Reviewerly/exHarmony huggingface dataset:
A Study on the Mediating Effects of Ego-Resilience on the Association between the Career Growth Opportunities and Withdrawal in Employees

The research on organizational behaviors has primarily focused on the promotion of employees' productive behaviors through positive attitudes and behaviors related to their organization and job and, ultimately, on the improvement in organizational effectiveness.This study was designed, taking into account the situation in which the relationships based on a psychological contract between an organization and an employee weakened as well as the issue of the decline in employment with the introduction of the concept of the Fourth Industrial Revolution.Specifically, it aimed to confirm the association between withdrawal, which is typical of unproductive behavior inhibiting organizational consolidation, and career growth opportunities in a situation characterized by increasing anxiety about job security and employability.It also verified the mediating effects of ego-resilience as a positive result of adjustment by employees perceiving the risk in the association between these two variables.A survey was conducted in employees at public and private enterprises in South Korea; a total of 293 questionnaires were analyzed.The career growth opportunities perceived by employees were found to be significant variables.In contrast, their ego-resilience partially mediated between career growth opportunities and physical withdrawal but had insignificant mediating effects on the association between career growth opportunities and psychological withdrawal.Lastly, the significance and limitations of this study and the issues for further research were discussed.
The variation of the posterior variance and Bayesian sample size determination

We consider Bayesian sample size determination using a criterion that utilizes the first two moments of the expected posterior variance. We study the resulting sample size in dependence on the chosen prior and explore the success rate for bounding the posterior variance below a prescribed limit under the true sampling distribution. Compared with sample size determination based on the expected average of the posterior variance the proposed criterion leads to an increase in sample size and significantly improved success rates. Generic asymptotic properties are proven, such as an asymptotic expression for the sample size and a sort of phase transition. Our study is illustrated using two real world datasets with Poisson and normally distributed data. Based on our results some recommendations are given.
The Realization of Transitivity Systems in Undergraduate Learnersâ€™ Argumentative Essay Texts

Transitivity systems define as a system to explore how language concernes in the overall grammatical resources for construing going on. The ability to realize transitivity systems in argumentative essays is important because by realizing these systems, the writer can explore language in process of doing, sensing, saying activities happened in the real world. Therefore, this study aims to explore the realization of transitivity systems in studentsâ€™ argumentative essay texts. There are five argumentative essays written by students who have a good quality in writing skill. The findings show that the writers have already realized six process types corresponding with participants and circumstances. By realizing all process types in essay, the readers can experience different world so that they will know the writersâ€™ experience in using language to mark experiential meaning in essays. In this case, the writers mostly asked readers to experience with process of doing by material process. Thus, the writers moved the readersâ€™ attention by showing the experience of stating relation of the participants. The relation was showed in a configuration of being in attributing and identifying the participants. Therefore, the occurrence of other processes as well as participants and circumstances makes the argumentative essay text more alive.&#x0D; Keywords: Transitivity Systems, Process types, Participants, Circumstances, argumentative essays.
Logical reduction of metarules

Many forms of inductive logic programming (ILP) use \emph{metarules}, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called \emph{derivation reduction}, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperforms subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.
THE EFFECT OF MOTIVATION FACTORS ON EMPLOYEES’ PERFORMANCE IN PENANG MANUFACTURING FIRMS, MALAYSIA

This study evaluated the relationship between motivation factors on employees' performance in manufacturing firms in Penang, Malaysia. A sample size of 222 respondents was taken from 28 electrical manufacturing firms with a 6322 population and 361 samples to examine the relationship. A questionnaire was designed for data collection to measure learning activities on employees' performance in manufacturing firms. A stratified sampling method was used, and the data was analyzed using SmartPls 3.7.8. The study showed that financial rewards and friendly work environment have a significant relationship with employees' performance in manufacturing firms. The result also showed that career development has not significant on employees’ performance in manufacturing firms. However, the limitation of this study only covers electrical manufacturing firms. Suggested for future study focus on electronic, plastic, and fabricated manufacturing firms to be more effective in improving manufacturing firms' motivation factors.
Investigating Correlations of Inter-coder Agreement and Machine Annotation Performance for Historical Video Data

