Reviewerly/exHarmony huggingface dataset:
Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching

Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling is necessary for the related disease diagnoses, treatments and epidemiological population analyses. We define a hypergraph representation of the abdominal arterial system as a family tree model with a probabilistic hypergraph matching framework for automated vessel labeling. Then we treat the labelling problem as the convex optimization problem and solve it with the maximum a posteriori(MAP) combined the likelihood obtained by geometric labelling with the family tree topology-based knowledge. Geometrically, we utilize XGBoost ensemble learning with an intrinsic geometric feature importance analysis for branch-level labeling. In topology, the defined family tree model of the abdominal arterial system is transferred as a Markov chain model using a constrained traversal order method and further the Markov chain model is optimized by a hidden Markov model (HMM). The probability distribution of the target branches for each candidate anatomical name is predicted and effectively embedded in the HMM model. This approach is evaluated with the leave-one-out method on 37 clinical patients' abdominal arteries, and the average accuracy is 91.94%. The obtained results are better than those of the state-of-art method with an F1 score of 93.00% and a recall of 93.00%, as the proposed method simultaneously handles the anatomical structural variability and discriminates between the symmetric branches. It is demonstrated to be suitable for labelling branches of the abdominal arterial system and can also be extended to similar tubular organ networks, such as arterial or airway networks.
Gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction

Human-robot interaction (HRI) is the key capability for assistive robots to provide support for the elders and impaired in daily activities. Implicit intention understanding is a challenge problem for natural and intuitive HRI. In this paper, an implicit intention inference framework based on gaze behavior is proposed. First, support vector machine classifier is used to classify human’s gaze behavior as intentional and unintentional visual behavior. Then, a Naive Bayes model based on implicit intention inference is presented with taking historical visual attention into accounts. The advantage of this model is that the human’s implicit intention could be inferred based on his/her gaze at multiple objects by dealing with the historical information of visual behavior. Finally, experiments are conducted in a scenario of “home care in the kitchen”. People are free to look at one or more objects among 14 kinds of objects provided in experiment. With the proposed model, four kinds of intentions could be inferred based on people’s historical gaze on objects. The experiment results validate that the proposed model, which considers the historical information of visual behavior, outperforms the previous gaze-based intention inference methods.
Cognitive architecture for intuitive and interactive task learning in industrial collaborative robotics

This paper introduces a cognitive architecture, implemented in python3, designed with industrial collaborative robotics specifications in mind, to engage in a mixed-initiative teacher/learner setting called interactive task learning: a human can teach the robot, with natural and multimodal communication means, how to perform a task. The architecture has been built around explainable, modular representations (relational graphs and behavior trees) to ease the upgradability of the system and AI modules to adapt to realistic and complex settings. A first prototype based on speech and gesture communication means is proposed and has been validated on an industrial system to learn an unknown task. A link to a video of this validation is attached in the article.
Accurate COVID-19 Health Outcome Prediction and Risk Factors Identification through an Innovative Machine Learning Framework Using Longitudinal Electronic Health Records

COVID-19 is a respiratory disease that has caused a worldwide pandemic and put a strain on the global healthcare systems. To better protect and treat higher risk individuals and alleviate the burden on healthcare systems, it is crucial to identify risk factors and build accurate predictive models for COVID-19 related health outcomes. However, current predictive models focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as CT scans and COVID-19 lab tests results after a positive COVID-19 diagnosis. To address these issues, we developed an innovative supervised machine learning framework using synthetic veteran Electronic Health Records (EHR) which were routinely collected during past clinic visits. Our models accurately predicted COVID-19 related health outcomes including mortality, ventilation, days in hospital or ICU. In particular, we developed a series of unique and effective data processing algorithms, including data cleaning, vector representation, initial feature screening. Then we trained models using state-of-the-art machine learning strategies combined with different parameter settings. Our machine learning pipeline not only consistently outperformed those developed by other research groups using the same set of EHR, but also achieved similar accuracy as those trained on medical data that were only available after COVID-19 diagnosis. In addition, top risk factors for COVID-19 were identified, which include age, diabetes, metabolic syndromes, heart disease, kidney disease etc., and are consistent with epidemiology findings. Built on veteran’s EHR, our results were especially relevant to veterans and filled in the gap of missing COVID-19 research for veterans who are at much higher risk of severe COVID-19 illness than the general population. This project also demonstrated that longitudinal EHR data can be successfully leveraged to provide a holistic prediction of an individual’s health risk based on past health records, which is critical for controlling emerging infectious diseases such as COVID-19.
Multi-branch Channel-wise Enhancement Network for Fine-grained Visual Recognition

