Reviewerly/exHarmony huggingface dataset:
Growth and yield of lemongrass (<i>Cymbopogon citratus</i>) in fly ash with nutrient amendments and Mycorrhiza for three-ratoon period

Environmental and health concerns due to fly ash dumpsite could be prevented by phytostabilization using non-food crops that will prevent pollutants’ migration and provide additional economic returns. The growth and yield of Cymbopogon citratus in the fly ash dumpsite were evaluated in a pot experiment for a three-ratoon period. Application of compost, mycorrhiza, and NPK fertilizer, singly or the combination compost + mycorrhiza, compost + NPK fertilizer, mycorrhiza + NPK fertilizer, and compost + mycorrhiza + NPK fertilizer had increased the dry matter yield and the total essential oil content compared to the control. NPK fertilization regardless of compost and mycorrhiza resulted in more than 5-fold yield over the control. Compost lowered the available heavy metals from fly ash and supplied some essential nutrients while mycorrhiza could have improved water and nutrients acquisition and uptake. However, the nutrients supplied by the compost cannot sustain the requirements for the three-ratoon period. NPK fertilization, compost, and mycorrhiza reduced the concentrations of Cr, Cu, Ni, and Pb in shoots and in essential oil. Overall, the combined application of compost, mycorrhiza, and NPK fertilization at the rate of 120-90-90 kg NPK/ha could enhance the phytostabilization potential of C. citratus in fly ash and produce a high amount of essential oil with a low concentration of heavy metals that could be safe for human use. Novelty StatementPhytostabilization using aromatic plants is a potential alternative for the on-site pollution mitigation strategy of fly ash dumpsite. In this report, the appropriateness of lemongrass as a phytostabilization agent for fly ash dumpsite of the Morupule Power Plant in Botswana was evaluated for a three-ratoon period. This is the first report from Botswana that considered the effect of soil amendments, mycorrhiza inoculation, and NPK fertilization on the growth and yield of lemongrass in fly ash as well as the heavy metal availability and uptake. The results from this study are very useful for identifying site-specific cultural management strategy of using lemongrass in the phytostabilization of fly ash dumpsite in Botswana and elsewhere.
End-to-end masked graph-based CRF for joint slot filling and intent detection

Slot filling and intent detection are the basic and crucial fields of natural language processing (NLP) for understanding and analyzing human language, owing to their wide applications in real-world scenarios. Most existing methods of slot filling and intent detection tasks utilize linear chain conditional random field (CRF) for only optimizing slot filling, no matter the method is a pipeline or a joint model. In order to describe and exploit the implicit connections which indicate the appearance compatibility of different tag pairs, we introduce a graph-based CRF for a joint optimization of tag distribution of the slots and the intents. Instead of applying the complex inference algorithm of traditional graph-based CRF, we use an end-to-end method to implement the inference, which is formulated as a specialized multi-layer graph convolutional network (GCN). Furthermore, mask mechanism is introduced to our model for addressing multi-task problems with different tag-sets. Experimental results show the superiority of our model compared with other alternative methods. Our code is available at https://github.com/tomsonsgs/e2e-mask-graph-crf.
An Efficient TP Model Transformation Algorithm for Robust Visual Servoing in the Presence of Uncertain Data

This paper presents a robust visual servoing controller based on an efficient TP model transformation method, while taking into account an uncertain image Jacobian matrix where, the camera intrinsic parameters, image features, and depth estimations are affected by unknown random uncertainties with known bounds.The convex vertex decomposition of image Jacobian matrix through uniform design greatly reduce the number of LMIs in the quasi-min-max model predictive control (MPC) scheme, in order to obtain the optimal control inputs of the constrained visual servoing system, while meeting the real-time requirements.Simulation and Experimental results demonstrate the effectiveness of the proposed method.
Analysis of the Educational Impact of M-Learning and Related Scientific Research

