Reviewerly/exHarmony huggingface dataset:
Land-Use competitiveness of photovoltaic and concentrated solar power technologies near the Tropic of Cancer

Solar radiation can be utilized for electricity generation through two main categories of solar power technologies: (1) photovoltaic (PV), and (2) concentrated solar power (CSP). In this study, three-dimensional computer models of electricity generation from 9 different solar power systems (4 PV and 5 CSP) using the simulation program Energy3D were used to establish a generic ranking of these systems in terms of the expected electric energy output during one year per unit land area. This performance metric is referred to as energy generation intensity (EGI), with ‘energy’ here referring to electric energy in particular. The analysis is aimed for low-latitude zones near the Tropic of Cancer. Solar power systems composed of long rows of connected PV modules, with single-horizontal-axis solar tracking (variable tilt), or with fixed optimized orientation were found to have the best EGI. On the other hand; heliostats with solar towers, PV units with single-vertical-axis solar tracking, and PV units with two-axis solar tracking were found to have the lowest EGI. Other four CSP systems: parabolic dishes, parabolic troughs, linear Fresnel reflectors (LFR), and compact linear Fresnel reflectors with two absorbers formed an intermediate group. Considering all of the 9 solar systems, the estimated EGI ranged from 113.1 kWh/year.m2 to 227.2 kWh/year.m2. The monthly contributions to EGI were contrasted; and daily and hourly energy generation intensities were examined on December 21 and June 21. Minor validation was performed using the simulation tool PVWatts Calculator for benchmarking, and using the web application PVGIS for PV systems calculation.
MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration

The Municipal solid wastes incineration (MSWI) process with complex mechanism and strong nonlinearity is an important part of resource cycle. It is a challenge to design its multivariable controlled object model and control strategy. To solve this problem, a multi-input multi-output (MIMO) data-driven model and a multi-loop PID controller are proposed in this paper. Firstly, a feature selection method based on Pearson correlation coefficient (PCC) and expert knowledge is used to analyze the relationship between the manipulated variable and the controlled variable of MSWI process. Secondly, a MIMO Takagi–Sugeno fuzzy neural network (TSFNN) based on multi-task learning (MTL) is designed to construct the multivariable controlled object model. Thirdly, a multi-loop PID controller based on quasi-diagonal recurrent neural network (QDRNN) is constructed, which has self-feedback channel and interconnection channel, and can adjust the control parameters automatically. Next, the stability of control strategy is proved by Lyapunov second method. Finally, the modeling effect and control performance are confirmed on the simulation experiments based on the real MSWI process data.
Layered input GradiNet for image denoising

In image denoising, the recovery of high-frequency regions, such as image edges, directly affects the quality of the denoised images. However, previous deep learning-based denoising methods fail to effectively allocate the transmission of different frequency information and have difficulty giving network attention to high-frequency regions. In this paper, we rethink the fusion of image gradients in a neural network and deeply mine the intrinsic structure of the input image to propose a novel layered input gradient network (LIGN) for image denoising. The core of our network focuses on the features of different frequencies through two networks, which contain several key elements: (a) The input noise image is layered to widen the shallow layer of the network and to promote the hierarchical learning of different types of frequencies. (b) A multiscale feature extraction (MFE) block and information shunting (IS) block are proposed to integrate and separate various frequency features. (c) A gradient network (GradiNet) is designed to extract high-frequency information by network training, and the information is adaptively added to the input of the parallel main network (MainNet) through normalization to obtain high-quality images. Furthermore, we propose a sharpening loss function to enhance the texture details of the denoised image and improve visual quality. Extensive experiments on synthetic and real-world datasets show that the proposed method greatly enhances perceptual visual quality and achieves state-of-the-art performance on both PSNR and SSIM. The source code and pretrained models are available at https://github.com/JerryYann/LIGN . • A layered input gradient network (LIGN) based on a dual U-Net for high-quality image denoising is proposed. • Layered input and sharpening loss greatly improve the perceptual quality of the denoised image. • Multi-scale feature extraction block can capture more semantic information. • LIGN achieves the SoTA performance compared with the latest methods on synthetic and real noise datasets.
Pronounce differently, mean differently: A multi-tagging-scheme learning method for Chinese NER integrated with lexicon and phonetic features

