Reviewerly/exHarmony huggingface dataset:
당대(唐代) 서승(書僧) 고한(高閑)의 서예(書藝)와 역대의 평가

In the Tang Dynasty, there were many monks who had knowledge and cultural literacy: monk poets(Shi seng) who wrote poems well; monk painters(Hua seng) who painted pictures well; and monk calligraphers(Shu seng) who were excellent in calligraphy. There are countless monks of the Tang Dynasty who were well known as monk calligraphers. Among them, Huai Su and Gao Xian are the representative monk calligraphers of the Tang Dynasty. As a second review on the monk calligraphers of the Tang Dynasty, this study, first, briefly introduces Gao Xian’s life and calligraphy. Next, this study analyzes the literary works of the Tang Dynasty that sang and evaluated Gao Xian’s calligraphy and the poems of the Tang Dynasty, as well as the evaluations of Gao Xian’s calligraphy by the literary men since the Tang Dynasty.Some discriminating features in the evaluations of Gao Xian’s calligraphy are as follows: Extreme praises and criticisms exist at the same time; the praises and criticisms of Gao Xian’s calligraphy are compared with those of Zhang Xu’s and Huai Su’s; the evaluations since the Tang Dynasty were very much mediated by Han Yu’s Farewell speech for monk Gao Xian. Although, unlike Zhang Xu and Huai Su, extreme praises and criticisms exist for Gao Xian’s cursive script, the fact that Gao Xian is compared with Zhang Xu or Huai Su, who were representative cursive script calligraphers in the Tang Dynasty, demonstrates the excellence of Gao Xian’s cursive script, who was the representative cursive script calligrapher in the Late Tang period.
Stable Marriage Matching for Homogenizing Load Distribution in Cloud Data Center

Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators with tools to seamlessly move VMs across physical machines. Several studies have shown that the resource utilization within a data center is not homogeneous across the physical servers. Load imbalance situations are observed where a significant portion of servers are either in overloaded or underloaded states. Apart from leading to inefficient usage of energy by underloaded servers, this might lead to serious QoS degradation issues in the overloaded servers.In this paper, we propose a lightweight decentralized solution for homogenizing the load across different machines in a data center by mapping the problem to a Stable Marriage matching problem. The algorithm judiciously chooses pairs of overloaded and underloaded servers for matching and subsequently VM migrations are performed to reduce load imbalance. For the purpose of comparisons, three different greedy matching algorithms are also introduced. In order to verify the feasibility of our approach in real-life scenarios, we implement our solution on a small test-bed. For the larger scale scenarios, we provide simulation results that demonstrate the efficiency of the algorithm and its ability to yield a near-optimal solution compared to other algorithms. The results are promising, given the low computational footprint of the algorithm.
EVALUASI PROGRAM PELAKSANAAN UJIAN ONLINE DENGAN MENGGUNAKAN LEARNING MANAGEMENT SYSTEM MOODLE BERBASIS ANDROID DI SMK NEGERI 1 JAKARTA

This study aims to determine whether the Evaluation Program of Testing Online by use Learning Management System Moodle android-based for students Program field DPIB (Desain Permodelan dan Infromasi Bangunan) class X at SMKN 1 Jakarta has been successful or not.Begins research in april 2013 until mei 2013. The method used is the evaluative research method with the type of CIPP evaluation model (context, input, process, product) with a sample of 30 students. Validity test results, 12 items questionnaire is valid. While the results of reliability test, R11 Value is 0,747. To access the online testing program, head of IT requires program of Moodle, Computer for server, internet networks, and android. The curriculum used is 2013. The average value of an android-based online exam for midterms for students is 81, while for the final school examinations is only 74, lower than the Minimum Completion Criteria (KKM) that has been set which is 75 while the midterm examination score is greater than the value (KKM) From the calculation of the questionnaire, percentage of successful online testing program is only 67.33% of the respondent. During the implementation of the Android-based online exam program there are still problems, lack of connectors to help chager android phones so that they are closer and reduce student activity during the exam and there is no Google blocking for exam security and internet connection 1 mbps. The impact of the online exam program that is felt by the school is the teacher and students can save their time in the implementation of the exam compared to using paperles and the internet as a means of evaluating learning. Thus, overall the implementation of an android-based online exam program at SMKN 1 Jakarta can be said to be almost successful, but there is a need for improvement especially in online exam program support facilities.
Deep learning for recognition of sedimentary microfacies with logging data

