Reviewerly/exHarmony huggingface dataset:
Consistent-Resolution Network for 3D Hand Shape Estimation from a Single RGB Image

Abstract We propose a novel method for 3D hand shape estimation from a single RGB image. Most exiting methods leverage a deep network to extract a low-resolution representation to estimate 3D coordinates, which always leads to the loss of spatial information. In contrast, we present a Consistent-Resolution Network (CRNet) to extract the same resolution representation as the original image, thus preserve more details about spatial information. Specifically, we introduce the recent high-resolution network (HRNet) to generate high-resolution feature maps, which can attain high-resolution representation of the original image. Then, we design a deconvolution module to recover this map to the size of the original image. Therefore, we can directly leverage this feature to learn the precise 2D shape and the depth map, and transfer them into 3D coordinates in the camera space. Through extensive experiments on a large real-world dataset FreiHAND, we show that our proposed method can predict precise and suitable 3D hand shape from a monocular view.
Development of Night Time Calling System by Eye Movement Using Wearable Camera

This paper proposes a night time call system using a wearable camera for patients. The proposed system consists of a wearable camera, computer, relay controller, and nurse call. The user wears the wearable camera. All captured eye images are fed to the convolutional neural network to detect the pupil center. When the detected pupil center exceeds a preset threshold value, the computer sends the signal to operate the nurse call via each relay controller. Two experiments were conducted to evaluate the proposed system: verification of the accuracy of pupil center detection and quantitative evaluation of call success. In the former experiment, we collected 2,800 eye images from seven people and conducted a pupil center detection experiment on several training conditions. As a result, an average error of 1.17 pixels was obtained. In the latter experiment, a call experiment was conducted on five healthy people. The experiment time for each subject was about five minutes. The subject experimented while lying on the bed. Twelve audio stimuli were given in one experiment, after getting the stimuli, the subject moved his eye. The correct call in response to the audio stimulus was considered successful, and the precision, recall, and F-measure were calculated. As a result, we obtained the precision, recall, and F-measure of 0.83, 1.00, and 0.91, respectively. These experimental results show the effectiveness of the proposed system.
PROFESSIONAL MOBILITY AS AN INDICATOR OF CONTINUOUS PEDAGOGICAL EDUCATION

The article deals with the continuous pedagogical education which provides to the teacher understanding of own place in multinational society cause of fast adaptation in it. The professional mobility which gives to the modern teacher an opportunity to adapt changes and to flexibly reconstruct the direction and content of own professional and pedagogical activity is one of indicators of continuous pedagogical education. Continuous pedagogical education provides constant development, independent increase educational and cultural levels (independent introductions of competences, necessity for professional activity) both adaptations in multinational and polycultural the environment. The analysis of scientific literature is presented. It is noted that professional mobility of specialists is studied by different sciences, such as: sociology (T. Zaslavskaya, S. Lipset, P. Sorokin and others); economics (P. Blau, N. Nichkalo, O. Simonchuk, O. Filatov, D. Chernylevsky); psychology (I. Martyniuk, L. Mitina, V. Osovsky); pedagogy (N. Kovalisko, B. Likhachev, P. Novikov, L. Ponomaryov, etc.). This allows us to argue that the concept of “professional mobility” is interdisciplinary. The structure of the teacher’s professional mobility is presented, which consists of four components: personal, cognitive, communicative and activity. On the basis of components and indicators the levels of professional mobility of the teacher are distinguished: reproductive, basic, productive. A role of continuous pedagogical education in formation and development of personality is educed.
The White Rose University Press: an academic-led open access publisher

The case study aims to outline the development of the White Rose University Press explaining why an open access press came into being as part of the long-established White Rose Consortium created by the universities of Leeds, Sheffield and York. The brief case study seeks to highlight the consortia motivations, appetite for innovation and attitudes towards risk and what this meant for the technology and functional model choices made during the project. The impact of the press thus far is assessed along with lessons learned during the project and developments for the future.
Method of Selecting Experts Based on Analysis of Large Unstructured Data and Their Relations

The paper describes the problems of automatic selection of experts for reviewing scientific texts. Existing methods are analyzed, and a new selection method is proposed, based on obtaining a ranked list of relevant experts by processing a large amount of unstructured data. A technique for evaluating the results of similar methods is proposed and the effectiveness of the proposed approaches are studied in experiments.
Anomaly Detection in High-Dimensional Data Based on Autoregressive Flow

