Reviewerly/exHarmony huggingface dataset:
Text-to-image synthesis with self-supervised bi-stage generative adversarial network

Text-to-image synthesis is challenging as generating images that are visually realistic and semantically consistent with the given text description involves multi-modal learning with text and image. To address the challenges, this paper presents a text-to-image synthesis model that utilizes self-supervision and bi-stage image distribution architecture, referred to as the Self-Supervised Bi-Stage Generative Adversarial Network (SSBi-GAN). The self-supervision diversifies the learned representation thus improving the quality of the synthesized images. Besides that, the bi-stage architecture with Residual network enables the generation of larger images with finer visual contents. Not only that, some enhancements including L1 distance, one-sided smoothing and feature matching are incorporated to enhance the visual realism and semantic consistency of the images as well as the training stability of the model. The empirical results on Oxford-102 and CUB datasets corroborate the ability of the proposed SSBi-GAN in generating visually realistic and semantically consistent images.
The role of data imbalance bias in the prediction of protein stability change upon mutation

There is a controversy over what causes the low robustness of some programs for predicting protein stability change upon mutation. Some researchers suggested that low-quality data and insufficiently informative features are the primary reasons, while others attributed the problem largely to a bias caused by data imbalance as there are more destabilizing mutations than stabilizing ones. In this study, a simple approach was developed to construct a balanced dataset that was then conjugated with a leave-one-protein-out approach to illustrate that the bias may not be the primary reason for poor performance. A balanced dataset with some seemly good conventional n-fold CV results should not be used as a proof that a model for predicting protein stability change upon mutations is robust. Thus, some of the existing algorithms need to be re-examined before any practical applications. Also, more emphasis should be put on obtaining high quality and quantity of data and features in future research.
A framework to estimate generating capacities of PV systems using satellite imagery segmentation

The growing interest in global warming has led to various efforts to rely on new renewable energy such as solar power. More accurate energy generation calculation is on-demand with the rise of publicly accessible residential photovoltaic (PV) panels. PV panel area computation is the first step towards the image-based estimation of energy generation from the residential solar arrays. Satellite image segmentation provides a low-cost and simple solution to calculate solar panel area installed on rooftops and the ground. The most relevant approach is to acquire high-quality labels rather than network architectures in many scenarios. This research aims to estimate potential solar energy from satellite images using deep learning segmentation techniques. We compared five architectures such as UNet, UNet++, Feature Pyramid Network (FPN), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3, with different encoders. We converted segmented image pixels into square meters regarding the average slope of panels. We considered the average solar panel efficiency, global tilted irradiation, and the loss coefficient to estimate the annual energy output from the calculated area. UNet with the EfficientNetB7 encoder performed the best with a dice similarity coefficient of 0.9982 for segmentation and a Mean Absolute Percentage Error (MAPE) of 3.5 for energy output estimation.
Satellite-derived solar radiation for intra-hour and intra-day applications: Biases and uncertainties by season and altitude

Regional to European scale intra-day forecasts of solar resources and photovoltaic (PV) power generation are of key relevance for operators of solar plants and power grids, as well as power traders, who seek to optimize revenues and maintain grid stability by matching power supply and demand. Our study addresses a key prerequisite for such forecasts: the accuracy of intra-hour and intra-day surface solar radiation (SSR) estimates of the HelioMont Meteosat product, a state of the art satellite product particularly suited for the Alpine region. A second satellite product, SARAH-2, as well as longer time scales, are also examined for comparison. The satellite SSR estimates are analyzed based on 136 ground stations across altitudes from 200 m to 3570 m in Switzerland in 2018. In peak daytime periods, the instantaneous satellite SSR deviates from the ground-measured SSR by a mean absolute deviation (MAD) of 110.4 and 99.6 W/m2 for SARAH-2 and HelioMont, respectively. For the daytime SSR, the instantaneous, hourly, and daily-mean MADs amount to 91.7, 81.1, 50.8, and 82.5, 66.7, 42.9 W/m2 for SARAH-2 and HelioMont, respectively. SARAH-2 drastically underestimates instantaneous SSR in winter at altitudes above 1000 m, possibly due to snow cover. HelioMont performs better in this respect, but the remaining substantial, spatially and temporally heterogeneous biases we identify pose a challenge with regard to the use of HelioMont for accurate intra-day PV estimates and forecasts.
Part-of-Speech Tagging of Odia Language Using Statistical and Deep Learning Based Approaches

