Reviewerly/exHarmony huggingface dataset:
Potts-Cox survival regression

A Bayesian semi-parametric survival regression model with latent partitions is introduced. Its goal is to predict survival and to cluster survival patients within the context of building prognosis systems. In order to drive cluster formation on individuals, the Potts random partition model is chosen as a prior on the covariates space. For any given partition, the proposed model assumes an interval-wise exponential distribution for the baseline hazard rate. The number of intervals is unknown. It can be estimated with a fused-lasso type penalty given by a sequential double exponential prior. Estimation and inference are done with the aid of Markov chain Monte Carlo. To simplify the computations, the Laplace's integral approximation method is used to estimate some constants and to propose parameter updates within Markov chain Monte Carlo. The methodology is illustrated with an application to cancer survival.
Review on Medical Image Compression

In today’s digital era, the demand for digital medical images is rapidly increasing. Hospitals are transitioning to filmless imaging systems, emphasizing the need for efficient storage and seamless transmission of medical images. To meet these requirements, medical image compression becomes essential. However, medical image compression typically necessitates lossless compression techniques to preserve the diagnostic quality and integrity of the images. There are several challenges associated with medical image compression and management. Firstly, medical image management and image data mining involve organizing and accessing large volumes of medical images efficiently for clinical and research purposes. Secondly, bioimaging, which encompasses various imaging modalities like microscopy and molecular imaging, presents specific requirements and challenges for compression algorithms. Thirdly, virtual reality technologies are increasingly utilized in medical visualizations, demanding efficient compression methods to handle the high resolution and immersive nature of VR medical imaging data. Lastly, neuro imaging deals with complex brain imaging data, requiring specialized compression techniques tailored to the unique characteristics of these images. As the amount of medical image data continues to grow, image processing and visualization algorithms have to be adapted to handle the increased workload. Researchers and developers have been working on various compression algorithms to address these challenges and optimize medical image compression. This review paper compares different compression algorithms that would provide valuable insights into the strengths, limitations, and performance metrics of various techniques. It would assist researchers, clinicians, and imaging professionals in selecting the most suitable compression algorithm for their specific needs, considering factors such as compression ratio, computational complexity, and image quality preservation. By comprehensively comparing compression algorithms, this review paper contributes to advancing the field of medical image compression, facilitating efficient image storage, transmission, and analysis in healthcare settings.
Development of Paint Thickness Simulator for Industrial Painting Robots

Path planning with a uniform paint thickness distribution for industrial painting robots is challenging. It is difficult and time-consuming to generate a path with a constant paint thickness because extensive trial and error are required. To realize painting paths with a uniform paint thickness distribution without specialized operator skills, it is useful to simulate all painting processes and verify the paint thickness distribution. Therefore, a simulator that can predict and analyze paint thickness is developed in this study. A distribution model of adhered paint ejected from a spray gun is proposed. The distribution model is identified by fitting the actual paint distribution to the model and a painting simulator is developed. The effectiveness of the developed simulator is confirmed by a comparison between the simulation results and actual painting results for a complex structure.
An interpretable ensemble method for deep representation learning

Abstract In representation learning domain, the mainstream methods for model ensemble include “implicit” ensemble approaches, such as using techniques like dropout, and “explicit” ensemble methods, such as voting or weighted averaging based on multiple model outputs. Compared to implicit ensemble techniques, explicit ensemble methods allow for more flexibility in combining models with different structures to obtain different perspectives on representations. However, the representations obtained from different models do not guarantee a linear relationship, and simply linearly combining multiple model outputs may result in a degraded performance. Meanwhile, employing non‐linear fusion mechanisms such as distillation and meta‐learning can be uninterpretable and time‐consuming. To this end, we propose the hypothesis of linear fusion based on the output representations of deep learning models, and design a interpretable linear fusion method based on this hypothesis. This method applies a transform layer to map the output representations of different models to the same classification center. Experimental results demonstrate that compared to directly averaging the representations, our method achieves better performance. Additionally, our method retains the convenience of direct averaging while offering improved performance in terms of time and computational efficiency compared to non‐linear fusion. Furthermore, we test the applicability of our method in both computer vision and natural language processing representation tasks using supervised and semi‐supervised approaches.
A generalized decision tree ensemble based on the NeuralNetworks architecture: Distributed Gradient Boosting Forest (DGBF)

