Reviewerly/exHarmony huggingface dataset:
Optimizing Vaccine Access: A Web-Based Scheduling System with Geo-Tagging Integration and Decision Support for Local Health Centers

The system created aims to produce an online vaccination appointment scheduling system with geo-tagging integration and a decision-support mechanism for neighborhood health clinics.With a decision support mechanism that suggests the essential vaccines based on their account details, it is made to meet the unique vaccination needs of each patient.The system includes immunizations that are accessible locally, and patients and midwives can manage their own corresponding information through personal accounts.Viewers of websites can visualize the distribution of vaccines by purok thanks to geotagging.The Agile Scrum Methodology was modified by the researchers for early delivery, change flexibility, and continual system improvement in order to accomplish the study's main goal.In order to assess the system's acceptability in terms of functional adequacy, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, it was designed in accordance with the ISO 25010 Product Software Quality Standards.Following the assessment, the system was given an average total weighted mean score of 4.62, which represents a verbal interpretation of "strongly agree".This score demonstrates that the evaluators were in agreement that the system met the requirements of ISO 25010 for Product Software Quality Standards.
Kualitas Pengalaman dalam Memoderasi Gaya Hidup, Kesadaran Merek dan Kualitas Rasa terhadap Revisit Konsumen Air Minum Kemasan Aqua

This study aims to find out that lifestyle, brand awareness and taste quality influence consumers' repurchase intentions towards AQUA bottled water products, and also to find out that the quality of experience as a moderation variable can strengthen or weaken. This research uses quantitative methods, where the subject or object of research is people who consume AQUA bottled mineral water in the Bekasi Regency area. On condition, so that this respondent is a proposive sampling. This shows that the population is non-probality so the size of the respondents taken is 80 respondents. The collected data was processed using regression test techniques processed with the SmartPLS 4.0 application. This study concludes: 1) consumers who buy Aqua mineral water products feel that their lifestyle is in accordance with Aqua mineral water products so that consumers consider buying again, 2) brand awareness affects the intention to repurchase Aqua mineral water, it can mean that the more consumers are aware of the brand, the encourage consumers to repurchase, 3) Aqua mineral water products have guaranteed product quality and are safe for consumption thereby encouraging consumers to repurchase again, 4) The quality of experience does not directly affect the consumer's intention to repurchase Aqua mineral water, because of necessity consumer drinking water is not influenced by lifestyle, 5) The quality of direct experience can encourage repurchase intention of consumers of Aqua mineral water, because consumers have personal preferences and previous experience of Aqua mineral water products, 6) The quality of experience can affect consumer repurchase intentions Aqua mineral water, because consumers are satisfied with the quality of Aqua mineral water products which are always maintained.
A Traffic Parameter Extraction Model Using Small Vehicle Detection and Tracking in Low-Brightness Aerial Images

It is still a challenge to detect small-size vehicles from a drone perspective, particularly under low-brightness conditions. In this context, a YOLOX-IM-DeepSort model was proposed, which improved the object detection performance in low-brightness conditions accurately and efficiently. At the stage of object detection, this model incorporates the data enhancement algorithm as well as an ultra-lightweight subspace attention module, and optimizes the number of detection heads and the loss function. Then, the ablation experiment was conducted and the analysis results showed that the YOLOX-IM model has better mAP than the baseline model YOLOX-s for multi-scale object detection. At the stage of object tracking, the DeepSort object-tracking algorithm is connected to the YOLOX-IM model, which can extract vehicle classification data, vehicle trajectory, and vehicle speed. Then, the VisDrone2021 dataset was adopted to verify the object-detection and tracking performance of the proposed model, and comparison experiment results showed that the average vehicle detection accuracy is 85.00% and the average vehicle tracking accuracy is 71.30% at various brightness levels, both of which are better than those of CenterNet, YOLOv3, FasterR-CNN, and CascadeR-CNN. Next, a field experiment using an in-vehicle global navigation satellite system and a DJI Phantom 4 RTK drone was conducted in Tianjin, China, and 12 control experimental scenarios with different drone flight heights and vehicle speeds were designed to analyze the effect of drone flight altitude on speed extraction accuracy. Finally, the conclusions and discussions were presented.
Pseudo-supervised image clustering based on meta-features

