Reviewerly/exHarmony huggingface dataset:
Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal

Removing undesired reflections from a photo taken in front of glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. There is a dearth of literature for efficient methods concerning single image reflection removal, which can generalize well in large-scale reflection types. In this study, we proposed an iterative gradient encoding network for single image reflection removal. Next, to further supervise the network in learning the correlation between the transmission layer features, we proposed a feature co-occurrence loss. Extensive experiments on the public benchmark dataset of SIR$^2$ demonstrated that our method can remove reflection favorably against the existing state-of-the-art method on all imaging settings, including diverse backgrounds. Moreover, as the reflection strength increases, our method can still remove reflection even where other state of the art methods failed.
Pemilihan Deteksi Tepi Terbaik Untuk Menganalisa Citra Ultrasonografi Kehamilan

In its development, image processing is very helpful for solving problems faced by humans. Imaging processing is an image processing technique of an object to distinguish the background and the object to be analyzed. This research uses a segmentation method that can distinguish between objects and backgrounds. Analyzing objects from ultrasound images requires the expertise of an experienced doctor. In addition, there are artificial factors that make automated analysis complicated. We aim to improve natural imagery. Therefore to overcome the potential difficulties in analysis, we present four Comparison of Edge Detection, namely Gradient Image, Roberts Operator, Sobel Operator and Prewitt Operator. In image processing, data accuracy and accuracy are required as well as knowledge of statistics because image processing is related to data processing. The result of this research is to determine which edge detection is more appropriate for analyzing ultrasound images. The conclusion from this research is that from the four operators that were tried, the results of the testing process showed that the Prewit operator succeeded in detecting existing objects, even objects that function as background were successfully detected. The resulting edge detection is smoother compared to other operators.
Solutions to Enhance Working Motivation in International High School Education in Vietnam

Based on an analysis of the current state of primary and secondary education in Vietnam, potentialchallenges and opportunities, the article presents motivational solutions to international high schools in general andeconomic educational experience of an Group of Asian international Education (GAIE) in particular. The results ofthe analysis can help the general education system in Vietnam improve its internationality and deepen integrationinto the regional and global educational environment.
영국의 사기범죄 방지체계에 대한 비판적 고찰

Due to the revolutionary development of the Internet and communication technology, fraud and related economic crimes have exploded over the past 30 years. In England and Wales, the likelihood of being a victim of fraud and computer fraud is four times higher than that of robbery. Law enforcement structures in many countries around the world are adapting from traditional crimes such as theft and robbery to new crimes such as fraud, hacking, and ransomware. In the UK, a new millennium began responding to these changes from 2000 to 2010 and new organizations such as National Fraud Intelligence Bureau (NFIB) and Action Fraud to enhance the existing limited capacity for fraudulent crime. The purpose of this study is to explain the changes in the police and judicial system in the UK to cope with the increasing number of fraudulent crimes, and to make a critical evaluation from this important point of view. The UK's fraud prevention system is theoretically very good with the establishment of the Integrated Fraud Reporting Center, Action Fraud, and NFIB, which preemptively blocks fraudulent phone numbers by analyzing and utilizing big data However, it was analyzed as a major problem that the actual manpower and resources administered to the police were very short, and that the priority of the police regarding fraud prevention was much lower than that of other crimes. It is hoped that this study will help to derive useful lessons and policy implications for Korea and other similar countries fighting the explosively increasing tsunami of fraudulent crimes.
Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study

Despite current advancement in the field of biomedical image processing, propelled by the deep learning revolution, multimodal image registration, due to its several challenges, is still often performed manually by specialists. The recent success of image-to-image (I2I) translation in computer vision applications and its growing use in biomedical areas provide a tempting possibility of transforming the multimodal registration problem into a, potentially easier, monomodal one. We conduct an empirical study of the applicability of modern I2I translation methods for the task of rigid registration of multimodal biomedical and medical 2D and 3D images. We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration. We evaluate these method combinations on four publicly available multimodal (2D and 3D) datasets and compare them with the performance of registration achieved by several well-known approaches acting directly on multimodal image data. Our results suggest that, although I2I translation may be helpful when the modalities to register are clearly correlated, registration of modalities which express distinctly different properties of the sample is not well handled by the I2I translation approach. The evaluated representation learning method, which aims to find abstract image-like representations of the information shared between the modalities, manages better, and so does the Mutual Information maximisation approach, acting directly on the original multimodal images. We share our complete experimental setup as open-source (https://github.com/MIDA-group/MultiRegEval).
Plagiarism Checker, Analysis of Random Page Replacement Algorithm in Operating System

