Reviewerly/exHarmony huggingface dataset:
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer

As the applications of deep learning models on edge devices increase at an accelerating pace, fast adaptation to various scenarios with varying resource constraints has become a crucial aspect of model deployment. As a result, model optimization strategies with adaptive configuration are becoming increasingly popular. While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space. Existing methods tend to either limit the architecture search space to a small set of options or limit the quantization policy search space to fixed precision policies. To this end, we propose BatchQuant, a robust quantizer formulation that allows fast and stable training of a compact, single-shot, mixed-precision, weight-sharing supernet. We employ BatchQuant to train a compact supernet (offering over $10^{76}$ quantized subnets) within substantially fewer GPU hours than previous methods. Our approach, Quantized-for-all (QFA), is the first to seamlessly extend one-shot weight-sharing NAS supernet to support subnets with arbitrary ultra-low bitwidth mixed-precision quantization policies without retraining. QFA opens up new possibilities in joint hardware-aware neural architecture search and quantization. We demonstrate the effectiveness of our method on ImageNet and achieve SOTA Top-1 accuracy under a low complexity constraint ($&lt;20$ MFLOPs). The code and models will be made publicly available at https://github.com/bhpfelix/QFA.
ORCEA: Object Recognition by Continuous Evidence Assimilation

ORCEA is a novel object recognition method applicable for objects describable by a generative model. The primary goal of ORCEA is to maintain a probability density distribution of possible matches over the object parameter space, while continuously updating it with incoming evidence; detection and regression are by-products of this process. ORCEA can project primitive evidence of various types (edge element, area patches etc.) directly on the object parameter space; this made possible by the study phase where ORCEA builds a probabilistic model, for each evidence type, that links evidence and the object-parameters under which they were created. The detection phase consists of building the joint distribution of possible matches resulting from the set of given evidence, including possible grouping to signal/noise; no additional algorithmic steps are needed, as the resulting PDF encapsulates all knowledge about possible solutions. ORCEA represents the match distribution over the parameter space as a set of Gaussian distributions, each representing a concrete probabilistic hypothesis about the object, which can be used outside its scope as well. ORCEA was tested on synthetic images with varying levels of complexity and noise, and shows satisfactory results.
打ちことばにおける長音符による イントネーション表現に関する考察 －キャスフィにおける小中高生掲示板の分析から－

The purpose of this paper is to clarify the types of expression and functions of intonation using long note ‘-’, ‘∼’, ‘…’ in Japanese internet-based language. Internet-based language has a shortcoming which cannot transfer informations of prosody messages. But many internet-based language users in Japan are using and making the methods of expression of prosody messages, expecially intonation. Thus, we focus on the types of expression and function of intonation using long note ‘-’, ‘∼’, ‘…’ in Japanese internet-based language on a youth bulletin site is called Casphy.com. As a result of investigation, we clarified that the intonation in Japanese internet-based language has five types of expression, ‘short rise(↑)’, ‘long rise(↗)’, ‘rise-fall(⇅)’, ‘flat(→)’, ‘long fall(↘)’. And we clarified the intonation in Japanese in internet-based language has nineteen functions. Expecially three functions ‘affinity’, ‘lingering’, ‘rising of feelings’ are unique functions in Japanese internet-based language.
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data

Railway systems require regular manual maintenance, a large part of which is dedicated to inspecting track deformation. Such deformation might severely impact trains' runtime security, whereas such inspections remain costly for both finance and human resources. Therefore, a more precise and efficient approach to detect railway track deformation is in urgent need. In this paper, we showcase an application framework for predicting vertical track irregularity, based on a real-world, large-scale dataset produced by several operating railways in China. We have conducted extensive experiments on various machine learning &amp; ensemble learning algorithms in an effort to maximize the model's capability in capturing any irregularity. We also proposed a novel approach for handling imbalanced data in multivariate time series prediction tasks with adaptive data sampling and penalized loss. Such an approach has proven to reduce models' sensitivity to the imbalanced target domain, thus improving its performance in predicting rare extreme values.
여성결혼이민자의 쓰기 자료에 나타난 오류 양상

The purpose of this study was to examine the writing errors found in the writings of immigrant women through marriage and to suggest a writing education plan. First, misspellings or spacing errors appear in spelling errors, and errors in investigation and endings appear in grammar errors. In vocabulary and semantic errors, an error of selecting an inappropriate vocabulary appears in the context, and an error in which the response of the word order or sentence component is not appropriate appeared in the sentence error. Second, errors in the concept of sentences and paragraphs are insufficient or the paragraph composition is not appropriate. Third, to reduce spelling or grammatical errors, theoretical education on language norms is required, and to reduce paragraph composition and structural errors in writing, education on the step-by-step process of writing must be provided at an intermediate level or higher.
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning

The increasing concerns about data privacy and security drive an emerging field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning. A class of federated learning, vertical federated learning, where different parties hold different features for common users, has a great potential of driving a great variety of business cooperation among enterprises in many fields. In machine learning, decision tree ensembles such as gradient boosting decision trees (GBDT) and random forest are widely applied powerful models with high interpretability and modeling efficiency. However, stateof-art vertical federated learning frameworks adapt anonymous features to avoid possible data breaches, makes the interpretability of the model compromised. To address this issue in the inference process, in this paper, we firstly make a problem analysis about the necessity of disclosure meanings of feature to Guest Party in vertical federated learning. Then we find the prediction result of a tree could be expressed as the intersection of results of sub-models of the tree held by all parties. With this key observation, we protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs. The advantages of Fed-EINI will be demonstrated through both theoretical analysis and extensive numerical results. We improve the interpretability of the model by disclosing the meaning of features while ensuring efficiency and accuracy.
Diagnosis Laboratorium pada COVID-19

Abstract COVID-19 (corona virus disease -19) is a respiratory tract infection caused by the SARS-CoV- 2 coronavirus. The spread of this virus is so fast that, even though the cure rate is high, the fatality rate is equally high. Controling of Corona virus transmission can be done by breaking the chain of transmission, examining for tracking, tracing people who have had contact with sufferers, and examining for diagnosis (testing) of COVID-19 cases. One of the functions of the COVID-19 laboratory examination is for diagnosis. Laboratory examination parameters that are often used for the diagnosis of COVID-19 are: Whole Blood, C-Reactive Protein (CRP), Antibody Rapid Test and RT-PCR. However, the gold standard for COVID-19 diagnosis is RT-PCR. The purpose of this literature review is to provide an insight into a precise, fast, and highly accurate diagnosis in order to immediately mitigate and prevent any spike in COVID-19 cases. Keywords : diagnosis of COVID-19, hematology examination, antibody rapid test, RT-PCR
Co-Factorization Model for Collaborative Filtering with Session-based Data

Matrix factorization (MF) is a common method for collaborative filtering. MF represents user preferences and item attributes by latent factors. Despite that MF is a powerful method, it suffers from not be able to identifying strong associations of closely related items. In this work, we propose a method for matrix factorization that can reflect the localized relationships between strong related items into the latent representations of items. We do it by combine two worlds: MF for collaborative filtering and item2vec for item-embedding. The proposed method is able to exploit item-item relations. Our experiments on several datasets demonstrates a better performance with the previous work.
Real-Time Multi-View Face Recognition using Alignment-RMFRA

Face Recognition plays an important role in most of the disciplines in the recent world. One of the most common discipline is security and fraud detection. Facial alignment is a method of arranging the facial landmarks points on the face and those landmarks gives fine points for face recognition. Face detection and identification is important in the fraud detection. Therefore, detection of profile and semi-profile faces plays a vital role in security purpose.The face alignment by utilizing Hourglass model gives better accuracy for face recognition by using Haar- Cascade Classifier can be obtained by facial aligned dataset. The performance is measured by the accuracy rate and precision. It gives better results when compared to face recognition by using Support Vector Machine[SVM] and Principal Component Analysis[PCA]. The model gives alignment of facial points with 68 landmark points and the aligned data is sent as an input for the face recognition. The face alignment and recognition gives most accurate and precise results.
Directional GAN: A Novel Conditioning Strategy for Generative Networks

Image content is a predominant factor in marketing campaigns, websites and banners. Today, marketers and designers spend considerable time and money in generating such professional quality content. We take a step towards simplifying this process using Generative Adversarial Networks (GANs). We propose a simple and novel conditioning strategy which allows generation of images conditioned on given semantic attributes using a generator trained for an unconditional image generation task. Our approach is based on modifying latent vectors, using directional vectors of relevant semantic attributes in latent space. Our method is designed to work with both discrete (binary and multi-class) and continuous image attributes. We show the applicability of our proposed approach, named Directional GAN, on multiple public datasets, with an average accuracy of 86.4% across different attributes.
Contracts Without Promises: Blockchain-Based 'Smart Contracts' and the Title-Transfer Theory of Contract

