Reviewerly/exHarmony huggingface dataset:
The Influence of Usability of Oracle Cloud ERP Systems on the Effectiveveness of Its Implementation at PT. Bumi Sarana Utama in Indonesia

ERP systems (Enterprise Resource Planning) provide companies with software that combines functional pieces with a focus on the processes.This study aims to determine the effect of usability of the Oracle Cloud ERP System on the effectiveness of its implementation at PT. Bumi Sarana Utama in Indonesia. The design of this study is to use a quantitative approach. In this study there are two variables, namely the dependent variable and the independent variable. The population in this study are food and beverage industry companies. The sampling method used is random sampling. Data collection was carried out using library research and field research. Data analysis was performed using multiple linear regression analysis using the SPSS 23.0 system. The results of this study indicate that (1) There is a positive and significant effect on learnability variables on the implementation of the Oracle Cloud ERP system at PT. Bumi Sarana Utama. (2) There is a positive and insignificant effect on the efficiency variable on the implementation of the Oracle Cloud ERP system at PT. Bumi Sarana Utama. (3) There is a positive and insignificant effect on the memorability variable on the implementation of the Oracle Cloud ERP system at PT. Bumi Sarana Utama. (4) There is a positive and insignificant effect on the error variable on the implementation of the Oracle Cloud ERP system at PT. Bumi Sarana Utama. (5) There is a positive and not significant effect on the satisfaction variable on the implementation of the Oracle Cloud ERP system at PT. Bumi Sarana Utama
LibGuides: Online Academic Reputation Management: Grad Student Workshop: ResearchGate & Academia

Part 3 in the OGS Becoming a Strategic Scholar Series. This class invites graduate students to map, connect and leverage the various platforms that can document, shape, and convey their respective academic reputations.
Multi-level Graph Drawing using Infomap Clustering

Infomap clustering finds the community structures that minimize the expected description length of a random walk trajectory; algorithms for infomap clustering run fast in practice for large graphs. In this paper we leverage the effectiveness of Infomap clustering combined with the multi-level graph drawing paradigm. Experiments show that our new Infomap based multi-level algorithm produces good visualization of large and complex networks, with significant improvement in quality metrics.
Perumahan Tamarunang dalam memelihara drainase lingkungan

Tamarunang is located approximately 10 km from Makassar, and about 4 km from the border of Makassar City and Gowa Regency. As a developing area, Tamarunang Village is a reference for developers to build urban communities houses. The urbanization makes the growth of housing development more rapid. This has an impact on rainwater catchment areas turned into residential housing areas. The problems faced in this community partnership programs are: 1) the living area of partner 1 and 2 have poor drainage and reduced rain water catchment areas, 2) the impact of poor drainage causes water to overflow and pool on the public roads and yard, 3) the garbage and sedimentation that accumulates in the drainage causes clogged water flow, 4) due to the development of housing construction, causing the flow direction and pattern of environmental drainage flow in the partner area to be unclear. The method used in this community partnership program is mentoring method and participatory rural approach (PRA) method. The mentoring method was carried out to change the community's paradigm of thinking in handling environmental drainage, increasing the community's understanding of environmental cleanliness and structuring, and facilitating partners in making infiltration drainage, flow direction maps, and environmental arrangement. The PRA method is carried out to arouse the partner's sense of belonging to the product to be developed, and the partner's sense of responsibility towards the products and environment that surrounds them. The main method adopted in this Community Partnership Program (PKM) activity is the time of presentation of counseling material, the method used is lecture, discussion, question and answer, and simulation, while during the practice of making infiltration drainage, the method used is demonstration. The results are the installation of biopori pipes, overflowing water and pooling on the road and the people's home page has been reduced, and rainwater does not cause standing water on the road and downstream of the housing area. And the construction of the drainage channel is intended so that the drainage channel is not blocked again which can cause water to overflow into the highway.
Talking game design and the importance of building community with Siralim developer Zack Bertok

Zack Bertok is the lead designer and programmer of the Siralim game franchise and founder of Thylacine Studios. This interview explores a range of topics and issues relating to game development, design and content.
Using LSB Method For Hiding Hill Encrypted Grayscale And RGB Images In RGB Image

Availability of data anywhere and ease of access and interception the information in different forms as text ,images ,video…etc., create an important issue represented by data security both steganography and encryption was appeared to achieve this mission . In this paper a hybrid technique was used to secure messages of grayscale and colored images in RGB cover image by encrypting the message with Hill encrypting algorithm and hiding it by LSB method in RGB image with random locations generated by seed number and taking three LSB bits , many different sizes of images was experimented and results was analyzed by PSNR , Correlation and histogram analysis methods , all proves the efficiency of proposed technique in securing images .
AN INVESTIGATION OF TEACHING STRATEGY EMPLOYED BY ENGLISH TEACHER OF DEAF STUDENTS: A CASE STUDY AT AN INDONESIAN SPECIAL NEEDS SCHOOL