Automatic part-of-speech (POS) tagging is a preprocessing step of many natural language processing tasks, such as named entity recognition, speech processing, information extraction, word sense disambiguation, and machine translation. It has already gained promising results in English and European languages. However, in Indian languages, particularly in the Odia language, it is not yet well explored because of the lack of supporting tools, resources, and morphological richness of the language. Unfortunately, we were unable to locate an open source POS tagger for the Odia language, and only a handful of attempts have been made to develop POS taggers for the Odia language. The main contribution of this research work is to present statistical approaches such as the maximum entropy Markov model and conditional random field (CRF), as well as deep learning based approaches, including the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) to develop the Odia POS tagger. A publicly accessible corpus annotated with the Bureau of Indian Standards (BIS) tagset is used in our work. However, most of the languages around the globe have used the dataset annotated with the Universal Dependencies (UD) tagset. Hence, to maintain uniformity, the Odia dataset should use the same tagset. Thus, following the BIS and UD guidelines, we constructed a mapping from the BIS tagset to the UD tagset. The maximum entropy Markov model, CRF, Bi-LSTM, and CNN models are trained using the Indian Languages Corpora Initiative corpus with the BIS and UD tagsets. We have experimented with various feature sets as input to the statistical models to prepare a baseline system and observed the impact of constructed feature sets. The deep learning based model includes the Bi-LSTM network, the CNN network, the CRF layer, character sequence information, and a pre-trained word vector. Seven different combinations of neural sequence labeling models are implemented, and their performance measures are investigated. It has been observed that the Bi-LSTM model with the character sequence feature and pre-trained word vector achieved a result with 94.58% accuracy.
Use of Online Databases by Undergraduate Students at a Health Sciences College in Malawi

AbstractAbstractOnline databases, which are more recent electronic information advances have influenced the delivery of information services in institutions of higher learning. This major worry regarding resource under-utilization prompted this research in order to understand participants' use of these online databases as well as the reasons for such patterns at Saint John of God College of Health Sciences in Malawi. To investigate the utilization of online databases by students at Saint John of God College of Health Sciences in Malawi, this quantitative study employed questionnaires to collect the data. According to the findings, enhancing users' knowledge and awareness of online databases has the ability to increase online database usage levels. Online databases are beneficial to students' progress in completing diverse educational tasks. Finally, the study identified factors that may inspire or dissuade users to use online databases.Keywords: Electronic journalsInformation and Communication Technologyonline databasesSaint John of God College of Health SciencesMalawi
Robust Control for UAV Close Formation Using LADRC via Sine-Powered Pigeon-Inspired Optimization

This paper designs a robust close-formation control system with dynamic estimation and compensation to advance unmanned aerial vehicle (UAV) close-formation flights to an engineer-implementation level. To characterize the wake vortex effect and analyze the sweet spot, a continuous horseshoe vortex method with high estimation accuracy is employed to model the wake vortex. The close-formation control system will be implemented in the trailing UAV to steer it to the sweet spot and hold its position. Considering the dynamic characteristics of the trailing UAV, the designed control system is divided into three control subsystems for the longitudinal, altitude, and lateral channels. Using linear active-disturbance rejection control (LADRC), the control subsystem of each channel is composed of two cascaded first-order LADRC controllers. One is responsible for the outer-loop position control and the other is used to stabilize the inner-loop attitude. This control system scheme can significantly reduce the coupling effects between channels and effectively suppress the transmission of disturbances caused by the wake vortex effect. Due to the cascade structure of the control subsystem, the correlation among the control parameters is very high. Therefore, sine-powered pigeon-inspired optimization is proposed to optimize the control parameters for the control subsystem of each channel. The simulation results for two UAV close formations show that the designed control system can achieve stable and robust dynamic performance within the expected error range to maximize the aerodynamic benefits for a trailing UAV.
Quantum Secure Multi-Party Summation Using Single Photons

In this paper, we propose a secure multi-party summation based on single photons. With the help of a semi-honest third party, n participants can simultaneously obtain the summation result without revealing their secret inputs. Our protocol uses single photon states as the information carriers. In addition, each participant with secret input only performs simple single-particle operators rather than particle preparation and any complex quantum measurements. These features make our protocol more feasible to implement. We demonstrate the correctness and security of the proposed protocol, which is resistant to participant attack and outside attack. In the end, we compare in detail the performance of the quantum summation protocol in this paper with other schemes in terms of different indicators. By comparison, our protocol is efficient and easy to implement.
Improved Optimization Strategy Based on Region Division for Collaborative Multi-Agent Coverage Path Planning

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more "cohesive", thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase.
No-Reference Image Quality Assessment Using the Statistics of Global and Local Image Features

Methods of image quality assessment are widely used for ranking computer vision algorithms or controlling the perceptual quality of video and streaming applications. The ever-increasing number of digital images has encouraged the research in this field at an accelerated pace in recent decades. After the appearance of convolutional neural networks, many researchers have paid attention to different deep architectures to devise no-reference image quality assessment algorithms. However, many systems still rely on handcrafted features to ensure interpretability and restrict the consumption of resources. In this study, our efforts are focused on creating a quality-aware feature vector containing information about both global and local image features. Specifically, the research results of visual physiology indicate that the human visual system first quickly and automatically creates a global perception before gradually focusing on certain local areas to judge the quality of an image. Specifically, a broad spectrum of statistics extracted from global and local image features is utilized to represent the quality-aware aspects of a digital image from various points of view. The experimental results demonstrate that our method’s predicted quality ratings relate strongly with the subjective quality ratings. In particular, the introduced algorithm was compared with 16 other well-known advanced methods and outperformed them by a large margin on 9 accepted benchmark datasets in the literature: CLIVE, KonIQ-10k, SPAQ, BIQ2021, TID2008, TID2013, MDID, KADID-10k, and GFIQA-20k, which are considered de facto standards and generally accepted in image quality assessment.
Facial Motion Capture System Based on Facial Electromyogram and Electrooculogram for Immersive Social Virtual Reality Applications

