Reviewerly/exHarmony huggingface dataset:
Long-term evolution of the spectral content of continuous seismo-volcanic signals from a network-based analysis

Volcanoes are multi-physics systems where different phenomena interact, such as magma transport, degassing, crystallization, and pressure-induced faulting. These interactions create a series of seismic signals, among which we have volcano-tectonic earthquakes, long-period events, and volcanic tremors. Thanks to these signals, there has been an improvement in the comprehension of volcanic systems. However, due to its complexity, there is still a debate regarding the observed seismic signals, i.e., their precise origin and characteristics. In the past, some techniques, such as spectral analysis and simple earthquake location were used. However, these techniques lack the resolution that we currently need. In this regard, network-based methods have been developed to determine the level of wavefield coherence and to classify different seismicity types from complex continuous signals.In this work, we analyze eight years (from 2011 to 2020) of continuous seismic data of Piton de la Fournaise, la R&amp;#233;union, France, using a network array including approximately 20 stations. We use a method based on the covariance matrix combining interstation single-component cross-correlations. From the continuous velocity records, we create temporal overlapping windows in which we estimate the covariance matrix in the frequency domain. We then evaluate its rank through the estimation of the width of its eigen-values distribution,&amp;#160;in other terms, the number of independent seismic sources.&amp;#160;This method allows us to quantitatively measure the presence of coherent sources recorded by the array and to characterize their frequency content.The resulting distributions of the spectral width show that continuous signals are characterized by multiple narrow spectral peaks clearly observed in the co-eruptive tremors but also during periods without visible volcanic activity. To enhance these peaks, we re-normalize the distribution of spectral width in the frequency and time domains. As a result, we observe in the 1-3 Hz frequency band many spectral peaks that remain nearly constant during very long periods (weeks to months). At the same time, we observe a clear difference in the distribution of these frequencies between the co-eruptive and quiet periods and also some significant variations during long-standing eruptions. We suggest that variations of the spectral lines can be related to the properties of seismo-volcanic sources and eventually to the structural changes and, therefore, can be used in volcano monitoring.
Using FAIR and Open Science practices to better understand vegetation browning in Troms and Finnmark (Norway)

In most places on the planet vegetation thrives: it is known as &amp;#8220;greening Earth&amp;#8221;. However in certain regions, especially in the Arctic, there are areas exhibiting a browning trend. This phenomenon is well known but not fully understood yet, and grasping its impact on local ecosystems requires involvement of scientists from different disciplines, including social sciences and humanities, as well as local populations. Here we focus on the Troms and Finnmark counties in northern Norway to assess the extent of the problem and any link with local environmental conditions as well as potential impacts.&amp;#160;We have chosen to adopt an open and collaborative process and take advantage of the services offered by RELIANCE on the European Open Science Cloud (EOSC). RELIANCE delivers a suite of innovative and interconnected services that extend the capabilities of the European Open Science Cloud (EOSC) to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The RELIANCE project has delivered 3 complementary&amp;#160; technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining. RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work.&amp;#160;We will show how we are using these technologies along with EGI notebooks to work open and share an executable Jupyter Notebook that is fully reproducible and reusable. We use a number of Python libraries from the Pangeo software stack such as Xarray, Dask and Zarr. Our Jupyter Notebook is bundled with its computational environment, datacubes and related bibliographic resources in an executable Research Object. We believe that this approach can significantly speed up the research process and can drive it to more exploitable results.&amp;#160;Up to now, we have used indices derived from satellite data (in particular Sentinel-2) to assess how the vegetation cover in Troms and Finnmark counties has changed. To go a bit further we are investigating how to relate such information to relevant local parameters obtained from meteorological reanalysis data (ERA5 and ERA5-land from ECMWF). That should give a good basis for training an Artificial Intelligence algorithm and testing it, with the objective of getting an idea about the possibility of &amp;#8220;predicting&amp;#8221; what is likely to happen in the near future with certain types of vegetation like mosses and lichens which are essential for local populations and animals.
Assessing the quality of gridded Climate Data Records of the Surface Irradiance using global Reference Data Sets