Tree ensemble algorithms as RandomForest and GradientBoosting are currently the dominant methods for modeling discrete or tabular data, however, they are unable to perform a hierarchical representation learning from raw data as NeuralNetworks does thanks to its multi-layered structure, which is a key feature for DeepLearning problems and modeling unstructured data. This limitation is due to the fact that tree algorithms can not be trained with back-propagation because of their mathematical nature. However, in this work, we demonstrate that the mathematical formulation of bagging and boosting can be combined together to define a graph-structured-tree-ensemble algorithm with a distributed representation learning process between trees naturally (without using back-propagation). We call this novel approach Distributed Gradient Boosting Forest (DGBF) and we demonstrate that both RandomForest and GradientBoosting can be expressed as particular graph architectures of DGBT. Finally, we see that the distributed learning outperforms both RandomForest and GradientBoosting in 7 out of 9 datasets.
Evaluation of requirement engineering best practices for secure software development in GSD: An ISM analysis

Abstract Technological advancement makes the world a global village. Security is an evergreen and everlasting area, because of the continuous threat from Hackers and Crackers. The immense use of software systems has modernized human society in every aspect. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing the costs and shortening the time to market. Considering the significance of software security, it is important to consider the security practices from the early phase of the software development life cycle (SDLC), that is, requirements engineering (RE). Hence, this study aims to identify and categorize RE practices important to apply for secure software development (SSD) in a geographically distributed development environment. To study the RE practices concerning SSD, we conducted a questionnaire survey with industrial experts in the global software development (GSD) context. Furthermore, the interpretive structure modeling (ISM) approach was applied to evaluate the relationship between the RE security practice core categories. This paper identifies 70 practices and classifies them into 11 fundamental dimensions (categories) to assist GSD organizations in specifying the requirements for SSD. The ISM results show that the “Awareness of Secure Requirement Engineering (SRE)” category has the most decisive influence on the other 10 core categories of the identified RE security practices. With the help of empirical evidence and the ISM approach, this work attempts to identify potential security practices and to give a set of secure RE practices that can be used to improve the security of the software development process.
Human Activity Recognition Using Deep Learning : A Survey

With the use of deep learning algorithms from artificial intelligence (AI), several types of research have been conducted on video data. Object localization, behaviour analysis, scene understanding, scene labelling, human activity recognition (HAR), and event recognition make up the majority of them. Among all of them, HAR is one of the most difficult jobs and key areas of research in video data processing. HAR can be used in a variety of fields, including robotics, human-computer interaction, video surveillance, and human behaviour categorization. This research seeks to compare deep learning approaches on several benchmark video datasets for vision-based human activity detection. We suggest a brand-new taxonomy for dividing up the literature into CNN- and RNN-based methods. We further categorise these approaches into four subgroups and show several methodologies, their effectiveness, and experimental datasets. To illustrate the development of HAR techniques, a brief comparison is also provided with the handcrafted feature-based approach and its merger with deep learning. Finally, we go over potential future research areas and some unresolved issues with recognising human activities. This survey's goal is to present the most recent developments in HAR techniques for vision-based deep learning using the most recent literature analysis.
Development of Image Processing Tools

The development of image processing tools has revolutionised various fields, including computer vision, medical imaging, remote sensing, and multimedia applications. These tools utilise algorithms and techniques to analyse and manipulate images, allowing for enhanced visual perception, feature extraction, and information extraction. This abstract highlights the recent advancements and trends in the development of image processing tools. It discusses the importance of image preprocessing techniques such as noise reduction, image enhancement, and geometric transformations in improving image quality and extracting valuable information. Additionally, it explores the role of image segmentation, object detection, and recognition algorithms in identifying and classifying objects within images. Image processing tools have emerged as powerful tools for tasks such as image classification, object detection, and image generation. This article discusses some image processing tools that help achieve the above-mentioned tasks.
Twitter Sentiment Analysis