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Deep learning for recognition of sedimentary microfacies with logging dataAuthors: Hanpeng CaiYongxiang HuHanpeng CaiUniversity of Electronic Science and Technology of ChinaSearch for more papers by this author and Yongxiang HuUniversity of Electronic Science and Technology of ChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428018.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThere are two main problems in the traditional methods of identifying sedimentary microfacies from logging data: (1) whole morphological structural characteristics of logging curve with depth is often neglected, and (2)data partitioning, features extraction, and classification are usually carried out separately. Integrating deep learning and image semantic segmentation, we perform an image-to-image sedimentary microfacies division by using an improved U-net. Allowing for the features of logging data and U-net algorithm in deep learning, we change the pooling layer, convolution kernel and number of layers in U-net to establish an improved U-net. In addition, we propose a method to expand logging sample size so as to demand the basic requirement of deep learning for sufficient samples. And four attributes extended from logging curve are applied to enhance the features of the sample, so as to improve the accuracy of identifying sedimentary microfacies. The application of practical data turns out that the proposed method can be an effective tool for sedimentary microfacies recognition from logging data.Presentation Date: Tuesday, October 13, 2020Session Start Time: 9:20 AMPresentation Time: 11:25 AMLocation: Poster Station 1Presentation Type: PosterKeywords: machine learning, sedimentology, facies, well-log interpretationPermalink: https://doi.org/10.1190/segam2020-3428018.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Hanpeng Cai and Yongxiang Hu, (2020), "Deep learning for recognition of sedimentary microfacies with logging data," SEG Technical Program Expanded Abstracts : 1651-1655. https://doi.org/10.1190/segam2020-3428018.1 Plain-Language Summary Keywordsmachine learningsedimentologyfacieswell-log interpretationPDF DownloadLoading ...
GPU-based State Adaptive Random Forest for Evolving Data Streams

Random forest is an ensemble method used to improve the performance of single tree classifiers. In evolving data streams, the classifier needs to be adaptive and work under constraints of space and time. One benefit of random forest is its ability to be executed in parallel. In our research we introduce a random forest model utilizing a hybrid of both GPU and CPU, called GPU-based State-Adaptive Random Forest (GSARF). We address the pre-existing challenges of adapting random forest for data streams, specifically in the area of continual learning. Our novel approach reuses previously seen trees in the random forest when previous concepts reappear. This allows us to retain prior knowledge and provide a more stable predictive accuracy when changes occur in the data stream. Our random forest for data streams stores three types of trees, foreground trees which are trees that are currently used in prediction, background trees which are trees that are built when we are aware of possible changes in the data streams, and candidate trees which are trees that had been highly used in the previous concepts, but are now discarded due to changes in the data stream. We store candidate trees as they may be potentially useful at a later period in a repository and can be accessed when needed. We empirically show our technique performs up to 138 times the speed compared to current CPU-based random forest benchmarks. Our approach has shown to outperform a baseline GPU-based approach in terms of cumulative accuracy performance.
CTDGM: A Data Grouping Model Based on Cache Transaction for Unstructured Data Storage Systems.

Cache prefetching technology has become the mainstream data access optimization strategy in the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships, which can result in a heavy computational burden for the existing prefetching model and lead to severe degradation in the performance of data access. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art methods, our algorithm achieves an average increase in the cache hit rate of 12% on the MSR dataset with small cache size (0.001% of all the data), which in turn reduces the number of data I/O accesses by 50% when the cache size is less than 0.008% of all the data.
Research on MTCNN Face Recognition System in Low Computing Power Scenarios

This paper analyzes the time-consuming analysis of each cascading network module (PNet module, RNet module and ONet module) in MTCNN, and finds that the time-consuming of PNet module is the highest (about 70%). According to the results of time-consuming analysis, two improved methods are proposed, one is to reduce the number of candidate face frames in input PNet network and the other is to reduce the number of output face frames in PNet network. Then, aiming at the problem that MTCNN algorithm has low detection speed in high-resolution video and cannot meet real-time requirements, a series of optimization such as adjusting the minnisize parameter and PNet threshold in combination with the low computing power application scenarios of the channel bayonet. It is verified in the FDDB face test set and practical application, the detection speed has increased by 70.1% when the detection rate has dropped by only 3.5%, and the improved scheme has achieved good results. Compared with the performance of OpenCV-VJ and SURF face detection algorithms on FDDB, the optimized MTCNN algorithm has better performance. Through the analysis of the detection results of the specific FDDB data set pictures, it is found that the undetected face conditions do not meet the actual application scenarios in this article, which proves that the optimized algorithm has excellent performance in actual applications. The test results reflect that the reproduced and optimized MTCNN face detection algorithm has good robustness to face pose changes, and fully meets the requirements of face recognition systems in low computing power scenarios such as channel bayonet.
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein

