Reviewerly/exHarmony huggingface dataset:
Pedestrian Detection Based on Hand-crafted Features and Multi-layer Feature Fused-ResNet Model

One of the most sought-after research areas in object detection is pedestrian detection owing to its applications especially in automated surveillance and robotics. Traditional methods use hand-crafted features to characterize pedestrians. In this work, we have pro-posed a new hand-crafted feature extraction method that concatenates shape, color and texture features; which is then classified by using Support Vector Machine (SVM). As in recent years, deep learning models such as Convolutional Neural Networks (CNNs) have become an eminent state of the art in detection challenges, which unlike the manually designed feature extraction mechanism, results in more accuracy. Therefore, we have also proposed a CNN network, a modification of the pre-trained ResNet-18 named as Multi-layer Feature Fused-ResNet (MF2-ResNet). We have used the proposed modification for (1) feature extraction; which is then classified by using Support Vector Machine (SVM); (2) End-to-End feature extraction and classification by the CNN network and (3) MF2-ResNet based Faster-RCNN to include region proposals in the detection pipeline. To evaluate the proposed method, it is compared with existing pre-trained CNNs. The MF2-ResNet based Faster R-CNN is compared with state-of-the-art detection methods. Three benchmark pedestrian datasets are used in this work: INRIA, NICTA and Daimler.
Improving Continuous-time Conflict Based Search

Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and 2^k-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multi-agent path finding in continuous-time domains.
Data Augmentation for Spoken Language Understanding via Pretrained Language Models

The training of spoken language understanding (SLU) models often faces the problem of data scarcity. In this paper, we put forward a data augmentation method using pretrained language models to boost the variability and accuracy of generated utterances. Furthermore, we investigate and propose solutions to two previously overlooked semi-supervised learning scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts is given; ii) Rich-in-Utterance: a large number of unlabelled utterances are available. Empirical results show that our method can produce synthetic training data that boosts the performance of language understanding models in various scenarios.
Massive Ultrasonic Data Compression Using Wavelet Packet Transformation Optimized by Convolutional Autoencoders

Ultrasonic signal acquisition platforms generate considerable amounts of data to be stored and processed, especially when multichannel scanning or beamforming is employed. Reducing the mass storage and allowing high-speed data transmissions necessitate the compression of ultrasonic data into a representation with fewer bits. High compression accuracy is crucial in many applications, such as ultrasonic medical imaging and nondestructive testing (NDT). In this study, we present learning models for massive ultrasonic data compression on the order of megabytes. A common and highly efficient compression method for ultrasonic data is signal decomposition and subband elimination using wavelet packet transformation (WPT). We designed an algorithm for finding the wavelet kernel that provides maximum energy compaction and the optimal subband decomposition tree structure for a given ultrasonic signal. Furthermore, the WPT convolutional autoencoder (WPTCAE) compression algorithm is proposed based on the WPT compression tree structure and the use of machine learning for estimating the optimal kernel. To further improve the compression accuracy, an autoencoder (AE) is incorporated into the WPTCAE model to build a hybrid model. The performance of the WPTCAE compression model is examined and benchmarked against other compression algorithms using ultrasonic radio frequency (RF) datasets acquired in NDT and medical imaging applications. The experimental results clearly show that the WPTCAE compression model provides improved compression ratios while maintaining high signal fidelity. The proposed learning models can achieve a compression accuracy of 98% by using only 6% of the original data.
Strain monitoring of wind turbines using a semi-autonomous drone

In this work, an approach is proposed that can perform a nondestructive evaluation of wind turbine structures using a non-contact, three-dimensional full-field optical digital image correlation (DIC) technique. This approach can quantify the level of strain and loading conditions that rotating structures such as wind turbines experience during operation. The optical technique does not interfere with the structural functionality of the wind turbine. Moreover, the use of Unmanned Aerial Vehicles (UAVs) for remote inspection enables robust measurements for periodic inspection. The strain obtained using the proposed approach is validated using strain gauges mounted on the blades. A control algorithm is designed for the UAV to stabilized and obtain the desired field of interest and working distance based on the turbine size. Blending the benefits of the remote accessibility of UAVs and full-field dynamic evaluation of structures using strain data obtained with DIC is a novel method of monitoring wind turbines.
Geosemiotic Analysis of Signs in the Linguistic Cityscape of China

In the last several decades, adding English to public signboards has become a common phenomenon in China, and the inclusion of more foreign languages other than English for a sign in public sphere has drawn much attention in recent years.In sociolinguistic research, linguistic landscape is a more common term and has been adopted most in studies worldwide which always target at languages used in city centers as a tradition.This study is an attempt to investigate multilingualism in the public sphere of China.Focusing on the linguistic landscape of Xi'an, an ancient city of China, it explores the visibility and salience of languages used on signboards in the city center under the theoretical framework of Geosemiotics.The Geosemiotic analysis demonstrates that Chinese-English bilingual signs constitute about half of the linguistic cityscape of Xi'an; trilingual signs on commercial signboards often carry incoordinate information in different languages; for quadrilingual signs, a relatively fixed order is given to different languages.Besides, the display of traditional Chinese characters on signboards in the cityscape often appear on the wooden plates, which create an "ancient" feeling to visitors.This research also indicates that there is a tendency to standardize the display of language signs in the public sphere of Xi'an city, despite that the desire to be internationalized had been emphasized a lot by many researchers in early studies on linguistic landscape worldwide.
Ellipsoidal Path Planning for Unmanned Aerial Vehicles

