Reviewerly/exHarmony huggingface dataset:
Public relations in the digital era: New methods and tools

The beginning of the 21st century marked an evolutionary "leap" in Internet technology - the transition from static web pages whose content was determined solely by their creator to dynamic, interactive user-created web pages and the emergence and proliferation of social networks as a special and multi-purpose cyber info-sphere.In such a new and dynamic environment, naturally the need for new ways of functioning of public relations has emerged, finding new approaches and methods that would successfully fit into the new digital environment and take advantage of all the newly created potential. This next evolutive stage of the Internet - Web 2.0 brought interactive access web-pages, blogs, social networks/media, targeted advertising and pod-casts have become new and vital fields of contemporary PR. The new interactive technologies have enabled not only more accurate identification of target groups but also direct access to the collection of unimaginable quantity and quality of data that describes not only groups but individuals as well. This quantum leap in knowledge of target audiences made possible more precise "tailoring" of messages for each of them, thus dramatically increasing the effectiveness of these messages. This paper will analyze the principles, functions and possibilities of modern digital public relations, the methods and tools for it`s application as well as practical examples.
Building the Catalog

So far, we've created the essential pages for our site (or most of them), set up our data sources, and hooked in data from our data sources. Our shop is beginning to take shape, but this is where things get interesting – what about displaying products in our shop, ready for sale?
Student Actions in the Learning Process of Indonesian Language Subjects and Their Implications for Improving Discussion Ability

This study aims to analyze the types, functions and implications of students' speech acts, how the speech acts in speaking directly to students in class discussion activities in the Indonesian language subject at the Faculty of Economics, University of Prima Indonesia. This research method uses mequalitative descriptive research tode with a case study research design for Faculty students Economics of Prima Indonesia University, where the research data were collected using technique note, the method of observation, and listen. Sources of data were obtained from the results of students' speech acts in the learning process of discussing Indonesian Language courses. Types of speech acts used in discussion activities in the learning process are locus speech acts, illocutionary speech acts and perlocution speech acts. The speech act function used by students in discussion activities in the learning process is a locus speech act with a news function, with a question-speaking function, with a commanding speech function. Assertive illocutionary speech acts, directive illocutionary speech acts, expressive illocutionary speech acts, commissive illocutionary speech acts, and declarative illocutionary speech acts. Perlocutionary speech acts with the function of giving influence.
Single-Shot Motion Completion with Transformer

Motion completion is a challenging and long-discussed problem, which is of great significance in film and game applications. For different motion completion scenarios (in-betweening, in-filling, and blending), most previous methods deal with the completion problems with case-by-case designs. In this work, we propose a simple but effective method to solve multiple motion completion problems under a unified framework and achieves a new state of the art accuracy under multiple evaluation settings. Inspired by the recent great success of attention-based models, we consider the completion as a sequence to sequence prediction problem. Our method consists of two modules - a standard transformer encoder with self-attention that learns long-range dependencies of input motions, and a trainable mixture embedding module that models temporal information and discriminates key-frames. Our method can run in a non-autoregressive manner and predict multiple missing frames within a single forward propagation in real time. We finally show the effectiveness of our method in music-dance applications.
Analysis of Educational Needs for the Development of Elderly Learners’ Employment Competency

The purpose of this study is to analyze differences between the perceived importance and performance for the development of employment competency among elderly learners and is to identify prioritize their educational needs. Through the literature review, this study was divided the elderly learners’ employment competency such as preparation competency for job hunting, basic job competency, and vocational adjustment competency and analyzed the level of importance and performance with 12 competencies through t-test analysis. Borich Priority Formula and the Locus for Focus model were used to compare priorities and determine the items for educational needs of elderly learners’ employment competency. According to result, all items between the perceived importance and performance for the development of employment competency among elderly learners were found statistically significant differences. The largest item of competency for elderly learners was verified self-understanding and job-hunting design. The top priority item of educational needs for employment competency among elderly learners was shown as self-understanding and job-hunting design. The second items in priority were derived by ability of adjusting to change and interpersonal ability. Therefore, the findings of the study will contribute to the improved employment rate of elderly learners based on the development of educational programs around the employment competency of elderly learners on top priority at lifelong education institutions or at the local level.
Pifthon: A Compile-Time Information Flow Analyzer For An Imperative Language

