Reviewerly/exHarmony huggingface dataset:
Latency Minimization in Covert Communication-Enabled Federated Learning Network

Federated Learning (FL) as a promising technique is able to address the privacy issues in machine learning. However, due to the broadcast nature of wireless channel, one of the key challenges of FL is its vulnerability to wireless security threats. Thus, in this paper, we consider the model update security in FL. In particular, we propose to adopt a covert communication technique with which a friendly jammer transmits jamming signals to prevent a warden from detecting local model update transmissions of mobile devices in FL. The use of jamming signals reduces the transmission rate of the devices. Thus, we formulate an optimization problem that jointly determines the jamming power, local model transmission power, and local training accuracy to minimize the FL latency, given a security performance requirement. The problem is non-convex, and we propose an alternating descent algorithm to solve it. Extensive simulations are conducted and the results demonstrate the effectiveness and network performance improvement of the proposed algorithm.
Object-Based Augmentation for Building Semantic Segmentation: Ventura and Santa Rosa Case Study

Today deep convolutional neural networks (CNNs) push the limits for most computer vision problems, define trends, and set state-of-the-art results. In remote sensing tasks such as object detection and semantic segmentation, CNNs reach the SotA performance. However, for precise performance, CNNs require much high-quality training data. Rare objects and the variability of environmental conditions strongly affect prediction stability and accuracy. To overcome these data restrictions, it is common to consider various approaches including data augmentation techniques. This study focuses on the development and testing of object-based augmentation. The practical usefulness of the developed augmentation technique is shown in the remote sensing domain, being one of the most demanded in effective augmentation techniques. We propose a novel pipeline for georeferenced image augmentation that enables a significant increase in the number of training samples. The presented pipeline is called object-based augmentation (OBA) and exploits objects’ segmentation masks to produce new realistic training scenes using target objects and various label-free backgrounds. We test the approach on the buildings segmentation dataset with different CNN architectures (U-Net, FPN, HRNet) and show that the proposed method benefits for all the tested models. We also show that further augmentation strategy optimization can improve the results. The proposed method leads to the meaningful improvement of U-Net model predictions from 0.78 to 0.83 F1-score.
Weight adjustable photonic synapse by nonlinear gain in a vertical cavity semiconductor optical amplifier

In this paper, we report a high-speed and tunable photonic synaptic element based on a vertical cavity semiconductor optical amplifier (VCSOA) operating with short (150 ps-long) and low-energy (μW peak power) light pulses. By exploiting nonlinear gain properties of VCSOAs when subject to external optical injection, our system permits full weight tunability of sub-ns input light pulses, just by varying the VCSOA's applied bias current. Not only is the VCSOA-based synapse able to adjust the strength of incoming optical pulses, but it can also provide gain (applied weight factors &amp;gt;1). Moreover, we show that this simple approach permits dynamical weight tuning at high-speed (ns rates) with up to an 11.6-bit precision. These results are realized with commercially sourced, inexpensive vertical cavity surface emitting lasers, operating at the key telecom wavelengths of 1300 and 1550 nm and hence making our approach compatible with optical network and data center technologies. This VCSOA-based system, therefore, offers a hardware friendly, low-energy, and high-speed solution for photonic synaptic links with high potential for use in future neuromorphic photonic systems.
Semi-supervised Multi-label Learning for Graph-structured Data

The semi-supervised multi-label classification problem primarily deals with Euclidean data, such as text with a 1D grid of tokens and images with a 2D grid of pixels. However, the non-Euclidean graph-structured data naturally and constantly appears in semi-supervised multi-label learning tasks from various domains like social networks, citation networks, and protein-protein interaction (PPI) networks. Moreover, the existing popular node embedding methods, like Graph Neural Networks (GNN), focus on graphs with simplex labels and tend to neglect label correlations in the multi-label setting, so the easy adaption proves empirically ineffective. Therefore, graph representation learning for the semi-supervised multi-label learning task is crucial and challenging. In this work, we incorporate the idea of label embedding into our proposed model to capture both network topology and higher-order multi-label correlations. The label embedding is generated along with the node embedding based on the topological structure to serve as the prototype center for each class. Moreover, the similarity of the label embedding and node embedding can be used as a confidence vector to guide the label smoothing process, formulating as a margin ranking optimization problem to learn the second-order relations between labels. Extensive experiments on real-world datasets from various domains demonstrate that our model significantly outperforms the state-of-the-art models for node-level tasks.
Learning an epipolar shift compensation for light field image super-resolution

