Reviewerly/exHarmony huggingface dataset:
Study on Cultural Heritage Official Development Assistance in the Era Time of Sustainable Development Goals

This study investigates the values of cultural heritage Official Development Assistance (ODA). The excavation and restoration of cultural heritage stem from the emotional sympathy of the locals and contribute to poverty reduction through regional and tourism development. Cultural heritage ODA significantly contributes to achieving the Sustainable Development Goals (SDGs). Nevertheless, although our cultural heritage ODA has taken steps toward this objective, it does not have substantial budget, strategy, policy systems, or contents. The purpose of this research is to review the use of cultural heritage ODA in the era of SDGs and ask the following questions: What is cultural heritage and why is it necessary? How is it processed and implemented? How do we utilize it? The study answers this questions through a literature review and case study, analyzing the concept and features of cultural heritage ODA from the literature review, highlighting the issues and implications of it and evaluating the current status and implementation of cultural heritage ODA. It concludes that some countries have external strategies, acquisition of financing as well as private cooperation. In order to utilize cultural heritage ODA at a time of SDGs, it is necessary to prepare a long-term strategy and develop contents through careful selection and concentration in each country, based on field study and research. Furthermore, a framework for cooperation with North Korea needs to be established through the excavation and restoration of various elements of heritage. In other words, cultural heritage ODA is one of the initiatives proposed in the agenda in the era of SDGs.
Class-Incremental Learning of Convolutional Neural Networks Based on Double Consolidation Mechanism

Class-incremental learning is a model learning technique that can help classification models incrementally learn about new target classes and realize knowledge accumulation. It has become one of the major concerns of the machine learning and classification community. To overcome the catastrophic forgetting that occurs when the network is trained sequentially on a multi-class data stream, a double consolidation class-incremental learning (DCCIL) method is proposed. In the incremental learning process, the network parameters are adjusted by combining knowledge distillation and elastic weight consolidation, so that the network can better maintain the recognition ability of the old classes while learning the new ones. The incremental learning experiment is designed, and the proposed method is compared with the popular incremental learning methods such as EWC, LwF, and iCaRL. Experimental results show that the proposed DCCIL method can achieve better incremental accuracy than that of the current popular incremental learning algorithms, which can effectively improve the expansibility and intelligence of the classification model.
Estimating Vehicle Fuel Economy from Overhead Camera Imagery and Application for Traffic Control

In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with minimal impact to mobility. We introduce the ORNL Overhead Vehicle Data set (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for vehicle detection. We show the data set utility through three applications: estimation of fuel consumption based on segmentation bounding boxes, vehicle discrimination for vehicles with large bounding boxes, and fine-grained classification on a limited number of vehicle makes and models using a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set of web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation of Urban Mobility (SUMO) package. Our results demonstrate the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras.
Contextualized context2vec

Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence.There are two major approaches for lexical substitution: (1) generating contextualized word embeddings by assigning multiple embeddings to one word and (2) generating context embeddings using the sentence.Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution.Experiments demonstrate that our method outperforms the current state-ofthe-art method.We also create CEFR-LP, a new evaluation dataset for the lexical substitution task.It has a wider coverage of substitution candidates than previous datasets and assigns English proficiency levels to all target words and substitution candidates.
Target Privacy Threat Modeling for COVID-19 Exposure Notification Systems

The adoption of digital contact tracing (DCT) technology during the COVID-19pandemic has shown multiple benefits, including helping to slow the spread of infectious disease and to improve the dissemination of accurate information. However, to support both ethical technology deployment and user adoption, privacy must be at the forefront. With the loss of privacy being a critical threat, thorough threat modeling will help us to strategize and protect privacy as digital contact tracing technologies advance. Various threat modeling frameworks exist today, such as LINDDUN, STRIDE, PASTA, and NIST, which focus on software system privacy, system security, application security, and data-centric risk, respectively. When applied to the exposure notification system (ENS) context, these models provide a thorough view of the software side but fall short in addressing the integrated nature of hardware, humans, regulations, and software involved in such systems. Our approach addresses ENSsas a whole and provides a model that addresses the privacy complexities of a multi-faceted solution. We define privacy principles, privacy threats, attacker capabilities, and a comprehensive threat model. Finally, we outline threat mitigation strategies that address the various threats defined in our model
Elliptic Curve over a Local Finite Ring<i>R<sub>n</sub></i>

