Reviewerly/exHarmony huggingface dataset:
Towards Faster Offline Handwriting Recognition Using Temporal Convolution Networks

The problem of unconstrained handwriting recognition is quite challenging in the field of document analysis and recognition. The state of the art models utilize Recurrent Neural Networks (RNN) and its variants to extract the features with Connectionist Temporal Classification (CTC) as the cost function. Because of the recurrent connections these models are extremely slow to train and test but enable the recognition of the handwritten text images with high accuracy at character and word level. In this work, we present a faster system for handwritten text recognition which takes the input as a handwritten text line image and outputs the text transcription of the same. The model uses convolution structure based on Temporal Convolution Network (TCN) and CTC cost function at the output layer. The model shows a comparable Character Error Rate (CER) of 9.6% with respect to state of the art models on IAM handwritten lines dataset. Since our model is a TCN based architecture, it can effectively use the parallelism offered by GPUs. Our baseline model takes 52s per epoch to train and 3ms per image to test which is a significant improvement as compared with the existing models for the same task. It has comparatively lesser number of parameters. This makes our model suitable to use for real life applications and to be deployed on smart phone devices. To the best of our knowledge, ours is the first work which uses TCN based model for the task of transcribing the text of handwritten line images.
Cloud Storage Options in a Nutshell: Storing and Securing Your Stuff

One of the primary advantages of migrating to the cloud is the seemingly infinite amount of available storage. In the past, additional storage was only an option if you wanted to add on infrastruct...
Digital watermarks with adaptive width of the informative ring in task of control signal hidden transmission at multi–agent robotics system

Abstract In the paper the relevant scientific problem of development of new methods of data transmission in mobile robotic groups is considered. The originality is defined by rapid body height of distribution of group robotics in the solution of a wide range of tasks and at the same time lack of a methodological support for formation of the safe environment of data exchange between agents in such groups. The authors offer original approach and pilot signals transformation method with function of concealment of the fact of data transmission between agents of a diverse system. The technology of digital watermarks embedding into a video stream, which is transferred in interaction of agents, is applied. The results of computing and program experiments which show effectiveness of application of the offered methodical approach regarding counteraction to the attacks, distortions and other impacts on the transmitted data caused by the difficult conditions of an actuation medium are given in work. The results show applicability of the offered approach and the method in the solution of the put-forward scientific problem on creation of effective providing in the field of control of heterogeneous robots and robotic complexes and achievement of necessary reliability level and safety.
Stereotypes and hate crimes in the light of an Indian context

Stereotypes are most generally defined as “beliefs about the characteristics, attributes, and behaviours of members of certain groups”. Stereotypes are as old as humanity and today they have become a common phenomenon in cultures around the world. It is a cognitive construct based on the processing of perceptual information. Research indicates that stereotypes can imply both positive and negative ideas. A negative perspective based on some kind of generalisation paves way to prejudices and bias which then may influence people to commit crimes known as hate crimes. Hate crimes can be defined as crime, most commonly violence motivated by prejudice, bias, or hatred toward a particular group of which the victim is presumed to be a member. A comprehensive search of the literature was performed on books, journals and other credible online internet websites. This study reviewed the literature on the activation of stereotypes, the models used to explain bias, the relationship between stereotypes and hate crimes. Finally, research incorporating useful interventions for prejudice reduction.
Optimal strategies to infer the width of an infinite square well by performing measurements on the particle(s) contained in the well

We seek the optimal strategy to infer the width a of an infinite potential well by performing measurements on the particle(s) contained in the well. In particular, we address quantum estimation theory as the proper framework to formulate the problem and to determine the optimal quantum measurement, as well as to evaluate the ultimate bounds to precision. Our results show that in a static framework the best strategy is to measure position on a delocalized particle, corresponding to a width-independent quantum signal-to-noise ratio (QSNR), which increases with delocalisation. Upon considering time-evolution inside the well, we find that QSNR increases with time as t2 (at least for small t). On the other hand, it decreases with a and thus time-evolution is a metrological resource only when the width is not too large compared to the available time evolution. Finally, we consider entangled particles in the well and observe super-additivity of the QSNR: it is the sum of the single-particle QSNRs, plus a positive definite term, which depends on their preparation and may increase with the number of entangled particles. Overall, entanglement represents a resource for the precise characterization of potential wells.
Research and Construction of “Intelligence Plus” Air Re-Study Classroom--Take the “Higher Mathematics” Course as an Example

