Reviewerly/exHarmony huggingface dataset:
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network

We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings. We show that our model outperforms the current state of the art in learning a single enhancement mapping, while having significantly fewer parameters than its competitors. Furthermore, the model achieves even higher performance on learning multiple mappings simultaneously, by taking advantage of shared representations. Our network is based on the recently proposed SGN architecture, with modifications targeted at incorporating global features and style adaption. Finally, we present an unpaired learning method for multitask image enhancement, that is based on generative adversarial networks (GANs).
PENERAPAN MODEL SOMATIC, AUDITORY, VISUALIZATION, DAN INTELECTUALLY (SAVI) UNTUK MENINGKATKAN HASIL BELAJAR PADA MATA PELAJARAN IPS SISWA KELAS IV

The purpose of this study was to determine: (1) The Process of Application of the SAVI Model in Social Studies Subjects for Class IV MI DDI Jabal Nur Parepare Students, (2) Social Studies Learning Outcomes for Class IV MI DDI Jabal Nur Parepare Students through the application of the SAVI Model. The approach used is a qualitative approach and the type of research used is classroom action research. This research focuses on the process and student learning outcomes. Data collection techniques used are observation, testing, and documentation. Then, analyzed using qualitative descriptive data analysis. The conclusion in this study is that by applying the SAVI model can improve social studies learning outcomes about production equipment, communication and transportation in class IV MI DDI Jabal Nur Parepare students.
Self-adjustive DE and KELM-based image watermarking in DCT domain using fuzzy entropy

With advances in machine learning and development of neural networks that are efficient and accurate, this paper explores the use of kernel extreme learning machine (KELM) to develop a semi-blind watermarking technique for grey-scale images in discrete cosine transform domain. Fuzzy entropy is employed for selection of the blocks where the watermark bits are to be embedded. A dataset formed from these blocks is used to train KELM. The nonlinear regression property of KELM predicts the values where watermark bits are embedded. Self-adjustive differential evolution (SeAdDE) controls the strength of the scaling factors finds their optimal values. The adaptiveness of differential evolution (DE) helps in self-adjustment and varies the DE parameters to explore best solutions. This saves time as the manual hit and trial method for finding the appropriate parameter values is avoided. The scheme presented shows robustness against various attacks like histogram equalisation, resizing, JPEG compression, Weiner filtering, etc. and still also retains the quality of the watermarked image. Thus, the proposed technique can be used as a solution to ensure authenticity via watermarking.
Development and Research of Algorithms for the Formation of an Optimal Individual Educational Path for Online Courses

An important condition for making an effective decision in the field of e-learning is the analysis of data from participants in the educational process at its various stages.Currently, the volume of data circulating in a digital educational environment that supports working with online courses is growing exponentially.This is facilitated by the rapidly growing demand for open education.In such an environment, for the effective study of materials, it is necessary to carry out a personified process control of the flow of knowledge.One way is the formation of individual educational paths.In the framework of this study, a model for the formation of the individual educational path of the student based on the solution of the problem of minimal coverage of the graph is proposed.As the basic parameters of the model, a competency-based approach is applied, superimposed on the structural description of the relationships of the individual components of the online course.In the framework of the study, genetic and greedy algorithms were used to form an educational path, as well as a comparative analysis of the approaches presented.
Policy Study on Literacy Movement Reading in Southeast Sulawesi

Building a true culture requires a mature approach.Reading culture through the School Literacy Movement in its implementation has not run as expected.Reading 15 minutes of textbooks is not a solution in building a culture of reading and writing, because students are generally interested in reading as they wish, not based on material determined by the teacher.One of the UN's sustainable development agendas is to build quality education, so that with the maximum and effective realization the Literacy Movement Program is able to realize the development agenda for the benefit of people and planet earth.This study aims to create innovations in literacy in schools.The research method is descriptive qualitative with case study design.The results show that the First Availability of books is not able to accommodate the development of reading culture in Southeast Sulawesi, Second Orientation of School Literacy Movement which is fixated on reading books is also an obstacle to building student literacy skills, and Third Emphasis on the portion of time does not build awareness of literacy.Based on the results, it is concluded that the school literacy movement has not gone according to the expected goals.
Geochemical Fingerprinting of Natural Waters in Middle Tennessee

