Reviewerly/exHarmony huggingface dataset:
Detection of surgical instruments based on Gaussian kernel

In minimally invasive laparoscopic surgery, it is of practical significance to quickly locate the location and category information of the surgical instrument. It can remind medical personnel of irreversible injury caused to patients due to leaving surgical instruments after the operation. In this paper, the Gaussian kernel is introduced into each ground truth, which is conducive to making full use of label information to allocate positive and negative samples and improve the accuracy of location and classification. Then, we introduce SIoU Loss and Harmonic Loss function into a total loss. The former uses relative coordinates to make the network converge more quickly, and the latter solves the problem of asynchronous optimization of the two branches of classification and regression. Our experiment proves that the strategy based on Gaussian kernel sample allocation is very effective on a pubic data set m2cai16-tool-locations, displaying our method possesses conspicuous accuracy of classification and regression than other work.
Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems

This paper studies the state estimation of a class of distributed nonlinear systems. A new robust distributed moving horizon fusion estimation (DMHFE) method is proposed to deal with the norm-bounded uncertainties and guarantee the estimation performance. Based on the given relationship between a state covariance matrix and an error covariance matrix, estimated values of the unknown parameters in the system model can be obtained. Then, a local moving horizon estimation optimization algorithm is constructed by using the measured values of sensor nodes themselves, the measured information of adjacent nodes and the prior state estimates. By solving the above nonlinear optimization problem, a local optimal state estimation is obtained. Next, based on covariance intersection (CI) fusion strategy, the local optimal state estimates sent to the fusion center are fused to derive optimal state estimates. Furthermore, the sufficient conditions for the square convergence of the fusion estimation error norm are given. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed algorithm.
Personalized Learning Resources Recommendation for Interest-Oriented Teaching

An interest-oriented teaching method can stimulate students' interest in learning, thus generating great internal drive. The personalized learning resource recommendation method for interest-oriented teaching meets students' personalized learning preference needs, reduces students' learning resource selection cost, and provides students with more diversified and rational learning resource supply. Different from traditional recommendation algorithms, the recommendation algorithm constructed in this article essentially adjusts the recommendation results in real time based on college students' adoption behavior of historical recommendation information. This article describes the problem of personalized learning resource recommendation for interest-oriented teaching, and constructs a personalized learning resource recommendation model based on the communication power of high-scoring learning resources. Experimental results verify the effectiveness of the model.
Uncertainty Assessment of Wave Elevation Field Measurement Using a Depth Camera

The present study suggests a methodology for acquiring wave elevation fields using a depth camera sensor, with assessment results for the test uncertainty. The depth camera module of an Azure Kinect is calibrated using marker arrays on the optic table, to obtain a polynomial mapping function between the pixel-wise and physical position. For installation of the depth camera in a wave channel, the system is aligned using the built-in inertial measurement unit of Azure Kinect to be perpendicular to the calm water surface. The systematic and random standard uncertainties were evaluated from the analysis of calibration and repeated tests, respectively. The wave height measurement results for the depth camera were compared to those for the capacitance-type and ultrasonic wave height gauges. The test uncertainty of the depth camera increased in large wave stiffness conditions, where the time-of-flight sensor has a disadvantage in measurement.
EquiP: A Method to Co-Design for Cooperation

Abstract In Participatory Design (PD), the design of a cooperative digital solution should involve all stakeholders in the co-design. When one stakeholder’s position is weaker due to socio-cultural structures or differences in knowledge or abilities, PD methods should help designers balance the power in the design process at both the macro and micro levels. We present a PD method that addresses the power relations arising during the design process and draws on theories about participation and power in the design and organisation of change processes. We contribute to Computer Supported Cooperative Work (CSCW) by using the PD method to design computer support for cooperation on cognitive rehabilitation between people with Mild Acquired Brain Injuries (MACI) and their healthcare professionals, where strengthening the cooperation is considered an element of patient empowerment. This method is presented as a contribution to the intersection between PD and CSCW. The discussion of power in PD contributes to the discussion of cooperation in CSCW. We found that EquiP supported the creation of choices, and hence the ‘power to’ influence the design. This method can contribute to a power ‘equilibrium’ and a positive-sum power relation in PD sessions involving all stakeholders.
Defense against Adversarial Patch Attacks for Aerial Image Semantic Segmentation by Robust Feature Extraction

Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety-critical systems. Existing research mainly focuses on solving digital attacks for aerial image semantic segmentation, but adversarial patches with physical attack attributes are more threatening than digital attacks. In this article, we systematically evaluate the threat of adversarial patches on the aerial image semantic segmentation task for the first time. To defend against adversarial patch attacks and obtain accurate semantic segmentation results, we construct a novel robust feature extraction network (RFENet). Based on the characteristics of aerial images and adversarial patches, RFENet designs a limited receptive field mechanism (LRFM), a spatial semantic enhancement module (SSEM), a boundary feature perception module (BFPM) and a global correlation encoder module (GCEM), respectively, to solve adversarial patch attacks from the DL model architecture design level. We discover that semantic features, shape features and global features contained in aerial images can significantly enhance the robustness of the DL model against patch attacks. Extensive experiments on three aerial image benchmark datasets demonstrate that the proposed RFENet has strong resistance to adversarial patch attacks compared with the existing state-of-the-art methods.
More Than Words

iWareBatik is two digital tools (a website and a mobile app) designed and developed to communicate the value of Indonesian Batik, a UNESCO intangible cultural heritage in 2009. Such tools have been evaluated through a panel of 997 bachelor students recruited in 33 Indonesian universities. They have been involved in a process that encompassed user testing activities, filling in a survey related to them, participating in a focus group, and writing a short essay. 156 of them later on took part in hackathon type of competition, aimed at suggesting possible improvements to the iWareBatik set of tools. This paper outlines the overall design of the evaluation activities, and presents in detail the results of the user testing and the related survey, highlighting positive elements and dimensions to be improved. Such evaluation exercise is not only for the set of digital tools at stake, but can provide a relevant model for all those projects aiming at using digital media in the field of intangible cultural heritage, helping to fill-in the gap between design, development, and evaluation.
A Cognitive Environment Modeling Approach for Autonomous Vehicles: A Chinese Experience

Existing environment modeling approaches and trajectory planning approaches for intelligent vehicles are difficult to adapt to multiple scenarios, as scenarios are diverse and changeable, which may lead to potential risks. This work proposes a cognitive spatial–time environment modeling approach for autonomous vehicles, which models a multi-scenario-adapted spatial–time environment model from a cognitive perspective and transforms the scenario-based trajectory planning problem into a unified spatial–time planning problem. The commonality of multiple typical Chinese road scenarios is analyzed, and a unified spatial–time environment model for multi-scenario adaptation is defined and established. The adaptability and trajectory planning potential of the spatial–time environment model are analyzed, and the planning results are obtained through a hybrid A* algorithm. The simulation results show that the proposal is effective in blurring the boundary between scenarios, allowing a single planning approach to adapt to multiple scenarios and plan optimal trajectories (optimal in both path and speed domains) and introducing more flexibility to the planning.
The Use of Robotic Technology in the Healthcare of People above the Age of 65—A Systematic Review

The integration of robots can help provide solutions in regards to the need for an increase in resources in healthcare. The aim of this review was to identify how robots are utilized in the healthcare of people who are over the age of 65 and how this population experiences interacting with healthcare robots.A systematic literature review with an integrated design was conducted.A literature search was performed in the electronic databases CINAHL via EBSCO, EMBASE, and Medline via Ovid. Content analysis was performed to assess the studies that were included in this review.A total of 14 articles were included. Participants in the studies included 453 older people ranging from 65 to 108 years of age. Nine of the studies focused on people with dementia or cognitive impairment. Seven studies included different types of socially assistive human-like robots, six of the studies included two different types of animal-like robots, and one study focused on a robotic rollator. The robots mainly served as social assistive- or engagement robots.
Decoherence-Protected Implementation of Quantum Gates

We present a scheme to implement a universal set of quantum gates based on achievable interactions, and the gates can be protected against decoherence through a dynamical-decoupling approach without encoding. By properly designing system evolution, the desired system interactions commute with the elements forming dynamical decoupling pulses. Thus, the effect of decoherence can be eliminated by repeatedly applying the pulses, without noticeably affecting the system evolution governed by the desired system interactions given a small enough time interval between pulses. Moreover, due to the commutation between the elements forming the pulses and the desired system interactions, our scheme is resistant to different types of decoherence, and so not limited to specific decoherence. Our scheme also works well in the case that the desired system interactions cannot be achieved ideally due to imperfect control of system parameters, through the action of dynamical-decoupling pulses.
Event-based video deblurring based on image and event feature fusion