Light field imaging has drawn broad attention since the advent of practical light field capturing systems that facilitate a wide range of applications in computer vision. However, existing learning-based methods for improving the spatial resolution of light field images neglect the shifts in the sub-pixel domain that are widely used by super-resolution techniques, thus, fail in recovering rich high-frequency information. To fully exploit the shift information, our method attempts to learn an epipolar shift compensation for light field image super-resolution that allows the restored light field image to be angular coherent with the enhancement of spatial resolution. The proposed method first utilizes the rich surrounding views along some typical epipolar directions to explore the inter-view correlations. We then implement feature-level registration to capture accurate sub-pixel shifts of central view, which is constructed by the compensation module equipped with dynamic deformable convolution. Finally, the complementary information from different spatial directions is fused to provide high-frequency details for the target view. By taking each sub-aperture image as a central view, our method could be applied for light field images with any angular resolution. Extensive experiments on both synthetic and real scene datasets demonstrate the superiority of our method over the state-of-the-art qualitatively and quantitatively. Moreover, the proposed method shows good performance in preserving the inherent epipolar structures in light field images. Specifically, our LFESCN method outperforms the state-of-the-art method with about 0.7 dB (PSNR) on average.
Design and Control of Drones

The design and control of drones remain areas of active research, and here we review recent progress in this field. In this article, we discuss the design objectives and related physical scaling laws, focusing on energy consumption, agility and speed, and survivability and robustness. We divide the control of such vehicles into low-level stabilization and higher-level planning such as motion planning, and we argue that a highly relevant problem is the integration of sensing with control and planning. Lastly, we describe some vehicle morphologies and the trade-offs that they represent. We specifically compare multicopters with winged designs and consider the effects of multivehicle teams.
Secure Multi-Dimensional and Multi-Angle Electricity Data Aggregation Scheme for Fog Computing-Based Smart Metering System

Smart Meters (SMs) can regularly report their detailed real time electricity consumption data to utility company, which can improve the balance of electrical load and the efficiency of energy usage. However, the fine-grained electricity data reported by smart meters is easy to disclose the privacy of users. Therefore, data aggregation technology is often used to protect the privacy of users. Currently the existing data aggregation schemes cannot meet the requirement of further fine-grained analysis. In this paper, we propose a multi-dimensional and multi-angle electricity data aggregation scheme for fog computing-based smart metering system. In our proposed scheme, the smart meters use the super-incremental sequence matrix to structure and encrypt their obtained electricity consumption data; the fog nodes regularly collect and aggregate these ciphertexts of electricity consumption data by using the homomorphic Paillier cryptosystem; the control center decrypts the aggregated ciphertext to obtain multi-dimensional and multi-angle electricity data, which is accurate to the types of household appliances and their corresponding electricity angles (energy efficiency indexes). According to the security requirements, we analyze the security of our proposed scheme, which can achieve identity anonymity. Additionally, the experimental results show our scheme is efficient.
User Experience in Health &amp; Medicine: Building Methods for Patient Experience Design in Multidisciplinary Collaborations

Health and medical contexts have emerged as an important area of inquiry for researchers at the intersection of user experience and technical communication. In addressing this intersection, this article advocates and extends patient experience design or PXD (Melonçon, 2017) as an important framework for user experience research within health and medicine. Specifically, this article presents several PXD insights from a task-based usability study that examined an online intervention program for people with voice problems. We respond to Melonçon's call (2017) to build PXD as a framework for user experience and technical communication research by describing ways traditional usability methods can provide PXD insights and asking the following question: What insights can emerge from combining traditional usability methods and PXD research? In addressing this question, we outline two primary methodological and practical considerations we found central to conducting PXD research: 1) engaging patients as participants, and 2) leveraging multidisciplinary collaboration.
Crack tip monitoring by multiscale optical experimental techniques

This study focuses on the development of a complementary fatigue crack-tip evaluation approach with multiscale optical techniques with different resolutions. A middle tension (MT) specimen, of aluminium alloy AA6082-T6, was prepared and submitted to a cyclic fatigue loading to generate a fatigue crack. Then, it was statically loaded under a uniaxial tensile condition, and two non-contact full-field optical techniques, Digital Image Correlation (DIC) and Electronic Speckle Pattern Interferometry (ESPI), were employed to acquire the experimental data. While the specimen is loaded with incremental force values, the behaviour of the cracked area is monitored, with displacement and deformation fields acquired for each force increment. The obtained data is thereby used to calculate the Stress Intensity Factor (SIF) and monitor the crack opening evolution. An overdeterministic algorithm was developed for the SIF determination. The main contribution of the present work is the development of a complementary multiscale methodology, which employs available techniques with different resolutions to monitor the tip of a fatigue crack, drawing a comparison between calculated parameters for each system to shift from one technique to the higher resolution one and validating the proposed methodologies. The obtained SIF values are compared to the reference solution proposed by ASTM E647, and an acceptable agreement has been verified amongst the results.
YOLO-RTUAV: Towards Real-Time Vehicle Detection through Aerial Images with Low-Cost Edge Devices