Relational regularized autoencoder (RAE) is a framework to learn the distribution of data by minimizing a reconstruction loss together with a relational regularization on the latent space. A recent attempt to reduce the inner discrepancy between the prior and aggregated posterior distributions is to incorporate sliced fused Gromov-Wasserstein (SFG) between these distributions. That approach has a weakness since it treats every slicing direction similarly, meanwhile several directions are not useful for the discriminative task. To improve the discrepancy and consequently the relational regularization, we propose a new relational discrepancy, named spherical sliced fused Gromov Wasserstein (SSFG), that can find an important area of projections characterized by a von Mises-Fisher distribution. Then, we introduce two variants of SSFG to improve its performance. The first variant, named mixture spherical sliced fused Gromov Wasserstein (MSSFG), replaces the vMF distribution by a mixture of von Mises-Fisher distributions to capture multiple important areas of directions that are far from each other. The second variant, named power spherical sliced fused Gromov Wasserstein (PSSFG), replaces the vMF distribution by a power spherical distribution to improve the sampling time in high dimension settings. We then apply the new discrepancies to the RAE framework to achieve its new variants. Finally, we conduct extensive experiments to show that the new proposed autoencoders have favorable performance in learning latent manifold structure, image generation, and reconstruction.
Research guides: Equity Focused Digital Pedagogy and Learning: Fundamentals of Digital Pedagogy and Learning

This Research Guide is a collaboration between the OISE Library and the OISE Equity Committee, with resources on anti-racism, accessibility and international students in Digital Pedagogy and Learning.
The Construction and Application of the Vertical Scale System of College English Grading Test

It is an important purpose of testing to evaluate and recommend talents fairly. However, due to the lack of vertical measurement, the fairness of the assessment system of College English grading teaching has been questioned. At the same time, this kind of questioning has a negative backwash effect on the classified teaching mode. Therefore, it is of great theoretical and practical value to analyze how to construct the vertical scale and eliminate the drawbacks of College English graded teaching evaluation system, so as to realize the fairness of the evaluation system and the teaching concept of teaching students according to their aptitude.
The Computational Complexity of Evil Hangman

The game of Hangman is a classical asymmetric two player game in which one player, the setter, chooses a secret word from a language, that the other player, the guesser, tries to discover through single letter matching queries, answered by all occurrences of this letter if any. In the Evil Hangman variant, the setter can change the secret word during the game, as long as the new choice is consistent with the information already given to the guesser. We show that a greedy strategy for Evil Hangman can perform arbitrarily far from optimal, and most importantly, that playing optimally as an Evil Hangman setter is computationally difficult. The latter result holds even assuming perfect knowledge of the language, for several classes of languages, ranging from Finite to Turing Computable. The proofs are based on reductions to Dominating Set on 3-regular graphs and to the Membership problem, combinatorial problems already known to be computationally hard.
${\rm N{\small ode}S{\small ig}}$: Random Walk Diffusion meets Hashing for Scalable Graph Embeddings

Learning node representations is a crucial task with a plethora of interdisciplinary applications. Nevertheless, as the size of the networks increases, most widely used models face computational challenges to scale to large networks. While there is a recent effort towards designing algorithms that solely deal with scalability issues, most of them behave poorly in terms of accuracy on downstream tasks. In this paper, we aim at studying models that balance the trade-off between efficiency and accuracy. In particular, we propose ${\rm N{\small ode}S{\small ig}}$, a scalable embedding model that computes binary node representations. ${\rm N{\small ode}S{\small ig}}$ exploits random walk diffusion probabilities via stable random projection hashing, towards efficiently computing embeddings in the Hamming space. Our extensive experimental evaluation on various graphs has demonstrated that the proposed model achieves a good balance between accuracy and efficiency compared to well-known baseline models on two downstream tasks.
거시경제변수 변동에 따른 산업별 주식시장 반응도 분석

