Reviewerly/exHarmony huggingface dataset:
Community-centred Disaster Risk Reduction: Experiences from &amp;#8220;Our Flood Mural&amp;#8221; in Beira, Mozambique

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Based on the notion that Flood risk communication contributes to Disaster Risk Reduction (DRR), a novel community-centred approach called &amp;amp;#8220;Our Flood Mural&amp;amp;#8221;/&amp;amp;#8220;Nosso Mural de Cheias&amp;amp;#8221; was tested in Beira, Mozambique. &amp;amp;#8220;Our Flood Mural&amp;amp;#8221; centres around the co-creation of an interactive mural painting that highlights local experiences of past flood events, informs on the existing flood risk in the area and suggests possible risk-reducing measures. The mural brings together local knowledge and arts, and modern information technologies. &amp;amp;#8220;Our Flood Mural&amp;amp;#8221; can easily be upscaled and adapted to the local context in various settings. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;A key part of &amp;amp;#8220;Our Flood Mural&amp;amp;#8221; was active engagement with the local community. Survey results and plenary community sessions were held and showed that the people of the targeted community have a broad understanding of what they can do to reduce flood risk, such as strengthening of rooftops, moving to higher ground, and freeing drainage canals from clutter. These shared experiences were incorporated in the design of the mural which was made by a local artist. Also, two interactive QR-codes were included on the mural to offer additional (online) information: one linking to local weather forecasts and giving background information on the development process of the mural. The location of the mural was mutually decided to be at a local market where it is exposed to a wide audience on a daily basis. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;During the implementation of the mural, festivities were organized with local leaders present to draw attention to the purpose of the mural. Local leaders, community members, and aid organisations expressed ownership and pride as well as the desire to expand the idea to other locations and to address societal issues other than flood risk. It was also noticeable that the linkage of the mural to online information drew attention in the community, showing the mural&amp;amp;#8217;s potential as a means of introducing new technologies and information channels to reach a target audience. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Lessons-learned from our community-centred approach include the importance of organizing plenary sessions and carrying out local surveys to assure accurate representation of the communities&amp;amp;#8217; flood risk situation, and to assure adequate use of imagery and/or text. The visibility and accessibility of the different steps in the co-creation approach can help communities, technical experts, aid organizations, and officials interact constructively and identify potential improvements in each other&amp;amp;#8217;s actions. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
Construction of an Intelligent Perception System for Music Training Based on an Audio Signal Penetration Efficiency Model

This paper designs and implements a music training perception system AIS based on the audio signal penetration efficiency model. The AIS system uses the Doppler frequency shift effect of ultrasound to measure the speed of the mobile phone holder and calculate the distance change relative to the sound source through integration. The article defines the concept of perceptual music education, and discusses how to use the concept of perceptual education. The importance and possibility of improving the efficiency of music education, and the role of building a mobile music teaching cloud platform as an auxiliary tool for the implementation of perceptual music teaching is analyzed.
Database Repeated Record Detection Algorithm for Information Deep Learning

In order to improve the effectiveness of database repetitive recording detection, we propose a database iterative record detection algorithm based on deep learning. As a result, it was proven that the database repetition record detection rate and efficiency of this algorithm were high, and the result of the database repetition record was obviously superior to other algorithms. The results show that the database duplicate record detection results are significantly better than other current algorithms.
A Study of Teachers' Human Rights Consciousness in Reference to 'Human Rights Education

This research study gives an overview of Human Rights education, with subdivisions into the main categories of Human Rights Education, such as 'theory of Human Rights education.' 'Human Rights Education Implementation' is a result of "Human Rights Education." The expanding literature base on "Human Rights Education," which would be based on culture, an efficient educational system, classroom studies, curriculum analysis, textbooks, transformative learning, and youth development, is examined in this paper, especially at this time when the COVID-19 virus is active. This all follow the presentation of key results; the author propose that the future research might continue in the same vein, While COVID-19 is active and at the same time concentration will be fully on Impact related evolution.
A detection method for cyber-attack on connected signal phase and timing information

