Reviewerly/exHarmony huggingface dataset:
Using Smart Glasses to Support Situated Decision Making in Aquaculture

The rapidly expanding prawn farming industry is vital in providing high-quality protein for the increasing global population. The key to consistently high yield is the efficient and effective management of pond water, maintaining the optimal growth environment. Our consultations with seven Australian commercial prawn farms found that the lack of access to essential real-time water information and trends has hindered farmers from making confident and timely decisions while in the field, which contributes considerably to either prolonged suboptimal pond conditions or increases in operational costs. This paper describes two aquaculture pond water management tasks that can benefit from wearable technology and discusses general concerns about using smart eyewear in harsh outdoor farming environments. A Pondside Visualisation System was created to allow farmers to access visualised historical and real-time water data during field operations using Google Glass Enterprise Edition 2. The application uses a new data visualisation style that is optimised for small near-eye displays. A validation study was conducted with seven industrial practitioners (including a supervisor and six farm technicians) on a commercial farm with real data from the recent growing season, where each technician made 100 pond management decisions using our system. Objective measurements of task completion time and task accuracy indicated the farmers achieved an accuracy of 86.4% and 89.2% for two management tasks with at least 41.0% less time compared to existing desktop-based practice. A structured expert review confirmed the usability of our system and discussed methods to mitigate issues discovered during the study. We also discussed lessons learned from the project.
Multi-Scale Annulus Clustering for Multi-Label Classification

Label-specific feature learning has become a hot topic as it induces classification models by accounting for the underlying features of each label. Compared with single-label annotations, multi-label annotations can describe samples from more comprehensive perspectives. It is generally believed that the compelling classification features of a data set often exist in the aggregation of label distribution. In this in-depth study of a multi-label data set, we find that the distance between all samples and the sample center is a Gaussian distribution, which means that the label distribution has the tendency to cluster from the center and spread to the surroundings. Accordingly, the double annulus field based on this distribution trend, named DEPT for double annulusfield and label-specific features for multi-label classification, is proposed in this paper. The double annulus field emphasizes that samples of a specific size can reflect some unique features of the data set. Through intra-annulus clustering for each layer of annuluses, the distinctive feature space of these labels is captured and formed. Then, the final classification model is obtained by training the feature space. Contrastive experiments on 10 benchmark multi-label data sets verify the effectiveness of the proposed algorithm.
Learning text analytics without coding? An introduction to KNIME

The combination of the quantitative turn in linguistics and the emergence of text analytics has created a demand for new methodological skills among linguists and data scientists. This paper introduces KNIME as a low-code programming platform for linguists interested in learning text analytic methods, while highlighting the considerations necessary from a linguistics standpoint for data scientists. Examples from an Open Educational Resource created for the DiMPAH project are used to demonstrate KNIME’s value as a low-code option for text analysis, using sentiment analysis and topic modelling as examples. The paper provides detailed step-by-step descriptions of the workflows for both methods, showcasing how these methods can be applied without writing code. The results suggest that visual or low-code programming tools are useful as an introduction for linguists and humanities scholars who wish to gain an understanding of text analytic workflows and computational thinking. However, as with more traditional programming, caution must be exercised when using methods without fully understanding them. In conclusion, KNIME is a potential avenue for innovative research and teaching computational methods to humanities scholars.
A Hybridized Deep Learning Strategy for Course Recommendation

Recommender systems have been actively used in many areas like e-commerce, movie and video suggestions, and have proven to be highly useful for its users. But the use of recommender systems in online learning platforms is often underrated and less likely used. But many of the times it lacks personalisation especially in collaborative approach while content-based doesn't work well for new users. Therefore, the authors propose a hybrid course recommender system for this problem which takes content as well as collaborative approaches and tackles their individual limitations. The authors see recommendation as a sequential problem and thus have used RNNs for solving the problem. Each recommendation can be seen as the new course in the sequence. The results suggest it outperforms other recommender systems when using LSTMs instead of RNNs. The authors develop and test the recommendation system using the Kaggle dataset of users for grouping similar users as historical data and search history of different users' data.
mCrutch: A Novel m-Health Approach Supporting Continuity of Care

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.
Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest Recommendation