With the rapid development of virtual reality (VR) technology and the market growth of social network services (SNS), VR-based SNS have been actively developed, in which 3D avatars interact with each other on behalf of the users. To provide the users with more immersive experiences in a metaverse, facial recognition technologies that can reproduce the user's facial gestures on their personal avatar are required. However, it is generally difficult to employ traditional camera-based facial tracking technology to recognize the facial expressions of VR users because a large portion of the user's face is occluded by a VR head-mounted display (HMD). To address this issue, attempts have been made to recognize users' facial expressions based on facial electromyogram (fEMG) recorded around the eyes. fEMG-based facial expression recognition (FER) technology requires only tiny electrodes that can be readily embedded in the HMD pad that is in contact with the user's facial skin. Additionally, electrodes recording fEMG signals can simultaneously acquire electrooculogram (EOG) signals, which can be used to track the user's eyeball movements and detect eye blinks. In this study, we implemented an fEMG- and EOG-based FER system using ten electrodes arranged around the eyes, assuming a commercial VR HMD device. Our FER system could continuously capture various facial motions, including five different lip motions and two different eyebrow motions, from fEMG signals. Unlike previous fEMG-based FER systems that simply classified discrete expressions, with the proposed FER system, natural facial expressions could be continuously projected on the 3D avatar face using machine-learning-based regression with a new concept named the virtual blend shape weight, making it unnecessary to simultaneously record fEMG and camera images for each user. An EOG-based eye tracking system was also implemented for the detection of eye blinks and eye gaze directions using the same electrodes. These two technologies were simultaneously employed to implement a real-time facial motion capture system, which could successfully replicate the user's facial expressions on a realistic avatar face in real time. To the best of our knowledge, the concurrent use of fEMG and EOG for facial motion capture has not been reported before.
The Use of Typomorphic Features of Placer Gold of the Anabar Region for Determining Its Sources

Typomorphic features of placer gold of the Anabar region were studied as predictive-exploration criteria. The target of the study was to determine the typomorphic features of placer gold related to the intermediate sources (paleo-placers) and the supposed nearby primary ore occurrences. Two varieties of placer gold were identified. The first variety is well-rounded high-fineness lamellar gold with a highly modified internal structure. This native gold is associated with intermediate sources, Neogene–Quaternary watershed pebble beds. The second type includes slightly rounded gold with a wide variation in fineness (494‰–999‰). Its indicator is a block heterophase internal structure. The set of typomorphic features of this variety of placer gold indicates the vicinity of the primary source, what was the prerequisite for constructing prospecting traverses in order to find ore occurrences. As a result of these studies, hydrothermal-metasomatic formations with gold-sulfide mineralization were identified. The main primary substrate for them is fractured near-fault carbonate rocks of the Cambrian and Vendian–Cambrian age. Along with this, hydrothermalites developed on slightly cemented fine-pebble quartz conglomerates of the Middle Permian age were found in the core of exploration wells. Two types of metasomatic rocks are identified: quartz-potassium feldspar and jasperoid. The main ore minerals were galena and pyrite, different ratios by sites were revealed. Gold was identified in the form of small particles in the carbonate and siliceous substrate of hydrothermal-metasomatic formations. The lithological factor was one of the leading favorable factors for the ore formation due to the presence of near-fault highly permeable fractured carbonate and slightly cemented terrigenous rocks. The structural control of the studied ore occurrences is determined by their localization in the Mayat–Logoy and Dogoy–Kuoy faults of the Molodo–Popigay system of discontinuous faults. We assume a two-stage formation of the gold ore occurrences: during the first stage, the ore components in the form of primary hydrothermal-sedimentary ores in the near-gault zones were formed. The second stage was related to the processes of the Mesozoic tectonic-magmatic activation, when the intrusion of basite dikes initiated the mobilization of ore components the gold-sulfide occurrences were formed in the near-fault zone as a result of silicic-potassic metasomatosis of the carbonate and terrigenous rocks.
The unscented genetic algorithm for fast solution of GA-hard optimization problems

This work introduces the Unscented Genetic Algorithm (U-GA), which combines ideas from evolutionary computation and Kalman filters to devise a novel approach to solve GA-hard problems. The approach is justified based on how other Bayesian methods make strong assumptions on data, which could limit their performance in the long run. U-GA applies theory from unscented Kalman filters to relax this assumptions via Monte-Carlo simulation. The algorithm is explained in detail, showing how unscented Kalman filters equations could be adapted for the evolutionary computation framework. In the experiments, the proposed approach is compared to Bayesian optimization algorithm (BOA) and genetic algorithms (GAs) to investigate the strengths and limitations of U-GA. The results show how U-GA attains better performance than the benchmarks, even when the problem size is increased. Also U-GA attained a considerable speed-up (around 400%) when compared with similar methods.
Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts

COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products.We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology.Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.