Reviewerly/exHarmony huggingface dataset:
Zipf rank analysis highlights the exploration potential for Lithium-Caesium-Tantalum -type pegmatites in the Northern Cape, South Africa

Exploration activities for mineralised Lithium-Caesium-Tantalum (LCT) pegmatites in the metalliferous Orange River pegmatite belt (southern Africa) are hampered by a dearth of any reasonable estimates of the mineral province's residual metal endowment or of its prospectivity. To mitigate this, Zipf rank analysis has been used to estimate the likelihood of pegmatites occurring below Quaternary cover sediments which veneer extensive tracts of this mineral province. The Zipf rank analyses focused on the western extremity of the pegmatite belt near Vioolsdrif where historical mining activities have exploited several pegmatite bodies including the world-class Li–Bi–Be–Cs–Ta-mineralised Noumas 1 pegmatite. Zipf rank analyses were conducted on the surface exposures of both a comprehensive dataset of mineralised- and unmineralised pegmatites (N = 159), and on a subset which comprised only the mineralised pegmatites (N = 23). Results from the latter analysis indicate that up 11 500 m2 (or ∼11%) of mineralised pegmatite surface exposure remains to be found within the confines of the defined study area. Because our Zipf analyses considered surface exposure at the present day erosional level of the Meso-proterozoic basement rocks, we suggest this residual endowment is hidden beneath the unconsolidated cover sediments. This novel application of Zipf rank analysis to pegmatite surface exposure thus highlights the prospectivity of the Orange River pegmatite belt for several of earth's critical raw materials.
Emergence of norms in interactions with complex rewards

Abstract Autonomous agents are becoming increasingly ubiquitous and are playing an increasing role in wide range of safety-critical systems, such as driverless cars, exploration robots and unmanned aerial vehicles. These agents operate in highly dynamic and heterogeneous environments, resulting in complex behaviour and interactions. Therefore, the need arises to model and understand more complex and nuanced agent interactions than have previously been studied. In this paper, we propose a novel agent-based modelling approach to investigating norm emergence, in which such interactions can be investigated. To this end, while there may be an ideal set of optimally compatible actions there are also combinations that have positive rewards and are also compatible. Our approach provides a step towards identifying the conditions under which globally compatible norms are likely to emerge in the context of complex rewards. Our model is illustrated using the motivating example of self-driving cars, and we present the scenario of an autonomous vehicle performing a left-turn at a T-intersection.
Optimal synthesis of crank-rocker mechanisms with optimum transmission angle for desired stroke and time-ratio using genetic programming

Dimensional synthesis of crank-rocker mechanisms applied to provide some desired values of stroke and time ratio, is of utmost importance for designing an efficient mechanism. In the synthesis and manufacturing of crank-rocker mechanisms, the designers are further challenged by other design criteria, such as quality of motion. In this study, a novel approach based on genetic programming (GP) is proposed for dimensional synthesis of planar crank-rocker mechanisms with optimum transmission angle over the desired stroke and time-ratio. An analytical approach is elaborated which leads to an interesting relationship of length of the coupler and rocker links. It is, therefore, advised that by adopting equal lengths for coupler link and rocker link, one can guarantee the optimality of the transmission angle’s deviation ([Formula: see text]), ensuring the Grashof condition. Consequently, through an inverse modeling approach, GP method is utilized to construct some explicitly mathematical formulas to represent all the sizes and dimensions of the crank-rocker mechanism based on the any desired values of both stroke and time-ratio. In this way, an input-output data set consisting of all the dimensions and the sizes of the links as the input variables and both the stroke and time-ratio as output variables is first constructed using the pertinent mathematical equations. Indeed, such approach of inverse modeling using GP simplifies greatly the synthesis of the crack-rocker mechanisms for any desired values of both stroke and time-ratio. The proposed approach has been applied for kinematic synthesis of a crank-rocker mechanism. A comparison between the obtained results of this work and the analytic method, clearly illustrates the efficiency of the proposed approach.
Domain Adaptation for Unknown Image Distortions in Instance Segmentation

Data-driven techniques for machine vision heavily depend on the training data to sufficiently resemble the data occurring during test and application.However, in practice unknown distortion can lead to a domain gap between training and test data, impeding the performance of a machine vision system.With our proposed approach this domain gap can be closed by unpaired learning of the pristine-todistortion mapping function of the unknown distortion.This learned mapping function may then be used to emulate the unknown distortion in the training data.Employing a fixed setup, our approach is independent from prior knowledge of the distortion.Within this work, we show that we can effectively learn unknown distortions at arbitrary strengths.When applying our approach to instance segmentation in an autonomous driving scenario, we achieve results comparable to an oracle with knowledge of the distortion.An average gain in mean Average Precision (mAP) of up to 0.19 can be achieved.
Development of Mobile Learning Based Electronic Student Worksheets to Measure Learning Motivation on Sensing Systems Materials

