Reviewerly/exHarmony huggingface dataset:
Progressive Batching for Efficient Non-linear Least Squares

Non-linear least squares solvers are used across a broad range of offline and real-time model fitting problems. Most improvements of the basic Gauss-Newton algorithm tackle convergence guarantees or leverage the sparsity of the underlying problem structure for computational speedup. With the success of deep learning methods leveraging large datasets, stochastic optimization methods received recently a lot of attention. Our work borrows ideas from both stochastic machine learning and statistics, and we present an approach for non-linear least-squares that guarantees convergence while at the same time significantly reduces the required amount of computation. Empirical results show that our proposed method achieves competitive convergence rates compared to traditional second-order approaches on common computer vision problems, such as image alignment and essential matrix estimation, with very large numbers of residuals.
Linking Bank Clients using Graph Neural Networks Powered by Rich  Transactional Data

Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time. In this work, we focus on the task of predicting new interactions in the network of bank clients and treat it as a link prediction problem. We propose a new graph neural network model, which uses not only the topological structure of the network but rich time-series data available for the graph nodes and edges. We evaluate the developed method using the data provided by a large European bank for several years. The proposed model outperforms the existing approaches, including other neural network models, with a significant gap in ROC AUC score on link prediction problem and also allows to improve the quality of credit scoring.
Energy Efficient Boosting of GEMM Accelerators for DNN via Reuse

Reuse-centric convolutional neural networks (CNN) acceleration speeds up CNN inference by reusing computations for similar neuron vectors in CNN’s input layer or activation maps. This new paradigm of optimizations is, however, largely limited by the overheads in neuron vector similarity detection, an important step in reuse-centric CNN. This article presents an in-depth exploration of architectural support for reuse-centric CNN. It addresses some major limitations of the state-of-the-art design and proposes a novel hardware accelerator that improves neuron vector similarity detection and reduces the energy consumption of reuse-centric CNN inference. The accelerator is implemented to support a wide variety of neural network settings with a banked memory subsystem. Design exploration is performed through RTL simulation and synthesis on an FPGA platform. When integrated into Eyeriss, the accelerator can potentially provide improvements up to 7.75 \( \times \) in performance. Furthermore, it can reduce the energy used for similarity detection up to 95.46%, and it can accelerate the convolutional layer up to 3.63 \( \times \) compared to the software-based implementation running on the CPU.
Capitolo 16. Fonti extravaganti della lessicografia romanesca

The paper proposes some extravagant typologies for the creation of a Vocabulary of contemporary Romanesque: in the first place “colonial” Romanesco, that is, Romanesque outside the city perimeter. We propose then two types of written sources rarely used: the noir literature of the twentieth century and manuals of gastronomy.
Calculation of the main characteristics of polymer branching taking into account the exchange of activity between polymerization centres

Abstract The calculation of the main characteristics of the branching of the polymer is presented in the article using the example of polymerization of butadiene in the presence of an organolithium catalytic system modified with polyfunctional sodium and magnesium alcoholates. The addition of a modifier to the catalytic system leads to the participation in the polymerization of at least two types of active centers, which is the reason for some branching of macromolecules. The complemented mathematical model of the periodic process, taking into account the exchange of activity between the two centers, allows to calculate the main characteristics of branching.
An Overview of Behavioral Cooperative Control of Multiple Unmanned Surface Vehicles

This article combines the current research status of multi-agent system control theory and technology, takes the unmanned surface vehicle)USV(system as the control object, and gives a detailed overview of the theory and technology development. The related theories and application issues are discussed from the direction of behavioral cooperative control of multiple USVs system. Then this article introduces the design method of USVs cluster controller composed of kinematic controller and dynamic controller. Finally, it points out some problems existing in the current collaborative research and the future direction, indicating that the USVs cluster control is of great significance for enhancing social and military benefits and maximizing the effectiveness of offshore mission execution.
Fostering Faculty and Student Engagement through Digitalization in Higher Education Sector before and after COVID-19

