Reviewerly/exHarmony huggingface dataset:
RETRACTED: Research on Humanization of Life Ceramics Design Based on VR Technology

Abstract the Development of Design Has Been Accompanied by the Progress of Human Civilization and Culture. the Progress of Contemporary Society is Gradually Changing All Aspects of Human Production and Life. Living Ceramics is One of the Types of Ceramic Products, with Certain Practicality and Appreciation. It is a Relatively Traditional and Ancient Ceramic Product in Our Country. Life Ceramic Design Serves People, and Humanization Must Become the Core of All Designs. in the Contemporary Era, the Research on the Concept of Humanized Design Has Gone Deep into Many Fields and is Quite Mature. in Ceramic Modeling Design, the Attention of Humanized Design is Particularly Important. Virtual Reality (Vr) Technology Can Be Used as a Tool to Test the Performance of Ceramic Products, Which Can Be Evaluated and Corrected in All Aspects Before They Are Put into Production, So That Designers and Customers Can Feel and Identify the Advantages and Disadvantages of the Products through the Virtual Model on the Computer. in This Paper, Virtual Reality Technology is Applied in the Research of Life Ceramic Design, and the Application of Humanized Design in Life Ceramic Product Design is Analyzed.
External Assistance Techniques That Target Core Game Tasks for Balancing Game Difficulty

Game balancing is a time consuming and complex requirement in game design, where game mechanics and other aspect of a game are tweaked to provide the right level of challenge and play experience. One way that game designers help make challenging mechanics easier is through the use of External Assistance Techniques – a set of techniques outside of games' main mechanics. While External Assistance Techniques are well known to game designers (like providing onscreen guides to help players push the right buttons at the right times), there are no guiding principles for how these can be applied to help balance challenge in games. In this work, we present a design framework that can guide designers in identifying and applying External Assistance Techniques from a range of existing assistance techniques. We provide a first characterization of External Assistance Techniques showing how they can be applied by first identifying a game's Core Tasks. In games that require skill mechanics, Core Tasks are the basic motor and perceptual unit tasks required to interact with a game, such as aiming at a target or remembering a detail. In this work we analyze 54 games, identifying and organizing 27 External Assistance Techniques into a descriptive framework that connects them to the ten core tasks that they assist. We then demonstrate how designers can use our framework to assist a previously understudied core task in three games. Through an evaluation, we show that the framework is an effective tool for game balancing, and provide commentary on key ways that External Assistance Techniques can affect player experience. Our work provides new directions for research into improving and maturing game balancing practices.
Intellectual Resources in the Development of Smart University

Modern conditions are closely related to the development of information technology, new approaches to telecommunications, and digitalization of business. The emergence of a new digital generation of people requires a universal transition to smart education. With an increase in the intelligence of work, a need arises for knowledge management which focuses on the strategic development of the smart university and involves the effective use of its intellectual potential. Intellectual resources represent a unique capital, which occupies a leading position in the development of the smart university. Modern practice demonstrates the emergence of a new branch of the science of management and a new type of management activity that meets the requirements of the knowledge economy; this is the management of intellectual resources. Current market conditions pose new challenges for the development of a smart university; the definition and management of its intellectual resources are what determines the relevance of a scientific paper. The concept of intellectual resources is revealed in the paper, and the features of applying this category to a smart university are highlighted. The basic principles of managing the intellectual resources of a smart university have been clarified. The result of the study is the refinement of the conceptual apparatus of the intellectual resources of the smart university, as well as the development of an algorithm for managing the intellectual resources of the smart university. The results were tested in the management of intellectual resources of the smart unit of the university on the example of the department.
D-SRGAN: DEM Super-Resolution with Generative Adversarial Network

LIDAR (light detection and ranging) is an optical remote-sensing technique that measures the distance between sensor and object, and the reflected energy from the object. Over the years, LIDAR data has been used as the primary source of Digital Elevation Models (DEMs). DEMs have been used in a variety of applications like road extraction, hydrological modeling, flood mapping, and surface analysis. A number of studies in flooding suggest the usage of high-resolution DEMs as inputs in the applications improve the overall reliability and accuracy. Despite the importance of high-resolution DEM, many areas in the United States and the world do not have access to high-resolution DEM due to technological limitations or the cost of the data collection. With recent development in Graphical Processing Units (GPU) and novel algorithms, deep learning techniques have become attractive to researchers for their performance in learning features from high-resolution datasets. Numerous new methods have been proposed such as Generative Adversarial Networks (GANs) to create intelligent models that correct and augment large-scale datasets. In this paper, a GAN based model is developed and evaluated, inspired by single image super-resolution methods, to increase the spatial resolution of a given DEM dataset up to 4 times without additional information related to data.
Crowdsourcing and Blockchain-Based E-Government Applications: Corruption Mapping

