Reviewerly/exHarmony huggingface dataset:
A method for automatic segmentation of tactile pavement in outdoor environments

Aiming at the complex outdoor situation, the tactile pavement area in the captured images is prone to shadows and noise, which causes inaccurate tactile pavement segmentation. Based on the Y component of the YUV color space representing the luminance information, the shadow regions in the images are extracted and chromaticity correction is performed; a tactile pavement automatic segmentation algorithm in the outdoor environment is proposed by combining the maximum inter-class variance (OTSU) algorithm and the watershed segmentation algorithm. The OTSU algorithm is used to segment the pre-processed image, initially extract the tactile pavement region, combine the distance transform to label the image, and the watershed algorithm then uses the label for tactile pavement segmentation. The experiment proves that the method can avoid the influence of the shadow part in the image on the segmentation algorithm and improve the effectiveness of the segmentation of outdoor tactile pavement.
Pembangunan Sistem Informasi Manajemen dan Pelayanan Konsumen PT. Air Minum Giri Menang (PERSERODA)

PT. Air Minum Giri Menang is a drinking water service provider company located in the city of Mataram. The existence of news media and information on consumer complaints is also one of the services provided by this company, where consumers can easily get updates from the latest news about drinking water services, but the management of this information is still somewhat inefficient because the categories of Latest News and Disturbance Information are still managed by a different admin. This activity is carried out by the service team as a form of applying knowledge to help the community. The method used is the Waterfall method which makes it easier to build the system to be achieved. The purpose of this activity is to build an information portal and complaint service, as well as build a system that can help admins from PT. Air Minum Giri Menang manages news information and consumer complaints in one place. Based on the results of the test cases, it can be concluded that the application can run well and in accordance with the desired results regardless of the type of device the application is accessed from.
A Lightweight Image Super-Resolution Network Based on ESRGAN for Rapid Tomato Leaf Disease Classification

The Crop disease identification is an important task in intelligent agriculture. Image resolutions have a large impact on the overall accuracy of classification performance. Some crop diseases are so similar that low-resolution images cannot capture their differences. To reduce the losses caused by crop diseases, it is vital to study crop image super-resolution reconstruction. Recently, with the rapid development of deep learning, various Single Image Super-Resolution (SISR) methods based on convolutional neural network (CNN) have achieved remarkable performance. However, the existing SR networks mainly have large parameter sizes, which require numerous training images and computing resources. In this paper, a lightweight image super-resolution model is constructed and applied to tomato leaf disease identification. We introduce the Shuffle Blocks with an attention mechanism to replace the Residual in Residual Dense Blocks (RRDBs) in the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), which is an effective SR model and won first place in the PIRM2018-SR Challenge. By the special structure we designed, our model can significantly reduce the parameter size of ESRGAN while almost maintaining its performance. Besides, we employ the tomato leaf images from the Plant Village dataset to train and test our model. Finally, we use the VGG16 to classify tomato leaf diseases based on the reconstructed images. The experiment results show that our model can effectively reduce the parameter size, computational complexity, and image reconstruction time compared to other chosen SR networks. Furthermore, the accuracy of SR images generated by our model is closer to ESRGAN and higher than other state-of-the-art methods.
SELF-PERCEPTION OF DIGITAL COMPETENCE OF TEACHERS IN BASIC AND SECONDARY EDUCATION IN CAPE VERDE

INTED2022 was held virtually on March 7th and 8th 2022.After 14 years as an "in-person" conference, the 2021 and 2022 editions were held on-line due to the COVID-19 restrictions.Every year, INTED brings together lecturers and researchers from universities and educational institutions from all over the world.This edition counted with 1264 participants from more than 75 different countries, ensuring a geographic diversity and a multicultural environment.The main objective of INTED2022 was to offer a meeting point for educational experts to share and discuss their experiences and projects on education, learning and teaching technologies and educational Innovations.The conference program also provided different networking activities where participants could interact with other delegates and share their perspectives on education.In addition to the Technical Program, different plenary sessions with speeches, workshops and discussion panels were delivered by world-leading keynote speakers (keynote speeches available at IATED Talks, https://iated.org/talks/).
Mesh: a more equitable world