A personalized point-of-interest (POI) recommender system is of great significance to facilitate the daily life of users. However, it suffers from some challenges, such as trustworthiness and data sparsity problems. Existing models only consider the trust user influence and ignore the role of the trust location. Furthermore, they fail to refine the influence of context factors and fusion between the user preference and context models. To address the trustworthiness problem, we propose a novel bidirectional trust-enhanced collaborative filtering model, which investigates the trust filtering from the views of users and locations. To tackle the data sparsity problem, we introduce temporal factor into the trust filtering of users as well as geographical and textual content factors into the trust filtering of locations. To further alleviate the sparsity of user-POI rating matrices, we employ a weighted matrix factorization fused with the POI category factor to learn the user preference. To integrate the trust filtering models and the user preference model, we develop a fused framework with two kinds of integrating methods in relation to the different impacts of factors on the POIs that users have visited and the POIs that users have not visited. Finally, we conduct extensive experiments on Gowalla and Foursquare datasets to evaluate our proposed POI recommendation model, and the results show that our proposed model improves by 13.87% at precision@5 and 10.36% at recall@5 over the state-of-the-art model, which demonstrates that our proposed model outperforms the state-of-the-art method.
Examining the Perspectives of Gender Development and Inequality: A Tale of Selected Asian Economies

The rising concern about gender inequality among the economies in South, South-East, and Eastern Asia motivates us to study the context of gender development in terms of bridging gender disparity. To show the impact, the data has been extracted from various authentic sources- Varieties of Democracy (V-Dem), World Bank Development Indicators database, Sustainable Development Index, The Observatory of Economic Complexity and Human Development Reports of the 24 South, South-East, and East Asian economies from period 2000–2020. This research was carried out empirically using various techniques such as the Ordinary Least Squared Method (OLS), Generalized Methods of Moments (GMM), and Generalised Quantile Regression. The findings of the research show a significant impact of FDI and Economic Complexity in the reduction of gender inequality. Along with this, access to justice and electoral democracy will be providing more representation to women by reducing the gender gaps. Several policy implications have been proposed following the results of the study.
Tilt Angle Adjustment for Incident Solar Energy Increase: A Case Study for Europe

The work presented in this paper aims to analyze the efficiency of using optimum tilt angles defined for several time intervals to maximize the incident solar irradiation on a certain surface deployed in Europe. Such a technique would improve the solar energy harvesting process, both for photovoltaic panels and solar thermal collectors, while not investing in the more expensive sun-tracking devices. The optimum tilt angles have been determined for the yearly, bi-annual, seasonal, and monthly time intervals, which were delimited on a calendar and astronomical base, respectively, considering multiple locations from Europe, and using various mathematical models based on empirical equations and solar irradiation estimation. The total incident solar irradiation provided by adjusting the tilt angle multiple times per year was calculated and compared to that obtained when using the yearly optimum tilt angle. The gains for each type of adjustment were investigated, and the monthly optimization of the tilt angle is obviously the most effective one, ensuring gains of up to 7%, depending on the considered latitude. Otherwise, an optimization twice per year, using the bi-annual optimum tilt angles determined for the astronomical-defined warm and cold seasons, would be the next best solution.
Person-Specific Gaze Estimation from Low-Quality Webcam Images

Gaze estimation is an established research problem in computer vision. It has various applications in real life, from human-computer interactions to health care and virtual reality, making it more viable for the research community. Due to the significant success of deep learning techniques in other computer vision tasks-for example, image classification, object detection, object segmentation, and object tracking-deep learning-based gaze estimation has also received more attention in recent years. This paper uses a convolutional neural network (CNN) for person-specific gaze estimation. The person-specific gaze estimation utilizes a single model trained for one individual user, contrary to the commonly-used generalized models trained on multiple people's data. We utilized only low-quality images directly collected from a standard desktop webcam, so our method can be applied to any computer system equipped with such a camera without additional hardware requirements. First, we used the web camera to collect a dataset of face and eye images. Then, we tested different combinations of CNN parameters, including the learning and dropout rates. Our findings show that building a person-specific eye-tracking model produces better results with a selection of good hyperparameters when compared to universal models that are trained on multiple users' data. In particular, we achieved the best results for the left eye with 38.20 MAE (Mean Absolute Error) in pixels, the right eye with 36.01 MAE, both eyes combined with 51.18 MAE, and the whole face with 30.09 MAE, which is equivalent to approximately 1.45 degrees for the left eye, 1.37 degrees for the right eye, 1.98 degrees for both eyes combined, and 1.14 degrees for full-face images.
A Method for Spatially Registered Microprofilometry Combining Intensity-Height Datasets from Interferometric Sensors