According to the draft of China's intelligent education development plan (2020-2035), the goal of intelligent education is to build a new talent training system in the intelligent era, which requires the classroom revolution of inquiry, collaboration and hybrid teaching methods. This revolution needs to deal with the classroom reform in the era of intelligence. It is not only based on diversified, open, flexible and appropriate digital education resources, but also requires teachers to become high-level digital teachers with “artificial intelligence +”. On April 1, 2016, Tsinghua University “Xuetang online” launched a smart teaching tool “Rain Classroom” based on WeChat and PPT. The tool can conveniently collect teaching data before, during and after class, and interact with teachers and students, so as to achieve real-time classroom data-driven teaching and learning. So far, more than 1.3 million classrooms in more than 90 countries and regions around the world, and more than 16 million users are using them in depth.
Design and Implementation of Database Query System for Civil–Military Integration Regulations Based on B/S Mode

In order to solve the problem that the laws and regulations of civil–military integration are too scattered and large in quantity, which brings inconvenience to the relevant workers, a database query system of civil–military integration laws and regulations based on B/S mode is developed by using C# development language, ASP web page production technology and SQL Server 2005 database technology, which realizes the fast retrieval and query of civil–military integration laws and regulations. This paper focuses on the system requirements analysis, functional module design, database design, and system implementation. The application of the system greatly improves the query efficiency of civil–military integration laws and regulations, realizes resource integration, information sharing, and improves the ability of scientific and technological innovation [1].
Use of information and communication technology in agriculture development

Information and Communication Technology (ICT) has connected the world globally and is now changing the life style and social consciousness dynamically. ICT has emerged as a best tool for information sharing and mutual communication. None of the walks of life have been left untouched to the ICT sector. Agriculture has also been greatly influenced by ICT in the present era though the share of ICT in agriculture is increasing considerably especially after GATT (General Agreement on Tariffs and Trade). ICT is important sector in improving the agriculture through sustainable management of resources. The use of computers, smartphone applications and apps and internet has brought awareness on the potential of ICT in agriculture and agri-business. Rural communication and information management in ICT have important role in spreading information about successful farmers' innovations and getting access to fresh knowledge knowledge.
2015 개정 초등 6학년과 중등 1학년 간 영어 교과서 의사소통 기능의 연계성 분석

This study analyses the communicative functions of listening scripts extracted from the 2015 elementary school English 6 and middle school English 1 textbooks in terms of continuity. Auditory data corpora were drawn from all five elementary school English textbooks and five most widely used middle school English textbooks. Each sentence of listening scripts was manually tagged based on the classification of communication functions presented in the 2015 Revised National Curriculum. The corpora were analyzed using 14 syntactic complexity measures with the L2 Syntactic Complexity Analyzer (Lu, 2010). The findings of this study show that the continuity between the elementary school English 6 and the middle school English 1 textbooks is relatively well-organized. However, concerning the sequence, the elementary school English 6 was found to be more complex than the middle school English 1 in terms of syntactic complexity. It is suggested that future textbook development should correct the reversed complexity in listening scripts found in this study.
Knowledge Sharing Pada Mahasiswa Baru

Knowledge sharing in University level happens at the core activites of University. Almost all academics in University are doing knowledge sharing. Knowledge sharing could be found at learning, teaching and community services process. College students and lecturers are the one who mostly doing knowledge sharing. In University, knowledge sharing become an important process because there are difference between learning and studying in High School and in Univeristy. This research used SECI Model to analyze knowedge sharing. SECI Model is used to analyzed the creation and transfer of knowledge developed by Takeuchi and Nonaka (1995). SECI is an acronym for Socialization, Externalization, Combination and Internalization. By doing knowledge sharing college students could get the exsisted knowledge and even  new knowledge. The result of this research shows undergraduate students of library science at university of Indonesia batch 2019 are doing knowledge sharing and they are using LINE application as a media.
A Deep Learning Based Framework for Distracted Driver Detection

In this paper, we propose a novel, real-time, deep learning-based framework for distracted driver detection for driver Advanced Driver Assistance Systems (ADAS). We assume that the camera is assumed to be mounted inside the vehicle such that the side view of the driver is in view. Distracted driving is a serious problem leading to a large number of serious and even fatal road accidents worldwide every year. We propose a deep learning architecture that takes as input the captured images of the driver and classifies and recognizes the various distracted driving behaviors. It also recognizes if the driver is not distracted and is alert. The experiments are performed on the publicly available State Farm Distracted Driver Detection (SFDDD) dataset [1] which has 9 classes of distracted driver behavior and one class of alert driving. The training time for the proposed framework is minimal and approach works in real-time. Our experimental results show that our proposed framework is robust and performs better than the state-of-the-art approaches on this dataset.
Beyond Adjacency Pairs: Extracting Longer Regularities in Human-Machine Dialogues

