Reviewerly/exHarmony huggingface dataset:
Evaluation of Multi-Reanalysis Solar Radiation Products Using Global Surface Observations

Solar radiation incident at the Earth’s surface is an essential driver of the energy exchange between the atmosphere and the surface and is also an important input variable in the research on the surface eco-hydrological process. The reanalysis solar radiation dataset is characterized by a long time series and wide spatial coverage and is used in the research of large-scale eco-hydrological processes. Due to certain errors in their production process of the reanalysis of solar radiation products, reanalysis products should be evaluated before application. In this study, three global solar-radiation reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) in different temporal scales and climate zones were evaluated using surface solar-radiation observations from the National Meteorological Information Center of the China Meteorological Administration (CMA, Beijing, China) and the Global Energy Balance Archive (GEBA, Zürich, Switzerland) from 2000 to 2009. All reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) overestimated with an annual bias of 14.86 W/m2, 22.61 W/m2, and 31.85 W/m2; monthly bias of 15.17 W/m2, 21.29 W/m2, and 36.91 W/m2; and seasonal bias of 15.08 W/m2, 21.21 W/m2, and 36.69 W/m2, respectively. In different Köppen climate zones, the annual solar radiation of ERA-Interim performed best in cold regions with a bias of 10.30 W/m2 and absolute relative error (ARE) of 8.98%. However, JRA-55 and NCEP-DOE showed the best performance in tropical regions with a bias of 20.08 W/m2 and −0.12 W/m2, and ARE of 11.00% and 9.68%, respectively. Overall, through the evaluations across different temporal and spatial scales, the rank of the three reanalysis products in order was the ERA-Interim, JRA-55, and NCEP-DOE. In addition, based on the evaluation, we analyzed the relationship between the error (ARE) of the reanalysis products and cloud cover, aerosol, and water vapor, which significantly influences solar radiation and we found that cloud was the main cause for errors in the three solar radiation reanalysis products. The above can provide a reference for the application and downscaling of the three solar radiation reanalysis products.
Scalability analysis of request scheduling in cloud computing

Rapid advancement of distributed computing systems enables complex services in remote computing clusters. Massive applications with large-scale and disparate characteristics also create high requirements for computing systems. Cloud computing provides a series of novel approaches to meet new trends and demands. However, some scalability issues have to be addressed in the request scheduling process and few studies have been conducted to solve these problems. Thus, this study investigates the scalability of the request scheduling process in cloud computing. We provide a theoretical definition of the scalability of this process. By modeling the scheduling server as a stochastic preemptive priority queue, we conduct a comprehensive theoretical and numerical analysis of the scalability metric under different structures and various environment configurations. The comparison and conclusion are expected to shed light on the future design and deployment of the request scheduling process in cloud computing.
Performance Comparison of Machine Learning Platforms

In this paper, we present a method for comparing and evaluating different collections of machine learning algorithms on the basis of a given performance measure (e.g., accuracy, area under the curve (AUC), F-score). Such a method can be used to compare standard machine learning platforms such as SAS, IBM SPSS, and Microsoft Azure ML. A recent trend in automation of machine learning is to exercise a collection of machine learning algorithms on a particular problem and then use the best performing algorithm. Thus, the proposed method can also be used to compare and evaluate different collections of algorithms for automation on a certain problem type and find the best collection. In the study reported here, we applied the method to compare six machine learning platforms – R, Python, SAS, IBM SPSS Modeler, Microsoft Azure ML, and Apache Spark ML. We compared the platforms on the basis of predictive performance on classification problems because a significant majority of the problems in machine learning are of that type. The general question that we addressed is the following: Are there platforms that are superior to others on some particular performance measure? For each platform, we used a collection of six classification algorithms from the following six families of algorithms – support vector machines, multilayer perceptrons, random forest (or variant), decision trees/gradient boosted trees, Naive Bayes/Bayesian networks, and logistic regression. We compared their performance on the basis of classification accuracy, F-score, and AUC. We used F-score and AUC measures to compare platforms on two-class problems only. For testing the platforms, we used a mix of data sets from (1) the University of California, Irvine (UCI) library, (2) the Kaggle competition library, and (3) high-dimensional gene expression problems. We performed some hyperparameter tuning on algorithms wherever possible. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0825 .
New graph distance for deformable 3D objects recognition based on triangle-stars decomposition