Geochemical fingerprinting is the use of chemical species to determine the source and alteration of natural waters that comprise various bodies of water. These chemical fingerprints are defined as specific patterns of chemical species unique to each body of water. This research examines the presence of which chemical species are most prevalent in lakes that are located on two distinct physiographic regions of Tennessee: the Highland Rim and the Cumberland Plateau, by analysis of samples via ICP-MS. Lakes from these regions of Tennessee were chosen due to differences in local geology and land use patterns that each respective lake have been subjected to since their inception. Grab samples were obtained from various areas of each lake in order to obtain a true identity of each respective reservoir. Different ratios of chemical species were then determined so that a comparative analysis could occur between each body of water. An analysis of the deionized water used in the dilutions, performed prior to analysis via ICP-MS, was also performed to confirm that differences in chemical species are present due to the differences in land use and geology that each lake is subjected to. These ratios of chemical species should indicate how local geology and land use patterns affect the chemical identity of natural waters within Middle Tennessee.
Exploring Validation Techniques to Ensure Correctness in Aeronautical Databases

Data Chain is a conceptual representation of the path that a set or element of aeronautical data takes from its origination up to end-use.There are classical databases such as navigation or terrain database and configuration files for modularity and portability purposes.These databases can be used to activate or deactivate software items, adapt the software computation to the aircraft configuration, or provide computation data.The RTCA DO-200B establishes three Data Process Assurance Level (DPAL) as the level of rigor, representing the amount of verification and validation tasks performed, during data processing to assure data quality.This paper presents four different and complementary validation techniques to ensure correctness for Aeronautical Databases.This work aims to cover the existing gap in the available standards, presenting and exemplifying some proposed techniques.It also presents some techniques and provides different comparisons and specific intentions.At the end, some relevant needs of qualified tools are discussed to ensure that the use of techniques can be automated, and credits can be sought using tools.
A GPU enhanced LIDAR Perception System for Autonomous Vehicles

Environment vision and understanding is a crucial task in Autonomous Driving (AD) context. This mainly needs image processing approaches such as Convolutional Neural Networks (CNN). Nevertheless, cameras have shown their limits for such a task, especially in dealing with difficult light conditions. LIDAR is a powerful and widely used sensor for AD. Indeed, LIDAR can then cope with the lack of information gathered from cameras. For AD, data processing from the sensors is the key function to obtain a high quality perception. For this, Graphics Processing Unit (GPU) platforms show great performances and outperform other processing platforms such as FPGA and Multi-cores. This work presents a new approach to produce multiple 2D representation from 3D points cloud coming from LIDAR. The 2D representation can therefore be used by any efficient image processing applications. Our approach uses only LIDAR sensor and exploits the high GPU parallelism for its implementation. The resulting 2D representations are then used by CNN for AD applications such as image classification and segmentation. Finally, our contributions have been evaluated using the KITTI road benchmark and showed encouraging results.
SSD Public Website: Distance Curriculum Support for K-5 Teachers: Learning Disabilities Specialist

Here you will find Literacy and STEM specialist schedules, How To Videos related to iReady, ST Math and other tools, answers to FAQ related to the SSD Curriculum through distance learning.
Research Guides: Scholarly Communication for Librarians: Grant Support

This guide contains information and resources shared at the Scholarly Communication Boot Camp sessions.
The Effect of Inflation Threshold on Financial Development and Economic Growth: A Case Study of D-8 Countries

Although financial development facilitates the economic growth, inflationary conditions can negatively affect the relationship between the financial development and the economic growth. This paper studies the threshold inflation rate for the effect of financial development on the economic growth by using the panel smooth transition regression (PSTR) model and the data from eight Islamic developing countries of D-8 Group over the period 1990–2017. Results show an asymmetric relationship at different levels of inflation between financial development and the economic growth. The inflation rate will be transferred at the smooth transition speed of 0.93 by passing the threshold of 11.88 from the first regime to the second regime, which is a nonlinear relationship in the model. In fact, higher inflation levels reduce the motivation of the investor and consequently reduce the investment. On the other hand, the reduction in the productivity of production factors brings about the negative effect of the financial development on the economic growth. Thus, it is necessary for countries with a higher inflation rate threshold to have access to an efficient financial system in order to achieve the low inflation rates.
Virtually relevant: AR/VR and the theatre

