Reviewerly/exHarmony huggingface dataset:
Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks

Surgical tool segmentation in endoscopic images is an important problem: it is a crucial step towards full instrument pose estimation and it is used for integration of pre- and intra-operative images into the endoscopic view. While many recent approaches based on convolutional neural networks have shown great results, a key barrier to progress lies in the acquisition of a large number of manually-annotated images which is necessary for an algorithm to generalize and work well in diverse surgical scenarios. Unlike the surgical image data itself, annotations are difficult to acquire and may be of variable quality. On the other hand, synthetic annotations can be automatically generated by using forward kinematic model of the robot and CAD models of tools by projecting them onto an image plane. Unfortunately, this model is very inaccurate and cannot be used for supervised learning of image segmentation models. Since generated annotations will not directly correspond to endoscopic images due to errors, we formulate the problem as an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation using an adversarial model. Our approach allows to train image segmentation models without the need to acquire expensive annotations and can potentially exploit large unlabeled endoscopic image collection outside the annotated distributions of image/annotation data. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods.
Design and integration of a drone based passive manipulator for capturing flying targets

SUMMARY In this paper, we present a novel passive single degree-of-freedom (DoF) manipulator design and its integration on an autonomous drone to capture a moving target. The end-effector is designed to be passive, to disengage the moving target from a flying UAV and capture it efficiently in the presence of disturbances, with minimal energy usage. It is also designed to handle target sway and the effect of downwash. The passive manipulator is integrated with the drone through a single DoF arm, and experiments are carried out in an outdoor environment. The rack-and-pinion mechanism incorporated for this manipulator ensures safety by extending the manipulator beyond the body of the drone to capture the target. The autonomous capturing experiments are conducted using a red ball hanging from a stationary drone and subsequently from a moving drone. The experiments show that the manipulator captures the target with a success rate of 70% even under environmental/measurement uncertainties and errors.
Learning From Incomplete and Inaccurate Supervision

In plenty of real-life tasks, strongly supervised information is hard to obtain, and thus weakly supervised learning has drawn considerable attention recently. This paper investigates the problem of learning from incomplete and inaccurate supervision, where only a limited subset of training data is labeled but potentially with noise. This setting is challenging and of great importance but rarely studied in the literature. We notice that in many applications, the limited labeled data are with certain structures, which paves us a way to design effective methods. Specifically, we observe that labeled data are usually with one-sided noise such as the bug detection task, where the identified buggy codes are indeed with defects, while codes checked many times or newly fixed may still have other flaws. Furthermore, when there occurs two-sided noise in the labeled data, we exploit the class-prior information of unlabeled data, which is typically available in practical tasks. We propose novel approaches for the incomplete and inaccurate supervision learning tasks and effectively alleviate the negative influence of label noise with the help of a vast number of unlabeled data. Both theoretical analysis and extensive experiments justify and validate the effectiveness of the proposed approaches.
A graph for every analysis: Mapping visuals onto common analyses using flexplot

For decades, statisticians and methodologists have insisted researchers utilize graphical analysis much more heavily. Despite cogent and passionate recommendations, there has been no graphical revolution. Instead, researchers rely heavily on misleading graphics that violate visual processing heuristics. Perhaps the main reason for the persistence of deceptive graphics is software; most software familiar to psychological researchers suffer from poor defaults and limited capabilities. Also, visualization is ancillary to statistical analysis, providing an incentive to not produce graphics at all. In this paper, we argue that every statistical analysis must have an accompanying graphic, and we introduce the point-and-click software Flexplot, available both in JASP and Jamovi. We then present the theoretical framework that guides Flexplot, as well as show how to perform the most common statistical analyses in psychological literature.
Learning an Optimal Sampling Distribution for Efficient Motion Planning