The incoming surface solar radiation is an essential climate variable as defined by GCOS. Long term monitoring of this part of the earth&amp;#8217;s energy budget is required to gain insights on the state and variability of the climate system. In addition, climate data sets of surface solar radiation have received increased attention over the recent years as an important source of information for solar energy assessments, for crop modeling, and for the validation of climate and weather models.Gridded regional and global data records of the surface irradiance are available based on satellite measurements as well as derived from numerical models, e.g., reanalysis systems. For climatological analyses, long-term data records, covering about multiple decades, are required. Recently generated satellite-based climate data records from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), i.e., SARAH-3 and CLARA-A3, as well as the GEWEX SRB data set, fulfill this requirement. Corresponding reanalysis data are also available, i.e., ERA-5, MERRA-2.Here we will assess the quality of these satellite-based and reanalysis-derived climate data records of the surface irradiance by comparison with monthly surface reference data from the Global Energy Balance Archive (GEBA). The quality assessment will include the accuracy of the gridded data as well as their ability to realistically reproduce the anomalies and temporal trends as derived from the surface observations. The inter-comparison of the gridded data records allows to identify regions of high / low confidence in our knowledge of the surface irradiance and the surface radiation budget. &amp;#160;
L’impatto dell’emergenza sanitaria sull’insegnamento dell’italiano e delle lingue. Un’indagine esplorativa

The health emergency, caused by viruses SARS-CoV-2, has been a major challenge for the teaching of foreign languages and Italian.This essay investigates the main criticalities of distance learning by a representative sample of teachers, in the first phase of the health emergency.
Design and Implementation of Online Leadership Education Using Meeting Simulator and Peer Reflection

Since the 2008 academic year, we have been conducting leadership education for master’s program in-person classes. In this education, simulated experiences using a conference simulator on a computer and reflection on the action in real and simulated experiences are repeated to help students implement their leadership. This class was moved online because of the COVID-19 pandemic in 2020. The challenges of online classes include how instructors can understand and guide students’ simulations on their computers, and how to conduct peer reflection by students in online classes. As a solution, we shared composite images of students’ simulation screens and student camera images, allowing the instructor to remotely monitor and support the students’ learning status. Additionally, an online reflection sheet for peer reflection among students was created. Peer reflection among students was conducted online to promote collaborative learning among students.
SS4IoT: Secure Search Over Encrypted Outsourced IoT Data

The Internet of Things (IoT) is becoming increasingly popular, with estimates suggesting that by2050, more than 100 billion devices will have the ability to connect to the Internet. Since sensor data makes up the majority of the Io$\mathrm{T}'$s data, it is necessary to have large data handling capabilities. Sensor data might be unreliable due to measurement limitations, data update delays, and/or the requirement to protect data privacy. As a result, maintaining the privacy of data acquired from IoT devices that are sent to the cloud for analysis and storage is a major concern. Homomorphic encryption (HOM) is a potential solution for searching encrypted data stored in the cloud. We propose SS4IoT, a new secure search system for IoT data in the cloud. To encrypt data provided by data owners, we employ homomorphic and Order-Preserving Encryption (OPE). Our solution aims at protecting the dataset's privacy while also improving the efficiency of querying encrypted datasets. We also show the capacity of our approach to protect data and query privacy, as well as assess its effectiveness.
Adaptive Genetic Algorithm Based Particle Swarm Optimization for Industrial Robotic Arm Obstacle Avoidance Trajectory Optimization

In this paper, we propose an obstacle avoidance algorithm, which selects a point of the obstacle avoidance path as the chromosome, constructs the fitness function together with the path length, joint angle increment, and movement time as evaluation indexes, and performs scale transformation on the fitness to improve the competitiveness of the population. The algorithm cycles through the process of optimizing the velocity term in the chromosome in the first step with a particle swarm algorithm; selection in the second step; and crossover and mutation operations on individuals in the third step, in order to avoid the population falling into premature maturity, where the crossover and mutation probabilities vary adaptively with the results of the previous generation. The final smooth and continuous obstacle avoidance trajectory is obtained.
End-to-End Object Detection with Location-Sensitive Cues

A successful object detector should be consistent with the predicted results of human vision systems. However, object detectors can't accurately reflect the human perception of objects in the image. We develop an object detection algorithm based on spatial attention mechanism, combining with location-sensitive cues of objects. Firstly, the feature maps, which exactly describe objects at multiple scales, are extracted from the image to be detected using convolutional neural networks(CNNs). Secondly, an appropriate scale feature map is selected as the candidate feature map and a spatial attention model is adopted to obtain its weight matrix, then the feature map, which combines object saliency information, are generated using the candidate feature map and its weight matrix through mathematical modeling. Finally, the candidate feature map and other feature maps at different scales are used to predict objects through classification and regression strategies on CNNs. The experiment results demonstrate the proposed algorithm has good performance on PASCAL VOC 2007 and PASCAL VOC 2012. The mean average precision (mAP) of proposed algorithm is higher than that of attention-based object detection approaches, which indicates the proposed algorithm can well perceive the objects in the image.
Research on Keyword Selection and Usage in Scientific Papers