The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost.
Phase-Shifting Projected Fringe Profilometry Using Binary-Encoded Patterns

A phase unwrapping method for phase-shifting projected fringe profilometry is presented. It did not require additional projections to identify the fringe orders. The pattern used for the phase extraction could be used for phase unwrapping directly. By spatially encoding the fringe patterns that were used to perform the phase-shifting technique with binary contrasts, fringe orders could be discerned. For spatially isolated objects or surfaces with large depth discontinuities, unwrapping could be identified without ambiguity. Even though the surface color or reflectivity varied periodically with position, it distinguished the fringe order very well.
Temporal fringe projection profilometry: Modified fringe-frequency range for error reduction

Temporal techniques for fringe projection profilometry, in particular the multi-frequency class, require the projection of a sequence of fringes with preset frequencies. In this paper, we analyze the generation of fringes that offers better conditions to process them through the modification of the fringe-frequency range, which may reduce measurement errors. This will also serve to expand the possibilities of being able to process the fringe information through different signal-processing methods. Additionally, in this work, we derive a first-approximation frequency-to-height conversion formula and describe an algorithm to process the fringe image sequence through the Continuous Wavelet-Transform (CWT). Experimental results with simulations and real applications are presented as well.
Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: A case study of drinking water quality prediction

Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction. Although some traditional models were improved by introducing the cost matrix, little is known about whether cost matrix could enhance the prediction performance of DCF. Additionally, feature extraction is also an important way to potentially improve the model's ability to predict the imbalanced data. Here, we developed two novelty learning models based on DCF: cost-sensitive DCF (CS-DCF) and DCF that combines unsupervised learning models and greedy methods (USM-DCF-G). Subsequently, CS-DCF and USM-DCF-G were successfully verified by an imbalanced drinking water quality data set. Our data presented both CS-DCF and USM-DCF-G show better prediction performance than that of DCF alone did. In particular, USM-DCF-G shows the best performance with the highest F1-score (95.12 ± 2.56%), after feature extraction and selection by using unsupervised learning models and greedy methods. Thus, the two learning models, especially USM-DCF-G, were promising learning models to address environmental imbalanced issues and accurately predict environmental quality.
Identification of pegmatite bodies, at a province scale, using machine learning algorithms: preliminary results

Several raw materials for “green” energy production, such as high purity quartz, lithium, rare earth elements, beryllium, tantalum, and caesium, can be sourced from a rock type known as pegmatite. The GREENPEG project (https://www.greenpeg.eu/), started in May 2020, is developing and testing new and advanced exploration technologies and algorithms to be integrated and upscaled into flexible, ready-to-use economically efficient and sustainable methods for finding buried pegmatites and their “green” technology raw materials. One of the tasks of this project aims to apply different image processing techniques to different satellite images (Landsat, ASTER, and Sentinel-2) in order to automatically identify pegmatite bodies. In this work, we will present the preliminary results, regarding the application of machine learning algorithms (ML), more specifically, random forests (RF) and support vector machines (SVM) to one of the study areas of the project in Tysfjord, northern Norway, to identify pegmatite bodies. To be able to determine the classes that would make up the study area, geological data of the region, such as lithological maps, aeromagnetic data, and high-resolution aerial photographs, were used to define the four classes (1. pegmatites, 2. water bodies, 3. vegetation, 4. granite). All training locations were randomly selected, with 25% of the samples split into testing, and the remaining 75% split for training. The SVM algorithm presented more promising results in relation to overfitting and final image classification than RF. Testing the algorithms with several variables of parameters was able to make the process more efficient. Acknowledgments This study is funded by European Union’s Horizon 2020 innovation programme under grant agreement No 869274, project GREENPEG: New Exploration Tools for European Pegmatite Green-Tech Resources.
A Low-Code Tool Supporting the Development of Recommender Systems

The design of recommender systems (RSs) to support software development encompasses the fulfillment of different steps, including data preprocessing, choice of the most appropriate algorithms, item delivery. Though RSs can alleviate the curse of information overload, existing approaches resemble black-box systems, in which the end-user is not expected to fine-tune or personalize the overall process.
Introduction to the Mixed Reality Toolkit

In this chapter, we’ll learn more about the Mixed Reality Toolkit and its importance for Mixed Reality development. We’ll learn about the various components and example scenes included with the Mixed Reality Toolkit and how we can leverage this community resource for development.
Work pressures among teachers of learning disabilities in English language from their point of view

