Reviewerly/exHarmony huggingface dataset:
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

In medical image segmentation, supervised deep networks’ success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of the cohort’s images is feasible, annotating all available images is impractical. This issue is further exacerbated when pre-trained deep networks are exposed to a new image dataset from an unfamiliar distribution. Using available open-source data for ad-hoc transfer learning or hand-tuned techniques for data augmentation only provides suboptimal solutions. Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations from a single labeled atlas to the unlabeled data. Our work’s central tenet benefits from a combined view of one-shot generative learning and the proposed self-supervised training strategy that cluster unlabeled volumetric images with similar styles together. Unlike previous methods, our method does not require input volumes at inference time to synthesize new images. Instead, it can generate diversified volumetric image-segmentation pairs from a prior distribution given a single or multi-site dataset. Augmented data generated by our method used to train the segmentation network provide significant improvements over state-of-the-art deep one-shot learning methods on the task of brain MRI segmentation. Ablation studies further exemplified that the proposed appearance model and joint training are crucial to synthesize realistic examples compared to existing medical registration methods. The code, data, and models are available at https://github.com/devavratTomar/SST/.
Fuzzy Monotonic <i>K</i>-Nearest Neighbor Versus Monotonic Fuzzy <i>K</i>-Nearest Neighbor

In real-life applications, monotonic classification is a widespread task, where the improvement of a particular input value cannot result in an inferior output. A common drawback of the existing algorithms for monotonic classification is their sensitivity to noise data which particularly refer to monotonicity violations in the monotonic circumstance. Motivated by weakening the impact of noises, the fuzzy monotonic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -nearest neighbor (FM <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> NN) is proposed in this article, which constructs monotonic classifiers by taking advantage of the fuzzy dominance relation between a pair of instances, especially that between incomparable instances for the first time. Through tuning the thresholds of fuzzy dominance relation degrees, FM <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> NN intends to decrease the disturbance caused by noises which considerably affect the selection range of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -nearest neighbors in different extent. The experimental results show that the best average improvement degrees of FM <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> NN in terms of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> NN-based and non- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> NN-based classifiers on all the involved datasets arrive at 28 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , 11 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , and 29 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> with respect to ACCU, MAE, and NMI, respectively, which demonstrates the superiority of our proposed FM <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> NN over other state-of-the-art monotonic classifiers including the monotonic fuzzy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -nearest neighbor (MF <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> NN) which disperses the impact of noise data by converting crisp class labels into class membership vectors.
Chance-Constrained Motion Planning using Modeled Distance- to-Collision Functions

This paper introduces Chance Constrained Gaussian Process-Motion Planning (CCGP-MP), a motion planning algorithm for robotic systems under motion and state estimate uncertainties. The paper's key idea is to capture the variations in the distance-to-collision measurements caused by the uncertainty in state estimation techniques using a Gaussian Process (GP) model. We formulate the planning problem as a chance constraint problem and propose a deterministic constraint that uses the modeled distance function to verify the chance-constraints. We apply Simplicial Homology Global Optimization (SHGO) approach to find the global minimum of the deterministic constraint function along the trajectory and use the minimum value to verify the chance-constraints. Under this formulation, we can show that the optimization function is smooth under certain conditions and that SHGO converges to the global minimum. Therefore, CCGP-MP will always guarantee that all points on a planned trajectory satisfy the given chance-constraints. The experiments in this paper show that CCGP-MP can generate paths that reduce collisions and meet optimality criteria under motion and state uncertainties. The implementation of our robot models and path planning algorithm can be found on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/jacobjj/gp_prob_planning.
Lifelong 3D object recognition and grasp synthesis using dual memory recurrent self-organization networks

Humans learn to recognize and manipulate new objects in lifelong settings without forgetting the previously gained knowledge under non-stationary and sequential conditions. In autonomous systems, the agents also need to mitigate similar behaviour to continually learn the new object categories and adapt to new environments. In most conventional deep neural networks, this is not possible due to the problem of catastrophic forgetting, where the newly gained knowledge overwrites existing representations. Furthermore, most state-of-the-art models excel either in recognizing the objects or in grasp prediction, while both tasks use visual input. The combined architecture to tackle both tasks is very limited. In this paper, we proposed a hybrid model architecture consists of a dynamically growing dual-memory recurrent neural network (GDM) and an autoencoder to tackle object recognition and grasping simultaneously. The autoencoder network is responsible to extract a compact representation for a given object, which serves as input for the GDM learning, and is responsible to predict pixel-wise antipodal grasp configurations. The GDM part is designed to recognize the object in both instances and categories levels. We address the problem of catastrophic forgetting using the intrinsic memory replay, where the episodic memory periodically replays the neural activation trajectories in the absence of external sensory information. To extensively evaluate the proposed model in a lifelong setting, we generate a synthetic dataset due to lack of sequential 3D objects dataset. Experiment results demonstrated that the proposed model can learn both object representation and grasping simultaneously in continual learning scenarios.
Can Shape Structure Features Improve Model Robustness under Diverse Adversarial Settings?