Smart contracts are computer programs used to transfer digital assets according to user-generated specifications. Smart contracts run on decentralized blockchain networks. The fact that smart contracts, a new means for parties to digitally trade, greatly eliminate ex ante promissory obligations in favor of the actual execution of contracts through a single legal instrument presents a fundamental issue for a contract law paradigm based upon promissory obligations. Therefore, smart contracts offer a novel opportunity to re-examine the foundations of contract theory. The Title-Transfer Theory of Contract, premised not upon promissory obligations but on the transfer of property titles to scarce resources, saves contract theory from the theoretical issues posed by smart contracts by basing contract theory in the property theory of Rothbardian-libertarian ethics. Because neither smart contracts nor the Title-Transfer Theory of Contracts focus on promissory obligations, the two are inherently congruent.
A Study on the Current State of Artificial Intelligence Based Coding Technologies and the Direction of Future Coding Education

Artificial Intelligence (AI) technology is used in a variety of fields because it can make inferences and plans through learning processes. In the field of coding technologies, AI has been introduced as a tool for personalized and customized education to provide new educational environments. Also, it can be used as a virtual assistant in coding operations for easier and more efficient coding. Currently, as coding education becomes mandatory around the world, students’ interest in programming is heightened. The purpose of coding education is to develop the ability to solve problems and fuse different academic fields through computational thinking and creative thinking to cultivate talented persons who can adapt well to the Fourth Industrial Revolution era. However, new non-computer science major students who take software-related subjects as compulsory liberal arts subjects at university came to experience many difficulties in these subjects, which they are experiencing for the first time. AI based coding technologies can be used to solve their difficulties and to increase the learning effect of non-computer majors who come across software for the first time. Therefore, this study examines the current state of AI based coding technologies and suggests the direction of future coding education.
IMPLEMENTASI SISTEM JUST IN TIME PADA PERSEDIAAN BAHAN BAKU UNTUK MEMENUHI KEBUTUHAN PRODUKSI DI PT TSAMAROT INDONESIA

PT Tsamarot Indonesia, which is one of the companies engaged in the food processing industry with the resulting products, namely fruit pulp, chuncy and slincing, which in carrying out production activities uses the principle of make to order. Based on the system that makes the order, it is based on a good production management system to help and coordinate the use of various existing resources effectively and efficiently, so that there are no errors in the ongoing production process so that products can be delivered on time. PT Tsamarot Indonesia is experiencing difficulties in fulfilling consumer demand, this is due to the difficulty in controlling the flow of material for the production system so that it can hamper the ongoing production process. Thus the company needs the right method to solve these problems. Just In Time is a method that can increase lean production in the sense that there is no waste the company can do. Using a timely approach can provide suggestions for overcoming the production process. Based on the results of calculations using the punctual system at PT Tsamarot Indonesia, it can solve the problems that occur by looking at the level of efficiency of the company before using the system in only 54% of the time and has increased after using the system in just 78%.
Introducing scholarly communications: A cross-departmental approach for reaching students and faculty [slides]

Slides from a presentation discussing cross-departmental efforts to provide scholarly communications training to graduate students and faculty members at UNC Greensboro. This presentation was delivered on May 27, 2020 at the Azalea Coast Library Association Virtual Conference. Co-presented by Anna Craft, Amy Harris Houk, and Sam Harlow.
이중 언어 말뭉치와 분석기 개발

Corpus dealing only with a single language has been used in the past in corpus linguistics. In recent years, however, bilingual corpus that deals with multiple languages beyond its limits started to appear along with the emergence of the bilingual corpus analyzer exploiting this concept. The existing analyzers are mostly monolingual, however, and there are only a few corpus analyzers that handle two languages simultaneously. In this paper, the author will analyze the existing corpus analyzer and present a new corpus analyzer model that collects all necessary functions for language research.
Control PID basado en odometría visual monocular a un vehículo de superficie marino

The aim of this work is to study the behavior of a marine vehicle applying vision-based control. We use a DMVO algorithm (Direct Monocular Visual Odometry) that is based at the feature extraction of two image frames taken consecutively at two continuous tame frames in order to estimate a marine vehicle’s position; vehicle’s dynamics are modeled without external perturbations and therefore apply a vision based Proportional Integral Derivative (PID) control for the study of its behavior through simulations using computational tools to simulate a 3D scenario and get the matrix that contains the image from the inertial frame of reference view from the vehicle. First we need a static scenario that contains reference points such that we are able to apply the proposed vision based PID control; the virtual scenario was designed at the MATLAB’s virtual world editor that let us add sensors such as cameras, and set their parameters for make the study and simulations such as path following.
THE ROLE OF SPIRITUAL VALUES IN THE PROFESSIONAL ACTIVITY OF A MEDICAL WORKER

