Reviewerly/exHarmony huggingface dataset:
Program PDS Berkelanjutan dalam Pembelajaran Sintaksis Bahasa Indonesia Berbasis KKNI di STKIP Muhammadiyah Kotabumi

Program Penugasan Dosen di Sekolah (PDS) gives experience for the lecturers to understand comprehensively students’ characteristics in learning process. The learning process applied at STKIP Muhammadiyah Kotabumi based on KKNI-Dikti based curriculum with the characteristics which are interactive, holistic, integrative, saintific, contextual, them atic, collaborative, and student-centered. Indonesian syntax course focused on the students or how the students learn and understand the concept of material as well as implement it based on the concept understood. The method used in this article is descriptive method and qualitative approach in which describing the condition of fourth semester students in Indonesian syntax course. Based on the finding it was found that 88% students were active, creative and enthusiastic in learning process because the method and learning model are varied. It made the students to be more interested and active in following the learning. Meanwhile, 12 % students were active and motivated to learn. It can be concluded that the sudents’ achievement was increased.
Word Conversion in Diplomatic English-Chinese Translation

Cultural differences between China and the west countries lead to great differences in the expression of Chinese and English. In order to make the translation more accurate and fluent in foreign affairs translation, it is often necessary to use the translation strategy of word conversion. However, cultural differences increase the difficulty of word conversion. Aiming at this problem, this paper analyzes some methods and Strategies of word conversion in foreign affairs English-Chinese translation through examples.
Geochemical and geological evidence for a Triassic transition from subduction- to rift-related volcanic activity in the Cycladic Blueschist Unit in Attica (Greece)

&amp;lt;p&amp;gt;Triassic geodynamic phenomena in the Aegean area are largely controlled by subduction of the Paleotethys ocean and opening of the Neotethys oceans. Triassic volcanosedimentary sequences have a complex composition in many cases, reflecting both subduction and rifting setting.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Detailed mapping of NE Attica (Penteli Mt., Marathonas, Varnavas) revealed the existence of a structurally lower meta-volcanosedimentary sequence, which, comprises quartzofeldspathic rocks, schists, quartzite, metabasite and acid meta-volcanics This sequence is isoclinally folded in the macro-scale with marble layers and the axial plane foliation displays greenschist facies assemblages, whereas earlier HP minerals are mostly preserved as inclusions in albite porphyroblasts. The sequence of rocks has been investigated for their geochemical composition and their field relationships. Two assemblages of volcanic rocks are distinguished based on geochemical criteria: (a) a predominant subalkaline andesite-rhyolite series with a significant proportion of meta-tuffs in the stratigraphic sequence and (b) minor alkali basalts. Lenses of felsic meta-volcanic rocks alternate with siliciclastic layers showing sedimentary banding and allow for an interpretation of a volcano-sedimentary succession.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The geochemical characteristics of the alkali basalts are typical of rift settings (positive anomalies in Nb, Ta, Ti and P) and plot in the field of within plate basalts in the tectonic discrimination diagrams. &amp;amp;#160;The trace element and Rare Earth Element characteristics of the andesites and rhyolites in the subalkaline group show many characteristics of subduction zone melts e.g. negative Nb and Ta anomalies, positive Pb anomaly and LREE-enriched suggesting that a metasomatized mantle wedge source played an important role in the formation of the calcalkaline magmas. A geodynamic model of rift formation in the active continental margin of Pelagonia is proposed to explain the transition from a subduction- to an extension-related magmatic activity in the Late Permian/Triassic time in the broader NE Attica-central Evvia region.&amp;lt;/p&amp;gt;
AI융합 교육을 위한 교사 교육과정 개발

Socialchanges caused by software and AI are also affecting education. Changes in thecurriculum to cope with changing technologies or industries are leading todemands for enhancement of teachers’ capacity. Accordingly, this study wasconducted with an aim of developing a curriculum to strengthen teachers'AI-related fusion capabilities so that they can adapt to the curriculum when itis revised. To achieve this goal, we analyzed the AI-related curriculum of 18universities in and outside Korea. Based on the results, an AI convergencecurriculum was developed for elementary and secondary school teachers. AnAI convergence curriculum for graduate schools of education for elementaryand secondary teachers was developed, which covers 28 subjects in 6 areas. Inaddition to the essential areas, it includes six other areas: AI educationmethodology, software introduction, programming for artificial intelligence,data science and machine learning, and AI and education. Each area consists of3 to 8 subjects.
SVM &GA-CLUSTERING BASED FEATURE SELECTIONAPPROACH FOR BREAST CANCER DETECTION