Recent studies show that convolutional neural networks (CNNs) are vulnerable under various settings, including adversarial attacks, common corruptions, and backdoor attacks. Motivated by the findings that human visual sys-tem pays more attention to global structure (e.g., shapes) for recognition while CNNs are biased towards local texture features in images, in this work we aim to analyze whether "edge features" could improve the recognition robustness in these scenarios, and if so, to what extent? To answer these questions and systematically evaluate the global structure features, we focus on shape features and pro-pose two edge-enabled pipelines EdgeNetRob and Edge-GANRob, forcing the CNNs to rely more on edge features. Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm. Then EdgeNetRob trains down-stream learning tasks directly on the extracted edge features, while EdgeGANRob reconstructs a new image by refilling the texture information with a trained generative adversarial network (GANs). To reduce the sensitivity of edge detection algorithms to perturbations, we additionally propose a robust edge detection approach Robust Canny based on vanilla Canny. Based on our evaluation, we find that EdgeNetRob can help boost model robustness under different attack scenarios at the cost of the clean model accuracy. EdgeGANRob, on the other hand, is able to improve the clean model accuracy compared to EdgeNetRob while preserving the robustness. This shows that given such edge features, how to leverage them matters for robustness, and it also depends on data properties. Our systematic studies on edge structure features under different settings will shed light on future robust feature exploration and optimization.
Natural Language Understanding in Socially Interactive Agents

chapter Natural Language Understanding in Socially Interactive Agents Share on Author: Roberto Pieraccini View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 1: Methods, Behavior, CognitionSeptember 2021 Pages 147–172https://doi.org/10.1145/3477322.3477328Online:02 October 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Potential of Virtual Reality in the Current Digital Society: Economic Perspectives

The modern world is evolving at a rapid pace. Virtual space has changed our lives and society. Augmented and virtual reality technologies are growing in popularity. Virtual reality is referred to as a simulated virtual environment, representing a physical environment. This study focuses on a thorough review of these new technologies, which will analyze the evolution of each of them in recent years in the most important categories and in the countries that are most involved in these technologies. The aim of the article is to analyze the potential use of virtual space. The article considers the positive and negative consequences of the existence of virtual space. Global indicators of digitalization (ISI, NRI, DESI) around the world are analyzed. The advent of the Internet and the World Wide Web has given a powerful impetus to the latest technologies, knowledge of which has been able to spread around the world. Their development stimulates economic growth around the world.
High accuracy offering attention mechanisms based deep learning approach using CNN/bi-LSTM for sentiment analysis

Purpose Neural network (NN)-based deep learning (DL) approach is considered for sentiment analysis (SA) by incorporating convolutional neural network (CNN), bi-directional long short-term memory (Bi-LSTM) and attention methods. Unlike the conventional supervised machine learning natural language processing algorithms, the authors have used unsupervised deep learning algorithms. Design/methodology/approach The method presented for sentiment analysis is designed using CNN, Bi-LSTM and the attention mechanism. Word2vec word embedding is used for natural language processing (NLP). The discussed approach is designed for sentence-level SA which consists of one embedding layer, two convolutional layers with max-pooling, one LSTM layer and two fully connected (FC) layers. Overall the system training time is 30 min. Findings The method performance is analyzed using metrics like precision, recall, F1 score, and accuracy. CNN is helped to reduce the complexity and Bi-LSTM is helped to process the long sequence input text. Originality/value The attention mechanism is adopted to decide the significance of every hidden state and give a weighted sum of all the features fed as input.
Secure Outsourcing of Large-Scale Convex Optimization Problem in Internet of Things

With the development of cloud computing and the advent of Internet of Things(IoT), outsourcing computation, as an important application of cloud computing, has been widely researched in the field of academic and industry. The convex optimization problem, as a most common mathematical problem, often appears in some machine learning algorithms and smart grid designs. However, the process of solving the convex optimization problem is very complicated and time-consuming. For some resource-constrained IoT devices, there are no enough computation resources and storage resources to deal with this problem. In this paper, we proposed an efficient and secure outsourcing algorithm for solving the large-scale convex optimization problem with equality constraints in IoT. Our proposed algorithm can not only reduce the computational complexity on the client side, but also protect the client’s sensitive data from being disclosed to the dishonest cloud server. In addition, the client can detect the malicious behavior from the cloud server with probability approximately 1. Finally, we give a theoretical analysis about correctness and security, and conduct experiments to show the feasibility of our proposed algorithm.
Novel climate classification based on the information of solar radiation intensity: An application to the climatic zoning of Morocco