The objectives of this research were to find out the implementation of teaching strategies by English teacher for deaf students and the challenges faced by the English teachers in using teaching strategies for deaf students. This research used qualitative method as the research method and case study as the research design. To obtain the data, observation, interview and documentation were used. The result of the research showed and infered that frequent teaching strategies applied by teacher in teaching English for deaf students were direct teaching and cooperative strategy. The reserach also revealed the challenges encountered by the teacher during teaching english to the deaf students were due to communication problems.  Sometimes the students made mistakes in understanding words because of similar lip movements to other words; they got difficulties in differentiating similar words; they had limited vocabularies; and they were the lack of background knowledge. These led to the need of getting more the students’ attention, adapting the material and media, and repetition in delivering the material.
Human activity recognition from skeleton poses

Human Action Recognition is an important task of Human Robot Interaction as cooperation between robots and humans requires that artificial agents recognise complex cues from the environment. A promising approach is using trained classifiers to recognise human actions through sequences of skeleton poses extracted from images or RGB-D data from a sensor. However, with many different data-sets focused on slightly different sets of actions and different algorithms it is not clear which strategy produces highest accuracy for indoor activities performed in a home environment. This work discussed, tested and compared classic algorithms, namely, support vector machines and k-nearest neighbours, to 2 similar hierarchical neural gas approaches, the growing when required neural gas and the growing neural gas.
Soil Salinity Mapping Based on ETM+ Data in Arid Rangeland, Iran (Case Study: Damghan Region, Iran)

Soil salinity has concerned people in arid and semi-arid rangelands. One of the most essential cases in relation to information for natural resource managers is preparation of soil salinity maps. Developing such maps, using traditional methods spends a lot of time and costs. Satellite data have broadened and integrated our vision for this purpose. This study was conducted in order to develop a model for providing a salinity map using ETM+ satellite data collected in 2012 and salinity values in Damghan rangelands, Iran. The geometric and atmospheric correction of satellite images was carried out. Necessary processing such as fusion of multispectral bands with panchromatic bands, tasseled cap transformation, the analysis of Principal Components Analysis (PCA), and rationing for composite bands creation were also performed. A total number of 114 surface soil sample points with the depth of 0-15 cm were taken through a random sampling method and their Electrical Conductivity (EC) was measured. Different bands extracted spectral values for each sample and the relation between spectral values (i.e. main bands, Tasseled Cap bands, and soil and vegetation index) with EC values of the samples was investigated. Using PCA analysis, the variables were categorized into four principle components to develop soil EC map according to the highest correlation. Results revealed that there was the highest correlation between PCA1 and variables of blue, green, red bands (R=0.7), Tasseled cap 1, 2, 4 (R=0.68) and indicators SI1, SI2, SI3 (R=0.7), GVI, BI (R=0.68), INT1, INT2, MND, WDVI (R=0.7). In PCA2, the variables of NIR,OSAVI, NDVI, SAVI, VNIR1 and TVI had a significant correlation with PCA2. Finally, using stepwise regression, three models were developed to determine soil salinity maps according to the utilized independent variables. Results showed that Landsat ETM+ images are good tools to estimate salinity maps of arid rangelands.
Extraction of Petrophysical Information and Formation Heterogeneity Estimation from Core Photographs by Clustering Algorithms

Summary Core data is the most reliable and representative source of information, however it is very expensive. It is the only thing that can be used for precise description of geological situation in a target formation including its depositional environment, petrophysical properties and oil saturation distribution, reservoir heterogeneity and compartmentalization degree induced by secondary processes. Despite the fact that core retrieving operation can increase well cost in few times only a little amount of core information is utilized during field development projects as a huge part of it is represented by qualitative description which is hard to be implemented into digital quantitative geological modeling process.
Learning adaptively from the unknown for few-example video person re-ID

This paper mainly studies one-example and few-example video person re-identification. A multi-branch network PAM that jointly learns local and global features is proposed. PAM has high accuracy, few parameters and converges fast, which is suitable for few-example person re-identification. We iteratively estimates labels for unlabeled samples, incorporates them into training sets, and trains a more robust network. We propose the static relative distance sampling(SRD) strategy based on the relative distance between classes. For the problem that SRD can not use all unlabeled samples, we propose adaptive relative distance sampling (ARD) strategy. For one-example setting, We get 89.78\%, 56.13\% rank-1 accuracy on PRID2011 and iLIDS-VID respectively, and 85.16\%, 45.36\% mAP on DukeMTMC and MARS respectively, which exceeds the previous methods by large margin.
Parallel computing technologies in video stabilization for teaching purposes

