Reviewerly/exHarmony huggingface dataset:
Monocular Depth Parameterizing Networks

Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these are able to use recognition of image features to predict reasonably looking depth maps the result often has low metric accuracy. In contrast traditional stereo methods using multiple cameras provide highly accurate estimation when pixel matching is possible. In this work we propose to combine the two approaches leveraging their respective strengths. For this purpose we propose a network structure that given an image provides a parameterization of a set of depth maps with feasible shapes. Optimizing over the parameterization then allows us to search the shapes for a photo consistent solution with respect to other images. This allows us to enforce geometric properties that are difficult to observe in single image as well as relaxes the learning problem allowing us to use relatively small networks. Our experimental evaluation shows that our method generates more accurate depth maps and generalizes better than competing state-of-the-art approaches.
The present and the future of Russian higher education shown through university typology

In the article, through the study of various typologies of universities, the current state and the future of Russian higher education are assessed. There are different approaches to the classification of higher education institutions: classical, which views the university through the history; an approach that systematizes higher education institutions by belonging to a particular cultural tradition; economic approach. The authors of the typologies offer two opposite points of view regarding the future of the Russian university. According to the first point of view, the existing university system must be completely reformed. Supporters of the second one suggest adjusting the existing system to the requirements of modern society. Due to the analysis, we concluded that a hybrid option is more suitable for Russia, in which gradual changes allow preserving the advantages of Russian education and minimize the disadvantages.
Competitive Networks for AI Agents

In the last chapter, we studied the different parts of deep reinforcement learning (RL) and also studied the interactivity of the Python API to build custom models. Since there are various paradigms inside RL, we will be exploring adversarial and cooperative learning in addition to curriculum learning. Since we have an idea of the actor critic class of algorithms, including proximal policy operation (PPO), we will also look into an off-policy counterpart that we mentioned in the last chapter: deep deterministic policy gradient (DDPG). The important aspects in this chapter include understanding the significant improvement in training when curriculum learning is applied along with a policy gradient variant. This allows the agent to learn in incremental steps when placed in a new dynamic environment. In this section, we will be exploring more samples of machine learning (ML) agents to get an overview of adversarial self-play, where an agent has to compete with an adversary to gain rewards. After covering the fundamental topics, we will also be looking at certain simulations using ML Agents, including the Kart game (which we mentioned in the previous chapter). Let us begin with curriculum learning, and then we will explore competitive networks.
Deep Learning in Historical Kannada Document

Kannada is one of the famous ancient languages of the India and the official language of the State of Karnataka, which has a very large heritage. The digital analysis of these historical Kannada documents will provide us information about the culture and traditions that were practiced. Retrieving such information from paper documents, palm leaves or from stone carvings will enhance our knowledge. Investigating Historical document isn’t straight advance procedure because of low quality, differentiation, contrast and covering of characters. In this analysis, the authors propose a novel Scale invariant Feature Transform (SIFT) with deep learning classifier to recognize Historical kannada characters. To begin with, the character is divided utilizing Connected Component Analysis and later the Different SIFT Features are detached. At long last, form a powerful convolutional neural system classifier to recognize the Historical kannada archives. Proposed tale schemes during the preprocessing stage to guarantee strong, precise and constant grouping. They assess their strategy all alone datasets their characterization results surpass 97% on all datasets, which are superior to the cutting edge in this space.
PENERAPAN SISTEM PENDUKUNG KEPUTUSAN PENERIMA BANTUAN SOSIAL MENGGUNAKAN METODE FUZZY AHP

The distribution of Sosial Assistance (BANSOS) in Molombulahe Village remains to experience various issues where its one of them is the village has yet to indicate a significant change for the recipients of Social Assistance according to the data from Central Bureau of Statistics. In addition, the process of distribution for this assistance is still redundant at two types of assistance, while the selection process for the assistance recipients is still timeconsuming in regards to data processing so that an individual may take in more than one type of Social Assistance Dealing with the previos issues, a decision support system for recipients og Social Assistance by using the implementation of Fuzzy AHP method is required which could facilitate in data collection process and decision making in determining decent recipients of Social Assistance, so that there is no mare redundancy for the Assistance distribution in molumbulahe Village. Therefore, the distribution process of Social Assistence through applying the implementation of Fuzzy AHP method in the Decision Support System of Social Assistance Recipients could help the government of Molombulahe Village in the data management for proposed recipients of Social Assistance in the assistance distribution process. Therefore, the determination of decent Social Assistance recipients could be more accurate anf right on target. Keywords Decision Support System, Social Assistence, Implementation Of Fuzzy AHP
Imaginary protagonists in idiomatic expressions of the contemporary Italian language