Corruption represents a fundamental threat to the stability and prosperity of Morocco. Fighting it requires approaches based on both appropriate principles and practices. Information and Communication Technologies (ICT) allow initiatives such as the Smarter Crowdsourcing project innovated by GovLab to open new perspectives for the prevention, the detection and the criminalization of corrupt acts. In the same context, we propose a digital framework based on the principle of crowdsourcing, blockchain technology and the principle of smart contracts for the implementation of a tool for mapping corrupt acts. The framework is inspired by the concept of blockchain and would allow citizens to publish photos and videos with their reports on suspicious transactions. The developed application collects, aggregates, displays and stores the reported information in a secure manner on a limited access ledger based on a blockchain. Reporters will then be able to track and monitor the government’s response to actions taken via the application. Once confirmed, reports on suspicious transactions are reported to a public document-oriented database.
Finding Patterns as the Path from Input to Output

The output of a machine learning system is nothing more than statements of the following form: “such and such a new case is likely to behave similarly to other similar cases that belong to the training dataset that was used to train this machine.” It has been described as “just curve fitting”, in the sense of drawing a curve through the datapoints in the training dataset, perhaps smoothing out some irregularities; the “learning” in machine learning is nothing more than tuning parameter values to make a well-fitting curve. This curve is then used to make predictions for new cases. The patterns found by machine learning are not laws of nature like Newton’s laws, and they are not stipulative rules like those laid down in statutes, they are simply fitted curves. Analogously, Holmes said that law emerges as people and their institutions find patterns in human experience. He wrote that a page of history is worth a volume of logic. When lawmakers ask for “the logic involved” in machine learning, or refer to “inferring rules from data”, they should really be asking for “a story about the training dataset”.
Effect of Overhauling on the Performance of Boiler

The availability of electrical energy and its per person intake is viewed as the living standard in today’s civilization. The booming of power production is an indication of rising gross national products which reveals prosperity of the people. For power generation in the thermal power plant, boiler is the steam generator which is used to transform water into superheated steam. It is an attempt to utilize the maximum possible amount of heat energy supplied to furnace in order to get higher performance of boiler. This paper analyzes the factors which affect the boiler’s performance such as lignite quality, exit flue gas, excess air and slagging. Theoretical calculations have been carried out to find out the efficiency of boiler before and after major overhauling, and suitable methods have been adopted to improve the effectiveness of the boiler.
Research on Integrated Deployment Method of Digital Earth Thematic Application Model

Abstract Nowadays, the digital earth not only relates to the technologies of surveying and mapping geography, but also includes the analysis and cross-application of various scientific data related to geographic information. It requires a computing environment and management for the corresponding thematic application models which supports the online analysis on the digital earth. So we proposed an integrated deployment method for digital earth thematic application models based on container cloud, which provides a flexible and scalable information integration and analysis architecture for digital earth. Firstly, a Docker image building module is designed to facilitate the user to quickly build the application model into a image. Then the Docker technology is used to implement the containerization of the application model to provide a stable running environment for the application model. Finally, the Kubernetes is used to dynamically manage the cluster resources to realize rapid expansion of computing resources. In addition, the HDFS is deployed, and the image files and various scientific data respectively store with Hbase and Apache file systems. It can ensure the read and write speed of the application model and the related data and log files. It also used zabbix framework to manage the monitoring index of the container cloud cluster to ensure the execution effect of the application model. Through the tests of typical application model, it is verified that the method can integrated deploy and apply the cross-domain, hetero-architecture, multi-version and multi-class application models on the digital earth.
Colorectal Image Classification with Transfer Learning and Auto-Adaptive Artificial Intelligence Platform

In automatic (computer-based) interpretation of medical images, the use of deep learning techniques is limited because of the lack of large publicly available datasets. With just hundreds of samples (images) in a dataset, the application of deep learning techniques is very hard, and the results are under expectations. Training a multi layer convolutional neural network requires thousands or even millions of images for an acceptable level of correct classification. In this paper we will present a novel approach that can be used to solve computer vision related problems (e.g. medical image processing) even when only a small dataset of images are available for training. We will show that combining Transfer Learning and some auto-adaptive artificial intelligence algorithms we can obtain very good classification rates even with the use of a limited dataset. As a demonstration of the effectiveness of our approach we will show the use of this technique to solve the polyp detection problem in endoscopic image sets. We show that using just a subset of the available images (from the original dataset containing 4000 images) the results are comparable with the case when all the images were used.
Performance analysis on prediction structure for multi-view-based light field video coding