The relevance of the embodied actor and theatre maker is potentially tested alongside advances and emergences in performative technology, specifically Augmented and Virtual Reality (AR and VR). However, the general notion emerging amongst theorists, practitioners and academics seems to be favouring an evolutionary collaboration between these somewhat disparate disciplines: the former emerging from and refined over thousands of years of artistic development, the latter finding its more recent birth in entertainment and gaming. Rather than a consumption of art in a gamification of storytelling through emergent technologies, it is argued that tools of AR and VR will soon be used to enhance live, embodied narrative through dramatic storytelling and together these forms will generate a new style and technique of immersive theatre that takes artists and makers to the next stage of human creativity. Relevance, then, will be ensured through the consideration of the audience and their place in the theatrical event, as well as through up- and re-skilling of theatre makers, performers and actors in this technology in order to democratise its usefulness. Theatre’s long and proud history of subsuming emergent technologies for its own purposes also provides a blueprint for adoption of these future technologies. There are also significant and important opportunities for applied theatre and socially engaged arts practice to incorporate the technology into its forms, particularly in support of new and experimental research in healthcare. The current limitations of AR/VR on the dramatic stage are undeniable, yet by focusing on the experiential and communal nature of theatre the technology and the art can be reconciled into a new and innovative expression in the theatre arts. This new age of making should begin in the conservatory and academy for multiple reasons but will eventually spread to independent artists. The technician and theatre maker will combine, enabling a future for the stage that we cannot yet envisage.
Securing Web Application by Embedded Firewall at Gytech Indosantara Mandiri Ltd.

Gytech Indosantara Mandiri Ltd. in the last few years experienced many Cybercrime attacks on the Web Server which caused many moral and material losses. Therefore, it is necessary to consider ways to fight and prevent attacks on the webserver. One way to fight and prevent attacks is to use the Attack Signatures method by using ModSecurity and fail2ban as a Web Application Firewall (WAF). ModSecurity is used to detect and prevent the occurrence of Cyber Crime in the Http and https services. Whereas Fail2ban is used to prevent Bruteforce attacks on ssh, FTP and telnet services. Modesecurity, which acts as a Web Application Firewall (WAF) will send logs to Fail2ban when exploits occur on the Web Server. Meanwhile, Fail2ban will block the Attacker's IP address so that both can be used as a Web Application Firewall or can be used as layer 7 network security.
SPARQL Endpoint Interface to Python

This is a wrapper around a SPARQL service. It helps in creating the query URI and, possibly, convert the result into a more manageable format. The package is licensed under W3C license, and it can be downloaded in .zip and .tar.gz formats from GitHub, also from PyPi or from Debian, and eventually from SourceForge where was previously hosted.
MaskAAE: Latent space optimization for Adversarial Auto-Encoders.

The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse. Auto-Encoders (AE) with regularized latent space provide an alternative framework for generative models, albeit their performance levels have not reached that of GANs. In this work, we hypothesise that the dimensionality of the AE model's latent space has a critical effect on the quality of generated data. Under the assumption that nature generates data by sampling from a true generative latent space followed by a deterministic function, we show that the optimal performance is obtained when the dimensionality of the latent space of the AE-model matches with that of the true generative latent space. Further, we propose an algorithm called the Mask Adversarial Auto-Encoder (MaskAAE), in which the dimensionality of the latent space of an adversarial auto encoder is brought closer to that of the true generative latent space, via a procedure to mask the spurious latent dimensions. We demonstrate through experiments on synthetic and several real-world datasets that the proposed formulation yields betterment in the generation quality.
Penggunaan Deteksi Gerak untuk Pengurangan Ukuran Data Rekaman Video Kamera CCTV

Some cases the recording data of Closed Circuit Television (CCTV) is stored for future use. In the long term usage, the files size will grow larger and requiring large storage devices. In some cases, the recorded data not only image with the desired object but also the background images that may be recorded for long periods of time. This cases make data storage device usage to be less effective. So this research will design a system of CCTV devices that capable to select images to reduce the size of stored images data by image processing.The images selection of this system is based on based on adaptive median algorithm. When any object get detected, the images data to be saved is current input frame. Otherwise, the data to be saved is background model image. Background model on this system is adapted with the change visual data of background image.The results obtained from this research in the form of a CCTV system that are able to select recording data to be stored with image processing. The background model will be kept adapting with background visual data changes.
Human activity recognition by using MHIs of frame sequences

A motion history image MHI is a temporal template that collapses temporal motion information into a single image in which intensity is a function of recency of motion. In recent years, the popularity of deep learning architectures for human activity recognition has encouraged us to explore the effectiveness of combining them and MHIs. Based on this, two new methods are introduced in this paper. In the first method, which is called the basic method, each video splits into N groups of consecutive frames, and the MHI is calculated for each group. Transfer learning with the fine-tuning technique is used for classifying these temporal templates. The experimental results show that some misclassification errors are created because of the similarities between these temporal templates; these errors can be corrected by detecting specific objects in the scenes. Thus, spatial information consisting of a single frame is also added to the second method, called the proposed method. By converting video classification problems into image classification problems in the proposed method, less memory is needed and the time complexity is greatly reduced. They are implemented and compared with state-of-the-art approaches on two data sets. The results show that the proposed method significantly outperforms the others. It achieves recognition accuracies of 92% and 92.4% for the UCF Sport and UCF-11 action data sets, respectively.
Sudden birth of maximal hidden quantum correlations.