The Corona Virus outbreak has not only completely changed one’s lifestyle in every area and sector but also has compelled the faculty members as well as the students to adopt the digital platform for the teaching and learning process. There is no denying fact that today all the physical infrastructure is replaced by the online platform when one talks about the education sector. Already different researches have shown that engagement of faculty members as well students are a very difficult task for an organization, especially in the higher education sector. But the COVID-19 situation has certainly made somewhat situation worse as the online mode is very new for some and requires training. Whether it’s the question of commencement of classes, quizzes, classroom assignments, or the conduction of examination, the people connected with the education sector, no doubt have explored innovative ways to deal with the new challenges in this area. But one cannot forget that a digital platform is just a medium for the collection and distribution of knowledge and data in one or the other way. The first question that arises in the mind of one person is how far the digital platform is being successful in fostering engagement among the faculty members as well as the students, despite social distancing and many other challenges faced under this situation. The second question that we try to explore is whether, in this pandemic, the digital platform is acting as an opportunity or a challenge in the education sector. How successful is this digital platform in engaging the faculty as well as students and which online modes have helped to do the same? What new education model is one heading towards? In this research, an attempt is made to find the answers to the above questions by an extensive literature review conducted on ongoing research. In the end, it is discussed how the blended learning concept can transform the current education system in India.
Analyzing the Reversal of Public Opinion in the Internet Era From "Bao Yuming's Alleged Sexual Assault Case

With the rapid development of the Internet in China in recent years, the public tends to get information about news events from the Internet and publish their opinions on online social platforms. Guiding people's public opinion in the online world has become an essential social proposition. We choose the "Bao Yuming's alleged sexual assault case," which caused an intense social discussion on the Chinese Internet in 2020, as a case study for analysis and analysis of the various public opinion phenomena that emerged in society at that time discuss their causes and background. Finally, we propose ways to guide the case from three aspects.
Characterising the Multidimensional Nature of the “Pedagogical Control” Definition Using Content Analysis and Cluster Analysis

The aim of the study is to classify the existing definitions of the notion of “pedagogical control” in order to determine the research positions of their authors. The paper presents the results of a content analysis of the notion in question to identify the main semantic components that constitute this notion and the results of a cluster analysis to classify the existing definitions. Scientific novelty lies in identifying the essential features and structure of pedagogical control by analysing and classifying the components that make up the definitions selected by the continuous sampling method. As a result, 5 main components have been identified, which are more or less represented in the selected definitions, namely control, evaluative, prognostic, corrective and developmental components. The researcher has determined the quantitative and qualitative ratio of these components within the definitions of the notion of “pedagogical control” and has classified the existing definitions into groups.
ASSOCIATION FOR SYMBOLIC LOGIC

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ZONASI TINGKAT KERENTANAN TERHADAP BENCANA BANJIR DI KECAMATAN GUNUNG PUTRI KABUPATEN BOGOR

Flood is a phenomenon of submerging or inundating of an area. Based on the Bogor Regency Regional Regulation No. 4 of 2019 concerning the 2018-2023 Regional Medium-Term Development Plan, there are seven sub-districts in Bogor Regency that have a high level of vulnerability to flood disasters. One of these sub-districts is Gunung Putri District. This sub-district is the worst and most affected by the flood disaster due to the overflow of the Cikeas River and the Cileungsi River that surrounds it during high rainfall intensity for a long time. The purpose of this research is to arrange zoning based on the level of vulnerability to flood disasters. The method used is overlay and scoring techniques. The level of vulnerability is determined based on the river buffer, elevation, slope, and land use. The results showed a zoning map of the level of flood vulnerability in Gunung Putri District. The zone with a high level of flood vulnerability is 3864.83 Ha, the moderate zone is 1662.87 Ha, and the low is 561.68 Ha from the area of Gunung Putri District.
Ecological and geochemical assessment of the content of chemical elements and features of their spatial distribution in Fe nodules of soils in the Middle Cis-Urals

This work is about the ecological and geochemical assessment of the elemental chemical composition of soil nodules in the taiga zone of the Middle Cis-Urals. The features of the spatial distribution of chemical elements in concretions of Umbric Albeluvisols Abruptic, Luvic Stagnosols Dystric, and Haplic Fluvisols Oxyaquic are characterized for the first time using atomic absorption spectrometry and scanning electron microscopy with energy-dispersive analysis. A concentric ring structure of nodules with alternating zones rich in Fe and Mn has been revealed. Nodules act as geochemical barriers and accumulate not only iron and manganese, but also Cu, Ni, Co, Zn, and Pb. Keywords: ALBELUVISOLS, STAGNOSOLS, FLUVISOLS, ORTSHTEIN, RORENSTEIN, GEOCHEMISTRY CONSTANTS, ATOMIC ABSORPTION SPECTROMETRY, SEM/EDS, FE AND MN DISTRIBUTION, RING MICROSTRUCTURE
“Kebunku” Educational Game Using Construct 2