Compile-time information flow analysis has been a promising technique for protecting confidentiality and integrity of private data. In the last couple of decades, a large number of information flow security tools in the form of run-time execution-monitors or static type systems have been developed for programming languages to analyze information flow security policies. However, existing flow analysis tools lack in precision and usability, which is the primary reason behind not being widely adopted in real application development. In this paper, we propose a compile-time information flow analysis for an imperative program based on a hybrid (mutable + immutable) labelling approach that enables a user to detect information flow-policy breaches and modify the program to overcome violations. We have developed an information flow security analyzer for a dialect of Python language, PyX, called Pifthon using the said approach. The flow-analyzer aids in identifying possible misuse of the information in sequential PyX programs corresponding to a given information flow policy (IFP). Pifthon has distinct advantages like reduced labelling overhead that ameliorates usability, covers a wide range of PyX programs that include termination-and progress-sensitive channels, in contrast to other approaches in the literature. The proposed flow analysis is proved to be sound under the classical non-interference property. Further, case study and experience in the usage of Pifthon are provided.
SENTIMENT ANALYSIS AND OPINION MINING ON PRODUCTS, MOVIES, TOURISM REVIEWS USING MACHINE LEARNING TECHNIQUES

Sentiment Analysis and Opinion Minning are one of the fast -emerging fields.Today our world is technologically advanced.There is extensive use of social media among the people.People express their opinions about their concerned domain on social networking sites or online websites.Sentiment Analysis helps to analyze people's reviews and opinions.This helps businesses, organizations, hotels, institutions, tourism industry, to know people's needs and try to modify their products and services according to it to yield the profits.Sentiment Analysis and Opinion Mining is performed in various domains like product reviews, movie reviews, tourism, education, politics, sports, etc.Some of the domains and the techniques used to analyze the Sentiment Analysis and Opinion Mining are discussed in this paper.
The 35<sup>th</sup> Year of AI EDAM: Where are We Heading?

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Practitioner-generated blog posts as evidence for software engineering research: attitudinal survey and preliminary checklist.

Background: Blog posts are frequently used by software practitioners to share information about their practice. Blog posts therefore provide a potential source of evidence for software engineering (SE) research. The use of blog posts as evidence for research appears contentious amongst some SE researchers. Objective: To better understand the actual and perceived value of blog posts as evidence for SE research, and to develop guidance for SE researchers on the use of blog posts as evidence. Method: We further analyse responses from a previously conducted attitudinal survey of 44 software engineering researchers. We conduct a heatmap analysis, simple statistical analysis, and a thematic analysis. Results: We find no clear consensus from respondents on researchers' attitudes to the credibility of blog posts, or on a standard set of criteria to evaluate blog-post credibility. We show that some of the responses to the survey exhibit characteristics similar to the content of blog posts, e.g., asserting prior beliefs as claims, with no citations and little supporting rationale. We illustrate our insights with ~60 qualitative examples from the survey ~40% of the total responses. We complement our quantitative and qualitative analyses with preliminary checklists to guide SE researchers. Conclusion: Blog posts are relevant to research because they are written by software practitioners describing their practice and experience. But evaluating the credibility of blog posts, so as to select the higher-quality content, remains an ongoing challenge. The quantitative and qualitative results, with the proposed checklists, are intended to stimulate reflection and action in the research community on the role of blog posts as evidence in software engineering research. Finally, our findings on researchers' attitudes to blog posts also provide more general insights into researchers' values for SE research.
AI and Data Science in Earth Observation

Geoinformation derived from Earth observation satellite data is indispensable for many scientific, governmental and planning tasks. Geoscience, atmospheric sciences, cartography, resource management, civil security, disaster relief, as well as planning and decision support are just a few examples. Furthermore, Earth observation has irreversibly arrived in the Big Data era, e.g. with ESA's Sentinel satellites and with the blooming of NewSpace companies. This requires not only new technological approaches to manage and process large amounts of data, but also new analysis methods. Here, methods of data science and artificial intelligence (AI), such as machine learning, become indispensable.In this talk, explorative signal processing and machine learning algorithms, such as compressive sensing and deep learning, will be shown to significantly improve information retrieval from remote sensing data, and consequently lead to breakthroughs in geoscientific and environmental research. In particular, by the fusion of petabytes of EO data from satellite to social media, fermented with tailored and sophisticated data science algorithms, it is now possible to tackle unprecedented, large-scale, influential challenges, such as the mapping of global urbanization one of the most important megatrends of global changes.
Modelling and comparison of image-analysis algorithms to measure blood velocity with laser scanning microscopy