We address the problem of comparing deformable 3D objects represented by graphs such as triangular tessellations. We propose a new graph matching technique to measure the distance between these graphs. The proposed approach is based on a new decomposition of triangular tessellations into triangle-stars. The algorithm ensures a minimum number of disjoint triangle-stars, provides improved dissimilarity by covering larger neighbors and allows the creation of descriptors that are invariant or at least oblivious under the most common deformations. The present approach is based on an approximation of the Graph Edit Distance, which is fault-tolerant to noise and distortion, thus making our technique particularly suitable for the comparison of deformable objects. Classification is performed with supervised machine learning techniques. Our approach defines a metric space using graph embedding and graph kernel techniques. It is proved that the proposed distance is a pseudo-metric. Its time complexity is determined and the method is evaluated against benchmark databases. Our experimental results confirm the performances and the accuracy of our system.
Geochemical distribution mapping by combining number-size multifractal model and multiple indicator kriging

The Mirgeh-Naghshineh region located at the northwest of Iran has a high potential for Au mineralization due to its adjacency to shear zone as a plausible environment responsible for ore-bearing occurrences. In this study, geochemical samples were collected over promising regions of the study area to delineate favorable zones for exploratory drillings. A number-size (NS) fractal approach was taken into account to separate anomalous regions from regional and background ones, and the prospect zone was divided into some indicators based on the extracted thresholds. Such results were incorporated in the multiple indicator kriging (MIK) approach to augment the interpolation of Au distribution map. It was observed that the highest favorable zones cover about 1.6% of the area, while the drilled holes prove such Au mineralization. The applicability of the proposed method was also compared with the conventional interpolation techniques, such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Median Indicator Kriging (Median-IK). The Mean Error (ME) and Root Mean Square Error (RMSE) values for the proposed method were smaller than those of other methods. Moreover, cross validation indicated the superiority of the proposed method.
Preference based multi-objective algorithms applied to the variability testing of software product lines

Evolutionary Multi-Objective Algorithms (EMOAs) have been applied to derive products for the variability testing of Software Product Lines (SPLs), which is a complex task impacted by many factors, such as the number of products to be tested, coverage criteria, and efficacy to reveal faults. But such algorithms generally produce a lot of solutions that are uninteresting to the tester. This happens because traditional search algorithms do not take into consideration the user preferences. To ease the selection of the best solutions and avoid effort generating uninteresting solutions, this work introduces an approach that applies Preference-Based Evolutionary Multi-objective Algorithms (PEMOAs) to solve the problem. The approach is multi-objective, working with the number of products to be tested, pairwise coverage and mutation score. It incorporates the preferences before the evolution process and uses the Reference Point (RP) method. Two PEMOAs are evaluated: R-NSGA-II and r-NSGA-II, using two different formulations of objectives, and three kinds of RPs. PEMOAs outperform the traditional NSGA-II by generating a greater number of solutions in the Region of Interest (ROI) associated to the RPs. The use of PEMOAs can reduce the tester’s burden in the task of selecting a better and reduced set of products for SPL testing.
Pengembangan Bahan Ajar Berbasis CD Interaktif Power Point Untuk Meningkatkan Hasil Belajar Siswa

This study aims to describe the development of interactive CD learning materials based on power point to improve students’ learning outcomes within the topic of production technology, communication and transportation in grade IV. The research methods used are research and development, consisting of the preliminary study, model development, and model testing. The results show that the interactive CD learning materials developed are eligible according to the development steps. The results of media validation and the results of material validation were shown with average score of 79,8% and 87,2% respectively. The students' responses to interactive learning materials scored 81.75% on small-scale trials while large-scale trials obtained a score of 85%. Then the teacher's response scored 71.7% and 80.48% respectively. Based on the value obtained, then the interactive CD teaching materials are feasible to be applied to grade 4 elementary school students. Based on the test results of the student learning on small-scale, it showed that 63% of students passed with the score ≥75. While on a large-scale, 100% of the students passed and scored ≥75. Thus, it can be concluded that the development of interactive teaching materials is appropriate for students in grade IV of elementary school.
A High-Speed Division Algorithm for Modular Numbers Based on the Chinese Remainder Theorem with Fractions and Its Hardware Implementation

In this paper, a new simplified iterative division algorithm for modular numbers that is optimized on the basis of the Chinese remainder theorem (CRT) with fractions is developed. It requires less computational resources than the CRT with integers and mixed radix number systems (MRNS). The main idea of the algorithm is (a) to transform the residual representation of the dividend and divisor into a weighted fixed-point code and (b) to find the higher power of 2 in the divisor written in a residue number system (RNS). This information is acquired using the CRT with fractions: higher power is defined by the number of zeros standing before the first significant digit. All intermediate calculations of the algorithm involve the operations of right shift and subtraction, which explains its good performance. Due to the abovementioned techniques, the algorithm has higher speed and consumes less computational resources, thereby being more appropriate for the multidigit division of modular numbers than the algorithms described earlier. The new algorithm suggested in this paper has O (log2 Q) iterations, where Q is the quotient. For multidigit numbers, its modular division complexity is Q(N), where N denotes the number of bits in a certain fraction required to restore the number by remainders. Since the number N is written in a weighed system, the subtraction-based comparison runs very fast. Hence, this algorithm might be the best currently available.
Automatic Muscle Fiber Orientation Tracking in Ultrasound Images Using a New Adaptive Fading Bayesian Kalman Smoother

