Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation difficulties. In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0. Experimental results on two public IoT datasets demonstrate that the proposed framework outperforms state-of-the-art methods for IIoT data stream analytics.
Cedars Sinai Medical Center（西达赛奈医学中心）；University of Pennsylvania（宾夕法尼亚大学）
Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools/packages (e.g. scikit-learn) have been developed to make the various elements of data handling, processing, modeling, and interpretation accessible. However, it is not trivial for most investigators to assemble these elements into a rigorous, replicatable, unbiased, and effective data analysis pipeline. Automated machine learning (AutoML) seeks to address these issues by simplifying the process of ML analysis for all. Here, we…
Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of data stream challenges such as concept drift on the performance of AutoML methods, and which adaptation strategies can be employed to make them more robust. To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on different AutoML approaches. We do this for a variety of AutoML approaches for building…
Real-Time Adaptive Anomaly Detection in Industrial IoT Environments
CIISE, Concordia University, Montréal, QC, Canada（Concordia大学）；Ericsson Research, Lund, Sweden（爱立信研究）；Computer Science Programme, University of Western Cape, Capetown, South Africa（南非开普敦大学计算机科学项目）
Siemens AG（西门子股份公司）；Technical University of Munich（慕尼黑工业大学）
Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations. Various sensors and field devices play a central role, as they generate a vast amount of real-time data that can provide insights into manufacturing. The synergy of complex event processing (CEP) and machine learning (ML) has been developed actively in the last years in IIoT to identify patterns in heterogeneous data streams and fuse raw data into tangible facts. In a traditional compute-centric paradigm, the raw fiel…
Bournemouth University（伯恩茅斯大学）；INFOR/Logicblox；University of Technology Sydney（悉尼科技大学）
Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies wit…
Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagnostic support to medical practitioners and reduce mistakes via human error. To further the development of such methods, we propose a two-stream model for endoscopic image analysis. Our model fuses two streams of deep feature inputs by mapping their inherent relations through a novel relational network model, to better model symptoms and classify the image. In contrast to handcrafted feature-based models, our proposed …
Automated machine learning techniques benefited from tremendous research progress in recently. These developments and the continuous-growing demand for machine learning experts led to the development of numerous AutoML tools. However, these tools assume that the entire training dataset is available upfront and that the underlying distribution does not change over time. These assumptions do not hold in a data stream mining setting where an unbounded stream of data cannot be stored and is likely to manifest concept drift. Industry applications of machine learning on streaming data become more po…
Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complexity relying heavily on high dimensional input data, and data hungry Machine Learning (ML) algorithms. We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications. The method is based on a multi-variate statistical test and is data driven by design (full reference distributions are…
Valeo Vision Systems, Ireland（法雷奥视觉系统公司（爱尔兰））；Valeo Troy, United States（法雷奥特洛伊公司（美国））
Majority of semantic segmentation algorithms operate on a single frame even in the case of videos. In this work, the goal is to exploit temporal information within the algorithm model for leveraging motion cues and temporal consistency. We propose two simple high-level architectures based on Recurrent FCN (RFCN) and Multi-Stream FCN (MSFCN) networks. In case of RFCN, a recurrent network namely LSTM is inserted between the encoder and decoder. MSFCN combines the encoders of different frames into a fused encoder via 1x1 channel-wise convolution. We use a ResNet50 network as the baseline encoder …
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Chair of Process and Data Science, RWTH Aachen University（过程与数据科学教授席位，亚琛工业大学）；Artificial Intelligence & Machine Learning Lab, Technical University of Darmstadt（人工智能与机器学习实验室，达姆施塔特技术大学）
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
Cedars Sinai Medical Center（西达赛奈医疗中心）；University of Pennsylvania（宾夕法尼亚大学）；Fox Chase Cancer Center（福克斯蔡斯癌症中心）；University of Kansas（堪萨斯大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Charite University Hospital（夏里特大学医院）；Federal University of Sao Paulo（圣保罗联邦大学）；Landspitali University Hospital（兰德斯皮塔利大学医院）；University of Iceland（冰岛大学）；University of Sydney（悉尼大学）；Sir Charles Gairdner Hospital（查尔斯·盖尔德纳爵士医院）；University of Western Australia（西澳大利亚大学）；Linkou Chang Gung Memorial Hospital（林口长庚纪念医院）；Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）
Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A unified analytical framework for trustable machine learning has been presented in the literature. It proposed building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. In the proposed framewor…
Large Language Models Streamline Automated Machine Learning for Clinical Studies
University Hospital Aachen（亚琛大学医院）；RWTH Aachen University（亚琛工业大学）；Technical University Dresden（德累斯顿工业大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）
TransGAT: Transformer-Based Graph Neural Networks for Multi-Dimensional Automated Essay Scoring
Department of Computer Science, King Abdulaziz University（计算机科学系，国王阿卜杜勒阿齐兹大学）；English Language Institute, King Abdulaziz University（英语语言学院，国王阿卜杜勒阿齐兹大学）
The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framework which can work with massive imbalanced data streams. In this paper, we choose Racing Algorithm as an automated data balancing technique which optimizes the balancing techniques. We applied Random Forest classification algorithm which can deal with the massive data stream. We …
Pontifícia Universidade Católica do Paraná (PUCPR)（巴拉那天主教大学）；Université de Rouen Normandie（鲁昂诺曼底大学）