The leading cause of mortality in women in developing countries is breast cancer. Breast cancer is the world's secondmost common source of cancer mortality in women. In recent decades, the high incidence of breast cancer in womenhas significantly increased. Within this paper, we addressed many techniques of data mining used to diagnose breastcancer early. The diagnosis of breast cancer is a distinction between benign and malignant breast lumps. By using datamining techniques, we have approached the diagnosis of this disease. Data mining is an essential phase in exploringinformation in libraries where intelligent tools are used to identify trends. Several observational experiments have beenperformed on breast cancer using soft computation and machine learning techniques. Often say that their algorithms arequicker, simpler, or more detailed than others. This study is based on genetic programming and machine learningalgorithms designed to build a system to differentiate benign and malignant breast cancer accurately. The purpose ofthis analysis was to refine the research algorithm. We employed the genetic programming methodology to choose thebest features and parameter values of the classification machines. Data mining is an essential phase in exploringinformation in libraries where intelligent tools are used to identify trends. We are evaluating the breast cancer dataaccessible from the U.C.I. deep-learning data collection in Wisconsin. In this experiment, we compare four Wekasoftware classification techniques with genetic clustering. A comparison of the results shows that sequential minimaloptimization (S.M.O.) has higher accuracy than I.B.K. and B.F. Tree methods, i.e., 97.71%.
Image Enhancement Using Super Resolution Technique

Abstract The proposed super resolution technique finds it’s applicability in reconstructing a distorted image to a higher resolution image. The photographs consisting of the previous techniques were drawn towards lower force light which was considered to be a huge disadvantage. In view of this default, a novel strategy of Histogram. Equalisation is introduced and further detailed investigation is carried on. Light Enlightenment and picture quality is improved using the proposed technique. Image Enhancement (IE) and de-noising algorithm for executing the requirements of digital medical image enhancement is also used as an implied strategy. Keywords: Resolution, De-noising, Digital Image Processing Cite this Article Sudha.K, Arpit Arora, Harsh Rana, Chinmay Sharma, Shreyansh Jain. Image Enhancement Using Super Resolution Technique. Current Trends in Signal Processing. 2020; 10(2): 12–16p.
Revisión del trabajo de tesis

Through the process of making a professional Thesis, the revision of the elements of substance and form by the Director of Thesis is very important. Beginning with the Thesis protocol is very important the title of the Thesis and having current literature. In the stage of Review the correction of the original work and the support that is supposed to have the candidate for making a good quality work, and that includes giving information to pass the grade exam. The Director of Thesis should always supervise the most important points in the writing process and collect information resources. The recomendations gived by the exam jury are suggested to be integrated in the final Thesis work.
Exploring and exploiting hierarchies in Bayesian inverse problems

We make several novel contributions to aspects of the Bayesian approach to inverse problems: well-posedness, discretisation, and algorithms. The first main contribution of this work is a new concept of well-posedness of Bayesian inverse problems. In contrast to the existing concepts, our slightly simplified concept allows us to make well-posedness statements with respect to general mathematical models. Under e.g. finite-dimensional, non-degenerate Gaussian noise assumptions, we only need to show measurability of the underlying model.Next, we move on to the discretisation of Bayesian inverse problems. We consider hierarchical Bayesian inverse problems in which the prior random field is parameterised. Typically, random fields are discretised by truncated spectral expansions, such as the Karhunen–Loeve expansion. Such discretisation strategies may adhoc be not suitable in hierarchical settings, since the parameterisation may require a large number of random field discretisations. This is computationally infeasible. The second main contribution of this thesis is a reduced basis method allowing for a computationally cheap, parameterised discretisation.The solution of a Bayesian inverse problem is the posterior distribution. Sampling from the posterior distribution, with e.g. Markov chain Monte Carlo (MCMC) or Importance Sampling, may be unsuitable if the Bayesian inverse problems are constrained by a computationally tasking mathematical model, e.g. by a partial differential equation (PDE). More suitable are Sequential Monte Carlo strategies that use hierarchies of model discretisations and tempered likelihoods. An adaptive combination of these hierarchies leads to the third main contribution of this work: the highly efficient Multilevel Sequential² Monte Carlo algorithm. We derive this method and compare it numerically with standard Sequential Monte Carlo methods. Moreover, we interpret Sequential Monte Carlo in a framework where it is a Markov chain of random measures. In this setting, we discuss the long-time behaviour of these Markov chains and, thus, the convergence of Sequential Monte Carlo. This is the fourth main contribution of this work. We conclude by pointing the reader to directions for future research.
Library Guides: Get Books from Other CSU Libraries: The CSU+ Service