Abstract: Page replacement algorithms choose pages to swap out from memory, namely when a new page needs allocation memory. Several page replacement algorithms proposed by experts. Four well known page replacement algorithms are Least Recently Used (LRU), Optimal Page Replacement (OPR), First In First Out (FIFO), Random page replacement algorithms. Each one has the objective to minimize the page fault. If the minimum page fault achieved, the performance of the process increased. The algorithm discussed is random page replacement algorithm. In this paper three case studies of random page replacement algorithm are discussed thoroughly.Keywords: Random page replacement algorithm, page fault, page replacement algorithm, FIFO, LRU, OPR
Sistem Pendukung Keputusan Pemilihan Objek Wisata Menggunakan Metode AHP

Purwokerto is an interesting city to visit because it has many tourist attractions that offer natural beauty. Many tourists visiting Purwokerto have difficulty getting information about tourist objects and determining tourist destinations. This is based on the results of a survey of 114 respondents. Based on these problems, we need a system that provides information and provides recommendations regarding tourist destinations. The system is designed by applying the Analytical Hierarchy Process (AHP) algorithmas an algorithm that selects tourist destinations based on the criteria entered by the user. As for the methoddevelopment The software used in this research is a method Waterfall. Which systemhas beenbuilt tested to users using the User Acceptance Test testing method. This is meant to mengmeasure user acceptance. UAT testing conducted on 100 respondents measured 3 aspects, namely, software engineering aspects, functionality aspects, and visual communication aspectsal gives a value of 5,660. The value is converted using the Likert's Summated Rating (LSR) method, giving the conclusion that the system that has been built is accepted by the user.
GPRAR: Graph Convolutional Network based Pose Reconstruction and Action Recognition for Human Trajectory Prediction

Prediction with high accuracy is essential for various applications such as autonomous driving. Existing prediction models are easily prone to errors in real-world settings where observations (e.g. human poses and locations) are often noisy. To address this problem, we introduce GPRAR, a graph convolutional network based pose reconstruction and action recognition for human trajectory prediction. The key idea of GPRAR is to generate robust features: human poses and actions, under noisy scenarios. To this end, we design GPRAR using two novel sub-networks: PRAR (Pose Reconstruction and Action Recognition) and FA (Feature Aggregator). PRAR aims to simultaneously reconstruct human poses and action features from the coherent and structural properties of human skeletons. It is a network of an encoder and two decoders, each of which comprises multiple layers of spatiotemporal graph convolutional networks. Moreover, we propose a Feature Aggregator (FA) to channel-wise aggregate the learned features: human poses, actions, locations, and camera motion using encoder-decoder based temporal convolutional neural networks to predict future locations. Extensive experiments on the commonly used datasets: JAAD [13] and TITAN [19] show accuracy improvements of GPRAR over state-of-theart models. Specifically, GPRAR improves the prediction accuracy up to 22% and 50% under noisy observations on JAAD and TITAN datasets, respectively
CURRICULA DEVELOPMENT BASED ON THE SEMANTIC NETWORK (ONTOLOGY)

The effective educational curricula are developed and implemented by scientific and pedagogical staff for many years manually. The paper is above innovative methodology of automated curriculum creation as well as the described in the paper software uses methods for determining similar documents, where documents and queries are presented using index terms or keywords, and disciplines. The developed software builds automatically a graph of disciplines’ connectivity using a list of basic concepts that can be manually installed by experts or can be obtained automatically also from the ontological model of the subject area, presented in the form of a semantic network.While creating curricula, it is necessary to distribute studied disciplines effectively, to eliminate topics’ duplications, to ensure module’s consistency and interconnection. The content, aims and methods are included into the curriculum for every studying discipline. The developed software helps experts: 1) to estimate the quality of the curriculum using automatic analysis of the curriculum documents; 2) to identify relationships between disciplines and concepts inside of the discipline;3) to improve existing curriculum.The developed system is based on a principle of semantic relations’ identifying. The author’s hybrid algorithm selects the key concepts from the discipline’s description and determines the measure of connectedness. The map of concepts created by the system represents their location, which are relative to each other. The most related to each other concepts are nearby. To help to prepare the curriculum it is also necessary to determine the degree of disciplines’ interconnectedness. More interconnected disciplines need to be studied by students sequentially. The less connected disciplines can be arranged independently of each other. The result of the software work is a reduction of the academic hours needed. Acknowledgement:The discussed in this paper results were obtained partly in the framework of the Research Project titled "Component's digital transformation methods' fundamental reseach for micro- and nanosystems" (Project #0750-2020-0041) financially supported be the Russian Federation Ministry of Science and Higher Education
Flake-thrust structures and their role in gold deposits formation within Trans-Baikal region

