Reviewerly/exHarmony huggingface dataset:
Lasso Regression for Daily Rainfall Modeling at Citeko Station, Bogor, Indonesia

Rainfall is one of the climate components which is very important for an agrarian country with a tropical climate like Indonesia. There are so many variables that can affect the intensity of rainfall, including humidity, air temperature, speed and wind direction. However, not all of these variables can have a significant effect, therefore, we need a modeling technique that can select and shrink the predictor variables so as to make the model simpler. One of the techniques used is the Lasso (least absolute shrinkage and selection operator). The purpose of this study is to model rainfall intensity using lasso regression and determine which variable have the greatest influence on rainfall intensity. This paper also designs an application system that can be used to implement Lasso regression. The response variables in the form of daily rainfall at the Citeko observation station, Bogor Indonesia is assumed to have normal distribution. Rainfall intensity modeling involves 16 predictor variables, namely the maximum temperature, minimum temperature, average temperature, average humidity, sun exposure, maximum wind speed, wind direction when maximum wind speed and 8 other categorical variables are the most frequent wind direction. The resulting Lasso regression model successfully select and shrink the variables used to 9 variables only. In addition, based on the AIC and coefficient of determination indices for the model evaluation, the Lasso regression shows satisfying results rather than classical multiple linear regression. In Lasso regression, AIC value is 5901.20 where in classical model it is 5911.43. Also, Lasso regression coefficient of determination value is greater that is 22.73% while in classical model is 21.41%. From lasso regression model that has been obtained, it is known that the most frequent wind direction to the north is the variable that has the greatest influence on rainfall intensity. The Lasso regression application has been successfully created so that it can facilitate the user in uploading data, exploration of data and variables, Lasso regression analysis and report result.
Accelerated RANSAC for Accurate Image Registration in Aerial Video Surveillance

Compared with ground views and direct overhead views (for orbital satellites), aerial robotics allow for capturing videos from diverse viewpoints and scenes, thus, the content of aerial image is complex and changeable, and aerial video has complex inter-frame transforms stemming from the blend of camera motion, platform motion and jitter. In addition, poor quality and similar texture are common in long-distance and large-scale aerial video surveillance. All of these interferences make image registration of aerial video difficult. This article puts forward one image registration method suited to aerial video on the basis of the hypothesize-and-verify of RANSAC. The proposed accelerated RANSAC, named PSSC-RANSAC (Prior Sampling & Sample Check RANSAC), incorporates prior sampling, which comes from three levels of sample evaluation, including texture magnitude, spatial consistency and feature similarity, to generate more possibly correct samples in priority. Furthermore, prior information of sample, quality of sample subset and subset invariability are together used to check the sample subsets, and the incompatible arrangements of subsets are immediately ruled out in sample check stage, which speeds up the iteration further. Results of the experiment have proved the good performance of the presented PSSC-RANSAC at 90% contamination level. For typical image pairs, the number of iterations is reduced by at least 16.67% and evaluation computation is reduced by at least 11.01% compared with SVH-RANSAC, and the re-projection error is decreased by at least 4.44% and 6.31% compared with RANSAC and SVH-RANSAC, respectively. It can overcome the interferences, and is very suitable for image registration of aerial images.
VehSat: a Large-Scale Dataset for Vehicle Detection in Satellite Images

There has been this last decade a major improvement in earth observation imagery, both in terms of resolution and revisit rate. This technological leap comes with an increasing demand for detecting objects in satellite images. In this context, vehicle detection generates a great deal of interest in the scientific and industrial community. Indeed, detecting vehicles can have a range of applications from traffic monitoring to parking lots occupancy rate estimation. This interest is demonstrated in the wide range of public vehicle datasets that already exist. However, these datasets rely on the use of aerial images, acquired from planes, that are different in many aspects from satellite images. Machine learning algorithms trained on these datasets are therefore likely to underperform on satellite images. Therefore, we introduce a large-scale dataset for VEHicle detection on SATellite images (VehSat). To this end, we collected 4544 crops of satellite images, from 4 different satellites and on 8 different areas. In total, 36851 vehicles were annotated in various contexts and resolutions. To build a baseline for vehicle detection in satellite images, we also evaluate state-of-the-art object detection algorithms on VehSat. Results show that, although performing better than human annotators on crowdsourcing platforms, machine learning algorithms still have a lot of room for improvement due to the challenging nature of the dataset.
Fair auctioning and trading framework for cloud virtual machines based on blockchain