Anomaly detection of high-dimensional data is an important but yet challenging problem in research and application domains. Unsupervised techniques typically rely on the density distribution of the data to detect anomalies, where objects with low density are considered to be abnormal. The state-of-the-art methods solve this problem by first applying dimension reduction techniques to the data and then detecting anomalies in the low dimensional space. However, these methods suffer from inappropriate density estimation modeling and decoupled models with inconsistent objectives. In this work, we propose an effective Anomaly Detection model based on Autoregressive Flow (ADAF). The key idea is to unify the distribution mapping capability of flow-based models with the neural density estimation power of autoregressive models. We design an autoregressive flow-based model to infer the latent variables of input data by minimizing the combination of latent error and neural density. The neural density of input data can be estimated naturally by ADAF, along with the latent variable inference, rather than through an additional stitched density estimation network. Unlike stitching decoupled models, ADAF optimizes the same network parameters simultaneously by balancing latent error and neural density estimation in a unified training fashion to effectively separate the anomalies out. Experimental results on six public benchmark datasets show that, ADAF achieves better performance than state-of-the-art anomaly detection techniques by up to 20% improvement on the standard \(F_{1}\) score.
Continuous Information Monitoring in Software Startups

Abstract Software startups are central nowadays and considered primary drivers of economy and innovation. Lean and agile approaches are suggested for software startups to continuously build and validate the product. Thereby, they need to balance between the speed to deliver product and the quality of the product. It further urges startups to continuously monitor the versatile information, adjust their directions, and keep the bird’s-eye view. However, the preliminary literature review highlights that software startups, especially at the early stages, are not even aware of the need for information monitoring. This research project aims to identify how software startups decide what information needs to be monitored. The research plan proposes to utilize multiple case-study and surveys as data collection methods while grounded theory and factor analysis as data analysis procedures. Overall, both qualitative and quantitative research methods are expected to be implemented. The prospective research results encompass a framework to decide what information needs to be continuously monitored.
MERP

With the advent of the big data era, the research focuses on how to enhance the reliability, availability and high performance of the cloud storage system. Aiming to cope with extensive data storage, a replication placement strategy based on rack-awareness is applied in Hadoop Distributed File System (HDFS). Without synthetically considering the heterogeneity of cloud storage cluster and the load differences of each service node, however, the emergence of load imbalance is inevitable in HDFS. Focusing on the deficiency of the default replication placement method of HDFS, a multi-index evaluation replication placement scheme referred to as MERP is proposed in this paper. MERP takes a holistic view of the load characteristic, hardware performance and network topological distance of each datanode and leverages a combination-weighting TOPSIS model to comprehensively evaluate candidate datanodes and select the optimal one for replication placement. The simulation results conclusively demonstrated that our MERP outperforms the default replication placement of HDFS in terms of load balancing for distributed cloud storage cluster.
Human Action Recognition Method Based on Video-Level Features and Attention Mechanism

In order to capture the spatiotemporal information in the video and improve the long-term modeling capability of the network, two-stream network usually adopts the method of sparse sampling. However, there are two problems in the process of feature extraction on the sample. One is the unreasonableness of the snippet-level features corresponding to the video labels, and the other is that the salient features are not highlighted. In view of the above two points, we propose an action recognition method based on video-level features and attention mechanism (VFAM), which combines snippet-level features to generate video-level features, and adds attention mechanism to give effective features with greater weight, and good experimental results have been achieved on the dataset HMDB51, reflecting the superiority and robustness of our method.
67‐2: Lightweight Tone‐mapped HDRNET with Exposure Stack Generation

In this paper, we propose an efficient and lightweight deep neural network, LW‐HDRNET, which quickly and accurately reconstructs tone‐mapped high dynamic range (HDR) images. The proposed network generates over‐exposed and under‐exposed images from a single LDR image and then reconstructs the tone‐mapped HDR image. Experimental results show that the performance is better than the existing methods and has up to 4,000 times fewer parameters.
Story Summarization Using a Question-Answering Approach