Social media has entered further attention currently. Public and private opinions about a wide variety of subjects are expressed and spread continually via multitudinous social media. Twitter is one of the social media that's gaining trend. Twitter offers associations a fast and effective way to dissect guests ' perspectives toward the critical success in the request place. Developing a program for sentiment analysis is an approach to be used to computationally measure guests’ comprehension. This paper reports on the design of the sentiment analysis, rooting a vast quantum of tweets. Prototyping is used in this development. Results classify guests' perspectives via tweets into positive and negative, which is represented in a Graph. Still, the program has been planned to develop on a web operation system, but due to the limitation of Django which can be worked on a Linux Garcon or Beacon, further this approach needs to be done.
Exploratory Essay for Event Concepts: Focusing on the Concept of Media Event

Purpose - The purpose of this article is to find and present the starting point of the discussion on the definition of an event that stopped 20 years ago and resume it. Discussing the area and boundaries of study not only gives a new area, but also provides a new research method and makes it possible to pursue rigorous science. This article is an exploratory study to find the concept of 'event' through the concept of 'media event', a humanistic concept.Design, data, and methodology -This article explored the humanities, an adjacent disciplin e, for a more fundamental way of cognition and thinking, and repeated detailed reading and thick descripting using a method called 'theoretical detour'.Result - It was confirmed that aesthetic theories were useful as examples for the productio n process of creative events and interpretation of texts, and media events as examples for event industry and audience interpretation.Conclusions - In modern society where intersubjectivity is valued, it is hoped that event studies will pay attention to the research results of more diverse adjacent disciplines and establish a system of study.
Gaussian Filtering by Enabling Approximations

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General Gaussian Filtering

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General Gaussian Smoothing

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Bayesian Filtering Equations and Exact Solutions

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Constraint Manifolds for Robotic Inference and Planning

We propose a manifold optimization approach for solving constrained inference and planning problems. The approach employs a framework that transforms an arbitrary nonlinear equality constrained optimization problem into an unconstrained manifold optimization problem. The core of the transformation process is the formulation of constraint manifolds that represent sets of variables subject to equality constraints. We propose various approaches to define the tan-gent spaces and retraction operations of constraint manifolds, which are crucial for manifold optimization. We evaluate our constraint manifold optimization approach on multiple constrained inference and planning problems, and show that it generates strictly feasible results with increased efficiency as compared to state-of-the-art constrained optimization methods.
Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR

Deep learning has led to great progress in the detection of mobile (i.e. movement-capable) objects in urban driving scenes in recent years. Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self- supervised methods to avoid this, with much success. Whilst weakly and semi-supervised methods require some annotation, self-supervised methods have used cues such as motion to relieve the need for annotation altogether. However, a complete absence of annotation typically degrades their performance, and ambiguities that arise during motion grouping can inhibit their ability to find accurate object boundaries. In this paper, we propose a new self-supervised mobile object detection approach called SCT. This uses both motion cues and expected object sizes to improve detection performance, and predicts a dense grid of 3 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> oriented bounding boxes to improve object discovery. We significantly outperform the state-of-the-art self-supervised mobile object detection method TCR on the KITTI tracking benchmark, and achieve performance that is within 30 % of the fully supervised PV-RCNN++ method for IoUs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\leq$</tex> 0.5. Our source code will be made available online.
A Hierarchical Decoupling Approach for Fast Temporal Logic Motion Planning

Fast motion planning is of great significance, espe-cially when a timely mission is desired. However, the complexity of motion planning can grow drastically with the increase of environment details and mission complexity. This challenge can be further exacerbated if the tasks are coupled with the desired locations in the environment. To address these issues, this work aims at fast motion planning problems with temporal logical specifications. In particular, we develop a hierarchical decoupling framework that consists of three layers: the high-level task planner, the decoupling layer, and the low-level motion planner. The decoupling layer is designed to bridge the high and low layers by providing necessary information exchange. Such a framework enables the decoupling of the task planner and path planner, so that they can run independently, which significantly reduces the search space and enables fast planing in continuous or high-dimension discrete workspaces. In addition, the implicit constraint during task-level planning is taken into account, so that the low-level path planning is guaranteed to satisfy the mission requirements. Numerical simulations demonstrate at least one order of magnitude speed up in terms of computational time over existing methods.
Multi-embodiment Legged Robot Control as a Sequence Modeling Problem