The material of the sensory system is important because in the sensory system there are several kinds of senses and the nervous system which is still abstract and the problem that occurs is a misconception about the material. Motivation is one of the factors that support success in learning, but students learning motivation is still low. This study aims to produce valid and feasible mobile learning-based electronic student worksheets and to measure students learning motivation on sensing system materials. The research subjects were students of class VIII MTsN 8 Banyuwangi. This study uses Research and Development methods and 4D models with 4 stages, namely define, design, develop, and dissemination. The instrument used was a material validation questionnaire, medial, readability, and students' learning motivation. The validation results show the average percentage of the material test is 94% and the media is 94% with very feasible criteria. The percentage of the students' readability test has an average value of 71% with a good category and the students' learning motivation is very strong with a percentage of 82%. Thus, electronic student worksheets can measure students' learning motivation. For future researchers, it is recommended to use the product in classroom learning
How do ChatBots look like?

The development and use of electronic government chatbots grows every day. However, chatbots still fail to meet users' expectations, directly affecting citizen engagement and adoption. Prior research suggests impersonal interaction as the basis for rejecting such technologies. A common strategy to tackle this issue is using anthropomorphism in chatbots: clothes, body features, skin color, and even behavior can be considered during the human-chatbot interaction design process. Although extensive research has been conducted on chatbots in the electronic government domain, the comprehension of the use of anthropomorphism in such a context is yet to be discussed and investigated. This research examines the use of anthropomorphism on ten electronic government chatbots, five national (Brazilian) and five foreign (1 Argentine, 1 Portuguese, 1 American, 1 British, and 1 Singaporean). To understand and map its use, five anthropomorphism dimensions (appearance, language, behavior, identity, and personality), conceived after a literature review, were used to build the chatbot profiles. Our findings show different communicative profile strategies contributing to a preliminary anthropomorphism categorization.
Applying self-organizing maps to characterize hyperspectral drill core data from three ore prospects in Northern Finland

Hyperspectral (HS) data offers a fast, non-invasive and non-destructive way of obtaining information about the composition of rocks. In the Hyperspectral Lapland (HypeLAP) -project we are using HS imaging and point spectral data to map the mineralogical and mineral chemical variations of a total of 500 m of drill core data. These data, acquired in the visible-near infrared-short-wave infrared (VNIR-SWIR; 400-2,500 nm) and long-wave infrared (LWIR; 8,000-12,000 nm) wavelength regions, represent two gold prospects (Ruosselkä and Hirvilavanmaa) and one Cu-Zn volcanogenic massive sulphide (VMS) prospect (Pahtavuoma) in northern Finland. In this paper, we present preliminary results from the Hirvilavanmaa study area, where we mapped the mineralogy of a single drill core box using the self-organizing maps (SOM) multivariate data analysis technology. In the process, point spectral data, Mineral Liberation Analyzer (MLA) data and X-Ray Fluorescence (XRF) data were used to guide the analysis of the HS image data. The results suggest that HS data has the potential to offer valuable information for mineral exploration activities in the Hirvilavanmaa study area. In the next steps, we will focus on analyzing LWIR wavelength region data from the three study areas to obtain a holistic view of their mineralogy and mineral chemistry. The HypeLAP-project is financed through the European Regional Development Fund (Sustainable growth and jobs 2014-2020 -programme).
Two-step registration of near-space remote sensing images via deep neural networks

Near-space remote sensing image registration is an important foundation of near-space image processing. For large image jitter distortion, geometric and atmospheric distortion of its image, we propose a two-step method based on deep neural networks, which includes a coarse-to-fine registration process. We construct a near-space image registration dataset which is captured from Google Maps and hot air balloon platforms, etc. For obtaining candidates, the coarse alignment stage applies classical geometric validation methods to a corresponding set of pre-trained deep features. The fine alignment network is based on pyramidal feature extraction and optical flow estimation to realize local flow field inference from coarse to fine. We construct a regularization layer for each level to ensure smoothness. Applying our method to our synthetic dataset, the experimental result shows that it has a competitive result that is evaluated based on the root mean square error, peak signal to noise ratio and structural similarity.
The EDA TURBO project: software-based atmospheric turbulence mitigation