Laser scanning microscopy is widely used to measure blood hemodynamics with line-scans. Analysis of red blood cell (RBC) velocity involves two types of algorithms. The algorithms of the first kind are the image-processing algorithms such as the shear [1], the LSPIV [2], the radon-transform [3] and the Fourier transform [4, 5] algorithms. Each of them provides the apparent velocity (V<sub>RBCapp</sub>) of RBCs in the image, which does not consider the scanning conditions. The algorithms of the second kind give the real RBC velocity from V<sub>RBC app</sub> and the scanning conditions [6]. Here we developed a predictive model that provide the accuracy of one image-processing algorithms. We find that the accuracy of the image-processing algorithm depends on several parameters such as the design of the scanned line, the scanning velocity and the velocity of RBCs. Last we validate our model by testing various types of artificial linescan images. The same strategy will be followed for various image-processing algorithms. Overall, our analysis will allow accurate comparisons of blood velocity from all vessel types in control and pathological animal models.
Warum wir Wissen über die Cyberpsychologie benötigen

No AccessCyberpsychologie in der ArbeitsweltMar 2021Warum wir Wissen über die Cyberpsychologie benötigenRüdiger MaasRüdiger MaasSearch for more papers by this authorhttps://doi.org/10.3139/9783446468078.001SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2021Pages: 1-17Print ISBN: 978-3-446-46666-1eISBN: 978-3-446-46807-8 Copyright & Permissions© 2021 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
S R Ranganathan's five laws of librarianship: Principles that stood the test of time in knowledge management scenario

The paper traces the developments in Knowledge Management scenario in the context of Ranganathan's Five Laws of Library Science and opines that they have application in a wide area beyond time. In Digital Libraries, in World Wide Web as a whole; or in any information system the important objectives are saving user's time, facilitating convenient access mechanisms etc. In all these contexts Five Laws are as applicable to the present as they will be to tomorrow. And that is where Ranganathan's Five Laws of Library Science left off nearly ninety years ago. The paper traces out application of Five laws in numerous contexts with overarching themes of service, access, and a respect for the user in information managment field through time. Opines that the Five laws of Ranganathan continue to stand the test of time supporting information management and influencing Knowledge Management to this day.
PISE: Person Image Synthesis and Editing with Decoupled GAN

Person image synthesis, e.g., pose transfer, is a challenging problem due to large variation and occlusion. Existing methods have difficulties predicting reasonable invisible regions and fail to decouple the shape and style of clothing, which limits their applications on person image editing. In this paper, we propose PISE, a novel two-stage generative model for Person Image Synthesis and Editing, which is able to generate realistic person images with desired poses, textures, or semantic layouts. For human pose transfer, we first synthesize a human parsing map aligned with the target pose to represent the shape of clothing by a parsing generator, and then generate the final image by an image generator. To decouple the shape and style of clothing, we propose joint global and local per-region encoding and normalization to predict the reasonable style of clothing for invisible regions. We also propose spatial-aware normalization to retain the spatial context relationship in the source image. The results of qualitative and quantitative experiments demonstrate the superiority of our model on human pose transfer. Besides, the results of texture transfer and region editing show that our model can be applied to person image editing.
Pengelolaan Arsip Dinamis Aktif Sma Datuk Batu Hampar Pekanbaru

The aim of this research was to examine the management of active and dynamic archives in Senior High School Datuk Batu Hampar Pekanbaru and factors that influenced the management of active and dynamic. As for the problems in this researchwerehow the management of active and dynamic archives wasand what factors that influenced the management of active and dynamic archieves in Senior High School Datuk Batu Hampar Pekanbaru. Techniques used in collecting data were interview, observation, and documentation. Then, data was analyzed by reducing data, presenting data, and drawing conclusion.The research finding revealed that organizing actives and dynamic archives in Senior High School Datuk Batu Hampar PekanbaruThe head of administration dan staff implemented the maintenance in managing active and dynamic archives, implemented the archives storage, instructed the staffs to always take note for every activities, assigned the staffs to do checking for all archieves and destroyed archieves. It was found that the factors which influenced the management of of archieve was the lack of staffs’ understanding about archieves, limited facilities of archieves, authority in using facilities and maintained good communication.
Combination and fusion of some 2D invariant moments with generative and discriminative classifiers for recognition of isolated handwritten Tifinagh characters