Light Field (LF) image/video data provides both spatial and angular information of scene but at the cost of tremendous data volume for their storage and transmission. At the moment, the MPEG Multi-view Video Coding (MVC) is one of promising compression solutions for LF video data, so it deserves much investigation for better prediction structure to effectively reduce the redundancy in LF video data. Several prediction structures have been investigated but only with limited experimental evaluations due to lack of dataset and non-identical testing configurations. This practical problem can be mitigated now by availability of new datasets and common test condition recently proposed by MPEG. As the first step for designing a good compression method for LF video data, in this paper, we evaluate the performance of existing prediction structures for MVC-based LF video coding methods following the MPEG common test condition and its dataset.
SÍNTESIS DE ZEOLITA ZSM-5 UTILIZANDO DIATOMITA COMO PRECURSOR; SU APLICACIÓN EN EL PROCESO DE METANOL A HIDROCARBUROS (MTH)

Zeolite H-ZSM-5 is widely applied in the petrochemical industry, such as the synthetic process of methanol to hydrocarbons (methanol to hydrocarbons MTH).ZSM-5 zeolite is currently obtained through reagents and expensive processes, being available only a few cheaper alternative routes.In the present investigation, zeolite H-ZSM-5 was obtained from natural diatomite, then characterized, and finally tested in the synthetic MTH process.The natural diatomite was treated with sulfuric acid to perform hydrothermal synthesis with tetrapropyl ammonium hydroxide (TPA-OH).As a result, the H-ZSM-5 zeolite obtained had a Si / Al molar ratio of 23, it exhibited high thermal stability and showed Lowry-Brönsted and Lewis acid sites.The application of this zeolite in the MTH reaction showed a methanol conversion of 88%.The products were: dimethyl ether (DME) in 40%, olefins (mostly ethylene and propylene) in 40% and others in 20%.The reaction conditions were: 300 ° C, 1.2L metanol /g cat h at atmospheric pressure (0.65 bar).The selectivity of the products can be modified in such a way that DME can be
Classification with Rejection: Scaling Generative Classifiers with Supervised Deep Infomax

Deep Infomax (DIM) is an unsupervised representation learning framework by maximizing the mutual information between the inputs and the outputs of an encoder, while probabilistic constraints are imposed on the outputs. In this paper, we propose Supervised Deep InfoMax (SDIM), which introduces supervised probabilistic constraints to the encoder outputs. The supervised probabilistic constraints are equivalent to a generative classifier on high-level data representations, where class conditional log-likelihoods of samples can be evaluated. Unlike other works building generative classifiers with conditional generative models, SDIMs scale on complex datasets, and can achieve comparable performance with discriminative counterparts. With SDIM, we could perform classification with rejection. Instead of always reporting a class label, SDIM only makes predictions when test samples' largest class conditional surpass some pre-chosen thresholds, otherwise they will be deemed as out of the data distributions, and be rejected. Our experiments show that SDIM with rejection policy can effectively reject illegal inputs, including adversarial examples and out-of-distribution samples.
The Effects of Buyer-Supplier B2B Interaction Service Quality on Transaction Performance: Focusing on Steel Buyers

This study aims to define short-term exchange and long-term relationship factor in B2B interaction service quality and test the relationship between the short-term exchange, long-term relationship and transaction performance. Furthermore, based on the result, suggestion to steel industry on action plan regarding B2B interaction service quality will be made. Through these analysis, long-term relationship factors among B2B interaction service quality factors proved to be of great use. Moreover, Long-term supplier-buyer relationship in steel industry practically affected transaction performance. This study is the first to test the effect of supplier-buyer B2B interaction service quality on transaction performance in steel industry. Also, as the first study to use long-term relationship as indirect path in accordance with steel industry, the implication of this study can be considered significant both academically and practically.
Faster Graph Embeddings via Coarsening