As European optronic industries are facing an increasing demand for high-definition imagery, atmospheric turbulence becomes one of the main factors hindering the performance of military equipment used for intelligence, surveillance and reconnaissance. The TURBO project, which is funded by European Defence Agency and has Fraunhofer IOSB, TNO and Adimec as partners, aims to develop a real-time demonstrator capable of selecting the optimal algorithm for the given dataset from a repertoire of software-based turbulence correction algorithms. The work to be presented illustrates Fraunhofer IOSB's contribution to the first stage of the project. It consists of the selection of the most promising candidate methods for the demonstrator, as well as a detailed assessment of their limitations. A total of eight algorithms were selected. The first method performed global registration. For the correction of local motion, optical ow, block matching and iterative image generation methods were applied. To correct blur, we investigated various deconvolution approaches, including Richardson-Lucy approach and a blind deconvolution algorithm, and a Lucky fusion method. Finally, we focused on turbulence correction for scenes with moving content (people, vehicles). All methods were applied to several video sequences, which are recordings from field trials performed under different atmospheric conditions. The resulting corrected sequences were analysed first regarding their quantitative improvement in relation to the uncorrected sequences, and afterwards considering the execution time for each method. Three no-reference metrics were chosen for the quantitative analysis, namely Fisher information, edge variance and total variation. It was demonstrated that the selected methods achieved good correction results in many sequences. In particular optical ow and deconvolution achieved a high increase in image quality. Quantitative analysis was shown to be accurate, in the sense that it agreed systematically with visual perception. It was also demonstrated that certain methods produce good results within a short computation time, deconvolution for instance, which is of great interest for real-time use and it provides a good basis for future work.
Thinking about help systems

Online help systems are a fundamental support for users to better understand and master the different types of software that they need or intend to use. Despite different proposals of help systems being found in literature, the use of these resources is not broad or unanimous. To contribute with designers so that they can reflect upon the creation and updating of this type of resource, we conducted two studies. First, a Mapping Systematic, to verify the approaches and resources currently proposed. In this review, twenty five ways to categorize help resources were founded. In the second study - through a card sorting activity with specialists in the field of Human-Computer Interaction - these resources were classified. A first categorization sorted them in relation to how content was presented (Visual, Textual and Miscellany) and a second one in relation to its production (if by the Designer, the Community, or both). Besides these classifications - that can help the designer's reflection during the construction of these resources - we also highlight some points to be considered during this construction.
Accelerating a two-stage object detector for high quality in-orbit remote sensing

Object detection from remote sensing images has been performed on the ground. Recently, on-board object detection has been studied only to show its feasibility with single-stage detectors. However, highly accurate models such as two stage detectors are compute intensive so that they are too slow to run on power-constrained on-board computers. In this paper, we propose a speed-up method for two-stage detectors. Two-stage detectors extract features and ROIs(Region of Interest) in the first stage and then classify them at the second stage. This structure gives high accuracy but induces large inference latency. In remote sensing images from satellites, object size is small relative to the whole image. Based on this characteristic, we propose to exclude features related to the large objects in the first stage. To verify our concept, we have selected various R-CNN models as two-stage object detectors. We have implemented our methods on two NVIDIA Jetson boards. We have achieved 1.8x speed up in inference latency with 5% accuracy drop with the small object dataset.
Obstacle avoidance path planning of space manipulator based on improved RRT algorithm

The traditional rapid expansion random tree (RRT) algorithm has poor efficiency in the motion planning of the manipulator. Based on the traditional RRT algorithm, this paper introduces the target offset strategy in the process of expanding leaf nodes. When the algorithm falls into a local minimum, it will select the expansion point, so as to quickly break away from the minimum. The improved RRT algorithm and other algorithms are simulated in Mathematica. The experimental results show that the improved algorithm can guide the growth direction of the tree, improve the convergence speed of the algorithm, make it difficult to fall into local minimum, and improve the motion planning efficiency of the manipulator in simulation.
Multi-robot optimized sampling-base cooperative collision avoidance method in Lidar naviation

In multi-robot systems with dynamic and complex environments, robots are required to avoid not only the static objects but also other moving robots. To solve this problem, this paper presents an implementation of cooperative collision avoidance architecture based on optimized sampling-based collision avoidance paradigm. In our work, localization error is considered and bounded in adaptive Monte-Carlo localization process. Plus, we employ velocity obstacle paradigm in predicting collisions. Subsequently, by using Sampling-based planner and optimization theory, we get an optimizing velocity selection policy. Furthermore, we also introduce our distributed multi-robot system model in this paper. By applying the cooperative collision avoidance method in Gazebo self-driving car simulation environment and ROS mobile robots, it is illustrated that our approach is applicable and well-performed.
Image saliency detection based on regional label fusion

