Reviewerly/exHarmony huggingface dataset:
Generalizing Variational Autoencoders with Hierarchical Empirical Bayes

Variational Autoencoders (VAEs) have experienced recent success as data-generating models by using simple architectures that do not require significant fine-tuning of hyperparameters. However, VAEs are known to suffer from over-regularization which can lead to failure to escape local maxima. This phenomenon, known as posterior collapse, prevents learning a meaningful latent encoding of the data. Recent methods have mitigated this issue by deterministically moment-matching an aggregated posterior distribution to an aggregate prior. However, abandoning a probabilistic framework (and thus relying on point estimates) can both lead to a discontinuous latent space and generate unrealistic samples. Here we present Hierarchical Empirical Bayes Autoencoder (HEBAE), a computationally stable framework for probabilistic generative models. Our key contributions are two-fold. First, we make gains by placing a hierarchical prior over the encoding distribution, enabling us to adaptively balance the trade-off between minimizing the reconstruction loss function and avoiding over-regularization. Second, we show that assuming a general dependency structure between variables in the latent space produces better convergence onto the mean-field assumption for improved posterior inference. Overall, HEBAE is more robust to a wide-range of hyperparameter initializations than an analogous VAE. Using data from MNIST and CelebA, we illustrate the ability of HEBAE to generate higher quality samples based on FID score than existing autoencoder-based approaches.
Organization of Work of Volost Libraries and Village Libraries and Reading Rooms in the Districts of the Oryol Province in the First Years of Soviet Power

The article, based mainly on archival documents, considers the process of organizing the work of libraries in districts of the Orel province in the 1920s. Everywhere in towns and villages of the province, there were opened mass public peoples libraries, were created cultural and educational centres, village libraries and reading rooms, clubs, people’s houses and other cultural and educational institutions of new type. The author notes that during that period a lot of work was done in Oryol province to unite libraries into a single network. The article shows that the main “cell” of library network in the village becomes the volost library. Village libraries and reading rooms created in each locality were “the primary focus of cultural and educational work”. Special attention is paid to the study of forms and methods of library services to the population in the volost libraries and village libraries and reading rooms in the 1920s. The political and educational activities of the volost libraries and village libraries and reading rooms were very diverse in the studied period. Taking into account the high level of illiteracy of the population of Oryol villages, libraries preferred active forms of “living word and work with a book”: loud reading, conversation, agitation staging, drama club, illustrative evening, etc. The author notes that special attention was paid to the organization of work with children in the volost library. It is important to understand that along with the common cultural development of the rural population, active propaganda work was carried out in order to solve the current political problems. The 1920s is a separate period of library history, which is interesting for understanding by the modern Russian library science. The author concludes that this material expands and complements the existing understanding in regional library science of the librarianship of the Oryol province of the post-revolutionary decade, proves its stabilizing nature, which laid the basis for mass library construction in the following decades.
Streaming Media Players

Streaming media players are newer devices in the music teacher’s toolbox but are becoming more commonly found in the classroom. Being one of the more modern kids on the block, there are even more opportunities to create activities and resources specifically for these devices. Use your devices as a tool for your instruction. Such as a remote for your computer or as your recording device for assessments. Students can use interactive instrument apps to perform together, create VR and AR experiences on their own, or use their devices to interact with their recorders to practice. Mobile devices are perfect for allowing students to have more independence in their learning and create their own musical works of art.
Historian of the Russian State Library. In Memory of L.M. Koval (1933—2020)

The article is devoted to the memory of Lyudmila Mikhailovna Koval (October 17, 1933 – February 15, 2020), historian, Head of the History sector of the Russian State Library (RSL) and the Museum of Library history. The author presents brief biographical information about L.M. Koval, the author of more than 350 scientific and popular scientific works in Russian and in 9 foreign languages. She published 29 books in Publishing houses “Nauka”, “Kniga”, “Letniy Sad”, ”Pashkov Dom”, most of the works are dedicated to the Library. Special place in the work of L.M. Koval is given to the Great Patriotic War theme. The article considers the works devoted to the activities of Library staff during the War period. L.M. Koval paid much attention to the study of activities of the Library’s Directors. She prepared books and articles about the Directors of the Moscow Public and Rumyantsev Museums and Library from the end of the 19th century and almost to the end of the 20th century: N.V. Isakov, D.S. Levshin, V.A. Dashkov, M.A. Venevitinov, I.V. Tsvetaev, V.D. Golitsyn, A.K. Vinogradov, V.I. Nevsky, N.M. Sikorsky. The author notes contribution of L.M. Koval to the study of the Library’s history. Specialists in the history of librarianship widely use bibliography of L.M. Koval in their research. The list of sources contains the main works of L.M. Koval, and the Appendix includes reviews of publications by L.M. Koval and the works about her.
Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video

