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共 585 条记录，以下是81-90 订阅

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dataset Bias in Deception Detection 26

Dataset Bias in Deception Detection

引用

26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Mambreyan, Ara Punskaya, Elena Gunes, Hatice Univ Cambridge Dept Engn Cambridge England Univ Cambridge Dept Comp Sci & Technol Cambridge England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

With the advances in machine learning, lie detection technology gained significant attention. In recent years, several multi-modal techniques achieved as high as 99% accuracy results using the Real-life Trial dataset with only 121 data points. This led to considerable media hype and research interest in lie detection with machine learning. In this paper, we analyze the effect of dataset bias in deception detection. More specifically, we train a classifier to predict the sex of the identity appearing in the video. On a test data point, we use the sex predictor to predict sex which we use as a proxy for predicting deception, predicting lie for females and truth for males. This lie predictor simulates a classifier that uses nothing but dataset bias. Nevertheless, we find that the performance of this biased classifier is comparable to those of state-of-the-art papers. More specifically, when using IDT features, our biased classifier achieves 64.6% and 59.3% AUC while a classifier trained normally on truth/lie labels achieves 57.4% accuracy and 69.3% AUC. We perform similar experiments on the Bag-of-Lies dataset and show that it too is biased with respect to sex. In addition, we apply the state-of-the-art techniques on an unbiased dataset and show that their performance is no better than chance. Our experiments strongly suggest that the results of recent deception detection techniques can be explained by the bias inherent in the datasets.

关键词： Measurement Industries Ethics machine learning algorithms Government machine learning Detectors

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Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet recognition Through the Lens of Robustness 1

引用

1st ICLR international workshop on Trustworthy machine learning for Healthcare (TML4H)

作者： Cheng, Yanqi Liu, Lihao Wang, Shujun Jin, Yueming Schonlieb, Carola-Bibiane Aviles-Rivero, Angelica, I Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England UCL Wellcome EPSRC Ctr Intervent & Surg Sci London England UCL Dept Comp Sci London England

ISBN: (数字)9783031395390

ISBN: (纸本)9783031395383;9783031395390

Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides the surgeon with context-aware support and safety. The current go-to strategy for improving performance is the development of new network mechanisms. However, the performance of current state-of-the-art techniques is substantially lower than other surgical tasks. Why is this happening? This is the question that we address in this work. We present the first study to understand the failure of existing deep learning models through the lens of robustness and explainability. Firstly, we study current existing models under weak and strong d- perturbations via an adversarial optimisation scheme. We then analyse the failure modes via feature based explanations. Our study reveals that the key to improving performance and increasing reliability is in the core and spurious attributes. Our work opens the door to more trustworthy and reliable deep learning models in surgical data science. https://***/robustIVT/.

关键词： Robustness Deep learning Surgical Triplet recognition

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The 1st international workshop on Federated learning with Graph data (FedGraph) 22

The 1st International Workshop on Federated Learning with Gr...

引用

31st ACM international Conference on Information and Knowledge Management (CIKM)

作者： Yang, Carl Li, Xiaoxiao Baracaldo, Nathalie Shah, Neil He, Chaoyang Lyu, Lingjuan Sun, Lichao Avestimehr, Salman Emory Univ Atlanta GA 30322 USA Univ British Columbia Vancouver BC Canada IBM Res San Jose CA USA Snap Res Seattle WA USA FedML Inc Los Angeles CA USA Sony AI Tokyo Japan Lehigh Univ Bethlehem PA 18015 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450392365

The field of graph data mining, one of the most important AI research areas, has been revolutionized by graph neural networks (GNNs), which benefit from training on real-world graph data with millions to billions of nodes and links. Unfortunately, the training data and process of GNNs involving graphs beyond millions of nodes are extremely costly on a centralized server, if not impossible. Moreover, due to the increasing concerns about data privacy, emerging data from realistic applications are naturally fragmented, forming distributed private graphs of multiple "data silos", among which direct transferring of data is forbidden. The nascent field of federated learning (FL), which aims to enable individual clients to jointly train their models while keeping their local data decentralized and completely private, is a promising paradigm for large-scale distributed and private training of GNNs. FedGraph2022 aims to bring together researchers from different backgrounds with a common interest in how to extend current FL algorithms to operate with graph data models such as GNNs. FL is an extremely hot topic of large commercial interest and has been intensively explored for machine learning with visual and textual data. The exploration from graph mining researchers and industrial practitioners is timely catching up just recently. There are many unexplored challenges and opportunities, which urges the establishment of an organized and open community to collaboratively advance the science behind it. The prospective participants of this workshop will include researchers and practitioners from both graph mining and federated learning communities, whose interests include, but are not limited to: graph analysis and mining, heterogeneous network modeling, complex data mining, large-scale machine learning, distributed systems, optimization, meta-learning, reinforcement learning, privacy, robustness, explainability, fairness, ethics, and trustworthiness.