The goal of this chapter is to study some arithmetic proprieties of an elliptic curve defined by a Weierstrass equation on the local ring Rn=FqX/Xn, where n≥1 is an integer. It consists of, an introduction, four sections, and a conclusion. In the first section, we review some fundamental arithmetic proprieties of finite local rings Rn, which will be used in the remainder of the chapter. The second section is devoted to a study the above mentioned elliptic curve on these finite local rings for arbitrary characteristics. A restriction to some specific characteristic cases will then be considered in the third section. Using these studies, we give in the fourth section some cryptography applications, and we give in the conclusion some current research perspectives concerning the use of this kind of curves in cryptography. We can see in the conclusion of research in perspectives on these types of curves.
Real-world fence removal from a single-image via deep neural network

At public space such as a zoo and sports facilities, the presence of fence often annoys tourists and professional photographers. There is a demand for a post-processing tool to produce a non-occluded view from an image or video. This &#x201C;de-fencing&#x201D; task is divided into two stages: one is to detect fence regions and the other is to fill the missing part. For a decade or more, various methods have been proposed for video-based de-fencing. However, only a few single-image-based methods are proposed. In this paper, we mainly focus on single-image fence removal. Conventional approaches suffer from inaccurate and non-robust fence detection and inpainting due to less content information. To solve these problems, we combine novel methods based on a deep convolutional neural network (CNN) and classical domain knowledge in image processing. In the training process, we are required to obtain both fence images and corresponding non-fence ground truth images. Therefore, we synthesize natural fence image from real images. Moreover, spacial filtering processing (e.g. a Laplacian filter and a Gaussian filter) improves the performance of the CNN for detecting and inpainting. Our proposed method can automatically detect a fence and generate a clean image without any user input. Experimental results demonstrate that our method is effective for a broad range of fence images.
PERENCANAAN STRATEGIS SISTEM INFORMASI DAN TEKNOLOGI INFORMASI STIKES PALEMBANG

The purpose of this study resulted in strategic planning of information systems and information technology. STIKES 'Aisyiyah palembang as one private health university in south sumatera is still having difficulty in determining the priority scale of SI / IT development. Therefore it is needed an information system planning and information technology with qualitative approach using ward and peppard framework. In analyzing the internal business environment the researcher uses the value chain method to map the main activities and supporting activities, the main activity of STIKES 'Aisyiyah Palembang is described using Business Process Modeling Notation (BPMN). the current external business conditions use the five forces model and for internal and external environment analysis SI / TI uses current technology trends. Based on internal and external business and SI / IT environment analyzes then formulated the future SI / IT strategy using SWOT analysis then determine the portfolio of upcoming applications using Mc. farlan strategic grid. Keywords: Strategic planning of ward and peppard, Business process, BPMN, application portfolio
Myths of the New Ukrainian School: Some Tips for Educators to Choose Right Way under Conditions of Changes

This paper is about analysis of the teachers’ attitude towards school reform, their level of awareness of the need for changes in education; some difficulties, those arise in the process of implementation of the Concept "New Ukrainian School"; identifies sources and forms of teachers’ resistance to changes. Author of the paper proposes some ways to prepare educators to work under conditions of uncertainty and help them to become experts on change management.
A Deep Learning Approach to MRI Scanner Manufacturer and Model Identification

Forensics research has developed several techniques to identify the model and manufacturer of a digital image or videos source camera. However, to the best of our knowledge, no work has been performed to identify the manufacturer and model of the scanner that captured an MRI image. MRI source identification can have several important applications ranging from scientific fraud discovery, exposing issues around anonymity and privacy of medical records, protecting against malicious tampering of medical images, and validating AI-based diagnostic techniques whose performance varies on different MRI scanners. In this paper, we propose a new CNN-based approach to learn forensic traces left by an MRI scanner and use these traces to identify the manufacturer and model of the scanner that captured an MRI image. Additionally, we identify an issue called weight divergence that can occur when training CNNs using a constrained convolutional layer and propose three new correction functions to protect against this. Our experimental results show we can identify an MRI scanners manufacturer with 97.88% accuracy and its model with 91.07% accuracy. Additionally, we show that our proposed correction functions can noticeably improve our CNNs accuracy when performing scanner model identification.
ICT Investment Impact on Human Capital Development through the Channel of Financial Development in Bangladesh: An Investigation of Quantile ARDL and Toda-Yamamoto Test

