Reviewerly/exHarmony huggingface dataset:
Reflectance Spectroscopy of Global Parent Soils: Application to Aerosol Mineral Dust

Mineral dust particles originate from a variety of arid regions around the world.Mineral dust directly modifies the Earth's radiative balance through absorption and scattering.This radiative forcing varies strongly with mineral composition, yet there is still limited knowledge on the mineralogy of global dust source regions.Previously, 65 surface soil samples were collected worldwide, sieved to < 38 μm fraction and analyzed using XRD, SEM and re-suspended to determine scattering and absorption
Towards Natural Language Processing (NLP) based tool design for technical debt reduction on an agile project

This work presents the first approach towards a design of a tool to support agile projects documentation process based on Natural Language Processing (NLP). It is presented a proposed design of a tool as a result of the analysis of the NLP potential to improve agile project documentation processes. The tool has three different approaches: text-diagram, code-diagram, and a full version, which includes the above approaches being text-code-diagram. Finally, the potential advantages and disadvantages of each approach are discussed. In addition, we present the following steps on the future work towards the development of a tool from the proposed design.
Consideration of the side-channel attack to SIMON implemented on Arduino Uno

In this research, the authors conducted the side-channel attack (SCA)for the XOR in the final round of "SIMON." As of 2021, there is no research about side-channel attacks against SIMON, which makes this paper more worthwhile. As a result, the authors could attack with little computational resources and time, which means that "SIMON" can be weak against side-channel attacks. Furthermore, the authors checked the relationship between the hamming-weight of the round key and the easiness of attacking. The authors confirmed that there was no relationship between them.
My Personal HSE Commitment Program – A Valuable Web Portal to Improve the HSE Leadership at a Workplace

Abstract Safety at work is not just a priority, but a fundamental value, shared by employees, contractors and local stakeholders. People are both as the starting point and the final goal of all its actions. This is why we work hard to ensure the safety of each of us and those around us, in order to eliminate all accidents. we are all committed to spreading and strengthening a culture of safety among all our people, employees or contractors, as an essential part of our work. Companies develop campaigns and projects to promote a safe and healthy behavior in any workplace and, more in general, in everyday life. Virtuous tools and means of communication include workshops, theatre events and round table discussions, as well as video clips sharing what we learned from past safety hazards and the HSE Safety Golden Rules, our key safety rules. The continuous involvement of top management, contractors, representatives from local communities, or external guests, increases its commitment to safety. This paper presenting one of the effective program which is designed and launched by an Oil &amp; Gas Company so to allow all HSE leaders to self-evaluate their level of commitment towards HSE-related aspects and plan a number of important activities that would ultimately increase one's level of HSE commitment and enhance the safety culture through a Web Portal program called " My Personal HSE Commitment". The portal provides the possibility to develop an action plan related to specific corrective and/or proactive actions/tasks that the user wishes to keep track of. The most important part is also, The Web tool is furthermore capable of generating a personal HSE Commitment Poster that the user "The Safety Leader" can share worldwide thanks to the portal integration with Microsoft Outlook. Moreover, users can search others Posters – allowing for the widest dissemination of one's HSE Commitment among other users. By applying this program, companies engaged all its Leaders and Management in the process of creating a strong HSE Culture and also, increasing the competition between leaders and program users by allowing each one to get access to others’ commitment posters. By taking part of this program, each user can see - online - his direct HSE Commitment and be aware of others’ results which is creating a strong and transparent ability of positive competition and real outcomes based on each one's performed activities. Once the companies applied this program, we can immediately see a considerable increase of HSE activities performed by all users and HSE leaders worldwide and this paper highlighted the results achieved &amp; HSE performance improvement thanks to the way this program was created which helped a deep involvement of each leaders in the process of raising the HSE commitment of the company's management.
Text-independent Speaker Verification Using Hybrid Convolutional Neural Networks

Automatic speaker verification is an active research area for more than four decades, and the technology has gradually upgraded for real application. In this paper, a hybrid convolutional neural network (CNN) model is proposed where a combination of the 3D CNN &amp; 2D CNN model is used for speaker verification in the text-independent scenario. For speaker verification, this novel convolutional neural network architecture was built to capture and discard speaker and non-speaker information at the same time. In the training process, the network is trained to differentiate between different identities of a speaker to establish the background model. The model development of the speaker is one of the important aspects. Most conventional techniques employed the d-vector system to create speaker models by means of an average of the features collected from the speaker utterance. Here a hybrid of convolutional neural networks model is utilized in the development and registration phases for building a speaker model. The approach suggested exceeds the existing methods of speaker verification.
Fast Nonlinear Wavelet-Like Transforms