关键词： graph mining distributed learning data privacy federated learning graph neural networks

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Speeding up Heterogeneous Federated learning with Sequentially Trained Superclients 26

Speeding up Heterogeneous Federated Learning with Sequential...

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26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Zaccone, Riccardo Rizzardi, Andrea Caldarola, Debora Ciccone, Marco Caputo, Barbara Politecnico Torino Turin Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Federated learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, Le. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.(1)

关键词： Training Performance evaluation Federated learning Computational modeling Neural networks statistical distributions data models

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Rainfall Prediction Using machine learning 1

Rainfall Prediction Using Machine Learning

引用

1st international Conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Gupta, Akash Mall, Hitesh Kumar Janarthanan, S. Galgotias University School of Computing Science and Engineering UP Greater Noida India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Rainfall forecasting is a single of difficult and unpredictable undertakings that has a major influence on human society. Predictions that are accurate and timely can help to avert human and financial loss. This study contains a series of experiments that include the utilisation of basic machine learning techniques to build Weather forecasting models that estimate whether it will rain in major cities tomorrow based on the day's meteorological data. This comparative research looks at three aspects of modelling inputs, modelling methodologies, and preprocessing procedures. The findings demonstrate how different machine learning systems perform on a range of assessment parameters, as well as their capacity to forecast rainfall using weather data analysis. Agriculture is crucial to India's survival. The importance of rainfall in agriculture cannot be overstated. Rainfall forecasting has been a key problem in recent years. Individuals can be more aware of the weather and make more educated judgments by predicting rainfall. © 2022 IEEE.

关键词： Rain

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Optimal Feature Subset Selection for pattern Classification and recognition Using Spectral Entropy as a Complexity Measure from Alcohol and Sleep EEG data 1

Optimal Feature Subset Selection for Pattern Classification ...

引用

1st IEEE international Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Singh, Takveer Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350345957

The quantity of unprocessed data in multi-channel EEG analysis is enormous. Most datasets have some level of noise or redundancy, which could lower the accuracy of any relevant features that are uncovered. This result in a rise in computational complexity and a corresponding rise in the importance of concerns related to memory consumption. Consequently, feature selection has become the matter of significant research in the arenas of machine learning and statistics since it is crucial for efficient analysis of data while needing less memory and time. Based on the findings of this study, selecting features with high class separability from multichannel EEG recordings is an efficient method for performing supervised learning tasks. With this study, we intend to test the following theory: The best feature subset will have features that are substantially linked with the class labels but not connected with each other. Using the same alcoholic EEG database as these other investigations, we created and compared three feature selection algorithms. The purpose here is to use class knowledge in a feature selection technique to enhance the accuracy with which classes may be detected. © 2023 IEEE.

关键词： Feature Selection

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Efficient Large Scale Medical Image dataset Preparation for machine learning Applications 1

引用

1st MICCAI international workshop on data Engineering in Medical Imaging (DEMI)

作者： Denner, stefan Scherer, Jonas Kades, Klaus Bounias, Dimitrios Schader, Philipp Kausch, Lisa Bujotzek, Markus Bucher, Andreas Michael Penzkofer, Tobias Maier-Hein, Klaus German Canc Res Ctr Div Med Image Comp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Natl Ctr Tumor Dis NCT Heidelberg Germany Goethe Univ Frankfurt Univ Hosp Inst Diagnost & Intervent Radiol Frankfurt Germany Charite Univ Med Berlin Dept Radiol Berlin Germany

ISBN: (数字)9783031449925

ISBN: (纸本)9783031449918;9783031449925

In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be facilitated in machine learning algorithms. This paper introduces an innovative data curation tool, developed as part of the Kaapana (https://***/kaapana/ kaapana) open-source toolkit, aimed at streamlining the organization, management, and processing of large-scale medical imaging datasets. The tool is specifically tailored to meet the needs of radiologists and machine learning researchers. It incorporates advanced search, auto-annotation and efficient tagging functionalities for improved data curation. Additionally, the tool facilitates quality control and review, enabling researchers to validate image and segmentation quality in large datasets. It also plays a critical role in uncovering potential biases in datasets by aggregating and visualizing metadata, which is essential for developing robust machine learning models. Furthermore, Kaapana is integrated within the Radiological Cooperative Network (RACOON), a pioneering initiative aimed at creating a comprehensive national infrastructure for the aggregation, transmission, and consolidation of radiological data across all university clinics throughout Germany. A supplementary video showcasing the tool's functionalities can be accessed at https://***/MICCAI- DEMI2023.