The challenge in fine-grained visual classification (FGVC) is that the similarity within intra-class may be larger than inter-class, where the discriminative details require more attention than traditional classification tasks. To generate channel-wise complementary and discriminative features in beneficial details of FGVC, we propose a multi-branch channel-wise enhancement network (MCEN), which includes multi-pattern spatial disruption mechanism, inter-channel complementarity module(ICM), and novel soft target loss. The raw images are scrambled in multi-pattern and then the sub-images with different degrees of confusion are combined into three pairs as inputs, where the scrambled operation can force the channel to look for the discriminative details. And ICM can measure the complementarity between key features and overall features to restrain the redundancy of features. The soft target loss is designed for classification and the semantic relationship between the blocks is learned to judge the degree of the chaos of the image. Our designed multi-branched structure utilizes the shallow visual and deep semantic features to judge the outcome jointly, where the image pairs obtained by segmentation and rearrangement are input into the different branches to extract more complementary features from different patterns of the raw image. Our method is trained end-to-end with only class labels. Experimental results show that our model outperforms the state-of-the-art performance on three fine-grained benchmarks.
A Novel Approach of Product Recommendation Using Utility-Based Association Rules

An exorbitant source of data is easily available but the actual task lies in using this data efficiently. In this article, the aim is to analyse the significant information embedded in the customer purchase behaviour to recommend new products to them. Our proposed scheme is a two-fold approach. First, the authors retrieve various product correlations from the vast library of user transactions. Based on these product correlations, utility based association rules are learned which depict the customer purchase behaviour. These rules are then applied in a recommender system for novel product suggestions to the customers. With improved utility based mining the paper tries to incorporate the usefulness of an item set like cost, profit or any other factor along with their frequency. In this paper the authors have deployed the rules discovered from both the conventional Frequent Item Set Mining and Improved Utility Based Mining on an e-commerce platform to compare the accuracy of the algorithms. The obtained results establish the efficacy of the proposed algorithm.
Linear Inverse Problem for Depth Completion with RGB Image and Sparse LIDAR Fusion

Comprehensive depth information from surrounding scenes is important for perception in autonomous driving and robots. Sparse LIDAR sensors give a low-density point cloud of the environment, but are more affordable than their high-density counterparts. In this paper, we propose a novel sensor fusion architecture for sparse LIDAR depth completion. Instead of the traditional end-to-end neural network-based algorithm, we formulate depth completion as a Linear Inverse Problem (LIP) with a multi-modal proximal operator. This sensor fusion architecture allows a better signal prior and finds the unique optimal solution to the LIP. Instead of learning a unified network for the sparse input which treats pixels evenly, the proposed architecture guarantees both the data consistency and smoothness of the predicted depth map. To demonstrate the performance of our algorithm, we benchmark on the simulation dataset TartanAir, and the real indoor NYUdepthv2 and real outdoor KITTI datasets. Our proposed method outperforms previous methods and uses fewer parameters in both indoor and outdoor datasets.
Evolving 1D Convolutional Neural Networks for Human Activity Recognition

Human activity recognition is an important research field with a variety of applications in healthcare monitoring, fitness tracking and in user-adaptive systems in smart environments. The problem of human activity recognition can be solved using a 1D convolutional neural network (CNN) trained with accelerometric data. The design of an appropriate CNN architecture for solving a particular problem is not an easy task and usually requires considerable specialized knowledge to setup the network hyperparameters based on experimental evaluation. This article proposes an automated approach for CNN architecture optimization that uses genetic algorithms. The suggested approach for evolution of the architecture of 1D CNN is evaluated on two data sets for accelerometer-based human activity recognition and the results show that the GA based CNN design generates CNN architectures with competitive performance compared to the usage of other manually designed CNN models.
A Feature Decomposition Framework for Multi-Modal Image Patch Matching

Multi-modal remote sensing images have complementary information which is conducive to enhancing the performance of various applications. Image patch matching plays a crucial role in the combination of multi-modal images. However, there are great differences in appearance and texture of multi-modal images, which brings great difficulties to image patching matching. To solve this problem, we propose a novel feature decomposition framework for multi-modal image patch matching. It aims to eliminate the hinder caused by the significant difference in multi-modal images. Specifically, this paper proposes to decompose the feature of images into common feature and modal private feature. Then, only the common feature is used for image patch matching, so as to improve the matching accuracy. Experimental results on optical and SAR images demonstrate that our proposed feature decomposition framework can significantly improve the performance of multi-modal image patch matching.
Robust MIMO Detection using Hypernetworks with Learned Regularizers

Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Recently, there has been a growing interest to get reasonably close to the optimal solution using neural networks while keeping the computational complexity in check. However, existing work based on deep learning shows that it is difficult to design a generic network that works well for a variety of channels. In this work, we propose a method that tries to strike a balance between symbol error rate (SER) performance and generality of channels. Our method is based on hypernetworks that generate the parameters of a neural network-based detector that works well on a specific channel. We propose a general framework by regularizing the training of the hypernetwork with some pre-trained instances of the channel-specific method. Through numerical experiments, we show that our proposed method yields high performance for a set of prespecified channel realizations while generalizing well to all channels drawn from a specific distribution.
Jessy: A Conversational Assistant for Tutoring Digital Board Games

We present Jessy, an interactive intelligent digital board game assistant for The Royal Game of Ur. Jessy is capable of answering questions regarding the game rules, giving suggestions for best moves considering the player's state, and informing the player about the important events in the game. An explanatory non-comparative study was conducted to evaluate the usability and usefulness of Jessy in engaging and learning how to play the game. The study showed Jessy was helpful in general and the findings suggest insights on how to craft Jessy for its target application – difficult board games.
Evaluating generative networks using Gaussian mixtures of image features

We develop a measure for evaluating the performance of generative networks given two sets of images. A popular performance measure currently used to do this is the Fréchet Inception Distance (FID). FID assumes that images featurized using the penultimate layer of Inception-v3 follow a Gaussian distribution, an assumption which cannot be violated if we wish to use FID as a metric. However, we show that Inception-v3 features of the ImageNet dataset are not Gaussian; in particular, every single marginal is not Gaussian. To remedy this problem, we model the featurized images using Gaussian mixture models (GMMs) and compute the 2-Wasserstein distance restricted to GMMs. We define a performance measure, which we call WaM, on two sets of images by using Inception-v3 (or another classifier) to featurize the images, estimate two GMMs, and use the restricted 2-Wasserstein distance to compare the GMMs. We experimentally show the advantages of WaM over FID, including how FID is more sensitive than WaM to imperceptible image perturbations. By modelling the non-Gaussian features obtained from Inception-v3 as GMMs and using a GMM metric, we can more accurately evaluate generative network performance.
REIN-2: Giving birth to prepared reinforcement learning agents using reinforcement learning agents

Deep Reinforcement Learning (Deep RL) has been in the spotlight for the past few years, due to its remarkable abilities to solve problems which were considered to be practically unsolvable using traditional Machine Learning methods. However, even state-of-the-art Deep RL algorithms have various weaknesses that prevent them from being used extensively within industry applications, with one such major weakness being their sample-inefficiency. In an effort to patch these issues, we integrated a meta-learning technique in order to shift the objective of learning to solve a task into the objective of learning how to learn to solve a task (or a set of tasks), which we empirically show that improves overall stability and performance of Deep RL algorithms. Our model, named REIN-2, is a meta-learning scheme formulated within the RL framework, the goal of which is to develop a meta-RL agent (meta-learner) that learns how to produce other RL agents (inner-learners) that are capable of solving given environments. For this task, we convert the typical interaction of an RL agent with the environment into a new, single environment for the meta-learner to interact with. Compared to traditional state-of-the-art Deep RL algorithms, experimental results show remarkable performance of our model in popular OpenAI Gym environments in terms of scoring and sample efficiency, including the Mountain Car hard-exploration environment.
Industry Emergence between Technology and <i>Zeitgeist</i>

Abstract This chapter examines the dynamics of industry emergence using the case of the blockchain and crypto (BC) industry. The BC industry is a rapidly developing field that has—in less than a decade—transformed from initially being a volunteer project of a small group of cypherpunks to a global industry with a plethora of actors involving vivid entrepreneurial and corporate activity. Importantly, the review of the extant literature and evidence reveals that ideological driving forces, which have been neglected in prior industry emergence research, constitute a major catalyst of this rapid industry development. In this vein, the chapter shows that the ideological notion of ‘decentralization’ has strongly contributed to the BC industry’s rapid growth by matching the zeitgeist in times of financial crises with increasing centralization by large digital platforms.
Detecting Backdoor Attacks against Point Cloud Classifiers