In this paper, I analyze impacts on Korean individual industry stock market index by changes in macroeconomic variables such as one year monetary stabilization bond (MSB) yield chosen as Korean representative interest rate, Korean won - US dollar foreign exchange rate, and OPEC basket crude oil price. With the sample of 5,543 daily observations from the beginning of 1997 to the end of June, 2019 the summary findings from the regression results of GJR-GARCH-X model are as follows.Interest rate is shown to have significant impacts on only 3 industry stock index returns out of total 16 individual industries, whereas it is confirmed to affect positively nearly none of the stock indices volatilities except for the insurance industry. The reason for the insignificancy of the interest rate impact is due to a fact that most investors can react to the government’s monetary policy as perfectly as possible because the MSB yield does not contain any individual default risk premium.Won-Dollar foreign exchange rate is shown to be the most powerful risk factor in Korea because it has significant impacts on not only 11 industry stock indices returns including 7 manufacturings and 4 services but also nearly all the industry stock indices volatilities with only 3 exceptions of food & beverage, textile & garments, and paper & wood industries. The signs for the significant impacts on the returns of the industries are shown to be negative in the 9 sectors and positive in the two sectors as electrics & electonics and transportation equipments. The negative signs reflect the fact that industry return turns to negative when the FX rate goes up as a result of the deteriorating country fundamentals and the positive signs reflect the fact the industry return turns to positive as the price competitiveness improves as a result of the weakened country currency value.Crude oil price, contrary to the former two factors, shows relatively the same effects to the Korean stock market as expected, affecting significantly major export-oriented industry stock returns and volatilities including petrochemicals, machinery, and electrics & electronics.Considering the fact that won-US dollar FX rate is shown to be the most important risk factor in the Korean stock market affecting significantly almost all the return volatilities that belong to either domestic demand sectors or export oriented sectors, the need for fine tuning policy designs for stabilizing won-Dollar FX rate is clearly verified.
Adaptive Multi-grained Graph Neural Networks.

Graph Neural Networks (GNNs) have been increasingly deployed in a multitude of different applications that involve node-wise and graph-level tasks. The existing literature usually studies these questions independently while they are inherently correlated. We propose in this work a unified model, Adaptive Multi-grained GNN (AdamGNN), to learn node and graph level representation interactively. Compared with the existing GNN models and pooling methods, AdamGNN enhances node representation with multi-grained semantics and avoids node feature and graph structure information loss during pooling. More specifically, a differentiable pooling operator in AdamGNN is used to obtain a multi-grained structure that involves node-wise and meso/macro level semantic information. The unpooling and flyback aggregators in AdamGNN is to leverage the multi-grained semantics to enhance node representation. The updated node representation can further enrich the generated graph representation in the next iteration. Experimental results on twelve real-world graphs demonstrate the effectiveness of AdamGNN on multiple tasks, compared with several competing methods. In addition, the ablation and empirical studies confirm the effectiveness of different components in AdamGNN.
Tempesta in Asinara e Calibano sardo nella <i>Stoffa dei sogni</i> di Gianfranco Cabiddu

No AccessTempesta in Asinara e Calibano sardo nella Stoffa dei sogni di Gianfranco CabidduGonaria FlorisGonaria FlorisSearch for more papers by this authorhttps://doi.org/10.14220/9783737011327.333SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter FiguresReferencesRelatedDetails Download book coverWiener Arbeiten zur LinguistikVolume 5 1st editionISBN: 978-3-8471-1132-0 eISBN: 978-3-7370-1132-7HistoryPublished online:October 2020 PDF download
Docker forensics: Investigation and data recovery on containers

Container technology continuously grows in popularity, and the forensic area is less explored than other areas of research concerning containers. The aim of this thesis is, therefore, to explore Do ...
Technical Practice of Making Online Workplace Etiquette Courses Based on Camtasia Studio

With the emergence of the global new coronavirus pneumonia epidemic and the phenomenon of suspension of classes all over the world, how to make use of Internet technology and multimedia software to produce high-level online courses has become the key to improve the quality of teaching. The online course is an information-based teaching mode which has developed rapidly since the emergence of new pneumonia, and it is also an effective substitute for classroom teaching. It is not only suitable for the dissemination of knowledge in the era of mobile learning and the needs of personalized and in-depth learning, but also contains excellent teaching experience, teaching skills and teaching methods. However, the teaching of workplace etiquette has strong practical characteristics, how to effectively teach workplace etiquette on the Internet has become a major difficulty. The Internet has the advantages of openness, interaction and virtualization, and can disseminate the course contents in time and effectively. And the good use of multimedia course production tools and software, is conducive to the production of better quality multimedia course works, can enhance the online course effect. This article takes the etiquette teaching as the starting point, explores the online course production technology and practice under the new era background.