Named Entity Recognition (NER) aims to automatically extract specific entities from the unstructured text. Compared with performing NER in English, Chinese NER is more challenging in recognizing entity boundaries because there are no explicit delimiters between Chinese characters. However, most previous researches focused on the semantic information of the Chinese language on the character level but ignored the importance of the phonetic characteristics. To address these issues, we integrated phonetic features of Chinese characters with the lexicon information to help disambiguate the entity boundary recognition by fully exploring the potential of Chinese as a pictophonetic language. In addition, a novel multi-tagging-scheme learning method was proposed, based on the multi-task learning paradigm, to alleviate the data sparsity and error propagation problems that occurred in the previous tagging schemes, by separately annotating the segmentation information of entities and their corresponding entity types. Extensive experiments performed on four Chinese NER benchmark datasets: OntoNotes4.0, MSRA, Resume, and Weibo, show that our proposed method consistently outperforms the existing state-of-the-art baseline models. The ablation experiments further demonstrated that the introduction of the phonetic feature and the multi-tagging-scheme has a significant positive effect on the improvement of the Chinese NER task.
Dense and Small Object Detection in UAV-Vision Based on a Global-Local Feature Enhanced Network

Unmanned aerial vehicles (UAVs) have been widely used in post-disaster search and rescue operations, object tracking, and other tasks. Therefore, the autonomous perception of UAVs based on computer vision has become a research hotspot in recent years. However, UAV images include dense objects, small objects, and arbitrary object directions, which bring about significant challenges to existing object detection methods. To alleviate these issues, we propose a global-local feature enhanced network (GLF-Net). Considering the difficulty of processing UAV images with complex scenes and dense objects, we designed a backbone based on an involution and self-attention that can extract effective features from complex objects. A multiscale feature fusion module is also proposed to address the presence of numerous small objects in UAV images through multiscale object detection and feature fusion. To accurately detect rotated objects, a rotated regional proposal network was designed based on the midpoint offset representation, which can apply a rotated box to determine the real direction and contour of an object. GLF-Net achieves a state-of-the-art detection accuracy (86.52% mAP) on our created RO-UAV dataset, while achieving 96.95% and 97% mAP on the public datasets HRSC2016 and UCAS-AOU, respectively. The experimental results demonstrate that our method achieves a high detection accuracy and generalization, which can meet the practical requirements of UAVs under various complex scenarios.
CMKT: Concept Map Driven Knowledge Tracing

In this article, we advocate for and propose a novel concept map driven knowledge tracing (CMKT) model, which utilizes educational concept map for learner modeling. This article particularly addresses the issue of learner data sparseness caused by the unwillingness to practice and irregular learning behaviors on the learner side. CMKT considers the concept map as a new information source and explicitly exploits its inherent information to help the estimation of the learner's knowledge state. Specifically, the pairwise educational relations in the concept map are formulated as the ordering pairs and are used as mathematical constraints for model construction. The topology information in the concept map is extracted and used as the model input by employing the network embedding techniques. Integrating both educational relation information and topology information in the concept map, CMKT adopts the recurrent neural network to perform knowledge tracing tasks. Comprehensive evaluations conducted on five public educational datasets of four different subjects (more than 8000 learners and their 300 000 records) demonstrate the promise and effectiveness of CMKT: The average area under ROC curve (AUC) and overall prediction accuracy (ACC) achieve 0.82 and 0.75, respectively, and CMKT outperforms all the baselines by at least 12.2% and 9.2% in terms of AUC and ACC.
Polymorphic lambda calculus with context-free session types

Session types provide a typing discipline for structured communication on bidirectional channels. Context-free session types overcome the restriction to tail recursive protocols characteristic of conventional session types. This extension enables the serialization and deserialization of tree structures in a fully type-safe manner. We present the theory underlying the language FreeST 2, which features context-free session types in an extension of System F with linear types and a kinding system to distinguish message types, session types, and channel types. The system presents metatheoretical challenges which we address: contractivity in the presence of polymorphism, a non-trivial equational theory on types, and decidability of type equivalence. We also establish standard results on typing preservation, progress, and a characterization of erroneous processes.
Comparative Analysis Between Two Fuzzy Variants of Harmonic Search Algorithm: Fuzzy Fault Tolerant Control Application

The goal of this research is to improve the harmonic search (HS) algorithm by using type-1 and interval type-2 fuzzy systems to dynamically change one of the evolutionary method's parameters. We have previously used both sorts of fuzzy systems in a variety of benchmark challenges and discovered that using fuzzy logic in conjunction with the harmonic search algorithm produces good results. In some of the experiments, it is clearly demonstrated that our methodology is statistically superior to other algorithms. Using type-1 and interval type-2 fuzzy systems, the harmony memory (HMR) parameter is dynamically changed during the evolution process in this example. The fundamental contribution of this work is the capacity to establish, by experimentation in a benchmark control issue, which of the two types of fuzzy systems employed with the harmonic search method produces better results. This is because there are no previous studies to our idea that employ and compare type-1 and interval type-2 fuzzy systems. Furthermore, three type of uncertainties are employed in the benchmark two-tank level control system to assess the performance of both fuzzy systems, simulating the disturbances that may present in the actual world and therefore allowing statistical validation if there are substantial differences between type-1 and interval type-2 fuzzy systems.
GHOST—A New Face Swap Approach for Image and Video Domains