The sophistication of technology today can provide devices outside of computers, and edited into computers in the form of education. Games that have educational content are better known as educational games. This educational type game aims to provoke children's interest in learning subject matter while playing games, and is not focused on entertainment alone without any educational elements and is a fun learning tool for children aged 7-8 years of elementary school. The development of this game will use construct 2 as a means of making a game. Construct 2 uses the HTML 5 programming language and supports the development of 2-dimensional (2D) applications so that it is considered suitable for use in this game development process. The purpose of this research is to build an application for my Kebunku Educational Game using Construct 2 which is played on a computer and to learn the techniques for making a Kebunku Educational Game using construck 2 by going through three stages, namely the process of planting fruit seeds, watering fruit seeds and picking the fruit harvest. The result of making this game is my Kebunku Educational Game Application Using Construct 2.
MOTIVATION OF STAFF IN AN EDUCATIONAL ORGANIZATION

The article presents modern methods and methods of motivating the staff of an educational institution. The concepts of “motivation” and “stimulation” are considered. Measures to encourage employees of the educational institution are proposed. The article presents an individual approach to motivating and stimulating the work of a teacher to reveal the internal potential of the employee and his maximum return.
A Novel Approach for Sentiment Classification by Using Convolutional Neural Network

Social media platforms facilitate communication and data exchange. There is a substantial number of opinionated information available in digital form. It is essential to validate unstructured Web data in order to extract knowledge from it. Sentiment analysis offers a wide variety of applications across all domains. The primary objective of sentiment analysis is to asses if the input text is positive or negative. When a buyer purchases a product, they submit feedback of the product. These reviews are essential for getting a general sense of how people feel about the product or service. Customer reviews on the Internet help to make purchases. Sentiment analysis results assist businesses, understand customer expectations, and enhance service and product quality. Several deep learning algorithms have been used in this sector with promising results. This paper suggests a deep learning approach for sentiment analysis by using convolutional neural networks.
Seasonality Effects over the Classification Model using Machine Learning to Distinguish Natural from Anthropic Oil Slicks

Distinguishing between natural and anthropic oil slicks is considered a challenging task, especially in Gulf of Mexico where these events can be simultaneously observed and recognized as seeps or spills.In this context, the powerful data analysis provided by Machine Learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish the oil slick source (OSS).From a database containing 4,916 oil samples, detected using Synthetic Aperture Radar (SAR), there were 408 spectral and 10 geometric features.The exploratory data analysis successfully reduced the dataset without compromising the model accuracy, selecting 12 features for the CM designing.An innovative approach evaluated how external factors like seasonality limit or improve the OSS predictions.To accomplish this, specific classification models (SCM) were derived from the global, tuning the best algorithms and parameters according to different scenarios.The median accuracies results revealed winter and spring as the best seasons.Among the six tested ML algorithms, Random Forest (RF) was the most robust, performing better in more than half of the investigated scenarios.The global CM achieved 73.15% of maximum accuracy using RF.The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills.This represents a concrete contribution to reduce economic and geologic risks derived from exploration activities in offshore areas.Additionally, from the operational standpoint, specific models support specialists to select the best seasons for new acquisitions, as well as to optimize performances according to available data.
Multi Objective Optimization of Diesel Engine Emission System Based on NSGA-II

As a more efficient power machine at present, diesel engine is widely used in industry, large vehicles, ships, power generation and other industries. Because of its advantages of high thermal efficiency, low fuel consumption, strong power and long service life that diesel engine will continue to occupy a leading position in its application field in the next few decades.[1] However, because the diesel engine uses complex hydrocarbons as fuel, the air pollution caused by its exhaust gas is very serious, so the development of diesel engine in the future must shift from the concept of only power and economy to paying equal attention to both emission and economy. In this paper, the mainstream algorithm principle is introduced based on Non-dominated Sorting Genetic Algorithms – II. Aiming at solving the optimal solution between these conflicting parameters.
Evaluation of Language Training Programs in Luxembord using Principal Stratification

