Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Temperature-Based Global Solar Radiation Models—Case Study: Arar City, KSA

Solar radiation is considered the main renewable energy source which reshapes the global sustainability plan for future development. Due to the lack of solar radiation measurements, this work investigates the performance of several temperature-based hybrid solar radiation models combining the parametric, statistical and satellite data approaches to estimate the global solar radiation on a horizontal surface. Over 35 years of meteorological data in the new location, Arar City, KSA (Latitude 30°96′ N and longitude 41°05′ E) are employed to establish and validate the models. These models are validated using two datasets with different averaging time spans to investigate the accuracy and reliability of different models as forecasting tools for the solar radiation. The mostly common statistical indicators are calculated to identify the most accurate model. The results show that Model (1) has the best performance among all models with high reliability as a solar radiation forecasting tool in this new location. This model is also validated against the widely-used datasets, namely NASA, On-Site measurements and PVGIS-SARAH data. The model shows excellent values for statistical indicators with high values of coefficient of determination, R2 &gt; 0.955, presenting the best performance regardless of the time span of the validation datasets.
Sentiment Analysis for Movie Review in Bahasa Indonesia Using BERT

This study aims to analyze the sentiment in Indonesia Language towards the Gundala movie reviews on YouTube. However, sentiment analysis on YouTube comments are varying from positive, negative, and neutral comments which requires some automation in terms of classifying comments based on the polarity of sentiment. Sentiment analysis using traditional machine learning algorithms such as Naïve Bayes, SVM, etc cannot understand the context of comments in depth about the semantic of words because it only learns the given patters such as the frequency of occurrence of words. We need a transfer learning approach such as BERT (Bidirectional Encoder Representations from Transformers) which produces a bidirectional language model. The dataset used to do sentiment analysis goes through a pre-processing step which consists of case folding, data cleaning, tokenization, stop words removal, stemming, and normalization, using libraries from NLTK and Sastrawi. In this study, the hyperparameters used were 10 epochs, learning rate of 2e-5, and a batch size 16 based on experiments of hyperparameters used in another studies. In sentiment analysis, we will be using a multilingual-cased-model BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> model and it was carried out with three experiments. During this experiment, the accuracy gained in first experiment is 66%, while the second experiment was 68%, and the third experiment was 66%. So, the average accuracy obtained is 66,7%.
LISU: Low-light indoor scene understanding with joint learning of reflectance restoration

Semantic segmentation using convolutional neural networks (CNNs) achieves higher accuracy than traditional methods, but it fails to yield satisfactory results under illumination variants when the training set is limited. In this paper we present a new data set containing both real and rendered images and a novel cascade network to study semantic segmentation in low-light indoor environments. Specifically, the network decomposes a low-light image into illumination and reflectance components, and then a multi-tasking learning scheme is built. One branch learns to reduce noise and restore information on the reflectance (reflectance restoration branch). Another branch learns to segment the reflectance map (semantic segmentation branch). The CNN features from two tasks are concatenated together so as to improve the segmentation accuracy by embedding the illumination-invariant features. We compare our approach with other CNN-based segmentation frameworks, including the state-of-the-art DeepLab v3+, on the proposed real data set, and our approach achieves the highest mIoU (47.6%). The experimental results also show that the semantic information supports the restoration of a sharper reflectance map, thus further improving the segmentation. Besides, we pre-train a model with the proposed large-scale rendered images and then fine-tune it on the real images. The pre-training results in an improvement of mIoU by 7.2%. Our models and data set are publicly available for research. This research is part of the EU project INGENIOUS1. Our data sets and models are available on our website2.
Rapid land use prediction via portable X-ray fluorescence (pXRF) data on the dried lakebed of Avlan Lake in Turkey