The study aims to assess the causal effects of ICT investment, financial development, and human capital development in Bangladesh for the period 1990-2019. To do so, we applied liner ARDL, Quantile ARDL, and directional causality investigated by performing a non-granger causality test. The result of Quantile ARDL confirms long-run effects running from ICT investment and financial development to human capital development. Considering the result short-run estimation, study findings established a positive association between financial development and human capital development but both positive and negative observed in ICT investment on human capital development. Furthermore, the nonlinear relationship established with the standard Wald test. Second, the results of directional causality test following Toda and Yamamoto (1995) proposed framework. Study findings established bidirectional causality running between financial development and human capital development and unidirectional causality running from ICT investment to human capital development. Therefore, it assumed that human capital development in Bangladesh critically relies on financial sector growth and development in the ICT sector. Furthermore, it is also observed that the bidirectional causal relationship also confirmed that is the development of either independent variables can influence each other.
Using ACES Look Modification Transforms (LMTs) in a Visual Effects Production Environment &amp;#x2014; Part 1: Rec.709 Inputs and Outputs

Look Modification Transforms (LMTs) are a very powerful component of the Academy Color Encoding System (ACES) and offer extraordinary flexibility in ACES-based workflows. In ACES, the look of a project can be defined by a Look Transform, previously referred to as a Look Modification Transform, or LMT, applied before the Output Transform. The advantage of this approach is that technically the Look Transform should not have to change regardless of whether the Output Transform chosen is for [1]. The Academy Technical Bulletin TB-2014-10 describes the application of a Look Transform using the Academy Common LUT Format (CLF). Since CLF is not yet widely supported, any look applied using a LUT or grading operation, which spans an entire scene or show, and is used in series with per-shot adjustments can be considered to be a Look Transform. Therefore the import and export to other programs and color pipelines like 8 bit image processing programs, as well as video and film cameras, are of particular importance [1]. This paper aims to discuss the possibilities of importing and exporting Rec.709-coded imagery into the Foundry's Nuke 12 ACEScg workspace using the OCIO ACES1.1 configuration. Presented are the implemented Rec.709 transforms and possible alternatives. We also discuss the behavior of transforms concerning processing the transfer function (gamma) and color conversion of primaries and their complementary colors.
MODEL OF FORMATION OF STUDY EXAMPLES OF THE NEURAL NETWORK INTENDED FOR THE ANALYSIS OF THE KEYBOARD HANDWRITING

The article is devoted to increasing the efficiency of technologies of covert monitoring of operators' activity by information and control systems of various purposes for face recognition and emotional state. It is shown that from the standpoint of the possibility of using standard computer peripherals as a sensor for reading biometric parameters, inalienability from the user, the widespread use of information control systems of symbolic password and technological data, the complexity of forgery of biometric information, and the possibility of covert monitoring prospects have the means of keyboard analysis. The necessity of improving the methodology of neural network analysis of keyboard handwriting for authentication and recognition of the emotional state of information computer system operators is substantiated. The prospects of application of convolutional neural networks are determined, which leads to the need to improve the technology of determining the parameters of educational examples in terms of forming the input field of convolutional neural network and forming many parameters of keyboard handwriting to be analyzed. A model of formation of educational examples has been developed, which due to the application of a reasonable set of input parameters and the use of a rectangular input field of a convolutional neural network reduces the resource consumption of neural network recognition tools and provides accuracy of neural network analysis of keyboard handwriting at 75%. The proposed theoretical solutions were verified by computer experiments. The expediency of correlation of ways of further researches with development of representative databases of keyboard handwriting is shown.
The Movie Recommendation System Based on Differential Privacy

In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of encryption measures were adopted in the current recommendation systems in the cloud to protect users’ privacy security. However, there are many other privacy attacks in this recommendation system of the cloud-based device. Therefore, this paper studies the encryption interference of setting differential privacy protection mechanism for user data in user’s private devices based on untrusted servers. A dynamic privacy budget allocation method is proposed based on localized differential privacy protection technology and specific scenes recommended by movies.
Action Recognition in Video Using Human Keypoint Detection