Video indexing approaches such as visual concept classification and person recognition are essential to enable fine-grained semantic search in large-scale video archives such as the historical video collection of former German Democratic Republic (GDR) maintained by the German Broadcasting Archive (DRA). Typically, a lexicon of visual concepts has to be defined for semantic search. However, the definition of visual concepts can be more or less subjective due to individually differing judgments of annotators, which may have an impact on annotation quality and subsequently training of supervised machine learning methods. In this paper, we analyze the inter-coder agreement for historical TV data of the former GDR for visual concept classification and person recognition. The inter-coder agreement is evaluated for a group of expert as well as non-expert annotators in order to determine differences in annotation homogeneity. Furthermore, correlations between visual recognition performance and inter-annotator agreement are measured. In this context, information about image quantity and agreement are used to predict average precision for concept classification. Finally, the influence of expert vs. non-expert annotations acquired in the study are used to evaluate person recognition.
Scanclave: Verifying Application Runtime Integrity in Untrusted Environments

Data hosted in a cloud environment can be subject to attacks from a higher privileged adversary, such as a malicious or compromised cloud provider. To provide confidentiality and integrity even in the presence of such an adversary, a number of Trusted Execution Environments (TEEs) have been developed. A TEE aims to protect data and code within its environment against high privileged adversaries, such as a malicious operating system or hypervisor. While mechanisms exist to attest a TEE's integrity at load time, there are no mechanisms to attest its integrity at runtime. Additionally, work also exists that discusses mechanisms to verify the runtime integrity of programs and system components. However, those verification mechanisms are themselves not protected against attacks from a high privileged adversary. It is therefore desirable to combine the protection mechanisms of TEEs with the ability of application runtime integrity verification. In this paper, we present Scanclave, a lightweight design which achieves three design goals: Trustworthiness of the verifier, a minimal trusted software stack and the possibility to access an application's memory from a TEE. Having achieved our goals, we are able to verify the runtime integrity of applications even in the presence of a high privileged adversary. We refrain from discussing which properties define the runtime integrity of an application, as different applications will require different verification methods. Instead, we show how Scanclave enables a remote verifier to determine the runtime integrity of an application. Afterwards, we perform a security analysis for the different steps of our design. Additionally, we discuss different enclave implementations that might be used for the implementation of Scanclave.
The Implementation of Scaffolding Technique in Teaching English to The Students of High Schools in Semarang â€“A Case Of High School English Teachers in Semarang

The use of appropriate teaching technique to teach English in senior high schools in the context of EFL determines the quality of the studentsâ€™ achievement because the success of their efforts to reach the learning goals cannot be separated from teacherâ€™s role in assisting the process of learning.The research was conducted to describe the implementation of scaffolding technique in 6 senior high school in Semarang and to analyze the obstacles and factors underlying found in the process of the implementation used in the classroom by applying Deweriankaâ€™s scaffolding model.The data were taken from classroom interactions of 6 teachers in 6 public senior high schools in Semarang. The research subjects were 6 English teachers and 10th grade students in 6 different public schools. There were four stages of scaffolding technique suggested by Dewerianka, they were building the field, modeling, joint construction, and independent construction. The result shows that the implementation of scaffolding technique contributes to the studentsâ€™ ability. It was seen on (a) the students were able to produce the text depending on the purpose, social contexts, and medium in oral or written forms and (b) the teacher gave the contribution during individual work and reduced the contribution during pair work or group work. The strengths of the scaffolding technique in teaching English for 10th grade students are (a) to build the character of the students, and (b) to measure the ability of the students when producing the texts. Meanwhile, the obstacles were: (a) the differences of characters and ability among students, (b) limited time allotment for English class, and (c) school facility. This research will be beneficial as the source of information and reference related to the English teaching in EFL context.
The The Realization of Communication Strategies in Teacher â€“ Student Interactions in Tunas Harum Bangsa Trilingual National School