Cloud auction provides a cost-effective approach to cloud resource allocation. Most existing cloud auction mechanisms simply assume that the auctioneer is trustable, and overlook the fairness of auctions. However, in fact, such a trustable auctioneer may not exist, and the fairness is non-trivial to guarantee. In this work, for the first time, we propose a decentralized auctioning and trading framework for cloud virtual machines based on blockchain. We realize both auction fairness and trade fairness among participants (e.g., cloud provider and cloud users), which guarantees each participant will not suffer any loss as long as it follows the protocol. Furthermore, we implement our system through the local analog network and Ethereum official test network, carry out experimental simulation, and demonstrate the feasibility of our system.
Comparison of geochemical patterns from different sampling density geochemical mapping in Altay, Xinjiang Province, China

With the continuous development of geochemical mapping, especially with the proposal of the “Chemical Earth” big science project, low-density geochemical mapping technology has received increasing attention due to “low cost and high efficiency”. Due to the special geochemical properties of Au, the distribution of Au in water sediments is so complicated that the differences of low-density and high-density Au geochemical mapping are not well known, which makes it difficult for researchers to accurately evaluate the Au anomalies from low-density geochemical mapping. This paper compares Au geochemical mapping data and geochemical patterns from ultra-low-density geochemical mapping (ULGM) (1 sample/1600 km 2 ), super-low-density geochemical mapping (SLGM) (1 sample/180 km 2 ) and high-density geochemical mapping (HGM) (sampling density 1 sample/km 2 ) in Altay, Xinjiang province, China. We found that the standard deviation of Au increases with the mapping density. The median values of the HGM, SLGM and ULGM are 0.71 × 10 −9 , 0.77 × 10 −9 , and 0.94 × 10 −9 , respectively. Altay Au anomalous is mainly controlled by 3 factors and are consequently divided into 3 kinds of anomaly types: 1. regional-scale anomalies, caused by gold-rich stratum; 2. local Au anomalies, caused by gold mineralization; 3. anomalies around the catchment, caused by the supergene migration and enrichment of fine-grained Au. In general, as the mapping density decreases, the spatial correspondence between anomalies and geological factors become weaker. HGM can figure out the single gold deposit, SLGM can delineate Au deposits concentrated area; ULGM can delineate the Au metallogenic target area. So, the ultra-low density mapping is suitable for quickly obtaining the geochemical background of the Au region in the blank area, reflecting the large-scale enrichment behavior of Au in the supergene environment. • Au geochemical mapping projects in three kinds of density are compared for the first time. • High-density geochemical mapping can be used to figure out the single gold deposit. • Super-low-density geochemical mapping can delineate Au deposits concentrated area. • Ultra-low-density geochemical mapping can delineate the Au metallogenic target area.
Containment Logics: Algebraic Completeness and Axiomatization

The paper studies the containment companion (or, right variable inclusion companion) of a logic $$\vdash $$ . This consists of the consequence relation $$\vdash ^{r}$$ which satisfies all the inferences of $$\vdash $$ , where the variables of the conclusion are contained into those of the set of premises, in case this is not inconsistent. In accordance with the work started in [10], we show that a different generalization of the Płonka sum construction, adapted from algebras to logical matrices, allows to provide a matrix-based semantics for containment logics. In particular, we provide an appropriate completeness theorem for a wide family of containment logics, and we show how to produce a complete Hilbert style axiomatization.
Markov Decision Processes with Unknown State Feature Values for Safe Exploration using Gaussian Processes