We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time, the basic idea of which is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. To handle the common low texture regions on tissue surfaces, we propose effective post-processing steps for the local stereo matching method to enlarge the radius of constraint, which include outliers removal, hole filling and smoothing. Since the tissue models obtained by stereo matching are limited to the field of view of the imaging modality, we propose a model mosaicking method by using a novel feature-based simultaneously localization and mapping (SLAM) method to align the models. Low texture regions and the varying illumination condition may lead to a large percentage of feature matching outliers. To solve this problem, we propose several algorithms to improve the robustness of SLAM, which mainly include (1) a histogram voting-based method to roughly select possible inliers from the feature matching results, (2) a novel 1-point RANSAC-based P$n$P algorithm called as DynamicR1PP$n$P to track the camera motion and (3) a GPU-based iterative closest points (ICP) and bundle adjustment (BA) method to refine the camera motion estimation results. Experimental results on ex- and in vivo data showed that the reconstructed 3D models have high resolution texture with an accuracy error of less than 2 mm. Most algorithms are highly parallelized for GPU computation, and the average runtime for processing one key frame is 76.3 ms on stereo images with 960x540 resolution.
Survey of Human Factors in Crisis Responsive Software Development

Software development, despite all the significant improvements it contributes to society, is a very expensive high-risk venture. Every software project commences with the intention to deliver a software product on time and within budget, but a great percentage of these software projects either fail to achieve their full potential or fail entirely. A lot of factors come to play in this regard and the dominant amongst them, which is often overlooked and oftentimes the biggest or strongest factor, is the human factor, as software development is a basic human endeavor. Knowledge and understanding of human factors can greatly influence the success of software development. The purpose of this survey is to find out and throw more light on the human factors that have influences in crisis responsive software development, spell out some recommendations, and suggest future research directions. In this paper, we use a case study to investigate the human factors.
SISTEM INFORMASI KEPEGAWAIAN SEBAGAI PENYEDIA INFORMASI PEMILIHAN PEGAWAI PENERIMA PENGHARGAAN SATYA LENCANA KARYA SATYA DI RADIO REPUBLIK INDOSESIA PALANGKA RAYA

Staffing information system (Simpeg) as an information provider of election Officers Work Medal Awardees Satya Lencana In RRI Palangkaraya aims to be a place of useful information provider for submission of award Satya lencana karya satya so that it could be effective, efficient, and accurate by simplifying the existing system. By having this website is expected to handle the process of logging employees so that it can help in the process of registration of a Nobel Prize Medal work of satya.&#x0D; Methodology in the making of this website using the method waterfall, waterfall method which consists of several parts or stages – stages, namely analysis, design, coding and testing. This methodology was chosen because every step of the development of this website is structured, and clearly documented. The programming language used is PHP and MySQL as a database.&#x0D; At this stage of testing this website using black box testing. Black-Box Testing to focus on the functional specifications of the program. After doing the testing, can be seen the result of this site that this website can perform its function, namely to manage data, time data family history, history of education, employment, lencan setya, data filing SKP, and PKP
Fair and autonomous sharing of federate learning models in mobile Internet of Things

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency.
IMPACT OF BROKEN HOMES ON MATHEMATICS STUDENTS ACADEMIC ACHIEVEMENT IN SENIOR SECONDARY SCHOOLS IN NASSARAWA ZONAL EDUCATION AREA, KANO STATE – NIGERIA