With the popularization of the internet and the increase of video facilities, the recognition and segmentation of actions in the video have become research highlights of high application value. Different from images, the information in the video is more complex and also brings time sequences as a new dimension. This paper proposes a video action recognition and segmentation model in the human keypoint detection task. The main contributions are as follows:1) Based on the speech signal processing method, this paper designs an analysis framework for video action, which consists of three steps. The first step is to obtain data from the key point frame of the human body; the second is the action segmentation model; the third is to visualize the model results;2)the dynamic time warping algorithm is used and improved from calculation cost and constraint conditions;3) a distance function is designed to measure the similarity between time series. Four kinds of features are introduced, and the final distance is the weighted sum of the four kinds of features;4) a non-maximum suppression method is designed to filter the overlapped segments to get the final results. Experiment design verifies the validity of the proposed model and the importance of proposed features is illustrated.
Synergy through Making: Co-designing with Educational Stakeholders in Northeastern Nigeria

Nigerian educational statistics reflect a country in crisis.Several factors drive this trend: early marriages; poorly-trained and underpaid teachers; and extensive armed conflicts.This paper describes a collaboration between three universities on a [removed for peer review] grant-funded project to intervene in this crisis.Focusing on the educational system in two states, the project aims to improve educational materials and teaching strategies in 200 schools.Our research team employs a codesign methodology informed by the "co-creation" work of Dr.
Lingkungan Keluarga, Lingkungan Sosial dan Pergaulan Teman Sebaya terhadap Hasil Belajar

Abstract&#x0D; Learning outcomes are changes in behavior obtained by students after experiencing learning activities. Learning outcomes include three domains, namely cognitive, affective and psychomotor. High and low learning outcomes achieved by students are influenced by factors of the family environment, social environment, and the association of students with peers. This study aims to analyze the influence partially (individually) or simultaneously (together) between the family environment, social environment and peer relationships simultaneously on learning outcomes. The method used is quantitative with explanatory research types and research samples of 69 students or 31% of the population taken using proportional random sampling techniques. The results showed that the family environment Social environment and peer relationships affect learning outcomes
A note on differentially private clustering with large additive error

In this note, we describe a simple approach to obtain a differentially private algorithm for k-clustering with nearly the same multiplicative factor as any non-private counterpart at the cost of a large polynomial additive error. The approach is the combination of a simple geometric observation independent of privacy consideration and any existing private algorithm with a constant approximation.
Criticality Assessment of Critical Information Infrastructure Objects: A Category Based Methodology and Ukrainian Experience

The paper outlines the basic principles and assumptions used to assess the criticality of critical infrastructure object (CIO) and critical information infrastructure objects (CIIO). Methods for assigning critical information infrastructure objects to the criticality levels are described. The sequence of carrying out the criticality assessment of CIOs is provided. The recommendations concerning evolving regulation in the field of critical information infrastructure objects protection are given. According to the results of the research, several drafts of the Ukrainian state-level normative documents were developed such as “Classification of critical information infrastructure objects by severity (criticality)” and “Criteria and procedure for assigning critical information infrastructure objects to one of the significance (criticality)”. The implementation of the developed documents is an important step in the construction of the Ukrainian state system of protection of critical information infrastructure.
Multimodal Multiobject Tracking by Fusing Deep Appearance Features and Motion Information

Multiobject Tracking (MOT) is one of the most important abilities of autonomous driving systems. However, most of the existing MOT methods only use a single sensor, such as a camera, which has the problem of insufficient reliability. In this paper, we propose a novel Multiobject Tracking method by fusing deep appearance features and motion information of objects. In this method, the locations of objects are first determined based on a 2D object detector and a 3D object detector. We use the Nonmaximum Suppression (NMS) algorithm to combine the detection results of the two detectors to ensure the detection accuracy in complex scenes. After that, we use Convolutional Neural Network (CNN) to learn the deep appearance features of objects and employ Kalman Filter to obtain the motion information of objects. Finally, the MOT task is achieved by associating the motion information and deep appearance features. A successful match indicates that the object was tracked successfully. A set of experiments on the KITTI Tracking Benchmark shows that the proposed MOT method can effectively perform the MOT task. The Multiobject Tracking Accuracy (MOTA) is up to 76.40% and the Multiobject Tracking Precision (MOTP) is up to 83.50%.
A Study in Estonian Pronominal Coreference Resolution

The first study for Estonian pronominal coreference resolution using machine learning is presented. Appropriate machine learning algorithms and techniques for balancing the data are tested on a human-annotated corpus. The results are encouraging, showing an F-score comparable with the results obtained for English before the advent of deep neural networks.