Backdoor attacks (BA) are an emerging threat to deep neural network classifiers. A classifier being attacked will predict to the attacker’s target class when a test sample from a source class is embedded with the backdoor pattern (BP). Recently, the first BA against point cloud (PC) classifiers was proposed, creating new threats to many important applications including autonomous driving. Such PC BAs are not detectable by existing BA defenses due to their special BP embedding mechanism. In this paper, we propose a reverse-engineering defense that infers whether a PC classifier is backdoor attacked, without access to its training set or to any clean classifiers for reference. The effectiveness of our defense is demonstrated on the benchmark ModeNet40 dataset for PCs.
Visual Perspective Taking for Opponent Behavior Modeling

In order to engage in complex social interaction, humans learn at a young age to infer what others see and cannot see from a different point-of-view, and learn to predict others&#x2019; plans and behaviors. These abilities have been mostly lacking in robots, sometimes making them appear awkward and socially inept. Here we propose an end-to-end long-term visual prediction framework for robots to begin to acquire both these critical cognitive skills, known as Visual Perspective Taking (VPT) and Theory of Behavior (TOB). We demonstrate our approach in the context of visual hide-and-seek &#x2013; a game that represents a cognitive milestone in human development. Unlike traditional visual predictive model that generates new frames from immediate past frames, our agent can directly predict to multiple future timestamps (25 s), extrapolating by 175% beyond the training horizon. We suggest that visual behavior modeling and perspective taking skills will play a critical role in the ability of physical robots to fully integrate into real-world multi-agent activities.
From now on

The COVID-19 disease changed our society in many ways. The social distancing restrictions became mandatory as governments measure to limit the virus spread. These restrictions forced medical appointments, events, meetings, classes, and research experiments, to name a few, to move to an online format. In this new scenario, researchers were also required to redesign their studies, especially the user-based ones. While the virus is still a threat, researchers focus on understanding its long-term effects in research. From this perspective, we explored experiences from three case studies carried out last year: a focus group, a communicability evaluation, and a user observation study. We report on adaptations, challenges, opportunities, and lessons learned in conducting researches with human participation during the social restrictions. The main contribution of this work is a reflection on difficulties, advantages, and changes that may remain in a post-pandemic period from the experiences we met as Human-Computer Interaction researchers in conducting such remote studies. We conclude that the three methods analyzed could be successfully performed with small adjustments without compromising the user involvement in performing the tasks of each study.
Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery

In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) [1] and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar (SAS) data. We examine the sensitivity to factors in the fine tuning process such as class imbalance. Our findings show not only an improvement in seafloor texture classification, but also provide greater insight into what features play critical roles in improving performance as well as a knowledge of the importance of balanced data for fine tuning deep learning models for seafloor classification in SAS imagery.
Dual contextual module for neural machine translation

Abstract Self-attention-based encoder-decoder frameworks have drawn increasing attention in recent years. The self-attention mechanism generates contextual representations by attending to all tokens in the sentence. Despite improvements in performance, recent research argues that the self-attention mechanism tends to concentrate more on the global context with less emphasis on the contextual information available within the local neighbourhood of tokens. This work presents the Dual Contextual (DC) module, an extension of the conventional self-attention unit, to effectively leverage both the local and global contextual information. The goal is to further improve the sentence representation ability of the encoder and decoder subnetworks, thus enhancing the overall performance of the translation model. Experimental results on WMT’14 English-German (En $$\rightarrow $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>→</mml:mo> </mml:math> De) and eight IWSLT translation tasks show that the DC module can further improve the translation performance of the Transformer model.
Adaptation of the Periodic Table to Kichwa: An Ecuadorian Native Language

Minorities’ languages face transformation processes and struggle against many social and linguistic limitations. Education systems in these languages are not optimal to promote the teaching of ancestral knowledge and scientific research. This is the case of Kichwa, an Ecuadorian native language that more than half-million people speak with different variations among the country. Kichwa lacks science tools to respond to educational needs, unleashing the gradual loss of intercultural diversity. The adaptation of the periodic table to Kichwa was developed in this work, taking into account its different language variations and the Kichwa speaker’s opinion to ensure the acceptance of this scientific tool. This research aims to break barriers in the development and conservation of indigenous people’s intellect by promoting Kichwa deep-rooted writing and enhancing an excellent bilingual intercultural education system in Ecuador.