Abstract Stable semantics is a prerequisite for achieving excellent image clustering. However, most current methods suffer from inaccurate class semantic estimation, which limits the clustering performance. For the sake of addressing the issue, we propose a pseudo-supervised clustering framework based on meta-features. First, the framework mines meta-semantic features (i.e., meta-features) of image categories based on instance-level features, which not only preserves instance-level information but also ensures the semantic robustness of meta-features. Ulteriorly, we propagate pseudo-labels to its global neighbor samples with meta-features as the center, which effectively avoids the accumulation of errors caused by the misclassification of samples at the cluster boundary. Finally, we exploit the cross-entropy loss with label smoothing to optimize the pseudo-label optimization network. This optimization method not only achieves a direct mapping from features to stable semantic labels, but also effectively avoids suboptimal solutions caused by multi-level optimization. Extensive experiments demonstrate that our method significantly outperforms twenty-one competing clustering methods on six challenging datasets.
Video frame interpolation for high dynamic range sequences captured with dual‐exposure sensors

Abstract Video frame interpolation (VFI) enables many important applications such as slow motion playback and frame rate conversion. However, one major challenge in using VFI is accurately handling high dynamic range (HDR) scenes with complex motion. To this end, we explore the possible advantages of dual‐exposure sensors that readily provide sharp short and blurry long exposures that are spatially registered and whose ends are temporally aligned. This way, motion blur registers temporally continuous information on the scene motion that, combined with the sharp reference, enables more precise motion sampling within a single camera shot. We demonstrate that this facilitates a more complex motion reconstruction in the VFI task, as well as HDR frame reconstruction that so far has been considered only for the originally captured frames, not in‐between interpolated frames. We design a neural network trained in these tasks that clearly outperforms existing solutions. We also propose a metric for scene motion complexity that provides important insights into the performance of VFI methods at test time.
A tracking method for GEO space targets based on adaptive memory attenuation UKF

Abstract Aiming at the problem that the nonlinearity of the tracking state equation and the problem of strong noise in the measurement process in the process of tracking the target, a non-cooperative target tracking method in geostationary earth orbit based on adaptive memory attenuation UKF is proposed. Firstly, the relative motion model of the non-cooperative target is constructed, and the state equation and measurement equation are established. Use the memory attenuation adjustment factor to enhance the correction effect of the new measurement on the state estimation, improve the adaptability of the filtering method to nonlinearity and noise, and finally the relative motion trajectory of the space non-cooperative target is obtained. Finally, the standard UKF algorithm and improved UKF algorithm are applied to target tracking and compared by simulation. The deviation of the three coordinate axis components of the actual motion trajectory and the predicted trajectory of the space target is used as the evaluation index. The simulation results show that the filtering accuracy of the improved UKF algorithm is significantly improved, while taking into account the real-time performance of on-orbit tracking.
Simulating analogue film damage to analyse and improve artefact restoration on high‐resolution scans

Abstract Digital scans of analogue photographic film typically contain artefacts such as dust and scratches. Automated removal of these is an important part of preservation and dissemination of photographs of historical and cultural importance. While state‐of‐the‐art deep learning models have shown impressive results in general image inpainting and denoising, film artefact removal is an understudied problem. It has particularly challenging requirements, due to the complex nature of analogue damage, the high resolution of film scans, and potential ambiguities in the restoration. There are no publicly available high‐quality datasets of real‐world analogue film damage for training and evaluation, making quantitative studies impossible. We address the lack of ground‐truth data for evaluation by collecting a dataset of 4K damaged analogue film scans paired with manually‐restored versions produced by a human expert, allowing quantitative evaluation of restoration performance. We have made the dataset available at https://doi.org/10.6084/m9.figshare.21803304. We construct a larger synthetic dataset of damaged images with paired clean versions using a statistical model of artefact shape and occurrence learnt from real, heavily‐damaged images. We carefully validate the realism of the simulated damage via a human perceptual study, showing that even expert users find our synthetic damage indistinguishable from real. In addition, we demonstrate that training with our synthetically damaged dataset leads to improved artefact segmentation performance when compared to previously proposed synthetic analogue damage overlays. The synthetically damaged dataset can be found at https://doi.org/10.6084/m9.figshare.21815844, and the annotated authentic artefacts along with the resulting statistical damage model at https://github.com/daniela997/FilmDamageSimulator. Finally, we use these datasets to train and analyse the performance of eight state‐of‐the‐art image restoration methods on high‐resolution scans. We compare both methods which directly perform the restoration task on scans with artefacts, and methods which require a damage mask to be provided for the inpainting of artefacts. We modify the methods to process the inputs in a patch‐wise fashion to operate on original high resolution film scans.
Efficient image blur detection via hierarchical edge guidance and region complementation