This paper proposes a method to detect cyber-attacks by cross-checking Signal Phase and Timing (SPaT) information and connected vehicle trajectories data. The proposed method is only applicable to cyber-attacks that involve information tempering. The proposed V2I cyber-attacks detection algorithm includes the following features: i) effective in detecting the authenticity of broad-casted SPaT information; ii) feasible to identify the occurrence of cyber-attacks when the defensive function fails; iii) functional in the environment of partially connected and automated traffic; iv) leveraging transportation knowledge for cybersecurity assurance. Furthermore, a sensitivity analysis on the connected vehicle penetration rate is conducted. The best performance is observed when the penetration rate is 100%. Under this penetration rate, the experimental result demonstrates that the accuracy rate of the proposed method reached 94%.
Humanization in Educational Environments; Aspects Necessary for Living in School Today

While it is true, quality has always been measured through statistics and percentages, often forgetting the training of the whole person.&#x0D; Now, when we talk about issues such as humanism and education supported by technology, we can fall into extremes: it is common to label technology as the provider of all the evils of today's civilization; or the opposite assumption; to argue that incorporating technologies into education would be the panacea for all the problems it faces and finally, to assume that the subject of the humanities is more typical of actions and attitudes of the past than as a current need, giving vitality to any educational process. However, and fortunately for the educational community, today there are global efforts to make the educational task a human action that minimizes inequities and the abysmal differences that exist in countries like ours. Fernando Reimers says: “Equal educational opportunities must be the priority objective of educational policies, the aim of education must be to contribute to creating just societies. This requires improving the learning environments of the poor, but not only along the paths that have been started over the last decade. This aspect pointed out by Reimers is precisely the central aspect that all educational action must contemplate, the educational policies that are implemented in our country must guarantee, above all, the construction of a just and egalitarian society, strengthening the cultural wealth that we have, with respect to to the diversity of people, their past, their present and especially their future.
COMPUTATIONAL POLLUTANT OF SO2/NO2 IN THE ENVIRONMENT USING AERMOD IN SEMI-URBAN AREA, STUDI CASE IN TUBAN, EAST JAVA

Air pollution is a problem that often occurs in Indonesia, especially in cities where the industry is growing rapidly, such as Tuban City. This is due to the low level of environmental management planning, including air quality management. Many models can be used to predict pollutant concentrations in ambient air. Currently, the model often used is Meteorological Forecast Models, one of which is AERMOD. The effort made here is to evaluate the performance of the AERMOD (AERMOD + Background concentration) model. The AERMOD model was used to predict ambient air quality in Tuban, East Java, and evaluated using ratio-to-observer, MAPE, and relative mean bias values. Air quality modeling was carried out in the rainy and dry seasons in 2018 and 2019. The modeling results using the AERMOD model resulted in an acceptable model. The results of the NO2 parameter modeling result in an overpredicted model trend, and changes in grid size affect the MAPE value and the resulting relative means bias. Meanwhile, the SO2 parameter modeling results produce a model that tends to be underpredicted, and the change in grid size does not have a significant effect. The performance evaluation of the model is expected to be useful for air quality management and further development of this model.
The Digital Environment and the Crisis of Transition Towards e-learning in Light of the Corona Pandemic: A Socio Analytical Study on the Reality of Electronic Education in Iraq in the Context of the Coronavirus Pandemic and After

In most countries of the world, university education faces several crises that vary according to their severity and the nature of their impact. Some are serious and some are harmful, and some have negative long-term effects where they occur. Some are light and ephemeral. The educational system in all countries of the world has witnessed the Coronavirus pandemic crisis, which has no alternative that this system could do. This led them to resort to electronic education as a solution to the crisis and to work to integrate education in its traditional and electronic way at best to proceed with the academic year with the least damage. This research therefore comes to know how to deal with the crisis, as the pandemic has swept all social sectors, including the university education sector, in which three parties are represented (the student, the educational institution, with an indication of the importance of digital transformation) and the need for this to be done through the field of university education in the first century, and through effective mechanisms, such as the upgrading it, through university education, and what can be done through the university education field Twenty-three major advances in science and technology and modern means of communication were made possible by digital technology, which provided new and flexible media in electronic or distance education.
KBART Phase III: Changes and Unresolved Questions

Knowledge Bases and Related Tools (KBART) is one of the most successful National Information Standards Organization (NISO) recommended practices. Formally supported by over eighty organizations across all stakeholder groups, it enables a standardized transfer of data between content providers and knowledgebases. The KBART Standing Committee has begun work on Phase III of KBART, which was last updated in 2014. In this NASIG 2021 session, three members of the KBART Standing Committee provided an overview of plans around Phase III of KBART, reviewing progress to date and highlighting efforts to resolve issues around KBART files for which there are no easy answers, such as the challenges of supporting additional content types beyond serials and monographs, how best to handle gap coverage for serials, and how to communicate Open Access content. The audience was asked to provide input on their use of KBART files, how they would like to use KBART that is not possible today, their thoughts on support for additional content types, and their opinions on proposed changes to KBART around the treatment of gap coverage and the handling of Open Access content.
NETWORK TRAINING PROGRAMS WITH PARTICIPATION OF NON-EDUCATIONAL ORGANIZATIONS: QUALITY ISSUES

