Reviewerly/exHarmony huggingface dataset:
Model Mitigasi Bencana Berbasis Multisensoris Untuk Anak Berkebutuhan Khusus Di SD Inklusi Kota Yogyakarta

The potential for earthquakes, floods, tornadoes in the city of Yogyakarta needs to be addressed with training and simulations for disaster mitigation efforts, especially for elementary school-aged children who are a group vulnerable to disasters and among them there are the most vulnerable groups, namely children with special needs ( crew). Children with special needs have the same rights as other children to know about how to protect and save themselves when a disaster occurs. Some of them have mobility barriers to carry out protection and even self-rescue independently. So it is necessary to have information on how the procedures/plans for rescue crews from disasters are needed. The training and simulations in this service activity aim to: (1) equip students and teachers with knowledge about the potential for earthquakes, floods, and tornadoes related to the condition of the area in the service area; (2) equip students and teachers with knowledge and attitudes in dealing with disasters; (3) equip students and teachers with knowledge about the importance of preserving the environment to prevent disasters; (4) providing alternative efforts to mitigate earthquakes, floods, and tornadoes based on multisensory disasters; and (5) train teachers to provide disaster mitigation assistance to students, especially children with special needs (ABK).&#x0D; This community partnership program is carried out by means of lectures, questions and answers, and demonstrations. Lecture and question and answer methods were carried out for socialization aimed at providing understanding to students about knowledge and attitudes in dealing with earthquakes, floods, and tornadoes, as well as socialization to explain the importance of environmental sustainability in order to prevent natural disasters, as well as socialization of geographic information related to potential disasters. in the city of Yogyakarta, as well as the concept of multisensory-based earthquake, flood and tornado disaster mitigation. Demonstration method for carrying out multisensory-based earthquake, flood and tornado disaster mitigation simulations. Partners in this community partnership program are SDN Karaganyar and SDN Minggiran. The two elementary schools are inclusive elementary schools located in the city of Yogyakarta. Based on observations at SDN Karanganyar and SDN Minggiran, there are students with special needs who do not understand how to protect and save themselves when a disaster occurs. Therefore, it is necessary to conduct disaster mitigation training to improve the ability to face multisensory-based disaster threats. The target in this community partnership program is ABK students at SDN Karanganyar and SDN Minggiran, and it was carried out from April 2018 to November 2018. Through training to increase the ability to face multisensory-based disaster threats, it is hoped that it can improve the ability of ABK students in dealing with disaster threats.
Working with Power Automate

Microsoft Power Automate (previously known as Flow) helps you create automated workflows between your favorite cloud and on-premise apps. It provides services to synchronize files, get notifications, collect data, and more.
Pelatihan Penulisan Artikel Jurnal untuk Jurnal Fakultas Ilmu Sosial Universitas Negeri Semarang

In addition to have an important meaning for the college, where the journal is also important for teachers who take shelter in it. For teachers, existence junal can be used as a medium to pour a wide variety of knowledge, ranging from the idea of â€‹â€‹thinking, nor Mr studies. Various kinds of knowledge that will provide many benefits to the public, both for the world of education mapun general public. It is important for faculty to continue to improve their knowledge and their ability to write journal articles.Faculty of Social Sciences, State University of Semarang (Unnes) has had five journals: the Journal of Paramitha, Community Journal, Journal of Social Science Forum, Integralistic Journal, and the Journal of Geography. The fifth journal has been going well. Participation of lecturers in writing in these journals are also high. But the existence of these journals are still to be supported by an increase in the quality and quantity of publications in journals, so the usefulness of the journal for the higher audiences. One way to increase the quality and quantity that can be achieved is by training writing journal articles for lecturers in the FIS Unnes.
PROPTOSIS IN ACUTE MYELOID LEUKEMIA: AN UNDER RECOGNIZED PRESENTATION OF HEMATOLOGICAL MALIGNANCY

OBJECTIVE: To determine the frequency of proptosis and associations of proptosis with demographic, clinical and hematological characteristics with acute myeloid leukemia (AML) in children. METHODS: This descriptive study was conducted at Combined Military Hospital, Rawalpindi, Pakistan from January 2018 to July 2020. Patients of AML between 1-12 years of age who presented with and without orbital granulocytic sarcomas (OGS), selected by non-probability convenience sampling technique were evaluated for proptosis. Patients were classified into different subtypes of AML according to French-American-British (FAB) classification. Cytogenetic studies by karyotyping were done to identify different genetic abnormalities associated with AML. Kaplan- Meier survival analysis was used to analyze the overall survival of AML patients. RESULTS: Out of 230 patients diagnosed with AML, 34 (14.78%) patients presented with proptosis. Mean age of presentation was 6.80±3.69 years, with male-female ratio of 2.1:1. Proptosis was unilateral in 19 (55%) and bilateral in 15 (45%) patients. Ten (29.40 %) patients presented primarily with proptosis while 24 (71%) patients presented with proptosis and systemic features of leukemia. Overall AML-M2 was found in 102 (44.3%) cases and other types were observed in 128 (55.6%) cases. Most common FAB AML subtype associated with proptosis was AML-M2 (n=26; 76%). Median duration of survival in AML patients with OGS was 867 days and as compared to 353 days in AML patients without OGS. CONCLUSION: Proptosis is a frequent finding in children with AML. AML-M2 is associated with proptosis in children with AML. Survival in patients with OGS was better than patients without OGS.
PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR KESEHATAN LINGKUNGAN MENGGUNAKAN METODE PARTITIONING AROUND MEDOIDS DENGAN VALIDASI INDEKS INTERNAL