When exploring an unknown environment, a mobile robot must decide where to observe next. It must do this whilst minimising the risk of failure, by only exploring areas that it expects to be safe. In this context, safety refers to the robot remaining in regions where critical environment features (e.g. terrain steepness, radiation levels) are within ranges the robot is able to tolerate. More specifically, we consider a setting where a robot explores an environment modelled with a Markov decision process, subject to bounds on the values of one or more environment features which can only be sensed at runtime. We use a Gaussian process to predict the value of the environment feature in unvisited regions, and propose an estimated Markov decision process, a model that integrates the Gaussian process predictions with the environment model transition probabilities. Building on this model, we propose an exploration algorithm that, contrary to previous approaches, considers probabilistic transitions and explicitly reasons about the uncertainty over the Gaussian process predictions. Furthermore, our approach increases the speed of exploration by selecting locations to visit further away from the currently explored area. We evaluate our approach on a real-world gamma radiation dataset, tackling the challenge of a nuclear material inspection robot exploring an a priori unknown area.
Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Spectroscopic methods combined with statistics have recently gathered substantial interest in pedological studies. Near-infrared (NIR) spectroscopy has been utilized, for example, for reconstructions of the history and transformations of Chernozems, although no similar research was conducted based on mid-infrared (MIR). In this paper, the relevance of MIR spectroscopy was tested in studies on the origin/affinity of organic matter from chernozemic soils. Samples collected from three vegetation classes (grasslands, forests and arable lands) were investigated using MIR spectroscopy in order to create a statistical model, which was applied on buried profiles of unknown origin. The results showed a clear disjunction of vegetation classes. Samples of buried soil were placed in the space between all classes, indicating the relation to variable vegetation. Therefore, arable lands should not be omitted in paleoecological reconstructions, because we cannot exclude the cultivation of fertile soils before their burial. It was concluded that MIR methods may have similar applicability to NIR spectroscopy. Additionally, MIR spectra may also be discriminated according to the recognized soil type, which allows for direct reconstructions of the transformation trends in buried profiles.
Quantitative Mineral Mapping of Drill Core Surfaces II: Long-Wave Infrared Mineral Characterization Using<i>μ</i>XRF and Machine Learning

Abstract Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (±7–15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation.
Multimodal temporal-clinical note network for mortality prediction

Mortality prediction is an important task to achieve smart healthcare, especially for the management of intensive care unit. It can provide a reference for doctors to quickly predict the course of disease and customize early intervention programs for the patients in need. With the development of the electronic medical records, deep learning methods are introduced to deal with the prediction task. In the electronic medical records, clinical notes always contain rich and diverse medical information, including the clinical histories and reports during admission. Mortality prediction methods mostly rely on the temporal events such as medical examinations and ignore the related reports and history information in the clinical notes. We hope that we can utilize both temporal events and clinical notes information to get better mortality prediction results.We propose a multimodal temporal-clinical note network to model both temporal and clinical notes. Specifically, the clinical text are further processed for differentiating the chronic illness patients in the historical information of clinical notes from non-chronic illness patients. In order to further mine the information related to the mortality in the text, we learn the time series embedding with Long Short Term Memory networks and the clinical notes embedding with a label aware convolutional neural network. We also propose a scoring function to measure the importance of clinical note sections. Our approach achieved a better AUCPR and AUCROC than competing methods and visual explanations for word importance showed the interpretability improvement of the model.We have tested our methodology on the MIMIC-III dataset. Contributions of different clinical note sections were uncovered by visualization methods. Our work demonstrates that the introduction of the medical history related information can improve the performance of the mortality prediction. Using label aware convolutional neural networks can further improve the results.
Deep neural network and model-based clustering technique for forensic electronic mail author attribution

Abstract Electronic mail is the primary source of different cyber scams. Identifying the author of electronic mail is essential. It forms significant documentary evidence in the field of digital forensics. This paper presents a model for email author identification (or) attribution by utilizing deep neural networks and model-based clustering techniques. It is perceived that stylometry features in the authorship identification have gained a lot of importance as it enhances the author attribution task's accuracy. The experiments were performed on a publicly available benchmark Enron dataset, considering many authors. The proposed model achieves an accuracy of 94% on five authors, 90% on ten authors, 86% on 25 authors and 75% on the entire dataset for the Deep Neural Network technique, which is a good measure of accuracy on a highly imbalanced data. The second cluster-based technique yielded an excellent 86% accuracy on the entire dataset, considering the authors' number based on their contribution to the aggregate data.
Designing Gaming Wearables: From Participatory Design to Concept Creation

In this pictorial, we depict our design process on gaming wearables starting from participatory design workshops to concept creation. Wearables possess strong qualities for gaming such as performativity, sociality and interactivity. However, it is an emergent field and there is a dearth of design knowledge especially when it comes to designing wearables for mainstream gaming platforms such as game consoles. Our aim is to explore this field elaborately with a research through design approach and also clearly exemplify how our design process progressed through different phases. Our results, apart from helping wearables designers to understand critical features for mainstream gaming, will also demonstrate the techniques and methods for extracting knowledge from PD workshops and incorporating it in a conceptual design phase.
Higher-Order Graph Convolutional Networks With Multi-Scale Neighborhood Pooling for Semi-Supervised Node Classification