We report on the existence of the phenomenon of sudden birth of maximal hidden quantum correlations in open quantum systems. Specifically, we consider the CHSH-inequality for Bell-nonlocality, the $\rm F_3$-inequality for EPR-steering, and usefulness for teleportation as quantum correlations in a bipartite qubit system under collective decoherence due to an environment. We show that, even though the system may undergo a sudden birth of entanglement, neither of the aforementioned correlations are present and yet, these can still be revealed by means of local filtering operations, thus evidencing the presence of hidden quantum correlations. Furthermore, there are extreme versions of this phenomenon for which the revealed correlations achieve their maximal amount allowed by quantum theory. The phenomenon of maximal hidden correlations relies on the qubits collective damping, and may take place even in long-distance separated qubits. These results prove that apparently-useless physical systems as quantum registers could still exhibit maximal correlations, which are hidden from us, but that can still be accessed by means of local filtering operations.
YANG Model Conversion

This document introduces the grammer extensions and relavant rules forYANG 1.0 [RFC6020] to complete the mapping between two similar YANGmodules that describe the same entity.
Students&amp;rsquo; Perceptions on the Use of E-Quiz (Kahoot!(R)) to Enhance Learning Engagement

An institution-based cross-sectional study on exploring the effectiveness of one of the electronic teaching-learning technologies (Kahoot!App) was undertaken.A 15-item e-quiz was conducted to a cohort of third-year MBBS students on the topic of Community Medicine previously taught to them.The students' participation and feedback on the experience was quite positive.E-quiz using Kahoot!App seems to be an appropriate tool for formative assessment for the medical students and to improve their engagement in the classroom.
Edmodo-Based E-Learning Media Development in the Field of Science

Abstract This research uses the Research &amp; Development (R&amp;D) method with quantitative and qualitative approaches conducted in schools. This research aims to develop Edmodo media in the field of science, find out the feasibility of using media, find out the response of users in using the media. Based on the feasibilitys testing of the media and the field of science conducted by 2 material expert validators and 1 media expert validator it was found that Edmodo media developed according to experts was declared feasible and could be used as a learning medium in the field of science.
THE INFLUENCE OF THE DARK WEB

The Dark Web is the web that provides more freedom, so the main goal of the Dark Web is the freedom that some do not feel on the Surface Web. It is the Web content that is available on darknet and to access them, you need to have specific software. The dark network is often confused with Deep Web, which refers to unmarked (searchable) internet sites from traditional search engines. The Dark Web is at the center of the debate over whether anonymity online should be kept regardless of the illegal activity it provides. It is growing fast thanks to new tools, which makes the navigation easier. Websites which are the part of the Dark Web are accessible only by using Tor (The Onion Routing) and I2P (Invisible Internet Protocol). Tor provides anonymous access to the Internet and I2P specializes in allowing the sites to be accessed anonymously. The Darknet is used for illegal activities such as selling and buying drugs, hacking, fraud, pornography, etc. But also anonymity, privacy, access to restricted materials can be used in many positive ways. This research describes the effect of the Dark Web respectively what the Dark Web and the Deep Web are also this document aims to provide recommendations belong to the Dark Web, based on open- source research. This paper endeavors to provide a broader understanding of the dark Web and its influence on our lives.
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning

Human society had a long history of suffering from cognitive biases leading to social prejudices and mass injustice. The prevalent existence of cognitive biases in large volumes of historical data can pose a threat of being manifested as unethical and seemingly inhuman predictions as outputs of AI systems trained on such data. To alleviate this problem, we propose a bias-aware multi-objective learning framework that given a set of identity attributes (e.g. gender, ethnicity etc.) and a subset of sensitive categories of the possible classes of prediction outputs, learns to reduce the frequency of predicting certain combinations of them, e.g. predicting stereotypes such as `most blacks use abusive language', or `fear is a virtue of women'. Our experiments conducted on an emotion prediction task with balanced class priors shows that a set of baseline bias-agnostic models exhibit cognitive biases with respect to gender, such as women are prone to be afraid whereas men are more prone to be angry. In contrast, our proposed bias-aware multi-objective learning methodology is shown to reduce such biases in the predictied emotions.