Reviewerly/exHarmony huggingface dataset:
Technical and economic viability of greenfield large scale photovoltaic plants in Romania

In 2021 a favorable environment reemerged in Romania for the development of greenfield large scale photovoltaic plants, after all the developments for large scale projects were halted at the end of 2016 when the former subsidy scheme ended. The conclusion of long-term power purchase agreements, a regulatory framework for contracts for difference, grants opportunities, the decreasing cost of technology, rising energy prices and sustainability commitments are all creating a promising context for greenfield development of solar photovoltaics in Romania. This paper covers an existing gap in the scientific literature by assessing technically and economically the viability of large scale photovoltaics in Romania. The paper shows the yield and performance gap between the major regions in Romania and how this gap affects the financial return and the levelized costs of electricity. The assessment shows that using 1-axis tracking is improving the specific yield with 10–13% in comparison with a fixed tilt technology and bifacial modules add an additional 3% to the yield. It was identified that 1-axis tracking improves the captured price in comparison with fixed tilt with up to 4.5 EUR/MWh in the long-term. The same methodology can be applied for updating the assessment when cost or revenue assumptions are changing. The modelling and analysis framework can be used in the technical and economic benchmarking of real projects in Romania and gives a flavor of what can be expected in terms of pricing for long term power purchase agreements, auctions for contracts for difference, but also for the expected returns if the projects remain merchantly exposed.
Novel single and multi-layer echo-state recurrent autoencoders for representation learning

Representation learning impacts the performance of Machine Learning (ML) models. Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform efficiently on a specific task, in terms of: (1) high accuracy, (2) large short-term memory and (3) low execution time. The Echo-State Network (ESN) is a recent specific kind of a Recurrent Neural Networks (RNN), that presents very rich dynamics on account of its reservoir-based hidden layer. It is widely used in dealing with complex non-linear problems and has been shown to outperform classical approaches in a number of benchmark tasks. In this paper, the powerful dynamism and large memory provided by the ESN and complementary strengths of AEs in feature extraction are integrated, to develop a novel Echo-State Recurrent Autoencoder (ES-RA). In order to devise more robust alternatives to conventional reservoir-based networks, both single- (SL-ES-RA) and multi-layer (ML-ES-RA) models are formulated. The new features, once extracted from ESN’s hidden layer, are applied to various benchmark ML tasks including classification, time series prediction and regression. A range of evaluation metrics are shown to improve considerably compared to those obtained when applying original data features. An accuracy-based comparison is performed between our proposed recurrent AEs and two variants of ELM feed-forward AEs (Single and ML), for both noise free and noisy data. In summary, a comparative empirical study reveals the key contribution of exploiting recurrent connections in improving benchmark performance results.
Intelligent Correction Method of Shooting Action Based on Computer Vision

Aiming at the problem that the students' long-term use of nonstandard shooting action leads to poor basketball teaching effect, an intelligent correction method of shooting action based on computer vision is proposed. Combined with the principle of computer vision, the image acquisition model of basketball shooting action is constructed. The edge contour and adaptive feature segmentation of basketball images are detected, and abnormal shooting movements are recognized. The intelligent correction model of shooting action is constructed, and the intelligent correction of shooting action is realized. Finally, through experiments, it is proved that the visual analysis and intelligent correction effect of basketball shooting action are obviously better, and it can correct shooting action in real time and accurately.
An Analysis of Archive Digitization in the Context of Big Data

With the advancement of technology and the accumulation of archival information resource data in the era of big data, making use of archival information resources to improve the archival service capability and promote intelligent archive management has become a pressing task for data resource management. Keeping the significance of archive management in consideration, this research focuses on the implementation of data mining technology in the archive information resource sharing platform by discussing the construction of the data mining model, which is based on explaining the ideas related to data mining. The functional modules and operational process of the data mining model are comprehensively examined in order to select and create archival information data sources. The process of preparing data mining models as well as the creation of the prediction and the profiling models are discussed to lay down the foundation of a data mining-based model for archival information resources. Furthermore, this study also provides a more effective scientific solution to achieve the goal of comprehensive development of archival information digitization.
My High Horse Is Dying: Agitating Internalized Neoliberalism in Higher Education With(Out) Compassion