This paper proposes a new algorithm for automatic estimation of muscle fiber orientation (MFO) in musculoskeletal ultrasound images, which is commonly used for both diagnosis and rehabilitation assessment of patients. The algorithm is based on a novel adaptive fading Bayesian Kalman filter (AF-BKF) and an automatic region of interest (ROI) extraction method. The ROI is first enhanced by the Gabor filter (GF) and extracted automatically using the revoting constrained Radon transform (RCRT) approach. The dominant MFO in the ROI is then detected by the RT and tracked by the proposed AF-BKF, which employs simplified Gaussian mixtures to approximate the non-Gaussian state densities and a new adaptive fading method to update the mixture parameters. An AF-BK smoother (AF-BKS) is also proposed by extending the AF-BKF using the concept of Rauch-Tung-Striebel smoother for further smoothing the fascicle orientations. The experimental results and comparisons show that: 1) the maximum segmentation error of the proposed RCRT is below nine pixels, which is sufficiently small for MFO tracking; 2) the accuracy of MFO gauged by RT in the ROI enhanced by the GF is comparable to that of using multiscale vessel enhancement filter-based method and better than those of local RT and revoting Hough transform approaches; and 3) the proposed AF-BKS algorithm outperforms the other tested approaches and achieves a performance close to those obtained by experienced operators (the overall covariance obtained by the AF-BKS is 3.19, which is rather close to that of the operators, 2.86). It, thus, serves as a valuable tool for automatic estimation of fascicle orientations and possibly for other applications in musculoskeletal ultrasound images.
3D Visual passcode: Speech-driven 3D facial dynamics for behaviometrics

Face biometrics have achieved remarkable performance over the past decades, but unexpected spoofing of the static faces poses a threat to information security. There is an increasing demand for stable and discriminative biological modalities which are hard to be mimicked and deceived. Speech-driven 3D facial motion is a distinctive and measurable behavior-signature that is promising for biometrics. In this paper, we propose a novel 3D behaviometrics framework based on a "3D visual passcode" derived from speech-driven 3D facial dynamics. The 3D facial dynamics are jointly represented by 3D-keypoint-based measurements and 3D shape patch features, extracted from both static and speech-driven dynamic regions. An ensemble of subject-specific classifiers are then trained over selected discriminative features, which allows for a discriminant speech-driven 3D facial dynamics representation. We construct the first publicly available Speech-driven 3D Facial Motion dataset (S3DFM) that includes 2D-3D face video plus audio samples from 77 participants. The experimental results on the S3DFM show that the proposed pipeline achieves a face identification rate of 96.1%. Detailed discussions are presented, concerning anti-spoofing, head pose variation, video frame rate, and applicability cases. We also give comparison with other baselines on "deep" and "shallow" 2D face features.
Geothermal and Mineralogic Analysis of Hot Springs in the Puracé-La Mina Sector in Cauca, Colombia

Thermal waters are natural resources of great value to geothermal sciences, the tourism industry, and health. In this work, geochemical classification of physicochemical results of 17 sources at the Puracé-La Mina (Cauca, Colombia) sector was implemented in order to strengthen and determine their potential applications and enhance the continental tourism in Colombia. The analyzed parameters were developed following the Standard Methods 22nd edition, at Universidad Nacional de Colombia-Manizales. According to the results obtained by means of a geochemical classification, it was found that most of the sources have a sulfated-acid nature which makes them heated vapor waters and volcanic waters. Likewise, it was observed that all the sources are immature waters and still do not reach chemical equilibrium. On the other hand, mineralogical and chemical characterization by means of XRD and XRF showed a high content of silica isomorphous minerals with a low concentration. In addition, the presence of Fe 2 O 3 was observed, which is insoluble at pH &gt; 5 and remains in the rock. Nevertheless, considering that mine sources possess pH ± 2 and temperatures of 40°C, leaching is possible for iron justifying its presence in the water. Instead, elements like Na + , K + , Mg 2+ , and Ca 2+ have high mobility at the conditions of mine sources (low pH) as a consequence of hydrolysis processes, which produce variations in water composition.
Detection of Speech Smoothing on Very Short Clips