This work proposes a framework to predict sequences in dialogues, using turn based syntactic features and dialogue control functions. Syntactic features were extracted using dependency parsing, while dialogue control functions were manually labelled. These features were transformed using tf-idf and word embedding; feature selection was done using Principal Component Analysis (PCA). We ran experiments on six combinations of features to predict sequences with Hierarchical Agglomerative Clustering. An analysis of the clustering results indicate that using word embeddings and syntactic features, significantly improved the results.
A Deep Quantitative Type System

We investigate intersection types and resource lambda-calculus in deep-inference proof theory. We give a unified type system that is parametric in various aspects: it encompasses resource calculi, intersection-typed lambda-calculus, and simply-typed lambda-calculus; it accommodates both idempotence and non-idempotence; it characterizes strong and weak normalization; and it does so while allowing a range of algebraic laws to determine reduction behaviour, for various quantitative effects. We give a parametric resource calculus with explicit sharing, the collection calculus, as a Curry-Howard interpretation of the type system, that embodies these computational properties.
Design Thinking with Agile

I had an aversion to the phrase “fail fast” for many years. To me, it implied a sense of carelessness and was a reminder of the first .com boom and bust, in which plenty of early .com companies did indeed fail fast because they did not value nor pay any attention to basic business fundamentals. As I learned more about agile and design thinking, the idea of “fail fast” morphed (for me) into an understanding that this meant building working prototypes to get early customer feedback as well as attempting technically challenging work early, all for the purpose of learning from early “failures.”
예비 한국어 교사의 문법 평가 양상 분석: 교사 교육용 기초 자료 구축을 목적으로

This study stems from the recognition that there is a need for methods to strengthen the grammar evaluation characteristics of preservice Korean language teachers. A ‘Grammar Evaluation Skills Assessment’ form was introduced therefore in this research, to first examine the competence and characteristics on grammar evaluation of preservice Korean language teachers. Using the Grammar Evaluation Skills Assessment, the following four teaching errors were found: (1) the lack of teachers’ awareness on the learners’ attempts to use new grammar forms, (2) teaching complex grammar structures that exceed the learners’ competence, (3) excessive modification of word orders and expressions that the learners produced, and (4) varying degree of awareness among teachers on Korean grammar errors. A few additional aspects such as ‘deviating from the target grammar form’ and ‘failure to capture specific meaning of a grammar form which has several uses’ were also found, when providing further explanations on learner’s grammar errors. This study reveals that practical training, which emphasizes ‘common grammar error detection’, ‘corrections with minimal edits, based on the learners’ level’, and ‘providing right amount of relevant knowledge in the right moment’ are essential in order to improve the grammar evaluation skills of preservice Korean language teachers. This article expects to guide preservice teachers to assess and supplement their weakness in advance, and to open up more discussions on improving the grammar evaluation skills of Korean educators.
Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration

This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation. We use constraints provided by orthogonal scene planes to recover the focal length. We show that solvers using feature combinations can recover more accurate calibrations than solvers using only one feature type on scenes that have a balance of lines and texture. We also show that the proposed solvers are complementary and can be used together in a RANSAC-based estimator to improve auto-calibration accuracy. State-of-the-art performance is demonstrated on a standard dataset of lens-distorted urban images. The code is available at this https URL.
Impact of the Application of National Integrity Standards on the Disclosure in the Country's General Budget (The Jordanian Public Sector)

This study aims to contribute in promoting the adoption and application of integrity standards as well as analyze the impact of applying standards of national integrity on the disclosure of the State`s general budget in the Jordanian public sector in order to achieve various benefits, namely influencing disclosure and reinforcing integrity standards. The study sample included a number of expertise in the financial control and accountancy in the Jordanian public sector. The study results showed that the standards of national integrity are well applied with an arithmetic mean of 3.60, at a rate of more than 60% in the Jordanian public sector and to a favorable extent for each standard of the national integrity standards under study. Results also showed that the standards of national integrity have an impact on the State`s public budget disclosure in the Jordanian public sector. Integrity standards varied in relation to their impact on the State`s public budget disclosure, where the highest was the transparency standard and the lowest the rule of law standard.
Machine learning in production

Now that you hopefully feel more comfortable experimenting with machine learning and building applications using different models and inputs, let’s talk about the different aspects of putting machine learning models and systems into production.
Making Non-Stochastic Control (Almost) as Easy as Stochastic

