Reviewerly/exHarmony huggingface dataset:
Availability and technological knowledge of university mathematics teachers in the time of covid-19

The purpose of the study was evaluating, in the face of the contingency caused by the Coronavirus Disease pandemic (COVID-19), the mathematics teachers knowledge and availability of technological tools for teaching remote university courses. The research adopted the quantitative approach and the design is non-experimental, transactional and descriptive. Eighty-one teachers participated and the survey consisted of 38 items with dichotomous answers, Likert scale and multiple selection. The questions were related to the remote modality, the availability of technology devices and services, and the use of technological tools. According to the results, the mathematics teachers of one south Sonora university have internet access and technological devices to distance teaching, they know or have used virtual classroom platforms and applications that allow communication with students. The areas of opportunity that were detected are the distance courses design, the online exams and didactic material preparation, as well as the implementation of tools that promote collaborative work.
Identity 5.0: How to Fight Algorithms Online (Fast). Heuristic Compressions of Personality Concepts (Dis)Obedient to Algorithmic Power—from Film, Television and a Cult Classic Novel

Identity 5.0: How to Fight Algorithms Online (Fast). Heuristic Compressions of Personality Concepts (Dis)Obedient to Algorithmic Power—from Film, Television and a Cult Classic Novel was published in (Dis)Obedience in Digital Societies on page 342.
PELATIHAN PENULISAN DAN PUBLIKASI ARTIKEL ILMIAH MENGGUNAKAN DIGITAL PLATFORM PADA MASA PANDEMI

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Abstract.&lt;/em&gt;&lt;/strong&gt; &lt;em&gt;Writing and publishing scientific articles are essential for researchers, lecturers, and students in Indonesia. However, many of them still have difficulty writing their research results in the form of scientific articles and publishing them in reputable international journals. So, this service activity is carried out to develop academic writing and publish scientific articles skills for researchers, lecturers, and students in Indonesia. The community service activities were carried out using a digital platform, namely Zoom Meeting, during the COVID-19 pandemic. The participants involved in this activity consisted of 193 participants, consisting of researchers, lecturers, and students from various regions in Indonesia. The collecting data uses a questionnaire which was analyzed descriptively. The activity results showed an increase in the participants' academic writing and publication skills, with an average increase of 71.45% after attending this training through a digital platform. The participants gained a lot of knowledge about academic writing and scientific publications in reputable international journals.&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstrak.&lt;/strong&gt; Keterampilan menulis dan menerbitkan artikel ilmiah sangat penting bagi para peneliti, dosen, dan mahasiswa di Indonesia. Namun, sejumlah dari mereka masih mengalami kesulitan dalam menuliskan hasil penelitian mereka dalam bentuk artikel ilmiah dan menerbitkannya pada Jurnal internasional bereputasi yang baik. Sehingga, kegiatan pengabdian ini dilakukan untuk menumbuhkan keterampilan menulis dan menerbitkan artikel ilmiah bagi para peneliti, dosen, dan mahasiswa di Indonesia. Adapun kegiatan pengabdian dilakukan menggunakan platform digital, yang bernama Zoom Meeting, pada saat masa pandemi COVID-19. Para peserta yang terlibat dalam kegiatan ini terdiri dari 193 peserta, yang terdiri dari peneliti, dosen, dan mahasiswa dari berbagai daerah di Indonesia. Pengumpulan data menggunakan kuesioner yang dianalisis secara deskriptif. Hasil pengabdian ini menunjukkan peningkatan keterampilan menulis dan publikasi para peserta dengan rata-rata peningkatan di angka 71,45%, setelah mereka mengikuti pelatihan ini melalui platform digital. Para peserta mendapatkan banyak pengetahuan tentang dunia penulisan dan publikasi ilmiah di jurnal internasional bereputasi yang baik.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
COLD SMART EXTREME