Environmental health is an important aspect in efforts to achieve public health. The condition of environmental health in Indonesia is varies in each province, so the priorities for increasing environmental health are also different. This study aims to grouping provinces in Indonesia based on environmental health indicators in order to know the high/low environmental quality in each province to assist the government in optimizing environmental health efforts. The grouping of provinces is done partitioning around medoids method which is robust to data containing outliers. The measure of similarity objects is calculated using the Euclidean and Manhattan distances, the selection of the best number of clusters is done by validating the internal index, namely the Calinski-Harabasz index, Baker-Hubert index, silhouette index, C-index, and Davies-Bouldin index. The result of this study is that the best number of clusters are two clusters using the Manhattan distance measurement method, with the largest Calinski-Harabasz index value = 24.10072, the largest Baker-Hubert index = 0.8466251, the largest silhouette index = 0.4246581, the smallest C-index = 0.07290109, and the smallest Davies-Bouldin index = 1.094805.
A Perspective of the General Scientific Picture of the World: Collisions and Trends

The article discusses the problems of constructing a scientific picture of the world in a technogenic civilization at the stage of its globalization. The interdependence of science, technology and society generates a number of issues of a socio-humanitarian and, in particular, ideological nature. Interdisciplinary forms of organization of sciences contribute to the development of borderline methodologies. These methodologies integrate the achievements and problems of specific disciplines into a certain overall picture. The ambitions of this worldview include space (near, far), planetary nature with all geo – shells, as well as the biosphere, technosphere, sociosphere, anthroposphere. In the course of communicative interactions, scientists have developed methodological principles for constructing a picture of the world. These principles claim to be universal: integrity, consistency, coherence, structurality, evolution/involution, complexity, self-organization, human dimension. However, epistemic and communicative difficulties accompany the construction of a general scientific picture of the world. There are methodological, experimental, linguistic, cognitive barriers between disciplines. Also, the unevenness of their development is strong. Competition between disciplines and reductionist programs are often caused by social reasons – politicization, ideologization and commercialization of big science. Philosophy seeks to connect the idea of a scientific picture of the world with a change in self-consciousness and a person’s place in the world. The prospect of intellectual synthesis fluctuates between the transdisciplinary form of organization of collective scientific thinking and the ideological imperialism of individual philosophical and scientific programs.
EFFICIENT AND COST-EFFECTIVE AWS CLOUD-BASED SOLUTION FOR TRAFFIC VIDEOS ANALYSIS

Different research are underway, to make urbanite's life good, with the help of technology.Providing solutions to different urbanite problems such as traffic characterization, traffic congestion, identifying road accidents, and vehicle classification.The current solution offered in ITS involves two types of solution either streaming of traffic video to cloud for its computation or using edge device to compute.We made an alternate solution for the same purpose on the AWS cloud and trained our own YOLO model.The recorded video of the traffic was sent to AWS cloud for classification of vehicles.We got a detailed summary of video analysis using our trained model, its implementation using AWS in our proposed solution.The proposed model successfully classifies 1200 vehicles into 7 types in a 3 hour long video for which it was trained with a pretty good accuracy.
The pillars of knowledge management and its role in enhancing the dimensions of the competitive spectrum : Exploratory study in pharmaceutical companies in the city of Sulaymaniyah (pioneer company and Rasan company) as a model