In order to improve the recognition rate and accuracy of the Tifinagh OCR, this document proposes an approach to build a powerful automatic recognition system of isolated handwritten Tifinagh characters by using a combination and fusion of some features extraction methods. The Krawtchouk, Chebyshev and Gaussian Hermite moments are used as descriptors in the features extraction phase due to their invariance to translation, rotation and scaling changes. In the classification phase, the discriminative power of the neural networks, the multiclass SVM (Support Vector Machine), the nearest neighbour classifiers, and the generative nature of the Bayesian networks, are combined together in order to benefit from their complementarity. The experimental results of each single features extraction method and each single classification method are compared with our approach to show its robustness. To approve the claimed hypothesis and results, the experiments are conducted using databases of Handwritten Tifinagh Characters.
2020 Charleston Conference Report: Innovation Session

From the column editors:Welcome to another edition of E-Resource Round Up! This issue covers a wide range of e-resource topics, including: Two reports from the 2020 Charleston ConferenceReport from...
Real-time RGBD-based Extended Body Pose Estimation

We present a system for real-time RGBD-based estimation of 3D human pose. We use parametric 3D deformable human mesh model (SMPL-X) as a representation and focus on the real-time estimation of parameters for the body pose, hands pose and facial expression from Kinect Azure RGB-D camera. We train estimators of body pose and facial expression parameters. Both estimators use previously published landmark extractors as input and custom annotated datasets for supervision, while hand pose is estimated directly by a previously published method. We combine the predictions of those estimators into a temporally-smooth human pose. We train the facial expression extractor on a large talking face dataset, which we annotate with facial expression parameters. For the body pose we collect and annotate a dataset of 56 people captured from a rig of 5 Kinect Azure RGB-D cameras and use it together with a large motion capture AMASS dataset. Our RGB-D body pose model outperforms the state-of-the-art RGB-only methods and works on the same level of accuracy compared to a slower RGB-D optimization-based solution. The combined system runs at 30 FPS on a server with a single GPU. The code will be available at this https URL
Image Processing Technology in Computer Technology for Modern Printing Quality Monitoring

Nowadays, with the increasing demand for the quality of printing materials, the printing process is becoming more and more complex. At the same time, due to the continuous improvement of the speed of modern printing press, the requirements of quality control are higher, and the traditional quality control methods cannot meet its needs. In addition to the development of computer technology, image processing technology is widely used in many areas of life. The image processing technology is to be applied and investigated to monitor and monitor modern pressure quality. This article is based on the image recording based on the average filter denomination and property output. In order to suggest a modern print quality monitoring method based on image processing technology, it applies to monitoring the quality of modern printing. In order to verify the application of this method, the application of this type of print technology can be reduced to a higher degree than the quality of the print subsystem. We compare it with a traditional pressure quality monitoring method and analyse it in two directions of the amount and type of control. The results of this article’s research show that the average type of perception accuracy of the traditional pressure method is 50%, while the average type of perception accuracy of the image processing technology proposed in this article shows that the average type of perception accuracy of the printing method is 91.7%, high honesty and specific advantages and applicability.
The State of Crime in Bratsk in 1955–1991 According to the Prosecutors

This article presents and analyzes data on the dynamics of crime, presents the General directions of criminal activity in the city of Bratsk in 1955–1991. Also, the causes of the growth of crimes are considered. The study of regional indicators of crime dynamics in the Irkutsk region at the present stage is a significant step in the formation of ideas about the spread of social anomalies in the Soviet period. The archival materials of the Prosecutor’s office not previously used in publications have been introduced into scientific circulation.
Causal-aware Safe Policy Improvement for Task-oriented dialogue

The recent success of reinforcement learning's (RL) in solving complex tasks is most often attributed to its capacity to explore and exploit an environment where it has been trained. Sample efficiency is usually not an issue since cheap simulators are available to sample data on-policy. On the other hand, task oriented dialogues are usually learnt from offline data collected using human demonstrations. Collecting diverse demonstrations and annotating them is expensive. Unfortunately, use of RL methods trained on off-policy data are prone to issues of bias and generalization, which are further exacerbated by stochasticity in human response and non-markovian belief state of a dialogue management system. To this end, we propose a batch RL framework for task oriented dialogue policy learning: causal aware safe policy improvement (CASPI). This method gives guarantees on dialogue policy's performance and also learns to shape rewards according to intentions behind human responses, rather than just mimicking demonstration data; this couple with batch-RL helps overall with sample efficiency of the framework. We demonstrate the effectiveness of this framework on a dialogue-context-to-text Generation and end-to-end dialogue task of the Multiwoz2.0 dataset. The proposed method outperforms the current state of the art on these metrics, in both case. In the end-to-end case, our method trained only on 10\% of the data was able to out perform current state in three out of four evaluation metrics.