Y. Denisova 1, V. Sopin 1, T. Bukina 2 1Kazan National Research Technological University (RUSSIAN FEDERATION) 2Kazan Federal University (RUSSIAN FEDERATION)
Binary codes from graphs related to the Johnson graph Γ(<i>n</i>, <i>k</i>, <i>k</i> − 1)

We show that the binary codes generated by the row span of adjacency matrices of the uniform subset graph Γ(2k, k, 1) and the Johnson graph Γ(2k, k, k − 1) coincide despite the graphs being non-isomorphic. We extend our results to the binary codes of Γ(2k, k, i) and k Γ(2k, k, k − i) where (k even), by showing that their adjacency matrices are equivalent. Further, we discuss the binary codes from the generalised uniform subset graph Γ(2k, k, I) for I = {1, k − 1}, and show that they are self-orthogonal for k ≥ 3.
Fall Motion Detection in Humans with Long-Short Term Memory

In today&#x2019;s life health is one thing that is considered by the country and even the whole world. One thing to pay more attention to is the age of the elderly who at that age are very vulnerable to diseases both internally and externally. One of the incidents that often occurs is the number of sick conditions caused by falling due to a fragile and uncoordinated physical condition. For this reason, it is necessary to have a detection device that is able to detect falling movements so that they are able to provide preventive actions for medical and nursing staff later.
Using remote sensing and GIS to project climate risk for asset management users

&amp;lt;div&amp;gt;At Climate X we are producing risk estimates for the UK to help businesses and communities mitigate and adapt for climate change related losses. Climate X provides risk scores and expected financial losses from a plethora of hazards including flooding, subsidence, landslides, drought, fire and extreme heat. To do this at the scales we need, Earth Observation (EO) and other geospatial data sets play a crucial role in both physical modelling and risk estimation. Generating rich geospatial datasets to sit as the bedrock of risk models requires intelligent use of multiple data sources, involving the fusion of EO data from synthetic aperture radar, lidar and optical instruments and across processing levels from L1 to L3. This talk will cover the generation and use of these datasets that drive physical risk models (flooding) as well as ML enabled models (Landslides and subsidence).&amp;lt;/div&amp;gt;
Reevaluating and Strengthening Publishing Partnerships between Librarians and Researchers

This article describes a program session covering the unique ways in which librarians can form increasingly collaborative partnerships with researchers at their institution. Specifically, session leaders drew from the six stages of the academic publishing lifecycle – Research, Write, Review, Publish, Distribute, and Read – to illustrate how librarians can assist tenure-track faculty throughout the publishing process beyond common practices in data access, discovery, and preservation.
A Viewpoint on Belief Networks and Their Applications

Belief networks are developed for addressing issues in classical networks with layered structures. This addresses the problem of the delayed training and getting stuck at minima, which happens when we select poor performance parameters. This requires a huge amount of datasets, which is a big problem in the current scenario. Belief networks are probability models built with stochastic calculated layers with latent parameters. These layers have hidden layers that contain binary values, called features or hidden values. The layers on top are not structured, and there is an accurate connection between all layers. The layers at the bottom get the data from the connected layers, and the parameters of classes have to change according to the datasets and according to the case being used. There appears to be an ideal layer-by-layer technique in training the top-down, creating parameters that describe whether all parameters within one layer rely on the variables inside the level below.. In the real world, this format of layered structure helps to build different applications with fast execution and accurate results of training. This chapter gives an insight into how belief networks are formed and their use cases. Furthermore, we discuss the current world applications of belief networks, including key aspects and drawbacks.
Multitopic underwater image quality assessment with visual attention factors