In this paper, an image saliency detection method based on regional label fusion is proposed to solve the problems with fuzzy boundaries, unclear profile, and less interior density commonly existing in the researches of salient region detection. The image is segmented by super pixel segmentation algorithm, then the spectral clustering is carried out for the super pixel region to reduce the number of regions, thereby the label set with the boundary information could be obtained. Next, three salient features of the image have been fused under conditional random field model to generate the coarse saliency map. Afterwards, regional label fusion method is operated, which organically fuses the boundary information into the coarse saliency map by using the salient mean value calculated with the label information as the regional salient features, moreover, together with adaptive threshold segmentation algorithm to acquire reconstructed saliency map. At last, accurate salient region detection is achieved by calculating with a tag indicating vector defined and reconstructed coarse saliency map. Experimental results show that the salient regions obtained by this algorithm display clearer boundary contours and that the density of salient regions has been greatly improved compared with the other six significant detection methods prevailed in recent years.
On the binary codes of length 552 which admit the simple group Co<sub>3</sub> as a transitive permutation group

In this this paper all binary codes of length 552 which admit the sporadic simple group Co3 as an imprimitive transitive permutation group are determined. Our aim is to understand the results also by using theoretical arguments and to discuss the combinatorial properties of the codes as well as their relation to some special properties of the Leech lattice group Co3. For all codes (with two exceptions) we obtain the weight enumerators and in many interesting cases the classification of codewords under the action of the group of code automorphisms Co3. The exceptional codes are both self-dual and have minimum weight 12.
A pedestrian detection algorithm based on deep learning

In order to solve the problem of crowd counting and crowd density statistics in the security intelligent video surveillance system, this paper adopts the method based on deep learning to optimize the algorithm. This method mainly uses the VGG16 backbone network with 1*1 and 3*3 small convolution Classification and feature extraction of people information in a crowd. In order to reduce the sharp reduction in the number of positive samples after increasing the threshold, and to avoid the situation where using different thresholds during training and testing will cause the performance of the detector to degrade, this paper draws on the cascade structure of the Cascade R-CNN network for input video. The frame images are analyzed and processed, and different IoU thresholds are set at different stages to obtain enough positive samples to reduce over-fitting, and use the multi-task loss function and the Hadamard product to obtain the pedestrian detection network, and output the final number of people. The improved pedestrian counting algorithm in this paper is tested in the public dataset WorldExpo'10 Crowd Counting, and compared with other algorithms to verify the feasibility and effectiveness of this method.
Deep Graph Neural Networks for Text Classification Task

Text classification is to organizing documents into predetermined categories, usually by machinery learn algorithms. It is a significant ways to organize and utilize the large amount of information that exists in unstructured text format. Text classification is an important module in text processing, and its applications are also very extensive, such as garbage filtering, news classification, part-of-speech tagging, and so on. With the continuous development of deep learning in recent years! Its applications are also very extensive, such as: garbage filtering, news classification, part-of-speech tagging, and so on. But the text also has its own characteristics. According to the characteristics of the text, the general process of text classification is: 1. Preprocessing; 2. Text representation and feature selection; 3. Construction of a classifier; 4. The task of text classification refers to classifying texts into only single or many types in TC system. Some researchers are beginning to apply deep neural networks to tasks such as the text classification we mentioned above. Although the research around the task has made great progress, the review of this task is very scarce, and there is a lack of a comprehensive review of the development of the task in recent years. Therefore, we present a survey of research in text classification to create taxonomies. Finally, it is by giving vital effects, the direction of future research, and those challenges that may counter in the research field.
A dual-channel SSD pedestrian detection algorithm based on feature fusion

In order to solve the phenomenon of false detection and missed detection in the pedestrian counting algorithm caused by the change of occlusion and illumination in crowded scenes, this paper proposes a dual-channel SSD pedestrian detection algorithm based on feature fusion, which improves pedestrian detection under illumination and occlusion conditions. In the dual-channel SSD network structure, the Conv4_3 and FC7 layers in the color image channel are fused with the Conv4_3 and FC7 layers in the depth image channel in Concat or Eltwise ways to obtain the optimal dual-channel SSD network model. Then, the Conv4_3_Fuse layer in the network is fused with the Conv10_2_Fuse and Conv11_2_Fuse layers to fully learn the feature information of pedestrian heads. The experimental results show that the improved algorithm is tested on the TVHeads data set, and the detection accuracy obtained is 95.4%, which is 13.16% higher than that of the SSD algorithm, which improves the problem of missed detection caused by illumination changes and occlusion, and enhances the detection of pedestrians head recognition.