Event-based video deblurring is a method that performs deblurring by taking the event sequence data obtained from an event camera, which is composed of bio-inspired sensors, along with blurry frames as input. Event-based video deblurring has gained attention as a method that can overcome the limitations of conventional frame-based video deblurring. In this study, we propose a novel event-based video deblurring network based on convolution neural networks (CNNs). Unlike the existing event-based deblurring methods that only use event data, the proposed method fuses all the available information from current blurry frames, previously recovered sharp frames, and event data to deblur a video. Specifically, we propose an image and event feature fusion (IEFF) module to fuse event data with current intensity frame information. Additionally, we propose a current-frame reconstruction from previous-frame (CRP) module for acquiring a pseudo sharp frame from a previously recovered sharp frame and a fusion-based residual estimation (FRE) module, which fuses the event features with the image features of the previous sharp frame extracted from the CRP module. We demonstrate through a verification experiment using synthetic and real datasets that the proposed method has superior quantitative and qualitative results compared to state-of-the-art methods.
An automated accurate sound-based amateur drone detection method based on skinny pattern

Nowadays, the popularity of amateur drones (ADr) has increased marginally, and many research areas, such as machine learning, security systems, and the military industry, have been interested in ADrs. The prime goals of this paper are to present a new sound dataset for ADr detection and present an accurate classification model for ADr detection. This research proposes a novel classification model using the proposed skinny pattern and iterative neighborhood component analysis (INCA) feature selector using ADr sounds. The skinny pattern is a new nonlinear pattern that uses the skinny cipher's substitution box (S-Box). Tunable Q-factor wavelet transform (TQWT) and skinny pattern are used together to extract features in multilevel. The most informative features from the generated features are selected using the INCA selector. 16 classifiers were used in five categories to illustrate the general success of the presented model, and these categories are decision tree (DT), discriminant (D), support vector machine (SVM), k nearest neighborhood (kNN), and ensemble classifiers (EC). An ADr sound dataset was collected, and the presented skinny pattern and INCA-based model are applied to the gathered sound dataset to implement the presented model. The best classification accuracy was calculated as 99.72% using Fine kNN. The proposed model was also compared to other ADr detection/classification models, and it outperformed. These obtained results demonstrate the success of the skinny pattern and INCA-based ADr detection model.
Federated learning framework integrating REFINED CNN and Deep Regression Forests

Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as Images with NEighborhood Dependencies CNN framework can be combined with the proposed Federated Deep Regression Forests to provide improved performance as compared to existing approaches.The Python source code for reproducing the main results are available on GitHub: https://github.com/DanielNolte/FederatedDeepRegressionForests.ranadip.pal@ttu.edu.Supplementary data are available at Bioinformatics Advances online.
A Simulation of a Robot Operating System Based Autonomous Wheelchair with Web Based HMI Using Rosbridge

Our society is aging, which will result in a dramatic increase in the number of individuals suffering from various types of physical impairment and disability. It is necessary to provide mobility support to these persons so that they can live independently at home and be fully integrated into society. This work aims to show a simulation of a ROS (Robot Operating System)-based autonomous wheelchair, as well as a cloud-based online platform for controlling and tracking the wheelchair's mobility and the patient's location. Which allows the user to remotely control the wheelchair from any Internet-enabled device and location. With an intuitive human-machine interface, this wheelchair may be useful for disabled and elderly individuals. ROS offers a simple and user-friendly architecture that enables the development of complex systems at a cheaper cost. Several simulation results are shown in this paper to demonstrate the practicability and performance of the designed system.
XI Guitar: Development of a Slide-Type Guitar Application that Reproduces the Playing Sensation of Analog Guitar

In this paper, we propose and prototype a virtual slide-type guitar application for a smartphone. However, most of them place the guitar’s fretboards and strings on one same smartphone screen and require users to tap buttons or images of strings. In short, they can produce guitar sounds, but users cannot simulate the sensation or experience of playing an analog guitar. A possible substitute for a real guitar is JammyG, which has a MIDI controller function, but this requires a device other than a computer or a smartphone and it is not necessarily a smartphone application that simulates an analog guitar. It is a very complicated system in fact and the smartphone just works as a controller. Besides, it is expensive and difficult to call it ‘popular system’. There are many problems to be solved. In this research, inspired by the method of playing, a slide guitar, we have developed a prototype eXtended Intelligence Guitar (XI-Guitar) that can be played like an actual guitar on a single smartphone with physical sensation of horizontal movement of a finger, by utilizing the smartphone’s acceleration sensor.
Recent Trends and Open Challenges in Blind Quantum Computation