The cold winter is a challenge not only for us, but also for our smartphones. To prevent this, we concluded that the best solution in such a situation is to sew cases to protect our smartphones from the cold. We took into account the practicality of creating a product and usability. For the basis, we chose penofol as the most frost-resistant material in the conditions of our Yakut winter. Penofol now is used everywhere: to insulate clothes, houses, etc. For the shape of our covers, we chose a book for the convenience of use outside the home.&#x0D; As the experiment progressed, it show how each participant's phone reacted. IPHONE 6/6S and REDMI 4A brand phones were used in the experiment. During the study, we noticed that the phone lasted much longer with our cases than without them. In Yakutsk weather between -33 ºC and -40 ºC the phones went through a series of tests, namely, we compared how the gadget behaves and what happens when it shuts down.&#x0D; The main results of our experiment showed that with our covers the devices last on the street for a long time, which is necessary in conditions of extremely low temperatures in winter in Yakutia.
Research on edge‐guided disambiguation module for shadow detection

In shadow detection area, deep learning-based methods have achieved outstanding performance. However, the potential ambiguity in shadow images is still the main reason for the poor performance of the algorithms. By analysing the relationships among shadow edges, shadow false-positive information, and shadow areas, an edge-guided disambiguation module for better shadow detection performance is proposed. The edge-guided disambiguation module consists two parts: 1) the shadow edge feature is used to guide the comprehensive shadow area feature generation of different resolution. 2) The shadow false-positive features is applied to reduce ambiguity in the comprehensive shadow area features. In this way, the missed detections is added in the first step and the false detections is reduced in the second step. More accurate detection results is obtained. The comprehensive experiments are proceeded on the three public shadow detection datasets: SBU, UCF, and ISTD. The experimental results demonstrated the effectiveness and robustness of the proposed method.
The Reality of High Performing Deep Learning Models: A Case Study on Document Image Classification

This paper presents an approach to interpret the predictions of deep learning models in the domain of document image classification.
Personalized Product Recommendation Model of Automatic Question Answering Robot Based on Deep Learning

The collaborative filtering algorithm widely used in recommendation systems has problems with the sparsity of scoring data and the cold start of new products. A personalized product recommendation model for automated question-answering robots using deep learning is proposed. First, a personalized attention mechanism at the word level and the comment level is proposed, and the comments and users are individually coded. Then, the bidirectional gated recurrent unit (Bi-GRU) is used to construct the score prediction matrix, and through the dynamic collaborative filtering algorithm to integrate the time characteristics of the user’s interest changes. Finally, the feature codes of the users and products are input into the Bi-GRU model for learning, so as to output the recommendation list of personalized products of the automated question answering robot. Experimental results based on the JD and Tianchi datasets show that the training loss of the proposed model is lower than 45 and 23, respectively. And HR@15 and MRR@15 exceed 48 and 15, respectively, which are better than other comparison models. It can better adapt to the actual needs of automatic question-answering robots.
Forecasting Rice Paddy Production in Aceh Using ARIMA and Exponential Smoothing Models

Indonesia targets Aceh to be one of the paddy production centers and be able to carry out self-sufficient production in paddy and become a national granary. However, in reality, Aceh's paddy production in its province is not consistent from year to year. This province has not been able to meet the food needs of paddy independently, so that it supplies paddy from other regions due to the difficulty of detecting the presence of a surplus of paddy. The purpose of this research is to estimate the results of paddy production in Aceh for the future. The mathematical model that can be used is a time series model namely Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing. The results of the estimation of paddy production in the next 5 years using the ARIMA (1,1,3) model are 2000616; 2017549; 1903585; 1944499; and 1929810. While the estimation results using the Winter Exponential Smoothing model are 1625925; 1645196; 1687667; 1605530; and 1555213. ARIMA model (1,1,3) produces an MSE/MAD value of , while the Winter Exponential Smoothing model produces an MSE/MAD value of . Therefore, it can be concluded that the Winter Exponential Smoothing model.
Quantum computer: The state of the problem in the world and in Ukraine