Recently, the need to communicate has been a major concern by many experts. Learners tend to do better in written test than spoken one. It raises the awareness that students deem speaking as the most difficult task to do in learning a language. It may lead to the vast assumption that communicating in L2 is impossible to do. However, experts find that Communication Strategies (CSs) may become the solution. To fill in the gaps in using a language, learners may use CSs. This study aimed to see the most frequently used CSs by young learners. There were 16 Primary School Students who were divided into two categories based on their proficiency levels: High Proficient (HP) and Low Proficient (LP) Students. The data were gathered from the observation of the usage of CSs in real classroom settings and the result of the interview. The result revealed that HP Students tended to use more CSs than LP Students. Then, the students tended to use more Direct Strategies rather than Indirect and Interactional Strategies. Thus, it can be stated that certain kinds of CSs were more familiar for the students to use and may help them to overcome language problem in communicating using L2.
THE EFFECT OF CODE SWITCHING ON ENGAGING NON-ENGLISH MAJOR STUDENTS

When speakers share two or more languages in common, they often switch their languages in a conversation. It is true in the classes where both teachers and students must use a certain language as the language of instructions, as happening in the English classes of Computer Science students in Indonesia. Some teachers tend to always use English all the time with some considerations, whereas others often practice code-switching in the instructional process with other reasons, as well. This study is an observation of the effect of code switching to engage students in two English for computer science classes, in which the teacher in one class used code-switching and the other did not.DOI: doi.org/10.24071/ijiet.2019.030206
Post-synaptic potential regularization has potential

Improving generalization is one of the main challenges for training deep neural networks on classification tasks. In particular, a number of techniques have been proposed, aiming to boost the performance on unseen data: from standard data augmentation techniques to the $\ell_2$ regularization, dropout, batch normalization, entropy-driven SGD and many more.\\ In this work we propose an elegant, simple and principled approach: post-synaptic potential regularization (PSP). We tested this regularization on a number of different state-of-the-art scenarios. Empirical results show that PSP achieves a classification error comparable to more sophisticated learning strategies in the MNIST scenario, while improves the generalization compared to $\ell_2$ regularization in deep architectures trained on CIFAR-10.
The Realization of Commissive Speech Acts in the First Debate of South Carolina Governor Election 2018

In communication, commissive is a kind of speech acts that commit a speaker to do some future action. People should have the ability to produce and understand sentences which are appropriate in order to communicate effectively. The aims of this study are to analyze the candidatesâ€™ utterance and the candidatesâ€™ visual expression, and also to explain the roles of power and social distance in the debate. Qualitative method is applied in this study. There are five instruments which are used in this study, such as observation, recording technique, Searle and Vandervekenâ€™s (1985) theory, Navarro and Karlinâ€™s (2008) theory, and Trosborgâ€™s (1994) theory. The collected data are especially in form of words, sentences, or pictures. The findings showed that there are six types of commissive speech acts used by two candidates, namely promise, assure, threaten, guarantee, offer, and refuse. It indicated that the most common commissive speech acts found is a promise. In addition, the second findings presented that there are seven types of visual expressions by two candidates, such as putting the hand in the pocket, tongue-jutting behavior, furrowed forehead, arm-distancing, palms-up, interlaced fingers, and hand-steepling. The most dominant visual expression by two candidates is palms-up. It meant that the candidates wanted to be believed or wanted to be accepted. The last findings explained that power (status) and social distance. They had equal status in power, had different perception, and had a space line in the debate. Hence, this study can be used in pragmatics class for language learners, especially they will apply appropriately commissive speech acts for the purpose of persuading voters or audiences in communication.
Automated Word Stress Detection in Russian

In this study we address the problem of automated word stress detection in Russian using character level models and no part-speech-taggers. We use a simple bidirectional RNN with LSTM nodes and achieve the accuracy of 90% or higher. We experiment with two training datasets and show that using the data from an annotated corpus is much more efficient than using a dictionary, since it allows us to take into account word frequencies and the morphological context of the word.
Sistem Pengelolaan Peserta Didik yang Baik demi Pendidikan Berkualitas