The aim of this paper is to present imaginary personalities from oral and written literature who have found their place in Italian fixed expressions due to their character, specific circumstances, events or the things they have done or said. Most of the analysed characters in this paper are fictional, while some are associated with the most diverse stories and legends, mostly of unclear origin. If the analysed characters have been taken from a literary work, their creator is an individual and therefore a known subject. The creator of these characters can also be a collective author, and therefore an unknown subject. The characteristics of the protagonists in folk fiction and folklore have been created for a long time and they have been constantly attributed new meanings and language varieties. Although the subject of research in this paper are phrases of the contemporary Italian language, when it comes to these language forms, we cannot talk about contemporaneity in a narrower sense. Namely, due to their stability, these expressions represent a kind of antiquity, passed down from generation to generation through time and space. We will consider as contemporary those idioms which are recognizable in form and meaning in the language and speech of the XX and XXI century, and we will extract them from general and phraseological dictionaries and collections.
Influential Factors of the Research Performance in Business Administration

The concerns of this study are the factors that influence the research performance of the business administration professors in Korea. The factors are classified into two groups; organizational and personal factors. Therefore the organizational factor model is for finding the environmental factors that affect the research performance. These factors are the location and the type of the university, the salary and research fund. The type of the university and the research fund are found to be significant. The personal factor model is for finding the personal factors. The personal factors are career period, major, industry experience, US doctorate, school, and sex. Career period and industry experience are related positively but US doctorate is negatively to the research performance. Regarding the major the research performance of MIS or marketing is higher than that of other majors. The test whether the school where professors graduated from affects the research performance shows professors from some schools exhibit higher research performance than others. The integrated model is used for confirming the relationship between the research performance and the significant factors found in the above two models. The results of this study can be useful in evaluation and compensation system based on the research performance.
DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption

The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients' medical imaging data. In this paper, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three datasets, namely: the Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset, and the BraTS18 dataset. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.
Gadget-Based iNTRU Lattice Trapdoors.

We present two new related families of lattice trapdoors based on the inhomogeneous NTRU problem (iNTRU) defined in Genise et al. [16] (ASIACRYPT 2019). Our constructions are “gadget-based” and offer compact secret keys and preimages and compatibility with existing, efficient preimage sampling algorithms. Our trapdoors can be used as a fundamental building block in lattice-based schemes relying lattice trapdoors. In addition, we implemented our trapdoors using the PALISADE library.
Signed Graph Diffusion Network

Given a signed social graph, how can we learn appropriate node representations to infer the signs of missing edges? Signed social graphs have received considerable attention to model trust relationships. Learning node representations is crucial to effectively analyze graph data, and various techniques such as network embedding and graph convolutional network (GCN) have been proposed for learning signed graphs. However, traditional network embedding methods are not end-to-end for a specific task such as link sign prediction, and GCN-based methods suffer from a performance degradation problem when their depth increases. In this paper, we propose Signed Graph Diffusion Network (SGDNet), a novel graph neural network that achieves end-to-end node representation learning for link sign prediction in signed social graphs. We propose a random walk technique specially designed for signed graphs so that SGDNet effectively diffuses hidden node features. Through extensive experiments, we demonstrate that SGDNet outperforms state-of-the-art models in terms of link sign prediction accuracy.
Sketch-Inspector: A Deep Mixture Model for High-Quality Sketch Generation of Cats