The significant role of tectonometamorphic gold mineralization style thrust structures in the formation of some gold deposits (Kultuminskoye, Andryushkinskoye, Sepchugur, etc.) within Trans-Baikal region is shown. Thrust structures may present certain prospects for major relatively low-grade gold deposit prospecting, which requires a reassessment of some deposits.
MONITORING THE USE OF FORMATIVE ASSESSMENT TOOLS IN SCIENCE SUBJECTS AT PRIMARY SCHOOLS IN SLOVAKIA

The approach to school assessment in Slovakia is based on summative evaluation of the outcomes of the educational process. OECD education analysts recommend that teachers assess their students not only using grades, but also by providing them with targeted feedback on the quality of their learning process, i.e. formative assessment. The purpose of formative assessment is to provide students with regular and meaningful feedback and at the same time, keep teachers constantly informed about the way students are learning. Therefore, teachers can modify the educational process if necessary. This paper presents the results of research that aimed to investigate the use of formative assessment tools in school practice. The study answers the question: Which assessment tools are currently used in teaching science at primary schools in Slovakia? The opinions of primary school science teachers (N=65) were collected using a quantitative research method, specifically, a scale questionnaire. The reliability (Cronbach’s Alpha) of the questionnaire in the formative vs. summative assessment subscale was α=0.822. The research results indicated statistically significant differences between the frequency of use of formative vs. summative assessment (p<0.001). Primary school teachers preferred summative assessment tools to formative ones. As the most frequent reason, the respondents stated the lack of familiarity with formative assessment tools and their use. These findings indicate that it is necessary to focus on teacher education in formative assessment, as teachers need to acquire the proper knowledge and skills necessary to implement formative assessment tools into their teaching. Thus, students can be exposed to the long-term and overarching positive influence of formative assessment.Acknowledgements:This research was supported by the KEGA No. 004UPJŠ-4/2020 “Creation, Implementation, and Verification of the Effectiveness of Digital Library with the Formative Assessment Tools for the Natural Sciences, Mathematics and Informatics at the Elementary School” and VEGA No. 1/0265/17 “Formative Assessment in Natural Sciences, Mathematics and Informatics” grants.
MODERN APPROACH TO THE APPLICATION OF EDUCATIONAL GRAPHIC MODELING IN THE PROCESS OF TEACHING GEOGRAPHICAL DISCIPLINES

The article analyzes the current problem of modernization of higher education teaching methods, which is associated with the problem of improving the efficiency of training of future geography teachers. Based on the review of the most important publications on the research topic, aspects of the problem that require further research are identified. The necessity of introduction of pedagogical technologies by teachers, in particular technologies of application of educational graphic modeling is proved, and its main algorithm is defined. Foreign methodological innovations that are pro-actively used in the practice of teaching geography are specified. The results of the survey of geography teachers, conducted to specify ways to improve the efficiency of the application of educational graphic modeling technology, are analyzed. It was found that educational models were used by teachers, but mainly in order to organize the memorization of the basic meaning of educational material. Not all teachers understand the didactic nature and objectives of educational modeling. Attention is paid to the definition of the conceptual and categorical apparatus; the author's definitions of the concepts "pedagogical technology", "educational graphic model", "educational reception" are formulated. The authors highlight the specific features of educational graphic models. They prove that future geography teachers will master the didactic principles of the relevant technology, only when they use educational graphic modeling while the studying geographical disciplines. The article presents the educational graphic model "Nature of Poltava region" and characterizes its purpose. For the purpose of modernization of educational graphic modeling the paper offers to use scribing which combines the basic didactic characteristics of verbal and illustrative-demonstration methodical techniques. The areas of further researches of the problem in issue are defined: development of new educational graphic models, simplification of graphic symbolics and increase of a share of their associative-vivid images, application of available software.
Multiclass triplet metric-learning network combined with feature mixing block for few shot learning