Summarization is the process of selecting representative data to produce a reduced version of the given data with a minimal loss of information; so, it generally works on text, images, videos, and speech data. The chapter deals with not only concepts of text summarization (types, stages, issues, and criteria) but also with applications. The two main categories of approaches generally used in text summaries (i.e., abstractive and extractive) are discussed. Abstractive techniques use linguistic methods to interpret the text; they produce understandable and semantically equivalent sentences with a shorter length. Extractive techniques mostly rely on statistical methods for extracting essential sentences from the given text. In addition, the authors explore the SACAS model to exemplify the process of summarization. The SACAS system analyzed 50 stories, and its evaluation is presented in terms of a new measurement based on question-answering MOS, which is also introduced in this chapter.
Toward developing performance metrics for library print-based specialized collections using available circulation data and ratio analysis

The purpose of this study is to contribute to the establishment of metrics that can be used by libraries to analyze, anticipate, and plan for resource allocation for specialized print-based collections within an academic library. In the context of transactions associated with the circulation of library materials, that is checkouts, renewals, and holds, the resources this study will propose includes two ratios that can be used for evaluating and justifying continued allocation of library resources to specialized print collections. These ratios proposed are the Proportionate Number (P/N) and Internal P/N (I-P/N) Ratios. Beginning with the conceptual framework of how data driven decision making can contribute to the operation of the library, the study will also provide concrete examples of how to analyze data from an Integrated Library System (ILS). Data will be presented for the case study of Gumberg Library at Duquesne University. The study hopes to address a perceived gap in the literature that percentage of users accessing a collection is often not considered in the analysis of circulation data.
ServiceMarq

In an era, where large numbers of academic research papers are submitted to conferences and journals, the voluntary services of academicians to manage them, is indispensable. The call for contributions of research papers -- through an e-mail or as a webpage, not only solicits research works from scientists, but also lists the names of the researchers and their roles in managing the conference. Tracking such information which showcases the researchers' leadership qualities is becoming increasingly important. Here we present ServiceMarq - a system which proactively tracks service contributions to conferences. It performs focused crawling for website-based call for papers, and integrates archival and natural language processing libraries to achieve both high precision and recall in extracting information. Our results indicate that aggregated service contribution gives an alternative but correlated picture of institutional quality compared against standard bibliometrics. In addition, we have developed a proof of concept website to track service contributions and is available at https://cfp-mining-fe.herokuapp.com and our github repo is available at https://github.com/shitian007/cfp-mining
Script Converter for Automated Testing of Laptops

Notebook testing in production stage is a very important link, which is an important guarantee for high quality and high reliability in the process of computer production. Efficient software and hardware testing can ensure the quality of the products, at the same time improve the efficiency of production. At present, there have been many open source test automation tools and automated tools for commercial purposes, but these automated testing tools need manually writing test scripts, which consumes a lot of time on the test script development, increasing the notebook production cycle. Therefore, for the development of the test scripts, a test script converter is designed to automatically convert test scripts. First of all, according to the test requirements for operation, testers use Ranorex, a manual recording, generate XML format file, and then use the script converter converts it to corresponding test scripts in the end. Through the experiment, the script converter can effectively and accurately work out test scripts, which brings convenience for the notebook computer test, improves the quality of the production of notebook and shortens the notebook generation cycle.
Phenomenological Layer Structure of an Intelligent Agent for IoT

The main components in the study of multi-agent systems in the field of IoT are intelligent agents and contexts. From the viewpoint of each agent, the primary objective is to choose actions that maximize the agent's future utility. The choice of a correct action depends on a process of complex data collection from context. Using a neuroscience model, we consider the receptive field to slice the context area and to group the appropriate sensors. To permit the phenomenological analysis and consequent cognitive evaluation, we have applied Bellmund and Doeller's model of hippocampus identifying two types of cells: the map cells and the grid cells. The first linked to the positional aspects and the second to cognitive aspects. This paper describes only the phenomenological layer architecture. From a scientific point of view there is a novel fusion between fluent calculus and neural network ensemble. Such net is used to associate to situations' fluent the appropriate schema of actions.
Markovian Segmentation Of Textured Color Images

This paper presents an application of textured color images segmentation using hidden Markov chain model. We propose two comparative studies, The first one is between EM(Expectation-Maximization) algorithm,SEM (Stochastic Expectation-Maximization) algorithm and ICE (Iterative Conditional Estimation) algorithm, these estimators are used to estimate the parameters of Hidden Markov Chain with Independent Noise. The second one is between MAP(Maximum a Posteriori) and MPM(Maximum Marginal Posteriori) algorithms. These strategies are used to estimate the resulted images of segmentation. The obtained results show that EM algorithm, SEM, and ICE algorithm give the same results of segmentation under MAP and MPM algorithms. But, MPM provides better segmentation results than MAP.
Promotion Path of Children’s Literary Works Reading in the New Media Age