The deep penetration and global impact of mobile devices has led the scientific community to undertake a thorough analysis of the implications of this phenomenon. Researchers need to determine their true real effect and how they can best be used to manage information and build knowledge. This study has examined recent investigations on the subject of mobile learning and carried out a descriptive analysis of a sample of registers indexed on the Web of Science research platform in 2015-16. An exhaustive content analysis has revealed new areas where M-Learning is being implemented, especially in the teaching of foreign languages, the emergence of social interaction methodologies in Secondary Education, evolving forms of collaborative relationships, work with clearly defined student profiles and the use of virtual immersive and innovative spaces in Higher Education. This paper also emphasizes the presence of previously unknown, transcendent problems associated with M-learning, such as the collateral addiction effect, and its interference in the classroom. Finally, our study suggests that teachers could embrace these technological proposals and include them in their strategies. Indeed, it might be necessary to flip the process, so this research could be the start of the generation and design of innovative guidelines to manage these forms and content.
Pb and Nd isotopic correlation in Belingwe komatiites and basalts

Komatiites and basalts from two locations in the Ngezi Group of the Belingwe Greenstone Belt define parallel Pb-Pb isochrons with ages of 2692±9 Ma and 2675±173 Ma, and µ1 values of 8.2 and 8.4. Data from a third location scatter and do not give useful age information. The Pb-Pb ages are consistent with reliable ages from other greenstone belts in Zimbabwe, and are believed to be correct. Nd isotopic data on the same samples form a linear array in the Sm-Nd diagram with a slope corresponding to about 2.9 Ga. Initial eNd values, calculated at 2.7 Ga, range from +3.2, to+0.4 and show a negative correlation with the upvalues. The anomalously old Nd age and the co-variation in initial eNd and µ1 lvalues are explained by contamination of the lavas by old surrounding basement.
FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction

The implementation of neural network regression prediction based on digital circuits is one of the challenging problems in the field of machine learning and cognitive recognition, and it is also an effective way to relieve the pressure of the Internet in the era of intelligence. As a nonlinear network, the stochastic configuration network (SCN) is considered to be an effective method for regression prediction due to its good performance in learning and generalization. Therefore, in this paper, we adapt the SCN to regression analysis, and design and verify the field programmable gate array (FPGA) framework to implement SCN model for the first time. In addition, in order to improve the performance of the SCN model based on the FPGA, the implementation of the nonlinear activation function on the FPGA is optimized, which effectively improves the prediction accuracy while considering the utilization rate of hardware resources. Experimental results based on the simulation data set and the real data set prove that the proposed FPGA framework successfully implements the SCN regression prediction model, and the improved SCN model has higher accuracy and a more stable performance. Compared with the extreme learning machine (ELM), the prediction performance of the proposed SCN implementation model based on the FPGA for the simulation data set and the real data set is improved by 56.37% and 17.35%, respectively.
STATE OF THE ART IN DIGITAL SURFACE MODELLING FROM MULTI-VIEW HIGH-RESOLUTION SATELLITE IMAGES

Abstract. Data from the optical satellite imaging sensors running 24/7, is collecting in embarrassing abundance nowadays. Besides more suitable for large-scale mapping, multi-view high-resolution satellite images (HRSI) are cheaper when comparing to Light Detection And Ranging (LiDAR) data and aerial remotely sensed images, which are more accessible sources for digital surface modelling and updating. Digital Surface Model (DSM) generation is one of the most critical steps for mapping, 3D modelling, and semantic interpretation. Computing DSM from this dataset is relatively new, and several solutions exist in the market, both commercial and open-source solutions, the performances of these solutions have not yet been comprehensively analyzed. Although some works and challenges have focused on the DSM generation pipeline and the geometric accuracy of the generated DSM, the evaluations, however, do not consider the latest solutions as the fast development in this domain. In this work, we discussed the pipeline of the considered both commercial and opensource solutions, assessed the accuracy of the multi-view satellite image-based DSMs generation methods with LiDAR-derived DSM as the ground truth. Three solutions, including Satellite Stereo Pipeline (S2P), PCI Geomatica, and Agisoft Metashape, are evaluated on a WorldView-3 multi-view satellite dataset both quantitatively and qualitatively with the LiDAR ground truth. Our comparison and findings are presented in the experimental section.
Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.
System-based concept for a mixed reality supported maintenance phase of an industrial plant

