Reviewerly/exHarmony huggingface dataset:
Self-Supervised Video Representation Learning by Uncovering Spatio-Temporal Statistics.

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs. In order to alleviate the learning difficulty, we employ several spatial partitioning patterns to encode rough spatial locations instead of exact spatial Cartesian coordinates. Our approach is inspired by the observation that human visual system is sensitive to rapidly changing contents in the visual field, and only needs impressions about rough spatial locations to understand the visual contents. To validate the effectiveness of the proposed approach, we conduct extensive experiments with four 3D backbone networks, i.e., C3D, 3D-ResNet, R(2+1)D and S3D-G. The results show that our approach outperforms the existing approaches across these backbone networks on four downstream video analysis tasks including action recognition, video retrieval, dynamic scene recognition, and action similarity labeling. The source code is publicly available at: https://github.com/laura-wang/video_repres_sts.
Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels

Abstract Reflectance confocal microscopy (RCM) is a non-invasive imaging tool that reduces the need for invasive histopathology for skin cancer diagnoses by providing high-resolution mosaics showing the architectural patterns of skin, which are used to identify malignancies in-vivo. RCM mosaics are similar to dermatopathology sections, both requiring extensive training to interpret. However, these modalities differ in orientation, as RCM mosaics are horizontal (parallel to the skin surface) while histopathology sections are vertical, and contrast mechanism, RCM with a single (reflectance) mechanism resulting in grayscale images and histopathology with multi-factor color-stained contrast. Image analysis and machine learning methods can potentially provide a diagnostic aid to clinicians to interpret RCM mosaics, eventually helping to ease the adoption and more efficiently utilizing RCM in routine clinical practice. However standard supervised machine learning may require a prohibitive volume of hand-labeled training data. In this paper, we present a weakly supervised machine learning model to perform semantic segmentation of architectural patterns encountered in RCM mosaics. Unlike more widely used fully supervised segmentation models that require pixel-level annotations, which are very labor-demanding and error-prone to obtain, here we focus on training models using only patch-level labels (e.g. a single field of view within an entire mosaic). We segment RCM mosaics into “benign” and “aspecific (nonspecific)” regions, where aspecific regions represent the loss of regular architecture due to injury and/or inflammation, pre-malignancy, or malignancy. We adopt Efficientnet, a deep neural network (DNN) proven to accurately accomplish classification tasks, to generate class activation maps, and use a Gaussian weighting kernel to stitch smaller images back into larger fields of view. The trained DNN achieved an average area under the curve of 0.969, and Dice coefficient of 0.778 showing the feasibility of spatial localization of aspecific regions in RCM images, and making the diagnostics decision model more interpretable to the clinicians.
Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks

The training of a deep learning model requires a large amount of data. In case of sidescan sonar images, the number of snippets from objects of interest is limited. Generative adversarial networks (GAN) have shown to be able to generate photo-realistic images. Hence, we use a GAN to augment a baseline sidescan image dataset with synthetic snippets. Although the training of a GAN with few data samples is likely to cause mode collapse, a combination of pre-training using simple simulated images and fine-tuning with real data reduces this problem. However, for sonar data, we show that this approach of transfer-learning a GAN is sensitive to the pre-training step, meaning that the vanishing of the gradients of the GAN’s discriminator becomes a critical problem. Here, we demonstrate how to overcome this problem, and thus how to apply transfer-learning to GANs for generating synthetic sidescan snippets in a more robust way. Additionally, in order to further investigate the GAN’s ability to augment a sidescan image dataset, the generated images are analyzed in the image and the frequency domain. The work helps other researchers in the field of sonar image processing to augment their dataset with additional synthetic samples.
Autonomous wheelchair for patient’s transportation on healthcare institutions