Grace Hwang leads a cross-disciplinary team at Microsoft that explores what the future of 5-year-old HoloLens mixed-reality project looks like—a glowing, secondary world of holographic objects and people that can appear in an ordinary home or office. This year, the company debuted its Mesh platform—a virtual conference room where people can meet not just via HoloLens but also through an Android tablet, iPhone, or Oculus VR headset. Current customers include Accenture and Ray and Mark Dalio’s non-profit, OceanX.
A Neural Language Model with Attention Mechanism Based on Convolutional Neural Network

The convergence of artificial intelligence (AI) technology and natural language processing (NLP) has rapidly increased the demands for an analysis on the natural language that involves plenty of ambiguities not present in formal language.For this reason, the language model (LM), a statistical approach, has been used as a key role in this area.Recently, the emerging field of deep learning, which applies complex deep neural networks for machine learning tasks, has been applied to language modeling and achieved more remarkable results than traditional language models.One of the important techniques that have led neural network-based LM success is the attention mechanism.Attention mechanism makes neural networks pay attention to specific words in the input sentence when generating the output words.However, although the attention mechanism has improved the performance of many neural network models, it requires tons of parameters to achieve the state-of-art level performance.This is because attention mechanism encodes the context of a word by simply accumulating the outputs from the network for all the input words, which may cause information loss.To compensate for this limitation, we propose an extension of attention mechanism by adopting a convolutional neural network to replace the accumulation.With only far fewer parameters, our model achieved comparable performance to the recent state-of-the-art models on the very popular benchmark datasets, yielding perplexity scores of 58.4 on the Penn Treebank dataset and 50.1 on the Wikitext-2 dataset, respectively.
Expert System to Diagnose Bonsai Plant Pests with Certainty Factor Method

Bonsai ornamental plants are dwarf plants which are generally planted in shallow pots. The main element in bonsai is harmony between pots and plants, in addition to harmony between pots and plants there are also other elements such as trunk size, tree height, root distribution, twigs, and leaf size as well as diseases and pests that attack the bonsai plants need to be considered. The difficulty of the community in conducting consultations and the unavailability of funds and time, so that the community has never conducted consultations about diseases and pests that attack their bonsai plants to experts or plant extension workers from the relevant agencies. Therefore, it is necessary to take action to anticipate the increasing number of bonsai farmers who do not consult on diseases and pests of bonsai plants. This is due to the delay in the diagnosis of the disease. The unavailability of experts or plant extension workers who are close, the community does not have time to conduct consultations and requires money. An expert system is a computer-based system that uses knowledge, facts, and techniques and reasoning in solving problems that can usually only be solved by an expert in the field. The Certainty Factor method is efficient enough to be used in diagnosing the diseases and pests of the bonsai plant. This system can provide early diagnosis of diseases and pests on the bonsai plant based on the symptoms and intensity of the symptoms that are visible from the outside. Users only need to enter the Web to take the first step in solving bonsai plant diseases and pests and choose the symptoms of the disease they are experiencing without having to ask an expert directly. This system is made web-based with PHP programming language and MySQL database.
Quality assessment of video with film grain

Film grain noise originally arises from small metallic silver particles on processed photographic celluloid. Although modern digital video acquisition systems are capable of largely reducing noise, sometimes to nearly invisible levels, the look of cinematic film grain has not gone away. Instead, content creators often purposely introduce simulated film grain in post-production to emulate dust in the environment, enrich texture details, and develop a certain visual tone. Despite the artistic benefits, film grain has posed significant challenges to video delivery systems. Compressing and transmitting videos containing film grain noise is extremely costly due to the large number of bits required to encode the noisy pixels of much higher entropy than the typical visual content of the scene. Heavy compression may remove film grain, but meanwhile, remove meaningful texture content in the visual scene or deteriorate the artistic effect of the creator's intent. It also casts major challenges to quality control of video delivery systems, for which film grain-susceptible fidelity measures are highly desirable for measurement and optimization purposes. Here after describing the characteristics of film grain and its impact to video quality, we present a novel framework that unifies natural video quality assessment and creative intent friendly video quality assessment. We also demonstrate an instantiation of the framework in the context of film-grained content in terms of predicting the perception of different groups of subjects.
Application Of Geochemical Weathering Indices To Loess -Paleosol Sequences From Central Asia (Tajikistan)