With the involvement of artificial intelligence (AI), sketches can be automatically generated under certain topics. Even though breakthroughs have been made in previous studies in this area, a relatively high proportion of the generated figures are too abstract to recognize, which illustrates that AIs fail to learn the general pattern of the target object when drawing. This paper posits that supervising the process of stroke generation can lead to a more accurate sketch interpretation. Based on that, a sketch generating system with an assistant convolutional neural network (CNN) predictor to suggest the shape of the next stroke is presented in this paper. In addition, a CNN-based discriminator is introduced to judge the recognizability of the end product. Since the base-line model is ineffective at generating multi-class sketches, we restrict the model to produce one category. Because the image of a cat is easy to identify, we consider cat sketches selected from the QuickDraw data set. This paper compares the proposed model with the original Sketch-RNN on 75K human-drawn cat sketches. The result indicates that our model produces sketches with higher quality than human’s sketches.
Using Professionally Oriented Tasks When Teaching Mathematics to 7<sup>th</sup>-9<sup>th</sup>-Grade Pupils

The research objectives are as follows: to explore possibility of introducing professionally oriented tasks in mathematical education of 7th-9th-grade pupils, to evaluate efficiency of these tasks from the viewpoint of pupils’ professional self-determination. The paper proposes a system of tasks and discusses possibility of their usage at Math lessons for 7th-9th-grade pupils. Professionally oriented tasks are considered as an efficient means to raise education quality, to improve learners’ motivation, to stimulate their cognitive activity. The results of learners’ survey on influence of professionally oriented teaching on career choice are provided. The study enriches approaches to the problem of professional self-determination in terms of Math teaching methodology, which constitutes its scientific originality. The research findings are as follows: the authors justify efficiency of professionally oriented tasks when teaching Math to secondary school pupils.
An algorithm for detecting irregularities in images of material surfaces in the digital image correlation method

The paper is devoted to solving the problem of excluding areas of irregularities in material surface images. To solve this problem, we propose an algorithm for automatic mask construction with identifying the areas with material, areas without material, and crack areas; this combines image processing methods (pattern search, threshold flood filling, boundary search) with crack detection algorithms. It is demonstrated that this algorithm is applicable to experimental images with a large number of frames in a series.
Improvement of the prediction quality of electrical load profiles with artificial neural networks

Abstract Against the backdrop of the economically and ecologically optimal management of electrical energy systems, accurate predictions of consumption load profiles play an important role. On this basis, it is possible to plan and implement the use of controllable energy generation and storage systems as well as energy procurement with the required lead-time, taking into account the technical and contractual boundary conditions. The recorded electrical load profiles will increase considerably in the course of the digitization of the energy industry. In order to make the most accurate predictions possible, it is necessary to develop and investigate models that take account of the growing quantity structure and, due to the significantly higher number of observations, improve the forecasting quality as far as possible. Artificial neural networks (ANN) are increasingly being used to solve non-linear problems for a growing amount of data that is affected by human and other unpredictable influences. Consequently, the model approach of an ANN is chosen for predicting load profiles. Aim of the thesis is the simulative investigation and the evaluation of the quality and optimality of a prediction model based on an ANN for electrical load profiles.
Deep Domain Adaptation under Label Scarcity

The goal behind Domain Adaptation (DA) is to leverage the labeled examples from a source domain to infer an accurate model for a target domain where labels are not available or in scarce at the best. Recently, there has been a surge in adversarial learning based deep-net approaches for DA problem – a prominent example being DANN approach [9]. These methods require a large number of source labeled examples to infer a good model for the target domain; but start performing poorly with reduced labels. In this paper, we study the behavior of such approaches (especially DANN) under such scarce label scenarios. Further, we propose an architecture, namely TRAVERS, that amalgamates TRAnsductive learning principles with adVERSarial learning so as to provide a cushion to the performance of these approaches under label scarcity. Experimental results (both on text and images) show a significant boost in the performance of TRAVERS over approaches such as DANN under scarce label scenarios.
UPAYA MENINGKATKAN HASIL BELAJAR ILMU PENGETAHUAN SOSIAL MELALUI METODE PEMBELAJARAN WORD SQUARE PADA SISWA KELAS VI SD NEGERI 163087 TEBING TINGGI

