an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses using the data from a single monitoring device. By predicting theupcoming failure, we can stop th…
Duke Kunshan University（昆山杜克大学）；Wuhan University（武汉大学）；Lenovo Research（联想研究院）；Sun Yat-sen University（中山大学）
This paper introduces our approaches for the Mask and Breathing Sub-Challenge in the Interspeech COMPARE Challenge 2020. For the mask detection task, we train deep convolutional neural networks with filter-bank energies, gender-aware features, and speaker-aware features. Support Vector Machines follows as the back-end classifiers for binary prediction on the extracted deep embeddings. Several data augmentation schemes are used to increase the quantity of training data and improve our models' robustness, including speed perturbation, SpecAugment, and random erasing. For the speech breath monito…
scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；College of Life Science, Northeast Agricultural University（东北农业大学生命科学学院）；SKL-IOTSC and Department of CIS, University of Macau（澳门大学SKL-IOTSC和CIS系）；Jilin University（吉林大学）
Hybrid Deep Learning with Temporal Data Augmentation for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries
School of Physics and Electronic Information, Anhui Normal University（安徽师范大学物理与电子信息学院）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；School of Design, NingboTech University（浙大宁波理工学院设计学院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Bay Laboratory（深圳湾实验室）
Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming graph convolutional operation undermine the efficiency of these methods. To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function. As to the architecture, our network includes two main parts, i.e., pre-processing and network backbone. A simple low-pass denoising opera…
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Colorado School of Mines（科罗拉多矿业学院）；Michigan State University（密歇根州立大学）
Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for a given input, which may not properly reflect the distribution of the testing data, if it has a shift with respect to that of the training data. The Simultaneous Quantile Regression (SQR) method can estimate the entire conditional distribution of the target variable of a neural …
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to capture this information for the purpose of clinical reporting are rather poor. In date, endoscopists do not have any access to tools that enable them to browse the video data in an efficient and user friendly manner. Fast and reliable video retrieval methods could for example, allow them to review data from previous exams and therefore improve their ability to monitor disease progression. Deep learning provides new ave…
Time-series data are one of the fundamental types of raw data representation used in data-driven techniques. In machine condition monitoring, time-series vibration data are overly used in data mining for deep neural networks. Typically, vibration data is converted into images for classification using Deep Neural Networks (DNNs), and scalograms are the most effective form of image representation. However, the DNN classifiers require huge labeled training samples to reach their optimum performance. So, many forms of data augmentation techniques are applied to the classifiers to compensate for th…
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras
Laboratory for Intelligent & Safe Automobiles (LISA)（智能与安全汽车实验室（LISA））；University of California San Diego（加利福尼亚大学圣迭戈分校）；Visual Analysis and Perception Lab（视觉分析与感知实验室）；Aalborg Universitet（奥尔堡大学）
University of Central Florida（中佛罗里达大学）；West Virginia University（西弗吉尼亚大学）
There has been a major advance in the field of Data Science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) algorithms have been utilized for civil Structural Health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking;…
Deep Learning-Based Detection of Cognitive Impairment from Passive Smartphone Sensing with Routine-Aware Augmentation and Demographic Personalization
Department of Electrical and Computer Engineering, Cockrell School of Engineering, The University of Texas at Austin（电气与计算机工程系，Cockrell工程学院，德克萨斯大学奥斯汀分校）；Department of Neurology, Dell Medical School, The University of Texas at Austin（神经病学系，德克萨斯医学学院，德克萨斯大学奥斯汀分校）；Department of Neurology, University of Texas Southwestern Medical Center（神经病学系，德克萨斯西南医学中心）；Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center（彼得·奥·唐纳德·杰罗姆脑研究所，德克萨斯西南医学中心）；Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin（语言病理学系，摩依学院，德克萨斯大学奥斯汀分校）
Blinking Beyond EAR: A Stable Eyelid Angle Metric for Driver Drowsiness Detection and Data Augmentation
Institue of Smart Engineering and Machine Elements at the Hamburg University of Technology（汉堡技术大学智能工程与机械元件研究所）；Intelligent Transport Systems group at the Australian Center for Robotics, The University of Sydney（澳大利亚机器人中心智能交通系统组）
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation
Hadley Research, LLC（哈德利研究有限责任公司）；Case Western Reserve University（凯斯西储大学）；Louis Stokes Cleveland Department of Veterans Affairs Medical Center（路易斯·斯托克斯克利夫兰退伍军人事务部医疗中心）
Beijing Technology and Business University（北京工商大学）
Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The main ideas included that first, two image patches with size of 128 in three dimensions were selected according to glioma information of ground truth labels from the magnetic resonance imaging data of any two patients with the same modality. Next, a tensor in which all elements were independently sampled from…
Twitter and, in general, social media has become an indispensable communication channel in times of emergency. The ubiquitousness of smartphone gadgets enables people to declare an emergency observed in real-time. As a result, more agencies are interested in programmatically monitoring Twitter (disaster relief organizations and news agencies). Therefore, recognizing the informativeness of a Tweet can help filter noise from the large volumes of Tweets. In this paper, we present our submission for WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. Our most successful model …
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
Imperial College London（帝国理工学院）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）；UK Dementia Research Institute, Imperial College London（帝国理工学院英国痴呆症研究所）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments
Department of Experimental Physics（实验物理系）；Department of International Relations（国际关系系）；Department of Mathematics（数学系）；University of Zürich（苏黎世大学）；Faculteit der Bètawetenschappen（理学院）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Department of Physics（物理系）
Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection
Know-Center GmbH（Know-Center 公司）；Graz University of Technology（格拉茨工业大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Materials Center Leoben Forschung GmbH（莱奥本材料研究中心公司）；CERATIZIT Austria GmbH（赛瑞特奥地利有限公司）
Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
Machine Learning Group, Center for Cognitive Interaction Technology (CITEC), University of Bielefeld（认知交互技术中心（CITEC）机器学习组，比尔费尔德大学）；Honda Research Institute EU（本田欧洲研究院）；Models and Algorithms for Data and Text Mining Laboratory (MADLab), Department of Informatics, Systems and Communication (DISCo), University of Milano - Bicocca（数据与文本挖掘模型与算法实验室（MADLab），信息学、系统与通信系（DISCo），米兰-比可卡大学）