The research aims to diagnose the contribution of the pillars of knowledge management with its main dimensions (cognitive leadership, organizational structure, learning, technology, knowledge sharing), in enhancing the dimensions of the competitive spectrum represented by (cost leadership strategy, differentiation strategy, and response strategy), to express the ability of organizations In reaching a comprehensive and focused vision that contributes to extracting and deducing the main contents of the dimensions of the competitive spectrum through which it is possible to improve performance and achieve excellence through the contents and pillars of knowledge. In order to achieve the objectives of the research, the research was conducted in the pharmaceutical companies sector and within selected companies by designing a questionnaire that was prepared in accordance with the goal of the research. , division manager, unit manager), and by (46) forms, while the number of the form retrieved and valid for analysis reached (43) and a recovery rate of (93%). The data obtained from the investigated field was analyzed using the correlation coefficient and the linear regression equation, which helped to reach a set of results, in the light of which a number of conclusions were crystallized, perhaps the most important of which are: The competitive spectrum, and its different dimensions, especially the response dimension. In light of the conclusions, the research presented its recommendations, and perhaps the most important of them are: the recommendation that urges the surveyed organizations and organizations in the surveyed sector to pay attention to the contents of knowledge in general and pay more attention to the availability of knowledge pillars in particular to achieve a kind of compatibility with the nature of the work of the surveyed organizations and competitive challenges that it can face in order to achieve the desired harmony between the dimensions of the appropriate competitive spectrum with the nature of its business.
View Sub-sampling and Reconstruction for Efficient Light Field Compression

Compression is an important task for many practical applications of light fields. Although previous work has proposed numerous methods for efficient light field compression, the effect of view selection on this task is not well exploited. In this work, we study different sub-sampling and reconstruction strategies for light field compression. We apply various sub-sampling and corresponding reconstruction strategies before and after light field compression. Then, fully reconstructed light fields are assessed to evaluate the performance of different methods. Our evaluation is performed on both real-world and synthetic datasets, and optimal strategies are devised from our experimental results. We hope this study would be beneficial for future research such as light field streaming, storage, and transmission.
High-Fidelity Face Swapping with Style Blending

PAPER RETRACTED
Towards Temporal Stability in Automatic Video Colourisation

Much research has been carried out into the automatic restoration of archival images. This research ranges from colourisation, to damage restoration, and super-resolution. Conversely, video restoration hasremained largely unexplored. Most efforts to date have involved extending a concept from image restoration to video, in a frame-by-frame manner. These methods result in poor temporal consistency between frames. This manifests itself as temporal instability or flicker. The purpose of this work is to improve upon this limitation. This improvement will be achieved by employing a hybrid approach of deep-learning and exemplar based colourisation. Thus, informing current frame colourisation about its neighbouring frame’s colourisations and therefore alleviating the inter-frame discrepancy issues. This paper has two main contributions. Firstly, a novel end-to-end automatic video colourisation technique with enhanced flicker reduction capabilities is proposed. Secondly, six automatic exemplar acquisition algorithms are compared. The combination of these algorithms and techniques allow for an 8.5% increase in non-referenced image quality over the previous state of the art.
A Data Augmentation and Pre-processing Technique for Sign Language Fingerspelling Recognition

The reliance of deep learning algorithms on large scale datasets is a significant challenge for sign language recognition (SLR). The shortage of data resources for training SLR models inevitably leads to poor generalisation, especially for low-resource languages. We propose novel data augmentation and preprocessing techniques based on synthetic data generation to overcome these generalisation difficulties. Using these methods, our models achieved a top-1 accuracy of 86.7% and a top-2 accuracy of 95.5% when evaluated against an unseen corpus of Irish Sign Language (ISL) fingerspelling video recordings. We believe that this constitutes a state-of-the-art performance baseline for an Irish Sign Language recognition model when tested on an unseen dataset.
Box Supervised Video Segmentation Proposal Network

Bounding box supervision provides a balanced compromise between labeling effort and result quality for image segmentation. However, there exists no such work explicitly tailored for videos. Applying the image segmentation methods directly to videos produces sub-optimal solutions because they do not exploit the temporal information. In this work, we propose a box-supervised video segmentation proposal network. We take advantage of intrinsic video properties by introducing a novel box-guided motion calculation pipeline and a motion-aware affinity loss. As the motion is utilized only during training, the run-time remains fixed during inference time. We evaluate our model on Video Object Segmentation (VOS) challenge. The method outperforms the state-of-the-art self-supervised methods by 16.4% and 6.9% J&amp;F score, and the majority of fully supervised ones on the DAVIS and Youtube-VOS dataset. Code is available at https://github.com/Tanveer81/BoxVOS.git.
Dynamic Channel Selection in Self-Supervised Learning

Whilst computer vision models built using self-supervised approaches are now commonplace, some important questions remain. Do self-supervised models learn highly redundant channel features? What if a self-supervised network could dynamically select the important channels and get rid of the unnecessary ones? Currently, convnets pre-trained with self-supervision have obtained comparable performance on downstream tasks in comparison to their supervised counterparts in computer vision. However, there are drawbacks to self-supervised models including their large numbers of parameters, computationally expensive training strategies and a clear need for faster inference on downstream tasks. In this work, our goal is to address the latter by studying how a standard channel selection method developed for supervised learning can be applied to networks trained with self-supervision. We validate our findings on a range of target budgets td for channel computation on image classification task across different datasets, specifically CIFAR-10, CIFAR-100, and ImageNet-100, obtaining comparable performance to that of the original network when selecting all channels but at a significant reduction in computation reported in terms of FLOPs.
Integrating feature attribution methods into the loss function of deep learning classifiers