Children’s literary works, as the main spiritual food for children’s reading, have a very important impact on stimulating children’s reading interest, and improving children’s cognitive analysis ability and aesthetic appreciation ability. Under the new media environment, various new media technologies are widely used, affecting people’s lives, and also have a huge impact on traditional reading methods. Although the “fragmented reading” with mobile phones, e-books, Internet and other electronic terminal as the main carrier gets both praise and blame, it is undeniable that children’s literature reading in the new media environment presents a richer way of interaction and communication. In the context of new media, the reading of children’s literary works should be actively promoted. Based on the childlike innocence and aesthetic characteristics of children’s literary works, the establishment of an online and offline reading promotion system is proposed, so that reading can promote the healthy growth of children and lay a solid foundation for their future life.
Video classification by fusing two-stream image template classification and pretrained network

A motion energy image (MEI) is a spatial template that collapses regions of motion into a single image in which more moving pixels are brighter than others. The forward single-step history image (fSHI) is a spatiotemporal template that shows the presence and direction of motion. Each video can be described using these templates. Recently, the popularity of deep learning architectures for human activity recognition encourages us to explore the effectiveness of combining them with these templates. Hence, three new methods are introduced to convert the problem of human activity recognition in video into image templates classification. In method 1, each video is split into N groups of consecutive frames, and the MEI is computed for each group. Transfer learning with the fine-tuning technique is used for classifying these templates. fSHIs or spatiotemporal templates are used for classification in method 2, similar to method 1. Fusing the two streams of these templates is introduced as method 3. Among these methods, method 3 outperforms the others and is called the proposed method. It achieves recognition accuracies of 92.60% and 93.40% for the UCF Sport and UCF-11 action datasets, respectively. Also, it is compared with state-of-the-art approaches, and the results show that the proposed method has the best performance.
A Framework for Decentralized Private Random State Generation and Maintenance for Multiplayer Gaming Over Blockchain

The transparency and immutability properties offered by the burgeoning field of blockchain technology make it an increasingly popular choice for applications across many domains. One such application is online gaming. Blockchain offers participants the ability to verify the fairness of games in a manner previously unattainable by the classical centralized approach. However, the introduction of blockchain incurs additional overhead and poses unique challenges necessary to overcome in order to be a viable alternative. The transparency blockchain provides opens potential avenues for collusion, particularly in multiplayer games, which must be addressed. Furthermore, the emulation of random state, a core component of online gaming, is rendered difficult in a decentralized context. No approach exists which manages random state for multiplayer gaming in a completely decentralized manner. We propose a novel approach toward this end, significantly extending the utility of blockchain technology to online gaming.
Making researchers’ lives easier and managing risk at the University of Adelaide: The research data project

The case study describes the research data (ReDa) project that was executed at the University of Adelaide between 2017 and 2019 to provide systems and services to support improved management of research data. The ReDa project is considered to have been very successful, and the case study presents the key reasons for that success; namely, good strategic planning, highly effective change management and engagement with researchers, skilled project management including good management of staff turnover, skilled and expert staff and significant effort to transition to business as usual throughout the project.
Using LatInfLexi for an Entropy-Based Assessment of Predictability in Latin Inflection

This paper presents LatInfLexi, a large inflected lexicon of Latin providing information on all the inflected wordforms of 3,348 verbs and 1,038 nouns. After a description of the structure of the resource and some data on its size, the procedure followed to obtain the lexicon from the database of the Lemlat 3.0 morphological analyzer is detailed, as well as the choices made regarding overabundant and defective cells. The way in which the data of LatInfLexi can be exploited in order to perform a quantitative assessment of predictability in Latin verb inflection is then illustrated: results obtained by computing the conditional entropy of guessing the content of a paradigm cell assuming knowledge of one wordform or multiple wordforms are presented in turn, highlighting the descriptive and theoretical relevance of the analysis. Lastly, the paper envisages the advantages of an inclusion of LatInfLexi into the LiLa knowledge base, both for the presented resource and for the knowledge base itself.