This study aimed at identifying the work pressures among teachers of learning disabilities in English language in Irbid governorate from their point of view. The total number of the study population was (60) male and female teachers, while the study sample included (30) male and female teachers. To collect the study information, the researcher developed a questionnaire to reveal the level of work stress among male and female teachers, and it included (40) items. The study found that there are pressures at work for teachers of LDs in English language. These pressures are the poor achievement of some students, large financial obligations for teachers, poor standards used to promote teachers, the large number of lessons taught by the teacher per week, daily planning, and overcrowding of students in class. The study recommended holding training courses for teachers in the private sector, similar to their colleagues in other sectors, to improve the educational process, and so that teachers can keep pace with the rapid scientific and technological development that the world is witnessing around them
Remote sensing detection enhancement

Abstract Big Data in the area of Remote Sensing has been growing rapidly. Remote sensors are used in surveillance, security, traffic, environmental monitoring, and autonomous sensing. Real-time detection of small moving targets using a remote sensor is an ongoing, challenging problem. Since the object is located far away from the sensor, the object often appears too small. The object’s signal-to-noise-ratio (SNR) is often very low. Occurrences such as camera motion, moving backgrounds (e.g., rustling leaves), low contrast and resolution of foreground objects makes it difficult to segment out the targeted moving objects of interest. Due to the limited appearance of the target, it is tough to obtain the target’s characteristics such as its shape and texture. Without these characteristics, filtering out false detections can be a difficult task. Detecting these targets, would often require the detector to operate under a low detection threshold. However, lowering the detection threshold could lead to an increase of false alarms. In this paper, the author will introduce a new method that improves the probability to detect low SNR objects, while decreasing the number of false alarms as compared to using the traditional baseline detection technique.
SSLP: Spatial Guided Self-supervised Learning on Pathological Images

Nowadays, there is an urgent requirement of self-supervised learning (SSL) on whole slide pathological images (WSIs) to relieve the demand of finely expert annotations. However, the performance of SSL algorithms on WSIs has long lagged behind their supervised counterparts. To close this gap, in this paper, we fully explore the intrinsic characteristics of WSIs and propose SSLP: Spatial Guided Self-supervised Learning on Pathological Images. We argue the patch-wise spatial proximity is a significant characteristic of WSIs, if properly employed, shall provide abundant supervision for free. Specifically, we explore three semantic invariance from 1) self-invariance: the same patch of different augmented views, 2) intra-invariance: the patches within spatial neighbors and 3) inter-invariance: their corresponding neighbors in the feature space. As a result, our SSLP model achieves \(82.9\%\) accuracy and \(85.7\%\) AUC on CAMELYON linear classification and \(95.2\%\) accuracy fine-tuning on cross-disease classification on NCTCRC, which outperforms previous state-of-the-art algorithm and matches the performance of a supervised counterpart.
Large-bandwidth Transduction Between an Optical Single Quantum Dot Molecule and a Superconducting Resonator

Quantum transduction between the microwave and optical domains is an outstanding challenge for long-distance quantum networks based on superconducting qubits. For all transducers realized to date, the generally weak light-matter coupling does not allow high transduction efficiency, large bandwidth, and low noise simultaneously. Here we show that a large electric dipole moment of an exciton in an optically active self-assembled quantum dot molecule (QDM) efficiently couples to a microwave resonator field at the single-photon level. This allows for transduction between microwave and optical photons without coherent optical pump fields to enhance the interaction. With an on-chip device, we demonstrate a sizeable single-photon coupling strength of 16 MHz. Thanks to the fast exciton decay in the QDM, the transduction bandwidth between an optical and microwave resonator photon reaches several 100 s of MHz. We also show that the transduction process via the QDM is fully coherent within the measurement error range.Received 10 March 2022Revised 3 July 2022Accepted 10 August 2022DOI:https://doi.org/10.1103/PRXQuantum.3.030336Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasHybrid quantum systemsOptical & microwave phenomenaQuantum communicationQuantum interconnectsQuantum networksPhysical SystemsDouble quantum dotsSuperconducting devicesQuantum InformationGeneral PhysicsInterdisciplinary PhysicsCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical
STARdom: An Architecture for Trusted and Secure Human-Centered Manufacturing Systems

There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users’ feedback and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed at all levels. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study.
Blind image deblurring with Gaussian curvature of the image surface

This paper presents a blind image deblurring algorithm by utilizing Gaussian curvature (GC) of the image surface. GC is an intrinsic geometric feature and related to the developability of the surface. In recent years, numerous variational models based on GC for image denoising and image reconstruction have been proposed. In this paper, we show that GC regularization is also effective for blind image deblurring when combines it with L0-norm of image gradients. By minimizing the combined regularization, our algorithm gradually preserves sharp edges and removes detrimental structures and noises in intermediate latent images. The sharp latent images can then accurately guide the estimation of the blur kernel. However, a complicated optimization problem will occur once the proposed regularization has been involved. As we know, traditional diffusion methods for minimizing the GC regularization not only converges slowly but also requires the differentiability of signals. Moreover, the L0-norm of image gradients is non-convex. Consequently, Gaussian curvature filter (GCF) and the half-quadratic splitting strategy are adopted to solve the optimization problem. Extensive experimental results show that the proposed deblurring method achieves state-of-the-art results on benchmark datasets and performs favorably on real-world blurry images.