Sampling-based motion planners (SBMP) are commonly used to generate motion plans by incrementally constructing a search tree through a robot's configuration space. For high degree-of-freedom systems, sampling is often done in a lower-dimensional space, with a steering function responsible for local planning in the higher-dimensional configuration space. However, for highly-redundant systems with complex kinematics, this approach is problematic due to the high computational cost of evaluating the steering function, especially in cluttered environments. Therefore, having an efficient, informed sampler becomes critical to online robot operation. In this study, we develop a learning-based approach with policy improvement to compute an optimal sampling distribution for use in SBMPs. Motivated by the challenge of whole-body planning for a 31 degree-of-freedom mobile robot built by the Toyota Research Institute, we combine our learning-based approach with classical graph-search to obtain a constrained sampling distribution. Over multiple learning iterations, the algorithm learns a probability distribution weighting areas of low-cost and high probability of success, which a graph search algorithm then uses to obtain an optimal sampling distribution for the robot. On challenging motion planning tasks for the robot, we observe significant computational speed-up, fewer edge evaluations, and more efficient paths with minimal computational overhead. We show the efficacy of our approach with a number of experiments in whole-body motion planning.
Comparison and Optimization of Floating Bifacial and Monofacial Solar PV System in a Tropical Region

The installation of solar panels in the world has started to increase over the last few decades, due to the declining price of solar panels and on the other hands the electricity tariff from the utility grids is gradually rising. Monofacial crystalline solar panel is very common installed in tropical region especially in Indonesia, while the bifacial crystalline PV panel in our campus lake is still being tested for the first experience. This paper presents the comparison of two types of monofacial and bifacial PV performances, solar panels installed on the lake by using floating system infrastructure. The methods which are used in this research are actual experimental measurement and simulation with the PVSyst application software. Data obtained from actual measurement at the location of the floating solar panel installation was compared with the simulation results. This paper shows that the energy output delivered to the grid by BFSPV is greater by 6.75 percent than MFSPV for each string. The simulation results also show that the optimum tilt angle in the floating PV system is 8.3 degrees and rotation angle is 0 degree towards the north direction.
Training very deep neural networks: Rethinking the role of skip connections

State-of-the-art deep neural networks (DNNs) typically consist of several layers of features representations, and especially rely on skip connections to avoid the difficulty of model optimization. Despite the proliferation of different DNN models that employ various forms of skip connections to achieve remarkable results on benchmarking datasets, a concrete explanation for the successful operation and improved generalization capability of these DNNs is surprisingly still lacking. In this paper, we focus on investigating the role of skip connections for training very deep DNNs. Our exposition directly provides interesting insights and new interpretations to the following important questions (i) why model optimization is easier (ii) why model generalization is better. Theoretical results reveal that skip connections allow DNNs to circumnavigate the singularity of latent representations that translate to optimization and generalization problems, which plague models without skip connections referred to as PlainNets. For substantiating our analysis, our investigation puts into context some of the most successful skip-connection based DNNs, which include residual networks (ResNets) and residual network with aggregated features (ResNeXt) in relation to PlainNets. Experimental evaluations of these models support the theoretical analysis.
Multi-mode Systems for Resilient Security in Industry 4.0

In the era of the Internet of Things and Industry 4.0, machines and devices are increasingly getting connected. These connections go hand in hand with security vulnerabilities and potential threats to these devices. In regular IT systems, we typically provide updates to eliminate vulnerabilities. In industrial automation and control systems, especially in mass production, legacy systems are widespread and installing updates causes downtime. Availability is one of the top goals; stopping a machine in case of a cyber-security threat is often too expensive. But, system integrators and asset owners should not have to wait until product or component suppliers release appropriate updates. Due to safety and warranty requirements, developing and distributing updates can take a long time. In the meantime, attackers can pose threats by taking advantage of devices’ known vulnerabilities. In this paper, we propose the design of resilient systems based on multi-modal architectures with several operational modes. When vulnerabilities of systems become known, or when systems get even attacked at some point, mode switching can overcome the time between vulnerability disclosure or attack, and the availability of corresponding security patches. Therefore, system integrators and asset owners can actively protect themselves by implicitly or explicitly switching to modes with reduced attack surfaces and, thus, with limited ranges of activity for attackers.
The Robot as Scientist: Using Mental Simulation to Test Causal Hypotheses Extracted from Human Activities in Virtual Reality

