Reviewerly/exHarmony huggingface dataset:
Impact of Knowledge Management Environment on Knowledge Sharing Processes

The purpose of this study is to examine the mediating effect of organizational cultural factors on knowledge sharing processes in the banking sector of Pakistan.A survey technique was used for the collection of data. The data was collected by email and onsite survey. The collected data was analyzed using Windows PLS Smart.The results show that knowledge management environmental factors (tacit and explicit), plus the organizational factors (collaboration, trust, and learning) have significantly and positively contributed to knowledge sharing processes (internal and external) in Pakistans banking sector.This study sets out to explore knowledge management as a set of processes and cultural and environmental aspects that affect knowledge sharing in the banking sector of Pakistan. This relationship has not yet been investigated in the context of the banking sector of Pakistan. By managing knowledge, organizations can gain a competitive advantage in local as well as international markets. This study is helpful for organizations for the creation of a collaborative culture which can enhance employees knowledge and the success of the organization. This study is also a contribution to existing literature on knowledge management.
Learning to Infer Run-Time Invariants from Source code

Source code is notably different from natural language in that it is meant to be executed. Experienced developers infer complex about run-time state while reading code, which helps them to constrain and predict program behavior. Knowing these invariants can be helpful; yet developers rarely encode these explicitly, so machine-learning methods don't have much aligned data to learn from. We propose an approach that adapts cues within existing if-statements regarding explicit run-time expectations to generate aligned datasets of code and implicit invariants. We also propose a contrastive loss to inhibit generation of illogical invariants. Our model learns to infer a wide vocabulary of invariants for arbitrary code, which can be used to detect and repair real bugs. This is entirely complementary to established approaches, which either use logical engines that scale poorly, or run-time traces that are expensive to obtain; when present, that data can complement our tool, as we demonstrate in conjunction with Daikon, an existing tool. Our results show that neural models can derive useful representations of run-time behavior directly from source code.
Library: Using the EBSCO Mobile App: FAQs

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Analisis Pemodelan Curah Hujan - Limpasan dengan Metode NAM dan Sacramento pada Sub DAS Cikeruh, Jawa Barat

Floods in the Cikeruh watershed often occur every year during the rainy season. In addition to the rainy season there are several other factors that influence, such as slope, land height, soil type and land use. With this study, it is expected to be able to provide information on the location of floods in the Cisadane watershed with the aim of helping to make decisions in carrying out actions, prevention, reducing the risk of flooding. The data used in this study are secondary data, namely the average annual rainfall data from 13 stations, soil type data, DEMNAS data (National Elevation Data Model), RBI maps with a scale of 1: 25,000 and Cisadane watershed maps. The method used in this study uses the scoring and overlay method where each parameter used to analyze the level of flood vulnerability in the Cisadane watershed is rainfall, slope, elevation, soil type, distance to the river (river buffer), and use land that is given a value and weight according to the classification of the level of vulnerability. Processing data in this study using ArcGis 10.2.2 software. The results of the study resulted in a map of flood vulnerability levels in the Cisadane watershed which provided information on the distribution of locations prone to flooding. Around 76.77% or 116,178,559 ha of the Cisadane watershed area is prone to flooding with a very high vulnerability to the level of medium vulnerability that is spread from upstream to downstream
Sound Stations

This activity is an open-ended and flexible experience for university students to experiment with effects pedals to change the tone of musical sound sources. With multiple instruments, effects pedals, and amplifiers organized into “sound stations” around the room, students can explore the power of various effects pedals to shape the tone, texture, and essence of an instrument. Real hardware effects provide a concrete and tactile experience for tone-shaping, but DAW-based pedals, such as those found in GarageBand or Logic are viable substitutes as well. The tactile experience provided by a hardware-oriented approach to effects has many crossovers in enhancing performance practices and can help students to “think outside the box” in terms of developing their own musical projects and identities.
Technology Defenses to Fight the Root Causes of Breach: Part One

In the first chapter of this book, we focused on identifying the root causes of data breaches based on hard data from over 9,000 breaches. Then, in the first part of this book, we provided examples of mega-breaches that occurred as a result of these root causes. The root causes are phishing, malware, software vulnerabilities, third-party compromise or abuse, unencrypted data, and inadvertent employee mistakes (separate from phishing). We opened the second part of this book by delving into the key habits that need to be cultivated to support a security program oriented around continuous improvement. We followed up with advice to leadership on how to have discussions about cybersecurity at the board level.
렌즈기반 미디어 수업이 수업참여도와 수업만족도에 미치는 영향요인 분석