Abstract Blur detection is aimed to recognize the blurry pixels from a given image, which is increasingly valued in vision-centered applications. Albeit great improvement achieved by recent deep learning-based methods, the overweight model and rough boundary still pose challenges to blur detection. In this paper, we propose a Hierarchical Edge-guided Region-complemented Network (HER-Net) to tackle the above issues in quest of a favorable accuracy–complexity trade-off. First, we propose novel olive-shaped and pear-shaped inverted bottleneck structures based on large-kernel depth-wise convolutions to build a very concise architecture. Second, we provoke and exploit region-concerned and edge-concerned morphological priors to refine the boundary. To this end, we propose a reverse-region spatial attention to mine the complementary affinities between blurry and sharp regions so as to enrich the residual details around the boundary. In addition, we propose an edge spatial attention to guide the edge-concerned cues to emphasize the features related to the boundary. Both attentions are embedded into the model with hierarchical manners. Extensive experiments on three benchmark datasets demonstrate that the proposed method can achieve better detection performance using fewer parameters and lower floating-point operations compared to competitive methods. It proves the efficiency and effectiveness of our method in blur detection task.
TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework

TRON is a decentralized digital platform that provides a reliable way to transact in cryptocurrencies within a decentralized ecosystem. Thanks to its success, TRON’s native token, TRX, has been widely adopted by a large audience. To facilitate easy management of digital assets with TRON Wallet, users can securely store and manage their digital assets with ease. Our goal is first to develop a methodology to predict the future price using regression and then move on to build an effective classifier to predict whether a profit or loss is made the next day and then make a prediction of the transaction success rate. Our framework is capable of predicting whether there will be a profit in the future based on price prediction and forecasting results using regressors such as XGBoost, LightGBM, and CatBoost with R2 values of 0.9820, 0.9825 and 0.9858, respectively. In this work, an ensemble-based stacking classifier with the Whale optimization approach has been proposed which achieves the highest accuracy of 89.05 percent to predict if there will be a profit or loss the next day and an accuracy of 98.88 percent of TRX transaction success rate prediction which is higher than accuracies obtained by standard machine learning models. An effective framework will be useful for better decision-making and management of risks in a cryptocurrency.
Optimal trajectory planning technology for the cooperative flight of unmanned aerial vehicles

Abstract In the face of a complex battlefield environment, a multi-dimensional dynamic planning algorithm is proposed to solve the trajectory planning problem of the cooperative flight mission process of unmanned aerial vehicles. Under the constraints of aircraft kinematics, the collision between aircraft and space-time cooperation, and under the influence of random wind field, threat area, and other interference factors, the algorithm can make the UAV group pass through the mission points as much as possible, and at the same time, smooth the track points to make the trajectory reach the optimal solution. Finally, the simulation results of the program written in C++ and MATLAB show that the algorithm can meet the global optimization, and can calculate in real-time and online. It has strong engineering application value.
From Meaning to Expression: A Dual Approach to Coupling

Coupling is a key concept in the field of embodied interaction with digital products and systems, describing how digital phenomena relate to the physical world. In this paper, we present a Research through Design process in which the concept of coupling is explored and deepened. The use case that we employed to conduct our research is an industrial workplace proposed by Audi Brussels and Kuka. Our aim was to enrich this workplace with projection, or Spatial Augmented Reality, while focusing on operator interaction. We went through three successive design iterations, each of which resulted in a demonstrator. We present each of the three demonstrators, focusing on how they propelled our understanding of coupling. We establish a framework in which coupling between different events, be they physical or digital, emerges on four different aspects: time, location, direction, and expression. We bring the first three aspects together under one heading—coupling of meaning—and relate it to ease of use and pragmatic usability. We uncover the characteristics of the fourth aspect—coupling of expression—and link it to the psychological wellbeing of the operator in the workplace. We conclude this paper by highlighting its contribution to the embodied interaction research agenda.
Face Editing Using Part‐Based Optimization of the Latent Space