The report analyzes the current state of the problem of creating a quantum computer as a real working device, which the world scientific community identifies as one of the most important fundamental tasks of physics in the XXI century. It is emphasized that the work of the quantum computer is based on the results of basic research in the field of quantum physics. Today, the world is actively developing quantum technologies, which are expected to be implemented in the near future and provide breakthrough results in many areas of public life: from cryptography to modeling large-scale systems, description and prediction of complex processes and more. The contribution of Ukrainian scientists to the study of quantum systems, the development of quantum technologies and the solution of physical problems that arise in the implementation of elements of the quantum computer is considered.
Guided Inquiry Collaborative Learning (GICL) for Online Teaching in Cybersecurity: Challenges and Recommendations

The COVID-19 pandemic has required many educators to offer online courses. Given the evidence of the effectiveness of the Process-Oriented Guided Inquiry Learning (POGIL), many educators are interested in implementing POGIL in online environments. This paper first discusses the challenges of using the POGIL approach to teach courses. Then we share our experience and our proposed approach (GICL) for teaching cybersecurity topics via the Zoom platform in the online environment. Recommendations for overcoming some of these challenges for online teaching are provided.
Classification and Retrieval of Multimedia Book Cloud Resources Based upon Hash Algorithm

Classified retrieval is one of the important factors of library management. It can help readers and administrators quickly find the required materials and better enjoy the convenience of modern network. Through data mapping, hash algorithm can directly convert high-dimensional data into low-dimensional data and store it in Hamming space, which greatly reduces storage consumption and increases computational convenience. For the multimedia retrieval and classification in the era of big data, hash algorithm is right to the point. This paper is aimed at studying the classification and retrieval method of multimedia book cloud resources, extracting different classification categories, determining classification items; cloud resources contain a large amount of image text information, and according to the characteristics of the image text, the features can be well extracted for classification and retrieval, find the optimal book retrieval and classification method, and realize the classification and retrieval of multimedia book cloud resources with the help of hash algorithm. The experimental results show that the hash algorithm can help the classification and retrieval of multimedia books, optimize the classification of multimedia book cloud resources, and improve the efficiency by 20% and user satisfaction by 30%.
IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAIVE BAYES DENGAN FEATURE SELECTION UNTUK PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU

The Education Efficiency Rate (AEE) is one of the parameters of the quality of the education program. The quality is measured based on 7 main standards, one of which is students and graduates. Meanwhile, to predict students' graduation rates accurately based on manually owned data set characteristics is very difficult. Data Mining by Naïve Bayes method was chosen to find patterns in analyzing and predicting timely graduation of students. As for the test will be done by comparing the initial dataset and dataset characteristics using the algorithm attribute selector Gain Ratio Attribute with the help of tools WEKA. The results showed that there was a difference to the accuracy of the results, and the larger ROC or AUC curves on the dataset characteristics using the selector attribute by using the Gain Ratio Attribute, although not very significant. And the result of this research yield 81% accuracy level with precision equal to 83.563% and recall 88.41%. The method used is included in Good Classification and will become the reference of the college management side, to address the problems that may arise in the decrease of the quality of education (e.g. decrease ratio of lecturers with students).
Target Detection Alarm System Based on Video Surveillance System

Aiming at the problems of heavy video workload and low efficiency of staff in the video monitoring system, a target detection system model combining camera and virtual electronic fence is established, and a virtual electronic fence alarm system with low equipment cost and higher safety factor is developed. The system can set any geometric area independently, recognize the human posture of pedestrians entering the area through the video information collected by the monitoring lens, detect pedestrian intrusion and give an alarm, which plays the role of shock and warning. On this basis, Open Pose algorithm is proposed to further improve the accuracy and rapidity of the system. Experimental results show that this method is feasible and effective.
Blockchain Enabled Diabetic Patients’ Data Sharing and Real Time Monitoring

According to the World Health Organization worldwide diabetes report, the number of diabetic patients has surged from 108 million in the 1980s to 422 million in 2014. According to researchers, the numbers will continue to climb in the next decades. Diabetes is a sickness that requires long-term self-care and close monitoring to be appropriately put under control. As a result, continuous monitoring of blood sugar levels has the potential to save millions of lives. This paper proposes a Blockchain-based platform that connects the patients, healthcare practitioners (HP), and caregivers for a continuous monitoring and care ofdiabetic patients. It lets the patients to securely connected to HP for the purpose of remote patient monitoring (telemedicine), whilst preserving patient data privacy using the blockchain technology. IoT sensors are used to read sugar levels and store these data in a tamper-proof immutable ledger (Hyperledger). This platform provides an End-to-End movement of the patient's data. That is, from the point where it is formed (sensors) to the point it ends up in the HP side. It gives patient a control-and-track function to maintain/track data movement. It provides a unique feature in allowing the patient to keep track of the private data and to pick who they want to share the data with and for how long (and for what reason). The platform is developed in two stages. Initially, the concept is implemented using the Hyperledger Fabric. Then, a Blockchain based on a novel Proof-of-Review (PoR) consensus model is included on to provide efficient performance and scalability in the Hyperledger fabric. Essentially, this proposed platform is to alleviate the pain points in traditional healthcare systems in the scopes of information exchange, data security, and privacy maintenance for real-time diabetic patient monitoring.
Announcing Phase 4 of PAGES 2k: Hydroclimate of the Common Era

&amp;lt;p&amp;gt;&amp;lt;img src=&amp;quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.d17138223ed164871591461/sdaolpUECMynit/22UGE&amp;amp;app=m&amp;amp;a=0&amp;amp;c=811fe277410d9ff3699ef0224ece3bef&amp;amp;ct=x&amp;amp;pn=gnp.elif&amp;amp;d=1&amp;quot; alt=&amp;quot;&amp;quot; width=&amp;quot;353&amp;quot; height=&amp;quot;140&amp;quot;&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Understanding the climate of the past two millennia (2k) remains vital for developing our wider comprehension of the climate system, including the nature and scale of recent and future anthropogenic change. Phase 4 of the PAGES 2k network will build on previous phases and take us to a new level of understanding and science-policy integration.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;During previous phases, PAGES 2k members compiled global networks of proxy measurements, extending records beyond the instrumental period by more than an order of magnitude, reconstructing past climate and developing new knowledge of past variability and processes. Through data-model integration with state-of-the-art Earth systems models, proxy system modelling and data assimilation, we took key steps towards a more comprehensive understanding of climate dynamics.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Phase 4 will take us even further, challenging our community to turn its focus primarily towards the hydroclimate of the Common Era: performing new reconstructions and improving the interoperability, extent and scope of our data and model products. In doing so, we also seek to facilitate the translation of our science into evidence-based policy outcomes. Our overarching aim is to reconstruct hydroclimate variability over the Common Era from local to global spatial scales, at sub-annual to multi-centennial time scales. We propose to achieve this through new community-led data curation efforts and the development of new data-driven tools and practices to maximise the interoperability of convenient, efficient and widespread model/data products. We will aim for a process-level understanding of past hydroclimate events and variability by evaluating and constraining Earth system models and through data assimilation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our coordination team places a strong emphasis on respect and inclusion, aiming to foster a diverse and equitable community. Through a &amp;amp;#8216;hub and spoke&amp;amp;#8217; structure, our team will provide a facilitation, coordination and support role (the hub) for Pages 2k working groups (the spokes). We are actively seeking participation of those engaging in climate policy and climate services. Welcome to Phase 4!&amp;amp;#160; We warmly invite your collaborations and contributions!&amp;amp;#160;&amp;lt;/p&amp;gt;
Exploring the performance of phase association algorithms

&amp;lt;p&amp;gt;Seismic phase association plays an important role in earthquake detection and location workflows as it links together seismic phases detected on different seismometers into individual earthquakes. Together with improved phase picking algorithms, a phase association algorithm can generate large earthquake phase data sets and earthquake catalogs when applied to dense permanent or temporary seismic networks. Recently, many efforts have been made on improving seismic phase association performance, such as developing machine learning approaches that are trained on millions of synthetic sequences of P and S arrival times, to generate more precise and complete catalogs including more small earthquakes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As part of project MILESTONE, which aims at the automatic creation of large microseismicity catalogs in subduction settings, the present study evaluates the performance of the deep-learning based phase association algorithm PhaseLink (Ross et al. 2019) by comparison with a traditional grid-based method and a small handpicked benchmark dataset.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We used seismic data from the IPOC (Integrated Plate boundary Observatory Chile) permanent deployment of broadband stations in Northern Chile, dedicated to the study of earthquakes and deformation at the continental margin of Chile.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;For an initial calibration, we manually picked P and S phases of raw waveforms on 15 stations on two randomly chosen days. All events that were visually recognizable were picked and located, which led to a dataset of 251 events comprising 1823 P and 1468 S picks, spanning a depth range from the surface down to 240 km. We use this handpicked dataset as &amp;amp;#8216;ground truth&amp;amp;#8217;, and evaluate the performance of PhaseLink and the grid-based method coupled with a STA/LTA trigger against this benchmark, considering both the numbers of (correctly/falsely) associated events and the number of constituent picks per event.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In a second experiment, we compare PhaseLink and conventional phase associator using a much larger set of STA/LTA alerts from the same region, but without the additional ground truth.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The presented research represents first steps towards an integrated automated workflow for detecting, picking, associating and locating microseismicity in subduction zone settings.&amp;lt;/p&amp;gt;
Unraveling the secrets of the Earth through nanogeology: A correlative microscopy approach

&amp;lt;p&amp;gt;Correlative analytical approaches involving high-spatial resolution microscopy techniques allow for the compositional measurements and spatial imaging of materials at the near-atomic scale. By combining electron backscatter diffraction (EBSD) mapping, electron channeling contrast imaging (ECCI), scanning transmission electron microscopy (STEM) and atom probe tomography (APT) on various geological materials such as minerals and glasses, we have successfully documented element mobility regulated by structural defects. Although these techniques were initially developed in the materials sciences, they are now being applied to a broad range of applications within many subdisciplines of geosciences including geochemistry, geochronology, and economic geology. In one set of experiments, we applied a correlative approach on naturally deformed pyrite from an orogenic gold mine in northern Canada to assess the impact of crystal-plastic deformation on the remobilization of trace elements. This study has led us to propose a new paragenetic model for metallic ore deposits in which deformation creates nanostructures that act as traps for base- and precious-metals. By applying our approach on pyrite that is rich with fluid inclusions, we have also documented two processes that led to proposing a new fluid inclusion-induced hardening model, which is in contrast to the more commonly reported weakening effect of fluids on minerals. To broaden the applications of our approach, we have applied the same suite of analytical techniques to a synthetic andesitic glass to assess whether nanoscale chemical heterogeneities can act as nucleation sites for gas bubbles. The combined results demonstrate the existence of nanoscale chemical heterogeneities within the melt and at the bubble-melt interface supporting the hypothesis that homogeneous nucleation could in fact be a variety of heterogeneous nucleation. The interactions between trace elements and structural defects plays a vital role in determining the mechanical properties of minerals, particularly in fluid-rich environments. These sub-nanometer scale exchanges consequently control meso- to tectonic-scale geological processes. Our research work not only demonstrates the latest advancements in analytical microscopy resolving long-standing geological problems but also brings us closer to bridging the gap between the fields of materials sciences and geosciences.&amp;lt;/p&amp;gt;