With the rapid development of the Internet, search engines have become the main way for researchers to obtain academic literature. In order to keep up with the research hotspots in a certain field, and to make our papers easier to be located, downloaded, read and cited by academic peers, it is particularly important to select reasonable keywords for the papers. This paper firstly clarifies the concept of keywords. Next, the basic principles of keyword selection are expounded. It is necessary to maintain the inheritance and also take into account the innovation. The keywords of the paper should include thematic keywords, process keywords and conclusive keywords as much as possible. According to the basic principles of keyword selection, 12 rules that should be followed and avoided in keyword selection are summarized. On this basis, the paper analyzes and discusses how to choose reasonable keywords. Finally, the keywords of research papers in the field of computer science are analyzed using traditional word frequency statistics and semi-supervised learning methods. Based on the analysis results, the usage of paper keywords in the prediction of scientific research hotspots is discussed.
Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning

Federated learning is a distributed machine learning framework that has been widely applied in scenarios that require data privacy. To obtain a neural network model that performs well, when the model falls into a bug, existing solutions retrain it on a larger training dataset or the carefully selected samples from model diagnosis. To overcome this challenge, this paper presents Fed-DNN-Debugger, which can automatically and efficiently fix DNN models in federated learning. Fed-DNN-Debugger fixes the federated model by fixing each client model. Fed-DNN-Debugger consists of two modules for debugging a client model: nonintrusive metadata capture (NIMC) and automated neural network model debugging (ANNMD). NIMC collects the metadata with deep learning software syntax automatically. It does not insert any code for metadata collection into modeling scripts. ANNMD scores samples according to metadata and searches for high-quality samples. Models are retrained on the selected samples to repair their weights. Our experiments with popular federated models show that Fed-DNN-Debugger can improve the test accuracy by 8% by automatically fixing models.
Childhood Leukemia Classification via Information Bottleneck Enhanced Hierarchical Multi-Instance Learning

Leukemia classification relies on a detailed cytomorphological examination of Bone Marrow (BM) smear. However, applying existing deep-learning methods to it is facing two significant limitations. Firstly, these methods require large-scale datasets with expert annotations at the cell level for good results and typically suffer from poor generalization. Secondly, they simply treat the BM cytomorphological examination as a multi-class cell classification task, thus failing to exploit the correlation among leukemia subtypes over different hierarchies. Therefore, BM cytomorphological estimation as a time-consuming and repetitive process still needs to be done manually by experienced cytologists. Recently, Multi-Instance Learning (MIL) has achieved much progress in data-efficient medical image processing, which only requires patient-level labels (which can be extracted from the clinical reports). In this paper, we propose a hierarchical MIL framework and equip it with Information Bottleneck (IB) to tackle the above limitations. First, to handle the patient-level label, our hierarchical MIL framework uses attention-based learning to identify cells with high diagnostic values for leukemia classification in different hierarchies. Then, following the information bottleneck principle, we propose a hierarchical IB to constrain and refine the representations of different hierarchies for better accuracy and generalization. By applying our framework to a large-scale childhood acute leukemia dataset with corresponding BM smear images and clinical reports, we show that it can identify diagnostic-related cells without the need for cell-level annotations and outperforms other comparison methods. Furthermore, the evaluation conducted on an independent test cohort demonstrates the high generalizability of our framework.
Geometric Representation of Obstacles Depth in a Partially Unknown Environment for Achieving Optimized Navigation by Mobile Robots