Robots are traditionally bounded by a fixed embodiment during their operational lifetime, which limits their ability to adapt to their surroundings. Co-optimizing control and morphology of a robot, however, is often inefficient due to the complex interplay between the controller and morphology. In this paper, we propose a learning-based control method that can inherently take morphology into consideration such that once the control policy is trained in the simulator, it can be easily deployed to real robots with different embodiments. In particular, we present the Embodiment-aware Transformer (EAT), an architecture that casts this control problem as conditional sequence modeling. EAT outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired robot embodiment, past states, and actions, our EAT model can generate future actions that best fit the current robot embodiment. Experimental results show that EAT can outperform all other alternatives in embodiment-varying tasks, and succeed in an example of real-world evolution tasks: stepping down a stair through updating the morphology alone. We hope that EAT will inspire a new push toward real-world evolution across many domains, where algorithms like EAT can blaze a trail by bridging the field of evolutionary robotics and big data sequence modeling.
Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation

Powered by deep representation learning, re-inforcement learning (RL) provides an end-to-end learning framework capable of solving self-driving (SD) tasks without manual designs. However, time-varying nonstationary environments cause proficient but specialized RL policies to fail at execution time. For example, an RL-based SD policy trained under sunny days does not generalize well to rainy weather. Even though meta learning enables the RL agent to adapt to new tasks/environments, its offline operation fails to equip the agent with online adaptation ability when facing nonstationary environments. This work proposes an online meta reinforcement learning algorithm based on the conjectural online lookahead adaptation (COLA). COLA determines the online adaptation at every step by maximizing the agent's conjecture of the future performance in a lookahead horizon. Experimental results demonstrate that under dynamically changing weather and lighting conditions, the COLA-based self-adaptive driving outperforms the baseline policies regarding online adaptability. A demo video, source code, and appendixes are available at https://github.com/Panshark/COLA
Trajectory Optimization for 3D Shape-Changing Robots with Differential Mobile Base

Service robots have attracted extensive attention due to specially designed functions, such as mobile manipulators or robots with extra structures. For robots that have changing shapes, autonomous navigation in the real world presents new challenges. In this paper, we propose a trajectory optimization method for differential-drive mobile robots with controllable changing shapes in dense 3D environments. We model the whole-body trajectory as a polynomial trajectory that satisfies the nonholonomic dynamics of the base and dynamics of the extra joints. These constraints are converted into soft constraints, and an activation function for dense sampling is applied to avoid nonlinear mutations. In addition, we guarantee the safety of full shape by limiting the system's distance from obstacles. To comprehensively simulate a large extent of height and width changes, we designed a novel Shape-Changing Robot with a Differential Base (SCR-DB). Our global trajectory optimization gives a smooth and collision-free trajectory for SCR-DB at a low computational cost. We present vast simulations and real-world experiments to validate our performance, including coupled whole-body and independent differential-driven vehicle motion planning.
NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics

There are a multitude of emerging imaging technologies that could benefit robotics. However the need for bespoke models, calibration and low-level processing represents a key barrier to their adoption. In this work we present NOCaL, Neural Odometry and Calibration using Light fields, a semi-supervised learning architecture capable of interpreting previously unseen cameras without calibration. NOCaL learns to estimate camera parameters, relative pose, and scene appearance. It employs a scene-rendering hypernetwork pre-trained on a large number of existing cameras and scenes, and adapts to previously unseen cameras using a small supervised training set to enforce metric scale. We demonstrate NOCaL on rendered and captured imagery using conventional cameras, demonstrating calibration-free odometry and novel view synthesis. This work represents a key step toward automating the interpretation of general camera geometries and emerging imaging technologies. Code and datasets are available at https://roboticimaging.org/Projects/NOCaL/.
Shape visual servoing of a tether cable from parabolic features

In this paper we propose a visual servoing approach that controls the deformation of a suspended tether cable subject to gravity from visual data provided by a RGB-D camera. The cable shape is modelled with a parabolic curve together with the orientation of the plane containing the tether. The visual features considered are the parabolic coefficients and the yaw angle of that plane. We derive the analytical expression of the interaction matrix that relates the variation of the visual features to the velocities of the cable extremities. Singularities are demonstrated to occur if and only if the cable is taut horizontally or vertically. An image processing algorithm is also developed to extract in real-time the current features fitting the parabola to the cable from the observed point cloud. Simulations and experimental results demonstrate the efficiency of our visual servoing approach to deform the tether cable toward a desired shape configuration.