Feature attribution methods are typically used post-training to judge if a deep learning classifier is using meaningful concepts in an input image when making classifications. In this study, we propose using feature attribution methods to give a classifier automated feedback throughout the training process via a novel loss function. We call such a loss function, a heatmap loss function. Heatmap loss functions enable us to incentivize a model to rely on relevant sections of the input image when making classifications. Two groups of models were trained, one group with a heatmap loss function and the other using categorical cross entropy (CCE). Models trained with the heatmap loss function were capable of achieving equivalent classification accuracies on a test dataset of synthesised cardiac MRI slices. Moreover, HiResCAM heatmaps suggest that these models relied to a greater extent on regions of the MRI slices within the heart. A further experiment demonstrated how heatmap loss functions can be used to prevent deep learning classifiers from using noncausal concepts that disproportionately co-occur with images of a certain class when making classifications. This suggests that heatmap loss functions could be used to prevent models from learning dataset biases by directing where the model should be looking when making classifications.
Influence of Magnification in Deep Learning Aided Image Segmentation in Histological Digital Image Analysis

The use of digital pathology has grown significantly for both healthcare and research purposes in recent years. With this comes opportunity to develop systems supported by computer vision (CV) and artificial intelligence (AI), with the potential to improve patient management and quality of care. The accessibility of CV and AI toolboxes have resulted in the rapid application of image analysis in this domain driven by accuracy related metrics. However, in this short paper we illustrate common pitfalls in the field through a semantic segmentation task, specifically how magnification can influence training data quality and demonstrate how this can ultimately affect model robustness.
Cryptographic algorithms: examples and software codes

The presented textbook is intended for students and all persons interested in information security issues. The book will introduce them to cryptographic algorithms and their software implementations
Abduction threat surviving skills in teacher education curriculum key to learners’ safety in school environment

The study adopted survey design to determine MICHAEL OKPARA UNIVERSITY OF AGRICULTURE UMUDIKE and ALVAN IKOKU FEDERAL COLLEGE OF EDUCATION OWERRI teacher educators’ perceptions on infusing abduction threat surviving skills on teacher education curriculum for students’ safety in school environment. Researchers’ made attitude questionnaire titled” Teachers-educator Questionnaire on Infusing Abduction Threat Surviving Skills in Teacher education curriculum” (TEQIAST) was used for data collection. It had reliability coefficient of 0.77, determined using Cronbach Alpha. The data collected was analyzed using mean and standard deviation in answering research questions while the hypothesis was tested using t-test statistical tool tested at 0.05 level of significance. The findings showed that teacher educators have positive perceptions on infusing abduction threat surviving skills in teacher education curriculum for students’ safety in school environment It was recommended among others that there is need to infuse abduction threat surviving skills in relevant courses in teacher education curriculum for safety of students in school environment.
Architecture Growth of Dynamic Feedforward Neural Network Based on the Growth Rate Function

At present, when artificial neural network is widely used to deal with practical problems, there is no theoretical choice on network scale guidance. Users often use empirical or trial-and-error methods to find an appropriate scale of the network. When the scale is too large, the neural network will need to calculate a large number of parameters when carrying out forward and back propagation, which will cause a waste of resources and time, as well as overfitting. If the scale is too small, underfitting will happen. To solve this problem, a simple, effective and easy method that to realize network growth is proposed in this paper. A growth function is designed to continuously adjust the growth of the network architecture, it takes into account multiple factors which can reflect network performance, including the current recognition rate and the changing rate of the loss of error before and after growth. Experiments show that the architecture of network can grow to a suitable scale for solving problems.
Invertible Single Image Rescaling via Steganography

High-resolution (HR) images are typically downscaled by the bicubic method to fit the different resolutions of target devices and save the transmission bandwidth. However, the bicubic downsampling may cause the loss of high-frequency information (HFI), raising great challenges for recovering the original high-resolution images. Inspired by image steganography, wherein certain information needs to be written into an image as steganographic information and recovered later, in this work, we propose an invertible steganography rescaling network (ISRN) for single image rescaling. The proposed ISRN can preserve HFI that would otherwise be lost as stegano-graphic information in low-resolution (LR) images. Moreover, we develop an attentive steganography separate block (ASSB) to decompose the steganographic information into a case-aware part that can be embedded into the downscaled image and a case-agnostic part that can be captured using a prior distribution. Extensive experiments demonstrate that the proposed ISRN outperforms prior arts in terms of both quantitative and qualitative evaluations.