The rapid, non-destructive, and low-cost characterization of soil by using portable X-ray fluorescence (pXRF) has been increasingly attractive among soil scientists for soil pedogenesis research. Therefore, pXRF can aid to characterize by measuring elemental concentration, weathering indices, soil spectra for agricultural and lakebed areas. This study aimed to characterize the agricultural area (AA) and lakebed area (LA) through elemental concentration, weathering indices, and soil spectra based on pXRF spectral behavior and to evaluate the prediction and characterization performance of decision tree models for AA and LA. In total 108 soil samples (36 locations) were collected from three fixed depth intervals such as 0–20, 20–40, and 40–60 cm in both AA and LA in Elmalı, Antalya, Turkey. All soil samples measured using pXRF to obtain elemental concentrations and soil spectra and results of input variables were randomly separated into 70% for calibration and 30% for validation. Savitzky-Golay Filter was applied for spectral data preprocessing. The area under the ROC curve (AUC) and validation accuracy were used to compare the prediction performance of seven different models. The results showed that the AA had the highest Mg, Si, Ca, Ti, P, Mn, Zn, Sr, and Zr concentrations compared with LA. The prediction performance of AUC (0.79) and validation accuracy (0.77) for elemental concentrations were acceptable, but other models were less satisfactory compared to the best model. In particular, P, Sr, and Fe were robust indicators to separate AA and LA using the decision tree model. These results suggested that the possibility of using pXRF for soil characterization and high prediction performance of AA and LA. This study highlights that elemental concentrations, weathering indices, and soil spectra based on pXRF measurements can be used as a useful proximal sensor to predict, characterize, separate, and investigate an understanding of soil characterization in AA and LA.
Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model

Theabnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision owns the high robustness and accuracy for detection. However, executing CNN model on the cloud possible incurs a privacy disclosure problem during data transmission, and the high computation overhead makes difficult to execute the model on edge-end IoT devices with a well real-time performance. In this paper, we realize a skeleton-based abnormal behavior detection, and propose a secure partitioned CNN model (SP-CNN) to extract human skeleton keypoints and achieve safely collaborative computing by deploying different CNN model layers on the cloud and the IoT device. Because, the data outputted from the IoT device are processed by the several CNN layers instead of transmitting the sensitive video data, objectively it reduces the risk of privacy disclosure. Moreover, we also design an encryption method based on channel state information (CSI) to guarantee the sensitive data security. At last, we apply SP-CNN in abnormal behavior detection to evaluate its effectiveness. The experiment results illustrate that the efficiency of the abnormal behavior detection based on SP-CNN is at least 33.2% higher than the state-of-the-art methods, and its detection accuracy arrives to 97.54%.
An exploratory study of bug prediction at the method level

During the past decades, researchers have proposed numerous studies to predict bugs at different granularity levels, such as the file level, package level, module level, etc. However, the prediction models at the method level are rarely investigated. In this paper, we investigate to predict bug-prone methods based on method-level code metrics or history measures, and analyze the prediction importance of each metric. To proceed our study, we first propose a series of code metrics and history measures for conducting method-level bug predictions. Next, we compare the performance of different types of prediction models. Finally, we conduct analyses about the prediction power of each metric, based on which, we further analyze whether we can simplify the prediction models. Through our evaluation on eighteen large-scale projects, we have presented: (1) conducting method-level bug prediction has potentials of saving a large portion of effort on code reviews and inspections; (2) models using the proposed code metrics or history measures could achieve a good prediction performance; (3) the prediction importance of each metric distributes differently; (4) a highly simplified prediction model could be derived by just using a few important metrics. This study presents how to systematically build models for predicting bug-prone methods, and provides empirical evidence for developers to best select metrics to build method-level bug prediction models.
Chinese keyword extraction model with distributed computing

The content and structure of the news text are relatively complex and cannot effectively capture the core content. Existing supervision models cannot achieve good results in areas with less annotated data. To this end, we propose a new Chinese keyword extraction model with a distributed computing method. Precisely, we first fused the Bidirectional Encoder Representation from Transformers (BERT) and Conditional Random Fields (CRF) so that each word learns its relationship with the context while reducing errors; secondly, the adversarial training encourages the model to retain a small amount of annotations Sample knowledge to help extract keywords from unannotated samples; and because the model contains a large number of time-consuming components, it creatively uses distributed computing to save overall computing time. The results show that our model can steadily improve the performance of keyword phrase extraction in areas with insufficient labeled samples.
Design Model of Urban Leisure Sports Public Facilities Based on Big Data and Machine Vision

Sports facilities are the material basis for people to participate in physical exercise. The construction of facilities is conducive to improving people’s health and their expectations for a happy life. Sports facilities are part of the infrastructure. The reasonable layout of sports facilities is conducive to shortening the gap between urban and rural areas, achieving common economic prosperity, and promoting social harmony and unity. Public sports facilities are of great significance to urban construction and people’s daily lives. Based on big data and machine vision, this document constructs a big data model framework for urban public sports and leisure facilities, quantifies the diversity and overall coordination of sports facilities, and conducts simulation experiments on the designed urban leisure and sports public facility model. The experimental results show that compared with the traditional method, this method effectively improves the coverage of urban leisure and sports public facilities, and the space utilization rate is increased by 15.32% compared with the traditional method, which maximizes the use of regional space and makes it more convenient for urban residents. It can carry out physical exercise quickly and improve the quality of life of residents.
Future touch in industry: Exploring sociotechnical imaginaries of tactile (tele)robots