This study was carried out to determine the impact of broken homes on mathematics students' academic achievement in senior secondary schools in nassarawa zonal education area, kano state.A sample of 349 out of a population of 21,403 students from 24 schools was randomly drawn.A survey research design was used.A researcher constructed questionnaire was used after being validated and subjected to a pilot trial.A reliability cofficient of r=0.86 was obtained while two psychology and one English language specialists validated the questionnaire.In addition to the questionnaire, the students' 2nd year third term senior secondary school examination mathematics results were used.Three hypotheses treating whether or not a significant difference exist between students from stable and broken homes, male and female students from broken homes as well as students from broken high and low income were formulated.The t-test analysis showed students from stable homes perform significantly better than those from broken homes with no significant difference in achievement between male and female students from broken home but there is a significant difference in academic achievement between students from high and low home.It was recommended that teaching of family ethics should be emphasized in schools.
Talent Demand Analysis of LIS Based on Python and Apriori

This article uses Python crawler technology to obtain 3715 recruitment demands data which is highly correlated to LIS, from mainstream recruitment website in China, such as 51job, liepin, lagou, etc., and divides them into three categories: Library Management, Archives Management, and Information Science. Different positions are analyzed from multiple dimensions such as city, industry, education, experience, salary, etc. Then use jieba word segmentation technology to analyze the key skills of different positions. Finally, use Apriori algorithm to do further data mining with the talent demands of different positions.
ERROR ANALYSIS BY USING SIMPLE PRESENT IN WRITTING DESCRIPTIVE TEXT OF X GRADE STUDENTS AT MAN TOLITOLI

The purposes of this study were to find out the dominant errors on simple present tense in descriptive texts made by the tenth grade students of MAN Tolitoli and to find out the possible causes of those errors. The sampling technique used in this study was purposive sampling. The researcher took one class as the subject of this study. The sample was X MIA 3. The result of the analysis shows that there were 10 types of errors. They are omission of be, wrong form of be, double be, wrong use of singular and plural form, addition of be (before and after verb), omission of suffix -s/-es, wrong use of verb, wrong form of modal auxiliary, omission of verb, and wrong form of negative sentence. It is concluded that the dominant errors lies on the omission of suffix –s/-es from the verb of third person singular subjects in the students’ descriptive texts whose proportion of the errors is 24.65%. Based on the result of the research, it is suggested that the tenth grade students of MAN Tolitoli should be given intensive exercises on the correct structure of simple present tense in their writing. It is quite necessary for the tenth grade students and the teacher of MAN tolitoli to be informed about the result of this study so that they can improve their teaching and learning process. Key terms: Error Analysis, Simple Present Tense, Descriptive text
Awareness of cloud-based library management software (LMS) in VTU engineering college libraries

Evolution of Information Communication Technology (ICT) trigger the development of many new technologies. Internet and cloud computing are few among them. Invasion of ICT and its applications inspired the librarians to automate libraries and provide good services to their stakeholders. Library automation is a giant leap in the library science filed. It speedup the process of in-house activities and increase the effectivity in library services. Cloud computing is the recent development in communication field. Latest development is entire world is embracing cloud computing and libraries are not exceptional. Cloud-based library management software is a cost effective, work effective to provide good services to the library users in academic libraries. Keywords: Cloud computing, Library Management Software, Cloud-based ILMS.
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation

In this paper, we are interested in generating fine-grained cartoon faces for various groups. We assume that one of these groups consists of sufficient training data while the others only contain few samples. Although the cartoon faces of these groups share similar style, the appearances in various groups could still have some specific characteristics, which makes them differ from each other. A major challenge of this task is how to transfer knowledge among groups and learn group-specific characteristics with only few samples. In order to solve this problem, we propose a two-stage training process. First, a basic translation model for the basic group (which consists of sufficient data) is trained. Then, given new samples of other groups, we extend the basic model by creating group-specific branches for each new group. Group-specific branches are updated directly to capture specific appearances for each group while the remaining group-shared parameters are updated indirectly to maintain the distribution of intermediate feature space. In this manner, our approach is capable to generate high-quality cartoon faces for various groups.
Value Chain Analysis At Bank Sampah Bersinar In Bandung Regency As A Competitive Advantage Strategy