Statistical information on underwater images related to subjective quality perception differs based on the environment. We initially propose whitish, yellowish, greenish, bluish topics for underwater images obtained by applying an integrated color distribution, that is, images under the same topic have the same hue. Inspired by the local multiscale, multidirectional statistical characteristics of the structure, contrast, and color of underwater images, we constructed visual attention factor sets for the above four typical underwater environment topics. Furthermore, a multitopic underwater image quality evaluation (MTUIQE) model is proposed to measure mix distortions in different underwater images. The method of measuring image quality based on image topic is proven to be more effective than directly fitting all the natural scene statistics features to the mean opinion scores. Experiments were conducted to show that the proposed MTUIQE approach is superior to existing objective underwater image quality evaluation methods. Compared with six commonly used blind image quality evaluation methods, the Pearson linear correlation coefficient and Spearman rank-order correlation coefficient are increased by more than 10%, which means that the quality assessment model adapted to specific degradation topics is more consistent with subjective underwater image quality perception, and it can effectively evaluate the performance of underwater image enhancement and restoration methods.
Reading strategies and attitudes of young learners of English during online extensive reading

This study aims to analyze reading strategies and reading attitudes of elementary school students during online extensive reading. A total of 69 students participated in this study and were divided into two groups: a higher-level group (n = 21) and a lower-level group (n = 21). Both groups were asked to complete surveys concerning their reading strategies and reading attitudes, and take a reading proficiency test. Results showed that there was no significant difference between the higher-level group and the lower-level group in terms of their use of reading strategies. However, a significant difference was observed between two groups regarding anxiety and discomfort. Moreover, male students and female students showed a significant difference in terms of their use of reading strategies. There was a meaningful relationship between reading strategies and the students’ intellectual value regardless of their proficiency level. For the lower-level group in particular, there was also a significant relationship between reading strategies and comfort as well as between reading strategies and anxiety. Based on these findings, an appropriate teaching of reading strategies and the support of interesting activities to lower students’ anxiety can be suggested for a successful online English reading.
The Intersubjectivity and Intercorporeality of Noise and Sonic Arts

An Introduction to the Phenomenology of Performance Art - SELF/s; An introduction to the study and application of performance art through phenomenology for radical artists, educators and practitioner-researchers. Features exercises to activate your practice, clear introductory definitions to key phenomenological terms, and a multimodal design that lets the reader choose how to read the book. 36 illus.
Highway accident detection and classification from live traffic surveillance cameras: a comprehensive dataset and video action recognition benchmarking

Action Recognition in video is known to be more challenging than image recognition problems. Unlike image recognition models which use 2D convolutional neural blocks, action classification models require additional dimensionality to capture the spatio-temporal information in video sequences. This intrinsically makes video action recognition models computationally intensive and significantly more data-hungry than image recognition counterparts. Unequivocally, existing video datasets such as Kinetics, AVA, Charades, Something-Something, HMDB51, and UFC101 have had tremendous impact on the recently evolving video recognition technologies. Artificial Intelligence models trained on these datasets have largely benefited applications such as behavior monitoring in elderly people, video summarization, and content-based retrieval. However, this growing concept of action recognition has yet to be explored in Intelligent Transportation System (ITS), particularly in vital applications such as incidents detection. This is partly due to the lack of availability of annotated dataset adequate for training models suitable for such direct ITS use cases. In this paper, the concept of video action recognition is explored to tackle the problem of highway incident detection and classification from live surveillance footage. First, a novel dataset - HWID12 (Highway Incidents Detection) dataset is introduced. The HWAD12 consists of 11 distinct highway incidents categories, and one additional category for negative samples representing normal traffic. The proposed dataset also includes 2780+ video segments of 3 to 8 seconds on average each, and 500k+ temporal frames. Next, the baseline for highway accident detection and classification is established with a state-of-the-art action recognition model trained on the proposed HWID12 dataset. Performance benchmarking for 12-class (normal traffic vs 11 accident categories), and 2-class (incident vs normal traffic) settings is performed. This benchmarking reveals a recognition accuracy of up to 88% and 98% for 12-class and 2-class recognition setting, respectively.
Functionality and Architecture for a Platform for Independent Learners: KEPLAIR

The state-of-the-art in Artificial Intelligence, the Internet, and the computational power reached by the current technologies, allow to think of Intelligent Tutoring Systems much more advanced than their original definition. The KEPLAIR project envisages an online platform, designed to help all players involved in the educational endeavors, especially learners, in improving performance and effectiveness of their activities. Using leading edge AI solutions, KEPLAIR will act as a personalized assistant, helping its users in the entire educational experience, from goal elicitation, to learning path definition, to selection of materials, to performance/attainment testing, to analytics and report building. This paper introduces KEPLAIR’s architecture and functionality, and the role played by AI technologies.