Abstract The transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability

We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. In this regime, the leading eigenvalue of the training loss Hessian hovers just above the value 2/(step size), and the training loss behaves non-monotonically over short timescales, yet consistently decreases over long timescales. Since this behavior is inconsistent with several widespread presumptions in the field of optimization, our findings raise questions as to whether these presumptions are relevant to neural network training. We hope that our findings will inspire future efforts aimed at rigorously understanding optimization at the Edge of Stability.
Ghost Elimination via Multi-Component Collaboration for Unmanned Aerial Vehicle Remote Sensing Image Stitching

Ghosts are a common phenomenon widely present in unmanned aerial vehicle (UAV) remote sensing image stitching that seriously affect the naturalness of stitching results. In order to effectively remove ghosts and produce visually natural stitching results, we propose a novel image stitching method that can identify and eliminate ghosts through multi-component collaboration without object distortion, segmentation or repetition. Specifically, our main contributions are as follows: first, we propose a ghost identification component to locate a potential ghost in the stitching area; and detect significantly moving objects in the two stitched images. In particular, due to the characteristics of UAV shooting, the objects in UAV remote sensing images are small and the image quality is poor. We propose a mesh-based image difference comparison method to identify ghosts; and use an object tracking algorithm to accurately correspond to each ghost pair. Second, we design an image information source selection strategy to generate the ghost replacement region, which can replace the located ghost and avoid object distortion, segmentation and repetition. Third, we find that the process of ghost elimination can produce natural mosaic images by eliminating the ghost caused by initial blending with selected image information source. We validate the proposed method on VIVID data set and compare our method with Homo, ELA, SPW and APAP using the peak signal to noise ratio (PSNR) evaluation indicator.
Semisupervised Semantic Segmentation by Improving Prediction Confidence

Most of the recent image segmentation methods have tried to achieve the utmost segmentation results using large-scale pixel-level annotated data sets. However, obtaining these pixel-level annotated training data is usually tedious and expensive. In this work, we address the task of semisupervised semantic segmentation, which reduces the need for large numbers of pixel-level annotated images. We propose a method for semisupervised semantic segmentation by improving the confidence of the predicted class probability map via two parts. First, we build an adversarial framework that regards the segmentation network as the generator and uses a fully convolutional network as the discriminator. The adversarial learning makes the prediction class probability closer to 1. Second, the information entropy of the predicted class probability map is computed to represent the unpredictability of the segmentation prediction. Then, we infer the label-error map of the segmentation prediction and minimize the uncertainty on misclassified regions for unlabeled images. In contrast to existing semisupervised and weakly supervised semantic segmentation methods, the proposed method results in more confident predictions by focusing on the misclassified regions, especially the boundary regions. Our experimental results on the PASCAL VOC 2012 and PASCAL-CONTEXT data sets show that the proposed method achieves competitive segmentation performance.
Improving AR-powered remote assistance: a new approach aimed to foster operator’s autonomy and optimize the use of skilled resources

Abstract Augmented reality (AR) has a number of applications in industry, but remote assistance represents one of the most prominent and widely studied use cases. Notwithstanding, although the set of functionalities supporting the communication between remote experts and on-site operators grew over time, the way in which remote assistance is delivered has not evolved yet to unleash the full potential of AR technology. The expert typically guides the operator step-by-step, and basically uses AR-based hints to visually support voice instructions. With this approach, skilled human resources may go under-utilized, as the time an expert invests in the assistance corresponds to the time needed by the operator to execute the requested operations. The goal of this work is to introduce a new approach to remote assistance that takes advantage of AR functionalities separately proposed in academic works and commercial products to re-organize the guidance workflow, with the aim to increase the operator’s autonomy and, thus, optimize the use of expert’s time. An AR-powered remote assistance platform able to support the devised approach is also presented. By means of a user study, this approach was compared to traditional step-by-step guidance, with the aim to estimate what is the potential of AR that is still unexploited. Results showed that with the new approach it is possible to reduce the time investment for the expert, allowing the operator to autonomously complete the assigned tasks in a time comparable to step-by-step guidance with a negligible need for further support.
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift

Policy gradient methods are among the most effective methods in challenging reinforcement learning problems with large state and/or action spaces. However, little is known about even their most basic theoretical convergence properties, including: if and how fast they converge to a globally optimal solution or how they cope with approximation error due to using a restricted class of parametric policies. This work provides provable characterizations of the computational, approximation, and sample size properties of policy gradient methods in the context of discounted Markov Decision Processes (MDPs). We focus on both: “tabular” policy parameterizations, where the optimal policy is contained in the class and where we show global convergence to the optimal policy; and parametric policy classes (considering both log-linear and neural policy classes), which may not contain the optimal policy and where we provide agnostic learning results. One central contribution of this work is in providing approximation guarantees that are average case — which avoid explicit worst-case dependencies on the size of state space — by making a formal connection to supervised learning under distribution shift. This characterization shows an important interplay between estimation error, approximation error, and exploration (as characterized through a precisely defined condition number).
Inductive Topic Variational Graph Auto-Encoder for Text Classification

Graph convolutional networks (GCNs) have been applied recently to text classification and produced an excellent performance.However, existing GCN-based methods do not assume an explicit latent semantic structure of documents, making learned representations less effective and difficult to interpret.They are also transductive in nature, thus cannot handle out-of-graph documents.To address these issues, we propose a novel model named inductive Topic Variational Graph Auto-Encoder (T-VGAE), which incorporates a topic model into variational graph-auto-encoder (VGAE) to capture the hidden semantic information between documents and words.T-VGAE inherits the interpretability of the topic model and the efficient information propagation mechanism of VGAE.It learns probabilistic representations of words and documents by jointly encoding and reconstructing the global wordlevel graph and bipartite graphs of documents, where each document is considered individually and decoupled from the global correlation graph so as to enable inductive learning.Our experiments on several benchmark datasets show that our method outperforms the existing competitive models on supervised and semi-supervised text classification, as well as unsupervised text representation learning.In addition, it has higher interpretability and is able to deal with unseen documents.
Early Prediction of Malignant Mesothelioma: An Approach Towards Non-invasive Method

Background: Malignant Mesothelioma (MM) is a rare but aggressive tumor that arises in the lungs. Commonly, costly imaging and laboratory resources, i.e. (X-rays imaging, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, biopsies, and blood tests) have already been utilized for the diagnosis of MM. Even though these diagnostic measures are expensive and unavailable in distant areas, some of these diagnosis methods are also very painful for the patient, i.e., biopsy and cytology of pleural fluid. Objective: In this study, we proposed a diagnosis model for early identification of MM via machine learning techniques. We explored the health records of 324 Turkish patients, which show the symptoms related to MM. The data of patients include socio-economic, geographical, and clinical features. Methods: Different feature selection methods have been employed for the selection of significant features. To overcome the data imbalance problem, various data-level resampling techniques have been utilized to obtain efficient results. The Gradient Boosted Decision Tree (GBDT) method has been used to develop the diagnostic model. The performance of the GBDT model is also compared with traditional machine learning algorithms. Results : Our model's results outperformed other models, both on balance and imbalance data. The results clearly show that undersampling techniques outperformed by imbalanced data even without resampling based on accuracy and Receiving Operating Characteristic (ROC) value. Conversely, it has also been observed that oversampling techniques outperformed undersampling and imbalanced data based on accuracy and ROC. All classifiers employed in this study achieved efficient results utilizing feature selection-based methods (OneR, information gain, and Relief-F), but the results of the other two methods (gain ratio and Correlation) were not entirely promising. Finally, when the combination of Synthetic Minority Oversampling Technique (SMOTE) and OneR was applied with GBDT, it gave the most favorable results based on accuracy, F-measure, and ROC. Conclusion: The diagnosis model has also been deployed to assist doctors, patients, medical practitioners, and other healthcare professionals for early diagnosis and better treatment of MM.
Learning Generalizable Robotic Reward Functions from “In-The-Wild” Human Videos