Object detection in aerial images has been an active research area thanks to the vast availability of unmanned aerial vehicles (UAVs). Along with the increase of computational power, deep learning algorithms are commonly used for object detection tasks. However, aerial images have large variations, and the object sizes are usually small, rendering lower detection accuracy. Besides, real-time inferencing on low-cost edge devices remains an open-ended question. In this work, we explored the usage of state-of-the-art deep learning object detection on low-cost edge hardware. We propose YOLO-RTUAV, an improved version of YOLOv4-Tiny, as the solution. We benchmarked our proposed models with various state-of-the-art models on the VAID and COWC datasets. Our proposed model can achieve higher mean average precision (mAP) and frames per second (FPS) than other state-of-the-art tiny YOLO models, especially on a low-cost edge device such as the Jetson Nano 2 GB. It was observed that the Jetson Nano 2 GB can achieve up to 12.8 FPS with a model size of only 5.5 MB.
Explainable anomaly detection for Hot-rolling industrial process

Anomaly detection is emerging trend in manufacturing processes and may be considered as part of the Industry 4.0 revolution. It can serve both as diagnostic tool in predictive maintenance task, as well as trace back mechanism for assessing quality of production or services. In this paper we describe and approach for explainable anomaly detection in industrial data which contains sequential and static features. We based our solution on modified autoencoder architecture with Long Short-Term Memory layers. To address a problem of explinability in deep learning and find origin of the anomalies we have engaged the SHAP method, which gives both local and global explanations of the model. Analysis of SHAP explanations allowed us to determine the source of majority of anomalies detected by deep learning model. We demonstrated the feasibility of our approach on synthetic, reproducible dataset and on real-life data gathered from hot rolling industrial process.
Audio-Visual Transformer Based Crowd Counting

Crowd estimation is a very challenging problem. The most recent study tries to exploit auditory information to aid the visual models, however, the performance is limited due to the lack of an effective approach for feature extraction and integration. The paper proposes a new audiovisual multi-task network to address the critical challenges in crowd counting by effectively utilizing both visual and audio inputs for better modalities association and productive feature extraction. The proposed network introduces the notion of auxiliary and explicit image patch-importance ranking (PIR) and patch-wise crowd estimate (PCE) information to produce a third (run-time) modality. These modalities (audio, visual, run-time) undergo a transformer-inspired cross-modality co-attention mechanism to finally output the crowd estimate. To acquire rich visual features, we propose a multi-branch structure with transformer-style fusion in-between. Extensive experimental evaluations show that the proposed scheme outperforms the state-of-the-art networks under all evaluation settings with up to 33.8% improvement. We also analyze and compare the vision-only variant of our network and empirically demonstrate its superiority over previous approaches.
An Empirical Evaluation of Online Continuous Authentication and Anomaly Detection Using Mouse Clickstream Data Analysis

While the password-based authentication used in social networks, e-mail, e-commerce, and online banking is vulnerable to hackings, biometric-based continuous authentication systems have been used successfully to handle the rise in unauthorized accesses. In this study, an empirical evaluation of online continuous authentication (CA) and anomaly detection (AD) based on mouse clickstream data analysis is presented. This research started by gathering a set of online mouse-dynamics information from 20 participants by using software developed for collecting mouse information, extracting approximately 87 features from the raw dataset. In contrast to previous work, the efficiency of CA and AD was studied using different machine learning (ML) and deep learning (DL) algorithms, namely, decision tree classifier (DT), k-nearest neighbor classifier (KNN), random forest classifier (RF), and convolutional neural network classifier (CNN). User identification was determined by using three scenarios: Scenario A, a single mouse movement action; Scenario B, a single point-and-click action; and Scenario C, a set of mouse movement and point-and-click actions. The results show that each classifier is capable of distinguishing between an authentic user and a fraudulent user with a comparatively high degree of accuracy.
Self-calibrated driver gaze estimation via gaze pattern learning