Existing popular methods for semi-supervised node classification with high-order convolution improve the learning ability of graph convolutional networks (GCNs) by capturing the feature information from high-order neighborhoods.However, these methods with high-order convolution usually require many parameters and high computational complexity.To address these limitations, we propose HCNP, a new higher-order GCN for semi-supervised node learning tasks, which can simultaneously aggregate information of various neighborhoods by constructing high-order convolution.In HCNP, we reduce the number of parameters using a weight sharing mechanism and combine the neighborhood information via multi-scale neighborhood pooling.Further, HCNP does not require a large number of hidden units, and it fits a few parameters and exhibits low complexity.We show that HCNP matches GCNs in terms of complexity and parameters.Comprehensive evaluations on publication citation datasets (Citeseer, Pubmed, and Cora) demonstrate that the proposed methods outperform MixHop in most cases while maintaining lower complexity and fewer parameters and achieve state-of-the-art performance in terms of accuracy and parameters compared to other baselines.
Sensitive Information Detection Based on Convolution Neural Network and Bi-Directional LSTM

Electronic documents can carry lots of information and are widely used in daily lives. It will cause substantial economic losses to individual users, enterprises, and governments when the documents containing sensitive information are leaked. How to detect sensitive information to prevent data leakage is still a challenge in the field of information security. This paper mainly focuses on the detection of unstructured documents containing sensitive information. Governments, military, and other institutions can actively mark whether the electronic documents contain sensitive information according to the detection results. We propose a reliable method to detect sensitive electronic documents automatically and compare it with other basic methods. The algorithm structure can extract the characteristics of the data more comprehensively to obtain better detection results. Our model outperformed the other models with 93.44 % accuracy. Our model can also reduce the time cost, which is beneficial for realistic production.
Accelerating Bi-Directional Sampling-Based Search for Motion Planning of Non-Holonomic Mobile Manipulators

Determining a feasible path for nonholonomic mobile manipulators operating in congested environments is challenging. Sampling-based methods, especially bi-directional tree search-based approaches, are amongst the most promising candidates for quickly finding feasible paths. However, sampling uniformly when using these methods may result in high computation time. This paper introduces two techniques to accelerate the motion planning of such robots. The first one is coordinated focusing of samples for the manipulator and the mobile base based on the information from robot surroundings. The second one is a heuristic for making connections between the two search trees, which is challenging owing to the nonholonomic constraints on the mobile base. Incorporating these two techniques into the bi-directional RRT framework results in about 5x faster and 10x more successful computation of paths as compared to the baseline method.
PMAN: Progressive Multi-Attention Network for Human Pose Transfer

This paper presents a novel approach for human pose transfer, progressive multi-attention network (PMAN), which generates a new human image by transferring the pose of a given person to a target pose. The network gradually updates the pose feature and the image feature through a series of multi-attention transfer blocks (MATBs). Each MATB consists of two attention mechanisms: pose-conditioned batch normalization (PCBN) and cooperative attention mechanism (CAM). Specifically, in low-level feature space, the PCBN layer with pose information is used to replace the BN layer of the image channel to realize the preliminary guidance of pose to image. The CAM is implemented as two gated mechanisms in high-level feature space, which reveals the essence of human pose transfer, that is, mutual guidance and dynamic control between pose and image. Gated memory writing is used to calculate the pixel-wise weight of the pose by using global image information to guide the update of the pose. Gated response utilizes an adaptive gating mechanism to dynamically control the pose information flow so as to update the image. A large number of subjective and objective experiments on DeepFashion and Market-1501 demonstrate the superiority of our method. The proposed multi-attention mechanism is well adapted to the human pose transfer task and provides a possible new idea for other cross-domain generation tasks.
Scaling Data Physicalization – How Does Size Influence Experience?

Given the material nature of data physicalization, their creators need to make many design decisions, including material choices and scale. Our study explores the impact of scale in physicalization, motivated by the assumption that size can affect user experience. We created two different physicalizations (for the same dataset) in three sizes each, and evaluated the resulting six objects with a questionnaire approach and interviews. Our findings highlight that scale needs to be chosen wisely given its impact on representation legibility (ease of viewing and understanding) and affordances for interaction. We discuss factors to take into account when designing large-scale physicalizations and in further research on the potential role of scale in physicalizaton. In particular, we argue that for large-scale physicalizations, scale should matter and communicate meaning, for instance, supporting an intuitive understanding of magnitudes, or a specific experience. Thus, scale needs to be an explicit design decision, that interacts with other design parameters.