This paper explores sociotechnical imaginaries for industrial robotics. It is motivated by the prospect of promoting human-centred industrial futures. Investigating the tactility of labour through a critical social perspective the research attends to the future of tactile (tele)robots and elaborates on the concepts of pedagogic, collaborative and superhuman touch. These concepts are offered as starting points to foster productive dialogues between social scientists, roboticists, environmentalists, policy makers, industrial leaders and labourers (e.g. union representatives). This paper is framed through literature and ethnographic fieldwork that contextualises and maps the dominant sociotechnical imaginaries for a future touch in industry, identifying the role of a comparative-competitive frame in sustaining a splintering of the imaginary towards utopic and dystopic extremes. Against this, the paper draws on interviews with leading roboticists to chart alternative futures where humans and robots may work together as collaborators, not competitors.
Mapping and characterizing the Kīlauea (Hawaiʻi) lava lake through Sentinel-2 MSI and Landsat-8 OLI observations of December 2020–February 2021

On December 20, 2020, a new eruption started at Kīlauea volcano (Hawaiʻi, USA). During the eruption, the lava cascaded into the summit water lake, generating a vigorous steam plume and forming a new lava lake at the base of the crater. In this study, we investigate the lava lake through the Normalized Hot Spot Indices (NHI) algorithm. The latter runs operationally within a Google Earth Engine (GEE) App (NHI tool), using Operational Land Imager (OLI) and Multispectral Instrument (MSI) data, to map volcanic thermal anomalies at global scale. Results show that the used algorithm provided information about the lava lake and relative space-time variations in agreement with field observations. By correcting daytime data for the influence of the solar irradiation, we estimated values of the radiant flux in the range 1–5 GW from hottest pixels. This study opens some challenging scenarios about the quantitative characterization of volcanic thermal features through the NHI tool.
Exploration of temporal dynamics of frequency domain linear prediction cepstral coefficients for dialect classification

Speakers exhibit dialectal traits in speech at sub-segmental, segmental, and supra-segmental levels. Any feature representation for dialect classification should appropriately represent these dialectal traits. Traditional segmental features such as mel-frequency cepstral coefficients (MFCCs) fail to represent sub-segmental and supra-segmental dialectal traits. This study proposes to use frequency domain linear prediction cepstral coefficients (FDLPCCs) for dialect classification inspired by its long temporal summarization during pole estimation. The i-vectors and x-vectors derived from both baseline (MFCCs, linear prediction cepstral coefficients (LPCCs), perceptual LPCCs (PLPCCs), RASTA filtered PLPCCs (PLPCC-R) and proposed (FDLPCC) features are used for identifying the dialects with support vector machine (SVM) and feed-forward neural network (FFNN) as classifiers. Proposed FDLPCC features have shown to perform better than baseline features such as MFCCs and PLPCC-Rs (best among LPCCs variants) by an absolute improvement of 3.4% and 3.9% (in unweighted average recall (UAR)), with i-vector + SVM system and 1.6% and 4.6% (in UAR), i-vector + FFNN system respectively. It is also found that there exists a complementary information between the proposed and baseline features. Furthermore current studies are compared with previous studies and it is found that performances of current studies are better than previous studies.
Complex, but in a good way? How to represent encryption to non-experts through text and visuals – Evidence from expert co-creation and a vignette experiment

An ongoing discussion in the field of usable privacy and security debates whether security mechanisms should be visible to end-users during interactions with technology, or hidden away. This paper addresses this question using a mixed-methods approach, focusing on encryption as a mechanism for confidentiality during data transmission on a smartphone application. In study 1, we conducted a qualitative co-creation study with security and Human-Computer Interaction (HCI) experts (N = 9) to create appropriate textual and visual representations of the security mechanism encryption in data transmission. We investigated this question in two contexts: online banking and e-voting. In study 2, we put these ideas to the test by presenting these visual and textual representations to non-expert users in an online vignette experiment (N = 2180). We found a statistically significant and positive effect of the textual representation of encryption on perceived security and understanding, but not on user experience (UX). More complex text describing encryption resulted in higher perceived security and more accurate understanding. The visual representation of encryption had no statistically significant effect on perceived security, UX or understanding. Our study contributes to the larger discussion regarding visible instances of security and their impact on user perceptions.
An underwater-imaging-model-inspired no-reference quality metric for images in multi-colored environments