To act effectively in its environment, a cognitive robot needs to understand the causal dependencies of all intermediate actions leading up to its goal. For example, the system has to infer that it is instrumental to open a cupboard door before trying to grasp an object inside the cupboard. In this paper, we introduce a novel learning method for extracting instrumental dependencies by following the scientific approach of observations, generation of causal hypotheses, and testing through experiments. Our method uses a virtual reality dataset containing observations from human activities to generate hypotheses about causal dependencies between actions. It detects pairs of actions with a high temporal co-occurrence and verifies if one action is instrumental in executing the other action through mental simulation in a virtual reality environment which represents the system's mental model. Our system is able to extract all present instrumental action dependencies while significantly reducing the search space for mental simulation, resulting in a 6-fold reduction in computational time.
RoboEye, an Efficient, Reliable and Safe Semi-Autonomous Gaze Driven Wheelchair for Domestic Use

Any severe motor disability is a condition that limits the ability to interact with the environment, even the domestic one, caused by the loss of control over one’s mobility. This work presents RoboEYE, a power wheelchair designed to allow users to move easily and autonomously within their homes. To achieve this goal, an innovative, cost-effective and user-friendly control system was designed, in which a non-invasive eye tracker, a monitor, and a 3D camera represent some of the core elements. RoboEYE integrates functionalities from the mobile robotics field into a standard power wheelchair, with the main advantage of providing the user with two driving options and comfortable navigation. The most intuitive and direct modality foresees the continuous control of frontal and angular wheelchair velocities by gazing at different areas of the monitor. The second, semi-autonomous modality allows navigation toward a selected point in the environment by just pointing and activating the wished destination while the system autonomously plans and follows the trajectory that brings the wheelchair to that point. The purpose of this work was to develop the control structure and driving interface designs of the aforementioned driving modalities taking into account also uncertainties in gaze detection and other sources of uncertainty related to the components to ensure user safety. Furthermore, the driving modalities, in particular the semi-autonomous one, were modeled and qualified through numerical simulations and experimental verification by testing volunteers, who are regular users of standard electric wheelchairs, to verify the efficiency, reliability and safety of the proposed system for domestic use. RoboEYE resulted suitable for environments with narrow passages wider than 1 m, which is comparable with a standard domestic door and due to its properties with large commercialization potential.
Research on N-grams feature selection methods for text classification

Abstract Text classification requires previously extraction of features describing the text documents in the collection. Usually these features are based on the occurrence frequency of words, N-grams of words in documents, i.e. the vector space model for document representation is built. Feature selection allows to reduce redundancy in high-dimensional representation of text data, which can significantly improve text classification performance. In the present paper, research on feature selection methods is performed in terms of the accuracy and F-measure of text classification with different number of selected attributes (N-grams of words) for different classifiers and different datasets. The obtained results can be used to apply further pre-processing steps, which include modifying the vector space model in order to achieve its improvement in terms of the text classification.
On cointegration and cryptocurrency dynamics

Abstract This paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.
Adversarial Continuous Learning in Unsupervised Domain Adaptation

Domain adaptation has emerged as a crucial technique to address the problem of domain shift, which exists when applying an existing model to a new population of data. Adversarial learning has made impressive progress in learning a domain invariant representation via building bridges between two domains. However, existing adversarial learning methods tend to only employ a domain discriminator or generate adversarial examples that affect the original domain distribution. Moreover, little work has considered confident continuous learning using an existing source classifier for domain adaptation. In this paper, we develop adversarial continuous learning in a unified deep architecture. We also propose a novel correlated loss to minimize the discrepancy between the source and target domain. Our model increases robustness by incorporating high-confidence samples from the target domain. The transfer loss jointly considers the original source image and transfer examples in the target domain. Extensive experiments demonstrate significant improvements in classification accuracy over the state of the art.
Image super-resolution via enhanced multi-scale residual network