Prior knowledge of solar radiation potential is a necessity for the decision making and the establishment of solar projects. Current prediction methods are site-dependent and their performance outside the area of application is questionable. Therefore, a solar radiation zoning method using a combination of supervised and unsupervised machine learning techniques and satellite gridded data is proposed. The method uses Agglomerative Hierarchical Clustering (AHC) following the ward's method based on the monthly mean solar insolation on a horizontal surface (H), and Support Vector Machine Classifier (SVM-C) to assist AHC to effectively classify all the areas based on solar radiation data plus temperature, humidity, wind speed and precipitation. Four solar radiation zones have been established. The zones are homogeneous in space and have distinctive solar and meteorological characteristics. The t-distributed Stochastic Neighbor Embedding (t-SNE) is applied to the obtained zones to distinguish trends within solar radiation zones based on temperature, humidity and wind speed, and a total of 8 sub-zones with distinctive meteorological characteristics have been identified. The resulting solar radiation zones and sub-zones cover all Morocco can help for preliminary assessment and decision making, especially for areas with no solar radiation records.
Efficient Reinforcement Learning from Demonstration via Bayesian Network-Based Knowledge Extraction

Reinforcement learning from demonstration (RLfD) is considered to be a promising approach to improve reinforcement learning (RL) by leveraging expert demonstrations as the additional decision-making guidance. However, most existing RLfD methods only regard demonstrations as low-level knowledge instances under a certain task. Demonstrations are generally used to either provide additional rewards or pretrain the neural network-based RL policy in a supervised manner, usually resulting in poor generalization capability and weak robustness performance. Considering that human knowledge is not only interpretable but also suitable for generalization, we propose to exploit the potential of demonstrations by extracting knowledge from them via Bayesian networks and develop a novel RLfD method called Reinforcement Learning from demonstration via Bayesian Network-based Knowledge (RLBNK). The proposed RLBNK method takes advantage of node influence with the Wasserstein distance metric (NIW) algorithm to obtain abstract concepts from demonstrations and then a Bayesian network conducts knowledge learning and inference based on the abstract data set, which will yield the coarse policy with corresponding confidence. Once the coarse policy’s confidence is low, another RL-based refine module will further optimize and fine-tune the policy to form a (near) optimal hybrid policy. Experimental results show that the proposed RLBNK method improves the learning efficiency of corresponding baseline RL algorithms under both normal and sparse reward settings. Furthermore, we demonstrate that our RLBNK method delivers better generalization capability and robustness than baseline methods.
Identifying challenges and co-imagining futures for a design for health network

For the emerging field of Design for Health (D4H) to realize its potential, it is necessary to identify and address existing challenges faced by its community. The few papers that have identified challenges and opportunities in Design for Health confirm that healthcare is a challenging environment for designers to work in. In part this is because design is often misunderstood by health professionals. This paper describes a study that sought to understand the challenges and opportunities for a future D4H Global Network as identified by workshop participants spanning different backgrounds, contexts, and countries. Qualitative data from 59 participants were collected during the D4H Symposium 2019 workshop and analysed using a thematic method.Practical constraints identified by participants included lack of resources and differing regulatory and governance frameworks which acted as barriers to building and participating in transdisciplinary projects in this space. However, participants also acknowledged that broader philosophical barriers arising as a consequence of siloed perspectives and different research paradigms between design and health were equally problematic. Despite these challenges, the overall findings were inherently optimistic as participants co-imagined broad opportunities for a future global network and collectively identified targeted solutions for ‘breaking the “normal” and “doing things differently”’.
A scalable software solution for anonymizing high-dimensional biomedical data

Abstract Background Data anonymization is an important building block for ensuring privacy and fosters the reuse of data. However, transforming the data in a way that preserves the privacy of subjects while maintaining a high degree of data quality is challenging and particularly difficult when processing complex datasets that contain a high number of attributes. In this article we present how we extended the open source software ARX to improve its support for high-dimensional, biomedical datasets. Findings For improving ARX's capability to find optimal transformations when processing high-dimensional data, we implement 2 novel search algorithms. The first is a greedy top-down approach and is oriented on a formally implemented bottom-up search. The second is based on a genetic algorithm. We evaluated the algorithms with different datasets, transformation methods, and privacy models. The novel algorithms mostly outperformed the previously implemented bottom-up search. In addition, we extended the GUI to provide a high degree of usability and performance when working with high-dimensional datasets. Conclusion With our additions we have significantly enhanced ARX's ability to handle high-dimensional data in terms of processing performance as well as usability and thus can further facilitate data sharing.
A Finite Precision Implementation of an Image Encryption Scheme Based on DNA Encoding and Binarized Chaotic Cores

One dimensional chaotic maps suffer from limited number of control parameters and converged periodicity, specially under finite precision implementation, making them unsuitable for hardware based ciphering systems. This paper first discusses the limited periodicity of 1D maps under fixed point precision, then, an image encryption algorithm based on DNA encoding and two specially configured binarized chaotic cores is presented. The function of both cores is to perform the confusion and diffusion stages of the image by generating pseudo random numbers with excellent cryptographic properties. DNA encoding adds an extra layer of security to the algorithm by converting both the image and chaotic stream to DNA sequences using specific DNA encoding rule. Initial values of both chaotic cores are image dependent based on a calculated hamming distance. These initial condition and the utilized DNA rules composes the overall secret key of the system with a total length of 336 bits. On the condition that all calculations involved in the scheme are based on binary integer arithmetic, all performed security analysis subjected to the scheme proved the system to withstand known attacks with excellent encryption properties.