This study is to analyze the influencing factor of lens based media classes on on level of class participation and level of class satisfaction, and analyze the mediated effect of level of class participation in the relationship between lens based media classes and level of class satisfaction. Through this, this study is to analyze the relationship effect on the level of class participation from theory class, participation class and interest class, and find the way how to induce the students class participation on their own initiative and suggest an alternative to improve the level of class satisfaction. For this study, it was done based on the Chungnam and Incheon province where the is the work place of researchers and university students who have experience in taking lens based media classes were participated as a face to face and online survey. SPSS 23.0 was used and frequency analysis, correlation analysis, regression analysis and etc were conducted with 180 available questionnaires of total 220 questionnaires. The result of study, First, it shows that the lens based media classes are giving an effect on level of class satisfaction. In particular, participation class and interest class are in high level of class satisfaction. The theory class is most lowest level of class satisfaction. Second, it shows that the lens based media classes are giving an effect on the level of class participation. A practical training and experience are inducing students class participation on their own initiative and make students feel satisfied and an active response. Third, the level of class participation is a mediated effect in related between lens based class and level of class satisfaction. Based on the result of this study, we provide the basic information to plan and make the education program which can improve the level of class participation and satisfaction of student.
Assignment and Establishing Archives of Modern Korean Buddhist Culture Photos

This paper looked back on the temporal range and value of the photographic materials used in the ‘Establishing Archives of Modern Korean Buddhist Culture Photos’, and looked at how the constructed photos can be used in the future. First of all, in the study of Korean modern Buddhist cultural photo archives, the reason why the modern time range was assumed to be the 1980s, which is 100 years after 1880 was explained. By taking an example of the fact that photographs related to Buddhist paintings or sculptures belong to the category of photographs showing Buddhist culture, not just works, I explained what the value of photographs is and how it can be used. It is expected that the study of modern Buddhist culture will be expanded by understanding Korean modern culture easily and incorporating it with new fields by being utilized in various fields not only for educational purposes but also in various fields through the ‘Establishing Archives of Modern Korean Buddhist Culture Photos’.
Smart Derivatives Contracting: Automating Interest Rate Swaps in the Over-the-Counter (OTC) Market with the DAML

The over-the-counter (OTC) market sees the majority of trading volume in the finance industry, yet it remains vastly unregulated. Inherent to its nature are issues with transparency, efficiency and security, which have been barely addressed by legislators post the 2008 crisis. We propose that the utilization of Smart Contracts in the OTC market and creation of “smart derivatives”, solves the majority of issues that are associated with the market. While other researchers have debated on this matter and proposed the use of Smart Contracts in OTC markets, there are no papers offering an example of practical implementation. We selected interest rate swaps (IRS) as the test-case derivative for implementation in the form of a smart contract. Having unpacked the mathematical and theoretical framework behind structuring and pricing IRS, we produced a Vasicek Simulated Yield Curve from the Ten Year Constant Maturity Treasury Rates and found that the curve was inverted, which, combined with a characteristic LIBOR Simulated Yield Curve pointed to an increasingly negative swap spread for longer maturities that was demonstrated with a plotted Swap Spread. The parameters of the model were derived from live market data through Maximum Likelihood Estimation with Monte Carlo Simulation of Euler Scheme discretized Vasicek instantaneous rates. We concluded that short term interest rate swap contracts were preferable and implemented a one-year Interest Rate Swap by instantiating the DAML Ledger Sandbox. The implementation offers the likely innovation of merging a Swap Execution Facility and a Swap Data Repository into one platform, ensuring end-to-end automation, as well as providing the benefits of visibility, while preserving anonymity, improving efficiency and security in the market.
A Study of the Transformation of the “children"s story” described in two languages -From 1910s to 1920s: Focus on Korean elementary schools and Japanese language textbooks published by the Japanese Governmen -General of Chosun-

The Japanese language was the first language for Korean Children children during Japanese colonial occupation. Hangul was a second language because of the “bilingual” textbook.BR In the 1910s, the Korean Language Textbook was created to teach Korean and Chinese languages to Korean children at a time when the Japanese language was commercialized as an official Korean language. The textbook contains a version of the hung bu legend and The Old Man with the wen.BR In the 1920s, the Korean Language Textbook featured traditional fairy tales, Aesop’s fables, and the Simcheong legend.BR A large part of the Japanese language textbook had popular Japanese traditional folktales, such as Momotaro and Flower Grandfather.BR According to the classification of the fairy tales described in the Korean, the 1910s genre took the following format: characters, general fairy tales, Aesop’s fables, traditional tales, and myths, in that order. However, in the 1920s, the genre followed this orde: Aesop’s fables, general fairy tales, and characters.BR According to the classification of fairy tales described in Korean, the 1910s tales were characterized by the following in the order: general fairy tales, characters, folktales, and Aesop
ORGANIZATIONAL AND METHODOLOGICAL FUNDAMENTALS OF EXPERIMENTAL RESEARCH OF CONTINUOUS PROFESSIONAL TRAINING OF FUTURE MATH TEACHERS BY MEANS OF IСT

