Reviewerly/exHarmony huggingface dataset:
A Multi-Scale Feasibility Study into Acid Mine Drainage (AMD) Monitoring Using Same-Day Observations

Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (&lt;50 mm2 per pixel; r2 = 0.78) to medium-resolution PlanetScope Dove-R (3 m2 per pixel; r2 = 0.51) and low-resolution simulated Sentinel-2 (10 m2 per pixel; r2 = 0.23). A fractioned water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, S2: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors.
Robust Data Augmentation Generative Adversarial Network for Object Detection

Generative adversarial network (GAN)-based data augmentation is used to enhance the performance of object detection models. It comprises two stages: training the GAN generator to learn the distribution of a small target dataset, and sampling data from the trained generator to enhance model performance. In this paper, we propose a pipelined model, called robust data augmentation GAN (RDAGAN), that aims to augment small datasets used for object detection. First, clean images and a small datasets containing images from various domains are input into the RDAGAN, which then generates images that are similar to those in the input dataset. Thereafter, it divides the image generation task into two networks: an object generation network and image translation network. The object generation network generates images of the objects located within the bounding boxes of the input dataset and the image translation network merges these images with clean images. A quantitative experiment confirmed that the generated images improve the YOLOv5 model's fire detection performance. A comparative evaluation showed that RDAGAN can maintain the background information of input images and localize the object generation location. Moreover, ablation studies demonstrated that all components and objects included in the RDAGAN play pivotal roles.
A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism

Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look Once Version X (YOLOX). This method uses depthwise separable convolution to simplify and optimize the network, and greatly simplifies the total parameters, while the accuracy is only partially reduced. Meanwhile, a Squeeze-and-Extraction (SE) module is introduced into the backbone to improve the model′s ability to extract features; the introduction of a Convolutional Block Attention Module (CBAM) in the feature fusion network makes the network pay more attention to important features and suppress unnecessary features. Furthermore, Distance-IoU (DIoU) is used to replace Intersection over Union (IoU) to calculate the regression loss for model optimization, and data augmentation technology is used to expand the dataset to achieve a better detection effect. The experimental results show that the mean Average Precision (mAP) of the proposed method reaches 82.32%, approximately 2% higher than the baseline model, while the number of parameters is only about 1/10th of that of YOLOX-S, with the size of 3.85 MB. The proposed approach is, thus, a lightweight model with high detection accuracy and suitable for various edge computing devices.
Exploring the Effect of Virtual Environments on Passive Haptic Perception

Recent advances in virtual reality (VR) technologies such as immersive head-mounted display (HMD), sensing devices, and 3D printing-based props have become much more feasible for providing improved experiences for users in virtual environments. In particular, research on haptic feedback is being actively conducted to enhance the effect of controlling virtual objects. Studies have begun to use real objects that resemble virtual objects, i.e., passive haptic, instead of using haptic equipment with motor control, as an effective method that allows natural interaction. However, technical difficulties must be resolved to match transformations (e.g., position, orientation, and scale) between virtual and real objects to maximize the user’s immersion. In this paper, we compare and explore the effect of passive haptic parameters on the user’s perception by using different transformation conditions in immersive virtual environments. Our experimental study shows that the participants felt the same within a certain range, which seems to support the “minimum cue” theory in giving sufficient sensory stimulation. Thus, considering the benefits of the model using our approach, haptic interaction in VR content can be developed in a more economical way.
Crowd Density Level Estimation and Anomaly Detection Using Multicolumn Multistage Bilinear Convolution Attention Network (MCMS-BCNN-Attention)

The detection of crowd density levels and anomalies is a hot topic in video surveillance. Especially in human-centric action and activity-based movements. In some respects, the density level variation is considered an anomaly in the event. Crowd behaviour identification relies on a computer-vision-based approach and basically deals with spatial information of foreground video information. In this work, we focused on a deep-learning-based attention-oriented classification system for identifying several basic movements in public places, especially, human flock movement, sudden motion changes and panic events in several indoor and outdoor places. The important spatial features were extracted from a bilinear CNN and a multicolumn multistage CNN with preprocessed morphological video frames from videos. Finally, the abnormal and crowd density estimation was distinguished by using an attention feature combined with a multilayer CNN feature by modifying the fully connected layer for several categories (binary and multiclass). We validate the proposed method on several video surveillance datasets including PETS2009, UMN and UCSD. The proposed method achieved an accuracy of 98.62, 98.95, 96.97, 99.10 and 98.38 on the UCSD Ped1, UCSD Ped2, PETS2009, UMN Plaza1 and UMN Plaza2 datasets, respectively, with the different pretrained models. We compared the performance between recent modern approaches and the proposed method (MCMS-BCNN-Attention) and achieved the highest accuracy. The anomaly detection performance on the UMN and PETS2009 datasets was compared with that of a state-of-the-art method and achieved the best AUC results as 0.9953 and 1.00 for both scenarios, respectively, with a binary classification.
Probing Phases of Quantum Matter with an Ion-Trap Tensor-Network Quantum Eigensolver