Abstract We propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non‐linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub‐vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part‐based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces.
BuB: a builder-booster model for link prediction on knowledge graphs

Link prediction (LP) has many applications in various fields. Much research has been carried out on the LP field, and one of the most critical problems in LP models is handling one-to-many and many-to-many relationships. To the best of our knowledge, there is no research on discriminative fine-tuning (DFT). DFT means having different learning rates for every parts of the model. We introduce the BuB model, which has two parts: relationship Builder and Relationship Booster. Relationship Builder is responsible for building the relationship, and Relationship Booster is responsible for strengthening the relationship. By writing the ranking function in polar coordinates and using the nth root, our proposed method provides solutions for handling one-to-many and many-to-many relationships and increases the optimal solutions space. We try to increase the importance of the Builder part by controlling the learning rate using the DFT concept. The experimental results show that the proposed method outperforms state-of-the-art methods on benchmark datasets.
Completing Function Documentation Comments Using Structural Information

Abstract Source code comments are a cornerstone of software documentation facilitating feature development and maintenance. Well-defined documentation formats, like Javadoc, make it easy to include structural metadata used to, for example, generate documentation manuals. However, the actual usage of structural elements in source code comments has not been studied yet. We investigate to which extent these structural elements are used in practice and whether the added information can be leveraged to improve tools assisting developers when writing comments. Existing research on comment generation traditionally focuses on automatic generation of summaries. However, recent works have shown promising results when supporting comment authoring through a next-word prediction. In this paper, we present an in-depth analysis of commenting practice in more than 18K open-source projects written in Python and Java showing that many structural elements, particularly parameter and return value descriptions are indeed widely used. We discover that while a majority are rather short at about 6 to 9 words, many are several hundred words in length. We further find that Python comments tend to be significantly longer than Java comments, possibly due to the weakly-typed nature of the former. Following the empirical analysis, we extend an existing language model with support for structural information, substantially improving the Top-1 accuracy of predicted words (Python 9.6%, Java 7.8%).
Social and Communication Competences of Students: Future Teachers

Abstract Numerous studies have shown that teachers significantly shape student’s learning context. Socially and emotionally competent teachers tend to develop supportive and encouraging relationship with their students by promoting intrinsic motivation, focusing on student’s strengths and abilities, mediating through conflict situations and encouraging appropriate communication and prosocial behaviour. Teachers with good communication skills will create a more successful teaching and learning ambience for the students, and without communication, the teaching and learning process will not take place. Educators, parents and public today recognize the need for an educational agenda to improve academic performance, but also to enhance students’ social and communicational competence. The aim of the present study was to examine social and communication competence of the students of educational sciences and teacher education at the University of Zagreb Department Of Croatian Studies. As part of the most important aspects of the future educational work, the participants pointed out independence at work, the awareness that they would contribute to the future of the society and the awareness that they play a useful role in society. Participants also expressed optimism about their communication skills with students, planning and organizing teaching process and maintaining discipline, and to some less extent their competences for successful cooperation with students’ parents. Results were discussed in terms of possibilities to contribute to future teachers’ education.
DA-FER: Domain Adaptive Facial Expression Recognition

Facial expression recognition (FER) is an important field in computer vision with many practical applications. However, one of the challenges in FER is dealing with small sample data, where the number of samples available for training machine learning algorithms is limited. To address this issue, a domain adaptive learning strategy is proposed in this paper. The approach uses a public dataset with sufficient samples as the source domain and a small sample dataset as the target domain. Furthermore, the maximum mean discrepancy with kernel mean embedding is utilized to reduce the disparity between the source and target domain data samples, thereby enhancing expression recognition accuracy. The proposed Domain Adaptive Facial Expression Recognition (DA-FER) method integrates the SSPP module and Slice module to fuse expression features of different dimensions. Moreover, this method retains the regions of interest of the five senses to accomplish more discriminative feature extraction and improve the transfer learning capability of the network. Experimental results indicate that the proposed method can effectively enhance the performance of expression recognition. Specifically, when the self-collected Selfie-Expression dataset is used as the target domain, and the public datasets RAF-DB and Fer2013 are used as the source domain, the performance of expression recognition is improved to varying degrees, which demonstrates the effectiveness of this domain adaptive method.