All libraries in the CSU system now use OneSearch. This means you can search for, find, and request books from any CSU library. This Library Guide will show you how!
ARCA. Semantic exploration of a bookstore

In this demo paper, we present ARCA, a visual-search based system that allows the semantic exploration of a bookstore. Navigating a domain-specific knowledge graph, students and researchers alike can start from any specific concept and reach any other related concept, discovering associated books and information. To achieve this paradigm of interaction we built a prototype system, flexible and adaptable to multiple contexts of use, that extracts semantic information from the contents of a books' corpus, building a dedicated knowledge graph that is linked to external knowledge bases.
Classification of Breast Cancer Data for Recurrence Identification with Decision Tree Approach

Classification is one of the methods in data mining that can be used to group the data into class attributes based onsimilarities shared in the data. A decision tree will be used for classification purposes. The decision tree has beenwidely used in many field industries as its simplicity of computation is suitable to be used to analyse various kinds ofdata available. In this research, the performance of the decision tree will be used in analysing the Breast Cancerdata. Cancer is one of the leading causes of human death in the world and breast cancer has one of the highestrankings among other cancers. In Malaysia, about 17.3% of cancer patients suffered from breast cancer as stated bythe World Health Organization in 2018. Breast cancer is top cancer in women both in the developed and developingcountries. The breast cancer data were classified into two groups of recurrence and non-recurrence cancer and willbe discussed. The patients’ age, breast, breast quad, menopause, tumor size, involve nodes, node caps, degree ofmalignant, and irradiation are the attributes used for the classification process. The accuracy of the decision treecan be calculated using the confusion matrix.
Pengembangan Teknik Block Printing Dengan Metode Direct Print Untuk Produk Fashion

Abstract Block printing is one of the printing techniques that has a high aesthetic value because the printing results have characteristics that cannot be obtained through advance technology. The block printing technique is simple and relatively easy to do because print media and supporting tools are easy to get. The direct print method is one of the processes of block printing techniques on textiles. The direct print method is the process of attaching color into white material and the result is the color of the printing process. In designing fashion products, aesthetic elements are needed in the form of motifs or patterns to add aesthetic value and uniqueness to the product to be created. Along with the time block printing techniques have begun to be abandoned due to technological advances. Based on this phenomenon block printing can be used as a technique for decorating fabric surfaces because block printing techniques are considered to have the potential to be developed and applied to fashion products to add visual aesthetic value for the products to be created. The research method used is qualitative methods such as collecting data from various written sources such as books, journals, and documents related to the research topic. Then conduct experiments to develop block printing techniques on textile materials. The results of this research were applied to fashion products in the Ready-to-Wear Deluxe category because they are a limited number of handmade products. Visualization of block printing techniques is used as an aesthetic element applied to textile materials that will be used as clothing. Keywords: Block printing, Direct print, Textile, Fashion products.
Problemi di diatopia e di diacronia della lingua sarda. Un’ipotesi di sociolinguistica storica

No AccessProblemi di diatopia e di diacronia della lingua sarda. Un’ipotesi di sociolinguistica storicaMaurizio VirdisMaurizio VirdisSearch for more papers by this authorhttps://doi.org/10.14220/9783737011327.31SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next 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
Improving VAE Generations of Multimodal Data Through Data-Dependent Conditional Priors.

One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for the data generations. We propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), which learns to differentiate between the individual mixture components and therefore allows for generations from the distributional data clusters. We assume a two-level generative process with a continuous (Gaussian) latent variable sampled conditionally on a discrete (categorical) latent component. The new variational objective naturally couples the learning of the posterior and prior conditionals, and the learning of the latent categories encoding the multimodality of the original data in an unsupervised manner. The data-dependent conditional priors are then used to sample the continuous latent code when generating new samples from the individual mixture components corresponding to the multimodal structure of the original data. Our experimental results illustrate the generative performance of our new model comparing to multiple baselines.
Research on Intelligent Grid Data Center Operation and Maintenance Based on Artificial Intelligence Technology

The operating status of power equipment is closely related to the stability and safety of the power system. Comprehensively and accurately grasp the internal and external multi-source data of power equipment, and use scientific means to summarize and integrate the information, so as to make accurate assessment and prediction of the operating status and change trend of the equipment, and arrange a reasonable operation and maintenance plan, Is the key prerequisite and important foundation for the reliable and economic operation of the entire power system. The development and breakthrough of artificial intelligence technology (AI) in the aspects of efficiency, precision, and self-learning ability in identification, prediction, optimization, and decision-making tasks provide a new technical means and research ideas for the operation and maintenance of power equipment. The paper reviewed the key technologies of artificial intelligence in the operation and maintenance of power transmission and transformation equipment, sorted out the relevant data situation and existing problems of equipment operation and maintenance, pointed out the key technology framework of artificial intelligence and the application characteristics and scope of various algorithms. Finally, the problems faced by the application of artificial intelligence technology are analysed, and their development trends are prospected and suggested.
Hybrid Filtering for Image Sharpening and Smoothing Simultaneously