Audio editing software can easily be used to manipulate digital speech for forgery. Smoothing on the tampered boundary is usually performed to eliminate the obvious traces of forgery after tampering. This presents a considerable challenge for the forensic detection of tampered speech because the smoothing model is unknown and the smoothing operation often modifies only several tens of samples with the editing software. In this paper, we propose to apply six filtering models to approximate the smoothing in audio editing software for training the classifier. We analyze the impact of filtering operations on speech signals, especially on differential signals. On the basis of the local variance of the differential signal, we design a simple and yet efficient feature set. Theoretical analysis and extensive experiments show that the proposed features are very effective in detecting several common filtering operations on very short speech clips. The experimental results also show that the proposed method can detect unknown smoothing performed by commonly used audio editing software, such as Cooledit and Adobe Audition. This highlights the promising potential of the proposed method for use as a forgery localization tool of digital speech signals in practical forensic applications. The proposed method is capable of detecting smoothing on very short speech clips containing only several tens of samples and practical forgery used in audio editing software.
Local and Nonlocal Steering Kernel Weighted Total Variation Model for Image Denoising

To eliminate heavy noise and retain more scene details, we propose a structure-oriented total variation (TV) model based on data dependent kernel function and TV criterion for image denoising application. The innovative model introduces the weights produced from the local and nonlocal symmetry features involved in the image itself to pick more precise solutions in the TV denoising process. As a result, the proposed local and nonlocal steering kernel weighted TV model yields excellent noise suppression and structure-preserving performance. The experimental results verify the validity of the proposed model in objective quantitative indices and subjective visual appearance.
A novel method of text line segmentation for historical document image of the uchen Tibetan

Text line segmentation is a key step in Tibetan historical document recognition. A novel method for text line segmentation was proposed based on the baseline in uchen Tibetan, and a new dataset was released, which was used to evaluate the results of text line segmentation of uchen Tibetan historical documents. In this paper, there were two steps for the proposed method: baseline detection and text line segmentation using the baseline. In baseline detection, the upper edges of all characters in the document were obtained by a horizontal gradient operator, then an edge connectivity definition was proposed by which the upper edge set was divided into disjoint subsets. Eligible sets were selected from these subsets, and the edges in these sets were joined in turn to obtain the baseline. In text line segmentation, the document image was truncated at the baseline position, then the adhesion regions were segmented again. Each connected region in the image was assigned to its nearest baseline. All connected regions belonging to the same baseline formed a text line. Experiments on the proposed dataset showed that the method could effectively avoid document distortion, the accuracy of text line segmentation was high, and the text line adhesion could be handled.
An Improved Density-Based Approach to Spatio-Textual Clustering on Social Media

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm but may not be sufficient when the input data type is heterogeneous in terms of textual description.When we aim to discover clusters of geo-tagged records relevant to a particular point of interest (POI) on social media, examining only one type of input data (e.g., the tweets relevant to a POI) may draw an incomplete picture of clusters due to noisy regions.To overcome this problem, we introduce DBSTexC, a newly defined density-based clustering algorithm using spatio-textual information on social media (e.g., Twitter).We first characterize the POI-relevant and POI-irrelevant geotagged tweets as the texts that include and do not include a POI name or its semantically coherent variations, respectively.By leveraging the proportion of the POI-relevant and POI-irrelevant tweets, the proposed algorithm demonstrates much higher clustering performance than the DBSCAN case in terms of F 1 score and its variants.While DBSTexC performs exactly as DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with the textually heterogeneous inputs.Furthermore, to further improve the clustering quality by fully capturing the geographic distribution of geo-tagged points, we present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates the notion of fuzzy clustering into the DBSTexC.We then demonstrate the consistent superiority of F-DBSTexC over the original DBSTexC via intensive experiments.The computational complexity of our algorithms is also analytically and numerically shown.
Scalable quantum tomography with fidelity estimation

We propose a quantum tomography scheme for pure qudit systems which adopts a certain version of random basis measurements and a generative learning method, along with a built-in fidelity estimation approach to assess the reliability of the tomographic states. We prove the validity of the scheme theoretically, and we perform numerically simulated experiments on several target states that have compact matrix product state representation, demonstrating its efficiency and robustness. We find the number of replicas required by a fixed fidelity criterion grows only linearly as the system size scales up, which saturates a lower bound from information theory. Thus the scheme achieves the highest possible scalability that is crucial for practical quantum state tomography.
High-dynamic-range 3D shape measurement based on time domain superposition

Fringe projection is a widely used approach for 3D shape measurements. However, it is ineffective for scenarios with a wide range of reflectivity. In this paper, we propose a technique that can measure high-dynamic-range surfaces without changing camera exposure or projected light intensity. With the strategy of time domain superposition, we can use noisy fringe images to recover phase and geometric shapes accurately for low-reflection and high-reflection regions. The measurement accuracy of low-reflection regions obtained by the proposed method is about three times higher than that of the traditional phase-shifting method. Both simulations and experiments are performed to verify the proposed method.