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PanopticNDT: Efficient and Robust Panoptic Mapping

PanopticNDT: Efficient and Robust Panoptic Mapping

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Daniel Seichter Benedict Stephan Söhnke Benedikt Fischedick Steffen Müller Leonard Rabes Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent is, and how they can be reached; i.e., information about free space is also crucial. Panoptic mapping is a powerful instrument providing such information. However, building 3D panoptic maps with high spatial resolution is challenging on mobile robots, given their limited computing capabilities. In this paper, we propose PanopticNDT – an efficient and robust panoptic mapping approach based on occupancy normal distribution transform (NDT) mapping. We evaluate our approach on the publicly available datasets Hypersim and ScanNetV2. The results reveal that our approach can represent panoptic information at a higher level of detail than other state-of-the-art approaches while enabling real-time panoptic mapping on mobile robots. Finally, we prove the real-world applicability of PanopticNDT with qualitative results in a domestic application.

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CPK-Adapter: Infusing Medical Knowledge into K-Adapter with Continuous Prompt

CPK-Adapter: Infusing Medical Knowledge into K-Adapter with ...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Chen Liu Shaojie Zhang Chuanfu Li Haifeng Zhao Hefei Comprehensive National Science Center Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology & Institute of Artificial Intelligence Anhui University Hefei China Hefei Comprehensive National Science Ceter The First Affiliated Hospital of Anhui University of Chinese Medcine & Institute of Artificial Intelligence Hefei China

Recently, pre-trained language models (PLMs) have been significantly improved for downstream tasks by infusing knowledge. In the field of medical research, with the continuous updating and increasing of data, PLM often requires continuous learning of knowledge. Most existing methods jointly store new knowledge and historical knowledge in an entangled way by updating the overall parameters of PLM. However, when new knowledge infuses into PLM continuously, the historical knowledge is replaced by new knowledge. In this article, we propose CPK-Adapter, a method combines prompt learning and continual learning to balance the abilities of PLM to learn new knowledge and to memorize historical knowledge. We evaluated CPK-Adapter on a suit of BERT models, including BERT, BioBERT, ClinicalBERT, and BlueBERT. The experiments demonstrate that CPK-Adapter outperforms the comparative PLMs in medical tasks, and CPK-Adapter is significantly better than K-Adapter. Furthermore, the performance of CPK-Adapter approaches or exceeds DiseaseBERT which stores task knowledge by updating the overall parameters of PLM.

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A Framework for Fast Prototyping of Photo-realistic Environments with Multiple Pedestrians

A Framework for Fast Prototyping of Photo-realistic Environm...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Sara Casao Andrés Otero Álvaro Serra-Gómez Ana C. Murillo Javier Alonso-Mora Eduardo Montijano RoPeRt group at DIIS - I3A Universidad de Zaragoza Spain Cognitive Robotics at TU Delft The Netherlands

Robotic applications involving people often require advanced perception systems to better understand complex real-world scenarios. To address this challenge, photo-realistic and physics simulators are gaining popularity as a means of generating accurate data labeling and designing scenarios for evaluating generalization capabilities, e.g., lighting changes, camera movements or different weather conditions. We develop a photo-realistic framework built on Unreal Engine and AirSim to generate easily scenarios with pedestrians and mobile robots. The framework is capable to generate random and customized trajectories for each person and provides up to 50 ready-to-use people models along with an API for their metadata retrieval. We demonstrate the usefulness of the proposed framework with a use case of multi-target tracking, a popular problem in real pedestrian scenarios. The notable feature variability in the obtained perception data is presented and evaluated.

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Challenges of Testing cognitive EW Systems

Challenges of Testing Cognitive EW Systems

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international Automatic Testing conference, AUTOTESTCON

作者： Karen Zita Haigh Trung Nguyen Test Resource Management Center TRMC T&E/S&T Program

This paper describes some of the test infrastructure requirements for testing the performance of artificial intelligence (AI) systems that can learn from novel experiences in the field. As a test community, we must validate the learning process, rather than validating the learned model. cognitive capabilities will force multiple paradigm shifts from legacy test and evaluation (T&E) approaches. Notably, the system may never be the same twice. Testing must therefore become a continuous process, rather than a final exam, where we develop tests to understand system limitations and range of effectiveness. These paradigm shifts highlight some of the improvements needed in the current test infrastructure. The paper describes some of the requirements that should be placed on cognitive systems for data collection, transparency, and rules for how to interact with legacy systems. We use cognitive Electronic Warfare (CogEW) as the motivating scenario, where AI enables EW systems to respond more quickly and effectively to real-world conditions with complex and novel emitters.

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Implicit Feature Augmentation with Feature Transfer For Class-Imbalanced Medical Image Classification

Implicit Feature Augmentation with Feature Transfer For Clas...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Mei Yu Hao Zheng Xuewei Li Jie Gao Xuzhou Fu Zhiqiang Liu Ruiguo Yu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of Feature Technology Tianjin University Tianjin China

The class imbalance problem, which is prevalent in medical image datasets, seriously affects the diagnostic effectiveness of deep learning-based network models. To alleviate this problem, data re-sampling and loss re-weighting techniques are often used to reshape the decision boundary of the classifier. However, these techniques still lead to biased decision boundary due to the lack of sufficient and diversified samples in the tail classes of medical image datasets. In this paper, we propose an implicit feature augmentation with feature transfer (FT-IFA) method which solves the class imbalance problem by expanding the feature space of tail classes to reshape the decision boundary of the classifier. FT-IFA utilizes prototype similarity to transfer the rich transformation information from the head classes to tail classes on the basis of the balanced feature space, enriching the intra-class diversity of tail classes. Experimental results on two class-imbalanced medical image datasets show that FT-IFA outperforms the current state-of-the-art methods and effectively solves the class imbalance problem.