&amp;lt;p&amp;gt;Loess deposits are well known as repositories of information about climatic and environmental variations occurring over the Quaternary. Over the years, numerous weathering indices relating to the geochemical characteristics of loess sediments have been developed to provide insights into environmental changes through time. In this study, we characterize the major element chemistry of the uppermost 20 m of the Karamaidan loess deposit in Tajikistan, which spans the last full glacial cycle. We compare major element ratios (Al/Ti, Fe/Ti, and Al/Fe), together with ternary A-CN-K diagram and enrichment/depletion of the elements relative to the upper continental crust, down the Karamaidan sequence, and compare our results to other regional and supraregional loess deposits and their change through time. We investigate different weathering indices (A and B indices, PWI, bases vs. Al ratio, CIW, PIA, and YANG indices, WI-1, WI-2, and CPA and FENG) in order to identify those most applicable to our study. We compare our results magnetic susceptibility down the stratigraphic profile to derive a direct index for alteration of the deposit.&amp;lt;/p&amp;gt;
A Genetic Algorithm for Image Segmentation

In this paper we present a genetic algorithm for image segmentation. Instead of adopting the chromosome-like encoding of the classical genetic algorithm we propose a more “natural” representation that adheres to the basic building blocks philosophy underlying the genetic algorithm. As a result, we devise novel crossover and mutation operators. We also devise a hill-climbing procedure to speed up the convergence of the genetic algorithm. The basic philosophy underlying our algorithm is to first identify the building blocks, and then to choose a representation that allows us to manipulate the building blocks by means of the genetic operators. The results are quite illustrative of this philosophy.
Was 1974 the End of Music History? Universalism, cybernetics, and the International Conference of New Musical Notation

This chapter shows how efforts to standardise postwar notation, promoted by the Index of New Musical Notation (1970–1974, New York), share basic assumptions with Hegel's and Fukuyama's infamous ‘end of history’. I demonstrate that the Index, like many contemporary composers, projected the teleological development of Western Art music as a story of the progress of its notation towards a final moment of perfection and reinterpreted music-historical events in relation to this narrative. The creation of a universal standard that could accommodate different notational aesthetics relied on the cybernetic paradigm, which held humans and machines as equivalent. Within this paradigm, notation was understood as the codification of the musical message into an informational signal. Ultimately, however, the Index did not bring about an end to music history. Instead, the cybernetic equivalence of man and machine brought composers down from the transcendent plane they had occupied, elevating media as historical agents in the process. The chapter argues that recognising notation as an agent that can shape musical narratives and instantiations provides a pathway to rewriting the very concept of history in the ‘history of music’.
Motion Blur Fuzzy Blind Removal Algorithm for Character Images in Gradient Domain and Deep Learning

The imaging device is susceptible to factors such as the subject or the shooting environment when imaging, and complex variable blurring occurs in the final imaging. In most cases, we not only do not have the conditions to re-shoot a clear image but also do not know the specific parameters of the variable blur in advance. Therefore, the purpose of this study is to propose a motion blur fuzzy blind removal algorithm for character images based on gradient domain and depth learning. Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. The algorithm used in this study is to preprocess the image by using guided filtering and L0 filtering and send the preprocessed gradient domain image block to the designed convolutional neural network for training. Extract the trained model parameters and realize the fuzzy kernel estimation and image. Image deblurring is performed using the TV regular term during image restoration. The experiment proves that the algorithm can effectively suppress the ringing effect and reduce the noise, and the motion blur effect is better. In this study, the MLP method, the edge detection method, and the proposed method are discussed, respectively. The PSNR values of the three motion blur removal methods are 26.49, 27.51, and 29.18, respectively. It can be seen that the motion blur removal method proposed in this study can effectively remove image motion blur.
INTACT STUDENTS AS IMPORTANT ACTORS OF INCLUSIVE EDUCATION

The contribution focuses on the issue of inclusive education through the eyes of intact high school students. It aims to investigate the opinion of intact students on inclusive education and classmates with disabilities. The survey was conducted in a city-based high school with 121 students in four years. There were 107 participants (n=107), aged 15 to 20, in the survey. The data were collected by a questionnaire constructed by the author and contained 21 items (open, closed, gap-fill). The results reveal that the students of the school know the basic principles of inclusion at the theoretical level, as well as they have a positive attitude to the presence of the student with a disability in a regular class; however, they lack practical experience with actual inclusion. At the same time, only 24% of them could accurately name the disability of their classmate, while this fact was not of interest for 41% of students of the school. The findings declare insufficient readiness of high school students for implementation and realisation of inclusion in their school. Equally, it declares insufficient to no readiness of students not only for daily presence of their classmate with a particular disability in their classroom but also for their mutual interaction (they do not know how to communicate with the classmate with a disability, accept him into the group, solve the tasks together, spend time during the breaks together). The school's necessity is thus implementing and realising the transdidactic aspect of inclusive education, which clearly declares the need to implement inclusion in the school setting within formal and informal education.
Perceptual modelling for banding detection