Graph embeddings are a ubiquitous tool for machine learning tasks, such as node classification and link prediction, on graph-structured data. However, computing the embeddings for large-scale graphs is prohibitively inefficient even if we are interested only in a small subset of relevant vertices. To address this, we present an efficient graph coarsening approach, based on Schur complements, for computing the embedding of the relevant vertices. We prove that these embeddings are preserved exactly by the Schur complement graph that is obtained via Gaussian elimination on the non-relevant vertices. As computing Schur complements is expensive, we give a nearly-linear time algorithm that generates a coarsened graph on the relevant vertices that provably matches the Schur complement in expectation in each iteration. Our experiments involving prediction tasks on graphs demonstrate that computing embeddings on the coarsened graph, rather than the entire graph, leads to significant time savings without sacrificing accuracy.
An empirical study on the use of visual explanation in kidney cancer detection

In order to detect kidney cancer automatically from abdominal UCT (unenhanced CT) or CECT (contrastenhanced CT) images at an early stage, a promising approach is to use deep learning techniques with convolutional neural networks (CNNs). However, there still seem to be several challenges in detection of kidney cancer. For example, it is necessary to cope with the wide variety of abdominal CT images. In this paper, as an empirical study, we attempt to construct a CNN that detects kidney cancer well from abdominal CT images, with a special focus on how visual explanations produced by Gradient-weighted Class Activation Mapping (Grad-CAM) help us to construct such a CNN.
DeepMask: Face Masking System using Deep Neural Networks on Real-time Streaming

With a rapid increase in the use of personal broadcasting platforms (e.g., YouTube, afreecaTV, and Twitch), infringement of portrait rights has become a serious social problem as most people are unwilling to participate in the broadcasting and are exposed indiscriminately. To alleviate this problem, this paper proposes DeepMask, a real-time face masking system integrating face recognition and face masking technologies using advanced deep neural networks. Our system supports easy, selective masking of people via face registration. Thus, it can reduce the infringement of portrait rights.
A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing

We propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents. We focus on clustering extracted glyph images into underlying templates in the presence of multiple confounding sources of variance. Our approach introduces a neural editor model that first generates well-understood printing phenomena like spatial perturbations from template parameters via interpertable latent variables, and then modifies the result by generating a non-interpretable latent vector responsible for inking variations, jitter, noise from the archiving process, and other unforeseen phenomena associated with Early Modern printing. Critically, by introducing an inference network whose input is restricted to the visual residual between the observation and the interpretably-modified template, we are able to control and isolate what the vector-valued latent variable captures. We show that our approach outperforms rigid interpretable clustering baselines (c.f. Ocular) and overly-flexible deep generative models (VAE) alike on the task of completely unsupervised discovery of typefaces in mixed-fonts documents.
SOCIAL MEDIA USE INTENSITY AT WORKPLACE AMONG HUMAN RESOURCES EXECUTIVES OF A GOVERNMENT AGENCY HEADQUARTERS IN KUALA LUMPUR, MALAYSIA

Browsing social media is a common daily activity for most people all around the globe nowadays regardless of age and gender. Although the frequency, duration and motive of browsing social media differs from one person to another, the existence of employees who have social-media mania and keep browsing social media all the time even during their working hours causes the employers to monitor their employees‘ social media use intensity at workplace from time to time. This study looks at the level of social media use intensity at workplace among Human Resources executives of a government agency headquarters in Kuala Lumpur, Malaysia. Questionnaires were used to collect data on the sample. Due to its quite small population size, total population sampling method is applied. Out of 68 executives, only 30 completed the questionnaires (response rate = 44.12%). Findings revealed that the sample have high level of social media use intensity at workplace. However, there was no statistically significant difference between social media use intensity and gender. Other than that, it was found that the main motive behind using social media at workplace is interpersonal. As for the implications of this study, it contributes to the corpus of knowledge in the area of social media use intensity at workplace in local organization context and provides empirical data to assist organization in conducting appropriate policy to control social media use intensity at workplace among their employees.&#x0D; Keywords: Social Media Use Intensity, Motives Behind Using Social Media, Government Agency
End-to-end Optimized Video Compression with MV-Residual Prediction

We present an end-to-end trainable framework for P-frame compression in this paper. A joint motion vector (MV) and residual prediction network MV-Residual is designed to extract the ensembled features of motion representations and residual information by treating the two successive frames as inputs. The prior probability of the latent representations is modeled by a hyperprior autoencoder and trained jointly with the MV-Residual network. Specially, the spatially-displaced convolution is applied for video frame prediction, in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image. Finally, novel rate allocation and post-processing strategies are used to produce the final compressed bits, considering the bits constraint of the challenge. The experimental results on validation set show that the proposed optimized framework can generate the highest MS-SSIM for P-frame compression competition.