The use of modern information andcommunication and computertechnology is part of the mathematicaltraining of students in highereducation. Leading methods ofteaching higher mathematicsaccording to the model of blendedlearning are methods that stimulateactive systematic independent work ofstudents in the course of highermathematics: design andcommunication methods and methodsof research teaching. The level ofmathematical training of future mathteachers directly depends on theability to apply integrated computermathematics systems in theirprofessional activities. Afterconducting an experimental study ofcontinuing education of future mathteachers by ICT, we state that theeffectiveness and quality of continuingeducation of students of speciality 014Secondary Education (Mathematics)in organizing the training of disciplinesin the pedagogical institution of highereducation is most successful when thefunctional model is used in theeducational process, which is basedon learning in the information andcommunication educationalenvironment of higher education, andmethods of forming students'professional readiness by including inthe educational process of electroniceducational and methodologicalcomplexes, cloud-oriented learningenvironments in mathematics andspecialized professional programs.
Getting into Place/Space

The object of the activities outlined in this chapter is to experience the character and quality of the sound itself such as its pitch, duration, shape, gesture, timbre, and emotive impact. The following activities (Listening to Space, Writing Space, Capturing Space, and Thinking Space) explore some aspects of space that are important to embrace in music-making and provide exercises for a music and sound pedagogy that consider the “spatial” in music and sound art. Undergraduate and graduate students can become more comfortable with the basics of recording and working with a DAW while also developing an understanding of the role acoustic space plays in the recording process.
Multiregion segmentation of mammogram images by using parametric kernel graph cut algorithm / Nurhanani Abdul Rahim, Nor Farah Nabilah Mushtafa and Nadhirah Afiqah Zailan

Image segmentation is a crucial stage in image analysis. The role of segmentation technique is to partition an image into meaningful regions. Many methods are widely applied for image segmentation. However, several methods are faced with common issues, such as sensitivity to the noise and not robust in practices. Therefore, Parametric Kernel Graph Cut Algorithm was used in this study. Parametric Kernel Graph Cut Algorithm is an improvisation of the original Graph Cut. Parametric Kernel Graph Cut Algorithm has overcome these common issues by using the kernel trick instead of using different kinds of model in segmenting any images. In this study, the microcalcification from 25 mammogram images were extracted, whereby all the microcalcification were already con finned by the radiologist. The perfonnances of this method were measured based on Dice and Jaccard coefficient and also the accuracy and sensitivity by using percentage relative error of the area between method and expert. All the experimental results generated the outstanding results, where all images produced the average of 9 I .67% for Dice coefficient and 84. 72% for Jaccard coefficient. Meanwhile both accuracy and sensitivity results acquired 97.84% and 96%, respectively. Therefore, Parametric Kernel Graph Cut Algorithm had proved its ability to segment the microcalcification robustly and efficiently.
Realism of mammography tissue patches simulated using Perlin noise: a forced choice reading study

Software breast phantoms are central to the optimization of breast imaging, where in many cases the use of real images would be inefficient – or impossible. Establishing the realism of such phantoms is critical. For this study, patches of simulated breast tissue with different composition – fatty, scattered, heterogenous and dense tissue – were generated using a method based on Perlin noise. The composition of the patches is controlled by numerical parameters derived from input by radiologists and medical physicists with experience of breast imaging. Separate Perlin noise-based methods were used to simulate skin pores, high-frequency noise (representing quantum and electronic noise) and ligaments and vascular structures. In a forced choice reading study, the realism of the simulated tissue patches compared to patches from real mammograms was determined. Patches of 200-500 pixels were extracted from radiolucent, linear, nodular or homogenous (10 per category) mammograms randomly selected from a previously acquired dataset. Eighteen simulated patches in the same size range were added. Four readers, two radiologists and two medical physicists were shown the images in random order and asked to rate them as real or simulated. All readers accepted a substantial fraction of simulated images as real, ranging from 22% to 72%. Only two readers showed a significant difference in the number of images rated real in the real and simulated groups, 22% vs 73% (P= .0003) and 33% vs 63% (P=.04), respectively. These results suggest that the method employed can create images that are almost indistinguishable from patches of real mammograms.
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning

Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from common patterns of previously seen tasks so that a future task can be learned quickly. While recent works show the benefit of subspace-based representations, such results are limited to linear-regression tasks. This work explores a more general class of nonlinear tasks with applications ranging from binary classification, generalized linear models and neural nets. We prove that subspace-based representations can be learned in a sample-efficient manner and provably benefit future tasks in terms of sample complexity. Numerical results verify the theoretical predictions in classification and neural-network regression tasks.
AMITE: A Novel Polynomial Expansion for Analyzing Neural Network Nonlinearities

Polynomial expansions are important in the analysis of neural network nonlinearities. They have been applied thereto addressing well-known difficulties in verification, explainability, and security. Existing approaches span classical Taylor and Chebyshev methods, asymptotics, and many numerical approaches. We find that, while these have useful properties individually, such as exact error formulas, adjustable domain, and robustness to undefined derivatives, there are no approaches that provide a consistent method, yielding an expansion with all these properties. To address this, we develop an analytically modified integral transform expansion (AMITE), a novel expansion via integral transforms modified using derived criteria for convergence. We show the general expansion and then demonstrate an application for two popular activation functions: hyperbolic tangent and rectified linear units. Compared with existing expansions (i.e., Chebyshev, Taylor, and numerical) employed to this end, AMITE is the first to provide six previously mutually exclusive desired expansion properties, such as exact formulas for the coefficients and exact expansion errors. We demonstrate the effectiveness of AMITE in two case studies. First, a multivariate polynomial form is efficiently extracted from a single hidden layer black-box multilayer perceptron (MLP) to facilitate equivalence testing from noisy stimulus–response pairs. Second, a variety of feedforward neural network (FFNN) architectures having between three and seven layers are range bounded using Taylor models improved by the AMITE polynomials and error formulas. AMITE presents a new dimension of expansion methods suitable for the analysis/approximation of nonlinearities in neural networks, opening new directions and opportunities for the theoretical analysis and systematic testing of neural networks.
Learning Robust Models by Countering Spurious Correlations

Machine learning has demonstrated remarkable prediction accuracy over i.i.d data, but the accuracy often drops when tested with data from another distribution. One reason behind this accuracy drop is the reliance of models on the features that are only associated with the label in the training distribution, but not the test distribution. This problem is usually known as spurious correlation, confounding factors, or dataset bias. In this paper, we formally study the generalization error bound for this setup with the knowledge of how the spurious features are associated with the label. We also compare our analysis to the widely-accepted domain adaptation error bound and show that our bound can be tighter, with more assumptions that we consider realistic. Further, our analysis naturally offers a set of solutions for this problem, linked to established solutions in various topics about robustness in general, and these solutions all require some understandings of how the spurious features are associated with the label. Finally, we also briefly discuss a method that does not require such an understanding.
Human Pose Estimation Using Skeletal Heatmaps

We propose a novel skeletal attention module to generate keypoint heatmaps, which exploits skeletal, as well as overall body structure, information for human pose estimation. We first add augmenting convolutional layers to an existing deep neural network in order to yield skeletal heatmaps. These skeletal heatmaps emphasize keypoint relations connected either physically or virtually. By combining the skeletal heatmaps, we generate body attention maps for upper-body, lower-body, and full-body. Then, the skeletal heatmaps and the body attention maps are employed to estimate the heatmap for each keypoint. Finally, we perform weighted inference on the output heatmaps for more precise estimates. Experimental results demonstrate that the proposed algorithm enhances performance on two datasets for human pose estimation.
Pengklasteran Gaji Karyawan Pada Pt. Erba Primas Bogor Menggunakan Algoritma K-Medoids

PT. Erba Primas is located in Bogor, West Java and is part of the steel production industry. Inside PT. Erba Primas Bogor, there are several sections, among others, namely: Accounting Section, Administration Section, Human Resource Department (HRD), Logistics Section, Production Section Penjualan Sales and Purchasing Parts. In this study the authors found problems at PT. Erba Primas is specifically the accounting section where in classifying salaries to employees. So the authors use Datamining with the K-Medoids algorithm to facilitate decision making. K-Medoids Clustering Algorithm or also known as Partitioning Around Mendoid (PAM) Algorithm is an algorithm that uses partitioning clustering method to group n sets of objects into a number of k clusters. This algorithm uses objects in a group of objects to represent clusters. In this study aims to determine the salary clusters at high and low levels. The data source used was taken from PT. Erba Primas Bogor in 1 year. The number of records used by 100 employees and divided into two clusters, namely high and low. Based on calculations using the k-medoids algorithm the results of a high cluster of 6 employees and a low cluster of 94 employees. This research can be used as input to the company in an effort to help regulate the salary funds budget for employees and besides that in classifying employee salary data is done more  more effectively and efficiently.