Tensor-Network (TN) states are efficient parametric representations of ground states of local quantum Hamiltonians extensively used in numerical simulations. Here we encode a TN ansatz state directly into a quantum simulator, which can potentially offer an exponential advantage over purely numerical simulation. In particular, we demonstrate the optimization of a quantum-encoded TN ansatz state using a variational quantum eigensolver on an ion-trap quantum computer by preparing the ground states of the extended Su-Schrieffer-Heeger model. The generated states are characterized by estimating the topological invariants, verifying their topological order. Our TN encoding as a trapped ion circuit employs only single-site addressing optical pulses - the native operations naturally available on the platform. We reduce nearest-neighbor crosstalk by selecting different magnetic sublevels with well-separated transition frequencies to encode even and odd qubits.
A knowledge transfer-based adaptive differential evolution for solving nonlinear equation systems

Solving nonlinear equation systems (NESs) is an important yet challenging task in the field of numerical computation. It aims to locate multiple roots in a single run. However, the existing methods lack effective knowledge transfer. In this article, a knowledge transfer-based adaptive differential evolution is proposed to deal with NESs. Its main features are: (i) knowledge transfer between two niching techniques (crowding and speciation) is carried out to balance diversity and convergence; (ii) the variation characteristics of population diversity and convergence are used to judge knowledge transfer intensity; (iii) a knowledge transfer mechanism is designed to ensure that reasonable individuals are selected for the transfer to supplement the deficiencies of crowding and speciation; (iv) a parameter adaptation with niching level is introduced to improve search efficiency. Experiments on classical 30 NES problems have demonstrated that the proposed approach can outperform the state-of-the-art algorithms, in terms of root ratio and success rate.
Center-point-pair detection and context-aware re-identification for end-to-end multi-object tracking

Online multi-object tracking aims at generating the trajectories for multiple objects in the surveillance scene. It remains a challenging problem in crowded scenes because objects often gather together and occlude in tracking frames. The main impact of crowd occlusions is that it severely harms the performance of the detector and significantly increases the difficulty in extracting object features. In this paper, we propose an end-to-end tracking framework that alleviates such issues and estimates more accurate trajectories. Firstly, We design a Center-Point-Pair detection branch for object detection, which learns the correlations between the object head and the body to simultaneously predict the head and body regions to alleviate unreliable detection in tracking scenes. Secondly, we introduce the context information around the object to the tracker, inspired by the human search pattern. We propose a Context-Aware Re-Identification branch that includes the Previous-Frame Guided Spatial-Attention Model and the Previous-Frame Guided Channel-Attention Model to extract more discriminative object features. Thirdly, to harness the power of deep features for data association in generating reliable trajectories, we propose the Similarity Cluster Trajectory Management method that expands affinity descriptor and adopts the minority obeying the majority principle to association trajectories and detections. The experiments on diverse and challenging MOT datasets show that our tracking framework achieves superior results compared to other state-of-the-arts offline and online multi-object tracking methods.
From technology to community: the role of artefacts in teaching and learning during and beyond pandemic times

This article explores lecturers’ experience of adapting, shaping and transforming teaching and learning during the COVID-19 pandemic. The study focuses on understanding the challenges and opportunities that are afforded by pandemic-induced changes in terms of digital teaching and learning and their post-pandemic implications. Empirical data were collected through 16 semi-structured interviews with teaching staff at a Norwegian university. The article draws on Max W. Wartofsky’s work on artefacts, and uses the categorisation of primary, secondary and tertiary artefacts as a theoretical lens. The data indicates that designing and delivering courses that combine online and in-site teaching is a complex process requiring flexibility and creativity, which needs to be acknowledged by management and budget-allocating entities. Career development is an incentive to invest time in developing digital teaching. Finally, building a community of peers can support course quality and the professional welfare of the teaching staff.
Information cocoons in online navigation