Mining data streams is a challenge per se. It must be ready to deal with an enormous amount of data and with problems not present in batch machine learning, such as concept drift. Therefore, applying a batch-designed technique, such as dynamic selection of classifiers (DCS) also presents a challenge. The dynamic characteristic of ensembles that deal with streams presents barriers to the application of traditional DCS techniques in such classifiers. scikit-dyn2sel is an open-source python library tailored for dynamic selection techniques in streaming data. scikit-dyn2sel's development follows c…
Identifying threats in a network traffic flow which is encrypted is uniquely challenging. On one hand it is extremely difficult to simply decrypt the traffic due to modern encryption algorithms. On the other hand, passing such an encrypted stream through pattern matching algorithms is useless because encryption ensures there aren't any. Moreover, evaluating such models is also difficult due to lack of labeled benign and malware datasets. Other approaches have tried to tackle this problem by employing observable meta-data gathered from the flow. We try to augment this approach by extending it t…
CIDCO - Development Center for Ocean Mapping（CIDCO——海洋测绘开发中心）
This paper details a new method to recognize and detect underwater objects in real-time sidescan sonar data imagery streams, with case-studies of applications for underwater archeology, and ghost fishing gear retrieval. We first synthesize images from sidescan data, apply geometric and radiometric corrections, then use 2D feature detection algorithms to identify point clouds of descriptive visual microfeatures such as corners and edges in the sonar images. We then apply a clustering algorithm on the feature point clouds to group feature sets into regions of interest, reject false positives, yi…
Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper ti…
CDV AI Research（CDV人工智能研究院）；Valeo Vision Systems（法雷奥视觉系统公司）；Valeo R&D DVS（法雷奥研发DVS中心）；Nile University（尼罗河大学）
Motion is a dominant cue in automated driving systems. Optical flow is typically computed to detect moving objects and to estimate depth using triangulation. In this paper, our motivation is to leverage the existing dense optical flow to improve the performance of semantic segmentation. To provide a systematic study, we construct four different architectures which use RGB only, flow only, RGBF concatenated and two-stream RGB + flow. We evaluate these networks on two automotive datasets namely Virtual KITTI and Cityscapes using the state-of-the-art flow estimator FlowNet v2. We also make use of…
While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. Automatic question generation (QG) techniques can be utilized to satisfy the need for a continuous supply of new questions by streamlining their generation. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multi-task setting for QA, QG and answer extraction tasks using Turkish QA datasets. To the…
Contemporary patient surveillance systems have streamlined central surveillance into the electronic health record interface. They are able to process the sheer volume of patient data by adopting machine learning approaches. However, these systems are not suitable for implementation in many hospitals, mostly in developing countries, with limited human, financial, and technological resources. Through conducting thorough research on intensive care facilities, we designed a novel central patient monitoring system and in this paper, we describe the working prototype of our system. The proposed prot…
Leibniz University, Hannover（汉诺威莱布尼茨大学）；L3S Research Center, Hannover（汉诺威L3S研究中心）
The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to "fix" a (fair) model over the data stream as changes in the data might inc…
Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based …
National Institute of Technology Karnataka（卡纳塔克邦国家理工学院）；Indian Institute of Technology Roorkee（鲁尔基印度理工学院）；Amazon（亚马逊）
The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages…
In Video Analytics Pipelines (VAP), Analytics Units (AUs) such as object detection and face recognition running on remote servers critically rely on surveillance cameras to capture high-quality video streams in order to achieve high accuracy. Modern IP cameras come with a large number of camera parameters that directly affect the quality of the video stream capture. While a few of such parameters, e.g., exposure, focus, white balance are automatically adjusted by the camera internally, the remaining ones are not. We denote such camera parameters as non-automated (NAUTO) parameters. In this pap…
Nanyang Technological University（南洋理工大学）；Johannes Kepler University（约翰·开普勒大学）；Singapore Institute of Manufacturing Technology（新加坡制造技术研究院）
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent. This issue raises strong demand for automated real-time quality monitoring developed from data-driven approaches thus alleviating from operator dependence and adapting to various process uncertainties. Nonetheless, current approaches do not take into account the streaming nature of sensory information while relying heavily on hand-crafted features making them application-specific. This paper proposes the online quality monitoring methodology developed fro…
George Mason University（乔治梅森大学）；Universitat Pompeu Fabra（庞培法布拉大学）
High-quality human annotations are necessary to create effective machine learning systems for social media. Low-quality human annotations indirectly contribute to the creation of inaccurate or biased learning systems. We show that human annotation quality is dependent on the ordering of instances shown to annotators (referred as 'annotation schedule'), and can be improved by local changes in the instance ordering provided to the annotators, yielding a more accurate annotation of the data stream for efficient real-time social media analytics. We propose an error-mitigating active learning algor…
Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impr…
Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（Athinoula A. Martinos 生物医学成像中心、麻省总医院和哈佛医学院）；Department of Experimental Psychology, University College London（伦敦大学学院心理学系）；Department of Pharmacology and Physiology, University of Rochester School of Medicine（罗切斯特大学医学学院药理学与生理学系）；McLean Hospital（麦肯纳医院）
Institute of Infocomm Research, A*Star（新加坡科技研究局资讯通信研究院）；Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校）
The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments. An automated construction of a denoising autoeconder, namely deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. DEVDAN features an open structure both in the generative phase and in the discriminative phase where i…
Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating
