Reviewerly/exHarmony huggingface dataset:
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Meteoritics & Planetary ScienceVolume 54, Issue 9 p. i-i CoverFree Access Cover First published: 09 September 2019 https://doi.org/10.1111/maps.13164AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Volume54, Issue9September 2019Pages i-i RelatedInformation
A First Look at Accessibility Issues in GitHub

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Computer Aided Design on Course Resource Online Sharing Through Blockchain Technology and Information Digitization

This paper analyzes the existing problems of teaching resources in Colleges and universities, according to the characteristics of blockchain technology in resource sharing, this paper designs a blockchain based teaching resource sharing system in Colleges and universities, in order to provide more ways of teaching sharing in Colleges and universities.
Research on Online and Offline Mixed Teaching Model of Baking Course Based on OBE and Artificial Intelligence

OBE theory plays an important guiding role in the teaching reform of practical courses. Taking students as the centre, we divide the baking course teaching into three stages: online learning before class, internalization and sublimation in class and consolidation and strengthening after class. The classroom reform based on OBE and AI is supported by the update of teaching environment, teaching design and teaching implementation, realizing the classroom innovation and talent training. We pay attention to students' professional technology and ability to make the teaching goal clearer. We conduct the whole process assessment and evaluation of students' achievements, which is helpful to cultivate professional and skilled talents. Practice has proved that the theoretical goal completion, practical goal completion and teaching mode satisfaction of the online and offline hybrid teaching mode of baking course based on OBE concept are higher than the corresponding indicators under the traditional model. The online and offline mixed teaching mode of baking course based on OBE theory can fully enhance learning interest and learning effect.
Text Data

The economics and finance disciplines have been generally reluctant to integrate forms of unstructured data. One exception to this is text, which has been applied to a wide variety of empirical problems. In this chapter, we introduce a broad variety of natural language processing techniques -- including dictionary-based methods, topic modeling, text regression, and text classification -- and explain how they can be implemented for problems in economics using TensorFlow.
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Meteoritics & Planetary ScienceVolume 54, Issue 11 p. i-i CoverFree Access Cover First published: 01 November 2019 https://doi.org/10.1111/maps.13168AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Volume54, Issue11November 2019Pages i-i RelatedInformation
Meteorological Comfort Indices to Assess Extreme Warmness in Southwest, Nigeria

2015, 2016 and 2017 have been confirmed the three warmest years on record and the degree of warming during these three years has been exceptional. Extremes of heat and cold environmental conditions have been known to be very detrimental to the health and comfort of humans. The severity of these adverse effects is often local and varies from one place to another though they are happening on a global scale therefore Temperature variation, Heat Index (HI) temperature and Dew Point Temperature (DPT) ( Meteorological comfort indices) were used to assess the effect of this condition in southwest, Nigeria. Air temperature and Humidity data were collected from meteorological stations situated in the southwestern states selected for study and was processed using the rule of Thumb for DPT calculations and Steadman (1979) formula simplified by Rothfusz’ model (1990) for HI temperature. The data analysis was done using Microsoft Excel Package and Statistical Package for the Social Sciences (SPSS), the following results were obtained: Elevation may affect temperature yet during extreme warmness other factors can have a greater influence on temperature and meteorological comfort indices used to assess extreme warmness in southwest Nigeria from this study showed greater impacts thus: Heat Index Temperature has highest values in Lagos State, while Dew Point Temperature have highest values in Ogun state. None of these extreme warmness measuring variables seem to be solely in favour of state Location in terms of coordinates and elevation; this suggests more anthropogenic influence forming the characteristics of the states to geographic locations of the states however, between the start year of extreme warmness (2015) and 2017, HI temperature showed a significant increase while average DPT did not really change during the period of extreme warmness.
Cyber Security Patterns Students Behavior and Their Participation in Loyalty Programs

Despite of the number of public advice campaigns, researchers have found that individuals still engage in risky cyber behaviour. The first part of this article is focused on the general approach to the cyber security and safety of personal data kept and processed by different entities from the perspective of students, while the second part is dedicated to the privacy aspects from the perspective of loyalty programs. Researchers have found that individuals are typically aware of online security and how to protect their privacy in the network, Nonetheless, individuals are still inclined to take risks because they are unrealistically optimistic and believe that negative events are less likely to happen to them. The survey also shows that even though respondents are aware that retailers collect, and process personal data and respondents feel that the amount of personal data of program members is far beyond the accepted level they still participate in such programs. Authors found also interesting patterns related to behaviour of respondents influenced by demographical data and the area of loyalty.
Deploying Your Rails Applications

At some point, you may decide it's time to share your Rails application with the world. Though Rails offers a first-class local development environment, it's not feasible to host your public-facing web application from your development environment; besides potentially violating your residential Internet service's terms of service, you'd have to deal with dynamic IP addresses, opening your firewall (and a host of potential security issues), and many more issues. Instead, when you’re ready to launch your application for public use, you'll need to deploy your Rails application to a suitable hosting environment.
Dominance Solvable Games

This chapter first analyzes how to represent games in normal form (using matrices) and in extensive form (using game trees). We afterwards describe how to systematically detect strictly dominated strategies, i.e., strategies that a player would not use regardless of the action chosen by his opponents.
Risk-based availability modelling and reputation management on fault tolerant cloud computing systems

This paper exhibits a risk-based philosophy in cloud computing to estimate ideal quality and maintenance which maximise the high availability of cloud administration infrastructure. The procedure is confined into two stages: 1) accessibility displaying the cloud; 2) hazard based assessment. The projected procedure is practical to the healthcare scheme using Markov chain process. The proposed system predicts, mitigate and eliminate the risk-based inspection and Markov chain process to efficiently identify the repair and failures of each virtual machine and reduce the energy consumption rate. The result of our proposed method will definitely bound optimum solutions and specifies the conquest of an efficient risk-based availability modelling on minimal cost and energy consumption. Finally, the proposed model is verified by the Nagios and OPTIMIS toolkit. The result obtained in our proposed model definitely bounds optimum solutions and triumph of an efficient risk analysis of failures on minimal cost and energy consumption.
Earthquake Prediction

This third edition provides a concise yet approachable introduction to seismic theory, designed as a first course for graduate students or advanced undergraduate students. It clearly explains the fundamental concepts, emphasizing intuitive understanding over lengthy derivations, and outlines the different types of seismic waves and how they can be used to resolve Earth structure and understand earthquakes. New material and updates have been added throughout, including ambient noise methods, shear-wave splitting, back-projection, migration and velocity analysis in reflection seismology, earthquake rupture directivity, and fault weakening mechanisms. A wealth of both reworked and new examples, review questions and computer-based exercises in MATLAB®/Python give students the opportunity to apply the techniques they have learned to compute results of interest and to illustrate Earth's seismic properties. More advanced sections, which are not needed to understand the other material, are flagged so that instructors or students pressed for time can skip them.
Non-convex Pareto Set navigation

Many real world applications require the solution of multi-objective optimization problems. Exploration of the corresponding set of best compromises, i.e., the Pareto set, supports the decision making process.
Effect of background on sensory evaluation for walking movement

The elements that are perceived as beautiful in the beautiful gait are based on personal sensibility. Therefore, clarifying "elements of beauty" quantitatively requires analyzing the cognitive structure of individuals who perceived beautiful and why they evaluated as beautiful. However, the appearance, clothes, and background of a person who performs walking may affect the viewers' evaluation criteria. This study examined the effect of background on sensory evaluation of walking movements. We used an optical motion capture system to measure kinematic data such as joint angles during walking. The two types of gait were measured during the experiment; “slow (90 bpm)” and "fast (120 bpm)". Then the videos which were taken in the gait measurement were edited by adjusting the color saturation and brightness. The four videos were prepared; “slow – high color saturation and bright”, “slow – low color saturation and dark”, “fast – high color saturation and bright”, and “fast – low color saturation and dark”. Furthermore, we conducted a questionnaire survey as the subjective evaluation experiment. The evaluators watched the four videos and answered the questions how they felt. From the results of the kinematic data and the subjective evaluation experiment, we have concluded that the evaluators' impression for the walking movement might change depending on the walking speed and background color.
The International Conference Research Collaboration of Environmental Science

We would like to thank God for the blessing of International Conference Research Collaboration that was held on 12th March 2018 by The Biology Department, Faculty of Science and Technology, Universitas Airlangga. This conference is purposed to build promising international networking between our department, The University of Kitakyushu Japan, and some other Universities (Universitas Andalas, Universitas lampung, Universitas Pasundan, Universitas Pendidikan Indonesia, Institut Teknologi Bandung, Universitas Negeri malang, Universitas Padjadjaran, Universitas Langlangbuana, Universitas Sumatera Utara, Universitas Janabadra, and University Malaya, Malaysia), which have similar research interests for Improvement of City Environmental Quality. Hopefully through this seminar can help to improve the quality of our city environment.
Educational Scenario of a Technological – Vocational course, using ICTs, in the context of Constructivism, Pragmatic model and Cross-thematic integration

In this paper an Educational Scenario of a Technological – Vocational course is documented and presented. In this the potentials of ICTs are exploited, as management tools of the information and as back up tools of the teaching method such that Constructivist learning environments can be established inside the classroom and achieve a smooth and successful integration of the ICT in the main cognitive subject of the Technological – Vocational course, which is examined at the Higher Education entrance exams. The Scenario has characteristics of a model useful for other Technological – Vocational courses as it suggests Practical Educational Techniques and Cross-thematic integration approaches and promotes the Metacognitive skills of the Vocational Upper Secondary School (EPAL) students with a specialization.
Trees

Tree-based models have proven to be remarkably useful for prediction tasks in machine learning and have recently been applied to and modified for problems in economics and finance. In this chapter, we introduce the concept of tree-based models, including random forests and gradient-boosted trees, and then examine their implementation in the high-level Estimators API.
Form Recognition dan Character Mapping Menggunakan Image Segmentation dan Optical Character Recognition

Industry 4.0 revolve the way of thinking in manufacturing factory business. Speed and accuracy become the main focus to survive and To growth. This study aims to build a blue print of an system that will increase both speed and accuracy in form input. This research will use several computer vision technologies like CNN that will used to do form classification and image segmentation, there is also OCR that will take specific information from a document that have been classified with CNN and then transform it into a JSON format which have more generic format and can be used in most common platform.
Motor intent recognition using deep learning ensemble to be used in prosthetic control

This paper presents a new approach for constructing simple and computationally efficient improvements of generic Artificial Intelligence (AI) systems, including Multilayer and Deep Learning neural networks. The improvements are small network ensembles added to the existing AI architectures. Theoretical foundations of the approach are based on stochastic separation theorems and the ideas of the concentration of measure. We show that, subject to mild technical assumptions on statistical properties of internal signals in the original AI system, the approach enables fast removal of the AI’s errors with probability close to one on the datasets which may be exponentially large in dimension. The approach is illustrated with numerical examples and a case study of digits recognition in American Sign Language.
The Center of Ecological Culture for the society

The twenty-year experience of ecological activities of Krasnoyarsk Regional Scientific Library comprises working with various user groups in the city and region is discussed. The Head of the Ecological Culture Center reflects on how to become an indispensable player in solving ecological problems and, on equal grounds with the ecological science and agencies, to stand fast.
Effective and Trustworthy Implementation of AI Soft Law Governance

This double special issue (together with the IEEE Technology and Society Magazine, Dec 2021) is dedicated to examining the governance of artificial intelligence (AI) through soft law. This kind of law is considered “soft” as opposed to “hard” because it comes in the form of governance programs whose goal is to create substantive expectations that are not directly enforceable by government [1], [2]. Soft law materializes out of necessity to enable a technological innovation to thrive and not be hampered by disparate heterogeneous practices that may negatively impact its trajectory, causing a premature “valley of death” exit scenario [3]. Soft laws are meant to be “just in time” to grant industry fundamental guidance when dealing with complex socio-technical assemblages that may have significant socio-legal implications upon diffusion into the market. Anticipatory governance is closely connected with soft law, in that intended and unintended consequences of a new technology may well be anticipated and proactively addressed [4].Soft law’s role in governance is to influence the implementation of new technologies whose inception into society have outpaced hard law. Its usage is not meant to diminish the need for regulations, but rather be considered an interim solution when the roll-out of a new technology is happening rapidly, resisting the urge to create reactive and premature laws that may well take too long to enter legislation in a given state. Mutual agreement and conformance toward common goals and technical protocols through soft law among industry representatives, associated government agencies, auxiliary service providers, and other stakeholders, can lead to positive gains. Including the potential for societal acceptance of a new technology, especially where there are adequate provisions to safeguard the customer and the general public.
Comparative Analysis of Global and National Systems for Observing, Monitoring and Forecasting Natural Disasters and Hazards with а View to Reducing Risk

The object of research is modern systems for observing, monitoring and forecasting natural disasters and hazards. Although early warning systems are often used to predict the magnitude, location and time of potentially hazardous events, these systems rarely provide impact estimates, such as the expected amount and distribution of material damage, human consequences, service disruption or financial losses. Supplementing early warning systems with predictions of impact has the dual advantage of providing better information to governing bodies for informed emergency decisions and focusing the attention of various branches of science on the goal of mitigating or preventing negative effects.The publication analyses current trends in the growth of natural risks, taking into account the risks associated with global climate change. The issues related to the growing risks of natural disasters and catastrophes at the present stage of societal development and directions of activities at the international and national levels for their reduction are considered. Disaster risk prevention and mitigation measures are described and areas of work in this area are highlighted. The decision-making sequence model is given, global and regional systems of observation, analysis, detection, forecasting, preliminary warning and exchange of information on natural hazards related to weather, climate and water are described. The factors that «unbalance» the global economy in terms of intensity, magnitude, magnitude of losses due to catastrophic events are analyzed.Addressing disaster prevention requires a structure at the national level in each country that includes policy, institutional, legal, strategic and operational frameworks, as well as at the regional and societal levels. This structure will organize and implement disaster risk reduction activities and establish an organizational system that will understand disaster risk and ensure that it is reduced through public participation.
A short text conversation generation model combining BERT and context attention mechanism

The standard Seq2Seq neural network model tends to generate general and safe responses (e.g., I don't know) regardless of the input in the field of short text conversation generation. To address this problem, we propose a novel model that combines the standard Seq2Seq model with the BERT module (a pre-trained model) to improve the quality of responses. Specifically, the encoder of the model is divided into two parts: one is the standard Seq2Seq which generates a context attention vector; the other is the improved BERT module which encodes the input sentence into a semantic vector. Then through a fusion unit, the vectors generated by the two parts are fused to generate a new attention vector. Finally, the new attention vector is transmitted to the decoder. In particular, we describe two ways to acquire a new attention vector in the fusion unit. Empirical results from automatic and human evaluations demonstrate that our model improves the quality and diversity of the responses significantly.
Deep Learning &amp; Artificial Intelligence can Solve Measurement Problems with Estimated Confidence 

In the past, like many companies, we tried to automate the data collection of handheld meters and other devices using Optical Character Recognition (OCR) technology, only to learn OCR technology has its limitations; we discovered any change in position, lighting, angle and even glare would throw off the OCR, resulting in bad numbers. So we changed direction and switched to Artificial Intelligence (AI) with Deep Learning algorithms. Our goal was to implement a “learn as you go,” AI-assisted solution that will learn to read a handheld meter as good, or better than, a human. The continual learning/training would train the AI to read measurements at any angle, in most lighting conditions. The trained AI would even be smart enough to understand scaled values based on the prefixes. Over time, with a much larger data set, the AI would be able to read just about any new display.
AVALIAÇÃO CLÍNICA DA SENSIBILIDADE EM INDIVÍDUOS COM DIABETES MELITO

This study aimed to perform a clinical evaluation of low cost and easily applied to detect the presence of peripheral diabetic neuropathy (DPN) in individuals with DM and to correlate the score of the esthesiometry's test and MNSI.We evaluated 44 participants with a diagnosis of DM.The NDP was diagnosed by the Michigan Neuropathy Screening Instrument (MNSI) and by esthesiometry with Semmes-Weinstein monofilaments.Eighteen (40.9%) subjects were diagnosed with neuropathy by MNSI and 30 (68.2%) by esthesiometry, the two tests showed a significant correlation, then, is possible to conclude that these two tests are effective when used together for such a diagnosis.
Research on Dialogue Generation Technology Based on BERT-seq2seq

With the rapid rise of e-commerce, the industry's demand for chatbots has gradually increased.With the continuous development of deep learning technology, the research on generative chatbots has gradually been put on the agenda.The existing generative chatbots still have problems such as poor understanding of the problem and generating security responses.In this experiment, the method of dynamic word vector encoding text is used to construct a chatbot with seq2seq structure, which improves the understanding of the problem text semantics of the chatbot model, and improves the correlation and fluency of the model.