Driver’s eye gaze is regarded as an important clue for evaluating the driver’s awareness in the Advanced Driver Assistance Systems and the Automated Driving Systems. Existing driver gaze estimation methods have poor performance on uncalibrated drivers or camera views due to the tedious calibration process. To solve this problem, a knowledge-based solution based on domain prior of typical driver gaze patterns is proposed to realize self-calibration for real driving in naturalistic driving scenarios, which implicitly select the representative time samples of the forward-view gaze zone, the left-side mirror, the right-side mirror, and the rear-view mirror, the speedometer, and the center stack as calibration points. Supporting by the gaze pattern learning algorithm, the proposed method can extract the relevant driver status features gradually and update the estimation parameters periodically. Experimental results demonstrate that the auto-calibrated gaze estimation method can achieve automatic gaze calibration for gaze tracking during in-the-wild on-road driving, and does not require any cooperation from the driver.
Effect of Visual Cues on Pointing Tasks in Co-located Augmented Reality Collaboration

Visual cues are essential in computer-mediated communication. It is especially important when communication happens in a collaboration scenario that requires focusing several users' attention on a specific object among other similar ones. This paper explores the effect of visual cues on pointing tasks in co-located Augmented Reality (AR) collaboration. A user study (N = 32, 16 pairs) was conducted to compare two types of visual cues: Pointing Line (PL) and Moving Track (MT). Both are head-based visual techniques. Through a series of collaborative pointing tasks on objects with different states (static and dynamic) and density levels (low, medium and high), the results showed that PL was better on task performance and usability, but MT was rated higher on social presence and user preference. Based on our results, some design implications are provided for pointing tasks in co-located AR collaboration.
Learning with Immersive Virtual Reality: An Exploratory Study of Chinese College Nursing Students

As an emerging technology, virtual reality (VR) is now being used in education, particularly with STEM-related learning. However, as with all pedagogical innovation, there is a lack of empirical research on the application of VR in situations where students are using it with their second language (L2) and existing studies generally rely on the use of non-immersive VR tools. This paper is one step in a process of understanding the affordances and constraints of a fully immersive VR system in facilitating language learning for English as a foreign language (EFL) nurses. The results of a small-scale study suggested that learners appreciated the different features of VR, which enhanced the integration of virtuality, reality, and mentality. In addition, mediating factors of this VR learning experience were discussed based on the data set. Informed by the current findings, the paper puts forward suggestions for designing VR-supported language learning, which hopefully sheds some light on the complex nature of the effective use of VR in learning.
Graph Construction

Graphs are an effective and compelling way to present scientific results. With few rigid guidelines, researchers have many degrees-of-freedom regarding graph construction. One such choice is the range of the y-axis. A range set just beyond the data will bias readers to see all effects as big. Conversely, a range set to the full range of options will bias readers to see all effects as small. Researchers should maximize congruence between visual size of an effect and the actual size of the effect. In the experiments presented here, participants viewed graphs with the y-axis set to the minimum range required for all the data to be visible, the full range from 0 to 100, and a range of approximately 1.5 standard deviations. The results showed that participants’ sensitivity to the effect depicted in the graph was better when the y-axis range was between one to two standard deviations than with either the minimum range or the full range. In addition, bias was also smaller with the standardized axis range than the minimum or full axis ranges. To achieve congruency in scientific fields for which effects are standardized, the y-axis range should be no less than 1 standard deviations, and aim to be at least 1.5 standard deviations.
A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods

Understanding the inferences of data-driven, machine-learned models can be seen as a process that discloses the relationships between their input and output. These relationships consist and can be represented as a set of inference rules. However, the models usually do not explicit these rules to their end-users who, subsequently, perceive them as black-boxes and might not trust their predictions. Therefore, scholars have proposed several methods for extracting rules from data-driven machine-learned models to explain their logic. However, limited work exists on the evaluation and comparison of these methods. This study proposes a novel comparative approach to evaluate and compare the rulesets produced by five model-agnostic, post-hoc rule extractors by employing eight quantitative metrics. Eventually, the Friedman test was employed to check whether a method consistently performed better than the others, in terms of the selected metrics, and could be considered superior. Findings demonstrate that these metrics do not provide sufficient evidence to identify superior methods over the others. However, when used together, these metrics form a tool, applicable to every rule-extraction method and machine-learned models, that is, suitable to highlight the strengths and weaknesses of the rule-extractors in various applications in an objective and straightforward manner, without any human interventions. Thus, they are capable of successfully modelling distinctively aspects of explainability, providing to researchers and practitioners vital insights on what a model has learned during its training process and how it makes its predictions.