• The proposed metric is applied to multi-colored underwater environments. • The metric obtains a higher correlation between the predictions and true quality. • The calculation of true quality scores is based on the measured turbidity. • The proposed metric has the characteristics of strong generalization ability. • The proposed metric performs well in real-time image processing. The color of an underwater target is different under different lighting conditions and underwater environments. To date, no quality metric has been proposed for images of underwater targets in multi-colored environments. In this paper, we proposed a no-reference quality metric for images of underwater targets in multi-colored environments (QMICE). This metric is a weighted combination of colorfulness index, contrast index and sharpness index. The colorfulness index is used to measure the color loss caused by absorption. The contrast index and sharpness index are used to measure the blurring caused by scattering. The weighted coefficients of the three indexes are calculated by multiple linear regression (MLR). For the contrast index and sharpness index, we proposed a grayscale conversion method that can adaptively adjust the coefficients of red, green, and blue (RGB) values to enhance their generalization ability under multi-colored environments. During the calculation of weighted coefficients, the quality scores based on turbidity are regarded as the true quality scores. It is more reliable than subjective assessment scores. The experimental results show that compared with the leading underwater image quality metrics available in the literature, the proposed metric has the best correlation between the metric predictions and the true quality scores. More importantly, QMICE can also be used to process underwater images in real time and evaluate the performance of underwater image restoration algorithms.
Path planning to expedite the complete transfer of distributed gravel piles with an automated wheel loader

(2021). Path planning to expedite the complete transfer of distributed gravel piles with an automated wheel loader. Advanced Robotics: Vol. 35, Advanced Construction Robot System of Advanced Robotics, pp. 1418-1437.
Collaborative Avatar Platform for Collective Human Expertise

In this study, we developed a system in which two users are integrated into one actual robot arm to collaborate with each other. The method of dividing the roles allows flexible movements that is not dependent on the range of movement and rotational freedom of the human arm, and enables movements that are impossible for a single person. The mixing of movements in adjustable ratio allows an expert to support a beginner to make more stable movements, and induces collaboration between users with different perspectives. Additionally, we implement tactile feedback to enable interaction between users and between users and the robot. We investigate the effects of these on usability and cognitive behavior. This system is expected to become a new method of collaboration in the cyber-physical society.
Deciding Heavy Metal Levels in Soil Based on Various Ecological Information through Artificial Intelligence Modeling

The aim of this paper is to decide on heavy metal levels based on ecological parameters by effectively eliminating common disadvantages such as high cost and serious time-consuming laboratory procedures via an effective artificial intelligence approach. Therefore, this study is hinged on an artificial intelligence technique, ANN, because of its low cost and high accuracy in overcoming the mentioned limitations and obstacles in the determination process of the amounts of elements. The ANNs have thus been employed to determine essential heavy metals, such as Fe, Mn, and Zn depending on Ca, K, and Mg concentrations of soil samples obtained from different altitudes in Mount Ida. To the best knowledge of the authors, this is the first study in the literature in which altitude was considered as a parameter in the prediction of nutrient heavy metals. The computed relative errors are significantly low for each of the considered elements (Fe, Mn, and Zn); and are found to be between 1.0–4.1%, 1.0–4.2%, 1.5–7.1%, respectively, for the training, testing, and holdout data. The findings indicate that the relative errors could still be decreased further by assuming the altitude as a factor variable.
ReCom: A deep reinforcement learning approach for semi-supervised tabular data labeling

One of the main obstacles in applying machine learning to a new domain is the limited availability of labeled data. A common approach for overcoming this challenge is using semi-supervised learning, where labeled and unlabeled data are used together to label additional samples. One of the most common automatic labeling approaches is co-training, which trains two learners on different views of the data, and then proceeds to collaboratively and iteratively label additional samples. Despite their effectiveness in multiple domains, existing co-training approaches for tabular data are either heuristic, and therefore error-prone, or use a greedy approach that leads to sub-optimal performance. We present ReCom, a deep reinforcement learning-based co-training approach. Our approach models multiple aspects of both the dataset and the two learners and develops advanced labeling strategies that achieve state-of-the-art performance. ReCom overcomes the challenge of limited data availability by simultaneously training on multiple datasets, thus producing a generic and robust labeling policy that can be applied to new datasets without the need for any additional training. Our experiments, conducted on a diverse group of 32 datasets, demonstrate the merits of our approach.