The types of this research is classroom action research, there are 2 cycles in this research with four phases: planning, implementation, observation, reflection.The locaton of this research at SDN 163087 Tebing Tinggi. This research aim to describe this increase in teacher activity, student activity, and improvement of student learning result in social studies by appliying Word Square learning method. The subject of research is teacher and student third grader which amounted to 32 students. This study used a qualitative descriptive analysis technique. The result obtained from the learning results in the first cycle is 75% has increased in two cycle get 90,62 % and students activity in cycle I is 77, 4% has increased in cycle II get 81,2%. From the data obtained can be concluded that the word square learning model can improve students learning outcomes in class VI SDN 163087 Tebing Tinggi. Keywords: Word Square, IPS, Qualitative Descriptive
An experimental evaluation of refinement techniques for the subgraph isomorphism backtracking algorithms

Abstract In this paper, we study a well-known computationally hard problem, called the subgraph isomorphism problem where the goal is for a given pattern and target graphs to determine whether the pattern is a subgraph of the target graph. Numerous algorithms for solving the problem exist in the literature and most of them are based on the backtracking approach. Since straightforward backtracking is usually slow, many algorithmic refinement techniques are used in practical algorithms. The main goal of this paper is to study such refinement techniques and to determine their ability to speed up backtracking algorithms. To do this we use a methodology of experimental algorithmics. We perform an experimental evaluation of the techniques and their combinations and, hence, demonstrate their usefulness in practice.
ANALISIS PENERAPAN SISTEM INFORMASI AKUNTANSI KEUANGAN DAERAH PADA DINAS PERUMAHAN, KAWASAN PEMUKIMAN DAN PERTANAHAN PROVINSI SULAWESI SELATAN

A MUH SAFAR BASO PANCA. Analysis of the Application of Regional Financial Accounting Information Systems (RFAIS) in the Housing, Settlement and Land Offices of South Sulawesi Province. Guided by Dr. Firman Menne.SE., Ak., CA. and Dr. HJ. Herminawati Abu Bakar SE, MM.This study aims to obtain an overview of the application of the Regional Financial Accounting Information System in the Housing, Settlement and Land Areas of South Sulawesi Province, and to know the supporting factors and values ​​possessed by the Housing Agency, Settlement and Land Areas of South Sulawesi Province so that implement the Regional Financial Accounting Information System, as well as the quality of information generated from its application. Respondents in this study were 4 people consisting of the Chief of Staff and staff of the Service.Data is collected through an interview process and direct observation and the results of the study indicate that the Dinas employee has understood the meaning of the Regional Financial Accounting Information System
Prediksi Kedatangan Turis Menggunakan Algoritma Weighted Exponential Moving Average

Tourism is an important factor for the development of a country. Tourism can be used as a promotion to introduce natural beauty and cultural uniqueness. Government needs to predict how many tourists will come every year to do a planning. Therefore, an application is needed to help to predict the arrival of tourists in each country. In this paper, we use Weighted Exponential Moving Average (WEMA) method to predict the arrival of tourist, tourism expenditure in the country, and departure using data from 2008 to 2018. Error measurement is calculated using the Mean Absolute Percentage Error (MAPE). The result shows that the lowest average MAPE on arrival data with span 2 is at 3.28. The lowest average MAPE on tourism expenditure data with span 2 is at 3.99%. The result shows that the lowest average MAPE on departure data with span 2 is at 3.63%.
Records Management at PT. Bank Sulselbar Jeneponto Branch

Records management plays an important role in the running of an organization. This study aims to determine the description of archival management at PT. Bank Sulselbar Jeneponto Branch. To achieve this goal, the Researcher determined 3 (three) informants, namely: Operations Section Leader, Operational Assistant, and Administrative Assistant. This type of research and the approach used is descriptive qualitative. Collecting data through interview techniques, observation, and documentation. Data analysis techniques are carried out through data collection, data condensation, data presentation, data verification/conclusions. The results showed that archival management at PT. Bank Sulselbar Jeneponto Branch has been managed optimally. This can be seen from the indicators of the stages of receiving archives, the stages of archive storage, the stages of maintaining and securing archives, the stages of shrinking archives, and the stages of destroying archives that have fulfilled almost all aspects of the five stages. Evidenced by the management of records at PT. Bank Sulselbar Jeneponto Branch is adjusted to Standard Operating Procedures and archives that are easy to find as well as an archival storage system using a number system.