Few-shot learning aims to learn classification with only a few training examples per class. The metric-based approaches aim to learn a set of embedding functions, so that when represented in this embedding, images are easy to recognize. For metric-based few-shot learning, how to get the class feature representation under a few support samples and what metric to use are important. We propose a multiclass triplet metric-learning network combined with a simple foreground–background feature mixing block. With the foreground–background feature mixing block, we “hallucinate” the information from few support examples to get conceptual representation of classes, which is effective to promote few-shot learning. Furthermore, using the multiclass triplet loss, it learns a feature embedding function that could bring similar samples close to each other and keep samples of different classes far apart. Our proposed network is trained in an end-to-end manner from scratch, so as to learn a good embedding function, conceptual representation of classes, and a nonlinear metric simultaneously. Experimental results on the challenging datasets show that our method with Conv-64F feature extracting block is competitive and effective compared to the metric-based baselines with Conv-64F.
Efficient State Management in Distributed Ledgers.

Distributed ledgers implement a storage layer, on top of which a shared state is maintained in a decentralized manner. In UTxO-based ledgers, like Bitcoin, the shared state is the set of all unspent outputs (UTxOs), which serve as inputs to future transactions. The continuously increasing size of this shared state will gradually render its maintenance unaffordable. Our work investigates techniques that minimize the shared state of the distributed ledger, i.e., the in-memory UTxO set. To this end, we follow two directions: a) we propose novel transaction optimization techniques to be followed by wallets, so as to create transactions that reduce the shared state cost and b) propose a novel fee scheme that incentivizes the creation of “state-friendly” transactions. We devise an abstract ledger model, expressed via a series of algebraic operators, and define the transaction optimization problem of minimizing the shared state; we also propose a multi-layered algorithm that approximates the optimal solution to this problem. Finally, we define the necessary conditions such that a ledger’s fee scheme incentivizes proper state management and propose a state efficient fee function for Bitcoin.
Who Is Liable for a Quantum Adversary in a Cryptocurrency System?

Cryptocurrencies are based on digitally signed updates to a ledger maintained in a decentralized manner according to consensus mechanisms in compliance with consented protocols. Cryptography is used to secure all the aspects of a cryptocurrency system. Cryptography considered secure today is likely to be considered insecure in the future with the arrival of quantum computing. An adversary with access to quantum computers—a quantum adversary—may cause severe harm to a cryptocurrency system and its stakeholders. This paper discusses the liability of such harm. It finds that in addition to the liability of the quantum adversary, protocol developers, network participants, and misinforming agents may be held liable for the actions of the quantum adversary. However, in many cases, the harm is likely to be considered a risk accepted by the participants.
Learning Sentiment over Network Embedding for Recommendation System

With the rapid development of Internet, various unstructured information, such as user-generated content, textual reviews, and implicit or explicit feedbacks have grown continuously. Though structured knowledge bases (KBs) which consist of a large number of triples exhibit great advantages in recommendation field recently. In this paper, we propose a novel approach to learn sentiment over network embedding for recommendation system based on the knowledge graph which we have been built, that is, we integrate the network embedding method with the sentiment of user reviews. Specifically, we use the typical network embedding method node2vec to embed the large-scale structured data into a low-dimensional vector space to capture the internal semantic information of users and attractions and apply the user weight scoring which is the combination of user review ratings and textual reviews to get similar attractions among users. Experimental results on real-world dataset verified the superior recommendation performance on precision, recall, and F-measure of our approach compared with state-of-the-art baselines.
Psycholinguistic Study of Language Disorders in Autistic Children

Barriers experienced by someone in language is a language disorder, language disorders is a study of discussion in psycholinguistics. The purpose of this journal is to provide data relating to the relationship between psycholinguistics and disability in language in autistic children. The method is in the form of library research by collecting data from various scientific references. Based on data from several scientific references that are collected, analyzed, and studied, and get results that children with autism are influenced by the ability to interact and communicate because there is a behavior disorder caused by nerve development. Interference in speech is the main symptom caused by children with autism. Disability and deficiencies in communicating using verbal or other needs for children with autism will experience obstacles to get and understand communication in the community. language disorders is a study of discussion in psycholinguistics. In psycholinguistics a person applies an archetype in language acquisition. In the process of language acquisition, one's ability to speak can be influenced by mental or psychological symptoms. Mental or psychological symptoms, including those experienced by children with autism. Keywords : Psycholinguistics, Language Disorders, Autistic Children