This critical, poststructural autoethnography agitates internalized neoliberalism in the higher education context. Provocative and experimental, it is intended to trouble neoliberal norms that shape professional selves and higher-education-the-collective, while also interweaving what it means to be/come a scholar. Nonlinear flares, manifested in theorization, images, vignettes, checklists, and poetry illuminate the subtleties of inescapable neoliberalism at individual and systemic levels. Offering compassion as a throughline of inquiry, I wonder if/how it is possible for compassion to disrupt neoliberalism through the precarity of individual and systemic enaction. As higher education goes on, and uncertain institutions contemplate staff/faculty retention, along with the telos of college education, how do we come to terms with neoliberalism’s stronghold that stifles emancipating onto-epistemic possibilities? Can/does compassion create breeches for new possibilities?
Safe motion planning with environment uncertainty

We present an approach for safe motion planning under robot state and environment (obstacle and landmark location) uncertainties. To this end, we first develop an approach that accounts for the landmark uncertainties during robot localization. Existing planning approaches assume that the landmark locations are well known or are known with little uncertainty. However, this might not be true in practice. Noisy sensors and imperfect motions compound to the errors originating from the estimate of environment features. Moreover, possible occlusions and dynamic objects in the environment render imperfect landmark estimation. Consequently, not considering this uncertainty can wrongly localize the robot, leading to inefficient plans. Our approach thus incorporates the landmark uncertainty within the Bayes filter estimation framework. We also analyze the effect of considering this uncertainty and delineate the conditions under which it can be ignored. Second, we extend the state-of-the-art by computing an exact expression for the collision probability under Gaussian distributed robot motion, perception and obstacle location uncertainties. We formulate the collision probability process as a quadratic form in random variables. Under Gaussian distribution assumptions, an exact expression for collision probability is thus obtained which is computable in real-time. In contrast, existing approaches approximate the collision probability using upper-bounds that can lead to overly conservative estimate and thereby suboptimal plans. We demonstrate and evaluate our approach using a theoretical example and simulations. We also present a comparison of our approach to different state-of-the-art methods.
Federated Learning Cost Disparity for IoT Devices

Federated learning (FL) promotes predictive model training at the Internet of things (IoT) devices by evading data collection cost in terms of energy, time, and privacy. We model the learning gain achieved by an IoT device against its participation cost as its utility. Due to the device-heterogeneity, the local model learning cost and its quality, which can be time-varying, differs from device to device. We show that this variation results in utility unfairness because the same global model is shared among the devices. By default, the master is unaware of the local model computation and transmission costs of the devices, thus it is unable to address the utility unfairness problem. Also, a device may exploit this lack of knowledge at the master to intentionally reduce its expenditure and thereby enhance its utility. We propose to control the quality of the global model shared with the devices, in each round, based on their contribution and expenditure. This is achieved by employing differential privacy to curtail global model divulgence based on the learning contribution. In addition, we devise adaptive computation and transmission policies for each device to control its expenditure in order to mitigate utility unfairness. Our results show that the proposed scheme reduces the standard deviation of the energy cost of devices by 99 % in comparison to the benchmark scheme, while the standard deviation of the training loss of devices varies around 0.103.
Near-Shore ship segmentation based on I-Polar Mask in remote sensing

With the development of deep convolutional neural network, the performance of instance segmentation algorithms has been significantly improved. However, it is difficult to implement quick and accurate near-shore ship segmentation due to the complex background and arbitrary ship orientation, which makes segmentation challenging. To improve the segmentation efficiency of near-shore ships, this paper proposes a ship segmentation network based on Polar Mask, named I-Polar Mask. Specifically, we construct a Specific Polar Template (SPT) and Oriented Polar IoU (Intersection over Union) to better match the ship contour. Furthermore, oriented polar centre-ness was designed to reduce the weight of low-quality masks more effectively. In addition, a Context Information Extraction Module (CIEM) is built to reduce the influence of complex backgrounds and make the segmentation more accurate. To verify the effectiveness of the proposed algorithm, we collected 1015 images of near-shore areas from Google Maps and labelled them with Labelme to construct a ship instance segmentation dataset, called I-Ship. Extensive experiments on I-Ship show that the AP value of I-Polar Mask is improved by 5.9% compared with Polar Mask, which is a significant improvement. Compared with advanced methods, I-Polar Mask outperforms both quantitative and qualitative aspects.
The Use of Gaussian Processes in System Identification

Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistically conditioning it on the observed data. In system identification, Gaussian processes are used to form time series prediction models such as nonlinear finite-impulse response (NFIR) models as well as nonlinear autoregressive (NARX) models. Gaussian process state-space (GPSS) models can be used to learn the dynamic and measurement models for a state-space representation of the input-output data. Temporal and spatiotemporal Gaussian processes can be directly used to form regressor on the data in the time domain. The aim of this article is to briefly outline the main directions in system identification methods using Gaussian processes.
Extended Speckle Reduction Anisotropic Diffusion Filter to Despeckle Ultrasound Images

Speckle Reduction Anisotropic Diffusion filter which is used to despeckle ultrasound images, perform well at homogeneous region than in heterogeneous region resulting in loss of information available at the edges.Extended SRAD filter does the same, preserving better the edges in addition, compared to the existing SRAD filter.The proposed Extended SRAD filter includes the intensity of four more neighboring pixels in addition with other four that is meant for SRAD filter operation.So, a total of eight pixels are involved in determining the intensity of a single pixel.This improves despeckling performance by maintaining the information accessible at an image's edges.The proposed filter produces better Peak Signal to Noise Ratio, Root Mean Square Error and Structural Similarity Index values for standard test images with different noise levels with variance 0.3, 0.35 and 0.4.It also performs well in denoising breast ultrasound images at different noise levels.
Dark and Bright Channel Priors for Haze Removal in Day and Night Images

Removal of noise from images is very important as a clear, denoised image is essential for any application.In this article, a modified haze removal algorithm is developed by applying combined dark channel prior and multi-scale retinex theory.The combined dark channel prior (DCP) and bright channel prior (BCP) together with the multi-scale retinex (MSR) algorithm is used to dynamically optimize the transmission map and thereby improve visibility.The proposed algorithm performs effective denoising of images considering the properties of retinex theory.The proposed method removes haze on an image scene through estimation of the atmospheric light and manipulating the transmittance parameter.The coarse transmittance is refined using edge-preserving median filtering.Experimental results depict that the proposed technique enhances the image quality degraded by foggy weather during day time or night.The results show that the proposed algorithm enhances the image quality in terms of PSNR and SSIM to a considerate level.
FLSIR: Secure Image Retrieval Based on Federated Learning and Additive Secret Sharing

With the rapid deployment of electronic imaging devices, plenty of high-quality images are in the general public's hands.These images can be profitable, such as providing retrieval services; however, it is difficult for the individual to profit without the support of the cloud platform.The straightforward idea is that image owners upload their images to the cloud; yet, it is infeasible as the cloud platforms are not fullytrusted.In previous works, in order to protect the privacy of image owners, many researchers consider the Secure content-based Image Retrieval (SIR) task, which enables cloud servers to provide retrieval services while not exposing the images from the owners.However, the existing schemes are often not friendly to users as it's assumed that the owners have no profit demand and are unwilling to provide extra computation resources.This work introduces federated learning into SIR, which ensures better retrieval accuracy and efficiency; the additive secret sharing technology is utilized to protect the image information, and a better secure comparison protocol is proposed for better efficiency.We believe that the users can enjoy a better secure retrieval service with our proposed scheme.The experiment results and security analysis demonstrate that our scheme provides a significant accuracy advantage while ensuring efficiency and security.
Real-Time Classification of Real-Time Communications

Real-time communication (RTC) applications have become largely popular in the last decade with the spread of broadband and mobile Internet access. Nowadays, these platforms are a fundamental means for connecting people and supporting businesses that increasingly rely on forms of remote work. In this context, it is of paramount importance to operate at the network level to ensure adequate Quality of Experience (QoE) for users, and appropriate traffic management policies are essential to prioritize RTC traffic. This in turn requires the network to be able to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify media streams generated by RTC applications encapsulated in Secure Real-Time Protocol (SRTP) flows in real-time. Using carefully tuned features extracted from packet characteristics, we train models to classify streams into a variety of classes, including media type (audio/video), video quality, and redundant streams. We validate our approach using traffic from over 62 hours of multi-party meetings conducted using two popular RTC applications, namely Cisco Webex Teams and Jitsi Meet. We achieve an overall accuracy of 96% for Webex and 95% for Jitsi, using a lightweight decision tree model that makes decisions based solely on 1 second of real-time traffic. Our results show that models trained for a particular meeting software have difficulty when used with another one, although domain adaptation techniques facilitate the transfer of pre-trained models.
Multimodal-Based and Aesthetic-Guided Narrative Video Summarization