A recognized problem in profilometry applied to artworks is the spatial referencing of the surface topography at micrometer scale due to the lack of references in the height data with respect to the "visually readable" surface. We demonstrate a novel workflow for spatially referenced microprofilometry based on conoscopic holography sensors for scanning in situ heterogeneous artworks. The method combines the raw intensity signal collected by the single-point sensor and the (interferometric) height dataset, which are mutually registered. This dual dataset provides a surface topography registered to the artwork features up to the precision that is given by the acquisition scanning system (mainly, scan step and laser spot). The advantages are: (1) the raw signal map provides additional information about materials texture, e.g., color changes or artist marks, for spatial registration and data fusion tasks; (2) and microtexture information can be reliably processed for precision diagnostic tasks, e.g., surface metrology in specific sub-domains and multi-temporal monitoring. Proof of concept is given with exemplary applications: book heritage, 3D artifacts, surface treatments. The potential of the method is clear for both quantitative surface metrology and qualitative inspection of the morphology, and it is expected to open future applications for microprofilometry in heritage science.
Techno-Economic Potential of Urban Photovoltaics: Comparison of Net Billing and Net Metering in a Mediterranean Municipality

Solar photovoltaic self-consumption is an attractive approach to increase autarky and reduce emissions in the building sector. However, a successful deployment in urban rooftops requires both accurate and low-computational-cost methods to estimate the self-consumption potential and economic feasibility, which is especially scarce in the literature on net billing schemes. In the first part of this study, a bottom-up GIS-based techno-economic model has helped compare the self-consumption potential with net metering and net billing in a Mediterranean municipality of Spain, with 3734 buildings in total. The capacity was optimized according to load profiles obtained from aggregated real measurements. Multiple load profile scenarios were assessed, revealing that the potential self-sufficiency of the municipality ranges between 21.9% and 42.5%. In the second part of the study, simplified regression-based models were developed to estimate the self-sufficiency, self-consumption, economic payback and internal rate of return at a building scale, providing nRMSE values of 3.9%, 3.1%, 10.0% and 1.5%, respectively. One of the predictors with a high correlation in the regressions is a novel coefficient that measures the alignment between the load and the hours with higher irradiance. The developed correlations can be employed for any other economic or demand scenario.
Dense-HR-GAN: A High-Resolution GAN Model with Dense Connection for Image Dehazing in Icing Wind Tunnel Environment

To address the issue of blurred images generated during ice wind tunnel tests, we propose a high-resolution dense-connection GAN model, named Dense-HR-GAN. This issue is caused by attenuation due to scattering and absorption when light passes through cloud and fog droplets. Dense-HR-GAN is specifically designed for this environment. The model utilizes an atmospheric scattering model to dehaze images with a dense network structure for training. First, sub-pixel convolution is added to the network structure to remove image artifacts and generate high-resolution images. Secondly, we introduce instance normalization to eliminate the influence of batch size on the model and improve its generalization performance. Finally, PatchGAN is used in the discriminator to capture image details and local information, and then drive the generator to generate a clear and high-resolution dehazed image. Moreover, the model is jointly constrained by multiple loss functions during training to restore the texture information of the hazy image and reduce color distortion. Experimental results show that the proposed method can achieve the state-of-the-art performance on image dehazing the in icing wind tunnel environment.
Enhancing Neural Text Detector Robustness with μAttacking and RR-Training

With advanced neural network techniques, language models can generate content that looks genuinely created by humans. Such advanced progress benefits society in numerous ways. However, it may also bring us threats that we have not seen before. A neural text detector is a classification model that separates machine-generated text from human-written ones. Unfortunately, a pretrained neural text detector may be vulnerable to adversarial attack, aiming to fool the detector into making wrong classification decisions. Through this work, we propose μAttacking, a mutation-based general framework that can be used to evaluate the robustness of neural text detectors systematically. Our experiments demonstrate that μAttacking identifies the detector’s flaws effectively. Inspired by the insightful information revealed by μAttacking, we also propose an RR-training strategy, a straightforward but effective method to improve the robustness of neural text detectors through finetuning. Compared with the normal finetuning method, our experiments demonstrated that RR-training effectively increased the model robustness by up to 11.33% without increasing much effort when finetuning a neural text detector. We believe the μAttacking and RR-training are useful tools for developing and evaluating neural language models.
EduARdo—Unity Components for Augmented Reality Environments