Deep fake stands for a face swapping algorithm where the source and target can be an image or a video. Researchers have investigated sophisticated generative adversarial networks (GAN), autoencoders, and other approaches to establish precise and robust algorithms for face swapping. However the achieved results are far from perfect in terms of human and visual evaluation. In this study, we propose a new one-shot pipeline for image-to-image and image-to-video face swap solutions. We take the FaceShifter (image-to-image) architecture as a baseline approach and propose several major architecture improvements which include a new eye-based loss function, face mask smooth algorithm, a new face swap pipeline for image-to-video face transfer, a new stabilization technique to decrease face jittering on adjacent frames and a super-resolution stage. In the experimental stage, we show that our solution outperforms SoTA face swap architectures in terms of ID retrieval (+1.5% improvement), shape (the second best value) and eye gaze preserving (+1% improvement) metrics. We also established an ablation study for our solution to estimate the contribution of pipeline stages to the overall accuracy, which showed that the eye loss leads to 2% improvement in the ID retrieval and 45% improvement in the eye gaze preserving.
Modelling procurement problems in the environment of blockchain technology

• Developed a model for procurement problem considering blockchain technology cost. • Model captures transparency, security, and immutable data during the procurement. • Proposed model is a mixed integer linear program to optimize the procurement cost. • Model is validated on three different randomly generated datasets. In the highly competitive business environment, Supply chain (SC) disruption strongly impacted the manufacturing cost. The poor goods management and bottleneck have muddled the SC. It leads to cross-delivery, wastage of delivery time, burglary, quality and sometimes damages to the essential and precious goods. Only digitisation can be seen as an alternative to neutralise these obstacles from the procurement problems in SC. Security, immutable data, traceability, and transparency are the significant goal of the digital supply chain (DSC), which can only cut down the operational cost and are decisive factors for meeting the customer demand in real-time without disrupting the smooth flow of information. The rising concern towards cross-delivery, delivery delay time, hacking of data, transparency, intermediated and burglary have forced us to integrate blockchain technology (BCT) with the traditional supply chain. This work presents the DSC’s integrated mathematical model, transforming traditional procurement problems into digital procurement. This research paper optimises the procurement cost after adopting and integrating the blockchain (an infancy technology). The proposed model is a Mixed Integer-Linear programming mathematical model that incorporates the block development cost while purchasing, ordering, transporting and holding processes. These four processes are the susceptible stages where leakage can be reported; therefore, an integrated MILP model is proposed to eradicate the leakage from the system and endorse transparency, traceability, and immutable information for DSC. Finally, the model is authenticated and verified over the three different scenarios illustration using the LINGO 10.0 software for digital procurement problems based on the randomly generated datasets.
Visual inspection of fault type and zone prediction in electrical grids using interpretable spectrogram-based CNN modeling

In electrical grids, fault diagnosis (fault type and fault location classifications) are critical due to their economic and important implications. Numerous smart grid applications have embraced data-driven methodologies. While the majority of the work in this topic has been on increasing the predicted accuracy of machine-learning model for fault diagnosis, one important aspect that has received less attention is the interpretability of these systems. We advocate for a complementary perspective. To represent faulty signals, we propose a spectrogram-convolutional neural network based representation of the electrical signals where pre-trained models such as GoogleNet and SqueezeNet are trivially used. We then perform multiple fault classification tasks and offer a visual interpretation of the collected findings. The suggested approach makes the model more transparent through the use of Gradient-weighted Class Activation Mapping (Grad-CAM), which visualizes regions in the input spectrogram that are more relevant for predictions, assisting the end-user in the understanding and interpreting the results. We explore the merits of the suggested technique in terms of increasing the transparency of the black-box machine learning system, which is a critical requirement for designing modernized smart grids. • Visual representation of electrical signal by using spectrogram-based CNN modeling. • Building a visually interpretable fault type and zone classification system. • A highly accurate system able to provide individual or joint FTC or FZC. • Comparison with a large number of baselines using DFT or DWT.
Deep Learning Gated Recurrent Neural Network-Based Channel State Estimator for OFDM Wireless Communication Systems