The article presents a philosophical analysis of the role of spiritual values in the professional activity of a medical worker. It is noted that in the modern conditions of civilizational development there are new challenges that actualize the problem of the value dimension of human activity. This is primarily due to the coronavirus pandemic (COVID-19), which spread almost worldwide during the year. As a result of the intense spread of coronavirus disease around the globe, medical workers today find themselves in extremely difficult or even extreme working conditions. In addition, in some countries this category of workers does not have adequate financial and social guarantees. As a result, there is a problem of value measurement of professional activity of medical workers. The essence of this problem is the contradictory relationship between the state (government), society and medical workers, where the effectiveness of the latter is considered on the basis of its compliance with moral, legal and other social requirements, without taking into account the needs, interests and desires of health workers. It is stated that the formation of a professional medical worker should necessarily be aimed at the development of a spiritually rich, value-oriented personality, which is conscientious, strong-willed, responsible, competent, persistent, purposeful; uses trust, duty, self-improvement, rational use of its own essential forces, living space, constantly improves the forms and types of activities, cultivates a careful attitude to all forms of life.
Re-examining the Nexus Between Financial Development and Poverty Reduction: Evidence from Emerging Economies

During the past two decades, the research on financial development and growth has gained a lot of coverage. This research looks at the degree to which financial development helps alleviate poverty. Cointegration estimation of the system, called the FMOLS, was applied over the period 1995–2015 to a panel of five African emerging economies. Using liquid liability as a percentage of GDP and bank domestic credit as a percentage of GDP as the main financial development indicators, the findings indicate that bothfinancial development indicators minimize poverty. Other factors such as economic growth and inflation are not statistically significant. Government spending, on the other hand, does not tend to affect poverty irrespective of the measure of financial growth employed. These findings suggest that while financial growth is capable of reducing poverty, it must be combined with a reduction in inflation, as well as other macroeconomic variables. The use of liquid liability and bank domestic credit as measures offinancial growth is robust to the results.
Non-equilibrium Quantum Langevin dynamics of a charged particle in a magnetic field : Response function, position-velocity and velocity autocorrelation functions

We use the Quantum Langevin equation as a starting point to study the response function, the position-velocity correlation function and the velocity autocorrelation function of a charged Quantum Brownian particle in a magnetic field coupled to a bath. We study two bath models -- the Ohmic bath model and the Drude bath model and make a detailed comparison in various time-temperature regimes. For both bath models there is a competition between the cyclotron frequency and the viscous damping rate giving rise to a transition from an oscillatory to a monotonic behaviour as the damping rate is increased. In the zero point fluctuation dominated low temperature regime, non-trivial noise correlations lead to some interesting features in this transition. We study the role of the memory time scale which comes into play in the Drude model and study the effect of this additional time scale. We discuss the experimental implications of our analysis in the context of experiments in cold ions.
Estrategias y herramientas virtuales que dinamicen la enseñanza de la historia local, regional y nacional en básica primaria

The teaching of history in Colombia for decades has gone through a deterioration that has been reflected not only in textbooks and curricula but also in the discourse and thematic domain reflected by students and teachers; the return of this to the classrooms in accordance with the guidelines of Law 1874 of December 27, 2017 creates the need to look for possibilities and alternatives for the return of this to the classrooms. The objective of this research work is to identify which are the virtual strategies and tools that dynamize the teaching of national, regional and local history in elementary school students of the Valdivia Educational Institution, through a qualitative, descriptive study with a Sample population of 20 second grade students, through the application of different information gathering instruments, their analysis and documentary tracking. This research made it possible to understand the current state of the teaching of history at the institutional level and according to the interests and motivations of the students to define the strategies and tools that make it possible to carry out this, as well as to present within the recommendations a series of platforms and resources that make it possible to take them to the world of virtuality, responding to the current demands of the educational system in the framework of the pandemic. History and virtuality, two elements that begin to circulate strongly in the discourse of educational institutions and that converge in the present research work
MATHEMATICAL PACKAGES AND THE PROBLEM OF KNOWLEDGE TRANSFER

The paper considers mathematical packages that show real possibilities for the study of more complex mathematical models, develop stable practical skills for conducting mathematical reasoning.