Recently, a very deep convolutional neural network (CNN) has achieved impressive results in image super-resolution (SR).In particular, residual learning techniques are widely used.However, the previously proposed residual block can only extract one single-level semantic feature maps of one single receptive field.Therefore, it is necessary to stack the residual blocks to extract higher-level semantic feature maps, which will significantly deepen the network.While a very deep network is hard to train and limits the representation for reconstructing the hierarchical information.Based on the residual block, we propose an enhanced multi-scale residual network (EMRN) to take advantage of hierarchical image features via dense connected enhanced multi-scale residual blocks (EMRBs).Specifically, the newly proposed residual block (EMRB) is capable of constructing multi-level semantic feature maps by a two-branch inception.The twobranch inception in our proposed EMRB consists of 2 convolutional layers and 4 convolutional layers in each branch respectively, therefore we have different ranges of receptive fields within one single EMRB.Meanwhile, the local feature fusion (LFF) is used in every EMRB to adaptively fuse the local feature maps extracted by the two-branch inception.Furthermore, global feature fusion (GFF) in EMRN is then used to obtain abundant useful features from previous EMRBs and subsequent ones in a holistic manner.Experiments on benchmark datasets suggest that our EMRN performs favorably over the state-of-the-art methods in reconstructing further superior super-resolution (SR) images.
Exploring Cross-Modality Utilization in Recommender Systems

Multimodal recommender systems alleviate the sparsity of historical user–item interactions. They are commonly catalogued based on the type of auxiliary data (modality) they leverage, such as preference data plus user-network (social), user/item texts (textual), or item images (visual), respectively. One consequence of this categorization is the tendency for virtual walls to arise between modalities. For instance, a study involving images would compare to only baselines ostensibly designed for images. However, a closer look at existing models’ statistical assumptions about any one modality would reveal that many could work just as well with other modalities. Therefore, we pursue a systematic investigation into several research questions: which modality one should rely on, whether a model designed for one modality may work with another, which model to use for a given modality. We conduct cross-modality and cross-model comparisons and analyses, yielding insightful results pointing to interesting future research directions for multimodal recommender systems.
Who Owns What? Psychological Ownership in Shared Augmented Reality

Psychological ownership defines how we behave in and interact with the social world and the objects around us. Shared Augmented Reality (shared AR) may challenge conventional understanding of psychological ownership because virtual objects created by one user in a social place are available for other participants to see, interact with, and edit. Moreover, confusion may arise when one user attaches a virtual object in a shared AR environment onto the physical object that is owned by a different user. The goal of this study is to investigate tensions around psychological ownership in shared AR. Drawing on prior work, we developed a conceptualization of psychological ownership in shared AR in terms of five underlying dimensions: possession, control, identity, responsibility, and territoriality. We studied several shared AR scenarios through a laboratory experiment that was intended to highlight normative tensions. We divided participants into pairs, whereby one participant in each pair created the virtual object (object-creator) and placed it over the other person's (space proprietor) physical object or space. We recorded participants' perceptions of psychological ownership along the 5 dimensions through surveys and interviews. Our results reveal that the paired participants failed to form a mutual understanding of ownership over the virtual objects. In addition, the introduction of virtual objects called into question participants' sense of psychological ownership over the physical articles to which the virtual objects were attached. Building on our results, we offer a set of design principles for shared AR environments, intended specifically to alleviate psychological ownership-related concerns. Herein, we also discuss the implications of our findings for research and practice in this field.
SuperThermal: Matching Thermal as Visible Through Thermal Feature Exploration

Infrared Radiation (IR) images that capture the reflected IR signals from the surrounding environment have been applied to pedestrian detection and rescue tasks, especially for thermal images reflecting the surface temperature of the objects at nighttime. However, feature detection, extraction and matching are particularly challenging on thermal images as there are few textures compared to normal RGB images, which brings in an enormous gap in introducing various computer vision tasks such as image matching and retrieval, visual localization and SLAM. In this letter, we propose to design a full pipeline to learn a reliable and robust feature detector and descriptor. Our model trained on cross-spectral stereo and KAIST dataset is able to realize a reliable and accurate matching without further annotations. The proposed method is demonstrated to have superior performance compared with both widely used hand-crafted features and features extracted from the most recent learning-based models on visual and quantitative experiments. It also shows a good generalization and robustness ability on FLIR thermal benchmark even without training or fine-tuning on it.