Using deep learning technologies, the channel estimate for an orthogonal frequency division multiplexing system (OFDM) based on pilots is done in this work.To be more specific, deep learning gated recurrent unit (GRU) neural networks are used to present a new framework for channel estimation.Initially, it is trained offline using generated data sets, and thereafter it is used online to track the channel parameters, after which the data transmitted can be recovered.For the purpose of determining the performance of the proposed estimator, three alternative deep learning optimization techniques are used to test it.It is also compared to other commonly used estimators, such as least squares (LS) and minimum mean square error (MMSE).In addition, the proposed estimator is compared with two existing models.Deep learning GRU neural network-based channel state estimator, which are capable of learning and generalizing rapidly, are shown to outperform the comparable estimators when just a few pilots are available.In addition, there is no need for prior knowledge of channel statistics.So, estimating OFDM communication system channel states using the proposed estimator appears promising.
Context-aware image compression optimization for visual analytics offloading

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of running CNN on computation-constrained end devices. A critical component to ensure low-latency and accurate visual analytics offloading over low bandwidth networks is image compression that minimizes the amount of visual data to offload and maximizes the decoding quality of salient pixels for analytics. Despite the wide adoption, JPEG standard and traditional image compression do not address the accuracy of analytics tasks, leading to ineffective compression for visual analytics offloading. Although recent machine-centric image compression techniques leverage sophisticated neural network models or hardware architecture to support the accuracy-bandwidth trade-off, they introduce excessive latency in the visual analytics offloading pipeline. This paper presents CICO, a Context-aware Image Compression Optimization framework to achieve low-bandwidth and low-latency visual analytics offloading. CICO contextualizes image compression for offloading by employing easily-computable low-level image features to understand the importance of different image regions for a visual analytics task. Accordingly, CICO can optimize the trade-off between compression size and analytics accuracy. Extensive real-world experiments demonstrate that CICO reduces the bandwidth consumption of existing compression methods by up to 40% under a comparable analytics accuracy. In terms of the low-latency support, CICO achieves up to a 2x speedup over state-of-the-art compression techniques.
Methods of Intelligent Control in Mechatronics and Robotic Engineering: A Survey

Artificial intelligence is becoming an increasingly popular tool in more and more areas of technology. New challenges in control systems design and application are related to increased productivity, control flexibility, and processing of big data. Some kinds of systems require autonomy in real-time decision-making, while the other ones may serve as an essential factor in human-robot interaction and human influences on system performance. Naturally, the complex tasks of controlling technical systems require new modern solutions, but there remains an inextricable link between control theory and artificial intelligence. The first part of the present survey is devoted to the main intelligent control methods in technical systems. Among them, modern methods of adaptive and optimal control, fuzzy logic, and machine learning are considered. In its second part, the crucial achievements in intelligent control applications in robotic and mechatronic systems over the past decade are considered. The references are structured according to the type of such common control problems as stabilization, controller tuning, identification, parametric optimization, iterative learning, and prediction. In the conclusion, the main problems and tendencies toward intelligent control methods improvement are outlined.
Actual vs. perceived exertion during active virtual reality game exercise

Virtual exercise has become more common as emerging and converging technologies make active virtual reality games (AVRGs) a viable mode of exercise for health and fitness. Our lab has previously shown that AVRGs can elicit moderate to vigorous exercise intensities that meet recommended health benefit guidelines. Dissociative attentional focuses during AVRG gameplay have the potential to widen the gap between participants' perception of exertion and actual exertion.The aim of this study was to determine actual exertion (AEx) vs. perceived exertion (PEx) levels during AVRGs by measuring heart rate (HR) and ratings of perceived exertion (RPE) in two different settings.HR and RPE were collected on participants (N = 32; age 22.6 ± 2.6) during 10 min of gameplay in LabS and GymS using the HTC VIVE with the following games played: Fruit Ninja VR (FNVR), Beat Saber (BS), and Holopoint (HP).Participants exhibited significantly higher levels of AEx compared to reported PEx for all three AVRGs (Intensity): FNVR [AEx = 11.6 ± 1.8 (Light), PEx = 9.0 ± 2.0 (Very Light)], BS [AEx = 11.3 ± 1.7 (Light), PEx = 10.3 ± 2.1 (Very Light)], HP [AEx = 13.1 ± 2.3 (Somewhat Hard), PEx = 12.3 ± 2.4 (Light-Somewhat Hard)]. Additionally, participants playing in the GymS experienced significantly higher levels of AEx [12.4 ± 2.3 (Light-Somewhat Hard)] and PEx [10.8 ± 2.5 (Very Light-Light)] compared to the LabS [AEx = 11.6 ± 1.8 (Light), PEx = 10.3 ± 2.6 (Very Light-Light)].Perceptions of exertion may be lower than actual exertion during AVRG gameplay, and exertion levels can be influenced by the setting in which AVRGs are played. This may inform VR developers and health clinicians who aim to incorporate exercise/fitness regimens into upcoming 'virtual worlds' currently being developed at large scales (i.e., the "metaverse").