关键词： Medical Imaging data Curation machine learning Kaapana dataset Preperation Quality Control Bias Detection

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international workshop on Continual Semi-Supervised learning: Introduction, Benchmarks and Baselines 1

引用

1st international workshop on Continual Semi-Supervised learning (CSSLat IJCAI)

作者： Shahbaz, Ajmal Khan, Salman Hossain, Mohammad Asiful Lomonaco, Vincenzo Cannons, Kevin Xu, Zhan Cuzzolin, Fabio Oxford Brookes Univ Oxford England Huawei Technol Vancouver BC Canada Univ Pisa Pisa Italy

ISBN: (数字)9783031175879

ISBN: (纸本)9783031175879;9783031175862

The aim of this paper is to formalise a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 international workshop on Continual Semi-Supervised learning (CSSL@IJCAI) (https://***/view/sscl-workshop-ijcai-2021/), with the aim of raising the field's awareness about this problem and mobilising its effort in this direction. After a formal definition of continual semi-supervised learning and the appropriate training and testing protocols, the paper introduces two new benchmarks specifically designed to assess CSSL on two important computer vision tasks: activity recognition and crowd counting. We describe the Continual Activity recognition (CAR) and Continual Crowd Counting (CCC) challenges built upon those benchmarks, the baseline models proposed for the challenges, and describe a simple CSSL baseline which consists in applying batch self-training in temporal sessions, for a limited number of rounds. The results show that learning from unlabelled data streams is extremely challenging, and stimulate the search for methods that can encode the dynamics of the data stream.

关键词： Continual learning Semi-supervised learning Artificial intelligence

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Knowledge Graph data Enrichment Based on a Software Library for Text Mapping to the Sustainable Development Goals 2

Knowledge Graph Data Enrichment Based on a Software Library ...

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Joint of the 2nd international workshop on Knowledge Graph Generation From Text and the 1st international BiKE Challenge, TEXT2KG 2023 and BiKE 2023

作者： Mandilara, Ioanna Fotopoulou, Eleni Androna, Christina Maria Zafeiropoulos, Anastasios Papavassiliou, Symeon Institute of Communication and Computer Systems National Technical University of Athens Athens Greece

Over the last few years, there has been a significant increase in the release of massive amounts of data related to the Sustainable Development Goals (SDGs). This has been driven by the recognition that data is critical for monitoring progress towards the SDGs, identifying areas that require attention, and informing policy decisions. Given that a significant percentage of the provided information is made available in documents, the availability of software libraries that can enable scientists to easily extract information regarding the importance given to the SDGs in the documents’ text is considered crucial. Towards this direction, we have developed an open-source Python software library that provides the mapping between text and the SDGs, taking advantage of novel machine learning techniques. The provided software library is modular and permits the dynamic selection of trained machine learning models for analysis purposes. The outcomes from the usage of the software library are fed for data population of a knowledge graph that is targeted to the tracking of information around the SDGs. The overall approach is open and can be easily adopted by scientists and policy makers to support participatory modeling processes, as well as participatory decision making and action planning for the development of solutions for climate-resilient regions. © 2023 CEUR-WS. All rights reserved.

关键词： Sustainable development

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Sensor Fusion and Multimodal learning for Robotic Grasp Verification Using Neural Networks 26

Sensor Fusion and Multimodal Learning for Robotic Grasp Veri...

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26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Gohil, Priteshkumar Thoduka, Santosh Ploeger, Paul G. Bonn Rhein Sieg Univ Appl Sci Dept Comp Sci Autonomous Syst St Augustin Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Different sensors on a robot help in understanding different aspects of the environment they are working in;however, each sensor modality is often processed individually and information from other sensors is not utilized jointly. One of the reasons is different sampling rates and different dimensions of input modalities. In this paper, we use multimodal data fusion techniques such as early, late and intermediate fusion for grasp failure identification using four different 3D convolution-based multimodal neural networks (3D-MNN). Our results on a visualtactile dataset shows that the performance of the classification task is improved while using multimodal data. In addition, a neural network trained with 30:22 train-test split of multimodal data achieved accuracy comparable to a network trained with 78:22 train-test split of unimodal data(1).

关键词： Visualization Three-dimensional displays Fuses Neural networks Sensor fusion Robot sensing systems Minimization

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