Contemporary software applications have shifted focus from 2D representations to 3D. Augmented and Virtual Reality (AR/VR) are two technologies that have captured the industry’s interest as they show great potential in many areas. This paper proposes a system that allows developers to create applications in AR and VR with a simple visual process, while also enabling all the powerful features provided by the Unity 3D game engine. The current system comprises two tools, one for the interaction and one for the behavioral configuration of 3D objects within the environment. Participants from different disciplines with a software-engineering background were asked to participate in the evaluation of the system. They were called to complete two tasks using their mobile phones and then answer a usability questionnaire to reflect on their experience using the system. The results (a) showed that the system is easy to use but still lacks some features, (b) provided insights on what educators seek from digital tools to assist them in the classroom, and (c) that educators often request a more whimsical UI as they want to use the system together with the learners.
Parameter-Independent Event-Triggered Implicit UKF for the Celestial Navigation Using Time Delay Measurement

Celestial navigation using time delay measurement is an innovative autonomous navigation method. To calculate the equivalent measurement, the numerical method needs to be applied, which is time-consuming. The event-triggered mechanism intermittently and aperiodically processes measurements by judging if the update error has changed drastically. However, its performance is greatly affected by the constant threshold. To solve this problem, a parameter-independent event-triggered implicit unscented Kalman filter (UKF) is proposed and applied to the celestial navigation using time delay measurement. The innovation at the current moment and the updated estimate covariance at the last moment are compared with the previous value instead of the constant threshold. The event is automatically triggered when the accuracy of the state estimate is low. Simulation results indicate that the proposed parameter-independent event-triggered implicit UKF can reduce the running time by reducing unnecessary measurement updates, whose performance will not be affected by any parameter or window size. In a word, the proposed method substitutes the dynamic threshold for the constant threshold, ensuring that its performance will not be affected by any parameter or window size.
Global Relation-Aware-Based Oil Detection Method for Water Surface of Catchment Wells in Hydropower Stations

The oil in hydropower station catchment wells is a source of water pollution which can cause the downstream river to become polluted. Timely detection of oil can effectively prevent the expansion of oil leakage and has important significance for protecting water sources. However, the poor environment and insufficient light on the water surface of catchment wells make oil pollution detection difficult, and the real-time performance is poor. To address these problems, this paper proposes a catchment well oil detection method based on the global relation-aware attention mechanism. By embedding the global relation-aware attention mechanism in the backbone network of Yolov5s, the main features of oil are highlighted and the minor information is suppressed at the spatial and channel levels, improving the detection accuracy. Additionally, to address the problem of partial loss of detail information in the dataset caused by the harsh environment of the catchment wells, such as dim light and limited area, single-scale retinex histogram equalization is used to improve the grayscale and contrast of the oil images, enhancing the details of the dataset images and suppressing the noise. The experimental results show that the accuracy of the proposed method achieves 94.1% and 89% in detecting engine oil and turbine oil pollution, respectively. Compared with the Yolov5s, Faster R-CNN, SSD, and FSSD detection algorithms, our method effectively reduces the problems of missing and false detection, and has certain reference significance for the detection of oil pollution on the water surface of catchment wells.
Texture and semantic convolutional auto‐encoder for anomaly detection and segmentation

Abstract Anomaly detection is a challenging task, especially detecting and segmenting tiny defect regions in images without anomaly priors. Although deep encoder‐decoder‐based convolutional neural networks have achieved good anomaly detection results, existing methods operate uniformly on all extracted image features without considering disentangling these features. To fully explore the texture and semantic information of images, A novel unsupervised anomaly detection method is proposed. Specifically, discriminative features are extracted from images by using a deep pre‐trained network, where shallow and deep features are aggregated into texture and semantic modules, respectively. Then, a feature fusion module is developed to interactively enable feature information in two different modules. The texture and semantic segmentation results are obtained by comparing the texture features and semantic features before and after reconstruction, respectively. Finally, an anomaly segmentation module is designed to generate anomaly detection results by integrating the results of the texture and semantic modules by setting a threshold. Experimental results on benchmark datasets for anomaly detection demonstrate that our proposed method can efficiently and effectively detect anomalies, outperforming some state‐of‐the‐art methods by 2.7% and 0.6% in classification and segmentation.