In this paper, we propose a method for adjusting the sharpness, and in the meantime, it can enhance the smoothness of the black-and-white and color images simultaneously. Our filter can be divided into two terms, the smoothing filter and the sharpening filter. Both filters are connected with the binding filter used to define the filter weighting for a suitable area. The binding filter that we design based on a nonlinear combination of nearby image point values. The Laplacian filter is applied for constructing the binding filter that is designed similar to the Gaussian distribution function. The Gaussian Smoothing (GS) filter and High Boost (HB) filter are employed to construct the example of our hybrid filter. The processed image is compared with filtered image by the GS filter, HB filter, and bilateral filter for observing our filter performance, both the black-and-white images and color image. The experimental results show that our proposed filter can maintain the dominant properties of both GS filter and HB filter. It can reduce the noise in the flat area, and at the same time, it can increase the image sharpness in the edge region.
LibGuides: Accounting: Home

A guide to locate accounting and business related materials in the Moorpark College Library.
Automatic Recovery of Hidden Image from Image Steganography Using DNN and Local Entropy Features

Image steganography hides secret information in an image called cover image so naturally that the other users can not recognize the existence of information in the revealed image. This paper deals with an approach to recover the hidden image information from image steganography. The proposed approach investigates that the decoded hidden image information is a normal image or not. The normal and incorrectly decoded abnormal images have been trained using a deep neural network model and entropy features. The discrimination is processed with image patches since the information may be partially embedded in the cover image. The experiments are performed with respect to the various data capacities. The proposed approach discriminates and recovers the hidden image information automatically from a tremendously large number of steganography encoding methods.
WHEELCHAIR-PERSON FALL DETECTION WITH INTERNET OF THINGS

Falling down is among the major causes of medical problem that are faced by the elderly peopleand movement disability person. These people tend to injure themselves from falling down when they alone.When a falling event occurred, medical attention needs to provide immediately in order to reduce the risk offaller from getting severe injuries which may lead to death. Several technologies have been developed whichsome utilized webcams to monitor their activities. However, the cost of operation and installation isexpensive and only applicable for indoor environment. Some user also worried about their privacy issues.Current commercialized device is by wearing a wearable wireless emergency transmitter which restrictmovement of user and produce high false alarm. This research proposed a wheelchair-person fall detectionsystem with IoT which is cost effective and reliable to detect fall and alert surrounding to call for help. Forfall detection, gyroscope, GPS module, FSR pressure sensor and microcontroller are implemented into thesystem. The gyroscope is use to detect the position of the wheelchair while the FSR pressure sensor whichplaced on the sit pad of the wheelchair will be used to detect and recognized the gesture of the user. Bothworks together to detect fall event which increase the accuracy of the fall detection. GPS module used toallocate the location of the wheelchair when fall event occur. When fall event occur, all the data includes thelocation of the wheelchair will be sent to blynk mobile application. The IoT system will sent emailnotification to the registered person to alert them fall event happen and help needed. Moreover, this systemrequires less implementation cost and provides a quick response. It can install in the existing commercialwheelchair. However, the limitation of this system is it required a good WIFI connection. For futurerecommendation, a better GUI design can be implemented into the system besides that, more detectionsystem can be added on to increase the accuracy of the system
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization.

Transfer learning has been demonstrated to be successful and essential in diverse applications, which transfers knowledge from related but different source domains to the target domain. Online transfer learning(OTL) is a more challenging problem where the target data arrive in an online manner. Most OTL methods combine source classifier and target classifier directly by assigning a weight to each classifier, and adjust the weights constantly. However, these methods pay little attention to reducing the distribution discrepancy between domains. In this paper, we propose a novel online transfer learning method which seeks to find a new feature representation, so that the marginal distribution and conditional distribution discrepancy can be online reduced simultaneously. We focus on online transfer learning with multiple source domains and use the Hedge strategy to leverage knowledge from source domains. We analyze the theoretical properties of the proposed algorithm and provide an upper mistake bound. Comprehensive experiments on two real-world datasets show that our method outperforms state-of-the-art methods by a large margin.