We are motivated by the goal of generalist robots that can complete a wide range of tasks across many environments.Critical to this is the robot's ability to acquire some metric of task success or reward, which is necessary for reinforcement learning, planning, or knowing when to ask for help.For a general-purpose robot operating in the real world, this reward function must also be able to generalize broadly across environments, tasks, and objects, while depending only on on-board sensor observations (e.g.RGB images).While deep learning on large and diverse datasets has shown promise as a path towards such generalization in computer vision and natural language, collecting high quality datasets of robotic interaction at scale remains an open challenge.In contrast, "in-the-wild" videos of humans (e.g.YouTube) contain an extensive collection of people doing interesting tasks across a diverse range of settings.In this work, we propose a simple approach, Domain-agnostic Video Discriminator (DVD), that learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task, and can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos.We find that by leveraging diverse human datasets, this reward function (a) can generalize zero shot to unseen environments, (b) generalize zero shot to unseen tasks, and (c) can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.
Framework, Tools and Good Practices for Cybersecurity Curricula

Cybersecurity education and training are essential prerequisites of achieving a secure and privacy-friendly digital environment.Both professionals and the general public widely acknowledge the need for high-quality university education programs and professional training courses.However, guides, recommendations, practical tools, and good examples that could help institutions design appropriate cybersecurity programs are still missing.In particular, a comprehensive method to identify skills needed by cybersecurity work roles offered on the job market is missing.This paper aims to provide practical tools and strategies to help higher education providers design good cybersecurity curricula.First, we analyze the content of 89 existing study programs worldwide, collect recommendations of renowned institutions within and outside the EU, and provide a comprehensive survey accompanied by a dynamic web application called Education Map.Based on the knowledge about the current state in cybersecurity education, we design the SPARTA Cybersecurity Skills Framework that provides the currently missing link between work roles and required expertise and shows how to develop a curriculum that reflects job market requirements.Finally, we provide a practical tool that implements the framework and helps education and training providers design new study programs and analyze existing ones by considering the requirements of cybersecurity work roles.
Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.
Change Matters: Medication Change Prediction with Recurrent Residual Networks

Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all medications for the current visit, which often overlaps with medications from previous visits. A more clinically relevant task is to identify medication changes. In this paper, we propose a new recurrent residual networks, named MICRON, for medication change prediction. MICRON takes the changes in patient health records as input and learns to update a hid- den medication vector and the medication set recurrently with a reconstruction design. The medication vector is like the memory cell that encodes longitudinal information of medications. Unlike traditional methods that require the entire patient history for prediction, MICRON has a residual-based inference that allows for sequential updating based only on new patient features (e.g., new diagnoses in the recent visit), which is efficient. We evaluated MICRON on real inpatient and outpatient datasets. MICRON achieves 3.5% and 7.8% relative improvements over the best baseline in F1 score, respectively. MICRON also requires fewer parameters, which significantly reduces the training time to 38.3s per epoch with 1.5× speed-up.
Leadership and innovative work behaviour within Ghanaian metropolitan assemblies: mediating role of resource supply

Purpose This study explores the role of transformational leadership, transactional leadership and resource supply in enhancing innovative work behaviour using the mediation model. Design/methodology/approach Survey data was gathered from 314 local government staff from the six metropolitan assemblies in Ghana. Structural equation modelling was used for the analysis. Findings The results suggest that transformational and transactional leadership behaviours provided an impetus for innovative behaviours. Also, resource supply was found to mediate the relationship between transformational leadership behaviours and innovative work behaviour. Practical implications Public managers can improve the innovative behaviour of public servants by providing resources for innovation, setting up proper reward structures, communicating vision clearly and clarifying performance expectations. Originality/value This is the first study to investigate innovative work behaviour within the Ghanaian local government sector and the context of a developing country in Africa. The study extends the literature on innovative work behaviour by exploring the role of leadership and resource supply.