To achieve the goal of quality education requires that education management be able to collaborate with all the elements in the education system. That educational management is linked to the management of learners in which it is both management and the accomplishment. Home-management systems are still using conventional means and place more emphasis on developing intelligence in the narrow sense and giving less attention to the development of the creative talent of learners. Whereas creativity aside from good self-development the self-educated is also a need for self-realization as one of mankind's highest needs. The current education arrangement should be worked to provide special services to learners who have creativity as well as different incentives for education to be better directed.
Data by Proxy -- Material Traces as Autographic Visualizations

Information visualization limits itself, per definition, to the domain of symbolic information. This paper discusses arguments why the field should also consider forms of data that are not symbolically encoded, including physical traces and material indicators. Continuing a provocation presented by Pat Hanrahan in his 2004 IEEE Vis capstone address, this paper compares physical traces to visualizations and describes the techniques and visual practices for producing, revealing, and interpreting them. By contrasting information visualization with a speculative counter model of autographic visualization, this paper examines the design principles for material data. Autographic visualization addresses limitations of information visualization, such as the inability to directly reflect the material circumstances of data generation. The comparison between the two models allows probing the epistemic assumptions behind information visualization and uncovers linkages with the rich history of scientific visualization and trace reading.
Pelatihan Pembuatan Aplikasi Forecasting bagi Anggota Kelompok Kajian Pasar Modal Universitas Muria Kudus

The Capital Market Study Group (KKPM) is one of the Student Activity Units in the Faculty of Economics and Business, Muria Kudus University. Its members consist of Management students and Accounting students who are interested in capital markets. Forecasting capabilities are needed for people who want to invest in the world of capital markets. This ability can be used for better decision making in investing. However, the knowledge and abilities of KKPM members on forecasting are still lacking. Therefore, forecasting training has been held. There were 21 people who attended the training. The first activity was given material about forecasting with Linear Regression. The second activity was carried out training and assistance in making forecasting applications with PHP and MySQL programming languages. In addition to aiming to improve student forecasting skills, this training can increase the ability of students to make forecasting applications.
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF is evaluated in both synthetic processes and a simulated multi-robot warehouse, where it outperformed alternative filtering methods by exploiting passivity.
HistoKT: Cross Knowledge Transfer in Computational Pathology

The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. %Since pathologist time is expensive, dataset curation is intrinsically difficult. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer learning research follows a model-centric approach, tuning network parameters to improve transfer results over few datasets. In this paper, we take a data-centric approach to the transfer learning problem and examine the existence of generalizable knowledge between histopathological datasets. First, we create a standardization workflow for aggregating existing histopathological data. We then measure inter-domain knowledge by training ResNet18 models across multiple histopathological datasets, and cross-transferring between them to determine the quantity and quality of innate shared knowledge. Additionally, we use weight distillation to share knowledge between models without additional training. We find that hard to learn, multi-class datasets benefit most from pretraining, and a two stage learning framework incorporating a large source domain such as ImageNet allows for better utilization of smaller datasets. Furthermore, we find that weight distillation enables models trained on purely histopathological features to outperform models using external natural image data.
Hybrid Quantum-Classical Algorithms for Loan Collection Optimization with Loan Loss Provisions

Banks are required to set aside funds in their income statement, known as a loan loss provision (LLP), to account for potential loan defaults and expenses. By treating the LLP as a global constraint, we propose a hybrid quantum-classical algorithm to solve Quadratic Constrained Binary Optimization (QCBO) models for loan collection optimization. The objective is to find a set of optimal loan collection actions that maximizes the expected net profit presented to the bank as well as the financial welfare in the financial network of loanees, while keeping the LLP at its minimum. Our algorithm consists of three parts: a classical divide-and-conquer algorithm to enable a large-scale optimization, a quantum alternating operator ansatz (QAOA) algorithm to maximize the objective function, and a classical sampling algorithm to handle the LLP. We apply the algorithm to a real-world data set with 600 loanees and 5 possible collection actions. The QAOA is performed using up to 35 qubits on a classical computer. We show that the presence of the QAOA can improve the expected net profit by approximately $70\%$, compared to when the QAOA is absent from the hybrid algorithm. Our work illustrates the use of near-term quantum devices to tackle real-world optimization problems.