Regarding the point-to-point mobile robot navigation, multi-sensor fusion is a significant strategy in the domain of localization and mapping for near perfect depth data collection. This is even vital to capture the positional data under global positioning system (GPS) denied indoor environment like basement, cave, etc. Appropriate sensor synchronization, calibration, and mapping of data are a definite challenge in this strategy. This paper is primarily focussed on strategic path planning and representation of surrounding environment in a geometric cue. The related information then inferred for perfectly detecting on path obstacles and carrying out intelligent collision-free optimized path traversal by mobile robots. Both 2D–3D data are filtered out and combined for perfect scan of on-route obstacle within the considered indoor environment. Integration of multiple sensors and procedure of data collection with probability of precision has been presented and analyzed visually for better perception of localisation. Along with this, the obtained result has been proved numerically and presented in experimental repository for reference. The improvements of using fused sensor strategy have been vetted experimentally using two active wheeled customized differential drive point-to-point mobile agent. The outcome of the result analysis concludes with graceful proof of claim, sensor fusion strategies concerned with respect to perfect detection of obstacles, and clash free optimized navigation from start to desired goal position.
Building Scale-Attentive Visual Transformers for Intelligent Electric Power System Inspection

In the development of the intelligent electric power system, artificial intelligence techniques have shown critical importance. Especially, deep neural networks designed for computer vision tasks undoubtedly facilitate the inspection task. A strong vision perception framework provides safety and efficiency guarantee for the power system. However, we observe it is challenging to inspect the power transmission environment as it involves complex objects of various scales at different distances. Current convolutional neural networks (CNNs) have limited abilities for constructing large receptive fields and dynamically modeling context semantics. In this paper, we build a visual Transformer framework to handle scale-varying objects. A scale-attentive feature pyramid network (SA-FPN) is further proposed to enhance cross-level semantic information. Features from different stages are combined via an attention mechanism, not direct interpolation and summing widely used in conventional FPNs. Our method can detect risk devices accurately. Compared with previous CNN-based methods, we promote the detection performance by a significant margin, i.e. 3.4 mAP with negligible cost increase.
Multi-Objective Utility Optimization-Based Cooperative Mission Planning for UAVs

In recent years, benefited from their flexibility, low cost and high performance, unmanned aerial vehicles (UAVs) have been widely used in many fields. ln this paper, we investigate the problem of mission planning for heterogeneous UAVs. A joint trajectory planning and mission assignment problem is formulated as a constrained multi-objective system utility function maximization problem. To solve the formulated problem, a two-stage heuristic algorithm is proposed. Specifically, a hybrid Tabu simulated annealing-based trajectory optimization is proposed in the first stage. Then, based on the trajectory strategy of UAVs, we present a two-sided matching-based mission assignment algorithm. Simulation results show that compared to baseline algorithms, the proposed algorithm offers better performance.
Measuring uncertainty in human visual segmentation

Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same-different judgments and perform model-based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures. We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.
Dynamics of the Quantum Rabi Model in the Adiabatic Regime

Abstract The quantum Rabi model in the adiabatic and ultrastrong and deep strong coupling regimes is considered. Some of the differences with the Jaynes-Cummings model are illustrated by considering the evolution of states and the expected values and root-mean-square deviations of several physical quantities of the qubit-oscillator system.
AN EFFECT OF MOBILE TECHNOLOGY ON STUDENTS' ACADEMIC PERFORMANCE IN A DISTANCE LEARNING ENVIRONMENT

Mobile technology is an essential for everyone's daily use, especially in distance education. Learning with mobile technology is a solution to distance learning problems for students to be able to study anywhere and at any time. This paper presents an implementation of a distance education model with mobile technology, designed to include input, process, output, and feedback to help teachers support distance learning of undergraduate students who are studying one of the three majors within Sukhothai Thammathirat Open University's School of Liberal Arts. Input consists of learners' analysis; determining learning objectives; teachers, learners and supportive staff; organizing learning environment and technologies to support learning; and media of distance education lessons through mobile technology. Process consists of conducting teaching and learning activities with step one: preparation of teachers and learners, step 2: conducting learning activities, and step 3: measurement and evaluation. Output consists of learning achievement and the efficiency of a distance education model. Feedback consists of information reaction from students. In addition, distance education lessons were developed for students to learn through the MOODLE learning management system, which includes mobile technology. Students can select their learning options with infographics pages or video clips. Social networks were provided as available channels for students asking questions or giving feedback at any time. Moreover, pre-tests and post-tests were included for assessment by themselves. Finally, students had made significant progress in their studies and were satisfied with the teaching and learning via mobile technology at a high level. Another important consideration is that some students dropped out because of their family and work responsibilities. That is why this paper intends to propose the use of mobile technology to support and stimulate the teaching and learning of students to successfully graduate as intended. In further research, this distance education model could possibly be applied to other schools of undergraduate and graduate students who study in a distance learning environment.