In this paper, the development of a videostabilization program is described as part of the training in the subject Parallel Architectures in the Degree in Computer Engineering of the University of Vigo.The purpose is to take advantage of the parallelism methodologies in processors to teach students about computer vision and use it in applications like vibration sensors for maintenance in Industry 4.0 or computer vision for the autonomous vehicle.The main tool to implement this program is the C programming language and the OpenCV library.
Wind-Triggered Ground Shaking Masks Microseismicity

Ground motion caused by gusts of wind can drown out signals from the smallest earthquakes, potentially confusing earthquake detection algorithms.
Data Archiving and Sharing

<italic>Many journals require data archiving as a condition for publication. Archiving permits verification of results and future use of data. However, data archiving presents some challenges, the most important being the analysis of data in new ways either not approved by—or potentially scooping—the original authors. To whom do data belong and what is fair practice for future use? This chapter will discuss these issues and ways forward.</italic>
Knowledge Patterns within the Conception of Semantic Web

The article tries to contribute to answer a question if the general concept of knowledge pattern with its sub-concepts covers a great majority of the approaches used under this term in computer science literature.At one case, specialized software design patterns in the frame of object-oriented methodology become a very well used tool for software praxis; at a different case, there exists a large packet of tools for creating ontologies of various areas.As a third case, also RDF-based networks of linked data could be seen as knowledge patterns characterizing at least structures or defined activities of some social, working, or other organizations.We propose here to see the problem of knowledge pattern from knowledge representation especially at directions where the goal of using knowledge pattern meets the general goal of the semantic web.The motivation of this article is to apply knowledge patterns in the semantic web because knowledge at a higher professional level can and should usually be given in such a way that their specialized formal expertise incorporates the key to understanding their meaning.
Multi-party quantum privacy comparison of size based on d-level GHZ states

Quantum privacy comparison(QPC) plays an important role in secret ballot elections, private auctions and so on. To date, many multi-party QPC(MQPC) protocols have been proposed to compare the equality of $k(k\geq 3)$ participants. However, there are few examples of MQPC used to compare the sizes or values of their privacies. In this paper, we propose a MQPC protocol by which any $k(k\geq 3)$ participants can compare the sizes of their privacies with executing the protocol just once. The proposed MQPC protocol takes the $d-level$ GHZ states as quantum resources, and a semi-honest $TP$ is introduced to help the participants to determine the relationship of their privacies. Further more, only single-particle unitary transformations and measurements are involved, and the participants need not to share common secrets with each other beforehand which makes the proposed protocol much more efficient. Analysis shows that our protocol is secure against internal and external attack in theory.
Compositional Video Prediction

We present an approach for pixel-level future prediction given an input image of a scene. We observe that a scene is comprised of distinct entities that undergo motion and present an approach that operationalizes this insight. We implicitly predict future states of independent entities while reasoning about their interactions, and compose future video frames using these predicted states. We overcome the inherent multi-modality of the task using a global trajectory-level latent random variable, and show that this allows us to sample diverse and plausible futures. We empirically validate our approach against alternate representations and ways of incorporating multi-modality. We examine two datasets, one comprising of stacked objects that may fall, and the other containing videos of humans performing activities in a gym, and show that our approach allows realistic stochastic video prediction across these diverse settings. See https://judyye.github.io/CVP/ for video predictions.
Large Data Set Mining With Naive Bayes Classifier and Association Rule

Nowadays, large data contains limitless business opportunities. Companies and organizations begin to analyze their stored data to predict their potential customers and business decisions using data mining tools as examples Naive Bayes Classifier, Association Rule Mining, Decision Tree and other famous algorithms. An mining processing output produced accurate classification result it may help companies leading in its industry. Companies seek to find feasible business intelligences to obtain reliable prediction results. In this paper proposed method used an association rule mining to improve Naive Bayes Classifier. Naive Bayes Classifier is one of the famous algorithm in large data classification but based on an independent assumptions between features. Association rule mining is standard and useful for discovering relations between inputs in big data analysis. Proposed system use bank marketing data set to illustrate in this work. In general, this work is helpful to all the business data set.
EVALUASI PENERAPAN SISTEM INFORMASI AKUNTANSI PENGELUARAN KAS DENGAN APLIKASI SYSTEM APPLICATION AND PRODUCT IN DATA PROCESSING (SAP) DALAM RANGKA PENINGKATAN EFEKTIVITAS KINERJA (STUDI KASUS PADA SUBDIT TRESURI DAN PENGELOLAAN DANA PT. PELABUHAN INDONESIA III (PERSERO) KANTOR PUSAT)