In a world increasingly globalized, multiple language skills can create more employment opportunities.Several countries include language training programs in active labor market programs for the unemployed.We analyze the effects of a language training program on the re-employment probability and hourly wages simultaneously, using high-quality administrative data from Luxembourg.We address selection into training with an unconfoundedness assumption and account for the complication that wages are "truncated" by unemployment by adopting a principal stratification framework.Estimation is undertaken with a mixture model likelihood-based approach.To improve inference, we use the individual's hours worked as a secondary outcome and a stochastic dominance assumption.These two features considerably ameliorate the multimodality problem commonly encountered in mixture models.We also conduct a sensitivity analysis to assess the unconfoundedness assumption.Our results suggest a positive effect (of up to 12.7 percent) of the language training programs on the re-employment probability, but no effects on wages for those who are observed employed regardless of training participation.
The Effect of Auditory Feedback on Websites Users Perception

In this investigation, we design a website named UN.com that has three scenes and with different numbers of auditory feedback. An experiment was designed to explore the application of auditory feedback. The better situation to use auditory feedback has been found out. We found a significant correlation between auditory feedback and website usability. Too many auditory feedbacks may cause negative effect. The result may provide web designer some suggestion on auditory feedback to prevent negative effects.
A New Method to Compare Performance of Forecasting Models

Tremendous growth of solar and wind capacity worldwide has led to rapid development of renewable energy forecasting techniques, and the performance of various forecasting models are usually compared by statistical metrics. However, two factors are not considered in current comparison. One is the amount of the useful renewable energy, and another is the energy consumption during forecasting. These two factors are considered in the proposed new method. Performances of the ANN and RNN models with three different sampling frequencies from literature are compared using this proposed method. Furthermore, the relationships between four statistical metrics-RMSE, (CV)RMSE, E and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -and useful renewable energy are examined. Finally, the effect of adding a moving-window algorithm is tested. The use of the proposed method is expected to promote a virtuous cycle in which sufficient energy is available for our modern lives with fewer emissions of carbon dioxide.
An Simulation and Survival Based Mobile Game for Players Developing a Sense of Environmental Preservation using Modeling and Manual Manipulation Programming

The citizens nowadays are often born and raised in well developed urban areas and have rarely, even never experienced the difficulties that wildlife are suffering from due to human actions. They are likely to possess sympathy, but never do these individuals are aware of how their deeds may affect the living of other species on planet Earth [1]. However, if someone reveals the bloody truth to the people, they are likely to change for the greater good. In this paper, we mainly used unity modeling and java programming skills to develop an animal simulation game in order to show the damage done by mankind and resonate the feeling of empathy so the players may alter their actions to preserve the environment [2][3]. The player starts the game as an animal figure in a randomly generated map. The player will control the animal to move around to consume water and food for survival. Meanwhile, the animal must avoid the invasion of human poison lands closing in on the habitats for this figure. Eventually, the player will starve or be poisoned and fail to survive. By setting this result, we hope to arouse the sympathy in hearts and lead to some alteration to a person’s habits.
Prediction of hospital-onset COVID-19 using networks of patient contact: an observational study

among the priorities to address which could be supported by One Health strategies.A OHCF has been developed with a multidisciplinary effort to facilitate detection of threats at the human-animal-environment interface, assess risks for pandemic, support evaluation of possible impacts and provide input for prevention.The OHCF aims at guiding the relevant national sectors towards harmonised and context driven OH strategies in prevention and preparedness and identifies priority actions for Governance, Data collection and analysis and Capacity building, both at national and international level.Conclusion: At present, stand-alone national One Health plans are often developed, while the effort, in order to benefit from One Health approaches, should be to appropriately integrate One Health strategies into relevant national and international plans.It is therefore necessary to develop adequate frameworks and identify procedures that allow this integration and effective implementation of comprehensive prevention and preparedness strategies.The adoption of the proposed OHCF would facilitate the development of intersectoral data sharing and analysis platforms and support decision-making based on early signals which contemplate possible uncertainties.The OHCF will facilitate operationalisation of OH in prevention and preparedness and will guide assessment of the multiple potential risk factors involved before they became a threat.