Quantum mechanics with radically novel properties: such as superposition, entanglement, and the no-cloning theorem, has begun to open up Quantum Computation beyond the scope of Classical Computation. In recent years, quantum technology has seen tremendous leaps both in academic research and commercial exploration. Quantum computation has started to find applications in many more new domains every day, from disrupting modern cryptography to enabling unconventional security techniques, from quantum chemistry to physical simulations, and from quantum machine learning to financial optimization. This paper first provides the conceptual groundings of quantum computation and explores blind quantum computation, a one-of-its-kind sub-categorization of quantum computing active research. The use of Blind Quantum Computation in Quantum Cryptography is elaborated in detail. Finally concludes with highlights of the applicability of the subject. The paper is an easy-to-follow guide introducing the research trends and open challenges for the new researcher in follow-up on the field.
Visual Embedding Augmentation in Fourier Domain for Deep Metric Learning

Deep Metric Learning (DML) is very effective for many computer vision applications such as image retrieval or cross-modal matching. The common paradigm for DML is to seek metric spaces that can encode semantically similar objects close while locating the dissimilar ones far away from each other. To make features more discriminative, the mainstream methods usually design various specific loss functions to seek the help of hard negatives through complex hard mining strategies or hard synthesizing with additional networks. In spite of their fruitfulness, these approaches ignore the impact of low-level information in images on the performance, which may degrade the discerning ability of learned embedding. To alleviate these problems, we introduce a simple yet effective augmentation method to generate more hard negatives by swapping the low-frequency spectra of negative instances with anchors in the Fourier domain. Specifically, unlike previous methods, our proposed approach does not involve any complex design strategies but enriches hard negatives by manipulating the low-level variability of images only with simple Fourier transforms. In addition, our method is treated as a universal plug-in, which can be incorporated into different models for performance improvement. In the end, we conduct extensive experiments to evaluate our method on the widely-used datasets including CUB-200–2011, CARS-196, and Stanford Online Products. Our quantitative results demonstrate that the proposed plug-in outperforms previous approaches consistently and significantly across different datasets and evaluation metrics.
Multi-Modal Entity Alignment Using Uncertainty Quantification for Modality Importance

The knowledge graphs are structured data utilized for information retrieval purposes. Entity alignment using multi-modal supplementary information plays an important role in knowledge graph integration. However, if the supplementary information is missing or incorrect, it can negatively impact the retrieval of information. If we can quantify the usefulness of the information for retrieval as a degree of importance, the influence of unimportant supplementary information can be reduced. In this study, we proposed a method that quantifies the importance of each piece of information by using a probability distribution. Our proposed method improves an existing method by 7.7% and 7.3% in H@1 on two datasets (FB15K-DB15K, FB15K-YAGO15K). Qualitative experiments also showed that the importance of information quantified by uncertainty successfully captured data that was not useful for information retrieval. Our qualitative experiments also show that the importance of information, quantified by uncertainty, effectively captures data that is not beneficial for information retrieval.
Robust Adversarial Attacks on Deep Learning-Based RF Fingerprint Identification

Deep learning (DL)-based radio frequency fingerprint identification (RFFI), despite its state-of-the-art capability in improving the security performance of communication networks, is still vulnerable to carefully crafted and imperceptible adversarial attack. However, conventional radio frequency (RF) adversarial attacks ignore the impact of the practical fading channels between the attacker and the sensor. In this letter, a generation adversarial perturbations (GAP) problem is formulated in a DL-based RFFI network. Both the case of ideally perfect channel state information (CSI) and the practical imperfect CSI are considered. The closed-form expression of the adversarial example based on the perfect CSI is provided. In order to address the intractable non-convex robust optimization problem under the imperfect CSI, a spoofing attack algorithm is proposed by exploiting the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ S}}$ </tex-math></inline-formula> -procedure. Simulation results demonstrate that our proposed scheme achieves a superior spoofing ratio compared with other benchmark schemes.