Narrative videos usually illustrate the main content through multiple narrative information such as audios, video frames and subtitles.Existing video summarization approaches rarely consider the multiple dimensional narrative inputs, or ignore the impact of shot artistic assembly when directly applied to narrative videos.This paper introduces a multimodal-based and aesthetic-guided narrative video summarization method.Our method leverages multimodal information including visual content, subtitles and audio information through our specified key shot selection, subtitle summarization, and highlight extraction components.Furthermore, under the guidance of cinematographic aesthetic, we design a novel shot assembly module to ensure the shot content completeness and then assemble the selected shots into a desired summary.Besides, our method also provides the flexible specification for shot selection, to achieve which it automatically selects semantically related shots according to the user-designed text.By conducting a large number of quantitative experimental evaluations and user studies, we demonstrate that our method effectively preserves important narrative information of the original video, and it is capable of rapidly producing highquality and aesthetic-guided narrative video summaries.
Mobile Augmented Reality and Consumer Experience: A Mixed-Methods Analysis on Emotional Responses and Intention to Buy Household Items

Mobile Augmented Reality (MAR) technology offers a new and unique way for consumers to interact with products, increasing their desire to buy. Despite the popularity of MAR apps in the retail industry, the examination of the consumers’ emotional states, and the reasons evoking increased purchase intention are still under researched. To this end, this study seeks to explore the users’ emotional states and the design elements that affect their intention to buy a MAR viewed product, and use the app. A prototype MAR iOS app was developed, and a user test was conducted on 21 participants. The methodology was based on a mixed design approach, combining quantitative and qualitative data emerged from scaled questionnaire items, interviews, and open-ended questions. The thematic analysis revealed eight emotional codes, and six codes of perceived usefulness. Both analyses showed that the interaction with the app triggered positive emotions of enjoyment and fascination, as well as an increased desire to use the app and buy the product. The ability to test the product in the real space and the facility to combine different objects in space before the actual purchase were the strongest indicators of purchase intention. A set of specific functionality elements caused negative emotions of confusion and disappointment. Overall, the research findings provide useful insights on the MAR elements that can positively affect the consumers’ purchase intention.
Learning Spatial Transformations and their Math Representations through Embodied Learning in Augmented Reality

In Computer Aided Design, Computer Graphics, Robotics, etc., students suffer from inefficient and non-proficient use of the 3D modeling software due to a lack of mathematical knowledge. Deficient knowledge and skills may lead students to use the modeling software through trial-and-error without understanding the algorithms and mathematics. Spatial/geometric transformation is recognized as one of the key factors in learning 3D modeling software. This paper presents a newly developed educational Augmented Reality (AR) mobile application to help students intuitively learn the geometric reasoning of transformation matrices and the corresponding trigonometric equations through play. The application, developed in primary and advanced levels, intends to facilitate the understanding of fundamentals of spatial transformations and their mathematical representations in a self-learning approach. The results of a pilot user study conducted on 7 undergraduate students for the primary level reveal that students' math scores improved after playing with the application.
Electroencephalography and Self-assessment Evaluation of Engagement with Online Exhibitions: Case Study of Google Arts and Culture

The COVID-19 pandemic has and will continue to have an unprecedented impact on museums and exhibition galleries worldwide, with online visitors to museums and exhibitions increasing significantly. The most common method used by web user experience researchers to study user engagement is questionnaires, usually conducted after the user has completed the website experience and relying on the user’s memory and lingering feelings. Therefore, the purpose of this paper is to propose a new method of assessment based on a combination of user electroencephalography (EEG) signals and a self-assessment questionnaire (UES-SF). Since EEG signal measurement is a practical method to detect sequential changes in brain activity without significant time delays, it can comprehend visitors’ unconscious and sensory responses to online exhibitions. This paper employed the Google Arts & Culture (GA&C) website as an example to study 4 different exhibition formats and their impact on user engagement. The questionnaire results showed that the “game interaction” was significantly higher (p < 0.05) in terms of participation than the “2D information Kiosks” and “3D virtual exhibitions” and was the marginally significant (0.05 < p < 0.10) than “video explanation”. However, when we combined the EEG data, we could determine that “game interaction” had the highest user engagement, followed by “video explanation”, “3D virtual exhibition”, and the “2D information kiosk”. Therefore, our new evaluation approach can assist online exhibition user experience researchers in understanding the impact of different forms of interaction on engagement more comprehensively.