This study aimed to determine and analyze the value chain of waste management at Bank Sampah Bersinar in Bandung. The subjects in this study were the actors of the waste management value chain. In this study, there were three informants selected by purposive sampling and snow ball sampling methods. This study was conducted by using qualitative methods with the analysis method including value chains, SWOT analysis, value added and competitive advantage strategies. Based on the results of the discussion and analysis conducted in this study, it was found that in general Bank Sampah Bersinar only conducts basic value chain activities of waste management (input-process-output) without giving a higher value than the waste results obtained. For this reason, additional activities are needed to add more value to the treated waste. Furthermore, the results of the SWOT analysis revealed three main problems faced by Bank Sampah Bersniar namely distance or location, purchase price and public knowledge. The analyzed value chain improvement strategy is proved by the illustrations made, which indicates that it can increase waste value added from 6.86 percent to 74.36 percent.
EXPERIMENTAL DESIGN FOR DEPENDENT DATA

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30th ACM international conference on hypertext and social media

The ACM Hypertext conference is a premium venue for high quality peer-reviewed research on hypertext theory, systems and applications. It is concerned with all aspects of modern hypertext research including social media, semantic web, dynamic and computed hypertext and hypermedia as well as narrative systems and applications.
INCOME TAX ASPECTS OF CRYPTOCURRENCIES – LEGAL AND YOUNG ECONOMISTS’ VIEW IN THE CZECH REPUBLIC

Cryptocurrencies are used not only as a payment instrument but also as a speculative and investment instrument. In the context of their use, the question arises of how and whether they should be taxed. The aim of the paper is to analyse and to assess the taxation of income from the cryptocurrency operations from the perspective of a non-business natural person in compliance with the Act on Income Taxes in the Czech Republic, concurrently to find out the attitude of young future economists familiar with cryptocurrencies to this tax issue and to identify socioeconomic factors influencing their attitude. For this purpose, an online questionnaire survey was conducted among economists aged 19-35 years in May 2019. A total of 269 responses were obtained and evaluated using descriptive statistics and ordinal regression. While, according to the effective law, income from all cryptocurrency operations is to be taxed, the results show that most of the respondents (44.98%) would tax the income depending on how cryptocurrencies are used.
Robust Collective Classification against Structural Attacks

Collective learning methods exploit relations among data points to enhance classification performance. However, such relations, represented as edges in the underlying graphical model, expose an extra attack surface to the adversaries. We study adversarial robustness of an important class of such graphical models, Associative Markov Networks (AMN), to structural attacks, where an attacker can modify the graph structure at test time. We formulate the task of learning a robust AMN classifier as a bi-level program, where the inner problem is a challenging non-linear integer program that computes optimal structural changes to the AMN. To address this technical challenge, we first relax the attacker problem, and then use duality to obtain a convex quadratic upper bound for the robust AMN problem. We then prove a bound on the quality of the resulting approximately optimal solutions, and experimentally demonstrate the efficacy of our approach. Finally, we apply our approach in a transductive learning setting, and show that robust AMN is much more robust than state-of-the-art deep learning methods, while sacrificing little in accuracy on non-adversarial data.
Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation

Reinforcement learning algorithms such as hindsight experience replay (HER) and hindsight goal generation (HGG) have been able to solve challenging robotic manipulation tasks in multi-goal settings with sparse rewards. HER achieves its training success through hindsight replays of past experience with heuristic goals, but under-performs in challenging tasks in which goals are difficult to explore. HGG enhances HER by selecting intermediate goals that are easy to achieve in the short term and promising to lead to target goals in the long term. This guided exploration makes HGG applicable to tasks in which target goals are far away from the object's initial position. However, HGG is not applicable to manipulation tasks with obstacles because the euclidean metric used for HGG is not an accurate distance metric in such environments. In this paper, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment. We evaluated G-HGG on four challenging manipulation tasks with obstacles, where significant enhancements in both sample efficiency and overall success rate are shown over HGG and HER. Videos can be viewed at https://sites.google.com/view/demos-g-hgg/.
NOISE MODEL OF QUANTIZATION OF THE VECTOR WITH NON-ZERO MATHEMATICAL EXPECTATIONS

The mathematical model of quantization noise arising in high-precision measuring systems is obtained. A new formula of the probability distribution density is obtained for the quantization algorithm with rounding to the nearest whole normal vector with non-zero mathematical expectations. This formula is a generalization of the result obtained for the probability density of the echo signal from drops of atmospheric moisture . The formula is obtained on the basis of the probability theory, Fourier series expansions and Fourier integral. For this density, the formulas of the expectation vectors and the second initial moment are obtained.