A wide class of nonlinear wavelet-like transforms (NLWT) is introduced. Inside it, a subclass of NLWT is built with the structure of a fast algorithm. Each fast transformation from this class is represented as a parallel-serial superposition of nonlinear (2x2) -transforms. The reported architecture generalizes both linear and non-linear fast transforms, which can be considered as a formal framework for generalized signal processing. This approach leads to a number of new nonlinear transforms of potential interest for image processing.
Significance of Software Development Models in Current Context

As we know with the advancement of a various software development models over the past years, it became a subject of an extreme interest to a reason and segregates them relying upon the applications, favorable circumstances and inconveniences. There are different elements that influence the software development activities; they must even be taken care of once we choose a development model. Several software products come to fail because of reasons like an associate unskilled developer, a time limit, a poor quality, a less client association and considerably more. A software development models ought to be selected with wisdom watching the conditions, and a quality of the developer, a user, a time and the complexes of the project. Of these factors play an important role within the success of the project. A software development models will be categorized as lightweight models and hefty weight models. This paper discusses various models on completely different metrics with execs associated cons of each of them and additionally facilitate to choose an acceptable model relying upon the project.
Research on Optimization Methods of Flood Dispatching Scheme Based on Decision Trees

In river basin flood control, a huge database has been formed by the accumulation of massive data and dispatching information feedbacks. The potential relations or modes between the data can be fully mined using the decision tree algorithm. Therefore, the decision tree algorithm was used in this paper to preliminarily discuss and sufficiently mine the potential relations or modes through river basin flood control data to guide the reservoir scheduling.
A new case-deletion strategy for case-base maintenance based on K-means Clustering Algorithm applied to medical data

Case-Based Reasoning (CBR) system maintenance is an important issue for current medical systems research. Large-scale CBR systems are becoming more omnipresent, with immense case libraries consisting of millions of cases. Case-Base Maintenance (CBM) is the implementation of the following policies allowing to revise the organization and/or the content (information content, representation field of application, or the implementation) of the Case Base (CB) to improve future thinking. Diverse case-base deletion and addition policies have been proposed which claim to preserve case-base competence. This paper presents a novel clustering-based deletion policy for CBM that exploits the K-means clustering algorithm. Thus, CBM becomes a central subject whose objective is to guarantee the quality of the CB and improve the performance of CBM. The proposed approach exploited clustering, which groups similar cases using the K-means algorithm. We rely on the characterization made of the different cases in the CB, and we find this characterization by a method based on a criterion of competence and performance. From this categorization, case deletion becomes obvious. This quality depends on the competence and performance of the CB. Test results show that the proposed deletion strategy improved the efficiency of the CB while preserving competence.Furthermore, its performance was 13% more reliable. The effectiveness of the proposed approach examined on the medical databases and its performance has been compared with the existing approaches on deletion policy. Experimental results are very encouraging.
Investigation of Input Signal Representation to CNN for Improving SNR Classification Accuracy

With the increase in demand for wireless data traffic, high-speed communication systems are required in many environment. Adaptive modulation and coding is an important technique to realize this, but it requires feedback of communication environment information represented by SNR. Conventional SNR estimation methods have a problem of degrading estimation accuracy in fast-moving environments and the presence of carrier frequency offset (CFO). Convolutional neural network (CNN) is capable of estimating the SNR from the trained received signal dataset accurately. This paper investigates an input signal representation to further improve the SNR classification accuracy by CNN. We then propose to combine respective spectrogram data of IQ domains. It can extract the features related to SNR from the received signal to the maximum extent possible and thus achieve highly accurate SNR estimation. Simulation results show that the proposed approach outperforms the other existing candidates.
LabelUX! Guidelines to support software engineers to design data labeling systems