Inspection and maintenance processes have been researched for many years. However, the possible applications of extended reality in these processes is a new field of research that has to be developed. Within current paper an approach to optimize the existing, analogously documented processes by using extended reality is presented. The maintenance of an industrial plant is mainly based on analogue and manual methods and processes. Usually all maintenance information is documented analogously and transferred to the corresponding management systems with a certain delay after the inspection. The time delay can lead to data loss and errors and can also occur during transmission to an appropriate system. In the future, the maintenance data and changes to the real object should be determined digital and transferred to the data management system in real time. A digital twin should enable the unambiguous assignment of the data to the corresponding elements and thus represent a virtual representation of the AS-IS state of the industrial plant. The required process data will be collected in Mixed Reality and then transferred to a PDM-System. To transfer the data records without loss, a system model can be used as an interface between the Microsoft HoloLens mixed reality device and the PDM system. The HoloLens enables the determination of a coordinate point from the combination of gaze vector and the position of the observer. The coupled system model allows this coordinate point to be compared with a coordinate field within the reference model to determine the component. This paper presents a concept to create a system-based digital twin for the maintenance phase of an industrial plant to enable the data transmission from mixed reality to a PDM-System.
Cyber Security and Artificial Intelligence for Cloud-based Internet of Transportation Systems

The Internet of Things (IoT) has major implications in the transportation industry. Autonomous Vehicles (AVs) aim at improving day-to-day activities such as delivering packages, improving traffic, and the transportations of goods. AVs are not limited to ground vehicles but also include aerial and sea vehicles with a wide range of applications. The IoT systems consisting of a collection of AVs have come to be known as the Internet of Transportation systems. While such IoT systems manage large quantities of sensor data, much of the data is also sent to a cloud for offline analysis. While there is great potential in AVs and the improvements it can make to the transportation industry, security and privacy concerns pose new challenges that need to be addressed as we move forward. In addition, Artificial Intelligence techniques are also becoming crucial for such IoT systems to be able to intelligently manage the AVs. This paper discusses AI and security for cloud-based Internet of Transportation Systems.
Research on Movie Recommendation Model Based on LSTM and CNN

In order to further improve the accuracy of movie recommendation, while considering the characteristics of user data and movie data, this paper studies and proposes a combined recommendation model of LSTM and CNN. The model uses LSTM to capture the context dependency of user ratings data, and at the same time extracts the local relevant features of the movie title with CNN, and then fuse each feature to calculate the predicted ratings, through model training and optimization, the movie recommendation to the user is finally obtained according to the predicted ratings. The MovieLens data set is used to verify the effectiveness of the model, and the results show that compared with the traditional recommendation model and other recommendation models based on deep learning, the combined recommendation model of LSTM and CNN proposed in this paper have a MSE loss reduction of 4.4%~18.7% and a MAE loss reduction of 3.0%~52.2%.
Learn with diversity and from harder samples: Improving the generalization of CNN-Based detection of computer-generated images

Advanced computer graphics rendering software tools can now produce computer-generated (CG) images with increasingly high level of photorealism. This makes it more and more difficult to distinguish natural images (NIs) from CG images by naked human eyes. For this forensic problem, recently some CNN(convolutional neural network)-based methods have been proposed. However, researchers rarely pay attention to the blind detection (or generalization) problem, i.e., no training sample is available from "unknown" computer graphics rendering tools that we may encounter during the testing phase. We observe that detector performance decreases, sometimes drastically, in this challenging but realistic setting. To study this challenging problem, we first collect four high-quality CG image datasets, which will be appropriately released to facilitate the relevant research. Then, we design a novel two-branch network with different initializations in the first layer to capture diverse features. Moreover, we introduce a gradient-based method to construct harder negative samples and conduct enhanced training to further improve the generalization of CNN-based detectors. Experimental results demonstrate the effectiveness of our method in improving the performance for the challenging task of "blind" detection of CG images.
Mining Cross-Level High Utility Itemsets

Many algorithms have been proposed to find high utility itemsets (sets of items that yield a high profit) in customer transactions. Though, it is useful to analyze customer behavior, it ignores information about item categories. To consider a product taxonomy and find high utility itemsets describing relationships between items and categories, the ML-HUI Miner was recently proposed. But it cannot find cross-level itemsets (itemsets mixing items from different taxonomy levels), and it is inefficient as it does not use relationships between categories to reduce the search space. This paper addresses these issues by proposing a novel problem called cross-level high utility itemset mining, and an algorithm named CLH-Miner. It relies on novel upper bounds to efficiently search for high utility itemsets when considering a taxonomy. An experimental evaluation with real retail data shows that the algorithm is efficient and can discover insightful patterns describing customer purchases.
Propagating Over Phrase Relations for One-Stage Visual Grounding