Social media and online navigation bring us enjoyable experiences in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information. We provide a formal definition of IC in the scenario of online navigation. Subsequently, by analyzing real recommendation networks extracted from Science, PNAS, and Amazon websites, and testing mainstream algorithms in disparate recommender systems, we demonstrate that similarity-based recommendation techniques result in ICs, which suppress the system navigability by hundreds of times. We further propose a flexible recommendation strategy that addresses the IC-induced problem and improves retrieval accuracy in navigation, which are demonstrated by simulations on real data and online experiments on the largest video website in China. This paper quantifies the challenge of ICs in recommender systems and presents a viable solution, which offer insights into the industrial design of algorithms, future scientific studies, as well as policy making.
Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach

The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques.
Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System Architecture

Big data has revolutionized science and technology leading to the transformation of our societies. High-performance computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally, HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realization of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high-performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in situ and transit data analysis. This paper provides an extensive review of cutting-edge research on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.
Better be quiet about it! The Effects of Phantom Latency on Experienced First-Person Shooter Players

Users’ expectations about systems alter how they interact with them, thereby influencing their experience. Latency is also known to alter experience and performance in interactive systems, particularly in video games. Currently, it is unclear if users’ expectations influence latency-based effects. We report the results of an experiment (N = 24) in which participants played a video game with four levels of phantom latency (30 ms, 60 ms, 90 ms, and 120 ms). Crucially, all rounds were played with 75 ms of actual latency, we merely changed its presentation in the game. We show that a high phantom latency reduces game experience and performance. Participants were least accurate and effective when playing with 120 ms while feeling the least competent and tensest. We concluded that the effects of latency are partly expectation-based. Latency researchers, developers, and gamers must be aware of the effects induced by the expectation of latency.
Coverage Path Planning for SAR-UAV in Search Area Coverage Tasks Based on Deep Reinforcement Learning

Path planning is the most critical step in the search area coverage, which plays a vital role in many search tasks like regional surveillance, search, and rescue, hazard monitoring, etc. UAVs equipped with SAR sensors (SAR-UAV) are usually adopted in search area coverage tasks for their great superiority over optical sensors w.r.t the ability to “see” through the darkness, clouds, and rain. The traditional coverage path planning methods can hardly derive optimal plans or respond to environmental changes in time. We proposed a coverage path planning algorithm based on DRL to handle the search area coverage problem for SAR-UAV in this paper. We first modeled the imaging process of SAR-UAV, and we then designed the RL algorithm's action space, reward function, etc. Finally, we enabled the SAR-UAV to cover every point in the task area by controlling the actions of the SAR-UAV based on deep reinforcement learning. The experimental results show that the proposed method can complete the search area coverage tasks of various sizes, reduce the risk of exposure and minimize power loss, which demonstrates the effectiveness of the proposed method on search area coverage.
Applicable Image Security Based on Computational Genetic Approach and Self-Adaptive Substitution

With the emergence of new information technology fields and various usages of internet applications, securing massive amounts and multiple varieties of multimedia data has become a significant challenge. Hence, it is essential to investigate and develop new cryptographic algorithms to ensure the protection and privacy of multimedia data at rest and during transmission through the communication channel while attempting to overcome the limitations of traditional cryptographic methods. This paper suggests a novel image encryption and decryption technique based on a computational genetic approach and self-adaptive chaotic substitution. The proposed encryption method depends mainly on four steps: sequence generation, diffusion, confusion, and optimization. The USC-SIPI image dataset is being utilized to prove the correctness and effectiveness of our approach in defending against different attack types. Furthermore, the proposed technique could be capable of withstanding attacks while achieving an information entropy of 7. 999, Number of Pixel Change Rate of 99.62%, and a Unified Average Change in Intensity of 33.54%, respectively. The results of security testing and analysis revealed that our image security method is strongly recommended for modern communications security applications.
Performance Assessment of Traditional Software Development Methodologies and DevOps Automation Culture

Successful implementations of Software Development Methodologies significantly improve software efficiency, collaboration and security. Most companies are moving away from traditional development methodologies towards DevOps for faster and better software delivery. DevOps, which is a primary need of the IT industry, brings development and operation teams together to overcome communication gaps responsible for software failures. It relies on different sets of automation tools to robotize the tasks of software development from continuous integration, to testing, delivery, and deployment. The existence of several automation tools in each development phase raises the need for an integrated set of tools to reduce development time. For this purpose, we used the DevOps-based hybrid model Integrated Tool Chain (ITC), along with three sample java-based projects or code repositories to quantify the results. This paper evaluates and compares measurement metrics of java projects using traditional development methodologies and DevOps, and the results are shown in tabular and graphical format. The latest Google and Stack Overflow Trends have also been included to retrieve the best performer development methodology. This comparative and evaluative performance analysis will be beneficial to young researchers that study the metrics of software development, while also they will be introduced to the automotive environment of DevOps, the latest emerging buzzword in software development.