The demand for systems using artificial intelligence has substantially boosted in recent times, especially with Machine Learning (ML) techniques. Systems that use ML supervision techniques need representative and correctly categorized data to ensure its quality. In this context, a data labeling step plays a fundamental role during the development of such systems. The labeling is performed by users specialized in the data domain and aims to generate a database to enable a supervised ML model. However, labeling is exhausting for users, which can compromise the quality of the ML system, especially if the labeling is being done on systems that were not designed to assist the user in this activity. On the one hand, it can be difficult for a software engineer to design these kinds of systems. Depending on the type of data to be labeled, the interface needs different graphics and strategies to present and request user feedback. Aiming to help software engineers develop these kinds of systems, this work proposes the LabelUX guidelines. These guidelines aim to support software engineers in designing data labeling systems, defining a design with quality that provides a better user experience during the labeling task. We developed these guidelines from studies carried out in the literature and industry. We selected software engineers working on ML projects to participate in a feasibility study to evaluate the use of guidelines. The qualitative results obtained through the interview improved that the LabelUX guidelines supported a better design of textual type data labeling systems.
Blockchain in the Pharmaceutical and Healthcare Ecosystem

The pharma sector is highly regulated to ensure the safety and security of its consumers/patients. Pharmaceutical companies have fundamental responsibility for safety, quality, purity and effectiveness of their product and its disposal owing to its impact on society and humans. The chapter studies the various challenges in the pharma and healthcare ecosystem. With a process and system perspective it explores the various regulations, business protocols, recent trends and directions. Based on the studies it advocates the suitability and use of blockchain technology with the IoT to serve all these purposes for the benefit of people, process, compliance and security. It details the application of blockchain (with the IoT in some cases) for compliance aspects like the US Drug Supply Chain Security Act or EU Falsified Medicines Directive, transparency in clinical trials, transparency and monetization of genomics data management, prescription drugs transparency, etc. The chapter also evaluates and identifies unique use cases of blockchain for various aspects of drug manufacturing and the pharma supply chain. It touches upon the benefits that blockchain can bring in cases of pandemic like Covid-19. Lastly it also provides insight into how blockchain will play a critical role in the Covid-19 vaccination program.
Solar radiation in the cloud-free atmosphere

&amp;lt;p&amp;gt;The quantification of Earth&amp;amp;#8217;s solar radiation budget and its temporal changes is essential for the understanding of the genesis and evolution of climate on our planet. While the solar radiative fluxes in and out of the climate system can be accurately tracked and quantified from space by satellite programs such as CERES or SORCE, the disposition of solar energy within in the climate system is afflicted with larger uncertainties. A better quantification of the solar radiative fluxes not only under cloudy, but also under cloud-free conditions can help to reduce these uncertainties and is essential for example for the determination of cloud radiative effects or for the understanding of&amp;amp;#160; temporal changes in the solar radiative components of the climate system.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;We combined satellite observations of Top of Atmosphere fluxes with the information contained in surface flux observations and climate models to infer the absorption of solar radiation in the atmosphere, which we estimated at 73 Wm&amp;lt;sup&amp;gt;-2&amp;lt;/sup&amp;gt; globally under cloud-free conditions (Wild et al. 2019 Clim Dyn). The latest generation of climate models participating in CMIP6 is now able to reproduce this magnitude surprisingly well, whereas in previous climate model&amp;amp;#160; generations the cloud-free atmosphere was typically too transparent for solar radiation, which stated a long-standing modelling issue (Wild 2020 Clim Dyn, Wild et al. 1995 JClim).&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;With respect to changes in solar fluxes, there is increasing evidence that the substantial long-term decadal variations in surface solar radiation known as dimming and brightening occur not only under all-sky, but similarly also under clear-sky conditions (Manara et al. 2016 ACP, Yang et al. 2019 JClim; Wild et al. 2021 GRL). This points to aerosol radiative effects as major factor for the explanation of this phenomenon.&amp;lt;/p&amp;gt;
Fusion-based Medical Image Retrieval using Tamura and Wavelet features

In the last ten years, Content-based image retrieval (CBIR) is an emerging and challenging research area in medical image processing. The medical Image retrieval plays the accuracy of the retrieval process and further can be modified by a method that combines tamura and wavelet features in the retrieving process. The process is having calculation of distance between all the images and forms a matrix. The proposed method is projected as a process of CBIR in medical image processing with different statistical parameter calculations on different datasets. With the evidence of experimental results on medical images, the performance and effectiveness of the method is measured. The confusion matrix is evaluated in different views.
A recessive active learning method: enhancing the performance of predict models by adjusting the structure of data space

While training predict models to assess new data, relative researchers always incline to design some strategies to reduce amount of selected sampling points to save cost and time, especially when information about these sampling points is hard to get. Among these strategies, active learning is a popular and useful tool to enhance the efficiency of training predict model by improving the quality of sampling points selected. In biological field, making specific experiments to get result could cost some time and budgets, which fits the situation exactly. While when the information on each data points is limited, active learning method is relatively hard to apply. Thus, in order to relieve this situation, this paper attempts to solve the problem by optimizing the structure of datasets. In the paper, a specific dataset is selected to test the performance of several traditional active learning methods. Meanwhile, a special trick which aims to optimize the configuration of data space is proposed to enhance the performance of both predict models and active learning methods. According to experiments, it turns out that the optimization on data space could let the predict model fit better to the datasets and could help enhance the effect on active learning methods, which has performance enhancement of 5%~22% during the process (5%~20%) of training predict model. By combining with traditional active learning method, the increment could be risen up to 9%~32% under the same progress (5%, 10%, 15%, 20%), which stands for the percentage of data used to train predict model in all the dataset.
TEACHERS' DIGITAL COMPETENCE DURING THE COVID-19 PANDEMIC: AN ANALYSIS OF PERCEPTIONS OF LEARNING STAGES

This article examines educators' perceptions of their work when they were forced to conduct emergency distance learning because of the COVID-19 pandemic. A sample of closed-ended responses from educators at all levels of education (preschool, elementary and secondary education, vocational training, and higher education) is examined. Statistical analysis of the data shows that the greatest difficulties reported by educators are related to deficiencies in their digital skills training, causing them to perceive a higher workload during isolation along with negative emotions. Another finding is the existing digital divide between educators based on their gender, age, and type of educational organization. Another troubling finding is lower technological competence at lower levels of education, which are most vulnerable to distance learning. These results encourage us to think about the measures that need to be taken to improve the quality of educators' digital competence, social justice, and educational system sustainability that are consistent with the Sustainable Development Goals.
Robust Reconstruction of QR-Embedded Software using CRC-based Sequence Extraction of Asynchronous Time-Multiplexed QR Code

Recently, systems in which smart factories, smart cars, and several small edges work together are emerging. Error problems occur in software execution as often as small edges work together, which spreads to errors of the entire system or causes larger errors. In order to solve this problem, it is necessary to consider a communication method for determining the presence or absence of an edge error using power consumption data. In this paper, we try to segment data and communicate using QR codes. For the divided data part, the order is restored using Check Redundancy Check (CRC). If the order can be restored, data can be transmitted asynchronously. This architecture was implemented using ‘CW303’, ‘Chip-whisperer’ and ‘Raspberry Pi 3’ and showed successful data communication and error judgment.
DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification

In today’s era Big data classification is a very crucial and equally widely arise issue is many applications. Not only engineering applications but also in social, agricultural, banking, educational and many more applications are there in science and engineering where accurate big data classification is required. We proposed a very novel and efficient methodology for big data classification using Deep stack encoder and Rider chaotic biogeography algorithms. Our proposed algorithms are the combinations of two algorithms. First one is Rider Optimization algorithm and second one is chaotic biogeography-based optimization algorithm. So, we named it as RCBO which is integration is ROA and CBBO. Our proposed system also uses the Deep stack auto encoder for the purpose of training the system which actually produced the accurate classification. The Apache spark platform is used initial distribution of the data from master node to slave nodes. Our proposed system is tested and executed on the UCI Machine learning data set which gives the excellent results while comparing with other algorithms such as KNN classification, Extreme Learning Machine Random Forest algorithms.
Detection Of Chest Catheters Using Mask R-CNN

With the rise of deep learning and its limitless possibilities to solve the problems at hand as par to humans the researchers have been continually making efforts to apply the deep learning techniques in the medical field as well. Biomedical Image Segmentation has been one such field where it has performed exceptionally well because of the consistent efforts of the researchers across the computer vision community. In this paper we aim to address the problem of chest catheter detection using the well known Mask-RCNN benchmark architecture. While addressing the problem of chest catheter detection we focus on having a better recall for our model rather than focusing purely on accuracy.
Hot Topics and Frontier about ZigBee Research on CiteSpace

Taking 1729 papers related to ZigBee collected by the web of science as the research object, this paper takes spatiotemporal knowledge mapping and content knowledge mapping analysis as the main research methods, and based on the information visualization software CiteSpace, carries out the visualization analysis from four aspects: cooperative countries, research institutions, references, keywords, and burst terms. The research status and important literature in the field of ZigBee technology are clarified, and the research hotspots and frontiers in the field of ZigBee technology are revealed.