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Enhancing the Prediction of Diabetics using Bagging Ensambler Classifier

Enhancing the Prediction of Diabetics using Bagging Ensamble...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： M. Anand Kumar G. Manivasagam K. Kathirvel V Kavitha Anshika Gupta Dept of Computer Applications Graphic Era Deemed To Be University Dehradun India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation India Department of computer science Karpagam Academy of Higher education India Dept of Computer science with Cognitive Systems Sri Ramakrishna college of arts & science Coimbatore India Department of Computer Applications Graphic Era Deemed To Be University Dehradun India

Diabetes is a chronic disease exemplified by far above the ground level of blood sugar. It can up shot in a variety of complex diseases such as a stroke, a kidney failure, a heart attack, and so on. Widely, in the year of 2021 survey report based on diabetes affected almost nearly 512 million humans. The primary aspiration of this research investigation is to develop a machine learning (ML)-based system for predicting diabetic patients. In this paper, the disease of diabetics is to predict using “Machine Learning Classification and ensemble techniques” on a PIMA diabetes dataset. The Machine learning classifier techniques similar to “Logistic Regression”, “Naive-Bayes”, “KNN”, “Support Vector Machine”, “Decision Tree”, “Bagging meta-estimator” and “Random Forest classifier” are used for prediction. The proposed method employs various classification and ensemble methods that are implemented using Python. There were numerous missing values in the dataset; it will be resolved using the median replacement method. The performance factors of “Classification Accuracy, Precision, Recall and F1-Score” are measured using these machine learning classification methods. Finally, based on the results, we concluded that the Bagging Ensemble Method has the highest accuracy compared to other techniques.

关键词： Support vector machine classification Forestry Cardiac arrest Stroke (medical condition) Diabetes Ensemble learning Bagging

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Chiplet Solution with FO-MCM Package in Edge and Cloud computing (IMPACT 2023)

Chiplet Solution with FO-MCM Package in Edge and Cloud Compu...

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international Microsystems, Packaging, Assembly and Circuits Technology (IMPACT)

作者： David Ho Po Yuan James Su Jacy Pu Yu Po Wang Siliconware Precision Industries Co. Ltd. (SPIL) Tantzu Taichung Taiwan

Since the Meta, Amazon, Google and Microsoft are the popular social media and search website related with our daily life, the artificial intelligence (AI) with cloud computing become one of the most important for semiconductor industry. The cloud computing, high performance computing and artificial intelligence solutions require big data analysis processing with faster connections than ever, and all of these will need to have advanced technology supporting. The rise in energy costs and the increasing demand for sustainability to reduce environmental impacts further increase people's desire for high performance and high efficiency computing. The complex dataset has been tremendously grown on commercial application and widespread adoption of deep learning and neural networks. The advanced Node with System on Chip (SoC) chip size extension may the simplest design solution for good electrical performance, but the low yield rate cause of very expensive cost. The split die integration in package is the best solution to increase gross die with wafer good yield rate for cost effective [1]. Please see the Figure 1. The advantage of Chiplet The demand for higher functionality devices drive integration technologies to overcome limitations in Moore's Law and the Fan Out Multi Chip Module (FO-MCM) package can take the role for Cloud computing to Edge computing. Please see the Figure 2. FO-MCM Package. Today, the consumer product always requests not only thiner and power stamina but also high speed and massive connectivity for data transmission. The FO-MCM package can do the homogeneous integration and heterogeneous integration for each of different application because the cloud computing and Edge computing need to met the high performance and good electronic performance design requirement. That is the reason FO-MCM package solution can be the representative for homogeneous integration and heterogeneous integration. In this paper, we would like to discuss what the advantage for F

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Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods

Classification of executive functioning performance post-lon...

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IEEE India conference (INDICON)

作者： Akash K Rao Vishnu K Menon Shashank Uttrani Ayushman Dixit Dipanshu Verma Varun Dutt Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India

Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration.

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A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research

A Review of Multi-omics and Machine Learning: Advancements a...

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Mobile, Intelligent, and Ubiquitous computing conference (MIUCC), international

作者： Hazem Ayman Madonna Bassem Malak Madkour Mariam Fawzi Nada Amr Youmna Yousry Ziad Elgayar Ashraf Abdelraouf Department of Computer Engineering and Software Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem.

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LSTM deep learning model for Alzheimer’s disease prediction based on cost-effective time series cognitive scores

LSTM deep learning model for Alzheimer’s disease prediction...

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Novel Intelligent and Leading Emerging Sciences conference (NILES)

作者： Hager Saleh Nora El-Rashidy Tamer Abuhmed Shaker El-Sappagh Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University College of Computing and Informatics Sungkyunkwan University South Korea Faculty of Computer Science and Engineering Galala University Suez Egypt

Alzheimer’s disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer’s disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance.

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