Phrase level visual grounding aims to locate in an image the corresponding visual regions referred to by multiple noun phrases in a given sentence. Its challenge comes not only from large variations in visual contents and unrestricted phrase descriptions but also from unambiguous referrals derived from phrase relational reasoning. In this paper, we propose a linguistic structure guided propagation network for one-stage phrase grounding. It explicitly explores the linguistic structure of the sentence and performs relational propagation among noun phrases under the guidance of the linguistic relations between them. Specifically, we first construct a linguistic graph parsed from the sentence and then capture multimodal feature maps for all the phrasal nodes independently. The node features are then propagated over the edges with a tailor-designed relational propagation module and ultimately integrated for final prediction. Experiments on Flickr30K Entities dataset show that our model outperforms state-of-the-art methods and demonstrate the effectiveness of propagating among phrases with linguistic relations (Source code will be available at https://github.com/sibeiyang/lspn .).
Path Planning for Dense Drone Formation Based on Modified Artificial Potential Fields

Unmanned aerial vehicle (UAV) formation can accomplish complex and challenging tasks more efficiently. Path planning is one of the key issues to achieve formation flight. According to the drone light show, an artificial potential field (APF)-based path planning algorithm for dense drone formation method was proposed to realize the path planning of multiple drones and multiple targets in three-dimensional space. The problem of path oscillation was solved by improving the repulsive force model. By adding the target exchange algorithm, the problem that the individual cannot reach the target when being stuck in the local optimal solution was solved. The drone's path was improved following the performance by adding constraints. Finally, the dense formation transformation of 500 drones at 2.5m spacing was achieved and actual flight experiments were carried out. The path planning method studied in this paper can be widely used in a variety of military and civil multi rotors or helicopter clusters, which has a very high practical value.
Genetic Programming Based Hyper Heuristic Approach for Dynamic Workflow Scheduling in the Cloud

Workflow scheduling in the cloud is the process of allocating tasks to limited cloud resources to maximise resource utilization and minimise makespan. This is often achieved by adopting an effective scheduling heuristic. Most existing heuristics rely on a small number of features when making scheduling decisions, ignoring many impacting factors that are important to workflow scheduling. For example, the MINMIN algorithm only considers the size of the tasks when making scheduling decisions. Meanwhile, many existing works focused on scheduling a static set of workflow tasks, neglecting the dynamic nature of cloud computing. In this paper, we introduce a new and more realistic workflow scheduling problem that considers different kinds of workflows, cloud resources, and impacting features. We propose a Dynamic Workflow Scheduling Genetic Programming (DSGP) algorithm to automatically design scheduling heuristics for workflow scheduling to minimise the overall makespan of executing a long sequence of dynamically arriving workflows. Our proposed DSGP algorithm can work consistently well regardless of the size of workflows, the number of available resources, or the pattern of workflows. It is evaluated on a well-known benchmark dataset by using the popular WorkflowSim simulator. Our experiments show that scheduling heuristics designed by DSGP can significantly outperform several manually designed and widely used workflow scheduling heuristics.
Large depth-of-field three-dimensional shape measurement with the focal sweep technique

Three-dimensional (3D) shape measurement based on the fringe projection technique has been extensively used for scientific discoveries and industrial practices. Yet, one of the most challenging issues is its limited depth of field (DOF). This paper presents a method to drastically increase DOF of 3D shape measurement technique by employing the focal sweep method. The proposed method employs an electrically tunable lens (ETL) to rapidly sweep the focal plane during image integration and the post deconvolution algorithm to reconstruct focused images for 3D reconstruction. Experimental results demonstrated that our proposed method can achieve high-resolution and high-accuracy 3D shape measurement with greatly improved DOF in real time.
The Use of Unmanned Aerial Vehicle in Military Operations

In the twenty-first century, unmanned aerial vehicles (UAVs) have gone through big development, and more and more UAVs are used in operations. UAVs have a lot of advantages compared with manned aircraft. UAVs could not only release pilots from dangerous battlefield but also perform various tasks with good efficiency. UAVs can be used in reconnaissance and surveillance tasks and strike targets directly as well as carrying out other missions. However, UAVs still have some limitations, which should be overcome in the future. Especially, the command and control system of UAV is vulnerable. The future UAVs will be stealthy, more intelligent and autonomous. The self-defense capability of UAVs is also undergoing big improvement.