The aim of this study is to evaluate the application of a cash expenditure accounting information system with SAP as conducted by the PT. Pelabuhan Indonesia III (Persero) Head Office, especially in the Sub-Directorate of Treasury and Fund Management, in order to improve performance effectiveness. This research is a qualitative study using a case study. Data was obtained by conducting observations, documentation, and interviews. The utilized analysis technique of this study was descriptive analysis by identifying the main components of the cash expenditure accounting information system, internal control of cash expenditure accounting information system, and comparing the theories with the practices that occur in the field. The results of the analysis showed that the implemented cash expenditure accounting information system with SAP had not been effective and efficient. This is seen in the incompatibility of internal controls in the division of tasks between cash functions and accounting functions. From the results of the analysis, it is recommended that the company implement strategies that can be used to improve the effectiveness of its performance. Keywords: Accounting information system, cash expenditure.
NLNL: Negative Learning for Noisy Labels

Convolutional Neural Networks (CNNs) provide excellent performance when used for image classification. The classical method of training CNNs is by labeling images in a supervised manner as in "input image belongs to this label" (Positive Learning; PL), which is a fast and accurate method if the labels are assigned correctly to all images. However, if inaccurate labels, or noisy labels, exist, training with PL will provide wrong information, thus severely degrading performance. To address this issue, we start with an indirect learning method called Negative Learning (NL), in which the CNNs are trained using a complementary label as in "input image does not belong to this complementary label." Because the chances of selecting a true label as a complementary label are low, NL decreases the risk of providing incorrect information. Furthermore, to improve convergence, we extend our method by adopting PL selectively, termed as Selective Negative Learning and Positive Learning (SelNLPL). PL is used selectively to train upon expected-to-be-clean data, whose choices become possible as NL progresses, thus resulting in superior performance of filtering out noisy data. With simple semi-supervised training technique, our method achieves state-of-the-art accuracy for noisy data classification, proving the superiority of SelNLPL's noisy data filtering ability.
Deep Closed-Form Subspace Clustering

We propose Deep Closed-Form Subspace Clustering (DCFSC), a new embarrassingly simple model for subspace clustering with learning non-linear mapping. Compared with the previous deep subspace clustering (DSC) techniques, our DCFSC does not have any parameters at all for the self-expressive layer. Instead, DCFSC utilizes the implicit data-driven self-expressive layer derived from closed-form shallow auto-encoder. Moreover, DCFSC also has no complicated optimization scheme, unlike the other subspace clustering methods. With its extreme simplicity, DCFSC has significant memory-related benefits over the existing DSC method, especially on the large dataset. Several experiments showed that our DCFSC model had enough potential to be a new reference model for subspace clustering on large-scale high-dimensional dataset.
Multi-hypothesis classifier

Accuracy is the most important parameter among few others which defines the effectiveness of a machine learning algorithm. Higher accuracy is always desirable. Now, there is a vast number of well established learning algorithms already present in the scientific domain. Each one of them has its own merits and demerits. Merits and demerits are evaluated in terms of accuracy, speed of convergence, complexity of the algorithm, generalization property, and robustness among many others. Also the learning algorithms are data-distribution dependent. Each learning algorithm is suitable for a particular distribution of data. Unfortunately, no dominant classifier exists for all the data distribution, and the data distribution task at hand is usually unknown. Not one classifier can be discriminative well enough if the number of classes are huge. So the underlying problem is that a single classifier is not enough to classify the whole sample space correctly. This thesis is about exploring the different techniques of combining the classifiers so as to obtain the optimal accuracy. Three classifiers are implemented namely plain old nearest neighbor on raw pixels, a structural feature extracted neighbor and Gabor feature extracted nearest neighbor. Five different combination strategies are devised and tested on Tibetan character images and analyzed
Blockchain e Aplicações em Saúde

The book Short Courses of the Brazilian Symposium on Computation Applied to Health 2019 brings the texts of the mini-courses selected and presented in the 19th edition of the event, including four mini courses accepted. Short courses are an opportunity to update the community's knowledge with new themes related to health and computing, in a didactic way and with wide access to the public. The four papers selected are high quality, a result of the efforts of the authors, who dedicate many hours to the production of written content and presentation.
My Collection of #IndieWeb Summit Posts

My DemoReflection on learning CSS subgrid.Online in a DayA wrap up of the saturday session on how do we get people online in a day.Lurking