Banding is an annoying visual artifact that frequently appears at various stages along the chain of video acquisition, production, distribution and display. With the thriving popularity of ultra-high definition, high dynamic range, wide color gamut content, and the increasing user expectations that follow, the banding effect has been attracting a growing deal of attention for its strong negative impact on viewer experience in visual content that could otherwise have nearly perfect quality. What often frustrates many industrial practitioners is that simply increasing the bit-depth or bitrate of a video does not necessarily lead to removal or even reduction of banding. Indeed, with the recent accelerated growth of ultra-high definition (UHD), high dynamic range (HDR), wide color gamut (WCG) in content production, distribution services, and consumer display devices, severe banding occurs even more frequently than before and the visual effect is often much stronger. This is because UHD/HDR/WCG content typically covers a wider range of luminance levels and color variations than those of the traditional standard dynamic range (SDR) content. This, together with the limited and varying capabilities of display devices, creates major challenges to maintain smooth visual transitions simultaneously across all luminance levels and color variations. Automatic or objective image/video quality assessment (IQA/VQA) has been a highly active topic in the past two decades. However, popular IQA/VQA methods such as PSNR, SSIM, and MS-SSIM are insensitive to banding impairments. Therefore, the industry is in an urgent need of innovative approaches that are able to detect, control, and remove/reduce banding in an automated fashion. Here we present two different types of technologies of great promise on banding detection. The first is based on domain knowledge gained through deep and thorough understandings of the human visual system (HVS) and the video acquisition, production, distribution and display processes. Computational models derived from such domain knowledge are then combined to construct an overall banding detection and assessment model. In contrast to the first type of domain knowledge-driven methods, the second type of approaches are data-driven, with no or little domain knowledge assumed. Instead, a large number of images/videos and their ground-truth labels (with or without banding) are collected, and machine learning methods are then used to train black-box models such as the deep neural networks (DNNs) using the image/video dataset, so that the learned model may make good banding predictions on unseen image/video content.
Sulphur and carbon signatures of metamorphic processes in the Nepal Himalayas

&amp;lt;p&amp;gt;The Nepal Himalayas result from the India-Eurasia collision and the actual shortening is accommodated by a detachment ramp, the Main Himalayan Thrust (MHT). Separating high-grade metamorphic rocks from the Greater Himalayan Sequence to the north and low-grade metamorphic rocks from the Lesser Himalayan Sequence to the south, the Main Central Thrust (MCT) shear zone, is related to the MHT at depth where large Himalayan earthquakes nucleate. The MCT zone occurs from Far-Western to Eastern Nepal, associated at mid-crustal depth with active seismicity and high electrical conductivity; it exhibits carbon-rich rock layers and numerous active hydrothermal systems. Here, based on a multidisciplinary approach that includes geology, geochemistry and geophysics, we study the various sulphur and carbon signatures in the MCT zone in the Nepal Himalayas. First, we characterise the upper LHS rocks that include alternation of graphite-rich mica-schists (the so-called &amp;amp;#8220;black schists&amp;amp;#8221;) and carbonates (mainly siliceous dolomite). In the laboratory, we determine organic and inorganic carbon contents, as well as complex electrical conductivity. Second, we concentrate on numerous thermal springs in which we measure dissolved carbon and sulphur concentrations and their isotopic compositions (&amp;amp;#948;&amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;C and &amp;amp;#948;&amp;lt;sup&amp;gt;34&amp;lt;/sup&amp;gt;S). Third, we study the surface gaseous emissions, directly observed in the vicinity of hot springs, with the measurements of carbon dioxide (CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;) and hydrogen sulphide (H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;S) fluxes and isotopic compositions. By comparing the signatures of carbon and sulphur sequestration and carbon and sulphur release at a large spatial scale, our work provides insights into the carbon source-to-sink duality of large orogens, the metamorphic processes and the carbon and sulphur geochemical cycles.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;