Recent literature has made much progress in understanding \emph{online LQR}: a modern learning-theoretic take on the classical control problem in which a learner attempts to optimally control an unknown linear dynamical system with fully observed state, perturbed by i.i.d. Gaussian noise. It is now understood that the optimal on time horizon $T$ against the optimal control law scales as $\widetilde{\Theta}(\sqrt{T})$. In this paper, we show that the same rate (against a suitable benchmark) is attainable even in the considerably more general non-stochastic control model, where the system is driven by \emph{arbitrary adversarial} noise (Agarwal et al. 2019). In other words, \emph{stochasticity confers little benefit in online LQR}. We attain the optimal $\widetilde{\mathcal{O}}(\sqrt{T})$ when the dynamics are unknown to the learner, and $\mathrm{poly}(\log T)$ when known, provided that the cost functions are strongly convex (as in LQR). Our algorithm is based on a novel variant of online Newton step (Hazan et al. 2007), which adapts to the geometry induced by possibly adversarial disturbances, and our analysis hinges on generic policy regret bounds for certain structured losses in the OCO-with-memory framework (Anava et al. 2015). Moreover, our results accomodate the full generality of the non-stochastic control setting: adversarially chosen (possibly non-quadratic) costs, partial state observation, and fully adversarial process and observation noise.
Building a Firefighting Training System in MR

This manuscript builds a firefighting training system using AR functionality designed for home use. In the system, the user can practice manipulating a fire extinguisher to put out the fire burning in the room. The speed of fire is examined to yield the sense of urgency and a pilot experiment is conducted. The result shows that the mechanism a fire’s flame continues to spread if nothing is done, could give the subject a sense of urgency while there are opinions that the image quality of the camera is low, and that the brightness of the virtual object is different.
Avoiding Communication in Logistic Regression

Stochastic gradient descent (SGD) is one of the most widely used optimization methods for solving various machine learning problems. SGD solves an optimization problem by iteratively sampling a few data points from the input data, computing gradients for the selected data points, and updating the solution. However, in a parallel setting, SGD requires interprocess communication at every iteration. We introduce a new communication-avoiding technique for solving the logistic regression problem using SGD. This technique re-organizes the SGD computations into a form that communicates every $s$ iterations instead of every iteration, where $s$ is a tuning parameter. We prove theoretical flops, bandwidth, and latency upper bounds for SGD and its new communication-avoiding variant. Furthermore, we show experimental results that illustrate that the new Communication-Avoiding SGD (CA-SGD) method can achieve speedups of up to $4.97\times$ on a high-performance Infiniband cluster without altering the convergence behavior or accuracy.
The dynamics of the bipartite and tripartite entanglement in the three-qubit Dicke model

本文利用绝热近似方法和精确对角化方法研究三量子比特Dicke模型中的纠缠动力学. 处于两种典型的纠缠态GHZ态和W态上的量子比特在时间演化过程中与辐射光场发生强耦合作用, 在各种子系统间产生纠缠, 通过分析这些纠缠的演化特性发现初始GHZ态的三体纠缠鲁棒性比W态强, 这与旋波近似结论一致. 与旋波近似下结果不同的是, 两种态中任意一对量子比特间的纠缠都随时间演化到几乎为零, 而三体纠缠随时间周期演化, 且纠缠程度相对较强, 说明系统中的强耦合作用通过抑制量子比特中的对纠缠来支持三体纠缠.
SISTEM PAKAR IDENTIFIKASI KERUSAKAN MOTOR KAWASAKI NINJA 250 CC DENGAN METODE FORWARD CHANNING BERBASIS ANDROID

Refers to the times, expert systems have been used in various fields. In its use an expert system is a system developed to include expert thinking in solving problems in a field into a technology-based application or system. Seeing the many uses of motorized vehicles, in this study the expert system will be used as a solution to the problem of damage to the 250cc Ninja Kawasaki motor vehicle. This expert system is designed with an androidbased application and will identify existing damage based on the symptoms that will appear on the application screen and selected by the 250cc Kawasaki Ninja motorcycle users who want to repair the damage to the motorbike. Identification data and symptoms used are data from the company's mechanical staff who work directly with the motor of the brand. This expert system application is also built with the forward chaining method and using Android studio as a developer application
Software Development Security

In this chapter, you will learn tips about software development, various development models, the SDLC, testing, and effectiveness in software security.
The Background of Tax Reform and Changes of Tax-types in North Korea after Liberation — Focused on National Tax —

After liberation, while North Korea implemented ‘Democratic Reform’ in 1946 and economic planning in 1947, the financial support was necessary. Consequentially, North Korea carried out tax reform and set up new tax revenues suitable for ‘Democratic Reform.’ Income tax, turnover tax, agricultural tax-in-kind, and deduction from profit became the state''s significant new revenue. The introduction of Agricultural tax-in-kind reflected that rural area was the primary revenue for economic reconstruction. Income tax, introduced in the Japanese colonial period, was differently utilized for income redistribution and tax equity. North Korea reorganized income tax policy, which reinforced differential tax rates and reflected income character; this could be utilized for classification policy. Turnover tax, deductions from profit and other tax items were also transformed and managed for the necessity of North Korea as a newly formatted independent state while partially maintaining the existing tax systems from the Soviet Union or Japanese Colonial era.